Skip to main content
Cancers logoLink to Cancers
. 2022 Jul 10;14(14):3350. doi: 10.3390/cancers14143350

Circulating Exosome Cargoes Contain Functionally Diverse Cancer Biomarkers: From Biogenesis and Function to Purification and Potential Translational Utility

Megan I Mitchell 1, Junfeng Ma 2, Claire L Carter 1, Olivier Loudig 1,*
Editor: Giulio Cabrini
PMCID: PMC9318395  PMID: 35884411

Abstract

Simple Summary

The indolent nature of some cancers makes early detection challenging, as such significant effort is placed on identifying circulating cancer biomarkers using minimally invasive, highly sensitive diagnostic assays. Biological fluids contain small extracellular vesicles including exosomes, which have many tissue origins. Cancer cells increase production and release of exosomes in the circulation to deliver biologically active compounds that can reprogram recipient cells, which potentially represent a valuable source of biomarkers. In this review, we evaluate the biogenesis of exosomes to understand how cancer cells alter their production and how their molecular cargoes interact with the microenvironment. Next, we provide a comprehensive inventory of techniques available for exosome purification, as this represents the most critical aspect for sensitive and specific evaluation of their biomarker content. Finally, we provide a current evaluation of their use as human cancer biomarkers.

Abstract

Although diagnostic and therapeutic treatments of cancer have tremendously improved over the past two decades, the indolent nature of its symptoms has made early detection challenging. Thus, inter-disciplinary (genomic, transcriptomic, proteomic, and lipidomic) research efforts have been focused on the non-invasive identification of unique “silver bullet” cancer biomarkers for the design of ultra-sensitive molecular diagnostic assays. Circulating tumor biomarkers, such as CTCs and ctDNAs, which are released by tumors in the circulation, have already demonstrated their clinical utility for the non-invasive detection of certain solid tumors. Considering that exosomes are actively produced by all cells, including tumor cells, and can be found in the circulation, they have been extensively assessed for their potential as a source of circulating cell-specific biomarkers. Exosomes are particularly appealing because they represent a stable and encapsulated reservoir of active biological compounds that may be useful for the non-invasive detection of cancer. T biogenesis of these extracellular vesicles is profoundly altered during carcinogenesis, but because they harbor unique or uniquely combined surface proteins, cancer biomarker studies have been focused on their purification from biofluids, for the analysis of their RNA, DNA, protein, and lipid cargoes. In this review, we evaluate the biogenesis of normal and cancer exosomes, provide extensive information on the state of the art, the current purification methods, and the technologies employed for genomic, transcriptomic, proteomic, and lipidomic evaluation of their cargoes. Our thorough examination of the literature highlights the current limitations and promising future of exosomes as a liquid biopsy for the identification of circulating tumor biomarkers.

Keywords: extracellular vesicles, exosomes, circulating biomarkers, transcriptomics, proteomics, lipidomics, early cancer detection

1. Introduction

Despite recent advances in diagnostic and therapeutic strategies, cancer continues to significantly decrease life expectancy, and globally remains among the top five leading causes of death [1]. The World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) estimate that cancer is amongst the top two leading causes of death in developed countries, including the United States [2,3].

Advancements in the early detection of cancer have demonstrated the benefits of routine screening, as evidenced by decreased mortality rates, and have also led the way for the design of novel screening technologies and prognostic assays [4]. Early detection, to date, remains the most efficient way to improve cancer outcomes because advanced disease is inextricably correlated with tumor cell dissemination, treatment failure, and high recurrence rates, which are associated with ~90% of cancer deaths [5,6,7]. Unfortunately, many somatic cancer types (e.g., lung cancer, pancreatic cancer, brain cancer, ovarian cancer, and lymphoma), are still very difficult to detect early, often due to the indolence of their symptoms and the late onset of disease, and are generally associated with poor outcomes [8]. Thus, clinical efforts have been aimed at initiating early screening of patients who are at risk for these cancers (due to family history, risk exposure, etc.), while research efforts have been aimed at identifying circulating molecular biomarkers with the intent to develop non-invasive and universal early detection assays (i.e., liquid biopsies) [9,10]. Of relevance, single screening biomarkers such as prostate-specific antigen (PSA) for prostate cancer, the cancer antigen 125 (CA125) for ovarian cancer, the cancer antigen 15-3 (CA15-3), the carbohydrate antigen 19-9 (CA19-9), and the carcinoembryonic antigen (CEA) for breast cancer are routinely used in clinical cancer diagnostics [11,12,13,14]. Although single protein screening biomarkers are practical, generally blood-based, and minimally invasive, they display strong limitations due to high rates of both false-positive and false-negative results, confounding the accurate detection of disease and highlighting the need for exploration of other circulating biological materials as early prognostic tools [15,16,17,18,19,20].

In the last two decades, cancer genetics has improved our understanding of the hallmarks of cancer, along with the alterations associated with cancer development and progression [21,22,23]. The discovery of mutational drivers of carcinogenesis specific to each cancer type (i.e., BRCA-1/-2 for breast cancer, EGFR or KRAS for lung cancer, TP53 and RB1 for pancreatic cancer) has provided novel targets for blood-based fine-tuned molecular detection of specific tumors (breast cancer, prostate cancer, colorectal cancer, thyroid cancer, and lung cancer) [24,25,26,27,28]. The term “liquid biopsy” has been exemplified by the development of state-of-the-art methodologies for detecting circulating tumor cells (CTCs) [29,30,31] and circulating tumor DNA (ctDNA) [32,33,34,35]. For the detection of CTCs, which are released into the circulation by primary tumors during early stages of tumor growth and vascularization [36,37,38], novel technologies have been developed to detect as low as one CTC for every 1 × 109 red blood cells per milliliter of peripheral blood [39], by targeting their physical properties (i.e., size and/or density), using size-based selection/discrimination methods [40,41] and/or immunoaffinity-based selection methods that target their distinct surface tumor markers (e.g., antibodies targeting general CTC markers such as EpCam or more specific markers such as prostate-specific membrane antigen (PSMA) for prostate specific CTCs) [42,43,44,45]. Although CTC panels (e.g., CellSearch® CTC test) have been approved for clinical use for the detection of metastatic breast, prostate, and colorectal cancers, a significant lack of standardization between the different commercial technologies, including immunomagnetic and microfluidic variations, has hampered their widespread application [46,47,48].

In contrast, and advantageously, the screening for circulating tumor DNA (ctDNA), which relies on quantitative PCR, is highly specific and sensitive, and is based on the detection of unique somatic mutations harbored by the genomic DNA of dying circulating tumor cells and dying tumor cells (cell death, immune response, etc.) [49,50,51]. Screening panels for ctDNA have already been commercialized (e.g., Signatera tumor-informed assay [52]) for molecular monitoring of residual disease, response to therapeutic treatment, and recurrence. Considering the value of ctDNA for the detection of cancer, high-throughput targeted next-generation sequencing panels have been developed for clinical screening of ctDNA from circulating genomic DNA (i.e., Illumina TruSight cancer panel) [53,54]. However, sequencing depth remains an important limiting factor in detecting circulating genomic mutations at ultra-low levels of representation [55]. Although extremely accurate and sensitive, the methods developed for purification and detection of CTCs and ctDNA are limited by the amount of circulating material [56,57]. In fact, many studies have shown that both CTCs and ctDNA are often undetectable in a percentage of cancers [58,59,60], and both require knowledge of the mutations (e.g., protein or DNA) for tumor screening [61,62].

In contrast to CTCs and ctDNA, which are in very low abundance at early stages of tumor development, tumor secreted nanoparticles, also known as extracellular vesicles (EVs) or exosomes, have been shown to represent a robust, continuously produced, and unique source of biomarkers actively secreted by their tumor cells of origin [63]. Advantageously, membrane-bound stably encapsulated biomarkers (i.e., intact RNAs, fragmented gDNA, and intact surface and cytosolic proteins) are packaged and released in a higher abundance by tumor cells into both the microenvironment and the surrounding biofluids [64,65,66,67], and particularly into the circulation [68]. Although the nucleic acid and protein/lipid ratios of exosomes do not directly recapitulate those of their cell of origin, their biogenesis and content is representative of transformed cells, containing unique transcriptomic alterations and proteomic and lipidomic functional characteristics. Thus, exosomes and other EVs show great promise as molecular reservoirs for liquid biopsy in both cancer diagnosis and monitoring, but also as specifically targeted beacons for the delivery of clinical therapeutics [69,70,71].

This review is focused on understanding why exosomes are a unique and purifiable source of circulating tumor biomarkers. The detailed description of their biogenesis and their content will provide clues on why and how they hold both an intriguing and promising future as a minimally invasive source of liquid biopsy, as their presence in all tested biofluids, including blood, exhaled breath condensate (EBC), urine, sweat, saliva, and cerebral spinal fluid (CSF), provides localized and targetable biological material for clinical screening. Importantly, we will evaluate the technological landscape for their purification, including both global and targeted purification approaches. We will discuss the genomic, transcriptomic, and proteomic biomarker aspects of their cargo, which offer a unique and remote snapshot of tumor cell by/bio-products. By evaluating the ongoing technological efforts and weighing the current limitations, we provide a realistic view of the application of exosome biomarkers for the detection and monitoring of human cancers (e.g., early detection, treatment response, recurrence, and metastasis).

2. Extracellular Vesicles (EVs): A Journey from Discovery to Clinical Utility

The first description of EVs can be traced back to 1946, to the studies of Erwin Chargaff and Randolph West on blood coagulation [72,73]. They were the first to describe a platelet-free coagulation component from plasma which could be sedimented into a pellet by high-speed centrifugation (31,000× g) and could inhibit blood clotting [74].

The definition of exosomes was introduced in complementary seminal papers from Philip Stahl and Rose Johnstone’s laboratories in 1983 [75,76]. Stahl published stunning electron microscopic images which demonstrated the mechanical process of exosome release from the lumen of MVBs upon fusion with the plasma membrane, which helped define this novel intracellular sorting pathway then referred to as the exosome secretion pathway [75]. Functionally, Rose Johnstone and co-workers suggested that exosomes served as a cellular waste disposal mechanism, wherein they showed that under different cellular stressors the presence of the transferrin receptor on exosomes was altered at different times, thus suggesting that exosomes provided a major route for the shedding of obsolete membrane proteins [77]. However, many contradicting papers were published demonstrating the lateral cell–cell diffusion/transfer of proteins and lipids within the membranes of exosomes (i.e., membrane fluidity) [78], along with the identification of functional enzymes [79] and other active components (i.e., the tetraspanins, Rab4, and ARF [80]), suggestive of their active biological function. Additionally, several papers demonstrated that the production of exosomes and their composition could be altered at different stages of disease, being increased during transient brain ischemia, myocardial infarctions, angina, and Crohn’s disease, wherein exosomes were shown to act as functional activators [81,82,83].

Functional studies, many of which were conducted in the early 2000s, have highlighted the importance of the exosome cargoes and their functions as unique biological devices. Exosome proteomics, lipidomics, and transcriptomics analyses have led to the discovery of unique exosome cell-specific cargoes [84,85,86,87], confirming that they transport active proteins and also, importantly, carry unique and intact RNA molecules (i.e., mRNAs, miRNAs, and lncRNAs) that can post-transcriptionally modify the fate and behavior of recipient cells [88,89,90]. The study by Skog et al. demonstrated that the RNA found in glioblastoma-derived exosomes contains a “snapshot” of the cellular transcriptome of their cells of origin at a specific point in time [91]. With an ever-growing number of studies demonstrating the powerful and oncogenic effects of tumor exosomes, important breakthroughs such as the mechanistic description of metastatic dissemination have highlighted the unique intercellular communication properties of exosomes [92,93,94,95]. Jang et al. demonstrated in their study that the enhanced release of breast tumor-derived exosomes is critical for the education of pre-metastatic niche cells, which accommodate the colonization of circulating cancer cells from primary tumors [96,97]. This has in part been explained by the “seed and soil” hypothesis proposed by Stephen Paget [98], wherein exosomes carry oncogenic signals (i.e., mRNA, miRNA, and protein) specifically packaged by primary tumors’ (i.e., the “seed”) to prime target sites (i.e., the “soil”) prior to the dissemination of tumor cells, a process by which secondary tumors can prosper in a suitable microenvironment [99,100] (Figure 1).

Figure 1.

Figure 1

Exosomes and pre-metastatic niche formation. Exosomes released from primary tumors into the circulatory system specifically home to distant target organs (step 1). Upon their arrival, tumor-derived exosomes actively prepare the pre-metastatic niche through myofibroblast activation, induction of angiogenesis, and ECM remodeling (step 2). Local invasion of the primary tumor by cancer cells is followed by their intravasation into the tumor vasculature. These cancer cells survive and travel within the circulatory system, and upon their arrest in capillaries at distant sites, they extravasate into the parenchyma of target organs to commence metastatic colonization (step 3).

An important potential application of circulating tumor exosomes that has been extensively studied is their targeted purification for analysis of their content and identification of tumor biomarkers, including exosome studies of breast cancer [101], lung cancer [102], colon cancer [103,104], and pancreatic cancer [105,106]. Although many options are available for the purification of exosomes from biofluids, very few methods have been proven to provide both the sensitivity and specificity required for detecting low abundance tumor-specific circulating exosomes. Technological advancements aimed at enhancing tissue/cell-specific purification of exosomes from biofluids has significant potential to provide ultra-sensitive molecular assays for detection and monitoring of all human diseases, in particular cancer.

3. Current Exosome Isolation Techniques—Advantages and Limitations

The technologies and tools that have been used to purify exosomes from blood and other biofluids have evolved greatly in the past two decades, and this evolution has been driven by the need to accurately assess their biological function, but also and most importantly to decipher their molecular content, with a particular focus on tumor exosome biomarkers [107]. Isolation technologies are still evolving and include: (1) ultracentrifugation, (2) filtration, (3) precipitation, (4) immunoaffinity, and (5) microfluidics that often combine size separation and immunoaffinity [108]. Although some of these approaches are considered state-of-the-art for the isolation of exosomes, many of them have inherent limitations not adapted to the nanomolecular specificity and sensitivity required for purification of cell-type specific exosomes [109].

3.1. Ultracentrifugation-Based Isolation

Exosome isolation by means of ultracentrifugation is still the most widely used approach and is considered the gold standard for sedimentation of exosomes [110,111]. To date, it is estimated that ultracentrifugation accounts for ~56% of all exosome isolation techniques, as it is demonstrated to provide high-purity exosome fractions from biofluids [112]. Currently, two protocols for ultracentrifugation-based exosome isolations are used, including either differential ultracentrifugation or density gradient ultracentrifugation [113].

Differential ultracentrifugation: consists of a series of sequential centrifugation cycles at different centrifugal forces and durations [113]. The initial preparation of biofluids typically starts with several short, low-speed centrifugation steps (i.e., 2000× g for 10 min followed by 10,000 × g for 30 min [110]) that are necessary for the removal of contaminating cellular debris and larger microvesicles [114]. Next, a first round of ultracentrifugation is carried out at ~100,000× g for 90 min to generate an exosome pellet, which is washed with an appropriate isotonic buffer such as phosphate buffered saline (PBS) to remove protein and other soluble molecules. Subsequently, a second ultracentrifugation round (i.e., at ~100,000× g for 90 min) is performed to obtain the final exosome pellet, which is generally re-suspended in PBS and stored at −80 °C to await downstream analyses. Studies have also shown that exosomes purified by ultracentrifugation can be stably stored at 4 °C, where they maintain their intactness and retain their function for up to 20 months [115].

Density-gradient ultracentrifugation: (standard or isopycnic) has recently gained popularity because studies have shown that it increases the purity of exosome preparations [116]. Here, the separation of exosomes is achieved by the layering of a liquid sample as a narrow band on top of a medium, typically sucrose or iodixanol [117]. With the application of centrifugal force (i.e., at ~100,000× g for up to 18 h), the gradient allows for the separation of solutes, including exosomes, and their specific sedimentation into several distinct solute layers. After centrifugation, individual 1 mL gradient fractions are manually collected using a pipette [118]. The separated exosome fraction is then diluted with 1x PBS and subjected to a second round of ultracentrifugation (i.e., at ~100,000× g for ~70 min [116]). Similar to standard ultracentrifugation, the resulting exosome pellet is resuspended in PBS and stored at −80 °C. The biggest limitation when selecting density-gradient ultracentrifugation over differential ultracentrifugation is that the sample volume capacity for exosome isolation is greatly reduced in the former (~5% of the centrifuge tube capacity) [119].

Although ultracentrifugation remains the gold-standard for sedimentation of exosomes devoid of other EVs (i.e., larger sized microvesicles, cell debris, proteins) and lipoprotein contaminants, it requires expensive instrumentation, but most importantly only provides a bulk exosome isolate from a specific biofluid, rather that separating cell/tissue-specific exosome sub-populations from a biofluid [120]. Additionally, recent studies have reported that repeated rounds of ultracentrifugation reduce exosome yield and that extended and repeated high shear forces from high-speed ultracentrifugation adversely affect their integrity [121,122].

3.2. Size-Based Separation and Isolation

To date there are three main methods for the size-based isolation of exosomes, which include ultrafiltration, sequential filtration, and size-exclusion chromatography (SEC). These methods have been developed to bypass extended ultracentrifugation times and to facilitate the fractionation of exosomes from large biofluid volumes.

Ultrafiltration: relies on the use of 10–100 kDa molecular weight cutoff (MWCO) filters, which are most often utilized to reduce large sample volumes (i.e., urine or cell culture supernatants) by concentration to smaller more manageable volumes that often simplify downstream applications [123]. Currently, several commercially available kits are on the market which have been proven to provide pure populations of exosomes from biofluids. These include the Qiagen exoEasy kit, which allows isolation of exosomes from volumes up to 4 mL of plasma or 16 mL of cell culture media [124]; the Amicon® Ultra-15 centrifugal filter tubes (MilliporeSigma, Burlington, MA, USA), which allow filtration of up to 15 mL [125]; and the ExoLution® plus platform from Exosome Diagnostics, which allows the processing of 3 mL of plasma [126].

Sequential filtration:, unlike standard ultrafiltration, typically includes three steps [127]. First, larger cells and cellular debris are filtered out using standard filtration methods (0.2 µm filters). Second, free proteins are depleted by tangential flow filtration, where biofluids are passed parallel to the filter, allowing for continuous filtration and reduced membrane clogging [128]. Third, exosomes are sorted out with the use of a track-etched filter membrane (e.g., Whatman/GE Nucleopore 50, 80, 100, or 200 nm diameter membranes), allowing for size-based fractionation of exosomes [129].

Size exclusion chromatography (SEC): relies on passing a biofluid (known as the mobile phase) through a porous gel filtration polymer (i.e., the stationary phase [130]). The stationary phase of the chromatography column can be packed with different types of polymers (e.g., agarose, polyacrylamide, crosslinked dextrans, or allyldextran), the nature of which allows for differential elution of the sample into size-based fractions (i.e., larger particles travel faster and elute first, followed by smaller particles and finally non-membrane bound proteins [131]). In terms of its use for exosome isolation, SEC, when performed at lower flow rates, has been shown to possess several advantages, including reproducibility, cost efficiency, and the isolation of undamaged exosomes [121]. SEC significantly outcompetes other filtration-based isolation techniques in that it effectively removes protein contaminants, thereby yielding highly pure exosome fractions [132]. The isolation, purification, and enrichment of exosomes using SEC has been successfully used with a variety of biological fluids, including serum and plasma [133], cerebrospinal fluid [134], bovine and human milk [135], saliva [136], urine [137], synovial fluid [138], tears [136], nasal lavage [139], and seminal fluid [140].

Although great purity is achieved via SEC, which can accommodate large volumes of sample (i.e., up to 100 mL using the qEV100 column from IZON), this approach does not allow for separation of different exosome subpopulations or for separation of exosomes from other vesicles of the same size; thus, contamination by lipoproteins and other microvesicles cannot be ruled out [141].

3.3. Polyethylene Glycol (PEG) Precipitation-Based Isolation

The isolation of exosomes can also be achieved by precipitation by the addition of an aqueous polyethylene glycol (PEG) solution to a biofluid. Here, PEG coats the surface of exosomes and other microvesicles, facilitating the formation of exosome–PEG aggregates [142]. Exosomes are trapped in this porous microstructure, and are then precipitated (e.g., ExoQuick®) by low-speed centrifugation at 1500× g [143]. This method results in the isolation of exosomes within a size range consistent with ultracentrifugation; however, as PEG is a non-specific “coagulant”, it also results in the co-precipitation of soluble non-exosomal proteins, immunoglobulins, and lipoproteins, which significantly limits the purity of the final exosome pellet [144] as they carry unwanted biological material (i.e., proteins and nucleic acid species) [145]. The advantages of this procedure are that it is inexpensive, requires little to no training, and it allows for high-throughput processing of samples with little damage to exosomes. Although PEG-based precipitation results in high yield, low purity exosomes, studies have shown that when it is sequentially combined with an immuno-purification (such as anti-CD63 assay), the exosome fraction purity may be enhanced [146], making it an attractive approach for initial, fast, and crude isolation of exosomes [147].

3.4. Immunoaffinity-Based Isolation

In recent years, immunoaffinity-based capture has become one of the preferred methods for the isolation of exosomes from biofluids [148]. This technology allows for direct separation of exosomes via immobilized antibody targeting of membrane-surface specific exosomal proteins [149,150,151]. Most, if not all, commercially available immunoaffinity-based isolation kits are tailored with antibodies (either alone or in combination, i.e., a pan-exosome panel) targeting common exosome surface markers, such as the tetraspanins (CD9, CD63, and CD81) [152]. Other assays also include antibodies targeting markers such as epithelial cellular adhesion molecule (EPCAM) [153] and exosome-binding molecules such as heat shock proteins (HSP70, HSP90) [154] or heparin [155]. A large number of biotechnology companies, including MBL (EXOCAP, Sunnyvale, CA, USA), Systems Bioscience (ExoFlow, Palo Alto, Santa Clara, CA, USA), FujiFilm (MagCapture™, Wako Chemicals, Richmond, VA, USA), and BioLegend (MojoSort™, San Diego, CA, USA), provide ready-to-use immunoaffinity assays for exosome isolation [145] that utilize antibody-coated magnetic beads for the capture of exosomes, as they are convenient, allow rapid and easy magnetic isolation of exosomes, and ultimately allow for their concentration into a small volume [156]. Although the selection of exosomes from a biofluid, with antibodies targeting exosome surface proteins, provides the state-of-the-art selection, very few provide assays that allow for the gentle release of captured exosomes [149]. Another critical issue with magnetic beads, as we determined by quantitative PCR of small-RNA contaminants, is that they can bind small-RNAs and EVs (containing small-RNAs), which may be detrimental for downstream analyses, especially when analyzing the small-RNA content of exosome sub-populations that are in low abundance [149]. We hypothesized that the polymer coating is positively charged and the pore sizes between adjacent streptavidin/carboxyl molecules may be large enough to allow for non-specific binding of small biological material (i.e., nucleic acids, proteins, and lipids), creating a non-specific signal background that will interfere with subsequent analyses and the identification of exosome tumor biomarkers (i.e., transcriptomic signatures). To circumvent these limitations, we recently developed an assay that we termed EV-CATCHER (Extracellular Vesicles Capture by AnTibody of CHoice and Enzymatic Release), which greatly reduces non-specific binding of small-RNAs and EVs, which can be customized with any antibody, and which allows for the mild enzymatic release of intact EVs [149].

Overall, immunoaffinity-based capture generally reduces exosome yield (as only antibody-recognized exosomes are captured) and is generally more expensive; however, when coupled with an initial step of ultracentrifugation or ultrafiltration to concentrate exosomes, it can result in higher exosome purity and may help separate exosomes of specific cellular origin from the bulk exosome preparation. Many studies on lung [157], breast [158], colorectal [159], liver [160], and many other cancers have already demonstrated that with higher exosome purity and selectivity, the analysis of their molecular cargo can provide more specific and reproducibly detectable biomarkers [161,162]. Thus, the immuno-affinity separation of unique tumor exosome cargoes from biofluids has potential to lead to novel cancer diagnostic assays [163].

3.5. Microfluidics-Based Isolation

Microfluidics-based exosome isolation systems have become a sought-after nanotechnology for separating exosomes from other nanosized bioparticles. This technology provides high-speed, high-throughput, ultra-precise, and cost-efficient isolation of exosomes [164]. Emerging chip-based microfluidic exosome isolation techniques, including standard research laboratory-based approaches developed by Duke University using acoustic microfluidics (i.e., acoustofluidics) for the separation of exosomes from whole blood [165], the exosome total isolation chip (ExoTIC) device developed by Liu et al., and the ExoChip developed by Kanwar et al., have already been marketed for efficient isolation of high-yield, high-purity intact exosomes [166,167]. The ExoTIC device utilizes a simple filtration-based approach wherein EV-containing biofluids, such as culture media, plasma, and urine, are passed through a nanoporous membrane [166]. During this process, free-floating proteins, nucleic acids, and lipids are washed out, and the enriched exosomes (30–200 nm in size) can be collected from the membrane using a standard pipette [166]. In comparison to ultracentrifugation and precipitation-based exosome isolation, the ExoTIC device has been shown to possess undisputed advantages, which include the constant flow of clinical specimens through nanochannels that slow the EVs, the separation of large and small vesicles in separate nano-sized chambers via engineered ports, and the presence of antibodies for the capture of exosomes with potential for release [168]. Currently, many of the existing microfluidics-based systems, including acoustic microfluidics, ExoTIC, and ExoChip, incorporate immunoaffinity components for the separation and capture of exosomes by targeting specific surface markers with immobilized antibodies [167,169]. Interestingly, although our recent analyses revealed the strong limitations of magnetic beads for isolating pure exosomes, several microfluidic platforms have incorporated magnetic beads into their systems for the separation of cells from biofluids [166,167]. Currently, some of the most widely used microfluidics-based systems fully integrate size-based separation, immunoaffinity-based separation, and dynamic separation [170]. The most critical issue that microfluidic-based separation technologies have solved is that they avoid the non-continuous separation processes. Indeed, samples are processed from start-to-finish in a single run, in a closed loop, with limited user-interference or introduction of contaminants, which helps maintain yields that are otherwise reduced by the repeated washes performed during sequential ultracentrifugation [121]. Several problems still remain, however, which are similar to those mentioned for immunoaffinity-based isolation techniques and include the need for high immunoaffinity and the requirement for highly specific and sensitive antibodies [171]. Additionally, while microfluidic systems require less hands-on manipulation, they require expensive equipment and complex nano-sized chips that are often difficult to mass-produce [172]. Despite these challenges that remain to be solved, microfluidics-based purifications offer a promising technology for all-in-one chip-based robust isolation and characterization of circulating tumor exosome biomarkers.

3.6. Exosome Sorting by Fluorescence Activated Cell Sorting (FACS)

The separation of cells by means of flow cytometry is commonplace [173,174,175]. However, because exosomes are 30–150 nm in size, they fall under the threshold of even the most sensitive “reference noise” region of flow cytometers [176,177]. Nevertheless, selective sorting of antibody-labeled exosome sub-populations by FACS shows great potential for cancer biomarker screening and discovery. As such, extensive research efforts have been applied to the development of high-fidelity-based FACS systems for sorting exosomes [178,179,180] for subsequent proteomic and transcriptomic analyses [181]. One of the biggest advancements has been the development and marketing of advanced imaging flow cytometry (iFCM) by ImageStreamx (ISx, EMD Millipore, Seattle, WA, USA) [182] which offers significant advantages for the sorting of exosomes and other small EVs when compared to other available technologies [182,183]. Mastoridis et al. demonstrated that by combining iFCM with subset-specific markers, this technology allows for the high-throughput, multiparametric characterization, and functional assessment of exosomes [184]. Another promising avenue for FACS-based sorting and cancer diagnostics based on the presence of unique tumor exosomes in a biofluid (i.e., prostate cancer exosomes) is the multicolor multiplexed in situ proximity ligation, also termed exoPLA, developed in the laboratory of Dr. Kamali-Moghaddam [185,186]. This method relies on four oligonucleotide-conjugated antibodies whose combination uniquely targets a specific type of exosomes (i.e., prostate exosomes) and rolling circle PCR amplification guided by individual pairs of antibodies to generate different fluorescent signals. Ultimately, the combination of three different fluorescent signals allows for the unique detection and sorting of tumor exosomes by flow cytometry [187]. Altogether, while the FACS-based sorting of exosomes is still a relatively new method in comparison to the others described, it provides a promising avenue for highly specific and robust isolation of low-abundance exosome sub-populations and subsequent molecular analyses.

Collectively, while most of the described exosome isolation techniques are currently available and used in different applications (Figure 2), they all possess advantages and disadvantages, which need to be considered prior to initiating exosome analyses. The ideal method for exosome isolation should be relatively simple, efficient, inexpensive, and scalable, but most importantly ultra-sensitive in order to allow for the robust and accurate identification of encapsulated circulating tumor biomarkers and to propel exosome-based liquid biopsies toward ultra-sensitive cancer diagnostics.

Figure 2.

Figure 2

An estimate of the use of exosome isolation techniques over the past 10 years. Pie chart representation of the percentage utilization of each exosome isolation technique extracted from 173 publications spanning the years 2012–2022.

4. Biogenesis and Function of Exosomes in Normal and Pathological Processes

4.1. Biogenesis

Evidence supporting the endosomal origin of exosomes stems from observations that they contain proteins derived exclusively from the cytosol and are devoid of any nuclear proteins [188]. Initial proteomic analyses revealed that while exosomes harbor proteins specific to their cell and tissue of origin, for example integrins (e.g., α6β4, αvβ5, and α6β1 on breast cancer exosomes; αvβ3 and αvβ6 on prostate cancer exosomes; and β4 on pancreatic cancer exosomes), MHC class I and II on immune cell-derived exosomes, as shown for B lymphocytes and dendritic cells [189,190,191], prostate specific antigen (PSA) from prostate cells, asialoglycoprotein receptor 1 (ASGR1) from liver cells [192], microglial proteins (CD11b and CD45) from brain cells [193], placental alkaline phosphatase (PLAP) from placental cells [194,195], and Clara cell protein 16 (CC16) for exosomes produced and released by deep lung cells [196], they also contain proteins common to all exosomes irrespective of their cellular origin, which reflects a highly regulated sorting mechanism [186,197]. For instance, adapter protein ALG-2-interacting protein X (ALIX) and tumor susceptibility gene 101 (TSG101), two proteins extensively characterized for their roles in HIV budding (i.e., release of viral particles from the cell) and MVB formation [198,199], and several tetraspanins (CD63, CD81, CD9, and CD37) are present on all exosomes. The incorporation of specific tetraspanins into exosomes is dependent on cell-type specific needs, as exosomes are involved in a multitude of biological processes, including cell adhesion, motility, invasion, membrane fusion, intracellular signaling, and protein trafficking [200]. To better understand how cancer cells hijack the exosome machinery to regulate the immune response locally, for cell-to-cell communication between neighboring cells, and, as described by Lyden et al. [201], for the successful dissemination of tumor cells, we have divided the endosomal pathway for exosome biogenesis into three major sections (Figure 3).

Figure 3.

Figure 3

Exosome biogenesis. Exosomes originate from multivesicular bodies (MVBs) (also referred to as late endosomes). The inward budding of the late endosomal membrane around selectively packaged cargo results in the formation of exosomes. The selective packaging of proteins (e.g., tetraspanins, cytoplasmic proteins, and enzymes), nucleic acids (e.g., DNA, RNA, and miRNAs), and lipids (e.g., cholesterol) into exosomes is cell-type dependent and reflects the metabolic status of originating cells. Fusion of MVBs with either lysosomes or the plasma membrane results in either degradation or the release of exosomes into the extracellular matrix, respectively.

4.1.1. The Formation of Early Endosomes

Early endosomes were initially defined as the first cellular compartment to receive incoming endocytic vesicle cargoes [202] but are now recognized to be the main sorting station for initiation of the cellular endocytic pathway [203]. The exact mechanisms underlying the initiation of early endosome formation remain unclear; however, it is thought that their membrane is derived from the fusion of primary endocytic vesicles taken up from the extracellular space [204]. The function of early endosomes (i.e., their role in cargo sorting and delivery of vesicles to the plasma membrane) is heterogenous, cell-type dependent, and defined by the proteins present in their cytosol and on their surface [205]. Individually, early endosomes are complex in structure, with their membrane being comprised of a mosaic of protein subdomains, enriched in Rab5, Rab4, Rab11, Arf1/COPI, retromer, and caveolae of cellular origin [206]. Studies of these proteins have demonstrated that they are responsible for the molecular sorting and the direct transport of endosomes once matured to distinct organelles, including the trans-Golgi network and the plasma membrane [207].

4.1.2. The Maturation of Late Endosomes and Formation of Exosomes

Late endosomes (also termed MVBs) are described as having a multivesicular morphology because they contain intraluminal vesicles (ILVs) [208]. The membrane of these ILVs contains the majority of V-ATPases (i.e., protein pumps responsible for controlling intracellular and extracellular pH), cholesterol, sphingolipid-rich lipid rafts, clathrin coats, and most importantly the endosomal sorting complexes required for transport (ESCRT) machinery [209,210]. This ESCRT machinery sequesters and sorts cytosolic ubiquitinated proteins into ILVs and is the most well-defined and widely understood system for biogenesis of normal and cancer exosomes. The ESCRT complex is comprised of a series of sub-complexes (i.e., ESCRT-0, ESCRT-I, ESCRT-II, and ESCRT-III) that are sequentially activated by binding of cytosolic ubiquitinated proteins (Figure 4), which are then internalized as early endosomes mature [211].

Figure 4.

Figure 4

Endosomal sorting complexes required for transport (ESCRT)-dependent MVB formation. ESCRT-dependent MVB formation control the internalization of ubiquitinated proteins into the intraluminal vesicles (ILVs) of MVBs. The ESCRT complex is comprised of a series of sub-complexes which function uniformly during ILV production. ESCRT-0, -I, -II, and -III complexes function consecutively in a stepwise manner to control the selective sorting of ubiquitinated proteins into exosomes.

4.1.3. MVB Trafficking and Exosome Release

The biogenesis of mature MVBs terminates with their progression through one of two pathways: (1) they fuse with lysosomes, resulting in the rapid and irreversible degradation of their contents, or (2) they fuse with the plasma membrane, resulting in the release of their ILV content into the extracellular environment, at which point they are referred to as exosomes [212]. The mechanisms underlying this intracellular switch, which determines the fate of MVBs, is complex and to our knowledge remains unclear. However, recent studies suggest that both paths are interconnected and that decisions affecting one result in alterations of the other [213]. For instance, two studies showed that inhibition of lysosome fusion with bafilomycin A1 results in the increased secretion of exosomes [214,215]. The release of exosomes by fusion of the MVB with the plasma membrane involves two sequential steps: (i) The targeted movement of MVBs to the plasma membrane, which is reliant on the structure of the microtubule cytoskeleton and the dynamic mechanisms of molecular motors, including dynein, kinesins, and actin-based myosin [216,217], and is regulated by several small Ras-like GTPases (including Rab27 A/B, Rab11, and Rab35), and soluble NSF attachment protein receptor (SNARE) (i.e., small, abundant tail-anchored proteins which are post-translationally inserted into the plasma membrane) proteins [218,219], which have been shown to differ between different cell types [220,221]. (ii) The fusion with the plasma membrane, wherein Rab11 and Rab35 facilitate MVB and plasma membrane fusion [222,223] through the assembly and functionalization of the “SNAREpin” complex. This complex is comprised of one vesicle-SNARE protein (located on MVBs) and three target membrane SNARE proteins (located on the plasma membrane) and is responsible for bringing the two membranes in close apposition resulting in the opening of a fusion pore and the merging of the MVB and plasma membrane [224] and their releasing their exosome cargo into the intercellular space.

4.2. Biological Functions of Exosomes

Exosomes are the most extensively characterized type of extracellular vesicles (typically 30–150 nm in size) [225], which, as described above, are produced via the endosomal pathway (Figure 1) [226]. The release of exosomes is an evolutionary process that has been conserved across all archaea, bacteria, and eukaryotic cells [227]. In humans, exosomes have been shown to regulate cellular functions of neighboring/target cells either by: (1) extracellular interactions of their membrane proteins and lipids with cell-surface receptors and membranes of recipient cells, respectively, to induce signaling cascades [228] or (2) cellular uptake and intracellular pre- or post-transcriptional reprogramming, via the uptake of transcription factors, microRNAs, and long non-coding RNAs, or via the import of foreign messenger RNAs, viral RNAs, and other RNA species [229,230]. Although initially thought to be responsible for the removal of unnecessary material (proteins, lipids, nucleic acids, etc.) [76], exosomes have been shown to play critical roles in a broad range of essential regulatory cellular functions, including the modulation of immunity [231] by regulation of immune cells, particularly by the inhibition of natural killer cells [232] or by acting as antiviral and antibacterial beacons [149,233]. Such activities have also been described with seminal fluid exosomes (originating from seminal glands, prostate, etc.), which protect (i.e., have antimicrobial activities) and provide energy (i.e., produce ATP) to sperm during fertilization [234]. Other functional studies of exosomes have identified their important roles in the maintenance of cellular stemness [235], coagulation of blood and repair of tissues [236], and cardiac and skeletal muscle repair [237,238]. Studies of the function of exosomes highlight the essential roles they play in maintaining the homeostasis of human tissues, which is perturbed during disease [239,240]. Unfortunately, the biological functions of exosomes are “hijacked” in human pathologies, including cardiovascular physiological and pathological disorders (e.g., cardiomyocyte hypertrophy, peripartum cardiomyopathy, and sepsis-induced cardiomyopathy) [241], where exosomes appear to contain erroneous cargoes. Furthermore, exosomes containing aggregation-prone proteins have been found in the cerebral spinal fluid and blood of Parkinson’s disease (PD), Alzheimer’s disease (AD), Creutzfeldt–Jakob disease (CJD), and amyotrophic lateral sclerosis (ALS) patients and may play roles in the propagation of these diseases [242,243,244,245]. Many studies have also highlighted the critical role that tumor exosomes play during the progression of cancer [246]. The best example on the functional diversion of exosomes has been described by the group of David Lyden, which showed that circulating tumor exosomes educate host cells within a secondary organ to prime that environment for establishment of the metastatic niche [247]. In their studies, they demonstrated that exosomes from mouse or human lung, liver, and brain tumor cells preferentially fuse with specific recipient cells (i.e., those at predicted target sites), namely lung epithelial and fibroblast cells, brain endothelial cells, and Kupffer cells in the liver [248].

4.3. Alteration of Exosome Biogenesis in Tumor Cells

Numerous studies have demonstrated that the presence of tumors is associated with a significant increase in the levels of circulating exosomes when compared to healthy individuals [249]. In aggressive brain cancer, glioma cells carrying the EGFRvIII overexpressing mutation exhibit increased secretion of exosomes harboring EGFRvIII [250], and when transferred to astrocytes can confer their oncogenic activity (i.e., ability of exosomes to convey their tumorigenic signals to recipient cells) [251].

ESCRT protein silencing: In order to elucidate the mechanisms underlying the increase in exosome biogenesis observed in cancer cells, Colombo et al. performed a set of iconic experiments to evaluate the role of the different ESCRT components in exosome biogenesis using Hela-CIITA-OVA ovarian cancer cells, which were modified to express MHC-II molecules and allowed for monitoring of exosome secretion [252]. Using this modified cell line, they carried out experiments to silence all twenty-three ESCRT proteins [252]. The silencing of ESCRT-0/I (STAM1, Hrs, and TSG101) resulted in decreased production of CD63, CD81, and MHC-II in exosomes, whereas silencing of ESCRT-III (Vps4B) or ALIX led to increased production and secretion of all exosomes [253]. Three other independent studies, where Hrs (an ESCRT-0 component) was silenced, also led to alterations in exosome secretion [254]. Specifically, the depletion of Hrs in non-cancerous dendritic cells led to a reduction in exosome secretion as measured by the detection of ubiquitinated proteins [254], whereas knockdown of Hrs in HEK293 kidney cancer cells and in head and neck squamous cell carcinoma led to increased exosome secretion and exosomal Wnt3a levels [255]. These studies demonstrated that alterations in ESCRT proteins directly influence exosome production, which may partially help explain their increased production in cancer cells (Figure 5).

Figure 5.

Figure 5

Syndecan–syntenin–ALIX couples to ESCRT-dependent MVB formation in healthy versus cancer cells. Syntenin-1 interacts directly with syndecans and ALIX, the interaction of ALIX with Snf7 of the ESCRT-III complex forms the syndecan–syntenin–ALIX pathway which is directly linked to exosome biogenesis. In cancer, several proteins in this pathway are altered, leading to enhanced exosome production. Alterations leading to the upregulation of either the syndecans, ALIX, and/or CD90 all result in enhanced exosome production, whereas any alterations leading to the downregulation of the ESCRT-III protein Vsp4 A/B and/or CD63 results in the increased production of exosomes seen in cancer cells.

Increased expression of ALIX in tumor cells: The most dramatic alteration in ESCRT-dependent exosome biogenesis for many cancer cell types, including colorectal carcinoma and pancreatic cancer [256,257], has been observed with the overexpression of ALIX, (i.e., induced by a ALIXΔPRR mutation) [258]. ALIX plays an essential role in exosome formation, through stabilization of ESCRT-III, facilitation of ILV formation, and cargo sorting [259]. Thus, upregulation of ALIX expression, as noted in many cancer cell types [260], partially explains the enhanced secretion of exosomes from tumor cells [256].

Increased expression of syntenin-1 in tumor cells: The expression of syntenin-1, which interacts directly with ALIX (via three LYPXnL motifs located within ALIX’s conserved cytoplasmic domains), is upregulated in several cancer types, including breast cancer, lung cancer, and melanoma [261,262,263]. Furthermore, quantitative proteomics identified syntenin-1 to be the most abundant protein present in these exosomes, suggesting that it may serve as a putative cancer biomarker [264]. Syntenin-1 has also been shown to interact with the N-terminus of syndecans (a small family of transmembrane proteoglycans) [265], thus linking them to exosome biogenesis through the syndecan–syntenin–ALIX pathway (i.e., syndecans recruit syntenin-1, which in turn binds to ALIX, and finally ALIX is recruited, stabilizing ESCRT-III and thus increasing exosome biogenesis) [266].

Increased expression of syndecans: Syndecans are a family of four core transmembrane proteins which typically carry two to five heparan sulfate chains [267]. Heparan sulfates are glycosaminoglycans that interact with a wide range of binding proteins, including cytokines, chemokines, growth factors, enzymes, and lipoproteins [268], and are regulated by the heparanase enzyme [269]. Increased expression of heparanase in tumor cells has been associated with increased cell aggressiveness (i.e., invasiveness and metastatic potential) [270]. Because heparanase activity is essential for the regulation of heparan sulfate chains of syndecans, and because syndecans are upregulated in several cancers [271], it is not surprising that heparanase is present in exosomes (ExoCarta, https://www.exoscarta.org/ (accessed on 29 May 2022). Indeed, an increased abundance of heparanase is seen in exosomes isolated from cancer patients compared to those from healthy individuals [272], suggesting enhanced exosomal release via the syndecan–syntenin–ALIX pathway [267,273].

5. Exosomes: A Source of Tumor Biomarkers

Valadi et al. were the first to determine that exosomes carry messenger RNAs (mRNAs) and microRNAs (miRNAs) [88], but since then many studies have demonstrated that exosomes contain a plethora of other RNA species (long-noncoding RNA (lncRNA), piwi RNAs, circular RNAs, etc.) [274]. Exosome whole-transcriptome studies have demonstrated that mRNA molecules are full length and well-protected from RNase activities within circulating exosomes [275]. Some studies have shown that exosomal mRNAs can be transcribed by recipient cells and are possibly used for reprogramming these cells [276]. However, the majority of exosome transcriptomic studies have been performed on microRNAs, because their function as post-transcriptional regulators allows them to control the recipient cells upon delivery [277]. Additional studies on the content of exosomes have also revealed the presence of fragmented genomic DNA, proteins, and lipids from both normal and cancer cells [278]. Fragmented genomic DNA, for example, which is more stable (i.e., protected from degradation) within circulating exosomes than in the circulation, can provide critical biomarkers for the detection of circulating tumor DNA mutations [279]. Castellanos-Rizaldos et al. recently showed that the detection of circulating genomic DNA carrying the EGFRT790M mutation in patients with non-small cell lung cancer (NSCLC) was superior when isolated from circulating exosomes rather than circulating free DNA (cfDNA), particularly in patients with metastatic stage 0/1a lung cancer [126,280]. Overall, several free databases, including Vesiclepedia [281], EVpedia [282], ExoCarta [283] exoRBase [284], and EVmiRNA [285], have been made available for screening the transcriptomes, proteomes and lipidomes of exosomes from different cell types and tissues. For the purposes of this review, we will concentrate our evaluation of exosome biomarkers for known microRNAs, proteins, and lipids.

5.1. MicroRNA Biomarkers

MiRNAs represent a large class (~2600 identified in humans) of evolutionarily conserved small regulatory non-coding RNAs expressed from single genes, gene clusters, or intronic passengers, generally 19–25 nucleotides (nt) in length [277]. MiRNAs control gene expression by binding to imperfect complementary sites within the 3′ untranslated regions of their mRNA targets and orchestrate their degradation and/or post-transcriptional repression [286,287]. MiRNAs are involved in the control of all normal biological processes and their expression deregulation (i.e., increased or decreased expression) has been associated with the initiation and development of cancers [288,289,290,291]. Mature and functional cellular miRNAs can be detected in the blood and other biological fluids and can generally be found in two forms: either as cell/membrane-free molecules (i.e., free, bound to Argonaute (Argo) or to nucleophosmin proteins, complexed with high density lipoprotein (HDL)) or encapsulated within extracellular vesicles [274,291,292].

5.1.1. Current Technologies for Quantification of Exosomal MicroRNAs

Since the cargo of exosomes is specifically tailored and to an extent reflects the state and condition of their cells of origin, circulating miRNAs have a potentially strong diagnostic and prognostic value as disease biomarkers [293]. Considering the low amount of material available from exosomes, which may be extracted from limited amounts of biofluids (saliva, sweat, urine, semen, exhaled breath condensates, etc.), current technologies and protocols have been optimized for the global quantification and detection of exosomal miRNAs [294].

High-Throughput Expression Analyses

Next generation sequencing offers a unique unbiased opportunity to globally evaluate the content of exosomes [295,296]. It is important to note that miRNA quantification of cell specific exosomes, either from culture media or biofluids, requires highly purified exosomes [149], because miRNAs are extremely robust [297] and any external contamination can significantly affect quantification. Most next generation miRNA sequencing protocols require relatively high quantities of starting material; for instance, the Illumina TruSeq small-RNA sequencing platform requires a minimum of 10–50 ng of purified small-RNA (Illumina TruSeq small-RNA protocol) or 1000 ng of total RNA [298]. As such, many studies have been focused on global exosomal miRNA purifications from blood or biofluids, which provide workable amounts of total RNA [296,299]. In these studies, exosomes have been rarely isolated from plasma volumes less than 1 mL, and typically these isolations are carried out by means of ultracentrifugation, which yields higher exosome quantities [112]. However, currently many researchers are driving the development towards the utilization of optimized protocols adapted for NGS analysis of low miRNA abundant exosome sub-populations [300]. To this end, our laboratory developed and optimized a cDNA library preparation protocol which allows for 3′ barcoded miRNAs from up to 18 individual samples to be pooled prior to 5′ adapter ligation, reverse transcription, and PCR amplification (~15 to 20 cycles decided via a pilot PCR reaction), which increases the carrier effect of the combined RNA for subsequent enzymatic reactions and purifications [301,302]. This protocol was purposely designed for the analysis of samples with minute quantities of total RNA, and we demonstrated its robustness for exosomal small-RNA analyses with total RNA input <1.5 ng [149]. Using the EV-CATCHER assay for highly pure selection of CD63 exosomes from the serum of patients infected with the SARS-CoV-2 virus and admitted to our health network, we were able to detect miRNA expression differences in miR-146a and miR-126-3p between COVID-19 patients hospitalized with mild symptoms that did not receive mechanical ventilation and COVID-19 patients hospitalized with severe respiratory symptoms that received mechanical ventilation, within levels estimated at ~25 zeptomoles (10 e-21 mole), which we successfully validated by qPCR [149]. Although the detection miRNAs from low-input total RNA is challenging, our experiments strongly indicate that the specificity and purity of the exosome preparation is critical for the successful detection of unique and rare disease miRNA biomarkers.

Of note, the Nanostring nCounter miRNA platform is an advancing technology for the sensitive, reproducible, and highly multiplexed analysis of up to 800 pre-selected miRNAs. The nCounter miRNA platform is based on a novel method of direct molecular barcoding and digital detection using color-coded probe-pairs [303]. This technology relies on the use of unique miRtags (i.e., oligonucleotide tags), which are ligated to the 3′ end of specific miRNAs. Sequence specificity during ligation is ensured through stepwise controlled annealing and ligation temperature in addition to the use of Tm-optimized bridging oligonucleotides complementary to a portion of the target miRNA and miRtag [304]. To date, a few studies have employed this technology for the quantification of miRNAs from plasma-derived ultracentrifuged exosomes [305]. This methodology is very attractive because it bypasses PCR amplification, which is required during the preparation of small-RNA cDNA libraries, but it may not offer the proper sensitivity yet for the analysis of molecules in the zeptamole concentration range.

Quantitative PCR (qPCR) Analyses

Currently there are numerous commercially available platforms and qPCR-based assays for the analysis of miRNAs (i.e., TaqMan miRNA assays (Thermo Fisher), TaqMan Array microRNA 384-well cards (Thermo Fisher, Tokyo, Japan), OpenArray (Thermo Fisher), SmartChip (Takara, Kusatsu City, Japan), miScript miRNA PCR arrays (Qiagen, Hilden, Germany), miRCURY™ LNA™ Universal RT microRNA PCR (Exiqon, Vedbæk, Denmark), and miRNA Oligo chip (3D Gene)). Quantitative PCR technology is still considered the gold standard for validation of findings made during high-throughput analyses; however, it has inherent biological and experimental limitations for exosomal miRNA studies, which include: (1) The discovery of tumor miRNA panels specific to a tumor exosome remains limited to known or selected miRNAs (which can be multiplexed for up to 700 miRNAs using TaqMan card arrays) rather than global unbiased NGS analyses. In this fashion it does not allow discovery of IsomiR (i.e., miRNA variants of the same miRNA, which can differ in sequence and/or length composition and will bind to different mRNA targets than the original miRNA sequence [306]) expression ratio differences for a specific miRNA in exosome sub-populations. The study of isomiRs is relevant in cancer, as their expression ratio has been shown to be affected by and can elicit changes in tumor cell migration and invasion [307]. (2) qPCR assays typically require large inputs of RNAs for global evaluation and may still require a pre-amplification step (i.e., typically ~14 cycles) before the 40-cycle amplification for fluorescent quantification, which leaves room for potential amplification bias or errors. Most importantly, even when using single qPCR assays, the sensitivity of detection may not be sufficient for miRNA expression validation of total RNA extracted from a selected subpopulation of circulating exosomes. In fact, we have previously demonstrated that the validation of differentially expressed exosome miRNAs, identified by NGS below 25 zeptomoles, using RT-qPCR is challenging as this quantification lies within the threshold of detection (Ct values of ~36–39) [149]. Studies that have been performed using total RNA extracted from globally circulating exosomes for NSCLC [308], prostate cancer [309], metastatic colorectal cancer [310], and gastric cancer [311] have all failed to provide robust, reproducible, and diagnostic cancer-specific miRNA signatures. The lack of technical standardization and the total RNA purification from globally purified exosomes from plasma (i.e., most of them likely released by blood cells) may have contributed to these limitations.

Droplet Digital PCR (ddPCR) Analysis

Droplet digital PCR (ddPCR) technology has thus become very popular because of its inherent capability for the ultra-sensitive detection of rare molecules [312]. This technology enables absolute quantification by partitioning the reaction into individual droplets (i.e., using water-in-oil and microfluidic technology), wherein molecules are randomly distributed, with each droplet/compartment containing zero, one, or many molecules. In essence this allows for amplification of each compartmentalized PCR mix/target molecule(s) to occur independently of each other [313]. Following amplification, the absorbance of each droplet is counted [314] and using Poisson statistics and the number of positive reactions (i.e., compartments which contain molecules and thus positive signal) the initial copy number and target density are calculated. ddPCR relies on the analysis of individual droplet amplitude and scatter for the absolute quantification of nucleic acids [315]. BioRad has become the leader in ddPCR technology and currently markets their award-winning QX200 AutoDG ddPCR instrument for either EvaGreen or probe-based digital PCR applications. The instrument’s specifications indicate that in each 100 µL volume, up to 20,000 droplets may be generated; generally data from 12,000–16,000 droplets are used for accurate quantification of target molecules (https://www.bio-rad.com/webroot/web/pdf/lsr/literature/Bulletin_6407.pdf (accessed on 25 May 2022)). Wang et al. recently demonstrated that the analysis of miRNA targets from exosomes (i.e., purified from urine), when compared with traditional TaqMan qPCR technology, was significantly more accurate and sensitive for the detection of low concentrations (i.e., <100 copies/µL) [316]. However, although Bio-Rad offers a large array of mRNA-based detection using their ddPCR instrument, they have not established a standardized protocol for the analysis of miRNAs, and the only available protocols are from published studies [317,318]. The protocol of Hindson et al. suggests that using ddPCR with TaqMan probes improved the day-to-day reproducibility by 7-fold, as seen by a 37–86% decrease in the coefficient of variation, compared to standard qPCR [315]. Considering that tumor exosome miRNA biomarkers may be within or below the zeptomole concentration in a given biofluid, ddPCR technology offers a unique opportunity for the development of ultra-low, -sensitive, and -specific clinical assays and the detection of rare tumor-specific circulating exosomal miRNAs. Standardization of both the tumor exosome isolation techniques (i.e., antigen-based capture, nanoparticle enumeration) and miRNA ddPCR assay preparation will be critical.

5.1.2. Function of MicroRNA in Cancer Exosomes

Exosomal miRNAs from tumor exosomes have been shown to exert a multitude of biological effects during cancer progression which include: (1) the regulation of tumor growth, (2) The evasion from host immune responses, and (3) remodeling of the tumor microenvironment and metastasis.

Regulation of Tumor Growth

Cellular proliferation is a critical aspect to tumor progression, which commonly is a result of the deregulation of cell-cycle related proteins [316], and is a known hallmark of cancer [22]. Research studies have demonstrated that tumor-derived exosomal miRNAs can regulate cancer cell proliferation by targeting cell cycle-associated proteins and signaling proteins [317]. For example, in colorectal cancer, miR-200b is transferred between adjacent cancer cells, where it directly targets 3′-UTRs of p27 and Rho Family GTPase 3 (RND3), two proteins involved in cell cycle regulation [318,319], leading to their downregulation, which in turn induces proliferation in recipient cells [320]. Contrarily, miR-6869-5p, known to act as a tumor suppressor [321], is significantly downregulated in serum-derived exosomes originating from colorectal cancer cells [322]. The downregulation of exosomal miR-6869-5p delivery is associated with enhanced cell proliferation and increased production of the inflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) [323]. In metastatic breast cancer, the transfer of exosomal miR-1246 to non-malignant breast cells promotes proliferation, migration, and drug resistance through the direct downregulation of cyclin-G2 (CCNG2) [321]. Moreover, exosomal miRNAs profoundly regulate the apoptotic signaling pathway in cancer cells; for example, Jing et al. demonstrated that the delivery of exosomal miR-769-5p to gastric cancer cells directly targets caspase-9, a cysteine-aspartic protease that serves as the initiator of the intrinsic apoptosis pathway [324], leading to the inhibition of the downstream caspase-dependent apoptotic pathway, and promoting the degradation of the tumor suppressor protein p53 through the ubiquitin–proteosome pathway [325]. Furthermore, in breast cancer, the uptake of exosomal miR-128 leads to a reduction in the expression of the pro-apoptotic protein Bcl-2-associated X protein (Bax) in recipient breast cancer cells [326].

Evasion from Host Immune Responses

Exosomal miRNAs have been shown to function as critical mediators in the crosstalk between cancer cells and macrophages within the tumor microenvironment [327]. Macrophages can be divided into two classes: either M1-polarized or M2-polarized depending on their function [328]. M1 macrophages inhibit tumor growth whereas M2 macrophages have been demonstrated to provide immunosuppression around the tumor niche, while promoting tumor development and metastasis [329]. Exosomes secreted from the SKOV3 epithelial ovarian cancer cell line and containing miR-222-3p can induce macrophage polarization and differentiation to the M2 phenotype by suppression of the cytokine signaling 3 (SOCS3)/STAT3 pathway, which results in tumor growth and enhanced metastatic capability [330]. Similarly, exosomes from hypoxic ovarian cancer cells, present in the center of tumors, deliver miR-940 to macrophages and promote their polarization to the M2 phenotype, which results in tumor progression [331]. In a similar manner, hypoxic glioma-derived exosomes promote M2 macrophage polarization through the delivery of miR-1246, which targets TERF2 interacting protein (TER2IP) via the STAT3 and NF-κB pathways [332]. Dendritic cells are antigen presenting cells (APCs) linking innate and adaptive immunity which express a wide range of toll-like receptors (TLRs) and cytokines [333] that are critical for the activation of host immune responses to pathogens [334], including recognition of cancer cells [335]. In pancreatic cancer, the delivery of exosomes containing miR-203 can inhibit the expression of TLR4 and the production of cytokines, including TNF-α and interleukin-12 (IL-12) in dendritic cells [336], leading to dendritic cell dysfunction, which facilitates cancer cell evasion of the host’s immune response [337]. In addition to the effects that exosomal miRNAs have on macrophages and dendritic cells, they have also been shown to have profound effects on natural killer (NK) cells [338]. NK cells are a part of the innate immune system and can directly kill tumor cells [339] through the release of perforins and granzymes from their cytoplasm [340]. Briand et al. demonstrated that radiotherapy induced the release of exosomes containing increased levels of miR-378a-3p from a variety of cancer cell types, which resulted in the decreased release of granzyme-B from NK cells [341]. Natural killer group 2D receptor (NKG2D) is an activating receptor that is expressed on NK cells, which plays a pivotal role in tumor immunosurveillance [342]. Under hypoxic conditions, the IGR-Heu lung carcinoma and K562 myelogenous leukemia cell lines release exosomes containing increased levels of miR-23a, which when transferred to NK cells results in decreased expression of NKG2D, and thus reduced NK function [343].

Tumor Microenvironment Remodeling and Metastasis

The initiation of metastasis typically occurs when cancer cells undergo a process of epithelial-to-mesenchymal transition (EMT), wherein non-motile epithelial cells transform to a motile/invasive mesenchymal cell type [344]. Once these cells detach from primary tumors and travel through blood vessels, they establish the metastatic niche. Significant evidence suggests that tumor exosomes with their miRNA cargoes play a profound regulatory role in the metastatic process by controlling EMT, angiogenesis (i.e., formation of new blood vessels), niche cell education [345], and tumor cell invasion (Figure 1). Cai et al. demonstrated that exosomes isolated from glioblastoma patients carried increased levels of miR-148a compared to exosomes from healthy volunteers [346]. Exosomal delivery of miR-148a to T98G glioblastoma cells results in cell proliferation and metastasis by downregulation of the cell adhesion molecule 1 (CADM1) and activation of the STAT3 pathway. In triple negative breast cancer, exosomal miR-122 released from breast cancer cells has been shown to reduce the glycolytic pyruvate kinase PKM, and thus decreases glucose uptake in non-tumorigenic cells within the pre-metastatic niche, allowing for increased nutrient availability within the pre-metastatic niche and promoting survival of breast cancer cells that have metastasized [347]. In melanoma, tumor cells exchange miR-222 via exosomes to enhance tumor malignancy by the activation of the phosphoinositide 3-kinase (PI3K)/Akt pathway, via the downregulation of its target gene p27Kip1 [348]. Bone marrow-derived stem cells (BMSCs) present in the hypoxic tumor microenvironment are known to contribute to cancer progression [349]. In lung cancer, hypoxic BMSC-derived exosomes shuttle miR-193a-3p, miR-210-3p, and miR-5100 to lung epithelial cancer cells, which promotes their metastasis via the activation of STAT3 pathway-mediated EMT [350]. Moreover, Yang et al. demonstrated that hepatocarcinoma (HCC) cells with high metastatic potential are able to transfer exosomes containing miR-92a-3p to HCC cells with low metastatic potential, by promoting the EMT-mediated PEN/Akt pathway, as demonstrated by the increased expression of mesenchymal biomarkers (N-cadherin, β-catenin, Snail) and decreased expression of E-cadherin [275]. Altogether, these experiments demonstrate the powerful role that exosomes play in the intercellular communication of cancer cells for the self-propagation of cancer.

Altogether, these studies have demonstrated that cancer cells take advantage of exosomes to transfer tumorigenic miRNAs that can reprogram (i) adjacent tumor cells to promote tumor growth and (ii) a plethora of critical immune cells, to protect the tumor, and (iii) educate pre-metastatic niche cells for the formation of secondary tumor sites before metastasis. However, circulating cancer exosome studies conducted on the same human cancer types have not systematically identified the same deregulated miRNAs, globally suggesting that the miRNA packaging of exosomes may help differentiate healthy individuals from cancer patients (See Table 1). Along with the design of ultra-sensitive and specific cancer exosome purification assays, these studies highlight the potential clinical applications of exosome-based miRNA signatures for non-invasive diagnostic and prognostic evaluation of human cancers.

Table 1.

Published miRNA exosome cancer biomarkers identified in human biofluids. Lists of exosomal miRNAs circulating in biofluids of patients diagnosed with colorectal cancer, ovarian cancer, Glioblastoma, liver cancer, pancreatic cancer, lung cancer, extranodal natural killer/T-cell lymphoma, and prostate cancers.

Cancer Type Differentially Expressed Between Healthy and Cancer Reference
miRNA Biomarkers
Colorectal Cancer miR-224-5p, miR-548d-5p, miR-200a-3p, miR-320d, miR-200b-3p, miR-1246 Tang et al., 2019 [310]
novel_246, novel_301, miR-27a-5p
miR-135a-5p, miR-204-5p Sun et al., 2021 [104]
miR-6869-5p Yan et al., 2018 [321]
miR-486-5p, miR-3180-5p Yan et al., 2017 [322]
miR-638, miR-5787, miR-8075, miR-6869-5p, miR-548c-5p
Ovarian Cancer miR-21, miR-141, miR-200a, miR-200b, miR-200c, miR-203, miR-205, miR-214, miR-215 Taylor and Taylor, 2008 [89]
miR-940 Chen et al., 2017 [331]
miR-222-3p Ying et al., 2016 [330]
Glioblastoma let-7a, miR-15b, miR-16, miR-19b, miR-21, miR-26a, miR-27a, miR-92, miR-93, miR-320, miR-20 Skog et al., 2008 [91]
miR-148a Cai et al., 2018 [346]
Liver Cancer miR-17, miR-18a, miR-19a, miR-19b, miR-20a, miR-92a-3p Yang et al., 2020 [276]
miR-193a-3p, miR-210-3p, miR-5100 Zhang et al., 2019 [350]
Pancreatic Cancer miR-21, miR-210 Wu et al., 2020 [294]
miR-193a-3p, miR-210-3p, miR-5100 Zhang et al., 2019 [350]
Lung Cancer miR-132-3p, miR-181b-5p, miR-27a-3p, miR-27b-3p, miR-320a, miR-361-5p, let-7b-5p, miR-24-3p, miR-3184-5p, miR-486-5p, miR-486-3p, miR-320b Jin et al., 2017 [295]
let-7a-5p, let-7d-5p, let-7f-5p, miR-26b-5p, miR-30a-3p, miR-30e-3p, miR-744-5p, miR-744-5p, let-7e-5p, miR-191-5p, miR-191-5p, miR-206, miR-21-5p, miR-23a-5p, miR-23b-5p, miR-10b-5p, miR-15b-5p
miR-30b, miR-30c, miR-103, miR-122, miR-195, miR-203, miR-221, miR-222 Giallombardo et al., 2016 [308]
miR-193a-3p, miR-210-3p, miR-5100 Zhang et al., 2019 [350]
Extranodal Natural Killer/T-Cell Lymphoma miR-320e, miR-4454, miR-4516, miR-630, miR-122-5p, miR-574-5p, miR-22-3p, miR-486-3p, miR-1915-5p, miR-1972, miR-1285-5p, miR-222-3p, miR-1305, miR-891b, miR-4455, miR-21-5p, miR-1258, let-7b-5p, miR-25-3p, miR-1268a Ryu et al., 2020 [305]
miR-564, miR-196a-5p, miR-520c-3p, let-7d-5p, let-7i-5p, miR-212-3p, miR-29a-3p, miR-608, miR-503-5p, miR-587, miR-548g-3p, miR-765, miR-34c-3p, miR-770-5p, miR-301a-5p, miR-526a, miR-340-5p, miR-325, miR-199a-3p+miR-199b-3p, miR-423-3p
Prostate Cancer miR-196a-5p, miR-34a-5p, miR-501-3p, miR-92a-1-5p Rodríguez et al., 2017 [309]

↑ = miRNA expression observed in circulating exosomes isolated from cancer patients as compared to circulating exosomal miRNAs isolated from healthy individuals and ↓ = decreased miRNA expression observed in circulating exosomes isolated from cancer patients as compared to circulating exosomal miRNAs isolated from healthy individuals.

5.2. Protein Biomarkers

In addition to the many intact RNA species and fragmented genomic DNA available in exosomes, many proteins occupy the surface and interior compartments of these EVs. In the early days, the identification of protein components from EVs was performed by their separation on one-/two-dimensional gels by electrophoresis followed by tandem mass spectrometry [351,352,353,354]. Although these traditional approaches initially allowed for the identification of the mostly abundant proteins, it also allowed for the identification of important exosome protein biomarkers, such as tetraspanins (e.g., CD9, CD63, CD81, and CD151), 14-3-3 proteins, heat shock proteins (e.g., hsp70 and hsp90), and major histocompatibility complex (MHC) [351,352,353,354]. However, during the last two decades, great technical advances in proteomics have been made, which increased sensitivity and have allowed for the identification of thousands of proteins from exosomes and EV proteins obtained from various cell types and body fluids, including serum, urine, saliva, ascites, and breast milk, as shown in several databases (including EVpedia [282], ExoCarta [283], and Vesiclepedia [281]). The ever-growing list of proteins has largely deepened our understanding of EVs and facilitated the biomedical applications in multiple fields [355,356,357,358,359,360,361,362,363,364]. In this section, we will discuss the technological advances in EV proteomics and their applications in cancer liquid biopsy, particularly in the past several years.

5.2.1. Protein Extraction from Exosomes and EVs Prior to Analysis

The isolation of exosomes or other EV species is critical to factor proteomic analyses, and it is easy to understand that sample quality directly affects the final proteomics results. Particularly, the excess of certain proteins may overwhelm the proteomic analyses of exosomes as exemplified with the evaluation of exosomes from cell culture media, which often contains a high abundance of albumin due to the addition of serum (i.e., bovine serum), or with exosomes contained in biofluids which carry large amounts of endogenous components (e.g., albumin or other abundant serum proteins from blood samples, uromodulin for urine samples). The presence of such confounding molecules often severely obscures the detection of EV proteins, especially low abundant ones. Therefore, specific procedures should be carefully designed to reduce these non-exosomal proteins or other contaminants, which interfere with proteomic analysis [365,366]. Of note, despite great interest in EVs and their associated proteins, there is currently no consensus on the preferred method(s) for EV isolation. Moreover, it appears that a single isolation method might not reveal the total and specific proteomic content of EVs [367,368]. It is anticipated that the combination of more than one method may be used to describe the proteome content of particular exosome populations, especially considering that exosomes circulating in biofluids represent a heterogeneous population of exosomes from different cellular origins [365,366,367]. Recently a comprehensive and quantitative proteomic analysis was performed on EVs isolated from human primary dendritic cells [369], and it was found that common exosome markers (such as MHC, flotillin, and HSP70) are all identified in all EVs independently of their sedimentation speed, whereas tumor susceptibility 101 (TSG101), syntenin-1, EHD4, Annexin XI, and ADAM10 appeared to be better protein markers for ectosomes than the traditional exosomal protein markers previously used. Undoubtedly, appropriate isolation methods should be adopted for a particular EV population of interest so that meaningful proteomics data can be achieved; the purification methods currently available for selection or purification of exosomes are described in a previous paragraph. As we suggested in this previous section, the combination of different purification methods may help achieve the adequate selection of exosomes from different types of biofluids [365,366,367,368].

5.2.2. Exosome and EV Protein Processing Approaches

Traditional Methods

Unarguably, sample processing is a crucial step for successful proteomics studies.

With traditional proteomics workflows, proteins were often digested in gels or in solutions prior to analyses [351,352,353,354,365,366,367,368]. However, this approach suffers from several inherent drawbacks, such as tedious procedures (i.e., gel digestion and peptide purification/extraction) and lengthy processing time, which limits sample processing throughput and analytical reproducibility.

Filter-Aided Sample Preparation

As an alternative, filter-aided sample preparation (FASP), a simplified method for proteomics sample preparation, has been used for exosome and EV proteomics [370,371,372]. As a relatively new approach, suspension trapping (S-Trap)-based sample preparation has its own advantages [373]. In the S-Trap approach, samples are lysed and solubilized in 5% SDS to create fine particles so that they can be trapped in the filter column. Very strikingly, SDS and other contaminants can be easily removed into the flow-through (even more efficient than the FASP processing). Moreover, proteins retained on the column are readily digested into peptides, without an additional desalting process. All these advantages help further minimize sample loss and increase proteome coverage. Indeed, thorough comparison with the in-solution digestion processing and the FASP method suggests that the S-Trap approach provides the largest number of identifications and the highest reproducibility for EV proteomics [374]. Last but not least, the S-Trap approach allows analysis of substantially lower starting amounts of proteins (down to 50 ~ 200 ng), making it extremely appealing for characterization of minute amounts of EV samples.

5.2.3. Mass Spectrometric Analysis of EV and Exosomal Proteins

Top-Down vs. Bottom-Up Proteomics

Due to the high throughput and its unbiased nature, modern mass spectrometry-based proteomics has allowed for the global identification and quantification of exosomes and other EV proteins [375,376,377,378,379]. Indeed, in the past few decades, quickly evolving proteomics techniques have played an instrumental role in analyzing EV proteins (especially exosomes and ectosomes). As one of the two strategies, top-down proteomics has been tentatively used for the characterization of EV proteins. For example, >200 proteoforms (including multiple proteoforms of the pro-inflammatory mediators S100 A8 and A9) were identified from exosomes shed by murine myeloid-derived suppressor cells [380]. In contrast, bottom-up proteomics has gained much popularity and is widely employed to characterize EV protein cargoes [375,376,377,378,379]. Instead of analyzing intact proteins, proteins are subjected to digestion and then analyzed by tandem mass spectrometry in the bottom-up proteomics strategy. Although almost all major proteomics workflows can be adapted for EV proteomes, challenges remain. Despite great endeavors having been made in the preparation of exosomes from cultured cells or from biofluids (as aforementioned), purification of large amounts of exosomes with high purity is still not routine, which is either a tedious task or inherently limited by the low biofluid volume output. To that end, there is a strong need to develop highly sensitive and robust sample processing procedures and quantification strategy methods so that deep proteomics for EVs can be achieved. Herein, we do not intend to summarize all proteomic techniques proposed; rather, we will briefly present key factors that determine the output of EV proteomics: the purity/quality of isolated EVs, EV protein sample processing procedures, and tandem mass spectrometry (MS/MS) techniques.

In addition to sample processing, mass spectrometry is another key aspect for EV proteomics. The tandem mass spectrometry-based EV proteomics is usually performed via the data-dependent acquisition (DDA) mode. By combining high-resolution mass spectrometers with isotopic labeling techniques, quantitative information can be obtained for exosome proteomics [375,376,377,378,379]. For example, stable isotopic labelling method using isobaric tags for relative and absolute quantitation (iTRAQ) has been used for urinary EV proteomics and the identification of potential prostate cancer biomarkers, with candidate proteins verified using multiple reaction monitoring (MRM) mass spectrometry (candidate proteins include fatty acid binding protein 5, granulin, AMBP, CHMP4A, and CHMP4C) [381]. Stable isotope labelling with amino acids in cell culture (SILAC) was applied for the proteomics of EVs derived from microsatellite unstable colorectal cancer, allowing for the identification of transforming growth factor beta receptor type 2 (TGFBR2)-regulated EV proteins (such as FN1, GLUL, CTGF, and CDK1) [382]. As a relatively newly emerged approach, label-free data-independent acquisition (DIA) such as SWATH (sequential window acquisition of all theoretical fragment ion) has also been used for the quantitative proteomics of EVs and exosomes [374]. In one study, DDA and SWATH-MS were combined to identify exosome biomarkers indicative of acute or persistent radiation-induced responses [383]. In comparison to total urine proteomics, urine exosome proteomics was demonstrated to be superior for the identification of radiation signatures. Moreover, 23 biomarkers were identified from urine exosomes and 24 biomarkers from serum exosomes post whole-body irradiation (WBI) [383]. Interestingly, proteomic signatures of urinary exosomes seemed to be different from those obtained in serum, with the former indicating injury of the liver, gastrointestinal, and genitourinary track whereas the latter indicated vascular injuries and acute inflammation in response to radiation. Recently, DIA-MS proteomics was used for the analysis of EVs extracted from breast cancer cells, with performance comparison between DDA-MS ad DIA-MS performed [374]. The analyses revealed that DIA-MS outperformed DDA-MS, with deeper EV proteomic coverage and better quantification accuracy achieved [374].

5.2.4. Mass Spectrometric Analysis of EV and Exosomal Protein Modifications

Post-translational modifications (PTMs) add another layer to the regulation of protein functions [384,385]. In comparison to unmodified peptides, modified peptides are present in a low abundance and sub-stoichiometric ratio, making their characterization technically challenging. For such unique and low abundance peptides, enrichment (e.g., TiO2 for phosphopeptides and lectin chromatography for glycopeptides) is necessary prior to mass spectrometry analysis. So far, limited but very appealing studies have been performed for PTM proteomics of exosome samples. For examples, 19 phosphorylation sites corresponding to 14 phosphoproteins [386] and 126 N-glycopeptides belonging to 37 glycoproteins [387] were identified from human urine exosomes. In a recent study, by combining multi-lectin weak affinity chromatography and extensive fractionation (by using high pH reversed-phase chromatography), 378 glycoproteins with 604 glycosylation sites were identified from urine exosomes [388]. By analyzing exosomes isolated from the plasma of breast cancer patients and healthy subjects, a recent phosphoproteomics study identified 9225 and 1014 unique phosphopeptides, with 156 and 271 phosphorylation sites significantly regulated in the microvesicles and exosome fractions, respectively [389]. Among them, three potential phosphopeptide markers belonging to the RALGAPA2, PRKG1, and TJP2 proteins were found to be significantly different between breast cancer patients and healthy subjects. Recently, over 2300 phosphoproteins were identified in breast cancer EVs [390]. Among the identified proteins, many were found to be plasma membrane-based, further indicating that surface protein modifications on cancer exosomes are performed by imparting signaling functions to recipient cells (bypassing the need for the initiation of phosphorylations on membrane receptors). Collectively, although proteomics for PTMs has been largely under-studied in EVs, these very encouraging results suggest that PTMs of proteins should also be of great importance. Comprehensive characterization of protein PTMs in EVs will help understand the sorting mechanisms and identify novel PTM-specific functions of proteins secreted in EVs. They may also help with the discovery of novel biomarkers for translational applications as well as the identification of PTMs with therapeutic effects.

Taken together, proteomics has provided invaluable insights in all aspects of exosome research (i.e., EV biogenesis, secretion, and intercellular interactions). These analyses have revealed that exosomes are enriched with secreted proteins, plasma membrane proteins (e.g., adhesion proteins, receptors, tetraspanins, and transporters), vesicle trafficking-related proteins, cytoskeletal proteins (e.g., actins, myosins, and tubulins), and cytosolic proteins (such as molecular chaperones and metabolic enzymes), which play fundamental roles in their effects on recipient cells as is well studied with cancer metastasis [379,380,381,382,383,390,391]. It is also clear that EV proteomes are dependent not only on the parental cell type and conditions in which the EVs are secreted but are also based on the type of EVs. Very importantly for the diagnostic use of exosomes, the proteome content of each exosome population derived from different sources tends to retain unique signatures and markers of its origin (e.g., specific surface proteins), which may provide features for the early detection of cancer as a minimally invasive liquid biopsy approach.

5.3. Lipid Biomarkers

Lipids are diverse biomolecules that have a plethora of biological functions during developmental, physiological, and pathological processes [392,393,394]. Lipids provide structural integrity to cells and to their subcellular organelles, are an essential part of cellular organization, and allow intracellular trafficking [395,396,397]. Through direct and indirect signaling, lipids can regulate cellular proliferation [398], differentiation [399], and apoptosis [400] but also paly essential roles in energy homeostasis through mitochondrial fatty acid β-oxidation [401]. The dysregulation of lipids in cancer involves alterations of the membrane composition, up/down regulation of pleotropic signaling lipids, and changes in energy metabolism of the transformed cell [402]. These alterations have been linked to cancer cell growth [403], metastasis [404], immune evasion [405] and therapeutic resistance [394]. The study of exosome-derived lipids as cancer biomarkers is an emerging area of research. The lipidomic composition of cell-specific exosomes, their roles in exosome formation and biology, and the methods used for their analysis have been reviewed previously and thus will not be discussed in detail here [406,407,408,409,410,411]. A number of eloquent studies have also used lipidomic analysis to identify differences in cancer-derived exosome lipid composition compared to their parental cells [412,413,414,415]. While these studies are fundamental in understanding the composition of exosomes derived from diverse cancer cells, with an aim to elucidating their functional roles, their discussion is out of the scope of this review. This section will focus on the lipid profiles detected from cancer-derived exosomes that have demonstrated utility as diagnostic and prognostic biomarkers, which is a field in its infancy. All lipids presented in the forthcoming sections are annotated according to the nomenclature published by Fahy et al. [416,417].

5.3.1. Methods for the Analysis of Lipids from Exosomes

Mass spectrometry-based technology is the most sensitive and widely used approach for the analysis of lipids from isolated exosomes [418,419,420]. In the forthcoming section we will provide a brief overview of the different direct and indirect MS methods used for the analysis of lipids from exosomes. Lipids can be analyzed directly from intact exosomes using matrix-assisted laser desorption ionization (MALDI) mass spectrometry [418]. Alternatively, they can be analyzed following one of the four routinely used solvent-based liquid–liquid extraction techniques using both direct and indirect MS methods [419].

Analysis of Intact Exosomes

This technique offers the most rapid and high-throughput analysis of exosomes because they are spotted directly onto the MALDI target without any prior sample preparation. Data acquisition can be carried out within minutes (per sample), providing results that have been shown to match those carried out by MALDI MS of lipid extracts [418]. MALDI is more tolerant to salt impurities when compared to other MS techniques, providing robust and sensitive detection of a wide range of lipid species [421]. A limitation of this technique is the inclusion of high abundant matrix ions that can complicate the resulting mass spectra and the suppression of low abundant lipids or lipids that do not ionize readily. This is particularly evident in positive ion mode, where phosphatidylcholines (PCs) predominate the spectra. There is, however, a number of sample preparation techniques that can be utilized for the targeted analysis of lipids, such as the lithium base-hydrolysis technique introduced recently by Tran et al. which mitigates these ion suppression effects, enabling the sensitive detection of a range of sphingolipids in positive ion mode [422]. Another limitation of both intact analysis and the direct analysis discussed in the following section is the inability of these techniques to separate isomeric and isobaric lipid ions, thereby reducing the number and identification of the lipids detected when compared to indirect methods of analysis. The ability to separate lipid isomers during direct MS analysis of lipids is being realized with the advancement of more sophisticated mass analyzers, including ion mobility and high-resolution platforms, that are enabling the separation of these previously unresolvable lipid ions when analyzed directly [423,424]. Ion mobility platforms employ an electrophoretic-based separation of ions in the gas phase based on their charge, size, and conformation [425]. In this way, ion mobility MS platforms enable the separation of complex lipid mixtures prior to mass analysis [426]. Aside from MALDI, another ion source that has utility for the intact analysis of isolated exosomes is desorption electrospray ionization (DESI). While DESI MS is yet to be utilized for the analysis of exosomes, this technique requires no sample preparation as the samples are placed directly in the ambient ion source whereby desorption and ionization of lipids from the sample are facilitated through a stream of charged solvent (electrospray) that is rapidly applied to the surface of the sample [427]. Thus, DESI MS offers promise as a high-throughput analytical technique of intact exosomes for biomarker investigations.

Direct MS Analysis of Lipid Extracts

The direct analysis of lipid extracts by electrospray ionization (ESI) coupled to tandem MS, without any prior chromatographic separation, was introduced as “shotgun lipidomics” by Han and Gross [428]. Using this technique, lipids are continuously infused into a mass spectrometer and analyzed using simultaneous MS and MS/MS scans, thereby providing structural information that enables their identification and, with the addition of internal standards, enables their quantification [414,429]. The separation of isomeric and isobaric lipids can again be seen as a limitation of this technique; however, with the advancements of the aforementioned instruments and the addition of differential ion mobility to the shotgun lipidomics workflow, the separation of isobars by direct infusion techniques is a now reality [430]. Direct analysis of lipids from exosomes is also possible using MALDI and DESI MS, as discussed for the analysis of lipids from intact exosomes.

Indirect MS Analysis of Lipid Extracts

Indirect analysis of lipids extracted from exosomes involves prior separation utilizing chromatography techniques coupled to MS analysis. Common chromatography techniques include gas chromatography, liquid chromatography, and thin layer chromatography [411,419,431]. The simplest and most cost-effective of these techniques is thin layer chromatography (TLC) coupled to MALDI MS [418]. With this technique, lipid extracts are separated based on their head group polarity using a stationary phase more commonly consisting of silica gel and an apolar solvent mobile phase [432]. The TLC plate is then stained to visualize the lipid bands, which can then either be excised for MALDI MS analysis following an additional lipid extraction protocol or analyzed directly using MALDI imaging [433]. Liquid chromatography-based MS (LC-MS) techniques are the most widely used of the indirect MS methods for lipid analysis [434,435]. As with TLC, the polarity of the mobile and stationary phases is selected based on the selectivity and sensitivity desired [436]. The separation of lipid classes based on their polar head group is more commonly carried out using hydrophilic interaction liquid chromatography (HILIC) coupled to MS [434]. For comprehensive subclass coverage based on lipid fatty acid composition, reverse-phase liquid chromatography (RPLC) is the method of choice [437]. The hydrophobic interactions of RPLC separates lipids based on their carbon chain lengths and levels of saturation, with longer chain polyunsaturated acyl-containing lipids eluting last [434]. The mobile phases used for the elution of lipids can greatly differ from lab to lab, but all are based around organic–aqueous compositions that modify hydrophilic/hydrophobic interactions.

5.3.2. Lipidomic Profiling of Exosomes from Cancer Cells and Cancer Cell Lines

In a study of drug-sensitive vs. drug-resistant non-small lung cell cancer cells, Jung et al. used MALDI-ToF-MS to demonstrate that lipidomic profiles could stratify gefitinib-sensitive from gefitinib-resistant cells based on their exosome composition [438]. They detected 27 lipid signatures that were increased in resistant cells and 40 that were decreased when compared to their drug-sensitive counterpart. Lipids were identified as PCs and ether-linked PCs, lyso-PCs, sphingomyelins (SMs), phosphatidylglycerols (PGs), phosphatidylinositols (PIs), and lyso-PIs with varying fatty acid residues. In a comprehensive metabolic study of the pancreatic cell line PANC-1, the use of UPLC-ESI-Q-TOF-MS allowed identification of diacylglycerol (DAG) (20:2/18:1) as a biomarker of PANC-1 cells undergoing epithelial-to-mesenchymal transition (EMT) [420]. EMT is known to contribute to both metastatic disease and therapeutic resistance of pancreatic cancer cells [439,440]. These two studies demonstrate the utility of lipidomic exosome profiling for biomarker discovery, with potential to drive precision medicine by prediction of treatment response. LC-MS/MS analysis of EVs isolated from the prostate cell line, RWPE1, the prostate cancer cell line, NB26, and the metastatic/hormone resistant prostate cancer cell line, PC-3, showed similar lipid profiles when the mean abundance of lipid classes was compared [441]. Analysis of lipid subclasses and their fatty acyl residue composition from each sample, however, enabled the stratification of normal, cancer, and metastatic prostate cancer cells based on specific lipid profiles. In total, 187 lipid species were quantified as showing a differential abundance between these cell lines, highlighting the complexity of lipid analysis when considering full structural composition of each lipid class. DAG and triacylglycerol (TAG) species were decreased in tumor and metastatic cells when compared to their normal counterpart, with DG (16:0/22:6) detected with the highest fold change difference between normal vs. prostate cancer cells lines. Conversely, a number of PCs, SMs, phosphatidylethanolamine (PEs), ceramides (Cers), hexosylceramides (HexCers), phosphatidylserine (PS), PIs, cholesteryl esters (CEs), and ether-phospholipids (PLs), were enriched in both tumor and metastatic cell lines to degrees that were dependent on the structural combination of their subclass (head group) and fatty acid residues. A panel of biomarkers that could stratify prostate cancer and prostate cancer vs. metastatic disease was not proposed, possibly due to the number of lipids detected in this study and the processing method utilized to handle such complex data. Another extensive lipidomic study comparing the lipidomic profiles of exosomes from the ovarian cancer cell line, SKOV-3, to those detected in exosomes from normal ovarian surface epithelial cell line, HOSEPiC, identified 110 significantly altered lipids from over 1200 identified [442]. These again encompassed the phospholipid, sphingolipid, and sterol classes already mentioned in previous studies and these differences were dependent on the fatty acyl composition of the subclasses detected. The authors highlighted PG (34:1) and CE (18:2) as the lipids with the highest significance between the cell lines, along with PS (36:2) as a SKOV-3 cancer-specific EV lipid, and proposed the utility of these as potential biomarkers. Lipidomic analysis of EVs secreted from pancreatic ductal adenocarcinoma cells (PDACs) allowed detection of increased numbers of the pleotropic signaling lipid, ceramide-1-phosphate (C1P) [443]. While this was a functional study demonstrating that EVs containing C1P can promote pancreatic stem cell motility, the specificity of this sphingolipid subclass along with the report of C1P (d18:1/16:0) being the predominant species detected warrants further investigation for its use as a PDAC-specific EV biomarker.

5.3.3. Lipidomic Profiling of Exosomes from Liquid Biopsies

Lipidomic analysis of exosomes isolated from patient plasma samples successfully classified early and late-stage non-small cell lung cancer (NSCLC) by using the 430 detectable lipid ions [444]. Using ultra-high resolution mass spectrometry without any prior chromatography separation, Fan et al. were able to discriminate the different stages of NSCLC vs. healthy individuals from 91 patient samples. The data were classified using two different multivariate statistical analysis methods, random forests (RF) and least absolute shrinkage and selection operator (LASSO) regression analysis. RF analysis identified a higher number of lipid signatures with discriminating capabilities, of which 15 were identified and validated as PC (18:1/18:2), PC (18:0/18:2), PC (16:0/22:6), PC (16:0/18:2), SM (d18:1/16:0), PC (18:0/20:3), PC (16:0/20:4), PC (16:0/22:5), CE (20:4), TAG (52:5), SM (d18:1/24:1), PC (18:0/18:1), PC (16:0/16:0), TAG (54:6), and LysoPC (16:0). Of the seven LASSO features detected with discriminatory capabilities, three were identified and validated and they overlapped with those detected during the RF analysis, which increased the power of these lipids as classifiers for diagnosing and stratifying NSCLC. The three overlapping species were PC (18:1/18:2), PC (18:0/20:3), and TAG (54:6). The authors of this study showed that despite the discriminatory area under the ROC (aucROC) curve values being higher for RF analysis, the specificity was higher when using LASSO and fewer lipid species. This study demonstrates the power of a relatively small number of lipid species as valid diagnostic and prognostic biomarkers for cancer detection. The analysis of lipid profiles from serum exosomes isolated from pancreatic cancer (PC) patients identified 20 lipid species that were able to discriminate PC from healthy controls [445]. By correlating the data on the dysregulated lipids with clinical data, this group was able to identify several lipid species that provided a prognostic value. LysoPC (22:0) was shown to correlate with tumor stage, diameter, lymphocyte count, and the diagnostic markers CA19-9 and CA242. PC (P-14:0/22:2) was also shown to correlate with tumor stage and lymphocyte count, but this lipid species also correlated with three diagnostic markers, CA19-9, CA242, and CEA. PE (16:0/18:1) was also associated with tumor stage and the CA19-9 and CA242 diagnostic markers. This lipid, however, was shown to be an independent prognostic factor that correlated with patients’ overall survival. In a study comparing the lipidomic profiles of exosomes isolated from urine samples taken from prostate cancer patients compared to healthy controls, Skotland et al. identified nine lipid species that were significantly different between the two groups [446]. HexCer (d18:1/16:0), LacCer (d18:1/16:0), and PC (16:0/18:1) were all found to increase in cancer exosomes relative to the control. Contrarily, PE (O-18:0/18:1), PE(P-36:4/O-36:5), PE (P-18:1/20:4), PS (16:0/18:1), PS (18:0/18:1), and PS (18:1/18:1) were found to decrease in cancer exosomes relative to the controls. Despite the statistical differences of these lipids between prostate cancer and healthy patient urinary exosomes, their diagnostic performance was poor when used alone, with sensitivity values ranging from 20 to 67%. However, when the authors processed their data taking into account lipid ratio comparisons, the ratios of LacCer (d18:1/16:0) to PS (18:1/18:1) and PS (18:0/18:2) significantly separated the prostate cancer from healthy controls with a sensitivity of 93% and a specificity of 100%. This study again provides evidence that a very small number of lipid species are capable of acting as diagnostic exosome-based biomarkers, but also that care needs to be taken during data analysis and interpretation to ensure rigor in the identification of lipid-based biomarker panels from liquid biopsies.

5.3.4. Lipidomic-Specific Exosome Isolation for Use as Cancer Biomarkers

A number of studies have reported the utility of exosomes containing PS lipid species as diagnostic biomarkers. Although not technically lipidomic profiling analysis per se, PS-protein binding affinity assays have been used for the capture of cancer-based exosomes from cell culture and biological fluids [447,448,449,450,451]. In normal healthy cells, PS lipids are predominantly located on the inner leaflet of the lipid membrane, but in a diverse number of cancer cells, PS has been shown to externalize to the outer leaflet, and this has garnered interest for use as a novel biomarker and therapeutic target [452,453,454,455]. In a similar regard, tumor cell-derived exosomes also contain increased numbers of PS species that are predominantly located in the external membrane bilayer, where they are believed to play a role in the uptake of cancer-based exosomes by recipient cells [407,410,456]. In a proof-of-concept study, Lea et al. showed that PS-expressing exosomes detected from patient plasma could be used as a diagnostic biomarker for ovarian malignancies [457]. The group developed a stringent ELISA for selective binding and detection of PS-expressing exosomes that distinguished healthy individuals from patients with ovarian malignancies. The group also demonstrated the utility of their assay in detecting tumor recurrence from plasma-based liquid biopsies. This is a pivotal study as there are currently no standard diagnostic or prognostic biomarkers for ovarian cancers, and grading is often carried out following surgical resection and histological staining/analysis. The same group also utilized the PS ELISA-based assay system to quantify picogram amounts of PS-expressing exosomes from the blood of tumor-bearing mice [449]. In four preclinical model systems, the PS-expressing exosomes were detected weeks prior to the development of tumor masses, indicating that circulating PS-expressing exosomes could provide an excellent source of biomarkers for the early diagnosis of a number of tumors.

In summary, while the field of exosome-derived lipids as cancer biomarkers is in its infancy, the aforementioned studies demonstrate their promising utility as diagnostic and prognostic markers. The advancement of sophisticated MS instrumentation for the sensitive and high-throughput analysis of lipids will enable the rapid translation of data from the bench to the clinic. The biggest caveat is standardizing both MS workflows and the analysis of the complex lipid data from these studies. Standardized workflows, along with validated biomarker panels, are essential to enable the incorporation of exosome-derived lipids into clinical diagnostics.

6. Concluding Remarks

Advances in cancer diagnostic and therapeutic strategies have helped increase life expectancy. Indeed, molecularly guided early detection assays have improved personalized therapies and led the way toward better cancer care. Although advances in imaging technologies have greatly improved the detection of solid tumors, delays in cancer screening, generally due to the indolent nature of certain cancers (brain, pancreatic, lung, etc.), remain a major hurdle for improving prognosis. However, because tumors shed nucleic acids, proteins, lipids, and extracellular vesicles (i.e., exosomes) in their microenvironment and in the circulation, novel molecular strategies have emerged to enable non-invasive liquid biopsy screening. Highly sensitive and specific molecular assays for the detection of CTCs and ctDNA have provided the blueprint for the feasibility of liquid biopsies for non-invasive detection of solid tumors. Exosomes, whose biogenesis and cargoes are greatly altered in cancer cells, provide stably encapsulated and circulating molecular reservoirs of unique tumor biomarkers (i.e., proteins, RNAs, DNAs, and lipids), which are extensively studied for their potential to revolutionize the non-invasive detection of all human cancers. A diverse arsenal of methodologies and molecular assays has been designed to purify these exosomes, but to date many of these approaches (i.e., ultracentrifugation) still rely on global rather than cell-specific exosome selection. Incidentally, most circulating exosome studies aimed at non-invasively identifying cancer biomarkers have been performed with blood (i.e., serum or plasma) where exosome selectivity is necessary. Fortunately, during their biogenesis, exosomes and other extracellular vesicles inherit unique surface molecular fingerprints (i.e., proteins, lipids, etc.), specific to their cell of origin, which provide unique or uniquely combined targets for their immunoselection. A large body of circulating biomarker studies have been focused on evaluating exosome miRNA cargoes, because these non-coding small-RNA master regulators retain their function during transport and can still effectively reprogram/educate recipient cells after delivery. Although recent studies have shown that circulating cancer exosomes released by tumor cells contain unique miRNA ratios and species, most of them have not reached sufficient reproducibility for identification of actionable miRNA cancer biomarkers. It is because disease/tissue specific exosome sub-populations circulating in the blood represent a very small fraction of total circulating exosomes (i.e., blood cell exosomes), whose miRNA biomarker cargo detection is hindered by more abundant circulating contaminants. Using our EV-CATCHER assay, which was specifically tested and designed to improve the selectivity and purity of immunopurified extracellular vesicles prior to their miRNA analysis, we demonstrated that isolation is indeed the most critical step for identification of rare miRNA biomarkers (i.e., circulating contaminants quench low specific signals), especially when using blood as the source of exosomes. Given the stability of exosomes as well as the limited release of contaminants, of the evaluation tissue-specific biofluids, such as urine for bladder cancer, saliva for oral cancer, exhaled breath for lung cancer, and tears for ocular melanoma may allow for more concentrated but most importantly non-invasive access to cancer exosome biomarkers.

Currently, the lack of standardization for exosome isolation and purification of their analytes remains the biggest hurdle for reproducible cancer exosome biomarker identification. While certain guidelines have been established for the characterization of exosomes and other extracellular vesicles [458], the fact that multiple isolation techniques are available to researchers along with the lack of established standardized techniques for the highly sensitive quantification of their low abundance cargo (i.e., nucleic acids, proteins, and lipids) reveals that to date no FDA-approved exosomal biomarker signature is clinically available for the detection and monitoring of any cancer type.

Acknowledgments

The authors would like to thank Erika Shor for critical reading and editing of this review article.

Author Contributions

M.I.M. and O.L. conceptualized and were responsible for writing the Introduction, history, biogenesis and miRNA biomarker sections of this review. J.M. was responsible for writing the protein biomarker sections and C.L.C. was responsible for writing the lipid biomarker sections. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

This research received no external funding.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Bray F., Laversanne M., Weiderpass E., Soerjomataram I. The ever-increasing importance of cancer as a leading cause of premature death worldwide. Cancer. 2021;127:3029–3030. doi: 10.1002/cncr.33587. [DOI] [PubMed] [Google Scholar]
  • 2.World Health Organization (WHO) Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000–2019. WHO. 2020. [(accessed on 1 March 2022)]. Available online: Who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death.
  • 3.The Centers for Disease Control and Prevention (CDC) Trends in Breast Cancer Incidence, by Race, Ethnicity, and aGE aMONG Women Aged >20 Years—United Steates, 1999–2018. [(accessed on 10 May 2022)]; Available online: https://www.cdc.gov/mmwr/volumes/71/wr/mm7102a2.htm.
  • 4.Wender R.C., Brawley O.W., Fedewa S.A., Gansler T., Smith R.A. A blueprint for cancer screening and early detection: Advancing screening’s contribution to cancer control. CA Cancer J. Clin. 2019;69:50–79. doi: 10.3322/caac.21550. [DOI] [PubMed] [Google Scholar]
  • 5.Weigelt B., Peterse J.L., van’t Veer L.J. Breast cancer metastasis: Markers and models. Nat. Rev. Cancer. 2005;5:591–602. doi: 10.1038/nrc1670. [DOI] [PubMed] [Google Scholar]
  • 6.Mehlen P., Puisieux A. Metastasis: A question of life or death. Nat. Rev. Cancer. 2006;6:449–458. doi: 10.1038/nrc1886. [DOI] [PubMed] [Google Scholar]
  • 7.Dillekås H., Rogers M.S., Straume O. Are 90% of deaths from cancer caused by metastases? Cancer Med. 2019;8:5574–5576. doi: 10.1002/cam4.2474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Thalanayar P.M., Altintas N., Weissfeld J.L., Fuhrman C.R., Wilson D.O. Indolent, Potentially Inconsequential Lung Cancers in the Pittsburgh Lung Screening Study. Ann. Am. Thorac. Soc. 2015;12:1193–1196. doi: 10.1513/AnnalsATS.201412-577OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Zmrzljak U.P., Košir R., Krivokapić Z., Radojković D., Nikolić A. Detection of Somatic Mutations with ddPCR from Liquid Biopsy of Colorectal Cancer Patients. Genes. 2021;12:289. doi: 10.3390/genes12020289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Zou W., Yaung S.J., Fuhlbrück F., Ballinger M., Peters E., Palma J.F., Shames D.S., Gandara D., Jiang Y., Patil N.S. ctDNA Predicts Overall Survival in Patients With NSCLC Treated with PD-L1 Blockade or with Chemotherapy. JCO Precis. Oncol. 2021;5:827–838. doi: 10.1200/PO.21.00057. [DOI] [PubMed] [Google Scholar]
  • 11.Omri N., Kamil M., Alexander K., Alexander K., Edmond S., Ariel Z., David K., Gilad A.E., Azik H. Association between PSA density and pathologically significant prostate cancer: The impact of prostate volume. Prostate. 2020;80:1444–1449. doi: 10.1002/pros.24078. [DOI] [PubMed] [Google Scholar]
  • 12.Wu L.X., Li X.F., Chen H.F., Zhu Y.C., Wang W.X., Xu C.W., Xie D.F., Wan Y., Du K.Q. Combined detection of CEA and CA125 for the diagnosis for lung cancer: A meta-analysis. Cell Mol. Biol. 2018;64:67–70. doi: 10.14715/cmb/2017.64.15.11. [DOI] [PubMed] [Google Scholar]
  • 13.Charkhchi P., Cybulski C., Gronwald J., Wong F.O., Narod S.A., Akbari M.R. CA125 and Ovarian Cancer: A Comprehensive Review. Cancers. 2020;12:3730. doi: 10.3390/cancers12123730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Wang W., Xu X., Tian B., Wang Y., Du L., Sun T., Shi Y., Zhao X., Jing J. The diagnostic value of serum tumor markers CEA, CA19-9, CA125, CA15-3, and TPS in metastatic breast cancer. Clin. Chim. Acta. 2017;470:51–55. doi: 10.1016/j.cca.2017.04.023. [DOI] [PubMed] [Google Scholar]
  • 15.Diamandis E.P. Towards identification of true cancer biomarkers. BMC Med. 2014;12:156. doi: 10.1186/s12916-014-0156-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Maestroni U., Cavalieri D.M., Campobasso D., Guarino G., Ziglioli F. PSA-IgM and iXip in the diagnosis and management of prostate cancer: Clinical relevance and future potential. A review. Acta Biomed. 2022;92:e2021344. doi: 10.23750/abm.v92i6.12058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Zhang M., Cheng S., Jin Y., Zhao Y., Wang Y. Roles of CA125 in diagnosis, prediction, and oncogenesis of ovarian cancer. Biochim. Biophys. Acta Rev. Cancer. 2021;1875:188503. doi: 10.1016/j.bbcan.2021.188503. [DOI] [PubMed] [Google Scholar]
  • 18.Zhang B., Sun Z., Song M., Ma S., Tian Y., Kong Q. Ultrasound/CT combined with serum CEA/CA19.9 in the diagnosis and prognosis of rectal cancer. J. Buon. 2018;23:592–597. [PubMed] [Google Scholar]
  • 19.Taksler G.B., Keating N.L., Rothberg M.B. Implications of false-positive results for future cancer screenings. Cancer. 2018;124:2390–2398. doi: 10.1002/cncr.31271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Palsdottir T., Nordstrom T., Karlsson A., Grönberg H., Clements M., Eklund M. The impact of different prostate-specific antigen (PSA) testing intervals on Gleason score at diagnosis and the risk of experiencing false-positive biopsy recommendations: A population-based cohort study. BMJ Open. 2019;9:e027958. doi: 10.1136/bmjopen-2018-027958. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Hanahan D., Weinberg R.A. The hallmarks of cancer. Cell. 2000;100:57–70. doi: 10.1016/S0092-8674(00)81683-9. [DOI] [PubMed] [Google Scholar]
  • 22.Hanahan D., Weinberg R.A. Hallmarks of cancer: The next generation. Cell. 2011;144:646–674. doi: 10.1016/j.cell.2011.02.013. [DOI] [PubMed] [Google Scholar]
  • 23.Kumar-Sinha C., Chinnaiyan A.M. Precision oncology in the age of integrative genomics. Nat. Biotechnol. 2018;36:46–60. doi: 10.1038/nbt.4017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Krol I., Schwab F.D., Carbone R., Ritter M., Picocci S., De Marni M.L., Stepien G., Franchi G.M., Zanardi A., Rissoglio M.D., et al. Detection of clustered circulating tumour cells in early breast cancer. Br. J. Cancer. 2021;125:23–27. doi: 10.1038/s41416-021-01327-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Ried K., Tamanna T., Matthews S., Eng P., Sali A. New Screening Test Improves Detection of Prostate Cancer Using Circulating Tumor Cells and Prostate-Specific Markers. Front. Oncol. 2020;10:582. doi: 10.3389/fonc.2020.00582. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Zhao R., Cai Z., Li S., Cheng Y., Gao H., Liu F., Wu S., Liu S., Dong Y., Zheng L., et al. Expression and clinical relevance of epithelial and mesenchymal markers in circulating tumor cells from colorectal cancer. Oncotarget. 2017;8:9293–9302. doi: 10.18632/oncotarget.14065. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Ehlers M., Allelein S., Schwarz F., Hautzel H., Kuebart A., Schmidt M., Haase M., Dringenberg T., Schott M. Increased Numbers of Circulating Tumor Cells in Thyroid Cancer Patients. Horm. Metab. Res. 2018;50:602–608. doi: 10.1055/a-0651-4913. [DOI] [PubMed] [Google Scholar]
  • 28.Sequist L.V., Nagrath S., Toner M., Haber D.A., Lynch T.J. The CTC-chip: An exciting new tool to detect circulating tumor cells in lung cancer patients. J. Thorac. Oncol. 2009;4:281–283. doi: 10.1097/JTO.0b013e3181989565. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Aaltonen K.E., Novosadová V., Bendahl P.O., Graffman C., Larsson A.M., Rydén L. Molecular characterization of circulating tumor cells from patients with metastatic breast cancer reflects evolutionary changes in gene expression under the pressure of systemic therapy. Oncotarget. 2017;8:45544–45565. doi: 10.18632/oncotarget.17271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Qi L.N., Xiang B.D., Wu F.X., Ye J.Z., Zhong J.H., Wang Y.Y., Chen Y.Y., Chen Z.S., Ma L., Chen J., et al. Circulating Tumor Cells Undergoing EMT Provide a Metric for Diagnosis and Prognosis of Patients with Hepatocellular Carcinoma. Cancer Res. 2018;78:4731–4744. doi: 10.1158/0008-5472.CAN-17-2459. [DOI] [PubMed] [Google Scholar]
  • 31.Gorges T.M., Riethdorf S., von Ahsen O., Nastał Y.P., Röck K., Boede M., Peine S., Kuske A., Schmid E., Kneip C., et al. Heterogeneous PSMA expression on circulating tumor cells: A potential basis for stratification and monitoring of PSMA-directed therapies in prostate cancer. Oncotarget. 2016;7:34930–34941. doi: 10.18632/oncotarget.9004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Braig D., Becherer C., Bickert C., Braig M., Claus R., Eisenhardt A.E., Heinz J., Scholber J., Herget G.W., Bronsert P., et al. Genotyping of circulating cell-free DNA enables noninvasive tumor detection in myxoid liposarcomas. Int. J. Cancer. 2019;145:1148–1161. doi: 10.1002/ijc.32216. [DOI] [PubMed] [Google Scholar]
  • 33.Lv J., Chen Y., Zhou G., Qi Z., Tan K.R.L., Wang H., Lin L., Chen F., Zhang L., Huang X., et al. Liquid biopsy tracking during sequential chemo-radiotherapy identifies distinct prognostic phenotypes in nasopharyngeal carcinoma. Nat. Commun. 2019;10:3941. doi: 10.1038/s41467-019-11853-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Herrmann S., Zhan T., Betge J., Rauscher B., Belle S., Gutting T., Schulte N., Jesenofsky R., Härtel N., Gaiser T., et al. Detection of mutational patterns in cell-free DNA of colorectal cancer by custom amplicon sequencing. Mol. Oncol. 2019;13:1669–1683. doi: 10.1002/1878-0261.12539. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Xu R.H., Wei W., Krawczyk M., Wang W., Luo H., Flagg K., Yi S., Shi W., Quan Q., Li K., et al. Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nat. Mater. 2017;16:1155–1161. doi: 10.1038/nmat4997. [DOI] [PubMed] [Google Scholar]
  • 36.Onstenk W., Sieuwerts A.M., Weekhout M., Mostert B., Reijm E.A., van Deurzen C.H., Bolt-de Vries J.B., Peeters D.J., Hamberg P., Seynaeve C., et al. Gene expression profiles of circulating tumor cells versus primary tumors in metastatic breast cancer. Cancer Lett. 2015;362:36–44. doi: 10.1016/j.canlet.2015.03.020. [DOI] [PubMed] [Google Scholar]
  • 37.Zhang H., Lin X., Huang Y., Wang M., Cen C., Tang S., Dique M.R., Cai L., Luis M.A., Smollar J., et al. Detection Methods and Clinical Applications of Circulating Tumor Cells in Breast Cancer. Front. Oncol. 2021;11:652253. doi: 10.3389/fonc.2021.652253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Pereira-Veiga T., Martínez-Fernández M., Abuin C., Piñeiro R., Cebey V., Cueva J., Palacios P., Blanco C., Muinelo-Romay L., Abalo A., et al. CTCs Expression Profiling for Advanced Breast Cancer Monitoring. Cancers. 2019;11:1941. doi: 10.3390/cancers11121941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Auer M., Heitzer E., Ulz P., Geigl J.B., Speicher M.R. Single circulating tumor cell sequencing for monitoring. Oncotarget. 2013;4:812–813. doi: 10.18632/oncotarget.1026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Zinggeler M., Brandstetter T., Rühe J. Biophysical Insights on the Enrichment of Cancer Cells from Whole Blood by (Affinity) Filtration. Sci. Rep. 2019;9:1246. doi: 10.1038/s41598-018-37541-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Zhu Z., Wu D., Li S., Han Y., Xiang N., Wang C., Ni Z. A polymer-film inertial microfluidic sorter fabricated by jigsaw puzzle method for precise size-based cell separation. Anal. Chim. Acta. 2021;1143:306–314. doi: 10.1016/j.aca.2020.11.001. [DOI] [PubMed] [Google Scholar]
  • 42.Chen K., Amontree J., Varillas J., Zhang J., George T.J., Fan Z.H. Incorporation of lateral microfiltration with immunoaffinity for enhancing the capture efficiency of rare cells. Sci. Rep. 2020;10:14210. doi: 10.1038/s41598-020-71041-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Ates H.C., Ozgur E., Kulah H. Comparative study on antibody immobilization strategies for efficient circulating tumor cell capture. Biointerphases. 2018;13:021001. doi: 10.1116/1.5023456. [DOI] [PubMed] [Google Scholar]
  • 44.Liu Y., Li R., Zhang L., Guo S. Nanomaterial-Based Immunocapture Platforms for the Recognition, Isolation, and Detection of Circulating Tumor Cells. Front. Bioeng. Biotechnol. 2022;10:850241. doi: 10.3389/fbioe.2022.850241. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Mohme M., Riethdorf S., Pantel K. Circulating and disseminated tumour cells—Mechanisms of immune surveillance and escape. Nat. Rev. Clin. Oncol. 2017;14:155–167. doi: 10.1038/nrclinonc.2016.144. [DOI] [PubMed] [Google Scholar]
  • 46.Riethdorf S., O’Flaherty L., Hille C., Pantel K. Clinical applications of the CellSearch platform in cancer patients. Adv. Drug Deliv. Rev. 2018;125:102–121. doi: 10.1016/j.addr.2018.01.011. [DOI] [PubMed] [Google Scholar]
  • 47.Zhao W., Liu Y., Jenkins B.D., Cheng R., Harris B.N., Zhang W., Xie J., Murrow J.R., Hodgson J., Egan M., et al. Tumor antigen-independent and cell size variation-inclusive enrichment of viable circulating tumor cells. Lab. Chip. 2019;19:1860–1876. doi: 10.1039/C9LC00210C. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Gao W., Yuan H., Jing F., Wu S., Zhou H., Mao H., Jin Q., Zhao J., Cong H., Jia C. Analysis of circulating tumor cells from lung cancer patients with multiple biomarkers using high-performance size-based microfluidic chip. Oncotarget. 2017;8:12917–12928. doi: 10.18632/oncotarget.14203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Ziegler A., Zangemeister-Wittke U., Stahel R.A. Circulating DNA: A new diagnostic gold mine? Cancer Treat. Rev. 2002;28:255–271. doi: 10.1016/S0305-7372(02)00077-4. [DOI] [PubMed] [Google Scholar]
  • 50.Yi Z., Ma F., Li C., Chen R., Yuan L., Sun X., Guan X., Li L., Liu B., Guan Y., et al. Landscape of somatic mutations in different subtypes of advanced breast cancer with circulating tumor DNA analysis. Sci. Rep. 2017;7:5995. doi: 10.1038/s41598-017-06327-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Chin R.I., Chen K., Usmani A., Chua C., Harris P.K., Binkley M.S., Azad T.D., Dudley J.C., Chaudhuri A.A. Detection of Solid Tumor Molecular Residual Disease (MRD) Using Circulating Tumor DNA (ctDNA) Mol. Diagn. Ther. 2019;23:311–331. doi: 10.1007/s40291-019-00390-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Zhang S., Brazel D., Kumar P., Schafer L.N., Eidenschink B., Senthil M., Dayyani F. Utility of tumor-informed circulating tumor DNA in the clinical management of gastrointestinal malignancies. J. Gastrointest. Oncol. 2021;12:2643–2652. doi: 10.21037/jgo-21-484. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Zugazagoitia J., Ramos I., Trigo J.M., Palka M., Gómez-Rueda A., Jantus-Lewintre E., Camps C., Isla D., Iranzo P., Ponce-Aix S., et al. Clinical utility of plasma-based digital next-generation sequencing in patients with advance-stage lung adenocarcinomas with insufficient tumor samples for tissue genotyping. Ann. Oncol. 2019;30:290–296. doi: 10.1093/annonc/mdy512. [DOI] [PubMed] [Google Scholar]
  • 54.Nagahashi M., Shimada Y., Ichikawa H., Kameyama H., Takabe K., Okuda S., Wakai T. Next generation sequencing-based gene panel tests for the management of solid tumors. Cancer Sci. 2019;110:6–15. doi: 10.1111/cas.13837. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Wang T.T., Abelson S., Zou J., Li T., Zhao Z., Dick J.E., Shlush L.I., Pugh T.J., Bratman S.V. High efficiency error suppression for accurate detection of low-frequency variants. Nucleic Acids Res. 2019;47:e87. doi: 10.1093/nar/gkz474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Ignatiadis M., Lee M., Jeffrey S.S. Circulating Tumor Cells and Circulating Tumor DNA: Challenges and Opportunities on the Path to Clinical Utility. Clin. Cancer Res. 2015;21:4786–4800. doi: 10.1158/1078-0432.CCR-14-1190. [DOI] [PubMed] [Google Scholar]
  • 57.Li C., He Q., Liang H., Cheng B., Li J., Xiong S., Zhao Y., Guo M., Liu Z., He J., et al. Diagnostic Accuracy of Droplet Digital PCR and Amplification Refractory Mutation System PCR for Detecting EGFR Mutation in Cell-Free DNA of Lung Cancer: A Meta-Analysis. Front. Oncol. 2020;10:290. doi: 10.3389/fonc.2020.00290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Newman A.M., Bratman S.V., To J., Wynne J.F., Eclov N.C., Modlin L.A., Liu C.L., Neal J.W., Wakelee H.A., Merritt R.E., et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 2014;20:548–554. doi: 10.1038/nm.3519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Villaflor V., Won B., Nagy R., Banks K., Lanman R.B., Talasaz A., Salgia R. Biopsy-free circulating tumor DNA assay identifies actionable mutations in lung cancer. Oncotarget. 2016;7:66880–66891. doi: 10.18632/oncotarget.11801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Mego M., De Giorgi U., Dawood S., Wang X., Valero V., Andreopoulou E., Handy B., Ueno N.T., Reuben J.M., Cristofanilli M. Characterization of metastatic breast cancer patients with nondetectable circulating tumor cells. Int. J. Cancer. 2011;129:417–423. doi: 10.1002/ijc.25690. [DOI] [PubMed] [Google Scholar]
  • 61.Keller L., Belloum Y., Wikman H., Pantel K. Clinical relevance of blood-based ctDNA analysis: Mutation detection and beyond. Br. J. Cancer. 2021;124:345–358. doi: 10.1038/s41416-020-01047-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Wang Q., Zhao L., Han L., Tuo X., Ma S., Wang Y., Feng X., Liang D., Sun C., Wang Q., et al. The Discordance of Gene Mutations between Circulating Tumor Cells and Primary/Metastatic Tumor. Mol. Ther. Oncolytics. 2019;15:21–29. doi: 10.1016/j.omto.2019.08.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Wong C.H., Chen Y.C. Clinical significance of exosomes as potential biomarkers in cancer. World J. Clin. Cases. 2019;7:171–190. doi: 10.12998/wjcc.v7.i2.171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Zhao H., Achreja A., Iessi E., Logozzi M., Mizzoni D., Di Raimo R., Nagrath D., Fais S. The key role of extracellular vesicles in the metastatic process. Biochim. Biophys. Acta Rev. Cancer. 2018;1869:64–77. doi: 10.1016/j.bbcan.2017.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Guo Y., Ji X., Liu J., Fan D., Zhou Q., Chen C., Wang W., Wang G., Wang H., Yuan W., et al. Effects of exosomes on pre-metastatic niche formation in tumors. Mol. Cancer. 2019;18:39. doi: 10.1186/s12943-019-0995-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Hu C., Chen M., Jiang R., Guo Y., Wu M., Zhang X. Exosome-related tumor microenvironment. J. Cancer. 2018;9:3084–3092. doi: 10.7150/jca.26422. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Maia J., Caja S., Strano Moraes M.C., Couto N., Costa-Silva B. Exosome-Based Cell-Cell Communication in the Tumor Microenvironment. Front. Cell Dev. Biol. 2018;6:18. doi: 10.3389/fcell.2018.00018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Konoshenko M., Sagaradze G., Orlova E., Shtam T., Proskura K., Kamyshinsky R., Yunusova N., Alexandrova A., Efimenko A., Tamkovich S. Total Blood Exosomes in Breast Cancer: Potential Role in Crucial Steps of Tumorigenesis. Int. J. Mol. Sci. 2020;21:7341. doi: 10.3390/ijms21197341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Willms A., Müller C., Julich H., Klein N., Schwab R., Güsgen C., Richardsen I., Schaaf S., Krawczyk M., Krawczyk M., et al. Tumour-associated circulating microparticles: A novel liquid biopsy tool for screening and therapy monitoring of colorectal carcinoma and other epithelial neoplasia. Oncotarget. 2016;7:30867–30875. doi: 10.18632/oncotarget.9018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Torrano V., Royo F., Peinado H., Loizaga-Iriarte A., Unda M., Falcón-Perez J.M., Carracedo A. Vesicle-MaNiA: Extracellular vesicles in liquid biopsy and cancer. Curr. Opin. Pharmacol. 2016;29:47–53. doi: 10.1016/j.coph.2016.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Ko J., Bhagwat N., Black T., Yee S.S., Na Y.J., Fisher S., Kim J., Carpenter E.L., Stanger B.Z., Issadore D. miRNA Profiling of Magnetic Nanopore-Isolated Extracellular Vesicles for the Diagnosis of Pancreatic Cancer. Cancer Res. 2018;78:3688–3697. doi: 10.1158/0008-5472.CAN-17-3703. [DOI] [PubMed] [Google Scholar]
  • 72.Chargaff E., West R. The biological significance of the thromboplastic protein of blood. J. Biol. Chem. 1946;166:189–197. doi: 10.1016/S0021-9258(17)34997-9. [DOI] [PubMed] [Google Scholar]
  • 73.Hargett L.A., Bauer N.N. On the origin of microparticles: From “platelet dust” to mediators of intercellular communication. Pulm. Circ. 2013;3:329–340. doi: 10.4103/2045-8932.114760. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Chargaff E. Cell structure and the problem of blood coagulation. J. Biol. Chem. 1945;160:351–359. doi: 10.1016/S0021-9258(18)43131-6. [DOI] [Google Scholar]
  • 75.Harding C., Heuser J., Stahl P. Receptor-mediated endocytosis of transferrin and recycling of the transferrin receptor in rat reticulocytes. J. Cell Biol. 1983;97:329–339. doi: 10.1083/jcb.97.2.329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Pan B.T., Johnstone R.M. Fate of the transferrin receptor during maturation of sheep reticulocytes in vitro: Selective externalization of the receptor. Cell. 1983;33:967–978. doi: 10.1016/0092-8674(83)90040-5. [DOI] [PubMed] [Google Scholar]
  • 77.Johnstone R.M., Mathew A., Mason A.B., Teng K. Exosome formation during maturation of mammalian and avian reticulocytes: Evidence that exosome release is a major route for externalization of obsolete membrane proteins. J. Cell Physiol. 1991;147:27–36. doi: 10.1002/jcp.1041470105. [DOI] [PubMed] [Google Scholar]
  • 78.Gawrisch K., Stibenz D., Möps A., Arnold K., Linss W., Halbhuber K.J. The rate of lateral diffusion of phospholipids in erythrocyte microvesicles. Biochim. Biophys. Acta. 1986;856:443–447. doi: 10.1016/0005-2736(86)90135-5. [DOI] [PubMed] [Google Scholar]
  • 79.Vidal M., Sainte-Marie J., Philippot J.R., Bienvenue A. Asymmetric distribution of phospholipids in the membrane of vesicles released during in vitro maturation of guinea pig reticulocytes: Evidence precluding a role for “aminophospholipid translocase”. J. Cell Physiol. 1989;140:455–462. doi: 10.1002/jcp.1041400308. [DOI] [PubMed] [Google Scholar]
  • 80.Vidal M.J., Stahl P.D. The small GTP-binding proteins Rab4 and ARF are associated with released exosomes during reticulocyte maturation. Eur. J. Cell Biol. 1993;60:261–267. [PubMed] [Google Scholar]
  • 81.Lee Y.J., Jy W., Horstman L.L., Janania J., Reyes Y., Kelley R.E., Ahn Y.S. Elevated platelet microparticles in transient ischemic attacks, lacunar infarcts, and multiinfarct dementias. Thromb. Res. 1993;72:295–304. doi: 10.1016/0049-3848(93)90138-E. [DOI] [PubMed] [Google Scholar]
  • 82.Singh N., Gemmell C.H., Daly P.A., Yeo E.L. Elevated platelet-derived microparticle levels during unstable angina. Can. J. Cardiol. 1995;11:1015–1021. [PubMed] [Google Scholar]
  • 83.Powell J.J., Harvey R.S., Thompson R.P. Microparticles in Crohn’s disease—Has the dust settled? Gut. 1996;39:340–341. doi: 10.1136/gut.39.2.340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Kalra H., Adda C.G., Liem M., Ang C.-S., Mechler A., Simpson R.J., Hulett M.D., Mathivanan S. Comparative proteomics evaluation of plasma exosome isolation techniques and assessment of the stability of exosomes in normal human blood plasma. Proteomics. 2013;13:3354–3364. doi: 10.1002/pmic.201300282. [DOI] [PubMed] [Google Scholar]
  • 85.Wubbolts R., Leckie R.S., Veenhuizen P.T., Schwarzmann G., Möbius W., Hoernschemeyer J., Slot J.W., Geuze H.J., Stoorvogel W. Proteomic and biochemical analyses of human B cell-derived exosomes. Potential implications for their function and multivesicular body formation. J. Biol. Chem. 2003;278:10963–10972. doi: 10.1074/jbc.M207550200. [DOI] [PubMed] [Google Scholar]
  • 86.Bard M.P., Hegmans J.P., Hemmes A., Luider T.M., Willemsen R., Severijnen L.A., van Meerbeeck J.P., Burgers S.A., Hoogsteden H.C., Lambrecht B.N. Proteomic analysis of exosomes isolated from human malignant pleural effusions. Am. J. Respir. Cell Mol. Biol. 2004;31:114–121. doi: 10.1165/rcmb.2003-0238OC. [DOI] [PubMed] [Google Scholar]
  • 87.Subra C., Laulagnier K., Perret B., Record M. Exosome lipidomics unravels lipid sorting at the level of multivesicular bodies. Biochimie. 2007;89:205–212. doi: 10.1016/j.biochi.2006.10.014. [DOI] [PubMed] [Google Scholar]
  • 88.Mori M.A., Ludwig R.G., Garcia-Martin R., Brandão B.B., Kahn C.R. Extracellular miRNAs: From Biomarkers to Mediators of Physiology and Disease. Cell Metab. 2019;30:656–673. doi: 10.1016/j.cmet.2019.07.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Taylor D.D., Gercel-Taylor C. MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer. Gynecol. Oncol. 2008;110:13–21. doi: 10.1016/j.ygyno.2008.04.033. Erratum in Gynecol. Oncol. 2010, 116, 153. [DOI] [PubMed] [Google Scholar]
  • 90.Rosell R., Wei J., Taron M. Circulating MicroRNA Signatures of Tumor-Derived Exosomes for Early Diagnosis of Non-Small-Cell Lung Cancer. Clin. Lung Cancer. 2009;10:8–9. doi: 10.3816/CLC.2009.n.001. [DOI] [PubMed] [Google Scholar]
  • 91.Skog J., Würdinger T., van Rijn S., Meijer D.H., Gainche L., Sena-Esteves M., Curry W.T., Jr., Carter B.S., Krichevsky A.M., Breakefield X.O. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat. Cell Biol. 2008;10:1470–1476. doi: 10.1038/ncb1800. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92.Janowska-Wieczorek A., Wysoczynski M., Kijowski J., Marquez-Curtis L., Machalinski B., Ratajczak J., Ratajczak M.Z. Microvesicles derived from activated platelets induce metastasis and angiogenesis in lung cancer. Int. J. Cancer. 2005;113:752–760. doi: 10.1002/ijc.20657. [DOI] [PubMed] [Google Scholar]
  • 93.Hao S., Ye Z., Li F., Meng Q., Qureshi M., Yang J., Xiang J. Epigenetic transfer of metastatic activity by uptake of highly metastatic B16 melanoma cell-released exosomes. Exp. Oncol. 2006;28:126–131. [PubMed] [Google Scholar]
  • 94.Kosaka N., Iguchi H., Ochiya T. Circulating microRNA in body fluid: A new potential biomarker for cancer diagnosis and prognosis. Cancer Sci. 2010;101:2087–2092. doi: 10.1111/j.1349-7006.2010.01650.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Iguchi H., Kosaka N., Ochiya T. Secretory microRNAs as a versatile communication tool. Commun. Integr. Biol. 2010;3:478–481. doi: 10.4161/cib.3.5.12693. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96.Jung T., Castellana D., Klingbeil P., Cuesta Hernández I., Vitacolonna M., Orlicky D.J., Roffler S.R., Brodt P., Zöller M. CD44v6 dependence of premetastatic niche preparation by exosomes. Neoplasia. 2009;11:1093–1105. doi: 10.1593/neo.09822. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.Park J.E., Tan H.S., Datta A., Lai R.C., Zhang H., Meng W., Lim S.K., Sze S.K. Hypoxic tumor cell modulates its microenvironment to enhance angiogenic and metastatic potential by secretion of proteins and exosomes. Mol. Cell Proteom. 2010;9:1085–1099. doi: 10.1074/mcp.M900381-MCP200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Paget S. The distribution of secondary growths in cancer of the breast. Lancet. 1889;133:571–573. doi: 10.1016/S0140-6736(00)49915-0. [DOI] [PubMed] [Google Scholar]
  • 99.Mathias R.A., Gopal S.K., Simpson R.J. Contribution of cells undergoing epithelial-mesenchymal transition to the tumour microenvironment. J. Proteom. 2013;78:545–557. doi: 10.1016/j.jprot.2012.10.016. [DOI] [PubMed] [Google Scholar]
  • 100.Liu C.M., Hsieh C.L., Shen C.N., Lin C.C., Shigemura K., Sung S.Y. Exosomes from the tumor microenvironment as reciprocal regulators that enhance prostate cancer progression. Int. J. Urol. 2016;23:734–744. doi: 10.1111/iju.13145. [DOI] [PubMed] [Google Scholar]
  • 101.Halvaei S., Daryani S., Eslami S.Z., Samadi T., Jafarbeik-Iravani N., Bakhshayesh T.O., Majidzadeh A.K., Esmaeili R. Exosomes in Cancer Liquid Biopsy: A Focus on Breast Cancer. Mol. Ther. Nucleic Acids. 2018;10:131–141. doi: 10.1016/j.omtn.2017.11.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Chen R., Xu X., Qian Z., Zhang C., Niu Y., Wang Z., Sun J., Zhang X., Yu Y. The biological functions and clinical applications of exosomes in lung cancer. Cell Mol. Life Sci. 2019;76:4613–4633. doi: 10.1007/s00018-019-03233-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103.Jia S., Zhang R., Li Z., Li J. Clinical and biological significance of circulating tumor cells, circulating tumor DNA, and exosomes as biomarkers in colorectal cancer. Oncotarget. 2017;8:55632–55645. doi: 10.18632/oncotarget.17184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104.Sun B., Li Y., Zhou Y., Ng T.K., Zhao C., Gan Q., Gu X., Xiang J. Circulating exosomal CPNE3 as a diagnostic and prognostic biomarker for colorectal cancer. J. Cell Physiol. 2019;234:1416–1425. doi: 10.1002/jcp.26936. [DOI] [PubMed] [Google Scholar]
  • 105.Lu L., Risch H.A. Exosomes: Potential for early detection in pancreatic cancer. Future Oncol. 2016;12:1081–1090. doi: 10.2217/fon-2015-0005. [DOI] [PubMed] [Google Scholar]
  • 106.Chen K., Wang Q., Kornmann M., Tian X., Yang Y. The Role of Exosomes in Pancreatic Cancer from Bench to Clinical Application: An Updated Review. Front. Oncol. 2021;11:644358. doi: 10.3389/fonc.2021.644358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.Li P., Kaslan M., Lee S.H., Yao J., Gao Z. Progress in Exosome Isolation Techniques. Theranostics. 2017;7:789–804. doi: 10.7150/thno.18133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 108.Zhang P., Zhou X., He M., Shang Y., Tetlow A.L., Godwin A.K., Zeng Y. Ultrasensitive detection of circulating exosomes with a 3D-nanopatterned microfluidic chip. Nat. Biomed. Eng. 2019;3:438–451. doi: 10.1038/s41551-019-0356-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109.Ludwig N., Whiteside T.L., Reichert T.E. Challenges in Exosome Isolation and Analysis in Health and Disease. Int. J. Mol. Sci. 2019;20:4684. doi: 10.3390/ijms20194684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 110.Lobb R.J., Becker M., Wen S.W., Wong C.S., Wiegmans A.P., Leimgruber A., Möller A. Optimized exosome isolation protocol for cell culture supernatant and human plasma. J. Extracell. Vesicles. 2015;4:27031. doi: 10.3402/jev.v4.27031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111.Liangsupree T., Multia E., Riekkola M.-L. Modern isolation and separation techniques for extracellular vesicles. J. Chromatogr. A. 2020;1636:461773. doi: 10.1016/j.chroma.2020.461773. [DOI] [PubMed] [Google Scholar]
  • 112.Li W.J., Chen H., Tong M.L., Niu J.J., Zhu X.Z., Lin L.R. Comparison of the yield and purity of plasma exosomes extracted by ultracentrifugation, precipitation, and membrane-based approaches. Open Chem. 2022;20:182–191. doi: 10.1515/chem-2022-0139. [DOI] [Google Scholar]
  • 113.Livshits M.A., Khomyakova E., Evtushenko E.G., Lazarev V.N., Kulemin N.A., Semina S.E., Generozov E.V., Govorun V.M. Isolation of exosomes by differential centrifugation: Theoretical analysis of a commonly used protocol. Sci. Rep. 2015;5:17319. doi: 10.1038/srep17319. Erratum in Sci. Rep. 2016, 6, 21447. Livshts, Mikhail A [corrected to Livshits, Mikhail A] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 114.Purushothaman A. Exosomes from Cell Culture-Conditioned Medium: Isolation by Ultracentrifugation and Characterization. Methods Mol. Biol. 2019;1952:233–244. doi: 10.1007/978-1-4939-9133-4_19. [DOI] [PubMed] [Google Scholar]
  • 115.Kumeda N., Ogawa Y., Akimoto Y., Kawakami H., Tsujimoto M., Yanoshita R. Characterization of Membrane Integrity and Morphological Stability of Human Salivary Exosomes. Biol. Pharm. Bull. 2017;40:1183–1191. doi: 10.1248/bpb.b16-00891. [DOI] [PubMed] [Google Scholar]
  • 116.Zhang Z., Wang C., Li T., Liu Z., Li L. Comparison of ultracentrifugation and density gradient separation methods for isolating Tca8113 human tongue cancer cell line-derived exosomes. Oncol. Lett. 2014;8:1701–1706. doi: 10.3892/ol.2014.2373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 117.Brakke M.K. Density gradient centrifugation. A new centrifugation technique. J. Am. Chem. Soc. 1951;73:1847. doi: 10.1021/ja01148a508. [DOI] [Google Scholar]
  • 118.Dhondt B., Lumen N., De Wever O., Hendrix A. Preparation of Multi-omics Grade Extracellular Vesicles by Density-Based Fractionation of Urine. STAR Protoc. 2020;1:100073. doi: 10.1016/j.xpro.2020.100073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 119.Yang D., Zhang W., Zhang H., Zhang F., Chen L., Ma L., Larcher L.M., Chen S., Liu N., Zhao Q., et al. Progress, opportunity, and perspective on exosome isolation—Efforts for efficient exosome-based theranostics. Theranostics. 2020;10:3684–3707. doi: 10.7150/thno.41580. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 120.Ayala-Mar S., Donoso-Quezada J., Gallo-Villanueva R.C., Perez-Gonzalez V.H., González-Valdez J. Recent advances and challenges in the recovery and purification of cellular exosomes. Electrophoresis. 2019;40:3036–3049. doi: 10.1002/elps.201800526. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 121.Mol E.A., Goumans M.J., Doevendans P.A., Sluijter J.P.G., Vader P. Higher functionality of extracellular vesicles isolated using size-exclusion chromatography compared to ultracentrifugation. Nanomedicine. 2017;13:2061–2065. doi: 10.1016/j.nano.2017.03.011. [DOI] [PubMed] [Google Scholar]
  • 122.Konoshenko M.Y., Lekchnov E.A., Vlassov A.V., Laktionov P.P. Isolation of Extracellular Vesicles: General Methodologies and Latest Trends. Biomed. Res. Int. 2018;2018:8545347. doi: 10.1155/2018/8545347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 123.Haraszti R.A., Miller R., Stoppato M., Sere Y.Y., Coles A., Didiot M.C., Wollacott R., Sapp E., Dubuke M.L., Li X., et al. Exosomes Produced from 3D Cultures of MSCs by Tangential Flow Filtration Show Higher Yield and Improved Activity. Mol. Ther. 2018;26:2838–2847. doi: 10.1016/j.ymthe.2018.09.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 124.Stranska R., Gysbrechts L., Wouters J., Vermeersch P., Bloch K., Dierickx D., Andrei G., Snoeck R. Comparison of membrane affinity-based method with size-exclusion chromatography for isolation of exosome-like vesicles from human plasma. J. Transl. Med. 2018;16:1. doi: 10.1186/s12967-017-1374-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 125.Wang W., Lian J.Q., Wang P.Z., Pan L., Ji X.Y., Bai X.F., Jia Z.S. Isolation of exosomes derived from dendritic cells by ultrafiltration centrifugalization and their morphologic characteristics. J. Cell. Mol. Immunol. 2007;23:1119–1121. (In Chinese) [PubMed] [Google Scholar]
  • 126.Krug A.K., Enderle D., Karlovich C., Priewasser T., Bentink S., Spiel A., Brinkmann K., Emenegger J., Grimm D.G., Castellanos-Rizaldos E., et al. Improved EGFR mutation detection using combined exosomal RNA and circulating tumor DNA in NSCLC patient plasma. Ann. Oncol. 2018;29:700–706. doi: 10.1093/annonc/mdx765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 127.Heinemann M.L., Ilmer M., Silva L.P., Hawke D.H., Recio A., Vorontsova M.A., Alt E., Vykoukal J. Benchtop isolation and characterization of functional exosomes by sequential filtration. J. Chromatogr. A. 2014;1371:125–135. doi: 10.1016/j.chroma.2014.10.026. [DOI] [PubMed] [Google Scholar]
  • 128.van Reis R., Gadam S., Frautschy L.N., Orlando S., Goodrich E.M., Saksena S., Kuriyel R., Simpson C.M., Pearl S., Zydney A.L. High performance tangential flow filtration. Biotechnol. Bioeng. 1997;56:71–82. doi: 10.1002/(SICI)1097-0290(19971005)56:1&#x0003c;71::AID-BIT8&#x0003e;3.0.CO;2-S. [DOI] [PubMed] [Google Scholar]
  • 129.Xu R., Greening D.W., Zhu H.J., Takahashi N., Simpson R.J. Extracellular vesicle isolation and characterization: Toward clinical application. J. Clin. Investig. 2016;126:1152–1162. doi: 10.1172/JCI81129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 130.An M., Wu J., Zhu J., Lubman D.M. Comparison of an Optimized Ultracentrifugation Method versus Size-Exclusion Chromatography for Isolation of Exosomes from Human Serum. J. Proteome Res. 2018;17:3599–3605. doi: 10.1021/acs.jproteome.8b00479. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 131.Gaspar L.S., Santana M.M., Henriques C., Pinto M.M., Ribeiro-Rodrigues T.M., Girão H., Nobre R.J., Pereira de Almeida L. Simple and Fast SEC-Based Protocol to Isolate Human Plasma-Derived Extracellular Vesicles for Transcriptional Research. Mol. Ther. Methods Clin. Dev. 2020;18:723–737. doi: 10.1016/j.omtm.2020.07.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 132.Sidhom K., Obi P.O., Saleem A. A Review of Exosomal Isolation Methods: Is Size Exclusion Chromatography the Best Option? Int. J. Mol. Sci. 2020;21:6466. doi: 10.3390/ijms21186466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 133.Wei R., Zhao L., Kong G., Liu X., Zhu S., Zhang S., Min L. Combination of Size-Exclusion Chromatography and Ultracentrifugation Improves the Proteomic Profiling of Plasma-Derived Small Extracellular Vesicles. Biol. Proced. Online. 2020;22:12. doi: 10.1186/s12575-020-00125-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 134.Welton J.L., Loveless S., Stone T., von Ruhland C., Robertson N.P., Clayton A. Cerebrospinal fluid extracellular vesicle enrichment for protein biomarker discovery in neurological disease; multiple sclerosis. J. Extracell. Vesicles. 2017;6:1369805. doi: 10.1080/20013078.2017.1369805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 135.Vaswani K., Mitchell M.D., Holland O.J., Qin Koh Y., Hill R.J., Harb T., Davies P.S.W., Peiris H. A Method for the Isolation of Exosomes from Human and Bovine Milk. J. Nutr. Metab. 2019;2019:5764740. doi: 10.1155/2019/5764740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 136.Aqrawi L.A., Galtung H.K., Vestad B., Øvstebø R., Thiede B., Rusthen S., Young A., Guerreiro E.M., Utheim T.P., Chen X., et al. Identification of potential saliva and tear biomarkers in primary Sjögren’s syndrome, utilising the extraction of extracellular vesicles and proteomics analysis. Arthritis Res. Ther. 2017;19:14. doi: 10.1186/s13075-017-1228-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 137.Oeyen E., Van Mol K., Baggerman G., Willems H., Boonen K., Rolfo C., Pauwels P., Jacobs A., Schildermans K., Cho W.C., et al. Ultrafiltration and size exclusion chromatography combined with asymmetrical-flow field-flow fractionation for the isolation and characterisation of extracellular vesicles from urine. J. Extracell. Vesicles. 2018;7:1490143. doi: 10.1080/20013078.2018.1490143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 138.Foers A.D., Chatfield S., Dagley L.F., Scicluna B.J., Webb A.I., Cheng L., Hill A.F., Wicks I.P., Pang K.C. Enrichment of extracellular vesicles from human synovial fluid using size exclusion chromatography. J. Extracell. Vesicles. 2018;7:1490145. doi: 10.1080/20013078.2018.1490145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 139.Bartel S., La Grutta S., Cilluffo G., Perconti G., Bongiovanni A., Giallongo A., Behrends J., Kruppa J., Hermann S., Chiang D., et al. Human airway epithelial extracellular vesicle miRNA signature is altered upon asthma development. Allergy. 2020;75:346–356. doi: 10.1111/all.14008. [DOI] [PubMed] [Google Scholar]
  • 140.Rodriguez-Caro H., Dragovic R., Shen M., Dombi E., Mounce G., Field K., Meadows J., Turner K., Lunn D., Child T., et al. In vitro decidualisation of human endometrial stromal cells is enhanced by seminal fluid extracellular vesicles. J. Extracell. Vesicles. 2019;8:1565262. doi: 10.1080/20013078.2019.1565262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 141.Takov K., Yellon D.M., Davidson S.M. Comparison of small extracellular vesicles isolated from plasma by ultracentrifugation or size-exclusion chromatography: Yield, purity and functional potential. J. Extracell. Vesicles. 2018;8:1560809. doi: 10.1080/20013078.2018.1560809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 142.Buschmann D., Mussack V., Byrd J.B. Separation, characterization, and standardization of extracellular vesicles for drug delivery applications. Adv. Drug Deliv. Rev. 2021;174:348–368. doi: 10.1016/j.addr.2021.04.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 143.Fang X., Duan Y., Adkins G.B., Pan S., Wang H., Liu Y., Zhong W. Highly Efficient Exosome Isolation and Protein Analysis by an Integrated Nanomaterial-Based Platform. Anal. Chem. 2018;90:2787–2795. doi: 10.1021/acs.analchem.7b04861. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 144.Weng Y., Sui Z., Shan Y., Hu Y., Chen Y., Zhang L., Zhang Y. Effective isolation of exosomes with polyethylene glycol from cell culture supernatant for in-depth proteome profiling. Analyst. 2016;141:4640–4646. doi: 10.1039/C6AN00892E. [DOI] [PubMed] [Google Scholar]
  • 145.Hoffmann M.M., Bothe S., Gutmann T., Buntkowsky G. Unusual local molecular motions in the solid state detected by dynamic nuclear polarization enhanced NMR spectroscopy. J. Phys. Chem. C. 2017;121:22948–22957. doi: 10.1021/acs.jpcc.7b07965. [DOI] [Google Scholar]
  • 146.Shu S., Yang Y., Allen C.L., Hurley E., Tung K.H., Minderman H., Wu Y., Ernstoff M.S. Purity and yield of melanoma exosomes are dependent on isolation method. J. Extracell. Vesicles. 2019;9:1692401. doi: 10.1080/20013078.2019.1692401. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 147.Hessvik N.P., Llorente A. Current knowledge on exosome biogenesis and release. Cell Mol. Life Sci. 2018;75:193–208. doi: 10.1007/s00018-017-2595-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 148.Chen J., Li P., Zhang T., Xu Z., Huang X., Wang R., Du L. Review on Strategies and Technologies for Exosome Isolation and Purification. Front. Bioeng. Biotechnol. 2022;9:811971. doi: 10.3389/fbioe.2021.811971. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 149.Mitchell M.I., Ben-Dov I.Z., Liu C., Ye K., Chow K., Kramer Y., Gangadharan A., Park S., Fitzgerald S., Ramnauth A., et al. Extracellular Vesicle Capture by AnTibody of CHoice and Enzymatic Release (EV-CATCHER): A customizable purification assay designed for small-RNA biomarker identification and evaluation of circulating small-EVs. J. Extracell. Vesicles. 2021;10:e12110. doi: 10.1002/jev2.12110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 150.Wu D., Yan J., Shen X., Sun Y., Thulin M., Cai Y., Wik L., Shen Q., Oelrich J., Qian X., et al. Profiling surface proteins on individual exosomes using a proximity barcoding assay. Nat. Commun. 2019;10:3854. doi: 10.1038/s41467-019-11486-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 151.Hu Q., Su H., Li J., Lyon C., Tang W., Wan M., Hu T.Y. Clinical applications of exosome membrane proteins. Precis. Clin. Med. 2020;3:54–66. doi: 10.1093/pcmedi/pbaa007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 152.Jankovičová J., Sečová P., Michalková K., Antalíková J. Tetraspanins, More than Markers of Extracellular Vesicles in Reproduction. Int. J. Mol. Sci. 2020;21:7568. doi: 10.3390/ijms21207568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 153.Zhou Y.G., Mohamadi R.M., Poudineh M., Kermanshah L., Ahmed S., Safaei T.S., Stojcic J., Nam R.K., Sargent E.H., Kelley S.O. Interrogating Circulating Microsomes and Exosomes Using Metal Nanoparticles. Small. 2016;12:727–732. doi: 10.1002/smll.201502365. [DOI] [PubMed] [Google Scholar]
  • 154.Ghosh A., Davey M., Chute I.C., Griffiths S.G., Lewis S., Chacko S., Barnett D., Crapoulet N., Fournier S., Joy A., et al. Rapid isolation of extracellular vesicles from cell culture and biological fluids using a synthetic peptide with specific affinity for heat shock proteins. PLoS ONE. 2014;9:e110443. doi: 10.1371/journal.pone.0110443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 155.Balaj L., Atai N.A., Chen W., Mu D., Tannous B.A., Breakefield X.O., Skog J., Maguire C.A. Heparin affinity purification of extracellular vesicles. Sci. Rep. 2015;5:10266. doi: 10.1038/srep10266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 156.Koliha N., Wiencek Y., Heider U., Jüngst C., Kladt N., Krauthäuser S., Johnston I.C., Bosio A., Schauss A., Wild S. A novel multiplex bead-based platform highlights the diversity of extracellular vesicles. J. Extracell. Vesicles. 2016;5:29975. doi: 10.3402/jev.v5.29975. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 157.Nie H., Xie X., Zhang D., Zhou Y., Li B., Li F., Li F., Cheng Y., Mei H., Meng H., et al. Use of lung-specific exosomes for miRNA-126 delivery in non-small cell lung cancer. Nanoscale. 2020;12:877–887. doi: 10.1039/C9NR09011H. [DOI] [PubMed] [Google Scholar]
  • 158.Gotanda K., Hirota T., Saito J., Fukae M., Egashira Y., Izumi N., Deguchi M., Kimura M., Matsuki S., Irie S., et al. Circulating intestine-derived exosomal miR-328 in plasma, a possible biomarker for estimating BCRP function in the human intestines. Sci. Rep. 2016;6:32299. doi: 10.1038/srep32299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 159.Sun H., Meng Q., Shi C., Yang H., Li X., Wu S., Familiari G., Relucenti M., Aschner M., Wang X., et al. Hypoxia-Inducible Exosomes Facilitate Liver-Tropic Premetastatic Niche in Colorectal Cancer. Hepatology. 2021;74:2633–2651. doi: 10.1002/hep.32009. [DOI] [PubMed] [Google Scholar]
  • 160.Xu Y.F., Hannafon B.N., Zhao Y.D., Postier R.G., Ding W.Q. Plasma exosome miR-196a and miR-1246 are potential indicators of localized pancreatic cancer. Oncotarget. 2017;8:77028–77040. doi: 10.18632/oncotarget.20332. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 161.Dilsiz N. Role of exosomes and exosomal microRNAs in cancer. Future Sci. OA. 2020;6:FSO465. doi: 10.2144/fsoa-2019-0116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 162.Padda J., Khalid K., Khedr A., Patel V., Al-Ewaidat O.A., Tasnim F., Padda S., Cooper A.C., Jean-Charles G. Exosome-Derived microRNA: Efficacy in Cancer. Cureus. 2021;13:e17441. doi: 10.7759/cureus.17441. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 163.Lou D., Wang Y., Yang Q., Hu L., Zhu Q. Ultrafiltration combing with phospholipid affinity-based isolation for metabolomic profiling of urinary extracellular vesicles. J. Chromatogr. A. 2021;1640:461942. doi: 10.1016/j.chroma.2021.461942. [DOI] [PubMed] [Google Scholar]
  • 164.Salafi T., Zeming K.K., Zhang Y. Advancements in microfluidics for nanoparticle separation. Lab. Chip. 2016;17:11–33. doi: 10.1039/C6LC01045H. [DOI] [PubMed] [Google Scholar]
  • 165.Wu M., Ouyang Y., Wang Z., Zhang R., Huang P.H., Chen C., Li H., Li P., Quinn D., Dao M., et al. Isolation of exosomes from whole blood by integrating acoustics and microfluidics. Proc. Natl. Acad. Sci. USA. 2017;114:10584–10589. doi: 10.1073/pnas.1709210114. Erratum in Proc. Natl. Acad. Sci. USA 2020, 117, 28525. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 166.Liu F., Vermesh O., Mani V., Ge T.J., Madsen S.J., Sabour A., Hsu E.C., Gowrishankar G., Kanada M., Jokerst J.V., et al. The Exosome Total Isolation Chip. ACS Nano. 2017;11:10712–10723. doi: 10.1021/acsnano.7b04878. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 167.Kanwar S.S., Dunlay C.J., Simeone D.M., Nagrath S. Microfluidic device (ExoChip) for on-chip isolation, quantification and characterization of circulating exosomes. Lab. Chip. 2014;14:1891–1900. doi: 10.1039/C4LC00136B. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 168.Lin S., Yu Z., Chen D., Wang Z., Miao J., Li Q., Zhang D., Song J., Cui D. Progress in Microfluidics-Based Exosome Separation and Detection Technologies for Diagnostic Applications. Small. 2020;16:e1903916. doi: 10.1002/smll.201903916. [DOI] [PubMed] [Google Scholar]
  • 169.Gou Y., Jia Y., Wang P., Sun C. Progress of Inertial Microfluidics in Principle and Application. Sensors. 2018;18:1762. doi: 10.3390/s18061762. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 170.Ding L., Yang X., Gao Z., Effah C.Y., Zhang X., Wu Y., Qu L. A Holistic Review of the State-of-the-Art Microfluidics for Exosome Separation: An Overview of the Current Status, Existing Obstacles, and Future Outlook. Small. 2021;17:e2007174. doi: 10.1002/smll.202007174. [DOI] [PubMed] [Google Scholar]
  • 171.Yang F., Liao X., Tian Y., Li G. Exosome separation using microfluidic systems: Size-based, immunoaffinity-based and dynamic methodologies. Biotechnol. J. 2017;12:1600699. doi: 10.1002/biot.201600699. [DOI] [PubMed] [Google Scholar]
  • 172.Lee H., Lee J., Lee S.G., Doyle P.S. Hydrogel-Based Colorimetric Assay for Multiplexed MicroRNA Detection in a Microfluidic Device. Anal. Chem. 2020;92:5750–5755. doi: 10.1021/acs.analchem.9b05043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 173.Liao X., Makris M., Luo X.M. Fluorescence-activated Cell Sorting for Purification of Plasmacytoid Dendritic Cells from the Mouse Bone Marrow. J. Vis. Exp. 2016;117:e54641. doi: 10.3791/54641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 174.Kindlund B., Sjöling Å., Yakkala C., Adamsson J., Janzon A., Hansson L.E., Hermansson M., Janson P., Winqvist O., Lundin S.B. CD4+ regulatory T cells in gastric cancer mucosa are proliferating and express high levels of IL-10 but little TGF-β. Gastric Cancer. 2017;20:116–125. doi: 10.1007/s10120-015-0591-z. [DOI] [PubMed] [Google Scholar]
  • 175.Malmberg E.B., Ståhlman S., Rehammar A., Samuelsson T., Alm S.J., Kristiansson E., Abrahamsson J., Garelius H., Pettersson L., Ehinger M., et al. Patient-tailored analysis of minimal residual disease in acute myeloid leukemia using next-generation sequencing. Eur. J. Haematol. 2017;98:26–37. doi: 10.1111/ejh.12780. [DOI] [PubMed] [Google Scholar]
  • 176.Richards A.J., Staats J., Enzor J., McKinnon K., Frelinger J., Denny T.N., Weinhold K.J., Chan C. Setting objective thresholds for rare event detection in flow cytometry. J. Immunol. Methods. 2014;409:54–61. doi: 10.1016/j.jim.2014.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 177.Morales-Kastresana A., Musich T.A., Welsh J.A., Telford W., Demberg T., Wood J.C.S., Bigos M., Ross C.D., Kachynski A., Dean A., et al. High-fidelity detection and sorting of nanoscale vesicles in viral disease and cancer. J. Extracell. Vesicles. 2019;19:1597603. doi: 10.1080/20013078.2019.1597603. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 178.Cao Z., Li C., Higginbotham J.N., Franklin J.L., Tabb D.L., Graves-Deal R., Hill S., Cheek K., Jerome W.G., Lapierre L.A., et al. Use of fluorescence-activated vesicle sorting for isolation of Naked2-associated, basolaterally targeted exocytic vesicles for proteomics analysis. Mol. Cell Proteom. 2008;7:1651–1667. doi: 10.1074/mcp.M700155-MCP200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 179.Poncelet P., Robert S., Bouriche T., Bez J., Lacroix R., Dignat-George F. Standardized counting of circulating platelet microparticles using currently available flow cytometers and scatter-based triggering: Forward or side scatter? Cytom. A. 2016;89:148–158. doi: 10.1002/cyto.a.22685. [DOI] [PubMed] [Google Scholar]
  • 180.Chandler W.L., Yeung W., Tait J.F. A new microparticle size calibration standard for use in measuring smaller microparticles using a new flow cytometer. J. Thromb. Haemost. 2011;9:1216–1224. doi: 10.1111/j.1538-7836.2011.04283.x. [DOI] [PubMed] [Google Scholar]
  • 181.Groot Kormelink T., Arkesteijn G.J., Nauwelaers F.A., van den Engh G., Nolte-‘t Hoen E.N., Wauben M.H. Prerequisites for the analysis and sorting of extracellular vesicle subpopulations by high-resolution flow cytometry. Cytom. A. 2016;89:135–147. doi: 10.1002/cyto.a.22644. [DOI] [PubMed] [Google Scholar]
  • 182.Padda R.S., Deng F.K., Brett S.I., Biggs C.N., Durfee P.N., Brinker C.J., Williams K.C., Leong H.S. Nanoscale flow cytometry to distinguish subpopulations of prostate extracellular vesicles in patient plasma. Prostate. 2019;79:592–603. doi: 10.1002/pros.23764. [DOI] [PubMed] [Google Scholar]
  • 183.. Higginbotham J.N., Zhang Q., Jeppesen D.K., Scott A.M., Manning H.C., Ochieng J., Franklin J.L., Coffey R.J. Identification and characterization of EGF receptor in individual exosomes by fluorescence-activated vesicle sorting. J Extracell. Vesicles. 2016;5:29254. doi: 10.3402/jev.v5.29254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 184.Mastoridis S., Bertolino G.M., Whitehouse G., Dazzi F., Sanchez-Fueyo A., Martinez-Llordella M. Multiparametric Analysis of Circulating Exosomes and Other Small Extracellular Vesicles by Advanced Imaging Flow Cytometry. Front. Immunol. 2018;9:1583. doi: 10.3389/fimmu.2018.01583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 185.Löf L., Arngården L., Ebai T., Landegren U., Söderberg O., Kamali-Moghaddam M. Detection of Extracellular Vesicles Using Proximity Ligation Assay with Flow Cytometry Readout-ExoPLA. Curr. Protoc. Cytom. 2017;81:4–8. doi: 10.1002/cpcy.22. [DOI] [PubMed] [Google Scholar]
  • 186.Larssen P., Wik L., Czarnewski P., Eldh M., Löf L., Ronquist K.G., Dubois L., Freyhult E., Gallant C.J., Oelrich J., et al. Tracing Cellular Origin of Human Exosomes Using Multiplex Proximity Extension Assays. Mol. Cell Proteom. 2017;16:1547. doi: 10.1074/mcp.A116.064725. Erratum in Mol. Cell. Proteom. 2017, 16, 502–511. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 187.Löf L., Ebai T., Dubois L., Wik L., Ronquist K.G., Nolander O., Lundin E., Söderberg O., Landegren U., Kamali-Moghaddam M. Detecting individual extracellular vesicles using a multicolor in situ proximity ligation assay with flow cytometric readout. Sci. Rep. 2016;6:34358. doi: 10.1038/srep34358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 188.Morita E., Sandrin V., Chung H.-Y., Morham S.G., Gygi S.P., Rodesch C.K., Sundquist W.I. Human ESCRT and ALIX proteins interact with proteins of the midbody and function in cytokinesis. EMBO J. 2007;26:4215–4227. doi: 10.1038/sj.emboj.7601850. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 189.Théry C., Duban L., Segura E., Véron P., Lantz O., Amigorena S. Indirect activation of naïve CD4+ T cells by dendritic cell-derived exosomes. Nat. Immunol. 2002;3:1156–1162. doi: 10.1038/ni854. [DOI] [PubMed] [Google Scholar]
  • 190.Buschow S.I., Nolte-‘t Hoen E.N., van Niel G., Pols M.S., Ten Broeke T., Lauwen M., Ossendorp F., Melief C.J., Raposo G., Wubbolts R., et al. MHC II in dendritic cells is targeted to lysosomes or T cell-induced exosomes via distinct multivesicular body pathways. Traffic. 2009;10:1528–1542. doi: 10.1111/j.1600-0854.2009.00963.x. [DOI] [PubMed] [Google Scholar]
  • 191.Buschow S.I., van Balkom B.W., Aalberts M., Heck A.J., Wauben M., Stoorvogel W. MHC class II-associated proteins in B-cell exosomes and potential functional implications for exosome biogenesis. Immunol. Cell Biol. 2010;88:851–856. doi: 10.1038/icb.2010.64. [DOI] [PubMed] [Google Scholar]
  • 192.Newman L.A., Useckaite Z., Johnson J., Sorich M.J., Hopkins A.M., Rowland A. Selective Isolation of Liver-Derived Extracellular Vesicles Redefines Performance of miRNA Biomarkers for Non-Alcoholic Fatty Liver Disease. Biomedicines. 2022;10:195. doi: 10.3390/biomedicines10010195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 193.Chiasserini D., van Weering J.R., Piersma S.R., Pham T.V., Malekzadeh A., Teunissen C.E., de Wit H., Jiménez C.R. Proteomic analysis of cerebrospinal fluid extracellular vesicles: A comprehensive dataset. J. Proteom. 2014;106:191–204. doi: 10.1016/j.jprot.2014.04.028. [DOI] [PubMed] [Google Scholar]
  • 194.Pillay P., Maharaj N., Moodley J., Mackraj I. Placental exosomes and pre-eclampsia: Maternal circulating levels in normal pregnancies and, early and late onset pre-eclamptic pregnancies. Placenta. 2016;46:18–25. doi: 10.1016/j.placenta.2016.08.078. [DOI] [PubMed] [Google Scholar]
  • 195.Miranda J., Paules C., Nair S., Lai A., Palma C., Scholz-Romero K., Rice G.E., Gratacos E., Crispi F., Salomon C. Placental exosomes profile in maternal and fetal circulation in intrauterine growth restriction—Liquid biopsies to monitoring fetal growth. Placenta. 2018;64:34–43. doi: 10.1016/j.placenta.2018.02.006. [DOI] [PubMed] [Google Scholar]
  • 196.Seibold T., Schönfelder J., Weeber F., Lechel A., Armacki M., Waldenmaier M., Wille C., Palmer A., Halbgebauer R., Karasu E., et al. Small Extracellular Vesicles Propagate the Inflammatory Response after Trauma. Adv. Sci. 2021;8:e2102381. doi: 10.1002/advs.202102381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 197.Valadi H., Eksröm K., Bossios A., Sjöstrand M., Lee J.J., Lötvall J.O. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat. Cell Biol. 2007;9:654–659. doi: 10.1038/ncb1596. [DOI] [PubMed] [Google Scholar]
  • 198.Bissig C., Gruenberg J. ALIX and the multivesicular endosome: ALIX in wonderland. Trends Cell Biol. 2014;24:19–25. doi: 10.1016/j.tcb.2013.10.009. [DOI] [PubMed] [Google Scholar]
  • 199.Alenquer M., Amorim M.J. Exosome biogenesis, regulation, and function in viral infection. Viruses. 2015;7:5066–5083. doi: 10.3390/v7092862. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 200.Hemler M.E. Targeting of tetraspanin proteins—Potential benefits and strategies. Nat. Rev. Drug Discov. 2008;7:747–758. doi: 10.1038/nrd2659. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 201.Wortzel I., Dror S., Kenific C.M., Lyden D. Exosome-Mediated Metastasis: Communication from a Distance. Dev. Cell. 2019;49:347–360. doi: 10.1016/j.devcel.2019.04.011. [DOI] [PubMed] [Google Scholar]
  • 202.Helenius A., Mellman I., Wall D., Hubbard A. Endosomes. Trends Biochem. Sci. 1983;8:245–250. doi: 10.1016/0968-0004(83)90350-X. [DOI] [Google Scholar]
  • 203.Elkin S.R., Lakoduk A.M., Schmid S.L. Endocytic pathways and endosomal trafficking: A primer. Wien. Med. Wochenschr. 2016;166:196–204. doi: 10.1007/s10354-016-0432-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 204.Alberts B., Johnson A., Lewis J. Molecular Biology of the Cell. 4th ed. Garland Science; New York, NY, USA: 2002. [(accessed on 26 May 2022)]. Transport into the Cell from the Plasma Membrane: Endocytosis. Available online: https://www.ncbi.nlm.nih.gov/books/NBK26870/ [Google Scholar]
  • 205.Kaur G., Lakkaraju A. Early Endosome Morphology in Health and Disease. Adv. Exp. Med. Biol. 2018;1074:335–343. doi: 10.1007/978-3-319-75402-4_41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 206.Hotaru J., Helenius A. Endosome maturation. EMBO J. 2011;30:3481–3500. doi: 10.1038/emboj.2011.286. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 207.Pfeffer S.R. Multiple routes of protein transport from endosomes to the trans Golgi network. FEBS Lett. 2009;583:3811–3816. doi: 10.1016/j.febslet.2009.10.075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 208.Piper R.C., Katzmann D.J. Biogenesis and function of multivesicular bodies. Annu. Rev. Cell Dev. Biol. 2007;23:519–547. doi: 10.1146/annurev.cellbio.23.090506.123319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 209.Lu A., Tebar F., Alvarez-Moya B., López-Alcalá C., Calvo M., Enrich C., Agell N., Nakamura T., Matsuda M., Bachs O. A clathrin-dependent pathway leads to KRas signaling on late endosomes en route to lysosomes. J. Cell Biol. 2009;184:863–879. doi: 10.1083/jcb.200807186. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 210.Frankel E.B., Audhya A. ESCRT-dependent cargo sorting at multivesicular endosomes. Semin. Cell Dev. Biol. 2018;74:4–10. doi: 10.1016/j.semcdb.2017.08.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 211.Katzmann D.J., Babst M., Emr S.D. Ubiquitin-Dependent Sorting into the Multivesicular Body Pathway Requires the Function of a Conserved Endosomal Protein Sorting Complex, ESCRT-I. Cell. 2001;106:145–155. doi: 10.1016/S0092-8674(01)00434-2. [DOI] [PubMed] [Google Scholar]
  • 212.Piper R.C., Luzio J.P. Ubiquitin-dependent sorting of integral membrane proteins for degradation in lysosomes. Curr. Opin. Cell Biol. 2008;19:459–465. doi: 10.1016/j.ceb.2007.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 213.Peng X., Yang L., Ma Y., Li Y., Li H. Focus on the morphogenesis, fate and the role in tumor progression of multivesicular bodies. Cell Commun. Signal. 2020;18:122. doi: 10.1186/s12964-020-00619-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 214.Alvarez-Erviti L., Seow Y., Schapira A.H., Gardiner C., Sargent I.L., Wood M.J., Cooper J.M. Lysosomal dysfunction increases exosome-mediated alpha-synuclein release and transmission. Neurobiol. Dis. 2011;42:360–367. doi: 10.1016/j.nbd.2011.01.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 215.Miao Y., Li G., Zhang X., Xu H., Abraham S.N. A TRP Channel Senses Lysosome Neutralization by Pathogens to Trigger Their Expulsion. Cell. 2015;161:1306–1319. doi: 10.1016/j.cell.2015.05.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 216.Yoon S., Kovalenko A., Bogdanov K., Wallach D. MLKL, the Protein that Mediates Necroptosis, Also Regulates Endosomal Trafficking and Extracellular Vesicle Generation. Immunity. 2017;47:51–65.e7. doi: 10.1016/j.immuni.2017.06.001. [DOI] [PubMed] [Google Scholar]
  • 217.Olenick M.A., Holzbaur E.L.F. Dynein activators and adaptors at a glance. J. Cell Sci. 2019;132:jcs227132. doi: 10.1242/jcs.227132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 218.Hutagalung A.H., Novick P.J. Role of Rab GTPases in membrane traffic and cell physiology. Physiol. Rev. 2011;91:119–149. doi: 10.1152/physrev.00059.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 219.Koike S., Jahn R. SNARE proteins: Zip codes in vesicle targeting? Biochem. J. 2022;479:273–288. doi: 10.1042/BCJ20210719. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 220.Lorentz A., Baumann A., Vitte J., Blank U. The SNARE Machinery in Mast Cell Secretion. Front. Immunol. 2012;3:143. doi: 10.3389/fimmu.2012.00143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 221.Homma Y., Hiragi S., Fukuda M. Rab family of small GTPases: An updated view on their regulation and functions. FEBS J. 2021;288:36–55. doi: 10.1111/febs.15453. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 222.Yu X., Prekeris R., Gould G.W. Role of endosomal Rab GTPases in cytokinesis. Eur. J. Cell Biol. 2007;86:25–35. doi: 10.1016/j.ejcb.2006.10.002. [DOI] [PubMed] [Google Scholar]
  • 223.Ramel D., Wang X., Laflamme C., Montell D.J., Emery G. Rab11 regulates cell-cell communication during collective cell movements. Nat. Cell Biol. 2013;15:317–324. doi: 10.1038/ncb2681. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 224.Heo P., Coleman J., Fleury J.B., Rothman J.E., Pincet F. Nascent fusion pore opening monitored at single-SNAREpin resolution. Proc. Natl. Acad. Sci. USA. 2021;118:e2024922118. doi: 10.1073/pnas.2024922118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 225.Mathieu M., Névo N., Jouve M., Valenzuela J.I., Maurin M., Verweij F.J., Palmulli R., Lankar D., Dingli F., Loew D., et al. Specificities of exosome versus small ectosome secretion revealed by live intracellular tracking of CD63 and CD9. Nat. Commun. 2021;12:4389. doi: 10.1038/s41467-021-24384-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 226.Bebelman M.P., Smit M.J., Pegtel D.M., Baglio S.R. Biogenesis and function of extracellular vesicles in cancer. Pharmacol. Ther. 2018;188:1–11. doi: 10.1016/j.pharmthera.2018.02.013. [DOI] [PubMed] [Google Scholar]
  • 227.Gerlach P., Schuller J.M., Bonneau F., Basquin J., Reichelt P., Falk S., Conti E. Distinct and evolutionary conserved structural features of the human nuclear exosome complex. Elife. 2018;7:e38686. doi: 10.7554/eLife.38686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 228.Bahrami A., Moradi Binabaj M., AFerns G. Exosomes: Emerging modulators of signal transduction in colorectal cancer from molecular understanding to clinical application. Biomed. Pharmacother. 2021;141:111882. doi: 10.1016/j.biopha.2021.111882. [DOI] [PubMed] [Google Scholar]
  • 229.Schwarzenbach H., Gahan P.B. MicroRNA Shuttle from Cell-To-Cell by Exosomes and Its Impact in Cancer. Noncoding RNA. 2019;5:28. doi: 10.3390/ncrna5010028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 230.Liu H., Chen W., Zhi X., Chen E.J., Wei T., Zhang J., Shen J., Hu L.Q., Zhao B., Feng X.H., et al. Tumor-derived exosomes promote tumor self-seeding in hepatocellular carcinoma by transferring miRNA-25-5p to enhance cell motility. Oncogene. 2018;37:4964–4978. doi: 10.1038/s41388-018-0309-x. [DOI] [PubMed] [Google Scholar]
  • 231.Iguchi Y., Eid L., Parent M., Soucy G., Bareil C., Riku Y., Kawai K., Takagi S., Yoshida M., Katsuno M., et al. Exosome secretion is a key pathway for clearance of pathological TDP-43. Pt 12Brain. 2016;139:3187–3201. doi: 10.1093/brain/aww237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 232.Vulpis E., Soriani A., Cerboni C., Santoni A., Zingoni A. Cancer Exosomes as Conveyors of Stress-Induced Molecules: New Players in the Modulation of NK Cell Response. Int. J. Mol. Sci. 2019;20:611. doi: 10.3390/ijms20030611. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 233.Troyer Z., Tilton J.C. Extracellular vesicles as carriers of viruses. ExRNA. 2021;3:13. doi: 10.21037/exrna-21-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 234.Göran Ronquist K. Extracellular vesicles and energy metabolism. Clin. Chim. Acta. 2019;488:116–121. doi: 10.1016/j.cca.2018.10.044. [DOI] [PubMed] [Google Scholar]
  • 235.Rodríguez M., Silva J., Herrera A., Herrera M., Peña C., Martín P., Gil-Calderón B., Larriba M.J., Coronado M.J., Soldevilla B., et al. Exosomes enriched in stemness/metastatic-related mRNAS promote oncogenic potential in breast cancer. Oncotarget. 2015;6:40575–40587. doi: 10.18632/oncotarget.5818. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 236.Borges F.T., Melo S.A., Özdemir B.C., Kato N., Revuelta I., Miller C.A., Gattone V.H., 2nd, LeBleu V.S., Kalluri R. TGF-β1-containing exosomes from injured epithelial cells activate fibroblasts to initiate tissue regenerative responses and fibrosis. J. Am. Soc. Nephrol. 2013;24:385–392. doi: 10.1681/ASN.2012101031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 237.Nakamura Y., Miyaki S., Ishitobi H., Matsuyama S., Nakasa T., Kamei N., Akimoto T., Higashi Y., Ochi M. Mesenchymal-stem-cell-derived exosomes accelerate skeletal muscle regeneration. FEBS Lett. 2015;589:1257–1265. doi: 10.1016/j.febslet.2015.03.031. [DOI] [PubMed] [Google Scholar]
  • 238.Gartz M., Strande J.L. Examining the Paracrine Effects of Exosomes in Cardiovascular Disease and Repair. J. Am. Heart Assoc. 2018;7:e007954. doi: 10.1161/JAHA.117.007954. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 239.Pegtel D.M., Peferoen L., Amor S. Extracellular vesicles as modulators of cell-to-cell communication in the healthy and diseased brain. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2014;369:20130516. doi: 10.1098/rstb.2013.0516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 240.Bang C., Thum T. Exosomes: New players in cell-cell communication. Int. J. Biochem. Cell Biol. 2012;44:2060–2064. doi: 10.1016/j.biocel.2012.08.007. [DOI] [PubMed] [Google Scholar]
  • 241.Samanta S., Rajasingh S., Drosos N., Zhou Z., Dawn B., Rajasingh J. Exosomes: New molecular targets of diseases. Acta Pharmacol. Sin. 2018;39:501–513. doi: 10.1038/aps.2017.162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 242.Emmanouilidou E., Melachroinou K., Roumeliotis T., Garbis S.D., Ntzouni M., Margaritis L.H., Stefanis L., Vekrellis K. Cell-produced alpha-synuclein is secreted in a calcium-dependent manner by exosomes and impacts neuronal survival. J. Neurosci. 2010;30:6838–6851. doi: 10.1523/JNEUROSCI.5699-09.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 243.Rajendran L., Honsho M., Zahn T.R., Keller P., Geiger K.D., Verkade P., Simons K. Alzheimer’s disease beta-amyloid peptides are released in association with exosomes. Proc. Natl. Acad. Sci. USA. 2006;103:11172–11177. doi: 10.1073/pnas.0603838103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 244.Saá P., Yakovleva O., de Castro J., Vasilyeva I., De Paoli S.H., Simak J., Cervenakova L. First demonstration of transmissible spongiform encephalopathy-associated prion protein (PrPTSE) in extracellular vesicles from plasma of mice infected with mouse-adapted variant Creutzfeldt-Jakob disease by in vitro amplification. J. Biol. Chem. 2014;289:29247–29260. doi: 10.1074/jbc.M114.589564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 245.Basso M., Pozzi S., Tortarolo M., Fiordaliso F., Bisighini C., Pasetto L., Spaltro G., Lidonnici D., Gensano F., Battaglia E., et al. Mutant copper-zinc superoxide dismutase (SOD1) induces protein secretion pathway alterations and exosome release in astrocytes: Implications for disease spreading and motor neuron pathology in amyotrophic lateral sclerosis. J. Biol. Chem. 2013;288:15699–15711. doi: 10.1074/jbc.M112.425066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 246.Melo S.A., Sugimoto H., O’Connell J.T., Villanueva A., Vidal A., Qiu L., Vitkin E., Perelman L.T., Melo C.A., Lucci A., et al. Cancer exosomes perform cell-independent microRNA biogenesis and promote tumorigenesis. Cancer Cell. 2014;26:707–721. doi: 10.1016/j.ccell.2014.09.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 247.Psaila B., Kaplan R.N., Port E.R., Lyden D. Priming the ‘soil’ for breast cancer metastasis: The pre-metastatic niche. Breast Dis. 2007;26:65–74. doi: 10.3233/BD-2007-26106. [DOI] [PubMed] [Google Scholar]
  • 248.Hoshino A., Costa-Silva B., Shen T.L., Rodrigues G., Hashimoto A., Tesic Mark M., Molina H., Kohsaka S., Di Giannatale A., Ceder S., et al. Tumour exosome integrins determine organotropic metastasis. Nature. 2015;527:329–335. doi: 10.1038/nature15756. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 249.Silva J., Garcia V., Rodriguez M., Compte M., Cisneros E., Veguillas P., Garcia J.M., Dominguez G., Campos-Martin Y., Cuevas J., et al. Analysis of exosome release and its prognostic value in human colorectal cancer. Genes Chromosomes Cancer. 2012;51:409–418. doi: 10.1002/gcc.21926. [DOI] [PubMed] [Google Scholar]
  • 250.Al-Nedawi K., Meehan B., Micallef J., Lhotak V., May L., Guha A., Rak J. Intercellular transfer of the oncogenic receptor EGFRvIII by microvesicles derived from tumour cells. Nat. Cell Biol. 2008;10:619–624. doi: 10.1038/ncb1725. Erratum in Nat. Cell Biol. 2008, 10, 752. [DOI] [PubMed] [Google Scholar]
  • 251.Choi D., Montermini L., Kim D.K., Meehan B., Roth F.P., Rak J. The Impact of Oncogenic EGFRvIII on the Proteome of Extracellular Vesicles Released from Glioblastoma Cells. Mol. Cell Proteom. 2018;17:1948–1964. doi: 10.1074/mcp.RA118.000644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 252.Colombo M., Moita C., van Niel G., Kowal J., Vigneron J., Benaroch P., Manel N., Moita L.F., Théry C., Raposo G. Analysis of ESCRT functions in exosome biogenesis, composition and secretion highlights the heterogeneity of extracellular vesicles. Pt 24J. Cell Sci. 2013;126:5553–5565. doi: 10.1242/jcs.128868. [DOI] [PubMed] [Google Scholar]
  • 253.Villarroya-Beltri C., Gutiérrez-Vázquez C., Sánchez-Cabo F., Pérez-Hernández D., Vázquez J., Martin-Cofreces N., Martinez-Herrera D.J., Pascual-Montano A., Mittelbrunn M., Sánchez-Madrid F. Sumoylated hnRNPA2B1 controls the sorting of miRNAs into exosomes through binding to specific motifs. Nat. Commun. 2013;4:2980. doi: 10.1038/ncomms3980. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 254.Tamai K., Tanaka N., Nakano T., Kakazu E., Kondo Y., Inoue J., Shiina M., Fukushima K., Hoshino T., Sano K., et al. Exosome secretion of dendritic cells is regulated by Hrs, an ESCRT-0 protein. Biochem. Biophys. Res. Commun. 2010;399:384–390. doi: 10.1016/j.bbrc.2010.07.083. [DOI] [PubMed] [Google Scholar]
  • 255.Gross J.C., Chaudhary V., Bartscherer K., Boutros M. Active Wnt proteins are secreted on exosomes. Nat. Cell Biol. 2012;14:1036–1045. doi: 10.1038/ncb2574. [DOI] [PubMed] [Google Scholar]
  • 256.Valcz G., Galamb O., Krenács T., Spisák S., Kalmár A., Patai Á.V., Wichmann B., Dede K., Tulassay Z., Molnár B. Exosomes in colorectal carcinoma formation: ALIX under the magnifying glass. Mod. Pathol. 2016;29:928–938. doi: 10.1038/modpathol.2016.72. [DOI] [PubMed] [Google Scholar]
  • 257.Yang J., Zhang Y., Gao X., Yuan Y., Zhao J., Zhou S., Wang H., Wang L., Xu G., Li X., et al. Plasma-Derived Exosomal ALIX as a Novel Biomarker for Diagnosis and Classification of Pancreatic Cancer. Front. Oncol. 2021;11:628346. doi: 10.3389/fonc.2021.628346. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 258.Larios J., Mercier V., Roux A., Gruenberg J. ALIX- and ESCRT-III-dependent sorting of tetraspanins to exosomes. J. Cell Biol. 2020;219:e201904113. doi: 10.1083/jcb.201904113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 259.Roucourt B., Meeussen S., Bao J., Zimmermann P., David G. Heparanase activates the syndecan-syntenin-ALIX exosome pathway. Cell Res. 2015;25:412–428. doi: 10.1038/cr.2015.29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 260.Monypenny J., Milewicz H., Flores-Borja F., Weitsman G., Cheung A., Chowdhury R., Burgoyne T., Arulappu A., Lawler K., Barber P.R., et al. ALIX Regulates Tumor-Mediated Immunosuppression by Controlling EGFR Activity and PD-L1 Presentation. Cell Rep. 2018;24:630–641. doi: 10.1016/j.celrep.2018.06.066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 261.Yang Y., Hong Q., Shi P., Liu Z., Luo J., Shao Z. Elevated expression of syntenin in breast cancer is correlated with lymph node metastasis and poor patient survival. Breast Cancer Res. 2013;15:R50. doi: 10.1186/bcr3442. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 262.Das S.K., Guo C., Pradhan A.K., Bhoopathi P., Talukdar S., Shen X.N., Emdad L., Subler M.A., Windle J.J., Sarkar D., et al. Knockout of MDA-9/Syntenin (SDCBP) expression in the microenvironment dampens tumor-supporting inflammation and inhibits melanoma metastasis. Oncotarget. 2016;7:46848–46861. doi: 10.18632/oncotarget.10040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 263.Kim O., Hwangbo C., Tran P.T., Lee J.H. Syntenin-1-mediated small extracellular vesicles promotes cell growth, migration, and angiogenesis by increasing onco-miRNAs secretion in lung cancer cells. Cell Death Dis. 2022;13:122. doi: 10.1038/s41419-022-04594-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 264.Kugeratski F.G., Hodge K., Lilla S., McAndrews K.M., Zhou X., Hwang R.F., Zanivan S., Kalluri R. Quantitative proteomics identifies the core proteome of exosomes with syntenin-1 as the highest abundant protein and a putative universal biomarker. Nat. Cell Biol. 2021;23:631–641. doi: 10.1038/s41556-021-00693-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 265.Baietti M.F., Zhang Z., Mortier E., Melchior A., Degeest G., Geeraerts A., Ivarsson Y., Depoortere F., Coomans C., Vermeiren E., et al. Syndecan-syntenin-ALIX regulates the biogenesis of exosomes. Nat. Cell Biol. 2012;14:677–685. doi: 10.1038/ncb2502. [DOI] [PubMed] [Google Scholar]
  • 266.Tkachenko E., Rhodes J.M., Simons M. Syndecans: New kids on the signaling block. Circ. Res. 2005;96:488–500. doi: 10.1161/01.RES.0000159708.71142.c8. [DOI] [PubMed] [Google Scholar]
  • 267.David G., Zimmermann P. Heparanase tailors syndecan for exosome production. Mol. Cell Oncol. 2015;3:e1047556. doi: 10.1080/23723556.2015.1047556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 268.Gallagher J. Fell-Muir Lecture: Heparan sulphate and the art of cell regulation: A polymer chain conducts the protein orchestra. Int. J. Exp. Pathol. 2015;96:203–231. doi: 10.1111/iep.12135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 269.Hammond E., Khurana A., Shridhar V., Dredge K. The Role of Heparanase and Sulfatases in the Modification of Heparan Sulfate Proteoglycans within the Tumor Microenvironment and Opportunities for Novel Cancer Therapeutics. Front. Oncol. 2014;4:195. doi: 10.3389/fonc.2014.00195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 270.Ilan N., Elkin M., Vlodavsky I. Regulation, function and clinical significance of heparanase in cancer metastasis and angiogenesis. Int. J. Biochem. Cell Biol. 2006;38:2018–2039. doi: 10.1016/j.biocel.2006.06.004. [DOI] [PubMed] [Google Scholar]
  • 271.Mytilinaiou M., Nikitovic D., Berdiaki A., Kostouras A., Papoutsidakis A., Tsatsakis A.M., Tzanakakis G.N. Emerging roles of syndecan 2 in epithelial and mesenchymal cancer progression. IUBMB Life. 2017;69:824–833. doi: 10.1002/iub.1678. [DOI] [PubMed] [Google Scholar]
  • 272.Thompson C.A., Purushothaman A., Ramani V.C., Vlodavsky I., Sanderson R.D. Heparanase regulates secretion, composition, and function of tumor cell-derived exosomes. J. Biol. Chem. 2013;288:10093–10099. doi: 10.1074/jbc.C112.444562. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 273.Fares J., Kashyap R., Zimmermann P. Syntenin: Key player in cancer exosome biogenesis and uptake? Cell Adh. Migr. 2017;11:124–126. doi: 10.1080/19336918.2016.1225632. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 274.O’Brien K., Breyne K., Ughetto S., Laurent L.C., Breakefield X.O. RNA delivery by extracellular vesicles in mammalian cells and its applications. Nat. Rev. Mol. Cell Biol. 2020;21:585–606. doi: 10.1038/s41580-020-0251-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 275.Batagov A.O., Kurochkin I.V. Exosomes secreted by human cells transport largely mRNA fragments that are enriched in the 3’-untranslated regions. Biol. Direct. 2013;8:12. doi: 10.1186/1745-6150-8-12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 276.Yang B., Feng X., Liu H., Tong R., Wu J., Li C., Yu H., Chen Y., Cheng Q., Chen J., et al. High-metastatic cancer cells derived exosomal miR92a-3p promotes epithelial-mesenchymal transition and metastasis of low-metastatic cancer cells by regulating PTEN/Akt pathway in hepatocellular carcinoma. Oncogene. 2020;39:6529–6543. doi: 10.1038/s41388-020-01450-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 277.Zhang J., Li S., Li L., Li M., Guo C., Yao J., Mi S. Exosome and exosomal microRNA: Trafficking, sorting, and function. Genom. Proteom. Bioinform. 2015;13:17–24. doi: 10.1016/j.gpb.2015.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 278.Arenaccio C., Federico M. The Multifaceted Functions of Exosomes in Health and Disease: An Overview. Adv. Exp. Med. Biol. 2017;998:3–19. doi: 10.1007/978-981-10-4397-0_1. [DOI] [PubMed] [Google Scholar]
  • 279.Kalluri R., LeBleu V.S. Discovery of Double-Stranded Genomic DNA in Circulating Exosomes. Cold Spring Harb. Symp. Quant. Biol. 2016;81:275–280. doi: 10.1101/sqb.2016.81.030932. [DOI] [PubMed] [Google Scholar]
  • 280.Castellanos-Rizaldos E., Grimm D.G., Tadigotla V., Hurley J., Healy J., Neal P.L., Sher M., Venkatesan R., Karlovich C., Raponi M., et al. Exosome-Based Detection of EGFR T790M in Plasma from Non-Small Cell Lung Cancer Patients. Clin. Cancer Res. 2018;24:2944–2950. doi: 10.1158/1078-0432.CCR-17-3369. [DOI] [PubMed] [Google Scholar]
  • 281.Kalra H., Simpson R.J., Ji H., Aikawa E., Altevogt P., Askenase P., Bond V.C., Borràs F.E., Breakefield X., Budnik V., et al. Vesiclepedia: A compendium for extracellular vesicles with continuous community annotation. PLoS Biol. 2012;10:e1001450. doi: 10.1371/journal.pbio.1001450. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 282.Kim D.K., Lee J., Kim S.R., Choi D.S., Yoon Y.J., Kim J.H., Go G., Nhung D., Hong K., Jang S.C., et al. EVpedia: A community web portal for extracellular vesicles research. Bioinformatics. 2015;31:933–939. doi: 10.1093/bioinformatics/btu741. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 283.Keerthikumar S., Chisanga D., Ariyaratne D., Al Saffar H., Anand S., Zhao K., Samuel M., Pathan M., Jois M., Chilamkurti N., et al. ExoCarta: A Web-Based Compendium of Exosomal Cargo. J. Mol. Biol. 2016;428:688–692. doi: 10.1016/j.jmb.2015.09.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 284.Li S., Li Y., Chen B., Zhao J., Yu S., Tang Y., Zheng Q., Li Y., Wang P., He X., et al. exoRBase: A database of circRNA, lncRNA and mRNA in human blood exosomes. Nucleic Acids Res. 2018;46:D106–D112. doi: 10.1093/nar/gkx891. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 285.Liu T., Zhang Q., Zhang J., Li C., Miao Y.R., Lei Q., Li Q., Guo A.Y. EVmiRNA: A database of miRNA profiling in extracellular vesicles. Nucleic Acids Res. 2019;47:D89–D93. doi: 10.1093/nar/gky985. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 286.Ha M., Kim V.N. Regulation of microRNA biogenesis. Nat. Rev. Mol. Cell Biol. 2014;15:509–524. doi: 10.1038/nrm3838. [DOI] [PubMed] [Google Scholar]
  • 287.Ketting R.F. microRNA Biogenesis and Function: An overview. Adv. Exp. Med. Biol. 2011;700:1–14. doi: 10.1007/978-1-4419-7823-3_1. [DOI] [PubMed] [Google Scholar]
  • 288.Ardekani A.M., Naeini M.M. The Role of MicroRNAs in Human Diseases. Avicenna J. Med. Biotechnol. 2010;2:161–179. [PMC free article] [PubMed] [Google Scholar]
  • 289.Acunzo M., Romano G., Wernicke D., Croce C.M. MicroRNA and cancer—A brief overview. Adv. Biol. Regul. 2015;57:1–9. doi: 10.1016/j.jbior.2014.09.013. Erratum in Adv. Biol. Regul. 2015, 58, 53. [DOI] [PubMed] [Google Scholar]
  • 290.Santos R.M., Moreno C., Zhang W.C. Non-Coding RNAs in Lung Tumor Initiation and Progression. Int. J. Mol. Sci. 2020;16:2774. doi: 10.3390/ijms21082774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 291.Soares E., Reis J., Rodrigues M., Ribeiro C.F., Pereira F.C. Circulating Extracellular Vesicles: The Missing Link between Physical Exercise and Depression Management? Int. J. Mol. Sci. 2021;22:542. doi: 10.3390/ijms22020542. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 292.Cheng G. Circulating miRNAs: Roles in cancer diagnosis, prognosis and therapy. Adv. Drug Deliv. Rev. 2015;81:75–93. doi: 10.1016/j.addr.2014.09.001. [DOI] [PubMed] [Google Scholar]
  • 293.Schwarzenbach H. Clinical Relevance of Circulating, Cell-Free and Exosomal microRNAs in Plasma and Serum of Breast Cancer Patients. Oncol. Res. Treat. 2017;40:423–429. doi: 10.1159/000478019. [DOI] [PubMed] [Google Scholar]
  • 294.Wu L., Zhou W.B., Zhou J., Wei Y., Wang H.M., Liu X.D., Chen X.C., Wang W., Ye L., Yao L.C., et al. Circulating exosomal microRNAs as novel potential detection biomarkers in pancreatic cancer. Oncol. Lett. 2020;20:1432–1440. doi: 10.3892/ol.2020.11691. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 295.Jin X., Chen Y., Chen H., Fei S., Chen D., Cai X., Liu L., Lin B., Su H., Zhao L., et al. Evaluation of Tumor-Derived Exosomal miRNA as Potential Diagnostic Biomarkers for Early-Stage Non-Small Cell Lung Cancer Using Next-Generation Sequencing. Clin. Cancer Res. 2017;23:5311–5319. doi: 10.1158/1078-0432.CCR-17-0577. [DOI] [PubMed] [Google Scholar]
  • 296.Yagi Y., Ohkubo T., Kawaji H., Machida A., Miyata H., Goda S., Roy S., Hayashizaki Y., Suzuki H., Yokota T. Next-generation sequencing-based small RNA profiling of cerebrospinal fluid exosomes. Neurosci. Lett. 2017;636:48–57. doi: 10.1016/j.neulet.2016.10.042. [DOI] [PubMed] [Google Scholar]
  • 297.Siciliano V., Garzilli I., Fracassi C., Criscuolo S., Ventre S., di Bernardo D. MiRNAs confer phenotypic robustness to gene networks by suppressing biological noise. Nat. Commun. 2013;4:2364. doi: 10.1038/ncomms3364. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 298.Shore S., Henderson J.M., Lebedev A., Salcedo M.P., Zon G., McCaffrey A.P., Paul N., Hogrefe R.I. Small RNA Library Preparation Method for Next-Generation Sequencing Using Chemical Modifications to Prevent Adapter Dimer Formation. PLoS ONE. 2016;11:e0167009. doi: 10.1371/journal.pone.0167009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 299.Buschmann D., Kirchner B., Hermann S., Märte M., Wurmser C., Brandes F., Kotschote S., Bonin M., Steinlein O.K., Pfaffl M.W., et al. Evaluation of serum extracellular vesicle isolation methods for profiling miRNAs by next-generation sequencing. J. Extracell. Vesicles. 2018;7:1481321. doi: 10.1080/20013078.2018.1481321. Erratum in J. Extracell. Vesicles 2019, 8, 1581487. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 300.Olivares D., Perez-Hernandez J., Perez-Gil D., Chaves F.J., Redon J., Cortes R. Optimization of small RNA library preparation protocol from human urinary exosomes. J. Transl. Med. 2020;18:132. doi: 10.1186/s12967-020-02298-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 301.Loudig O., Liu C., Rohan T., Ben-Dov I.Z. Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens. J. Vis. Exp. 2018;135:57471. doi: 10.3791/57471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 302.Loudig O., Wang T., Ye K., Lin J., Wang Y., Ramnauth A., Liu C., Stark A., Chitale D., Greenlee R., et al. Evaluation and Adaptation of a Laboratory-Based cDNA Library Preparation Protocol for Retrospective Sequencing of Archived MicroRNAs from up to 35-Year-Old Clinical FFPE Specimens. Int. J. Mol. Sci. 2017;18:627. doi: 10.3390/ijms18030627. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 303.Geiss G.K., Bumgarner R.E., Birditt B., Dahl T., Dowidar N., Dunaway D.L., Fell H.P., Ferree S., George R.D., Grogan T., et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat. Biotechnol. 2008;26:317–325. doi: 10.1038/nbt1385. [DOI] [PubMed] [Google Scholar]
  • 304.Precazzini F., Detassis S., Imperatori A.S., Denti M.A., Campomenosi P. Measurements Methods for the Development of MicroRNA-Based Tests for Cancer Diagnosis. Int. J. Mol. Sci. 2021;22:1176. doi: 10.3390/ijms22031176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 305.Ryu K.J., Lee J.Y., Choi M.E., Yoon S.E., Cho J., Ko Y.H., Shim J.H., Kim W.S., Park C., Kim S.J. Serum-Derived Exosomal MicroRNA Profiles Can Predict Poor Survival Outcomes in Patients with Extranodal Natural Killer/T-Cell Lymphoma. Cancers. 2020;12:3548. doi: 10.3390/cancers12123548. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 306.Sanchez Herrero J.F., Pluvinet R., Luna de Haro A., Sumoy L. Paired-end small RNA sequencing reveals a possible overestimation in the isomiR sequence repertoire previously reported from conventional single read data analysis. BMC Bioinform. 2021;22:215. doi: 10.1186/s12859-021-04128-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 307.Zelli V., Compagnoni C., Capelli R., Corrente A., Cornice J., Vecchiotti D., Di Padova M., Zazzeroni F., Alesse E., Tessitore A. Emerging Role of isomiRs in Cancer: State of the Art and Recent Advances. Genes. 2021;12:1447. doi: 10.3390/genes12091447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 308.Giallombardo M., Chacártegui Borrás J., Castiglia M., Van Der Steen N., Mertens I., Pauwels P., Peeters M., Rolfo C. Exosomal miRNA Analysis in Non-small Cell Lung Cancer (NSCLC) Patients’ Plasma Through qPCR: A Feasible Liquid Biopsy Tool. J. Vis. Exp. 2016;111:53900. doi: 10.3791/53900. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 309.Rodríguez M., Bajo-Santos C., Hessvik N.P., Lorenz S., Fromm B., Berge V., Sandvig K., Linē A., Llorente A. Identification of non-invasive miRNAs biomarkers for prostate cancer by deep sequencing analysis of urinary exosomes. Mol. Cancer. 2017;16:156. doi: 10.1186/s12943-017-0726-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 310.Tang Y., Zhao Y., Song X., Song X., Niu L., Xie L. Tumor-derived exosomal miRNA-320d as a biomarker for metastatic colorectal cancer. J. Clin. Lab. Anal. 2019;33:e23004. doi: 10.1002/jcla.23004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 311.Yoon J.H., Choi B.J., Nam S.W., Park W.S. Gastric cancer exosomes contribute to the field cancerization of gastric epithelial cells surrounding gastric cancer. Gastric Cancer. 2022;25:490–502. doi: 10.1007/s10120-021-01269-3. [DOI] [PubMed] [Google Scholar]
  • 312.White T.B., McCoy A.M., Streva V.A., Fenrich J., Deininger P.L. A droplet digital PCR detection method for rare L1 insertions in tumors. Mob. DNA. 2014;5:30. doi: 10.1186/s13100-014-0030-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 313.Hindson B.J., Ness K.D., Masquelier D.A., Belgrader P., Heredia N.J., Makarewicz A.J., Bright I.J., Lucero M.Y., Hiddessen A.L., Legler T.C., et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 2011;83:8604–8610. doi: 10.1021/ac202028g. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 314.Zhu G., Ye X., Dong Z., Lu Y.C., Sun Y., Liu Y., McCormack R., Gu Y., Liu X. Highly Sensitive Droplet Digital PCR Method for Detection of EGFR-Activating Mutations in Plasma Cell-Free DNA from Patients with Advanced Non-Small Cell Lung Cancer. J. Mol. Diagn. 2015;17:265–272. doi: 10.1016/j.jmoldx.2015.01.004. [DOI] [PubMed] [Google Scholar]
  • 315.Hindson C.M., Chevillet J.R., Briggs H.A., Gallichotte E.N., Ruf I.K., Hindson B.J., Vessella R.L., Tewari M. Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat. Methods. 2013;10:1003–1005. doi: 10.1038/nmeth.2633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 316.Malumbres M., Carnero A. Cell cycle deregulation: A common motif in cancer. Prog. Cell Cycle Res. 2003;5:5–18. [PubMed] [Google Scholar]
  • 317.Su L., Zhang J., Zhang X., Zheng L., Zhu Z. Identification of cell cycle as the critical pathway modulated by exosome-derived microRNAs in gallbladder carcinoma. Med. Oncol. 2021;38:141. doi: 10.1007/s12032-021-01594-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 318.Xia H., Li M., Chen L., Leng W., Yuan D., Pang X., Chen L., Li R., Tang Q., Bi F. Suppression of RND3 activity by AES downregulation promotes cancer cell proliferation and invasion. Int. J. Mol. Med. 2013;31:1081–1086. doi: 10.3892/ijmm.2013.1321. [DOI] [PubMed] [Google Scholar]
  • 319.Razavipour S.F., Harikumar K.B., Slingerland J.M. p27 as a Transcriptional Regulator: New Roles in Development and Cancer. Cancer Res. 2020;80:3451–3458. doi: 10.1158/0008-5472.CAN-19-3663. [DOI] [PubMed] [Google Scholar]
  • 320.Zhang Z., Xing T., Chen Y., Xiao J. Exosome-mediated miR-200b promotes colorectal cancer proliferation upon TGF-β1 exposure. Biomed. Pharmacother. 2018;106:1135–1143. doi: 10.1016/j.biopha.2018.07.042. [DOI] [PubMed] [Google Scholar]
  • 321.Yan S., Liu G., Jin C., Wang Z., Duan Q., Xu J., Xu D. MicroRNA-6869-5p acts as a tumor suppressor via targeting TLR4/NF-κB signaling pathway in colorectal cancer. J. Cell Physiol. 2018;233:6660–6668. doi: 10.1002/jcp.26316. [DOI] [PubMed] [Google Scholar]
  • 322.Yan S., Han B., Gao S., Wang X., Wang Z., Wang F., Zhang J., Xu D., Sun B. Exosome-encapsulated microRNAs as circulating biomarkers for colorectal cancer. Oncotarget. 2017;8:60149–60158. doi: 10.18632/oncotarget.18557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 323.Wang M., Yu F., Ding H., Wang Y., Li P., Wang K. Emerging Function and Clinical Values of Exosomal MicroRNAs in Cancer. Mol. Ther. Nucleic Acids. 2019;16:791–804. doi: 10.1016/j.omtn.2019.04.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 324.Avrutsky M.I., Troy C.M. Caspase-9: A Multimodal Therapeutic Target With Diverse Cellular Expression in Human Disease. Front. Pharmacol. 2021;12:701301. doi: 10.3389/fphar.2021.701301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 325.Jing X., Xie M., Ding K., Xu T., Fang Y., Ma P., Shu Y. Exosome-transmitted miR-769-5p confers cisplatin resistance and progression in gastric cancer by targeting CASP9 and promoting the ubiquitination degradation of p53. Clin. Transl. Med. 2022;12:e780. doi: 10.1002/ctm2.780. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 326.Wei Y., Li M., Cui S., Wang D., Zhang C.Y., Zen K., Li L. Shikonin Inhibits the Proliferation of Human Breast Cancer Cells by Reducing Tumor-Derived Exosomes. Molecules. 2016;21:777. doi: 10.3390/molecules21060777. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 327.Kwon Y., Kim M., Kim Y., Jung H.S., Jeoung D. Exosomal MicroRNAs as Mediators of Cellular Interactions between Cancer Cells and Macrophages. Front. Immunol. 2020;11:1167. doi: 10.3389/fimmu.2020.01167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 328.Rőszer T. Understanding the Mysterious M2 Macrophage through Activation Markers and Effector Mechanisms. Mediat. Inflamm. 2015;2015:816460. doi: 10.1155/2015/816460. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 329.Owen J.L., Mohamadzadeh M. Macrophages and chemokines as mediators of angiogenesis. Front. Physiol. 2013;4:159. doi: 10.3389/fphys.2013.00159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 330.Ying X., Wu Q., Wu X., Zhu Q., Wang X., Jiang L., Chen X., Wang X. Epithelial ovarian cancer-secreted exosomal miR-222-3p induces polarization of tumor-associated macrophages. Oncotarget. 2016;7:43076–43087. doi: 10.18632/oncotarget.9246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 331.Chen X., Ying X., Wang X., Wu X., Zhu Q., Wang X. Exosomes derived from hypoxic epithelial ovarian cancer deliver microRNA-940 to induce macrophage M2 polarization. Oncol. Rep. 2017;38:522–528. doi: 10.3892/or.2017.5697. [DOI] [PubMed] [Google Scholar]
  • 332.Deregowska A., Wnuk M. RAP1/TERF2IP-A Multifunctional Player in Cancer Development. Cancers. 2021;13:5970. doi: 10.3390/cancers13235970. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 333.Medzhitov R. Toll-like receptors and innate immunity. Nat. Rev. Immunol. 2001;1:135–145. doi: 10.1038/35100529. [DOI] [PubMed] [Google Scholar]
  • 334.Martin-Gayo E., Yu X.G. Role of Dendritic Cells in Natural Immune Control of HIV-1 Infection. Front. Immunol. 2019;10:1306. doi: 10.3389/fimmu.2019.01306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 335.Veglia F., Gabrilovich D.I. Dendritic cells in cancer: The role revisited. Curr. Opin. Immunol. 2017;45:43–51. doi: 10.1016/j.coi.2017.01.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 336.Romano R., Picca A., Eusebi L.H.U., Marzetti E., Calvani R., Moro L., Bucci C., Guerra F. Extracellular Vesicles and Pancreatic Cancer: Insights on the Roles of miRNA, lncRNA, and Protein Cargos in Cancer Progression. Cells. 2021;10:1361. doi: 10.3390/cells10061361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 337.Zhou M., Chen J., Zhou L., Chen W., Ding G., Cao L. Pancreatic cancer derived exosomes regulate the expression of TLR4 in dendritic cells via miR-203. Cell Immunol. 2014;292:65–69. doi: 10.1016/j.cellimm.2014.09.004. [DOI] [PubMed] [Google Scholar]
  • 338.Lundholm M., Schröder M., Nagaeva O., Baranov V., Widmark A., Mincheva-Nilsson L., Wikström P. Prostate tumor-derived exosomes down-regulate NKG2D expression on natural killer cells and CD8+ T cells: Mechanism of immune evasion. PLoS ONE. 2014;9:e108925. doi: 10.1371/journal.pone.0108925. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 339.Childs R.W., Carlsten M. Therapeutic approaches to enhance natural killer cell cytotoxicity against cancer: The force awakens. Nat. Rev. Drug Discov. 2015;14:487–498. doi: 10.1038/nrd4506. [DOI] [PubMed] [Google Scholar]
  • 340.Thompson T.W., Kim A.B., Li P.J., Wang J., Jackson B.T., Huang K.T.H., Zhang L., Raulet D.H. Endothelial cells express NKG2D ligands and desensitize antitumor NK responses. Elife. 2017;6:e30881. doi: 10.7554/eLife.30881. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 341.Briand J., Garnier D., Nadaradjane A., Clément-Colmou K., Potiron V., Supiot S., Bougras-Cartron G., Frenel J.S., Heymann D., Vallette F.M., et al. Radiotherapy-induced overexpression of exosomal miRNA-378a-3p in cancer cells limits natural killer cells cytotoxicity. Epigenomics. 2020;12:397–408. doi: 10.2217/epi-2019-0193. [DOI] [PubMed] [Google Scholar]
  • 342.Duan S., Guo W., Xu Z., He Y., Liang C., Mo Y., Wang Y., Xiong F., Guo C., Li Y., et al. Natural killer group 2D receptor and its ligands in cancer immune escape. Mol. Cancer. 2019;18:29. doi: 10.1186/s12943-019-0956-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 343.Berchem G., Noman M.Z., Bosseler M., Paggetti J., Baconnais S., Le Cam E., Nanbakhsh A., Moussay E., Mami-Chouaib F., Janji B., et al. Hypoxic tumor-derived microvesicles negatively regulate NK cell function by a mechanism involving TGF-β and miR23a transfer. Oncoimmunology. 2015;5:e1062968. doi: 10.1080/2162402X.2015.1062968. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 344.Alečković M., Kang Y. Regulation of cancer metastasis by cell-free miRNAs. Biochim. Biophys. Acta. 2015;1855:24–42. doi: 10.1016/j.bbcan.2014.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 345.Yuan X., Qian N., Ling S., Li Y., Sun W., Li J., Du R., Zhong G., Liu C., Yu G., et al. Breast cancer exosomes contribute to pre-metastatic niche formation and promote bone metastasis of tumor cells. Theranostics. 2021;11:1429–1445. doi: 10.7150/thno.45351. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 346.Cai Q., Zhu A., Gong L. Exosomes of glioma cells deliver miR-148a to promote proliferation and metastasis of glioblastoma via targeting CADM1. Bull. Cancer. 2018;105:643–651. doi: 10.1016/j.bulcan.2018.05.003. [DOI] [PubMed] [Google Scholar]
  • 347.Fong M.Y., Zhou W., Liu L., Alontaga A.Y., Chandra M., Ashby J., Chow A., O’Connor S.T., Li S., Chin A.R., et al. Breast-cancer-secreted miR-122 reprograms glucose metabolism in premetastatic niche to promote metastasis. Nat. Cell Biol. 2015;17:183–194. doi: 10.1038/ncb3094. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 348.Felicetti F., De Feo A., Coscia C., Puglisi R., Pedini F., Pasquini L., Bellenghi M., Errico M.C., Pagani E., Carè A. Exosome-mediated transfer of miR-222 is sufficient to increase tumor malignancy in melanoma. J. Transl. Med. 2016;14:56. doi: 10.1186/s12967-016-0811-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 349.Sai B., Dai Y., Fan S., Wang F., Wang L., Li Z., Tang J., Wang L., Zhang X., Zheng L., et al. Cancer-educated mesenchymal stem cells promote the survival of cancer cells at primary and distant metastatic sites via the expansion of bone marrow-derived-PMN-MDSCs. Cell Death Dis. 2019;10:941. doi: 10.1038/s41419-019-2149-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 350.Zhang X., Sai B., Wang F., Wang L., Wang Y., Zheng L., Li G., Tang J., Xiang J. Hypoxic BMSC-derived exosomal miRNAs promote metastasis of lung cancer cells via STAT3-induced EMT. Mol. Cancer. 2019;18:40. doi: 10.1186/s12943-019-0959-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 351.Théry C., Regnault A., Garin J., Wolfers J., Zitvogel L., Ricciardi-Castagnoli P., Raposo G., Amigorena S. Molecular Characterization of Dendritic Cell-Derived Exosomes. Selective Accumulation of the Heat Shock Protein Hsc73. J. Cell Biol. 1999;147:599–610. doi: 10.1083/jcb.147.3.599. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 352.Théry C., Boussac M., Véron P., Ricciardi-Castagnoli P., Raposo G., Garin J., Amigorena S. Proteomic Analysis of Dendritic Cell-Derived Exosomes: A Secreted Subcellular Compartment Distinct from Apoptotic Vesicles. J. Immunol. 2001;166:7309–7318. doi: 10.4049/jimmunol.166.12.7309. [DOI] [PubMed] [Google Scholar]
  • 353.van Niel G., Raposo G., Candalh C., Boussac M., Hershberg R., Cerf-Bensussan N., Heyman M. Intestinal Epithelial Cells Secrete Exosome-like Vesicles. Gastroenterology. 2001;121:337–349. doi: 10.1053/gast.2001.26263. [DOI] [PubMed] [Google Scholar]
  • 354.Skokos D., Botros H.G., Demeure C., Morin J., Peronet R., Birkenmeier G., Boudaly S., Mécheri S. Mast Cell-Derived Exosomes Induce Phenotypic and Functional Maturation of Dendritic Cells and Elicit Specific Immune Responses In Vivo. J. Immunol. Baltim. Md. 2003;170:3037–3045. doi: 10.4049/jimmunol.170.6.3037. [DOI] [PubMed] [Google Scholar]
  • 355.György B., Szabó T.G., Pásztói M., Pál Z., Misják P., Aradi B., László V., Pállinger E., Pap E., Kittel A., et al. Membrane Vesicles, Current State-of-the-Art: Emerging Role of Extracellular Vesicles. Cell. Mol. Life Sci. 2011;68:2667–2688. doi: 10.1007/s00018-011-0689-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 356.Colombo M., Raposo G., Théry C. Biogenesis, Secretion, and Intercellular Interactions of Exosomes and Other Extracellular Vesicles. Annu. Rev. Cell Dev. Biol. 2014;30:255–289. doi: 10.1146/annurev-cellbio-101512-122326. [DOI] [PubMed] [Google Scholar]
  • 357.Yáñez-Mó M., Siljander P.R.-M., Andreu Z., Zavec A.B., Borràs F.E., Buzas E.I., Buzas K., Casal E., Cappello F., Carvalho J., et al. Biological Properties of Extracellular Vesicles and Their Physiological Functions. J. Extracell. Vesicles. 2015;4:27066. doi: 10.3402/jev.v4.27066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 358.Maas S.L.N., Breakefield X.O., Weaver A.M. Extracellular Vesicles: Unique Intercellular Delivery Vehicles. Trends Cell Biol. 2017;27:172–188. doi: 10.1016/j.tcb.2016.11.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 359.van Niel G., D’Angelo G., Raposo G. Shedding Light on the Cell Biology of Extracellular Vesicles. Nat. Rev. Mol. Cell Biol. 2018;19:213–228. doi: 10.1038/nrm.2017.125. [DOI] [PubMed] [Google Scholar]
  • 360.Mathieu M., Martin-Jaular L., Lavieu G., Théry C. Specificities of Secretion and Uptake of Exosomes and Other Extracellular Vesicles for Cell-to-Cell Communication. Nat. Cell Biol. 2019;21:9–17. doi: 10.1038/s41556-018-0250-9. [DOI] [PubMed] [Google Scholar]
  • 361.Teng F., Fussenegger M. Shedding Light on Extracellular Vesicle Biogenesis and Bioengineering. Adv. Sci. 2021;8:2003505. doi: 10.1002/advs.202003505. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 362.Kalluri R., LeBleu V.S. The Biology, Function, and Biomedical Applications of Exosomes. Science. 2020;367:eaau6977. doi: 10.1126/science.aau6977. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 363.Marar C., Starich B., Wirtz D. Extracellular Vesicles in Immunomodulation and Tumor Progression. Nat. Immunol. 2021;22:560–570. doi: 10.1038/s41590-021-00899-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 364.Cheng L., Hill A.F. Therapeutically Harnessing Extracellular Vesicles. Nat. Rev. Drug Discov. 2022;21:379–399. doi: 10.1038/s41573-022-00410-w. [DOI] [PubMed] [Google Scholar]
  • 365.Østergaard O., Nielsen C.T., Iversen L.V., Jacobsen S., Tanassi J.T., Heegaard N.H.H. Quantitative Proteome Profiling of Normal Human Circulating Microparticles. J. Proteome Res. 2012;11:2154–2163. doi: 10.1021/pr200901p. [DOI] [PubMed] [Google Scholar]
  • 366.Baranyai T., Herczeg K., Onódi Z., Voszka I., Módos K., Marton N., Nagy G., Mäger I., Wood M.J., El Andaloussi S., et al. Isolation of Exosomes from Blood Plasma: Qualitative and Quantitative Comparison of Ultracentrifugation and Size Exclusion Chromatography Methods. PLoS ONE. 2015;10:e0145686. doi: 10.1371/journal.pone.0145686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 367.Brennan K., Martin K., FitzGerald S.P., O’Sullivan J., Wu Y., Blanco A., Richardson C., Mc Gee M.M. A Comparison of Methods for the Isolation and Separation of Extracellular Vesicles from Protein and Lipid Particles in Human Serum. Sci. Rep. 2020;10:1039. doi: 10.1038/s41598-020-57497-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 368.Askeland A., Borup A., Østergaard O., Olsen J.V., Lund S.M., Christiansen G., Kristensen S.R., Heegaard N.H.H., Pedersen S. Mass-Spectrometry Based Proteome Comparison of Extracellular Vesicle Isolation Methods: Comparison of ME-Kit, Size-Exclusion Chromatography, and High-Speed Centrifugation. Biomedicines. 2020;8:246. doi: 10.3390/biomedicines8080246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 369.Kowal J., Arras G., Colombo M., Jouve M., Morath J.P., Primdal-Bengtson B., Dingli F., Loew D., Tkach M., Théry C. Proteomic Comparison Defines Novel Markers to Characterize Heterogeneous Populations of Extracellular Vesicle Subtypes. Proc. Natl. Acad. Sci. USA. 2016;113:E968–E977. doi: 10.1073/pnas.1521230113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 370.Kasprzyk J., Stępień E., Piekoszewski W. Application of Nano-LC-MALDI-TOF/TOF-MS for Proteomic Analysis of Microvesicles. Clin. Biochem. 2017;50:241–243. doi: 10.1016/j.clinbiochem.2016.11.013. [DOI] [PubMed] [Google Scholar]
  • 371.Fel A., Lewandowska A.E., Petrides P.E., Wiśniewski J.R. Comparison of Proteome Composition of Serum Enriched in Extracellular Vesicles Isolated from Polycythemia Vera Patients and Healthy Controls. Proteomes. 2019;7:20. doi: 10.3390/proteomes7020020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 372.Chan J.C., Morgan C.P., Adrian Leu N., Shetty A., Cisse Y.M., Nugent B.M., Morrison K.E., Jašarević E., Huang W., Kanyuch N., et al. Reproductive Tract Extracellular Vesicles Are Sufficient to Transmit Intergenerational Stress and Program Neurodevelopment. Nat. Commun. 2020;11:1499. doi: 10.1038/s41467-020-15305-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 373.Zougman A., Selby P.J., Banks R.E. Suspension Trapping (STrap) Sample Preparation Method for Bottom-up Proteomics Analysis. Proteomics. 2014;14:1006–1000. doi: 10.1002/pmic.201300553. [DOI] [PubMed] [Google Scholar]
  • 374.Wu C., Zhou S., Mitchell M.I., Hou C., Byers S., Loudig O., Ma J. Coupling Suspension Trapping–Based Sample Preparation and Data-Independent Acquisition Mass Spectrometry for Sensitive Exosomal Proteomic Analysis. Anal. Bioanal. Chem. 2022;414:2585–2595. doi: 10.1007/s00216-022-03920-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 375.Simpson R.J., Lim J.W., Moritz R.L., Mathivanan S. Exosomes: Proteomic Insights and Diagnostic Potential. Expert Rev. Proteom. 2009;6:267–283. doi: 10.1586/epr.09.17. [DOI] [PubMed] [Google Scholar]
  • 376.Schey K.L., Luther J.M., Rose K.L. Proteomics Characterization of Exosome Cargo. Methods. 2015;87:75–82. doi: 10.1016/j.ymeth.2015.03.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 377.Kreimer S., Belov A.M., Ghiran I., Murthy S.K., Frank D.A., Ivanov A.R. Mass-Spectrometry-Based Molecular Characterization of Extracellular Vesicles: Lipidomics and Proteomics. J. Proteome Res. 2015;14:2367–2384. doi: 10.1021/pr501279t. [DOI] [PubMed] [Google Scholar]
  • 378.Choi D.-S., Kim D.-K., Kim Y.-K., Gho Y.S. Proteomics of Extracellular Vesicles: Exosomes and Ectosomes. Mass Spectrom. Rev. 2015;34:474–490. doi: 10.1002/mas.21420. [DOI] [PubMed] [Google Scholar]
  • 379.Rosa-Fernandes L., Rocha V.B., Carregari V.C., Urbani A., Palmisano G. A Perspective on Extracellular Vesicles Proteomics. Front. Chem. 2017;5:102. doi: 10.3389/fchem.2017.00102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 380.Geis-Asteggiante L., Dhabaria A., Edwards N., Ostrand-Rosenberg S., Fenselau C. Top–down Analysis of Low Mass Proteins in Exosomes Shed by Murine Myeloid-Derived Suppressor Cells. Int. J. Mass Spectrom. 2015;378:264–269. doi: 10.1016/j.ijms.2014.08.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 381.Fujita K., Kume H., Matsuzaki K., Kawashima A., Ujike T., Nagahara A., Uemura M., Miyagawa Y., Tomonaga T., Nonomura N. Proteomic Analysis of Urinary Extracellular Vesicles from High Gleason Score Prostate Cancer. Sci. Rep. 2017;7:42961. doi: 10.1038/srep42961. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 382.Fricke F., Michalak M., Warnken U., Hausser I., Schnölzer M., Kopitz J., Gebert J. SILAC-Based Quantification of TGFBR2-Regulated Protein Expression in Extracellular Vesicles of Microsatellite Unstable Colorectal Cancers. Int. J. Mol. Sci. 2019;20:E4162. doi: 10.3390/ijms20174162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 383.Kulkarni S., Koller A., Mani K.M., Wen R., Alfieri A., Saha S., Wang J., Patel P., Bandeira N., Guha C., et al. Identifying Urinary and Serum Exosome Biomarkers For Radiation Exposure Using A DDA and SWATH-MS Combined Workflow. Int. J. Radiat. Oncol. Biol. Phys. 2016;96:566–577. doi: 10.1016/j.ijrobp.2016.06.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 384.Ole N., Jensen O.N. Interpreting the protein language using proteomics. Nat. Rev. Mol. Cell Biol. 2006;7:391–403. doi: 10.1038/nrm1939. [DOI] [PubMed] [Google Scholar]
  • 385.Muller M. Post-Translational Modifications of Protein Backbones: Unique Functions, Mechanisms, and Challenges. Biochemistry. 2018;57:177–185. doi: 10.1021/acs.biochem.7b00861. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 386.Gonzales P.A., Pisitkun T., Hoffert J.D., Tchapyjnikov D., Star R.A., Kleta R., Wang N.S., Knepper M.A. Large-Scale Proteomics and Phosphoproteomics of Urinary Exosomes. J. Am. Soc. Nephrol. 2009;20:363–379. doi: 10.1681/ASN.2008040406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 387.Saraswat M., Joenväära S., Musante L., Peltoniemi H., Holthofer H., Renkonen R. N-Linked (N-) Glycoproteomics of Urinary Exosomes. Mol. Cell. Proteom. 2015;14:2298. doi: 10.1074/mcp.A114.040345. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 388.Brown C.J., Gaunitz S., Wang Z., Strindelius L., Jacobson S.C., Clemmer D.E., Trinidad J.C., Novotny M.V. Glycoproteomic Analysis of Human Urinary Exosomes. Anal. Chem. 2020;92:14357–14365. doi: 10.1021/acs.analchem.0c01952. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 389.Chen I.-H., Xue L., Hsu C.-C., Paez J.S.P., Pan L., Andaluz H., Wendt M.K., Iliuk A.B., Zhu J.-K., Tao W.A. Phosphoproteins in Extracellular Vesicles as Candidate Markers for Breast Cancer. Proc. Natl. Acad. Sci. USA. 2017;114:3175–3180. doi: 10.1073/pnas.1618088114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 390.Rontogianni S., Synadaki E., Li B., Liefaard M.C., Lips E.H., Wesseling J., Wu W., Altelaar M. Proteomic Profiling of Extracellular Vesicles Allows for Human Breast Cancer Subtyping. Commun. Biol. 2019;2:325. doi: 10.1038/s42003-019-0570-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 391.Lo Cicero A., Stahl P.D., Raposo G. Extracellular Vesicles Shuffling Intercellular Messages: For Good or for Bad. Curr. Opin. Cell Biol. 2015;35:69–77. doi: 10.1016/j.ceb.2015.04.013. [DOI] [PubMed] [Google Scholar]
  • 392.Yu R.K., Tsai Y.-T., Ariga T. Functional Roles of Gangliosides in Neurodevelopment: An Overview of Recent Advances. Neurochem. Res. 2012;37:1230–1244. doi: 10.1007/s11064-012-0744-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 393.Sunshine H., Iruela-Arispe M.L. Membrane lipids and cell signaling. Curr. Opin. Lipidol. 2017;28:408–413. doi: 10.1097/MOL.0000000000000443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 394.Butler L.M., Perone Y., Dehairs J., Lupien L.E., de Laat V., Talebi A., Loda M., Kinlaw W.B., Swinnen J.V. Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention. Adv. Drug Deliv. Rev. 2020;159:245–293. doi: 10.1016/j.addr.2020.07.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 395.Gould S.B. Membranes and evolution. Curr. Biol. 2018;28:R381–R385. doi: 10.1016/j.cub.2018.01.086. [DOI] [PubMed] [Google Scholar]
  • 396.Singer S.J., Nicolson G.L. The fluid mosaic model of the structure of cell membranes. Science. 1972;175:720–731. doi: 10.1126/science.175.4023.720. [DOI] [PubMed] [Google Scholar]
  • 397.Jackson Catherine L., Walch L., Verbavatz J.-M. Lipids and Their Trafficking: An Integral Part of Cellular Organization. Dev. Cell. 2016;39:139–153. doi: 10.1016/j.devcel.2016.09.030. [DOI] [PubMed] [Google Scholar]
  • 398.Kang J.X., Wan J.B., He C. Concise Review: Regulation of Stem Cell Proliferation and Differentiation by Essential Fatty Acids and Their Metabolites. Stem Cells. 2014;32:1092–1098. doi: 10.1002/stem.1620. [DOI] [PubMed] [Google Scholar]
  • 399.Wang Q., Song Y.H., Tang Z., Wang Z.P., Xu Q., Bao N. Effects of ganglioside GM1 and neural growth factor on neural stem cell proliferation and differentiation. Genet. Mol. Res. 2016;15:1–10. doi: 10.4238/gmr.15038376. [DOI] [PubMed] [Google Scholar]
  • 400.Newton J., Lima S., Maceyka M., Spiegel S. Revisiting the sphingolipid rheostat: Evolving concepts in cancer therapy. Exp. Cell Res. 2015;333:195–200. doi: 10.1016/j.yexcr.2015.02.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 401.Houten S.M., Wanders R.J.A. A general introduction to the biochemistry of mitochondrial fatty acid β-oxidation. J. Inherit. Metab. Dis. 2010;33:469–477. doi: 10.1007/s10545-010-9061-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 402.Abuhusain H.J., Matin A., Qiao Q., Shen H., Kain N., Day B.W., Stringer B.W., Daniels B., Laaksonen M.A., Teo C., et al. A metabolic shift favoring sphingosine 1-phosphate at the expense of ceramide controls glioblastoma angiogenesis. J. Biol. Chem. 2013;288:37355–37364. doi: 10.1074/jbc.M113.494740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 403.Koizumi A., Narita S., Nakanishi H., Ishikawa M., Eguchi S., Kimura H., Takasuga S., Huang M., Inoue T., Sasaki J., et al. Increased fatty acyl saturation of phosphatidylinositol phosphates in prostate cancer progression. Sci. Rep. 2019;9:13257. doi: 10.1038/s41598-019-49744-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 404.Greenlee J.D., Subramanian T., Liu K., King M.R. Rafting Down the Metastatic Cascade: The Role of Lipid Rafts in Cancer Metastasis, Cell Death, and Clinical Outcomes. Cancer Res. 2021;81:5. doi: 10.1158/0008-5472.CAN-20-2199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 405.Sriram V., Cho S., Li P., O’Donnell Patrick W., Dunn C., Hayakawa K., Blum Janice S., Brutkiewicz Randy R. Inhibition of glycolipid shedding rescues recognition of a CD1+ T cell lymphoma by natural killer T (NKT) cells. Proc. Natl. Acad. Sci. USA. 2002;99:8197–8202. doi: 10.1073/pnas.122636199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 406.Skotland T., Hessvik N.P., Sandvig K., Llorente A. Exosomal lipid composition and the role of ether lipids and phosphoinositides in exosome biology. J. Lipid Res. 2019;60:9–18. doi: 10.1194/jlr.R084343. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 407.Skotland T., Sandvig K., Llorente A. Lipids in exosomes: Current knowledge and the way forward. Prog. Lipid Res. 2017;66:30–41. doi: 10.1016/j.plipres.2017.03.001. [DOI] [PubMed] [Google Scholar]
  • 408.Wang W., Zhu N., Yan T., Shi Y.-N., Chen J., Zhang C.-J., Xie X.-J., Liao D.-F., Qin L. The crosstalk: Exosomes and lipid metabolism. Cell Commun. Signal. 2020;18:119. doi: 10.1186/s12964-020-00581-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 409.Donoso-Quezada J., Ayala-Mar S., González-Valdez J. The role of lipids in exosome biology and intercellular communication: Function, analytics and applications. Traffic. 2021;22:204–220. doi: 10.1111/tra.12803. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 410.Skotland T., Sagini K., Sandvig K., Llorente A. An emerging focus on lipids in extracellular vesicles. Adv. Drug Deliv. Rev. 2020;159:308–321. doi: 10.1016/j.addr.2020.03.002. [DOI] [PubMed] [Google Scholar]
  • 411.Zebrowska A., Skowronek A., Wojakowska A., Widlak P., Pietrowska M. Metabolome of Exosomes: Focus on Vesicles Released by Cancer Cells and Present in Human Body Fluids. Int. J. Mol. Sci. 2019;20:3461. doi: 10.3390/ijms20143461. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 412.Llorente A., Skotland T., Sylvänne T., Kauhanen D., Róg T., Orłowski A., Vattulainen I., Ekroos K., Sandvig K. Molecular lipidomics of exosomes released by PC-3 prostate cancer cells. Biochim. Biophys. Acta. 2013;1831:1302–1309. doi: 10.1016/j.bbalip.2013.04.011. [DOI] [PubMed] [Google Scholar]
  • 413.Yi X., Li Y., Hu X., Wang F., Liu T. Changes in phospholipid metabolism in exosomes of hormone-sensitive and hormone-resistant prostate cancer cells. J. Cancer. 2021;12:2893–2902. doi: 10.7150/jca.48906. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 414.Lydic T.A., Townsend S., Adda C.G., Collins C., Mathivanan S., Reid G.E. Rapid and comprehensive ‘shotgun’ lipidome profiling of colorectal cancer cell derived exosomes. Methods. 2015;87:83–95. doi: 10.1016/j.ymeth.2015.04.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 415.Haraszti R.A., Didiot M.-C., Sapp E., Leszyk J., Shaffer S.A., Rockwell H.E., Gao F., Narain N.R., DiFiglia M., Kiebish M.A., et al. High-resolution proteomic and lipidomic analysis of exosomes and microvesicles from different cell sources. J. Extracell. Vesicles. 2016;5:32570. doi: 10.3402/jev.v5.32570. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 416.Fahy E., Cotter D., Sud M., Subramaniam S. Lipid classification, structures and tools. Biochim. Biophys. Acta. 2011;1811:637–647. doi: 10.1016/j.bbalip.2011.06.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 417.Fahy E., Subramaniam S., Murphy R.C., Nishijima M., Raetz C.R.H., Shimizu T., Spener F., van Meer G., Wakelam M.J.O., Dennis E.A. Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 2009;50:S9–S14. doi: 10.1194/jlr.R800095-JLR200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 418.Lobasso S., Tanzarella P., Mannavola F., Tucci M., Silvestris F., Felici C., Ingrosso C., Corcelli A., Lopalco P. A Lipidomic Approach to Identify Potential Biomarkers in Exosomes from Melanoma Cells with Different Metastatic Potential. Front. Physiol. 2021;12:748895. doi: 10.3389/fphys.2021.748895. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 419.Peterka O., Jirásko R., Chocholoušková M., Kuchař L., Wolrab D., Hájek R., Vrána D., Strouhal O., Melichar B., Holčapek M. Lipidomic characterization of exosomes isolated from human plasma using various mass spectrometry techniques. Biochim. Biophys. Acta Mol. Cell Biol. Lipids. 2020;1865:158634. doi: 10.1016/j.bbalip.2020.158634. [DOI] [PubMed] [Google Scholar]
  • 420.Altadill T., Campoy I., Lanau L., Gill K., Rigau M., Gil-Moreno A., Reventos J., Byers S., Colas E., Cheema A.K. Enabling Metabolomics Based Biomarker Discovery Studies Using Molecular Phenotyping of Exosome-Like Vesicles. PLoS ONE. 2016;11:e0151339. doi: 10.1371/journal.pone.0151339. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 421.Xu S., Ye M., Xu D., Li X., Pan C., Zou H. Matrix with high salt tolerance for the analysis of peptide and protein samples by desorption/ionization time-of-flight mass spectrometry. Anal. Chem. 2006;78:2593–2599. doi: 10.1021/ac051572a. [DOI] [PubMed] [Google Scholar]
  • 422.Tran A., Wan L., Xu Z., Haro J.M., Li B., Jones J.W. Lithium Hydroxide Hydrolysis Combined with MALDI TOF Mass Spectrometry for Rapid Sphingolipid Detection. J. Am. Soc. Mass. Spectrom. 2021;32:289–300. doi: 10.1021/jasms.0c00322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 423.Paglia G., Kliman M., Claude E., Geromanos S., Astarita G. Applications of ion-mobility mass spectrometry for lipid analysis. Anal. Bioanal. Chem. 2015;407:4995–5007. doi: 10.1007/s00216-015-8664-8. [DOI] [PubMed] [Google Scholar]
  • 424.Bowman A.P., Blakney G.T., Hendrickson C.L., Ellis S.R., Heeren R.M.A., Smith D.F. Ultra-High Mass Resolving Power, Mass Accuracy, and Dynamic Range MALDI Mass Spectrometry Imaging by 21-T FT-ICR MS. Anal. Chem. 2020;92:3133–3142. doi: 10.1021/acs.analchem.9b04768. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 425.Dodds J.N., Baker E.S. Ion Mobility Spectrometry: Fundamental Concepts, Instrumentation, Applications, and the Road Ahead. J. Am. Soc. Mass Spectrom. 2019;30:2185–2195. doi: 10.1007/s13361-019-02288-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 426.Giles K., Ujma J., Wildgoose J., Pringle S., Richardson K., Langridge D., Green M. A Cyclic Ion Mobility-Mass Spectrometry System. Anal. Chem. 2019;91:8564–8573. doi: 10.1021/acs.analchem.9b01838. [DOI] [PubMed] [Google Scholar]
  • 427.Claude E., Jones E.A., Pringle S.D. DESI Mass Spectrometry Imaging (MSI) Methods Mol. Biol. 2017;1618:65–75. doi: 10.1007/978-1-4939-7051-3_7. [DOI] [PubMed] [Google Scholar]
  • 428.Han X., Gross R.W. Shotgun lipidomics: Electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from crude extracts of biological samples. Mass. Spectrom. Rev. 2005;24:367–412. doi: 10.1002/mas.20023. [DOI] [PubMed] [Google Scholar]
  • 429.Hsu F.-F. Mass spectrometry-based shotgun lipidomics—A critical review from the technical point of view. Anal. Bioanal. Chem. 2018;410:6387–6409. doi: 10.1007/s00216-018-1252-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 430.Su B., Bettcher L.F., Hsieh W.-Y., Hornburg D., Pearson M.J., Blomberg N., Giera M., Snyder M.P., Raftery D., Bensinger S.J., et al. A DMS Shotgun Lipidomics Workflow Application to Facilitate High-Throughput, Comprehensive Lipidomics. J. Am. Soc. Mass Spectrom. 2021;32:2655–2663. doi: 10.1021/jasms.1c00203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 431.Wojakowska A., Zebrowska A., Skowronek A., Rutkowski T., Polanski K., Widlak P., Marczak L., Pietrowska M. Metabolic Profiles of Whole Serum and Serum-Derived Exosomes Are Different in Head and Neck Cancer Patients Treated by Radiotherapy. J. Pers. Med. 2020;10:229. doi: 10.3390/jpm10040229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 432.Fuchs B., Süß R., Teuber K., Eibisch M., Schiller J. Lipid analysis by thin-layer chromatography—A review of the current state. J. Chromatogr. A. 2011;1218:2754–2774. doi: 10.1016/j.chroma.2010.11.066. [DOI] [PubMed] [Google Scholar]
  • 433.Engel K.M., Prabutzki P., Leopold J., Nimptsch A., Lemmnitzer K., Vos D.R.N., Hopf C., Schiller J. A new update of MALDI-TOF mass spectrometry in lipid research. Prog. Lipid Res. 2022;86:101145. doi: 10.1016/j.plipres.2021.101145. [DOI] [PubMed] [Google Scholar]
  • 434.Cajka T., Fiehn O. Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends Anal. Chem. TRAC. 2014;61:192–206. doi: 10.1016/j.trac.2014.04.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 435.Holčapek M., Liebisch G., Ekroos K. Lipidomic Analysis. Anal. Chem. 2018;90:4249–4257. doi: 10.1021/acs.analchem.7b05395. [DOI] [PubMed] [Google Scholar]
  • 436.Lange M., Fedorova M. Evaluation of lipid quantification accuracy using HILIC and RPLC MS on the example of NIST® SRM® 1950 metabolites in human plasma. Anal. Bioanal. Chem. 2020;412:3573–3584. doi: 10.1007/s00216-020-02576-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 437.Rampler E., Criscuolo A., Zeller M., El Abiead Y., Schoeny H., Hermann G., Sokol E., Cook K., Peake D.A., Delanghe B., et al. A Novel Lipidomics Workflow for Improved Human Plasma Identification and Quantification Using RPLC-MSn Methods and Isotope Dilution Strategies. Anal. Chem. 2018;90:6494–6501. doi: 10.1021/acs.analchem.7b05382. [DOI] [PubMed] [Google Scholar]
  • 438.Jung J.H., Lee M.Y., Choi D.Y., Lee J.W., You S., Lee K.Y., Kim J., Kim K.P. Phospholipids of tumor extracellular vesicles stratify gefitinib-resistant nonsmall cell lung cancer cells from gefitinib-sensitive cells. Proteomics. 2015;15:824–835. doi: 10.1002/pmic.201400243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 439.Aiello N.M., Brabletz T., Kang Y., Nieto M.A., Weinberg R.A., Stanger B.Z. Upholding a role for EMT in pancreatic cancer metastasis. Nature. 2017;547:E7–E8. doi: 10.1038/nature22963. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 440.Zhou P., Li B., Liu F., Zhang M., Wang Q., Liu Y., Yao Y., Li D. The epithelial to mesenchymal transition (EMT) and cancer stem cells: Implication for treatment resistance in pancreatic cancer. Mol. Cancer. 2017;16:52. doi: 10.1186/s12943-017-0624-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 441.Brzozowski J.S., Jankowski H., Bond D.R., McCague S.B., Munro B.R., Predebon M.J., Scarlett C.J., Skelding K.A., Weidenhofer J. Lipidomic profiling of extracellular vesicles derived from prostate and prostate cancer cell lines. Lipids Health Dis. 2018;17:211. doi: 10.1186/s12944-018-0854-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 442.Cheng L., Zhang K., Qing Y., Li D., Cui M., Jin P., Xu T. Proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells and ovarian surface epithelial cells. J. Ovarian Res. 2020;13:9. doi: 10.1186/s13048-020-0609-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 443.Kuc N., Doermann A., Shirey C., Lee D.D., Lowe C.-W., Awasthi N., Schwarz R.E., Stahelin R.V., Schwarz M.A. Pancreatic ductal adenocarcinoma cell secreted extracellular vesicles containing ceramide-1-phosphate promote pancreatic cancer stem cell motility. Biochem. Pharmacol. 2018;156:458–466. doi: 10.1016/j.bcp.2018.09.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 444.Fan T.W.M., Zhang X., Wang C., Yang Y., Kang W.-Y., Arnold S., Higashi R.M., Liu J., Lane A.N. Exosomal lipids for classifying early and late stage non-small cell lung cancer. Anal. Chim. Acta. 2018;1037:256–264. doi: 10.1016/j.aca.2018.02.051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 445.Tao L., Zhou J., Yuan C., Zhang L., Li D., Si D., Xiu D., Zhong L. Metabolomics identifies serum and exosomes metabolite markers of pancreatic cancer. Metabolomics. 2019;15:86. doi: 10.1007/s11306-019-1550-1. [DOI] [PubMed] [Google Scholar]
  • 446.Skotland T., Ekroos K., Kauhanen D., Simolin H., Seierstad T., Berge V., Sandvig K., Llorente A. Molecular lipid species in urinary exosomes as potential prostate cancer biomarkers. Eur. J. Cancer. 2017;70:122–132. doi: 10.1016/j.ejca.2016.10.011. [DOI] [PubMed] [Google Scholar]
  • 447.Kang Y.-T., Purcell E., Palacios-Rolston C., Lo T.-W., Ramnath N., Jolly S., Nagrath S. Isolation and Profiling of Circulating Tumor-Associated Exosomes Using Extracellular Vesicular Lipid-Protein Binding Affinity Based Microfluidic Device. Small. 2019;15:e1903600. doi: 10.1002/smll.201903600. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 448.Matsumura S., Minamisawa T., Suga K., Kishita H., Akagi T., Ichiki T., Ichikawa Y., Shiba K. Subtypes of tumour cell-derived small extracellular vesicles having differently externalized phosphatidylserine. J. Extracell. Vesicles. 2019;8:1579541. doi: 10.1080/20013078.2019.1579541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 449.Sharma R., Huang X., Brekken R.A., Schroit A.J. Detection of phosphatidylserine-positive exosomes for the diagnosis of early-stage malignancies. Br. J. Cancer. 2017;117:545–552. doi: 10.1038/bjc.2017.183. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 450.Nakai W., Yoshida T., Diez D., Miyatake Y., Nishibu T., Imawaka N., Naruse K., Sadamura Y., Hanayama R. A novel affinity-based method for the isolation of highly purified extracellular vesicles. Sci. Rep. 2016;6:33935. doi: 10.1038/srep33935. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 451.Shih C.-L., Chong K.-Y., Hsu S.-C., Chien H.-J., Ma C.-T., Chang J.W.-C., Yu C.-J., Chiou C.-C. Development of a magnetic bead-based method for the collection of circulating extracellular vesicles. New Biotechnol. 2016;33:116–122. doi: 10.1016/j.nbt.2015.09.003. [DOI] [PubMed] [Google Scholar]
  • 452.Vallabhapurapu S.D., Blanco V.M., Sulaiman M.K., Vallabhapurapu S.L., Chu Z., Franco R.S., Qi X. Variation in human cancer cell external phosphatidylserine is regulated by flippase activity and intracellular calcium. Oncotarget. 2015;6:34375–34388. doi: 10.18632/oncotarget.6045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 453.Utsugi T., Schroit A.J., Connor J., Bucana C.D., Fidler I.J. Elevated Expression of Phosphatidylserine in the Outer Membrane Leaflet of Human Tumor Cells and Recognition by Activated Human Blood Monocytes. Cancer Res. 1991;51:3062–3066. [PubMed] [Google Scholar]
  • 454.Riedl S., Rinner B., Asslaber M., Schaider H., Walzer S., Novak A., Lohner K., Zweytick D. In search of a novel target—Phosphatidylserine exposed by non-apoptotic tumor cells and metastases of malignancies with poor treatment efficacy. Biochim. Biophys. Acta. 2011;1808:2638–2645. doi: 10.1016/j.bbamem.2011.07.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 455.Dong H.P., Holth A., Kleinberg L., Ruud M.G., Elstrand M.B., Tropé C.G., Davidson B., Risberg B. Evaluation of cell surface expression of phosphatidylserine in ovarian carcinoma effusions using the annexin-V/7-AAD assay: Clinical relevance and comparison with other apoptosis parameters. Am. J. Clin. Pathol. 2009;132:756–762. doi: 10.1309/AJCPAVFA8J3KHPRS. [DOI] [PubMed] [Google Scholar]
  • 456.Kelleher R.J., Jr., Balu-Iyer S., Loyall J., Sacca A.J., Shenoy G.N., Peng P., Iyer V., Fathallah A.M., Berenson C.S., Wallace P.K., et al. Extracellular Vesicles Present in Human Ovarian Tumor Microenvironments Induce a Phosphatidylserine-Dependent Arrest in the T-cell Signaling Cascade. Cancer Immunol. Res. 2015;3:1269–1278. doi: 10.1158/2326-6066.CIR-15-0086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 457.Lea J., Sharma R., Yang F., Zhu H., Ward E.S., Schroit A.J. Detection of phosphatidylserine-positive exosomes as a diagnostic marker for ovarian malignancies: A proof of concept study. Oncotarget. 2017;8:14395–14407. doi: 10.18632/oncotarget.14795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 458.Théry C., Witwer K.W., Aikawa E., Alcaraz M.J., Anderson J.D., Andriantsitohaina R., Antoniou A., Arab T., Archer F., Atkin-Smith G.K., et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): A position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J. Extracell. Vesicles. 2018;7:1535750. doi: 10.1080/20013078.2018.1535750. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Cancers are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

RESOURCES