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. 2019 Dec 19;23(1):100782. doi: 10.1016/j.isci.2019.100782

Detection of Extracellular Vesicle RNA Using Molecular Beacons

Getulio Pereira de Oliveira Jr 1, Eric Zigon 2, Gaenna Rogers 2, Danny Davodian 1,3, Shulin Lu 1, Tijana Jovanovic-Talisman 4, Jennifer Jones 5, John Tigges 2, Sanjay Tyagi 6,7, Ionita Calin Ghiran 1,7,8,∗
PMCID: PMC6992906  PMID: 31958756

Summary

Extracellular vesicles (EVs) have recently emerged as intercellular conveyors of biological information and disease biomarkers. Identification and characterization of RNA species in single EVs are currently challenging. Molecular beacons (MBs) represent an attractive means for detecting specific RNA molecules. Coupling the MBs to cell-penetrating peptides (CPPs) provides a fast, effective, and membrane-type agnostic means to deliver MBs across the plasma membrane and into the cytosol. Here, we generated RBCs-derived EVs by complement activation and tested the ability of MBs coupled with CPP to detect miRNAs from RBC-EVs. Our results showed that RBC and RBC-EVs miRNA-451a can be detected using MB-CPP, and the respective fluorescence levels can be measured by nano-flow cytometry. MB-based detection of RNA via nano-flow cytometry creates a powerful new analytical framework in which a simple addition of a reagent allows profiling of specific RNA species present within certain EV subsets.

Subject Areas: Biochemistry Methods, Biomolecules, Cell Biology, Functional Aspects of Cell Biology

Graphical Abstract

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Highlights

  • •

    RBC and RBC-EVs contain unequal amount of RNA

  • •

    Efficient detection of miRNA in vitro by MBs using nano-flow cytometry

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    CPPs effectively deliver cargo to cells and extracellular vesicles

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    MB-CPP can be used to detect cell-specific EV miRNAs

Biochemistry Methods; Biomolecules; Cell Biology; Functional Aspects of Cell Biology

Introduction

A novel paradigm in paracrine signaling has recently emerged based on the findings identifying extracellular vesicles (EVs) as multifaceted, intercellular conveyors of biological information (Tkach and Thery, 2016, Thery, 2015). EVs and their cargo have been shown to regulate gene expression and alter cell function in various cell types (Kreimer et al., 2015, Mantel et al., 2013). Isolation and molecular profiling of subsets of EVs (i.e., RNAs, proteins, lipids, metabolites) are critical for understanding the biogenesis of EVs and their potential utility as biomarkers. Simultaneous, multiparametric characterization of external protein and internal RNA components of single EVs is currently challenging owing to the limitations of currently available EV analysis assays. These require bulk analyses, thus limiting the detection of low abundant EV components and detection of EV DNA/RNA/proteins, and involves time-consuming (RNA-seq, dd/q-RT-PCR, southern/northern/western blot), as well as expensive (-omics) analyses.

Using the EV RNA cargo as an indicator of tissue origin or disease marker relies on identifying and quantifying the expression level of various RNA species from isolated EVs. When analyzing EV populations for low copy number of RNAs, it is easy to underappreciate rare signal, which may contain critical information about incipient processes in the host, as the information obtained represents an average and does not account for EV heterogeneity.

Molecular beacons (MBs) are hairpin-shaped oligonucleotides that contain a complementary sequence to a specific RNA or ssDNA molecule, a fluorochrome, and a quencher (Giesendorf et al., 1998, Tyagi and Kramer, 1996). Upon binding to target, MB undergoes a conformation change that separates the quencher from the fluorochrome, thus allowing the probe to fluoresce upon excitation. We have shown that the binding between the DNA/RNA target and MB is highly specific, with even one pair mismatch anywhere on the sequence preventing the opening of the stem and thus keeping the MB non-fluorescent (Bonnet et al., 1999, Marras et al., 1999, Tyagi et al., 1998). Most if not all extracellular and intracellular RNA species are bound to various RNA-binding proteins, interactions that could potentially interfere with the MB-based detection approach. Since the binding affinities of oligonucleotide probes to their targets are at least an order of magnitude higher than the binding affinities of RNA-binding proteins to their targets, MBs are successfully able to bind to mRNAs in living cells by displacing the bound proteins (Bratu et al., 2003, Chen et al., 2017b, Vargas et al., 2005).

Cell-penetrating peptides (CPPs) are short peptide sequences, rich in lysine or arginine, that have the ability to cross biological membranes through either passive or active processes. Based on their overall charge, CPPs belong to three distinct classes: cationic, amphipathic, and hydrophobic, each of which use a distinct mechanism for membrane fusion and internalization, although the precise molecular mechanisms are still poorly understood (Vives et al., 2003). As CPPs can be coupled to a wide range of biological compounds, they represent an attractive delivery vehicle perfectly suited to bridge the gap between specific RNA detection, imaging- and flow-friendly readouts, and simplicity of use. CPPs coupled to MBs have been used for tracking mRNA in living cells successfully before (Nitin et al., 2004).

We herein describe a novel method for interrogating the RNA content of circulating EVs, which combines the sensitivity and specificity of MBs to provide specific RNA information with the high throughput of nano-flow cytometry (nFC) for detection of specific RNA molecules in subsets of EVs. Our results show that interrogating cells and EVs with MBs coupled to CPPs provides fast and reliable results, which are fully consistent with more sophisticated, and time consuming, RNA detection methods such as q-RT-PCR.

Results

Extracellular Vesicles Contain Unequal Amount of RNA

Imaging and tracking small RNA molecules in cells, without the use of involved methods such as microinjection of labeled RNA molecules or RNA in situ hybridization, is complicated by the lack of exclusive RNA dyes, the presence of large amounts DNA in nucleus and mitochondria (∼13 kb), and the existence of significant levels of m, r, and t RNAs in cytosol. Red blood cells, by lacking organelles, DNA, and mRNA molecules, offer a significant advantage for studying EV biogenesis (originating from plasma membrane, i.e., microparticles) and RNA loading into EVs. We and others have shown that circulating RBCs contain few species and copies of small non-coding RNAs (sRNAs) relevant in host pathogen interaction (LaMonte et al., 2012, Mantel et al., 2016), and reviewed by Walzer et al. (Walzer and Chi, 2017). Using Syto9, a membrane-permeable RNA selective dye (Figure 1A, left panel, arrows), the RNA content of RBCs can be successfully labeled and tracked as it is packaged into EVs. Importantly, the RNA content of circulating RBCs decreases with the age of the cells, with newly released RBCs from bone marrow containing the largest amount of sRNAs (Figure 1A, middle panel, reticulocytes), whereas the older, smaller RBCs having virtually non-detectable sRNAs by fluorescence microscopy (Figure 1A, right panel, old RBC). A similar trend was observed when new, intermediate, and old RBCs were isolated from three independent donors using Percoll gradient and total RNA was quantified by fluorometry (Qubit, Thermo Fisher) (Figure 1B). Next, we took advantage of the uncluttered RNA landscape of human RBCs to quantify the range of sRNAs present in complement-generated EVs following our validated protocol (Kuo et al., 2017). The efficacy of complement-mediated EV generation was verified by transmission electron microscopy (Figure 1C), resistive pulse sensing (qNano, Izon) (Figure 1D), and nano-flow cytometry (Figure 1E, gate “EVs”). Next, the total RNA in RBC-EVs was labeled using Syto9 as show above. Our results show that even if the EVs were generated from the same cell type (RBCs) using the same method (complement activation, Figure 1F), their RNA content was not uniformly distributed among EVs (Figure 1G). Although virtually all EVs contain some amounts of sRNAs (Figure 1G, see the sub-log, unimodal shift of the main EV population), a subpopulation of EVs contained larger (over a log fluorescence difference) amounts, and presumably different types or sequences of sRNA (Figure 1G, arrow). Therefore, we investigated the effectiveness of MBs to label only EVs containing specific miRNA sequence, thus bypassing the need for EV isolation and purification as well as RNA-seq or qPCR.

Figure 1.

Figure 1

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EVs from the Same Cell Type Have Uneven RNA Loading

(A) Human RBC labeled for sRNA showing circulating age-related loss of cell sRNA (top row).

(B) Correlation plot of the total RNA content of old, intermediate, and new RBCs from three independent donors.

(C) Electron micrograph of an EV budding from RBC plasma membrane showing lack of or minimal amounts of hemoglobin content compared with the concentration of cytoplasmic hemoglobin of the parent cell.

(D) RBC-EVs diameters were measured using resistive pulse sensing (qNano).

(E–G) (E). Nano-flow cytometry of RBC-derived EVs in the presence of buffer (F) or 5 nM Syto9 (G), showing uneven RNA staining in EV population.

Detection of miRNA by MBs Using Fluorometry and Nano-Flow Cytometry

Current detection of specific miRNAs species is performed using RNA-seq or targeted qPCR-based approaches, which implies the use of involved and time-consuming procedures. MBs offer a one-step direct approach based on direct hybridization of the probe to the target nucleic acid sequence. We tested the ability of bead-attached MBs and CPP-coupled MB to identify the presence of specific miRNAs in buffer by incubating increasing concentrations of target or scrambled control miRNAs with complementary MBs immobilized on 10-μm sepharose beads (Figures S1 and S2). Our results, consistent with previous reports (Mhlanga and Tyagi, 2006, Nitin et al., 2004, Tyagi and Kramer, 1996), show a direct relationship between the concentration of the target miRNAs and fluorescence of the MBs attached to streptavidin-sepharose beads. The lowest concentration of miRNas immobilized on beads that generated a significant fluorescence signal (MFI 6.22) over the control scramble miRNAs (MFI 4.01) was 5 nM. Each streptavidin-sepharose bead would immobilize between 5,000 and 12,000 MBs, explaining the large fluorescence shift observed with higher concentrations of miRNAs. However, it is unlikely that such large numbers of the same miRNA species are present in any given EV or EV subpopulation. Thus, we tested the ability of our approach to detect physiologically relevant concentrations of miRNA using platelets and RBCs, two of the most abundant cell types in the blood and major contributors to circulating EVs.

Membrane-Penetrating Peptides Effectively Deliver Cargo to Cells and Extracellular Vesicles

We started by testing the efficacy of fluorescently labeled cell-perpetrating peptides, based on TAT sequence (CPP-FAM) to cross the plasma membrane and label circulating cells. We incubated freshly isolated buffy coat cells with 20 mM CPP-FAM for 20 min at RT without mixing (Figures 2A–2C). Our results show that, although all the circulating cells tested were labeled by CPP-FAM, the intensity of the signal varied significantly with the cell type. Platelets, lymphocytes, and PMNs had the lowest signals (MFI 964, 1,800, and 3,645, respectively) and RBCs and monocytes the highest (MFI 9,803 and 18,710, respectively). The variation was likely due to differences in the cell volume, internal pH, and, possibly, membrane composition. For lymphocytes, a subpopulation of 14.1% had a higher CPP-FAM uptake than the remaining 85.9%. We next tested (Figures 2D and 2E) the penetrating capabilities of CPP-FAM on EVs by incubating cell-free plasma with a diluted solution of CPP-FAM for 20 min at RT, following our published protocols (Danielson et al., 2016, Shah et al., 2017) with 2 ng/mL CPP-FAM. After 20 min, the mixture was analyzed, without washing, by nano-flow cytometry. Our data show a unimodal shift of the EV fluorescence following CPP-FAM treatment (Figure 2E, blue histogram, versus plasma control, orange), strongly suggesting that CPP-cargo penetrates all plasma EVs, without bias for a certain membrane type or cell origin. An alternative explanation could be that a population of CPP-FAM became attached to the outer leaflet of all circulating EVs, as it is commonly seen in cells pretreated with CPP-FAM (Illien et al., 2016). We investigated this alternative explanation by using FAM as the fluorophore bright as it is quenchable by trypan blue treatment. Thus, we tested whether the FAM fluorescence was originating from within the EVs, was associated with the plasma membrane, or both by incubating an aliquot of the EVs-CPP-FAM mixture with 0.2% trypan blue for 5 min. Our results showed that the fluorescence intensity did not change measurably following trypan blue-mediated quenching (Figure 2E red histogram behind the blue), strongly indicating that CPP-FAM complexes were in fact inside the EVs and therefore not quenchable in the trypan blue. While the signal associated with CPP-FAM depends almost exclusively on the amount of probe trapped by each EV, and thus by the EV volume, the signal intensity for a specific miRNA-CPP-FAM depends on the number or RNA targets when detecting specific RNA molecules by molecular beacons could be significantly lower.

Figure 2.

Figure 2

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CPPs Effectively Deliver Cargo to Cells and EVs

(A) Buffy coat cells were incubated with CPP-FAM, washed, and imaged using an Olympus BX62 microscope or quantified using a BD LSR II flow cytometer (BD Biosciences, NJ, USA). The respective histograms show autofluorescence (red) and penetration by CPP-FAM (blue).

(B) Dot-plot profile of RBCs.

(C) Penetration of RBC-membrane by CPP-FAM.

(D) Dot-plot profile of plasma EVs (arrow).

(E) Penetration of EV membrane by CPP-FAM (blue histogram). Incubation of EVs-CPP-FAM with 0.2% trypan blue does not decrease the fluorescence signal compared to buffer-treated sample (red histogram, behind the blue). Orange histogram represents the autofluorescence of EVs. Experiments performed four times.

Detection of Cell-Specific EV miRNAs by CPP-MBs

We designed a CPP-miRNA-MB against miR-495, an miRNA enriched in circulating platelets and also relevant in the pathogenesis of a number of cancers (Chen et al., 2017a, Li et al., 2016). Our results in Figure 3A show that incubation of whole blood with CPP-miR495-MB for 5 min at RT resulted in specific and strong labeling of the platelet population without any detectable labeling of the red blood cells or nucleated cells, even after 30 min of imaging. The CPP-miR495-MB labeling of the platelets (arrows in Figure 3A1) was both diffusely distributed throughout the cytoplasm and compartmentalized in vesicle-like structures, possibly primary- or alpha-granules (inset in Figure 3A1, upper left panel). Other circulating cells present in the blood, such as RBCs, seen as concave shadows, and PMNs, outlined in Figure 3A2–4, did not display any detectable signal in the cytoplasm. However, overnight incubation of whole blood with MBs showed discreet punctuated fluorescent pattern in circulating neutrophils, possibly in the lysosomal compartment. We next tested the efficacy of CPP-MBs to label platelet-specific EVs by imaging cell-free plasma incubated with CPP-miR495-MB using double-immersion, high-resolution dark field imaging (Figure 3B) achieved by immersion of both the high-numerical aperture (NA) cardioid-type condenser (NA = 1.4) and the 100x1.35 PlanApo objective fitted with iris diaphragm, coupled to fluorescence microscopy (Figure 3C) or by nano-flow cytometry (Figure 3D and 3E). Although dark-field microscopy has the advantage of identifying smaller particles with sizes down to 20–40 nm (Craig et al., 2010), it cannot differentiate between EVs and other particles such as lipoprotein complexes and protein aggregates within the same size range. The lack of registration between the dark-field and the fluorescence signals (arrows in Figures 3B and 3C) was likely due to (1) the Brownian motion of certain EVs, (2) the short exposure time required for dark-field images, and (3) the relatively long exposure times (tens to hundreds of milliseconds) for the fluorescence acquisition. When using fluorescence microscopy, the percent positive populations for CPP-miR495-MBs varied between 3% and 8%, depending on the donor. Analyzing the same specimen by nano-flow cytometry using miR495-MB without the cell penetrating peptide as negative control identified a CPP-miR495-MB-positive population between 13% and 18%, again depending on the donor (Figures 3D and 3E). One explanation for the lack of correlation between the two measurements could be attributed to the increased detection of EV and non-EV particles when using dark-field microscopy compared with nano-flow cytometry. Our results indicate that these combined methods allow an approximate characterization of the MB-generated signal and the percent of positive nanometer-sized structures.

Figure 3.

Figure 3

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Detection of EV miR-495 by Molecular Beacons

(A) Detection of platelet-associated miR-495 by fluorescence microscopy (arrows).

(B–E) (B) Detection of EV by dark-field microscopy and (C) miR-495-specific signal by fluorescence microscopy (arrows). Nano-flow cytometric profile of plasma EVs probed with either MB-miR-495 (D) or MB-miR495 coupled to cell penetrating peptide (E). Experiments performed three times, with similar results.

The RBC miRNA451 Expression Levels Are Variable among Donors

Human RBCs contain several miRNA species, some of which were described to be relevant in malaria pathogenesis as well as certain inflammatory conditions (Babatunde et al., 2018, Mantel et al., 2013). We tested the ability of CPP-MB to detect the amount of miRNAs in human RBCs and RBC-derived EVs using fluorescence microscopy, flow cytometry, and dark-field microscopy. Incubation of RBCs with 2 μg of CPP-MB miR451a RBCs for 1 h at 37°C, followed by labeling of the RBCs with a membrane stain (CellMask green) generated a distinct punctuated pattern in RBCs cytoplasm with some RBCs showing significantly higher levels of RBC miR451a than others, likely due to differences in the circulatory age of RBCs, similar with the total RNA staining. Figure 4A also shows that the vast majority of the signal originated from under the plasma membrane, suggesting that the MB could not penetrate deeper into the RBC, lack of miRNAs in areas away from the plasma membrane, or an inhibitory effect of hemoglobin on the fluorochromes. We next used real-time super-resolution microscopy (SRRF), a method that uses a combination of temporal fluctuation analysis and localization microscopy. This is a relatively recent (2016) (Culley et al., 2018) super-resolution method, which unlike classical super-resolution imaging methods that require use of laser-based illumination, extended acquisition and processing times, involved samples preparation, and dedicated fluorochromes, allows acquisition of super-resolution imaging of standard fluorescence samples in seconds. Our results show the same RBC imaged either with standard fluorescence microscopy (FL) or SRRF (Figure 4, upper panel). SRRF clearly identifies discreet MB signals including a miR451a MB located inside the cytoplasm, as well as an EV-like structure (arrows in insert), suggesting that super-resolution microscopy permits better quantification and localization of signals originating from MBs than standard fluorescence microscopy. Somehow unexpectedly, co-staining of RBC RNA with Syto9, a pan-RNA probe, and CPP MB-miR451a showed limited, if any, co-localization between miRNA451 and the Syto-9 labeled small RNA, likely, t and rRNAs (data not shown). Similar to the results obtained when investigating platelet-derived EVs by dark-field microscopy, fluorescence, and nano-flow cytometry, interrogation of RBC-derived EVs (see Figure 4A and B) rendered similar results with approximately 30% of the total circulating EVs being positive for red-cell-specific miRNA-451 (Figure 4C and D).

Figure 4.

Figure 4

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Imaging MB Staining with Super Resolution Microscopy, and Detection of EV miR451a by Molecular Beacon

Top. Increase spatial resolution of actin filaments when imaged by SRRF compared with standard fluorescence.

Bottom. (A) RBC labeled with membrane dye (CellMask, green) and CPP-MB-miR451a-Alexa 594 (red) was imaged sequentially with standard fluorescence microscopy (FL, left) followed by SR microscopy (SRRF, right). Inset arrow shows MB signal originating from an EV-like membrane structure located near plasma membrane (arrows). Detection of total EVs by high-resolution dark-field microscopy (arrows).

(B) Detection of EV miR451a-specific signal by fluorescence microscopy (arrows).

(C and D) (C) Nano-flow profile of plasma EVs probed with either non-penetrating MB-miR451a (C) or membrane-penetrating CPP-MB-miR451a shows a 30% positive EVs (D). Experiments performed three times, with similar results.

Validation of MB-Based Signal by qRT-PCR

We next performed qPCR analysis of miR451a using increasing numbers of RBCs from five unique donors. Although the standard curve showed as expected, a linear increase in the miR signal with the number of cells used for RNA extraction (from 103 to 108R > 0.94), our results also indicate that the amount of miR451a/RBC was significantly different among donors. Our analyses (Figures 5A and 5B), which were performed three times over a period of 8 weeks, showed that certain individuals consistently expressed higher number of miR451a copies/RBC (average Ct 13/108RBCs), or fewer copies of miR451a (average Ct 22/108RBCs). However, in several donors, the levels of RBC miR451a changed significantly, possibly due to changes in the proportion of circulating, new RBCs, which contain higher amounts of RNA compared with the older RBCs (see Figure 1B). We also noted that the detection of the RBC RNA when using fewer than 10,000 RBCs becomes unreliable (Ct values above 35).

Figure 5.

Figure 5

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qPCR Corroborates Molecular Beacon-Based Flow Cytometric Data

RNA from 108 to 103 RBCs was extracted from five donors, and hsa-miR451a was analyzed by qPCR. Heatmap showing all Ct levels from all samples were clustered showing miRNA-451 levels from high- and low-expression donors (A). Standard curves were generated showing a linear relationship between miRNA Ct levels and the final amount of RBC (B). Flow cytometry data showed higher geometric MFI for higher-expression-level donor compared with low-expression donor (C and D). Nano-flow cytometry showed the correlation between miR451a from parent RBCs and complement-generated EVs (E). Nano-flow cytometry was used to estimate the concentration of RBC-derived EVs obtained from high and low miR-451a expression donors (F).

A similar high/low expression pattern was noted in the expression levels of miR451a in circulating RBCs when using MBs and flow cytometry, with certain healthy donors displaying higher levels of miR451a (MFI = 72), compared with low expressers, MFI of 44 on average (see Figures 5C and 5D). Next, we measured the expression levels of RBC miR451a by flow cytometry in seven healthy donors, by incubating CPP-MB-miR451a with parent RBCs or complement-generated EVs. Most complement-generated EVs contained detectable amounts of miR451a, and overall the levels of miR451a in EVs, as detected by CPP-MB miR451a, matched those of parent RBCs (Figure 5E). Specifically, high miR451a RBCs expressers (MFImiR451a RBC = 37.0) released EVs with high miR451a signal, MFImiR451a EVs = 113.4, whereas low miR451a RBCs expressers MFImiR451a RBC = 18.0, generated EVs with lower fluorescence signal MFI miR451a EV = 52.7. These findings would suggest that, at least in the case of RBCs and miR451a, the expression-level profile of the parent cells is mirrored in EVs. The fluorescence values generated by MBs in EVs were higher than those measured in parent RBCs probed with the same MBs, probably due to the inhibitory effect of hemoglobin on beacon fluorescence. The electron microscopy analysis of RBC-EVs show low to no detectable levels of hemoglobin in RBCs, which may explain the higher MB fluorescence of EVs compared with that of the parent cell.

We next contrasted the data obtained when using flow cytometry and CPP-MBs by comparing the flow cytometry results with the gold standard method for detecting miRNA gene expression levels, quantitative PCR. We collected blood from five self-declared healthy donors and measured the expression levels of miR451a in RBCs and RBC-EVs by qPCR. Our results showed no correlation (R = 0.16, data not shown) between the levels of miR451a isolated from RBCs and RBC-EVs collected from low, intermediate, and high miR451a expressers measured by quantitative PCR, even when accounting for the number of RBC-EVs used for RNA quantification, measured by time-gated nano-flow cytometry (Figures 5F and S3).

Taken together, our results suggest that, even if there is little agreement between the two EV miRNA quantification approaches, using the one-step MB-based detection method and flow cytometry could provide a rapid, qualitative process for EV RNA detection. This approach is currently limited by the detection of weak fluorescence in rapid flow conditions, although new instruments are being developed using significantly slower flow rate to address this very issue (Morales-Kastresana et al., 2019).

Discussion

Characterizing EV RNA profiles is a difficult task owing to methodological impediments during the EV isolation and purification steps, which are further amplified by convoluted extraction protocols of already low quantities of RNAs present in EVs, as well as the involved biocomputational analyses of the RNA data. In addition, most EV isolation protocols rely on the assumption that the expression levels of the certain RNA targets present in tissue-specific EVs are abundant enough to stand out against the background noise generated by the RNA present in the rest of the EVs. Our data shown in Figure 1 indicate that, even from homogeneous cell types, such as RBCs, generation of EVs using complement activation produces EV populations heterogeneous in size and variable in RNA abundance. Therefore, enrichment protocols, even when using cell-type specific surface markers, may underappreciate or could even miss relevant RNA signals if their number is low.

Detection and quantification of EVs by flow cytometry has been made possible recently by the advent of new-generation instruments geared toward detection of small particles both by scatter and fluorescence signature. To date, detecting flow-based EV surface protein profiles relies either on direct antibody-based detection or on capturing the EV subsets by specific bead-bound antibodies targeting surface proteins. Extrapolating these methods to detection of specific RNA molecules is of little use owing to the shielding effect of the vesicle membranes.

Currently, there are several methods of introducing molecules into cells when interrogating the cargo with specific probes: (1) derivatizing the probes with acetoxymethyl (AM) groups; this approach, which in fact is a functional labeling, depends on the presence and activity of cytosolic esterases and is effective only for cargos below 0.6–0.8 kDa; (2) incorporating the probes into liposomes; although liposomes are the most effective delivery system, their fusion efficacy strictly depends on the physicochemical properties of their membranes, those of the target membranes, on their size, surface charge, and lipid organization (Bozzuto and Molinari, 2015). However, this approach was used successfully before as a means to detect RNA EV by fusing EVs with immobilized lipid nanospheres containing specific MBs; (3) cell/membrane-penetrating peptides (CPPs). We chose CPPs (based on HIV Tat peptide and PNABio-proprietary sequence), a more expensive alternative, but fast, highly effective, and, importantly, membrane-type agnostic, as seen in Figure 1.

MBs represent an attractive alternative for identification of specific RNA molecules owing to their specificity, ease of use, high signal to noise ratio, and the use of standard fluorochromes, which are easily detectable by instruments routinely used in virtually all research and clinical laboratories. Therefore, to overcome the current technological limitation of EV detection and RNA interrogation, we proposed herein an approach that combines the advantages of analyzing hundreds of thousands to millions of EVs, allowed by small particle flow cytometry, with the sensitivity and ease of use of RNA detection permitted by MBs. We next circumvented the inability of standard MBs to cross the EV membranes by attaching to MB membrane-penetrating peptides, an approach that was used for over 20 years to introduce small molecules in cells, but never with EVs. Recently, a fluorimeter-based detection method for MBs in EVs has been reported (Rhee and Jeong, 2017). The method uses Streptolysin O for permeabilization of EVs to allow MBs access to the RNA targets. We believe our approach, which does not rely on creating pores in plasma membrane, thus preventing the putative loss of molecules with MW under and of 100 kDa, may provide a more accurate representation of the miRNAs EV landscape. Figures 2 and 3 show that the method presented here can be readily used for the detection of specific RNA sequences by microscopy, as well as flow cytometers with nanometer resolution. The main disadvantage when using microscopy for EV detection is the Brownian motion, which may limit the detection of weak signals, or generate image artifacts. However, for immobilized EVs, either by using poly-L-lysine or capture antibodies-coated slides, microcopy offers significantly longer integration times (seconds) compared with flow cytometry (tens of microseconds) and is therefore better suited at detecting dim signals. Somehow unexpected, SR imaging the MB-based signal in cells did not reveal more structural details than regular high-resolution fluorescence microscopy, nor did it provide better quantification of the signal. It is possible that other super-resolution microscopy techniques with better resolving power may uncover yet unidentified details regarding the dynamics and behavior of individual miRNAs in cells and EVs.

If our results regarding the predetermined levels of miRNA451 in human circulating RBCs, obtained from a very limited number of self-declared healthy donors, can be validated in larger cohorts, it could have significant clinical implications. We have recently shown that miRNA451-rich EVs are released by circulating RBCs during malaria infection and fuse with and trigger a specific response in endothelial cells. Endothelial cell dysfunction in brain capillaries is a critical step leading to cerebral malaria, a complication with mortality of over 30%. Although signaling pathways involved several checkpoints and are redundant, these findings may suggest that the miR451a levels present in circulating RBCs could predispose patients with malaria to severe cerebral complication of malaria, and even death. A quick CPP-MB-based test readable by standard flow cytometry could help triage the patients at risk.

It is also worth noting that the number of cells in the subpopulation of lymphocytes having a larger uptake of CPP-FAM (14.1%) matches the ratio of B lymphocytes (5%–15%) in blood. This could suggest that B lymphocytes are more likely to be penetrated by CPP than T lymphocytes, although further studies would be needed to show whether this is the case or if this is just a coincidence.

There are several limitations in extrapolating the RNA detection results obtained by flow cytometry to those generated by qPCR, as we presented them in Figure 5. Although currently flow cytometry still lacks single EV sensitivity, it allows detection of fluorescence-associated signal for each trigger event, therefore permitting quantification of the MB-associated signal, which likely originates from several MBs and several EVs. On the other hand, qPRC has a sensitivity superior to that of flow cytometry-based RNA detection, but it is vulnerable to slight variations in EV input. Our results in Figure S3 show that the same method of EV generation (complement activation), using the same number of RBCs (109), can generate vastly different numbers of EVs depending on the donor. In addition, the proportion of circulating new RBCs compared with old (Figures 1A and 1B) would likely generate EVs with different RNA cargo loads (Figure 1G) further underscoring the need for normalization of the results based on the number of EVs used for analysis.

In summary, we have described a rapid and precise detection method that (1) allows identification of EV subpopulations based on specific sequences of RNA that is (2) compatible with standard fluorescence microscopes and nano-flow cytometers, (3) can be used with other MBs or antibodies against protein targets, for multiplex-RNA/protein type analyses, and (4) can be used for sorting of EV subsets with specific RNA sequences for further downstream validation and discovery work.

Limitation of the Study

The ability of flow cytometers to identify and measure individual EVs is still controversial, and the signal read by the instrument and assigned to one trigger event may in fact represent an average of signals originating in many EVs of various fluorescence and size profiles. Similarly, the fluorescence of one MB may be too weak to be reliably detected by the flow cytometer detector in the limited time (in average 22 μs) afforded to signal acquisition during the passage of the EV in front of the interrogation area of the instrument. MB detection limit may be constrained by the low volume of the EV and the steric hindrance of the MB-target miRNAs creating a negative bias against the actual miRNAs abundance. These suggest that, unlike fluorometers, which can integrate the MB fluorescence signal over tens of seconds, current flow cytometers can only detect signals originating from MBs if the fluorescence is bright enough for the limited dwell time of the signal on the detector. An added complication when detecting specific sRNA fluorescence signal originating from EVs is their reduced size range and limited number of RNA copies per EV. Although MBs present a novel avenue for detecting miRNAs inside EVs when paired with CPPs, this method is still subject to the inherent difficulties common to all MB experiments. Fluorescent intermittency, or blinking (Lukinavicius and Johnsson, 2014), common in all nanoscale light sources, depends on the power of light source, the length of exposure, and power-law distribution of the “on” and “off” fluorescence over time. Systems that have a power-law distribution do not have a characteristic scale, meaning that the state of the MB cannot be accurately predicted at a given time point. This implies that it is unlikely that repeated flow cytometric measurements under the same conditions would yield the same fluorescence intensity over time.

Methods

All methods can be found in the accompanying Transparent Methods supplemental file.

Acknowledgments

This publication is part of the United States National Institute of Health (NIH) extracellular RNA communication consortium paper package and was supported by the NIH Common fund's exRNA Communication Program.

This work was supported by the following grants to I.C.G. from the NIH: HL126497, HL147353, CA218500, UG3TR002881.

Author Contributions

G.P.O.J., I.C.G., J.J., T.J.-T., and S.T. designed the experiments and discussed about experimental results. G.P.O.J., E.Z., G.R., D.D., and S.L. performed the experiments pertinent to imaging and testing the molecular beacons. E.Z., G.R., and J.T. performed the nano-flow analyses, instrument settings, and result analyses. G.P.O.J., I.C.G., and E.Z. wrote the manuscript. T.J.-T. and S.T. provided detailed comments regarding the manuscript.

Declaration of Interests

The authors declare no competing interests.

Published: January 24, 2020

Footnotes

Supplemental Information can be found online at https://doi.org/10.1016/j.isci.2019.100782.

Supplemental Information

Document S1. Transparent Methods and Figures S1–S3
mmc1.pdf (1.6MB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Transparent Methods and Figures S1–S3
mmc1.pdf (1.6MB, pdf)

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