Mass spectrometry-based methods for investigating the dynamics and organization of the surfaceome: exploring potential clinical implications

Abstract

Introduction:

Cell-surface proteins are extremely important for many cellular events, such as regulating cell-cell communication and cell-matrix interactions. Aberrant alterations in surface protein expression, modification (especially glycosylation), and interactions are directly related to human diseases. Systematic investigation of surface proteins advances our understanding of protein functions, cellular activities, and disease mechanisms, which will lead to identifying surface proteins as disease biomarkers and drug targets.

Areas covered:

In this review, we summarize mass spectrometry (MS)-based proteomics methods for global analysis of cell-surface proteins. Then, investigations of the dynamics of surface proteins are discussed. Furthermore, we summarize the studies for the surfaceome interaction networks. Additionally, biological applications of MS-based surfaceome analysis are included, particularly highlighting the significance in biomarker identification, drug development, and immunotherapies.

Expert opinion:

Modern MS-based proteomics provides an opportunity to systematically characterize proteins. However, due to the complexity of cell-surface proteins, the labor-intensive workflow, and the limit of clinical samples, comprehensive characterization of the surfaceome remains extraordinarily challenging, especially in clinical studies. Developing and optimizing surfaceome enrichment methods and utilizing automated sample preparation workflow can expand the applications of surfaceome analysis and deepen our understanding of the functions of cell-surface proteins.

Plain Language Summary

The cell surface contains many important proteins such as receptors and transporters. These proteins are responsible for cells to communicate with each other, take nutrients from outside, and interact with their surroundings. Aberrant changes of surface protein expression, modifications, and interactions with other molecules directly result in various diseases, including infections, immune disorders, and cancer. Currently, mass spectrometry (MS)-based proteomics is very powerful to study proteins on a large scale, and there has been a strong interest in employing MS to investigate cell-surface proteins. In this review, we discuss different methods combining mass spectrometry with other approaches to systematically characterize protein abundance, dynamics, modification, and interaction on the cell surface. These methods help uncover protein functions and specific cell-surface proteins related to human diseases. A better understanding of the functions and properties of cell-surface proteins can facilitate the discovery of surface proteins as effective biomarkers for disease early detection and the identification of drug targets for disease treatment.

1. Introduction

Proteins on the cell surface are essential to mammalian cells and play extremely important roles in numerous extracellular events, including regulating intercellular communication and cell-matrix interactions, and sensing extracellular nutrients. The surfaceome comprises many proteins, such as receptors, transporters, enzymes, and binding proteins. Their significance is underscored by the fact that more than 60% of pharmaceutical agents listed in the Drugbank database specifically target cell-surface proteins [1]. Comprehensive analysis of cell-surface proteins, their dynamics, and interactions advances our understanding of protein functions and cellular activities. Moreover, it can contribute to the identification of surface proteins as biomarkers for disease detection and as drug targets for disease treatment.

In recent years, significant progress in mass spectrometry (MS)-based proteomics has profoundly enhanced our capacity to investigate proteins, including those on the cell surface [2–6]. It allows us to obtain valuable information about protein abundance changes, their modifications, and interactions. Despite the remarkable advancements, deciphering the human surfaceome still remains extraordinarily challenging because many surface proteins have low abundances, and surface proteins are typically membrane ones with high hydrophobicity, posing a notorious challenge for their characterization. Furthermore, modifications of these proteins make the surfaceome more complex. Additionally, the methods need to be specific to target proteins only on the cell surface.

To address these challenges, a variety of methods have been developed [1,3,4]. To enrich surface proteins with low abundance, several methods were developed, including lysine-targeting methods [7,8], glycan chain-targeting methods [6,9–14], and enzymatic oxidation [15,16] (discussed in detail below). Moreover, advanced instrumentation further facilitates the detection of peptides with low abundance. Given the hydrophobic nature of cell-surface proteins, efficient solubilization is crucial for their MS analysis. For instance, widely used detergents such as Nonidet P-40 and sodium dodecyl sulfate (SDS) help dissolve proteins with highly hydrophobic regions. Furthermore, to investigate modifications of cell-surface proteins, particularly glycosylation, glycan chain-targeting methods such as cell surface capture (CSC) [6], metabolic labeling-based methods [10,12], and enzymatic tagging [14] were reported. Glycopeptides treated with PNGase F allows for confident identification of N-glycosylation sites. Additionally, bioinformatic tools such as MSFragger [17], pGlyco3 [18], and StrucGP [19] were developed for glycosylation identification and interpretation.

This review aims to provide a comprehensive summary of recent progress in surfaceome analysis, including its biological and clinical applications. First, we include MS-based methods specific for global analysis of cell-surface proteins, and a summary of the methods is included in Figure 1. Subsequently, the investigation of the dynamics of cell-surface proteins is discussed. Moreover, we summarize the studies to study the surfaceome interaction networks. Systematic characterization of cell-surface proteins can advance our understanding of protein functions and the molecular mechanisms of human diseases, which allows us to identify surface proteins as biomarkers for disease detection and as drug targets for disease treatment.

Figure 1.

MS-based methods to systematically study surface proteins. (a) Peroxidase-mediated surface protein labeling; (b) Chemical biotinylation of the amine group of surface proteins; (c) Chemical glycocapture or enzymatic tagging of surface glycoproteins (galactose oxidase: GAO); (d) Metabolic labeling for enriching surface proteins; (e) Subtiligase-based surface protein labeling; (f) Ligand-receptor capture (LRC) method for studying surface protein interactions; (g) Photocatalytic proximity labeling for investigating surface protein interactions; (h) Proximity-dependent biotinylation for analyzing surface protein interactions.

2. Technologies for characterizing proteins on the cell surface

Modern MS-based proteomics has become very powerful to globally analyze proteins [20–23]. However, to specifically and comprehensively characterize proteins only located on the cell surface, their selective separation and enrichment are imperative prior to MS analysis. In this section, we summarize the methods for enriching surface proteins and discuss their advantages and weaknesses.

2.1. Chemical biotinylation of the amine group of surface proteins

In the human proteome, lysine accounts for approximately 6% of all amino acid residues [24]. Its nucleophilic ε-amine group makes it an appealing candidate for chemical labeling. Additionally, the amine group at the protein N-terminus can also be labeled [25]. N-Hydroxysuccinimide (NHS) esters are very useful labeling reagents due to their relatively fast labeling reaction under physiological conditions [26]. To date, a variety of NHS-ester derivatives have been developed for cell-surface protein enrichment. These reagents typically contain three components: 1) NHS ester for reacting with free amines on surface proteins; 2) a biotin moiety for selective enrichment of labeled proteins; 3) a linker connecting the NHS group with the biotin moiety. Using different linkers, NHS-ester reagents can vary in length, solubility, cell permeability, and cleavability. Regarding cell permeability, sulfo-NHS-LC-biotin and sulfo-NHS-SS-biotin with a negatively charged sulfate group exhibit membrane impermeability. This characteristic effectively prevents the inadvertent labeling of intracellular proteins. The number of identified proteins varies depending on the cell types and instruments. Typically, it is possible to identify more than 1000 proteins [8,27]. It is noteworthy, however, that the use of sulfo-NHS-LC-biotin carries a potential risk of contamination from intracellular proteins due to its hydrophobic long chain. Furthermore, intracellular proteins leaked from dead cells may also be biotinylated with NHS esters. According to the result of Nunomura et al., nearly 75% identified proteins were assigned as cell-surface proteins [7]. Additionally, all NHS-ester reagents label free amines, and thus, surface proteins with few or no exposed amines may be overlooked.

2.2. Peroxidase-mediated labeling

Horseradish peroxidase (HRP) and ascorbate peroxidases (APEX/APEX2) are enzymes that catalyze H₂O₂-dependent oxidation across a diverse range of substrates [28]. When H₂O₂ is present along with an oxidizable biotinylated probe near these peroxidases, it produces a reactive biotin species near the enzyme. This species can label proteins with spatial selectivity, including those localized on the cell surface. Li et al. developed a fast cell surface labeling method based on HRP-mediated oxidative tyrosine coupling, termed as peroxidase-mediated cell surface labeling (PECSL) [15]. They incubated living cells with exogenous HRP and a biotin-xx-phenol probe (BxxP). In the presence of H₂O₂ and HRP, phenoxyl radicals were produced, which subsequently reacted with neighboring tyrosine residues of cell-surface proteins. With a labeling time of 1 min, over 2000 proteins were identified from HeLa cells, including 1370 (51%) proteins being annotated as cell-surface proteins. The labeling reaction is very quick (~1 min), allowing for tracking transient dynamic changes of proteins on the cell surface.

A similar method was developed by Vilen et al [16]. They engineered the cell surface by conjugating modified cholesterol with recombinant APEX2. Then, the APEX2-mediated biotinylation reaction was initiated by supplementing biotin-phenol and H₂O₂ to the system. On average, approximately 200 proteins, constituting around 60% of the total identified proteins, were annotated as cell-surface proteins [15]. Additionally, HRP was conjugated to the extracellular domain of a transmembrane protein. HRP on the cell surface catalyzed the oxidation reaction for the surfaceome profiling in intact tissues [29]. These peroxidase-mediated labeling methods are dependent on the number of accessible and electron-rich amino acid residues, such as tyrosine, on the cell surface, which could potentially underestimate certain surface proteins [15,16].

2.3. Chemical glycocapture methods

Almost all proteins on the cell surface are glycosylated, which makes it possible to enrich cell-surface proteins by targeting the glycans of surface glycoproteins. Several methods have emerged for the enrichment of surface glycoproteins through their glycan moieties. These methods can be further categorized into three classes: chemical glycocapturing, metabolic labeling, and enzymatic tagging.

In 2009, two papers independently published from different groups included the application of mild periodate oxidation for the covalent labeling of the glycan moieties of cell-surface proteins [6,9]. The main difference about the surface glycan tagging between these two methods is to employ different reagents to label the aldehyde groups after the oxidation: one used aminooxy-biotin with aniline as a catalyst, while the other employed biocytin hydrazide. The latter is called cell surface capture (CSC) (Figure 2) [6]. In CSC, surface glycoproteins were labeled with biocytin hydrazides, followed by digestion and enrichment using the streptavidin beads. Subsequently, glycopeptides were released from the beads using PNGase F for MS analysis.

Figure 2.

CSC uses a multistep tandem affinity labeling strategy to confer the desired specificity for the glycoproteins on the cell surface: (1) tagging reactive groups from plasma-membrane proteins (yellow triangles, glycans; black, bi-functional linker molecules), (2) cell homogenization and protein digestion, (3) affinity purification, (4) peptide release, (5) peptide analysis by LC-MS/MS and (6) peptide or protein identification. (Adapted from Ref. [6], with permission from Springer Nature).

The CSC method has been employed for a broad range of cell lines, primary cells, and tissues, generating a mass-spectrometry-derived Cell Surface Protein Atlas (CSPA) with 1492 human and 1296 mouse cell-surface glycoproteins [30]. This compilation serves as an invaluable resource for exploring cell-surface proteins. To date, the CSPA is the most comprehensive database of the surfaceome. Combining the CSPA and machine learning, SURFY was developed to predict 2886 human surface proteins with an accuracy of 93.5% [31]. Nevertheless, glycan structures are destroyed during the oxidation of cis-diols. Furthermore, CSC requires a relatively large amount of samples (near 10⁸ cells or 200 mg - 1 g of tissue). To increase the sensitivity, reproducibility, and throughput, autoCSC was developed [32]. Benefiting from an automated sample processing system, the sample loss can be minimized, allowing for surfaceome quantification with 5- to 33-fold fewer samples compared with the original CSC.

2.4. Metabolic labeling-based methods

The combination of metabolic labeling and biorthogonal chemistry has offered another excellent opportunity for glycoprotein analysis, especially cell-surface glycoproteins. Typically, unnatural sugar analogs carrying a small biologically inert group can get into living cells, and then they are used by glycosyltransferases to modify proteins. These glycoproteins with sugar analogs are transported to the cell surface via the secretory pathway, and the functional group on labeled glycoproteins can react with membrane-impermeable probes using click chemistry for the following enrichment. Engineering cell-surface glycans using metabolic labeling was first introduced by Dr. Bertozzi and co-workers in 1997 [33]. Subsequently, different biorthogonal reactions and sugar analogs were developed for studying cell-surface glycoproteins [10,34–36]. Our lab developed a method integrating metabolic labeling, copper-free click chemistry, and MS-based proteomics to comprehensively and site-specifically analyze cell-surface N-glycoproteins (Figure 3), and the number of identified cell-surface glycoproteins ranges from 300 to 700, with nearly 95% of them annotated as cell-surface proteins [10,12,34,37,38]. A very similar method has also been reported by other research groups, known as surface-spanning protein enrichment with click sugars (SUSPECS) [13,39]. Due to the mild conditions, this method provides an opportunity to further investigate the dynamics of surface glycoproteins. Although metabolic labeling-based methods are powerful for studying surface glycoproteins, their applications to clinical samples are restricted.

Figure 3.

The principle of the site-specific identification of the cell surface N-sialoglycoproteome by integrating metabolic labeling, copper-free click chemistry and MS-based proteomics techniques. (Adapted from Ref. [12], which is under Creative Commons Attribution-NonCommercial 3.0 Unported Licence that permits use, sharing, adaptation, distribution, and reproduction in a noncommercial publication).

2.5. Enzymatic tagging of glycoproteins

Enzymatic tagging of glycans on the cell surface has been reported for surface glycoprotein analysis. Generally, these methods utilize an enzyme (such as glycosyltransferase or galactose oxidase (GAO)) to modify glycans of surface glycoproteins, providing a chemical handle for the enrichment. In the glycosyltransferase-based tagging methods, different glycotransferases, such as fructosyltransferases and sialyltransferases, are employed [40–42]. For example, Sun et al. utilized recombinant sialyltransferase (ST6Gal1) to transfer unnatural monosaccharides (azido-modified sialic acid) to glycoproteins on the cell surface. Azido-modified glycoproteins were subsequently biotinylated using sulfated dibenzocyclooctynylamide containing biotin (S-DIBO-biotin) for subsequent enrichment. This innovative approach was termed selective exoenzymatic labeling (SEEL) [41,43]. Galactose oxidase and aniline-catalyzed oxime ligation (GAL) was first introduced by Ramya et al [5]. In this method, GAO was used to selectively oxidize the hydroxyl group at the C6 position of galactose/N-acetylgalactosamine (Gal/GalNAc), generating an aldehyde group [44,45]. This aldehyde group can be utilized for enrichment through aniline-catalyzed ligation with aminooxy-biotin. Our group enhanced the GAO oxidation efficiency using HRP [14]. To further increase the tagging efficiency, sialic acids at the termini of glycans were removed using neuraminidase. Typically, enzymatic tagging of glycoproteins can identify 150–400 cell-surface proteins. Compared with CSC, enzymatic tagging of glycoproteins exhibits comparable specificity in the enrichment of cell-surface proteins. Furthermore, it has shown great potential for global analysis of surface glycoproteins in clinical research due to its mild experimental conditions.

2.6. Subtiligase-based labeling

Proteolysis of cell-surface proteins plays a critical role in many cellular activities, such as cell-cell communication and receptor activation, making cell-surface proteins highly dynamic. The subtiligase-catalyzed peptide ligation is a process that links a peptide ester acyl donor to the N-terminal α-amine of a peptide or protein [46,47]. An innovative method utilizing subtiligase to catalyze the ligation was developed to investigate cell-surface proteins in the Wells group [47,48]. First, they used periodate to oxidize glycans on the cell surface to generate the aldehydes. Then, they employed stabiligase with an N-terminal nucleophile to covalently attach to these aldehydes, forming glycan-tethered stabiligase on the cell surface. By introducing a biotinylated peptide ester, glycan-tethered stabiligase effectively links the biotinylated peptide to the N-termini of nearby proteins. This method allows for the targeted labeling of protein N-termini on the cell surface and provides valuable information on cell-surface protein proteolysis.

3. Systematic investigation of the dynamics of surface proteins for understanding protein functions and cellular activities

Cell-surface proteins are highly dynamic for promptly responding to ever-changing extracellular environments and adapting to their surroundings. For instance, toll-like receptor (TLR) families on the immune cell surface are rapidly activated in response to pathogens [49]. Endothelial cells also remodel their surfaceomes in response to a variety of inflammatory cytokines and chemokines [50]. To survive, cancer cells adjust their surfaceomes to increase nutrient import and escape immunological surveillance [51]. Therefore, conducting system-wide analysis of temporal protein changes on the cell surface enhances our understanding of biological systems. Benefiting from the combination of enrichment methods for cell-surface proteins and quantitative proteomics, it becomes possible to systematically study the dynamics of surface proteins. Cell-surface proteins are highly dynamic for promptly responding to ever-changing extracellular environments and adapting to their surroundings.

Governaa et al. developed a workflow, termed tumor surfaceome mapping (TS-MAP), to investigate the surfaceome and the internalized surfaceome fraction (endocytome) in glioblastoma [52]. In this workflow, they used sulfo-NHS-SS-biotin to profile both the surfaceome and the endocytome in patient glioma tumor tissue sections. They found that 299 cell-surface proteins were also identified in the endocytome, suggesting their involvement in endocytic internalization. Notably, proteins with high abundance in both the surfaceome and the endocytome, such as EGFR, BCAN, and TF, could potentially serve as targets for intercellular drug delivery. Upon comparing the surfaceomes across 10 patient samples, a notable heterogeneity in the expression of cell-surface proteins was observed. This underscores the importance of individualized target identification and the role of personalized medicine.

Proteins on the cell surface may exhibit different dynamics compared with intracellular proteins. Thus, studying the dynamics of surface proteins provides a better understanding of protein functions and cellular activities. Oostrum et al. systemically investigated the dynamics of surface proteins during neuronal development [53]. They used autoCSC to map the surfaceome every 2 days from 2 to 20 days. This time window enables the capture of the synapse formation period. The results showed that proteins involved in the synapse assembly were produced and trafficked to the cell surface before the onset of synapse formation. By comparing the expression of proteins on the cell surface and in the whole cells, they discovered that the neuronal surfaceome was more dynamic and the changes in surface trafficking were more pronounced at shorter time scales.

For cells in response to stimuli, not only surface protein abundances may change, but also alterations of protein N-glycosylation could happen. Our group combined metabolic labeling with quantitative proteomics to study the dynamics of surface glycoproteins and measure their half-lives [54]. To the best of our knowledge, this paper represents the first comprehensive study of the half-lives of cell-surface glycoproteins on a large scale. The half-lives of surface glycoproteins were measured as a function of time. It was found that surface glycoproteins with catalytic activities exhibited longer half-lives compared to those with binding and receptor activities. Additionally, the half-lives of N-glycosylated sites were analyzed. The median half-life of the sites located outside of any domain was longer than that of the sites located within domains. This observation suggests that glycans within domains primarily regulate interactions with other molecules, while glycans outside of domains primarily function to protect the proteins from degradation.

More recently, our group investigated the dynamics of surface glycoproteins on immune cells in response to bacterial infection [37]. Time-dependent changes of surface glycoproteins were quantified in both monocytes and macrophages treated with lipopolysaccharides (LPS). Some surface glycoproteins, such as CD83 and CD137, increased their abundances dramatically at 3 h after the infection, while other surface glycoproteins were found to have decreased abundances. To gain a deeper understanding of the different responses of monocytes and macrophages during the infection, we also quantified the dynamics of surface glycoproteins during the monocyte-to-macrophage differentiation. The results demonstrated that the differences in surface glycoprotein changes between these two types of immune cells can be attributed, at least in part, to the differentiation process. Furthermore, an ingenuity pathway analysis was performed, and several upstream regulators and downstream effects were identified during the immune response. Site-specific information regarding protein glycosylation changes during the immune response was also provided. Notably, this study identified new surface glycoproteins involved in the immune response, including APMAP, TSPAN3, and IGDF8. This workflow can be applied to identify the transient changes in surface glycoproteins of cells under various stimuli. Integrating bioinformatic tools with the analysis of surface protein dynamics advances our understanding of biological systems and the progression of diseases.

4. Investigation of the surfaceome interactions

4.1. Methods for characterizing the surfaceome interactions

As mentioned earlier, the surfaceome is intricately associated with numerous biological processes. These processes are frequently under the regulation of protein complexes organized through dynamic protein-protein interactions (PPIs) to facilitate rapid communication with the extracellular microenvironment. Many studies have shown that aberrant PPIs are associated with various diseases [55]. Thus, it becomes evident that elucidating PPIs on the cell surface can yield valuable information about protein functions, cellular activities, and disease mechanisms.

4.1.1. Ligand-receptor capture (LRC)

Ligand-receptor interactions on the cell surface play a central role in cell signaling, cellular communication, and various physiological processes. Ligands are molecules, such as hormones, growth factors, or neurotransmitters, that bind to specific receptors on the cell surface. This binding event triggers downstream signaling cascades, ultimately regulating gene expression. The ligand-receptor capture (LRC) method was specifically designed to analyze the interactions between a ligand and receptors on the cell surface. The Wollscheid group developed the LRC-based methods, termed TRICEPS-LRC [56] and HATRIC-LRC [57], to identify the complex interactions on the cell surface. Typically, the reagent used in LRC contains three moieties:1) a functional group reacting with a ligand; 2) a functional group for labeling the receptor; 3) a biotin moiety for enrichment. After the incubation of the probe containing a specific ligand with cells, the receptors binding to the ligand can be selectively enriched for MS analysis.

4.1.2. Proximity-dependent biotinylation

Proximity-dependent biotinylation approaches entail the use of enzymes to catalyze the transfer of biotin to neighboring proteins. Through the fusion of the enzyme with a specific protein (referred to as ‘bait’), adjacent proteins (referred to as ‘preys’) can be labeled and subsequently isolated for MS analysis. Proximity-dependent biotinylation approaches fall into two categories: 1) biotin ligase-based methods, such as BioID/BioID2 [58,59] and TurboID/miniTurboID [60]; 2) peroxidase-based methods, such as APEX/APEX2 [61,62]. These methods are frequently employed for the mapping of intracellular interactomes. By tagging the intracellular domains of cell-surface proteins, proximity-dependent biotinylation approaches can also be applied to investigate surface receptor signaling and internalization [63]. Numerous comprehensive reviews have introduced these methods [64,65]. In this context, we will focus on their applications for studying surfaceome interactions.

4.1.3. Photocatalytic proximity labeling

Proteins on the cell surface exhibit uneven distributions, giving rise to distinct microenvironments [66]. These microenvironments play a direct role in intercellular communication and the initiation of receptor signaling. Given that the organization of the surfaceome operates on the nanoscale, delineating the microenvironments of cells requires methods with enhanced spatial resolution. Recently, the MacMillan group introduced a photocatalytic carbene generation approach, known as MicroMap (μMap), to achieve higher-resolution labeling of cell-surface proteins (Figure 4) [67]. Employing an iridium catalyst and Dexter Energy Transfer (DET), μMap generates short-lived carbenes (t_1/2 = ~1 ns) with a limited diffusion range (nanometers) upon exposure to blue light (410 to 490 nm). By employing a secondary antibody-photocatalyst conjugation method, an average labeling resolution of ~500–600 Å can be achieved. Following this, μMap-Red was developed to mitigate the issue of poor tissue penetration associated with short-wavelength light (<500 nm) [68]. Subsequently, the Wollscheid group harnessed singlet oxygen generator (SOG)-coupled antibodies to achieve photocatalytic proximity labeling, termed LUX-MS [69]. Under visible light, short-lived singlet oxygen generated by the activated SOG converts the amino acid residues (His, Cys, Trp, Tyr, and Met) into photo-oxidation products. These products subsequently react with biocytin hydrazide for further isolation. The lifetime of singlet oxygen is ~67 μs in D₂O and ~3.5 μs in H₂O [70], allowing for tunable labeling resolution by modulating buffer conditions. These catalysts can be coupled to antibodies, ligands, and even intact viral particles.

Figure 4.

High-resolution proximity-based labeling by using carbene intermediates (μMap). (Adapted from Ref. [67], with permission from The American Association for the Advancement of Science).

4.2. Identification of the surfaceome interactions for drug development

Cell-surface proteins detect and respond to external stimuli through interactions with various molecules. Receptors, transporters, cell-adhesion proteins, and enzymes on the cell surface contribute to the extensive diversity and complexity of the surfaceome interactions. Exploring these interactions not only offers crucial insights into the mechanisms of signal transduction, but also unveils potential drug targets.

Cell-surface receptors are involved in signal transductions from the extracellular environment to the interior of cells. Sungkaworn et al. investigated the mechanism of the GPCR interactions on the cell surface using single-molecule imaging, which provides potential drug targets to control the receptor signaling [71]. Coupled with MS-based proteomics, it has become possible to identify unknown protein interactions on a large scale. The TRICEPS-LRC method was applied to identify pharmaceutically relevant targets of growth factors, therapeutic antibodies, and engineered affinity binders [56]. For example, when employing the therapeutic antibody trastuzumab (Herceptin) as a ligand, TRICEPS-LRC not only successfully identified its primary target, ErbB2, but also revealed several Fcϒ receptors in primary breast cancer tissue as potential off-targets. Additionally, through conjugation with vaccinia virus (VACV) mature virions as ligands, TRICEPS-LRC identified seven cellular VACV binding factor candidates on the surface of HeLa cells. These findings suggest that TRICEPS-LRC can serve as a valuable tool for mapping the relatively unexplored realm of cell surface interactions.

Several labeling reagents for studying ligand-receptor interactions on the cell surface were designed based on the TRICEPS-LRC method, such as ASB (the reagent containing an aldehyde-reactive aminooxy group, a sulfhydryl, and a biotin) [72] and HATRIC (the reagent with an acetone-protected hydrazone, an NHS, and an azide) [57]. The sensitivities of these labeling reagents are enhanced through modification and optimization of their functional groups. This enhancement empowers the LRC-based methods to identify target receptors for ligands on living cells, spanning from small molecules to biomolecules and even intact viruses. Frey et al. employed HATRIC-LRC to elucidate the interactions between high-density lipoprotein particles (HDL) and surface proteins of human endothelial cells (Figure 5) [73]. HDL comprises a complex mixture of approximately 300 proteins, along with an extensive array of lipid species and non-coding RNAs. The interactions between HDL and cells are responsible for regulating reverse cholesterol transport (RCT), maintaining endothelial barrier integrity, influencing angiogenesis, and affecting inflammation. Despite their significance, the molecular mechanisms behind these interactions remain inadequately understood. Using HATRIC-LRC, they identified the receptor tyrosine-protein kinase Mer (MERTK) as a novel interactor of HDL, influencing HDL binding and uptake.

Figure 5.

A schematic illustration depicting the tagging of receptors proximal to HDL using HATRIC-LRC: (1) Cells were first mildly oxidized (2) to tag receptors proximal to HDL using HATRIC-LRC. (3) HATRIC-LRC proteins were on-bead digested (4) on an automated liquid handling system. (5) Peptides were identified and quantified via mass spectrometry(Adapted from Ref. [73], which is under Creative Commons Attribution 4.0 license that permits use, sharing, adaptation, distribution, and reproduction in any medium or format.)

While the LRC-based methods enable the direct identification of receptors for orphan ligands without requiring genetic manipulations, high concentrations of ligand-coupled trifunctional reagents are normally required to ensure sufficient capture of cell-surface proteins for later identification by MS. Consequently, it is possible that the candidates identified through LRC experiments are proteins upregulated in response to the ligand treatment, causing inaccurate analysis. To address this problem, recently, Zheng et al. developed a nice method, termed interaction-guided crosslinking (IGC), which can identify the interactions between secreted ligands and their receptors [74]. Their IGC workflow requires approximately 1000-fold fewer ligands and about 10-fold fewer cells compared with the LRC-based methods. This advancement enables the capturing of protein-protein interactions in real biological scenarios.

BirA ligase or peroxidase is commonly conjugated to the intracellular domain of the cell-surface protein of interest to study surface receptor signaling and internalization. For instance, time-resolved APEX labeling was employed to examine the GPCR signaling network and internalization by attaching APEX to the intracellular domain of the GPCR [75]. This work revealed the molecular mechanism of GPCR endocytosis and signaling network. Subsequently, the same group employed a similar strategy to investigate protein networks associated with ligand-activated EGFR [76]. They traced the proximity proteome of EGFR during the EGFR endocytic trafficking process. This effort led to the identification of the Trk-fused gene (TFG) as a regulator of EGFR endosomal sorting. The integration of the APEX proximity labeling with time-resolved proteomics enables us to monitor the surfaceome reorganization and explore the molecular mechanisms underlying known ligand-receptor pairs. EMARS [77], split HRP [78], SPPLAT [79], and AAPL [80] have been developed for tagging an extracellular domain of the cell-surface protein of interest. Combining quantitative proteomics with proximity-dependent biotinylation approaches offers a unique opportunity to systematically investigate the surfaceome interaction network. Protein-protein interactions discovered from these methods can be further validated using an independent experiment.

Proximity-dependent biotinylation approaches, while powerful, often face challenges associated with limited spatiotemporal resolution due to their broad labeling radius. On the cell surface, specific protein-protein interactions occur within confined microenvironments. Furthermore, diverse post-translational modifications of a protein can influence its interactions with various binding partners. Consequently, the same protein in different microenvironments or after being modified may engage with a variety of binding partners [1], which emphasizes the significant impact of the local cellular environment on cell-surface protein interactions and underscores the necessity for developing novel methods with enhanced spatiotemporal resolution [66]. Photocatalytic proximity labeling methods, such as μMap [67] and LUX-MS [69], can spatially map the surfaceome network, and provide valuable information for the development of new therapeutic strategies.

Reyes-Robles et al. applied μMap to study epidermal growth factor receptor (EGFR) and c-MET tyrosine kinase surface microenvironment on non-small cell lung cancer cell lines [81]. EGFR and c-MET are two clinically significant receptor tyrosine kinases (RTKs) implicated in the initiation and advancement of cancer cells. Earlier research indicated that the cross-activation of receptors between epidermal growth factor receptor (EGFR) and c-MET tyrosine kinase was a major contributor to drug resistance [82,83]. Moreover, the combined targeting of EGFR and c-MET was shown to diminish tumor viability [84]. For more comprehensive insight into the microenvironments of EGFR and c-MET, they applied iridium photocatalyst-antibody conjugates to specifically target these receptors. This approach resulted in the identification of both documented and previously unknown proximal proteins associated with EGFR and c-MET, including HER2, FAT1, JAG1, JAG2, and NOTCH2. The results advance our understanding of the EGFR and c-MET signaling networks and offer valuable information for the development of innovative therapeutic strategies. μMap was also applied to identify interactions of the SARS-CoV-2 spike protein on the cell surface. A viral-host protein microenvironment mapping platform (ViraMap) based on photocatalysis was employed, involving the fusion of the SARS-CoV-2 spike protein with the iridium photocatalyst [85]. Besides the well-known receptor ACE2, other receptors such as PTGFRN and EFNB1 were identified as players for SARS-CoV-2 entry.

The catalyst in LUX-MS, the singlet oxygen generator (SOG), can also be conjugated with diverse ligands, such as antibodies, small molecule drugs, and even intact viral particles [80]. This flexibility enables the investigation of nanoscale surfaceome organization across a wide range of biomedically important scenarios. For example, the LUX-MS method was applied to elucidate surface signaling interactions in non-eukaryotic systems such as bacteria. Upon conjugating SOG with Thanatin, an anti-infective agent, and introducing it to E. coli cells, the direct binding partners of Thanatin and their proximal proteins were identified. Furthermore, this method was employed to investigate the surfaceome interaction network of intact viral particles. Through the use of thiorhodamine-conjugated SOG with bacteriophages that recognize a ubiquitous cell wall component of Listeria monocytogenes, over 80 bacterial surface proteins were found. These results offer valuable information for diagnosing infectious diseases and advancing vaccine discovery.

Several methods such as cross-linking mass spectrometry (XL-MS) [86], thermal proteome profiling (TPP) [87], and co-fractionation mass spectrometry (coFrac-MS) [88] were reported to study protein-protein interactions (PPIs). Although these methods haven’t been extensively applied to study surfaceome interactions, they demonstrate considerable potential. For example, chemical crosslinking is promising for investigating cell-surface protein interactions, allowing for the preservation of transient or labile protein interactions. XL-MS enables us for a large-scale exploration of surfaceome interactions. Our group has developed a method combining XL-MS and enzymatic tagging for studying surface glycoprotein interactions [89]. First, we used a membrane impermeable crosslinker, bis(sulfosuccinimidyl)suberate (BS³), to covalently link surface glycoproteins with their interactors, followed by enzymatic oxidation and chemical enrichment of surface glycoproteins. This method allowed us to identify over 300 proteins interacting with surface glycoproteins on MCF7 cells. Additionally, Mandal et al. developed a similar workflow employing disuccinimidyl sulfoxide (DSSO) and disuccinimidyl phenyl phosphonic acid (PhoX) as crosslinkers, combined with CSC, to investigate surfaceome interactions [90]. The use of two types of crosslinkers resulted in the identification of more than 2000 interlinked and intralinked peptides on acute myeloid leukemia (AML) cells. By comparing the cross-linked peptides with published structures in the Protein Data Bank (PDB), they identified AML-specific conformation of integrin β2 as a CAR T target. These studies have underscored the potency of integrating XL-MS with surfaceome enrichment methods for the exploration of surfaceome interactions.

4.3. Surfaceome profiling for disease studies

Cell-surface proteins are involved in a multitude of essential cellular activities, and alterations in cell-surface proteins have been implicated in different diseases. Cell-surface proteins can serve as valuable biomarkers and targets for disease diagnosis and therapy. A typical example is chimeric antigen receptor (CAR)-T cell therapy [91], which kills malignant cells by recognizing a specific cell-surface antigen. However, both flow cytometric immunophenotyping [92] and unique tumor cell-surface antigens that can be used for CAR-T cell therapy are constrained by our current knowledge of the surfaceome. This limitation hampers early clinical diagnosis and the development of immunotherapy strategies. Therefore, a thorough mapping of surface proteins can aid in clinical studies. Rose et al. utilized sulfo-NHS-SS-biotin to map the surfaceomes in two types of glioblastoma (GBM) cell lines (U87 and NCH8), aiming to discover novel, GBM-specific targets [93]. They identified the overexpression or exclusive expression of 87 surface proteins in the GBM cell lines compared with human healthy astrocytes. Among these, they found 11 potential therapeutic targets for GBM, such as RELL1, CYBA, EGFR, and MHC I. To establish a robust surface protein signature specific to GBM, Ghosh et al. integrated the surfaceome data from the GBM cell lines with primary GBM tissue transcriptomics [94]. They excluded genes exhibiting differentially expressed in non-GBM brain diseases, such as astrocytoma and oligodendroglioma, ultimately identifying 33 cell-surface proteins with a high possibility of GBM specificity. Notably, among these candidates, 17 proteins displayed an association with the transforming growth factor β signaling pathway. Subsequent gene knockdown experiments corroborated their pivotal roles in perturbed GBM invasion dynamics, further underlining their significance in GBM pathogenesis. Importantly, as cell-surface proteins have the potential to be secreted or shed into the bloodstream, these proteins may also hold promise as prospective blood-based biomarkers for the diagnosis of GBM.

Cell-surface proteins are not evenly distributed across the cell surface. The surfaceome of epithelial cells can be categorized into the apical and basolateral regions. Combining sulfo-NHS-SS-biotin labeling and SILAC, Koetemann et al. achieved quantitative mapping of the surfaceome in both the apical and basolateral regions [95]. Different protein expressions between these two regions were observed. They also discovered PTEN as the tumor suppressor regulating the polarized surfaceome architecture.

MYC is a transcription factor regulating the expression of more than 1,000 genes and is often found overexpressed in many cancers [96]. Chen et al. applied CSC to study three isogenic model cell lines characterized by the high or low MYC levels [97]. The results revealed that although the MYC-induced surfaceome remodeling displayed cell type-specific patterns, certain transporters, such as SLC1A5, SLC29A1, SLC30A1, and SLC43A3, were upregulated across all three cell lines. These proteins may serve as potential biomarkers and targets for drug development. Ferguson et al. also applied CSC to map the myeloma surfaceome under the baseline conditions, during drug resistance, and in response to acute drug treatment (Figure 6) [98]. Over 1200 surface proteins from four myeloma cell lines (KMS-12PE, AMO1, RPMI-8226, L363) were quantified, providing a unique resource to discover possible biomarkers of drug resistance and potential therapeutic targets. Integrating publicly accessible mRNA data with the myeloma surfaceome information, they discovered that CCR10 was overexpressed in these myeloma cell lines. High expression of CCR10 was found to correlate with worse overall survival among myeloma patients, indicating that CCR10 is a potential immunotherapy target in myeloma. The analysis of the surfaceome in drug-resistant myeloma cell lines, compared with wild-type cell lines, revealed potential markers for bortezomib (Btz) and lenalidomide (Len) resistance, such as CD53, CD10, EVI2B, and CD33. To identify appropriate targets for co-treatment approaches involving small molecules and immunotherapies, a comparison of the myeloma surfaceome was conducted between drug-sensitive and drug-resistant cells. Mucin-1 (MUC1) was found to be upregulated in response to the acute Len treatment, suggesting a viable co-treatment strategy for myeloma therapy by combining MUC-1-targeting CAR-T cells with Len. These findings provide a valuable resource to the myeloma research community.

Figure 6.

Overall schematic of surface proteomic investigation under baseline condition, during drug resistance, and in response to acute drug treatment. (Adapted from Ref. [98], which is under Creative Commons Attribution 4.0 license that permits use, sharing, adaptation, distribution, and reproduction in any medium or format.)

Furthermore, the surfaceome of neuroblastoma (NB) cells following chemotherapy was analyzed to identify novel targets for immunotherapy [99]. Cell-surface proteins exhibiting high abundance across all NB models and remaining unchanged under the selective pressure of chemotherapy were prioritized for further evaluation as potential immunotherapy targets. Notably, Protein Tyrosine Kinase 7 (PTK7), functioning as a pseudo tyrosine kinase without catalytic activity, was identified as a prime candidate. PTK7 is involved in the Wnt signaling pathway, as well as in tumor initiation and invasion. It was observed that elevated PTK7 expression significantly correlated with a lower probability of survival. Furthermore, PTK7 exhibited low expression levels in pediatric-specific normal tissues. This characterization indicates PTK7 as a highly promising candidate over GD2 for designing CAR-T based therapeutic. Subsequent analyses validated the efficacy of targeting PTK7 with gene-modified T cells as an effective approach for inducing NB cell death in vivo.

Weekes et al. developed a method called proteomic plasma membrane profiling (PMP) [100]. Compared with CSC, both tryptic and deglycosylated peptides from the enriched samples were collected in PMP, which resulted in a higher protein sequence coverage and improved quantitative accuracy. Also, aminooxy-biotin was used in PMP for the oxime ligation with the aldehyde group of surface glycoproteins, generating a more stable product compared with the hydrazone ligation using biocytin hydrazide. PMP was then applied to study the function of the endoplasmic reticulum chaperone gp96 in cell surface protein expression [100]. They discovered that the surface expression of the LDL receptor family (LDLR, LRP6, Sorl1, and LRP8) is gp96-dependent. Furthermore, through a structural analysis of these proteins, they identified potential requirements for gp96 in facilitating the proper folding of certain beta-propeller domains and leucine-rich repeats.

Examining the surfaceome through patient-derived samples presents notable challenges, largely attributed to the limited amount of these samples. To overcome this hurdle, Luecke et al. optimized the CSC method to reduce the sample amount required [101]. The method, named CellSurfer, can identify a similar number of surface proteins from ~100-fold fewer cells compared with the original CSC. They employed CellSurfer to map the surfaceome of isolated primary human heart cells, including cardiomyocytes, cardiac fibroblasts, cardiac microvascular endothelial cells, and coronary artery smooth muscle cells. Over 1400 N-glycoproteins were identified from human cardiac cells, providing a comprehensive surfaceome database with organ-specific, cell-type-specific, and region-specific information. From the cardiac surfaceome, they discovered that a specific surface protein, LSMEM2, is exclusively expressed on the cell surface of cardiomyocytes and is associated with heart failure, rendering LSMEM2 a potential target for delivering substances (payloads) to cardiomyocytes.

Modifications (especially glycosylation) on surface proteins play important roles in their functions and properties. Using the metabolic labeling method, our group globally characterized cell-surface glycoproteins from eight popular types of human cells [38]. More than 2000 N-glycosylation sites were identified from 1047 cell-surface glycoproteins. We systematically investigated the distribution and occurrence of N-glycosylation sites across eight cell types. The site-specific analysis revealed that besides surfaceome differences between cell types, N-glycosylation sites also showed the cell type uniqueness, which reflects another level of complexity of the surfaceome.

Large-scale surface protein profiling is crucial for understanding biological systems and can assist in identifying drug targets and discovering biomarkers for early disease detection. With advancements in instruments and technology, the applications of surface protein profiling are expected to become more prevalent in clinical studies.

5. Conclusion

The surfaceome plays a pivotal role in nearly every extracellular activity. Dysregulation of the surfaceome has been recognized as a hallmark in various diseases, such as cancer. Therefore, comprehensive and in-depth understanding of the surfaceome aids in the development of diagnostic tools, the design of vaccines, and the devising of targeted therapies. However, due to the low abundance of many surface proteins and the complexity of the surfaceome, comprehensive characterization of the surfaceome is exceptionally challenging. Different methods have been developed to separate and enrich surface proteins, including CSC, metabolic labeling-based methods, and proximity labeling-based ones. When coupled with quantitative proteomics, disease-associated changes in the surfaceome can be identified. Global analysis of surface protein modifications, especially glycosylation, further deepens our understanding of protein functions and cellular activities. With the advent of advanced instrumentation and technologies, more accurate and comprehensive characterization of surface proteins will reveal the molecular mechanisms of human diseases and will help discover surface glycoproteins as biomarkers for disease detection and as drug targets for disease treatment.

6. Expert Opinion

The emergence of immunotherapeutic strategies, notably the CAR-T (Chimeric Antigen Receptor T-cell) therapy and the TCR (T-Cell Receptor) therapy, mark a significant advancement in personalized cancer treatment. However, unlocking the full potential of these cutting-edge strategies hinges on our in-depth understanding of surface proteins. Given its high-throughput and untargeted nature, MS-based proteomics has emerged as a powerful tool for disease diagnosis, target discovery, and drug development. Systematic characterization of surface proteins using MS-based proteomics provides unprecedented and valuable information. Extensive reports have highlighted disease-associated alterations in cell-surface proteins. With the advancements in enrichment methods and quantitative proteomics, systematic study of the dynamics of surface proteins and their interactions has become feasible.

One of the missions in surfaceome mapping is to identify specific cell-surface proteins associated with particular diseases or disease stages, enabling early diagnosis and prognosis. As clinical practice heavily relies on patient-derived samples, enrichment methods must be straightforward, robust, and sensitive. Among the enrichment methods discussed earlier, the metabolic labeling approach yields a high number of identified cell-surface proteins. However, a major drawback is its incompatibility with clinical samples. While CSC can be applied to various sample types, from cell lines to tissues. Furthermore, the development of autoCSC extends its capability to clinical applications. Given that the experimental procedures are typically complex and time-consuming, the enrichment of surface proteins requires to be performed by highly skilled specialists. Incorporating an automated liquid handling workstation can dramatically enhance efficiency and reproducibility. Large-scale surfaceome analysis holds great potential for disease classification and subtyping, providing opportunities to develop therapeutic strategies with high efficacy and low drug toxicity. Moreover, through analyzing samples from individual patients, healthcare professionals can tailor treatment strategies for individual patients, thereby enhancing treatment effectiveness while minimizing adverse effects.

Monoclonal antibody drugs for cancer often encounter challenges such as low tumor perfusion rates and rapid development of drug resistance [102]. Exploring the surfaceome signaling networks can provide a deeper understanding of the environment surrounding cell-surface proteins of interest, which can help develop multi-target approaches in cancer therapy. Different methods have been developed to map the surfaceome interaction network. The surfaceome interaction network exhibits spatial selectivity, and the same protein may have distinct functions and interaction partners within different cell-surface microenvironments. Photocatalytic proximity labeling methods enable nanoscale mapping of the surfaceome, facilitating precise and spatial identification of PPIs.

Nearly all cell-surface proteins are glycosylated, and alterations in glycosylation often serve as hallmarks of disease states. It is widely recognized that glycosylation plays a crucial role in surface protein expression and interactions. For instance, hyper sialylation on the cancer cell surfaces aids in cancer immune evasion. However, most methods used for studying surfaceome interactions frequently overlook the influence of glycans. Only a limited number of studies have delved into the surface glycoprotein interaction network, but these investigations relied on crosslinkers that pose challenges in identifying direct protein-protein interactions on the cell surface [89,103]. While the data from certain enrichment methods, such as CSC and metabolic labeling, do include glycosylation site information, the experimental designs often hinder the observation of changes in the glycan component. Therefore, intact glycopeptide analysis can allow for measuring the changes in protein abundance and the alterations in the glycan component. Furthermore, protein abundance measured by glycan chain-targeting methods may be influenced by changes in the glycan component. Therefore, it may be necessary to measure non-glycosylated peptides from glycoproteins. Additionally, systematic study of other protein modifications such as phosphorylation and acetylation on the cell surface remains to be explored.

Beyond the discovery of alterations in the abundance of cell-surface proteins, it is important to note that these proteins may also undergo changes in conformation or thermal stabilities associated with diseases or treatment. Identifying such changes is more challenging due to technological limitations. The challenges may be addressed by combining cross-linking mass spectrometry (XL-MS) and CSC [90]. Despite the limitations, such as the requirement for a substantial sample amount, the challenge for obtaining structural information, and the low fraction of cross-linked peptides, this combined approach enables the exploration of structural changes of surface proteins. Thermal proteome profiling (TPP) utilizes the cellular thermal shift assay concept in conjunction with quantitative mass spectrometry to monitor drug-target interactions in living cells. Combining TPP with cell-surface protein enrichment methods enabled to measure ligand-induced changes of cell-surface protein thermal stabilities [104]. This integrative method allows for the direct identification of drug targets, measurement of target engagement, and elucidation of drug mechanisms. Therefore, a judicious combination of technologies has the potential to significantly deepen our understanding of the surfaceome.

As mass spectrometry instrumentation and computational technologies continue to advance, the potential of single-cell proteomics and mass spectrometry imaging (MSI) is gradually being recognized. Given the limited amount of clinical samples, these techniques will have more applications in the study of surfaceome from clinical samples. The glycans on cell-surface proteins contain a wealth of biological information. Several bioinformatic tools, such as MSFragger [17], pGlyco3 [18], and StrucGP [19], were developed for intact glycopeptide analysis. We envision that future surfaceome studies will not only identify cell-surface proteins but also unveil the influence of protein modifications. Machine learning and deep learning have demonstrated their significance in MS-based proteomics, empowering researchers to obtain fundamental biological insights from extensive omics datasets.

While the expansion of high-quality surfaceome databases is noteworthy, it is important to acknowledge that the majority of these datasets originate from cell lines, which may not fully recapitulate cellular behaviors within tissues or organs. Moreover, the accessibility of clinical samples poses a challenge for numerous research laboratories, and certain medical facilities may lack the requisite technologies for conducting surfaceome studies. These factors create a gap in the seamless transition of research discoveries from the laboratory to clinical practice. Furthermore, the strategies of dual and multi-targeting have gained increasing attention due to their enhanced effectiveness and reduced drug resistance. However, the pursuit of such strategies requires close interdisciplinary collaborations. Consequently, the effective translation of surfaceome research findings into clinical applications demands active engagement in interdisciplinary partnerships and initiatives.

Given the critical significance of surface proteins, surfaceome research and its practical applications are poised for further developments. Further surfaceome studies will significantly advance our understanding of protein functions, cellular activities, and the intricate molecular underpinnings of various diseases. Furthermore, they will help us identify surface proteins as valuable biomarkers for disease detection and promising targets for therapeutic interventions.

Article Highlights.

• Advancement of cell-surface protein enrichment methods allows for their systematic identification and quantification using MS-based proteomics.

• Profiling surface protein glycosylation is critical to understand protein functions and properties.

• Studying surface protein interaction networks uncovers receptors for orphan ligands, drug off-targets, signaling pathways, and protein complexes, shedding light on signal transduction mechanisms, and aiding in drug development.

• Promoting surfaceome studies in clinical practice requires further advancements in methodologies, instrumentation, bioinformatic tools, and the cultivation of interdisciplinary collaborations.