Summary

"Cell Surface Capture" Workflow for Label-Free Quantification of the Cell Surface Proteome

Published: March 24, 2023
doi:

Summary

Here, we describe a proteomics workflow for characterization of the cell surface proteome of various cell types. This workflow includes cell surface protein enrichment, subsequent sample preparation, analysis using an LC-MS/MS platform, and data processing with specialized software.

Abstract

Over the past decade, mass spectrometry-based proteomics has enabled an in-depth characterization of biological systems across a broad array of applications. The cell surface proteome (“surfaceome”) in human disease is of significant interest, as plasma membrane proteins are the primary target of most clinically approved therapeutics, as well as a key feature by which to diagnostically distinguish diseased cells from healthy tissues. However, focused characterization of membrane and surface proteins of the cell has remained challenging, primarily due to the complexity of cellular lysates, which mask proteins of interest by other high-abundance proteins. To overcome this technical barrier and accurately define the cell surface proteome of various cell types using mass spectrometry proteomics, it is necessary to enrich the cell lysate for cell surface proteins prior to analysis on the mass spectrometer. This paper presents a detailed workflow for labeling cell surface proteins from cancer cells, enriching these proteins out of the cell lysate, and subsequent sample preparation for mass spectrometry analysis.

Introduction

Proteins serve as the fundamental units by which the majority of cellular functions are carried out. Characterizing the structure and function of relevant proteins is an essential step to understand biological processes. Over the past decade, advances in mass spectrometry technology, analysis software, and databases have enabled the accurate detection and measurement of proteins at a proteome-wide scale1. Mass spectrometry-based proteomics can be utilized in a diverse array of applications, from basic science analysis of biochemical pathways, to identification of novel drug targets in a translational setting, to diagnosis and monitoring of diseases in the clinic2. When screening for novel drug targets, characterization of the cell surface proteome is particularly important, with over 65% of currently approved human drugs targeting cell surface proteins3. The field of cancer immunotherapy also wholly relies on cancer-specific cell surface antigens to target and specifically eliminate tumor cells4. Mass spectrometry-based proteomics can thus serve as a promising tool to identify new cell surface proteins toward therapeutic interventions.

However, there are several limitations when utilizing conventional proteomics methods to survey tumor cells for novel cell surface protein targets. A primary concern is that surface proteins make up a very small fraction of the total protein molecules in a cell. Therefore, fragments of these proteins are masked by a high abundance of intracellular proteins when performing mass spectrometry analysis of the whole-cell lysate5. This limitation makes it challenging to accurately characterize the cell surface proteome with a traditional proteomics workflow. To address this challenge, it is necessary to develop ways to enrich cell surface proteins out of the whole-cell lysate, prior to analysis on the mass spectrometer. One such method involves the oxidation and biotin labeling of glycosylated cell surface proteins in the intact cells, and subsequent enrichment of these biotinylated proteins from the lysate with a neutravidin pulldown, a process that has been termed "cell surface capture"6. Since ~85% of mammalian cell surface proteins are thought to be glycosylated7, this serves as an effective method of enriching the cell surface proteome out of the whole cell lysate. This paper describes a complete workflow, beginning with cultured cells, of cell surface biotin labeling, and subsequent sample preparation for mass spectrometry analysis (Figure 1). Over several replicates, this method provides robust coverage of the cell surface proteome of a particular sample. Utilizing this method to characterize the cell surface proteome of both tumor and healthy cells can facilitate the discovery of novel cell surface antigens to identify potential immunotherapeutic targets8.

Protocol

NOTE: AMO1 plasmacytoma cells were used for this cell surface proteome experiment. The same protocol could be used for other cell types as well, including a wide array of suspension and adherent cell lines9, as well as various types of primary samples10. However, cell numbers (starting material for the experiment) typically have to be optimized for equivalent proteome coverage. For details related to materials and equipment, see the Table of Materials. For …

Representative Results

For this experiment, we characterized the cell surface proteome of a tumor cell line by labeling N-glycosylated membrane proteins of intact cells with biotin, and enriching these labeled proteins from the whole cell lysate with a neutravidin pulldown (Figure 1). Further, we performed proteome analysis using LC-MS/MS to characterize enriched cell surface proteins. Unlike whole cell proteome analysis, here, the objective was to characterize only cell surface proteins. Hence, we starte…

Discussion

Mass spectrometry-based proteomics is a powerful tool that has enabled unbiased characterization of thousands of unknown proteins on a previously impossible scale. This approach allows us to identify and quantify the proteins, as well as glean a range of insights for the structural and signaling capacities of cells and tissues, by characterizing the variety of proteins present in a particular sample. Moving beyond global protein profiling in a sample, mass spectrometry allows us to characterize various post-translational…

Disclosures

The authors have nothing to disclose.

Acknowledgements

We thank Dr. Kamal Mandal (Dept of Laboratory Medicine, UCSF) for help with setting up the LC-MS/MS run, Deeptarup Biswas (BSBE, IIT Bombay) for help with data analysis, and Dr. Audrey Reeves (Dept of Laboratory Medicine, UCSF) for help with data analysis. Related work in the A.P.W. lab is supported by NIH R01 CA226851 and the Chan Zuckerberg Biohub. Figure 1 and Figure 2B were made using BioRender.com.

Materials

Kits
96X iST Sample Preparation Kit PreOmics P.O.00027 Proteomics sample preparation kit. Includes reagents for reduction, alkylation, and digestion. Also include desalting columns and reagents. 
Pierce Quantitative Colorimetric Peptide Assay Thermo 23275 Peptide quantification kit. Includes peptide standards and components of working reagents. 
Reagents
Acetonitrile Fisher A955-1
Ammonium bicarbonate Millipore Sigma 09830-1KG
Biocytin hydrazide Biotium 90060
D-PBS (w/o Calcium and Magnesium Salts) UCSF Cell Culture Facility CCFAL003-225B01
Formic Acid Honeywell 94318
Halt Protease and Phosphatase Inhibitor Single-Use Cocktail Thermo 1861280
High Capacity Neutravidin Agarose Resin Thermo 29204
Phosphate Buffered Saline UCSF Cell Culture Facility CCFAL001-22J01
RIPA Lysis Buffer, 10x Millipore Sigma 20-188
Sodium chloride Fisher BP358-212
Sodium metaperiodate Alfa Aesar 13798
Trypan Blue Stain (0.4%) Gibco 15250-061
Ultrapure 0.5 M EDTA, pH 8.0 Invitrogen 15575-038
Urea (Proteomics Grade) VWR M123-1KG
Equipment
TC20 Automated Cell Counter Bio-Rad 1450102
PrismR Microcentrifuge Labnet International C2500-R-230V
Sonicator VWR Branson Sonifier 240
Vacuum Manifold Promega Promega Vac-Man
Shaking Heatblock Eppendorf Eppendorf Thermomixer C
End-to-End rotator Labnet Revolver Adjustable Rotator
LC Thermo Ultimate 3000 HPLC and UHPLC
Q Exactive Plus Hybrid Quadrapole Orbitrap Mass Spectrometer Thermo IQLAAEGAAPFALGMBDK
Microplate Reader Biotek Biotek Synergy 2 
Vacuum Concentrator Labconco 7810010
Supplies
1.5 mL Protein LoBind Tubes Eppendorf 22431081
1.7 mL Microcentrifuge Tubes
Filtration Columns Bio-Rad 7326008
Spin Columns Thermo 69725

References

  1. Aebersold, R., Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature. 537 (7620), 347-355 (2016).
  2. Aslam, B., Basit, M. B., Nisar, M. A., Khurshid, M., Rasool, M. H. Proteomics: technologies and their applications. Journal of Chromatographic Science. 55 (2), 182-196 (2017).
  3. Bausch-Fluck, D., et al. The in silico human surfaceome. Proceedings of the National Academy of Sciences. 115 (46), 10988-10997 (2018).
  4. Takahashi, Y., et al. Research advance in tumor specific antigens: A narrative review. AME Medical Journal. 6, 35 (2021).
  5. Li, Y., Qin, H., Ye, M. An overview on enrichment methods for cell surface proteome profiling. Journal of Separation Science. 43 (1), 292-312 (2020).
  6. Wollscheid, B., et al. Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins. Nature Biotechnology. 27 (4), 378-386 (2009).
  7. Chandler, K. B., Costello, C. C. Glycomics and glycoproteomics of membrane proteins and cell-surface receptors: present trends and future opportunities. Electrophoresis. 37 (11), 1407-1419 (2016).
  8. Ferguson, I. D., et al. The surfaceome of multiple myeloma cells suggests potential immunotherapeutic strategies and protein markers of drug resistance. Nature Communications. 13 (1), 4121 (2022).
  9. Karcini, A., Lazar, I. M. The SKBR3 cell-membrane proteome reveals telltales of aberrant cancer cell proliferation and targets for precision medicine applications. Scientific Reports. 12 (1), 10847 (2022).
  10. Köhnke, T., et al. Integrated multiomic approach for identification of novel immunotherapeutic targets in AML. Biomarker Research. 10 (1), 43 (2022).
  11. Verma, A., Kumar, V., Ghantasala, S., Mukherjee, S., Srivastava, S. Comprehensive workflow of mass spectrometry-based shotgun proteomics of tissue samples. Journal of Visualized Experiments. (177), e61786 (2021).
  12. Leung, K. K., et al. Broad and thematic remodeling of the surfaceome and glycoproteome on isogenic cells transformed with driving proliferative oncogenes. Proceedings of the National Academy of Sciences. 117 (14), 7764-7775 (2020).
  13. Nix, M. A., et al. Surface proteomics reveals CD72 as a target for in vitro-evolved nanobody-based CAR-T cells in KMT2A/MLL1-rearranged B-ALL. Cancer Discovery. 11 (8), 2032-2049 (2021).
  14. Cox, J., et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. Journal of Proteome Research. 10 (4), 1794-1805 (2011).
  15. Waldman, A. D., Fritz, J. M., Lenardo, M. J. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nature Reviews Immunology. 20 (11), 651-668 (2020).
  16. Hosen, N., et al. The activated conformation of integrin β7 is a novel multiple myeloma-specific target for CAR T cell therapy. Nature Medicine. 23 (12), 1436-1443 (2017).
  17. Costa, A. F., Campos, D., Reis, C. A., Gomes, C. Targeting glycosylation: A new road for cancer drug discovery. Trends in Cancer. 6 (9), 757-766 (2020).
  18. Gundry, R. L., Boheler, K. R., Van Eyk, J. E., Wollscheid, B. A novel role for proteomics in the discovery of cell-surface markers on stem cells: Scratching the surface. Proteomics. Clinical Applications. 2 (6), 892-903 (2008).
  19. Itzhak, D. N., et al. A mass spectrometry-based approach for mapping protein subcellular localization reveals the spatial proteome of mouse primary neurons. Cell Reports. 20 (11), 2706-2718 (2017).
  20. Kirkemo, L. L., et al. Cell-surface tethered promiscuous biotinylators enable comparative small-scale surface proteomic analysis of human extracellular vesicles and cells. eLife. 11, 73982 (2022).
  21. Wojtkiewicz, M., Berg Luecke, L., Kelly, M. I., Gundry, R. L. Facile preparation of peptides for mass spectrometry analysis in bottom-up proteomics workflows. Current Protocols. 1 (3), 85 (2021).
  22. Välikangas, T., Suomi, T., Elo, L. L. A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation. Briefings in Bioinformatics. 19 (6), 1344-1355 (2018).
  23. Cox, J., Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology. 26 (12), 1367-1372 (2008).
  24. Bausch-Fluck, D., et al. A mass spectrometric-derived cell surface protein atlas. PLoS One. 10 (3), 0121314 (2015).
  25. Griss, J., et al. ReactomeGSA-efficient multi-omics comparative pathway analysis. Molecular & Cellular Proteomics. 19 (12), 2115-2125 (2020).
  26. Waas, M., et al. SurfaceGenie: a web-based application for prioritizing cell-type-specific marker candidates. Bioinformatics. 36 (11), 3447-3456 (2020).
  27. Mellacheruvu, D., et al. The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nature Methods. 10 (8), 730-736 (2013).
  28. van Oostrum, M., et al. Classification of mouse B cell types using surfaceome proteotype maps. Nature Communications. 10 (1), 5734 (2019).
  29. Berg Luecke, L., Gundry, R. L. Assessment of streptavidin bead binding capacity to improve quality of streptavidin-based enrichment studies. Journal of Proteome Research. 20 (2), 1153-1164 (2021).
check_url/64952?article_type=t

Play Video

Cite This Article
Naik, A., Srivastava, S., Wiita, A. P. “Cell Surface Capture” Workflow for Label-Free Quantification of the Cell Surface Proteome. J. Vis. Exp. (193), e64952, doi:10.3791/64952 (2023).

View Video