Transformative Opportunities for Single-Cell Proteomics

Journal of Proteome Research

Volumn 17, Issue 8, 2018, Pages 2565-2571

  Specht, Harrison ^a   Slavov, Nikolai ^a,b  

^a Northeastern University   (United States)

^b NORTHEASTERN UNIVERSITY   (United States)

Author keywords

causal inference; counting noise; disease diagnosis; network inference; sample preparation; Simpson's paradox; single cell analysis; single cell mass spectrometry; systems biology; ultrasensitive proteomics

Indexed keywords

ION; SINGLE CELL PROTEINS;

BIOLOGY; DATA ANALYSIS; HIGH THROUGHPUT SEQUENCING; MASS SPECTROMETRY; PEPTIDE ANALYSIS; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEOMICS; REVIEW; SENSITIVITY ANALYSIS; SEPARATION TECHNIQUE; SINGLE CELL ANALYSIS; ANALYSIS; ANIMAL; HUMAN; PROCEDURES; PROTEIN INTAKE; SPECIMEN HANDLING;

ANIMALS; DIETARY PROTEINS; HUMANS; MASS SPECTROMETRY; PROTEOMICS; SINGLE-CELL ANALYSIS; SPECIMEN HANDLING;

EID: 85049243239     PISSN: 15353893     EISSN: 15353907     Source Type: Journal    
DOI: 10.1021/acs.jproteome.8b00257     Document Type: Review

References (43)

1. Chalfie, M.; Tu, Y.; Euskirchen, G.; Ward, W. W.; Prasher, D. C. Green fluorescent protein as a marker for gene expression. Science 1994, 263, 802-805, 10.1126/science.8303295
2. Caprioli, R. M.; Farmer, T. B.; Gile, J. Molecular imaging of biological samples: localization of peptides and proteins using MALDI-TOF MS. Anal. Chem. 1997, 69, 4751-4760, 10.1021/ac970888i
3. Bandura, D. R. et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 2009, 81, 6813-6822, 10.1021/ac901049w
4. Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 2011, 332, 687-696, 10.1126/science.1198704
5. Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 2016, 14, 380-389, 10.1016/j.celrep.2015.12.021
6. Hughes, A. J. et al. Single-cell western blotting. Nat. Methods 2014, 11, 749-755, 10.1038/nmeth.2992
7. Elowitz, M. B.; Levine, A. J.; Siggia, E. D.; Swain, P. S. Stochastic gene expression in a single cell. Science 2002, 297, 1183-1186, 10.1126/science.1070919
8. Weissleder, R.; Pittet, M. J. Imaging in the era of molecular oncology. Nature 2008, 452, 580, 10.1038/nature06917
9. Raj, A.; van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 2008, 135, 216-226, 10.1016/j.cell.2008.09.050
10. Levy, E.; Slavov, N. Single cell protein analysis for systems biology. Essays in Biochemistry (in press) PeerJ Preprints 2018, 6, e26965v1 10.7287/peerj.preprints.26965v1.
11. Aebersold, R.; Mann, M. Mass spectrometry-based proteomics. Nature 2003, 422, 198, 10.1038/nature01511
12. Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. Nature 2014, 513, 382, 10.1038/nature13438
13. Macosko, E. Z. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell 2015, 161, 1202-1214, 10.1016/j.cell.2015.05.002
14. Brown, P. O.; Botstein, D. Exploring the new world of the genome with DNA microarrays. Nat. Genet. 1999, 21, 33, 10.1038/4462
15. Boyer, L. A. et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 2005, 122, 947-956, 10.1016/j.cell.2005.08.020
16. Slavov, N.; Macinskas, J.; Caudy, A.; Botstein, D. Metabolic cycling without cell division cycling in respiring yeast. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 19090-19095, 10.1073/pnas.1116998108
17. Slavov, N.; Budnik, B.; Schwab, D.; Airoldi, E.; van Oudenaarden, A. Constant Growth Rate Can Be Supported by Decreasing Energy Flux and Increasing Aerobic Glycolysis. Cell Rep. 2014, 7, 705-714, 10.1016/j.celrep.2014.03.057
18. Blyth, C. R. On Simpson's paradox and the sure-thing principle. J. Am. Stat. Assoc. 1972, 67, 364-366, 10.1080/01621459.1972.10482387
19. Franks, A.; Airoldi, E.; Slavov, N. Post-transcriptional regulation across human tissues. PLoS Comput. Biol. 2017, 13, e1005535, 10.1371/journal.pcbi.1005535
20. Budnik, B.; Levy, E.; Harmange, G.; Slavov, N. Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. bioRxiv 2017, 102681, 10.1101/102681
21. Hofstadler, S. A.; Swanek, F. D.; Gale, D. C.; Ewing, A. G.; Smith, R. D. Capillary Electrophoresis-Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry for Direct Analysis of Cellular Proteins. en. Anal. Chem. 1995, 67, 1477-1480, 10.1021/ac00104a028
22. Valaskovic, G. A.; Kelleher, N. L.; McLafferty, F. W. Attomole Protein Characterization by Capillary Electrophoresis-Mass Spectrometry. Science 1996, 273, 1199-1202, 10.1126/science.273.5279.1199
23. Mellors, J. S.; Jorabchi, K.; Smith, L. M.; Ramsey, J. M. Integrated Microfluidic Device for Automated Single Cell Analysis Using Electrophoretic Separation and Electrospray Ionization Mass Spectrometry. Anal. Chem. 2010, 82, 967-973, 10.1021/ac902218y
24. Virant-Klun, I.; Leicht, S.; Hughes, C.; Krijgsveld, J. Identification of maturation-specific proteins by single-cell proteomics of human oocytes. Mol. Cell. Proteomics 2016, 15, 2616-2627, 10.1074/mcp.M115.056887
25. Lombard-Banek, C.; Moody, S. A.; Nemes, P. Single-Cell Mass Spectrometry for Discovery Proteomics: Quantifying Translational Cell Heterogeneity in the 16-Cell Frog (Xenopus) Embryo. Angew. Chem., Int. Ed. 2016, 55, 2454-2458, 10.1002/anie.201510411
26. Sun, L. Single Cell Proteomics Using Frog (Xenopus laevis) Blastomeres Isolated from Early Stage Embryos, Which Form a Geometric Progression in Protein Content. Anal. Chem. 2016, 88, 6653-6657, 10.1021/acs.analchem.6b01921
27. Li, S. et al. An integrated platform for isolation, processing, and mass spectrometry-based proteomic profiling of rare cells in whole blood. Mol. Cell. Proteomics 2015, 14, 1672-1683, 10.1074/mcp.M114.045724
28. Zhu, Y.; Piehowski, P. D.; Zhao, R.; Chen, J.; Shen, Y.; Moore, R. J.; Shukla, A. K.; Petyuk, V. A.; Campbell-Thompson, M.; Mathews, C. E. Nanodroplet processing platform for deep and quantitative proteome profiling of 10-100 mammalian cells. en. Nat. Commun. 2018, 9, 882, 10.1038/s41467-018-03367-w
29. Milo, R.; Jorgensen, P.; Moran, U.; Weber, G.; Springer, M. BioNumbers-the database of key numbers in molecular and cell biology. Nucleic Acids Res. 2010, 38, D750-D753, 10.1093/nar/gkp889
30. Hicks, S. C.; Townes, F. W.; Teng, M.; Irizarry, R. A. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 2017, kxx053, 10.1093/biostatistics/kxx053
31. Hosic, S.; Murthy, S. K.; Koppes, A. N. Microfluidic sample preparation for single cell analysis. Anal. Chem. 2016, 88, 354-380, 10.1021/acs.analchem.5b04077
32. Kulak, N. A.; Pichler, G.; Paron, I.; Nagaraj, N.; Mann, M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat. Methods 2014, 11, 319, 10.1038/nmeth.2834
33. Hughes, C. S. et al. Ultrasensitive proteome analysis using paramagnetic bead technology. Mol. Syst. Biol. 2014, 10, 757, 10.15252/msb.20145625
34. Sielaff, M. et al. Evaluation of FASP, SP3, and iST Protocols for Proteomic Sample Preparation in the Low Microgram Range. J. Proteome Res. 2017, 16, 4060-4072, 10.1021/acs.jproteome.7b00433
35. Sun, L. et al. Over 10 000 Peptide Identifications from the HeLa Proteome by Using Single-Shot Capillary Zone Electrophoresis Combined with Tandem Mass Spectrometry. Angew. Chem., Int. Ed. 2014, 53, 13931-13933, 10.1002/anie.201409075
36. Meier, F. et al. Parallel accumulation-serial fragmentation (PASEF): multiplying sequencing speed and sensitivity by synchronized scans in a trapped ion mobility device. J. Proteome Res. 2015, 14, 5378-5387, 10.1021/acs.jproteome.5b00932
37. Thompson, A. et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 2003, 75, 1895-1904, 10.1021/ac0262560
38. Ross, P. L. et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics 2004, 3, 1154-1169, 10.1074/mcp.M400129-MCP200
39. Savitski, M. M. et al. Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. J. Proteome Res. 2013, 12, 3586-3598, 10.1021/pr400098r
40. Eng, J. K.; McCormack, A. L.; Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 1994, 5, 976-989, 10.1016/1044-0305(94)80016-2
41. Cox, J.; Mann, M. MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26, 1367-1372, 10.1038/nbt.1511
42. Sinitcyn, P.; Daniel Rudolph, J.; Cox, J. Computational Methods for Understanding Mass Spectrometry-Based Shotgun Proteomics Data. Annu. Rev. Biomed. Data Sci. 2018, 1, 207-34, 10.1146/annurev-biodatasci-080917-013516
43. Single Cell Proteomics 2018. www.northeastern.edu/scp2018.