Journal
CELLS
Volume 11, Issue 15, Pages -Publisher
MDPI
DOI: 10.3390/cells11152450
Keywords
single-cell proteomics; proteomics; mass spectrometry; spectral library; cellular heterogeneity; systems biology
Categories
Funding
- Cancer Prevention and Research Institute of Texas (CPRIT) [RP210111]
- National Institutes of Health (NIH) [R01CA180949, R01CA211892, P30CA125123]
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The integrated spectral library-based single-cell proteomics platform developed in this study allows for identification of over 2000 proteins in a single cell, distinguishing proteome landscapes associated with cellular types and heterogeneity. By characterizing normal and cancerous pancreatic ductal cells, a significant upregulation of multiple protein networks in cancer hallmarks was identified in the cancerous cells, functionally discriminating them from normal cells. This unique platform, built on high-resolution MS and widely accepted proteomic software, has great potential for community-wide applications.
Dissecting the proteome of cell types and states at single-cell resolution, while being highly challenging, has significant implications in basic science and biomedicine. Mass spectrometry (MS)-based single-cell proteomics represents an emerging technology for system-wide, unbiased profiling of proteins in single cells. However, significant challenges remain in analyzing an extremely small amount of proteins collected from a single cell, as a proteome-wide amplification of proteins is not currently feasible. Here, we report an integrated spectral library-based single-cell proteomics (SLB-SCP) platform that is ultrasensitive and well suited for a large-scale analysis. To overcome the low MS/MS signal intensity intrinsically associated with a single-cell analysis, this approach takes an alternative approach by extracting a breadth of information that specifically defines the physicochemical characteristics of a peptide from MS1 spectra, including monoisotopic mass, isotopic distribution, and retention time (hydrophobicity), and uses a spectral library for proteomic identification. This conceptually unique MS platform, coupled with the DIRECT sample preparation method, enabled identification of more than 2000 proteins in a single cell to distinguish different proteome landscapes associated with cellular types and heterogeneity. We characterized individual normal and cancerous pancreatic ductal cells (HPDE and PANC-1, respectively) and demonstrated the substantial difference in the proteomes between HPDE and PANC-1 at the single-cell level. A significant upregulation of multiple protein networks in cancer hallmarks was identified in the PANC-1 cells, functionally discriminating the PANC-1 cells from the HPDE cells. This integrated platform can be built on high-resolution MS and widely accepted proteomic software, making it possible for community-wide applications.
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