Spectral Library-Based Single-Cell Proteomics Resolves Cellular Heterogeneity

Authors

Lakmini Senavirathna
Cheng Ma
Ru Chen
Sheng Pan

Publication Date

8-7-2022

Journal

Cells

DOI

10.3390/cells11152450

PMID

35954294

PMCID

PMC9368228

PubMedCentral® Posted Date

8-7-2022

PubMedCentral® Full Text Version

Post-Print

Published Open-Access

yes

Keywords

Peptides, Proteome, Proteomics, Software, Tandem Mass Spectrometry, single-cell proteomics, proteomics, mass spectrometry, spectral library, cellular heterogeneity, systems biology

Abstract

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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