Enhanced Feature Matching in Single-cell Proteomics Characterizes IFN-γ Response and Co-existence of Cell States

Overview

Journal Nat Commun

Specialty Biology

Date 2024 Sep 26

PMID 39327420

Authors

Karl K Krull

Syed Azmal Ali

Jeroen Krijgsveld

Affiliations

Soon will be listed here.

Abstract

Proteome analysis by data-independent acquisition (DIA) has become a powerful approach to obtain deep proteome coverage, and has gained recent traction for label-free analysis of single cells. However, optimal experimental design for DIA-based single-cell proteomics has not been fully explored, and performance metrics of subsequent data analysis tools remain to be evaluated. Therefore, we here formalize and comprehensively evaluate a DIA data analysis strategy that exploits the co-analysis of low-input samples with a so-called matching enhancer (ME) of higher input, to increase sensitivity, proteome coverage, and data completeness. We assess the matching specificity of DIA-ME by a two-proteome model, and demonstrate that false discovery and false transfer are maintained at low levels when using DIA-NN software, while preserving quantification accuracy. We apply DIA-ME to investigate the proteome response of U-2 OS cells to interferon gamma (IFN-γ) in single cells, and recapitulate the time-resolved induction of IFN-γ response proteins as observed in bulk material. Moreover, we uncover co- and anti-correlating patterns of protein expression within the same cell, indicating mutually exclusive protein modules and the co-existence of different cell states. Collectively our data show that DIA-ME is a powerful, scalable, and easy-to-implement strategy for single-cell proteomics.

Citing Articles

Single-Cell Proteomics using Mass Spectrometry.

Momenzadeh A, Meyer J ArXiv. 2025; .

PMID: 40034135 PMC: 11875278.

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