Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states

• Verfasst von: Krull, Karl Kristian [VerfasserIn]
• Verfasst von: Ali, Syed Azmal [VerfasserIn]
• Verfasst von: Krijgsveld, Jeroen [VerfasserIn]
• Titel: Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states
• Verf.angabe: Karl K. Krull, Syed Azmal Ali & Jeroen Krijgsveld
• Jahr: 2024
• Umfang: 17 S.
• Illustrationen: Diagramme
• Fussnoten: Online veröffentlicht: 26. September 2024 ; Gesehen am 14.03.2025
• Titel Quelle: Enthalten in: Nature Communications
• Ort Quelle: [London] : Springer Nature, 2010
• Jahr Quelle: 2024
• Band/Heft Quelle: 15(2024), Artikel-ID 8262, Seite 1-17
• ISSN Quelle: 2041-1723
• DOI: doi:10.1038/s41467-024-52605-x
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Proteomics
• Sach-SW: Systems analysis
• K10plus-PPN: 1919786732

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.