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Status: Bibliographieeintrag

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Verfasst von:Krull, Karl Kristian [VerfasserIn]   i
 Ali, Syed Azmal [VerfasserIn]   i
 Krijgsveld, Jeroen [VerfasserIn]   i
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
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.
DOI:doi:10.1038/s41467-024-52605-x
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1038/s41467-024-52605-x
 kostenfrei: Volltext: https://www.nature.com/articles/s41467-024-52605-x
 DOI: https://doi.org/10.1038/s41467-024-52605-x
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Proteomics
 Systems analysis
K10plus-PPN:1919786732
Verknüpfungen:→ Zeitschrift

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