Status: Bibliographieeintrag
Standort: ---
Exemplare:
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| Online-Ressource |
Verfasst von: | Krull, Karl Kristian [VerfasserIn]  |
| Ali, Syed Azmal [VerfasserIn]  |
| 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 |
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 |
Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states / Krull, Karl Kristian [VerfasserIn]; 2024 (Online-Ressource)
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