Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states
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...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2024
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| In: |
Nature Communications
Year: 2024, Volume: 15, Pages: 1-17 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-024-52605-x |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41467-024-52605-x Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41467-024-52605-x 
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| Author Notes: | Karl K. Krull, Syed Azmal Ali & Jeroen Krijgsveld |
MARC
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| 520 | |a 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. | ||
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