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bioRxiv · 10.1101/2024.01.10.575010

Enhanced feature matching in single-cell proteomics characterizes response to IFN-gamma and reveals co-existence of different cell states

Krull, K. K.·Ali, S. A.·Krijgsveld, J.
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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 present DIA-ME, a 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 evaluate 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-{gamma}) in single cells, and recapitulate the time-resolved induction of IFN-{gamma} response proteins as observed in bulk material. Moreover, we observe 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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Krull, K. K., Ali, S. A., Krijgsveld, J.. 2024-01-10. Enhanced feature matching in single-cell proteomics characterizes response to IFN-gamma and reveals co-existence of different cell states. https://doi.org/10.1101/2024.01.10.575010

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