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

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

SUBMITTER: Krull KK 

PROVIDER: S-EPMC11427561 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature