期刊
JOURNAL OF PROTEOME RESEARCH
卷 21, 期 1, 页码 182-188出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.1c00670
关键词
single-cell proteomics; peptide identification optimization; computational proteomics; algorithms; MS/MS features
资金
- Biogen, Inc
The field of single-cell proteomics aims to identify and quantify proteins at the individual cell level, but the differences in spectral features between single-cell and bulk samples require adjustments in algorithms to compensate for these losses.
The goal of proteomics is to identify and quantify the complete set of proteins in a biological sample. Single-cell proteomics specializes in the identification and quantitation of proteins for individual cells, often used to elucidate cellular heterogeneity. The significant reduction in ions introduced into the mass spectrometer for single-cell samples could impact the features of MS2 fragmentation spectra. As all peptide identification software tools have been developed on spectra from bulk samples and the associated ion-rich spectra, the potential for spectral features to change is of great interest. We characterize the differences between single-cell spectra and bulk spectra by examining three fundamental spectral features that are likely to affect peptide identification performance. All features show significant changes in single-cell spectra, including the loss of annotated fragment ions, blurring signal and background peaks due to diminishing ion intensity, and distinct fragmentation pattern, compared to bulk spectra. As each of these features is a foundational part of peptide identification algorithms, it is critical to adjust algorithms to compensate for these losses.
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