Increasing Proteome Coverage Through a Reduction in Analyte Complexity in Single-Cell Equivalent Samples

• Published in: Journal of proteome research
• Main Authors: Pang, Marion, Jones, Jeff J, Wang, Ting-Yu, Quan, Baiyi, Kubat, Nicole J, Qiu, Yanping, Roukes, Michael L, Chou, Tsui-Fen
• Format: Journal Article
• Language: English
• Published: United States 04.06.2024
• Subjects: bottom-up proteomics; peptide identification optimization; single-cell proteomics; protease choice
• ISSN: 1535-3907
• DOI: 10.1021/acs.jproteome.4c00062

The advancement of sophisticated instrumentation in mass spectrometry has catalyzed an in-depth exploration of complex proteomes. This exploration necessitates a nuanced balance in experimental design, particularly between quantitative precision and the enumeration of analytes detected. In bottom-up proteomics, a key challenge is that oversampling of abundant proteins can adversely affect the identification of a diverse array of unique proteins. This issue is especially pronounced in samples with limited analytes, such as small tissue biopsies or single-cell samples. Methods such as depletion and fractionation are suboptimal to reduce oversampling in single cell samples, and other improvements on LC and mass spectrometry technologies and methods have been developed to address the trade-off between precision and enumeration. We demonstrate that by using a monosubstrate protease for proteomic analysis of single-cell equivalent digest samples, an improvement in quantitative accuracy can be achieved, while maintaining high proteome coverage established by trypsin. This improvement is particularly vital for the field of single-cell proteomics, where single-cell samples with limited number of protein copies, especially in the context of low-abundance proteins, can benefit from considering analyte complexity. Considerations about analyte complexity, alongside chromatographic complexity, integration with data acquisition methods, and other factors such as those involving enzyme kinetics, will be crucial in the design of future single-cell workflows.