Bulk vs single-cell proteomics: is there a need for identification optimization?

Author(s): Guillaume Deflandre,Samuel Grégoire,Laurent Gatto

Affiliation(s): UCLouvain



Single-cell proteomics (SCP) has emerged as a powerful tool for elucidating cellular heterogeneity, offering opportunities beyond traditional bulk sample analysis. However, the application of current peptide identifications crafted for bulk samples may lead to false discoveries in SCP. Challenges such as reduced peak counts, lower peak intensities, and degraded signal-to-noise ratios (as identified by Boekweg et al. [1]) raise the question: do current peptide scoring methods in search engines adequately perform in the context of SCP? To address these limitations, we explore the effectiveness of search engines and rescoring tools with the use of Bioconductor packages PSMatch and Spectra. Rescoring tools take profit of as many mass spectrometry-based features as possible, such as spectral characteristics and retention time models, which can be particularly relevant to mitigate the poor quality of SCP spectra. We used MS²Rescore to generate new features, Mokapot to rescore the SCP peptides as well as the above-mentioned packages to assess the efficiency of rescoring tools and potentially improve current scoring methods in the context of SCP. Our findings demonstrate a significant increase in confidently identified peptides upon rescoring. In addition, we suggest a 4-step methodology to evaluate the usefulness of current and new potential features. Finally, our results shed light on the differences between bulk and single-cell samples whilst providing insights that can inform more accurate and reliable data interpretation in the context of SCP. [1] Hannah Boekweg and Samuel H. Payne. Challenges and Opportunities for Single-cell Computational Proteomics. Molecular & Cellular Proteomics, 22(4):100518, April 2023. ISSN 15359476. doi: 10.1016/j.mcpro.2023.100518. URL https://linkinghub.elsevier.com/retrieve/pii/S1535947623000282.