CellSystems BiologyPeer-reviewed journal article
Global analysis of protein turnover dynamics in single cells
Pierre SabatieriD, Maico Y. LechneriD, Ulises H. GuzmániD, Christian Michel BeuschiD, Xinlei Zeng, Longteng WangiD, Fabiana IzaguirreiD, Anjali SethiD, Olga Gritsenko, Sergey RodiniD, Karl‐Henrik GrinnemoiD, Zilu YeiD, Jesper Velgaard OlseniD
Abstract
Even with recent improvements in sample preparation and instrumentation, single-cell proteomics (SCP) analyses mostly measure protein abundances, making the field unidimensional. In this study, we employ a pulsed stable isotope labeling by amino acids in cell culture (SILAC) approach to simultaneously evaluate protein abundance and turnover in single cells (SC-pSILAC). Using state-of-the-art SCP workflow, we demonstrated that two SILAC labels are detectable from [~]4000 proteins in single HeLa cells recapitulating known biology. We investigated drug effects on global and specific protein turnover in single cells and performed a large-scale time-series SC-pSILAC analysis of undirected differentiation of human induced pluripotent stem cells (iPSC) encompassing six sampling times over two months and analyzed >1000 cells. Abundance measurements highlighted cell-specific markers of stem cells and various organ-specific cell types. Protein turnover dynamics highlighted differentiation-specific co-regulation of core members of protein complexes with core histone turnover discriminating dividing and non-dividing cells with potential in stem cell and cancer research. Our study represents the most comprehensive SCP analysis to date, offering new insights into cellular diversity and pioneering functional measurements beyond protein abundance. This method distinguishes SCP from other single-cell omics approaches and enhances its scientific relevance in biological research in a multidimensional manner.
- organism: human
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- approach: experimental assay
- Citations
- 41
- FWCI
- 9.84
- Field percentile
- top 1%
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