Pierre Sabatier (Uppsala / SE; Copenhagen / DK), Zilu Ye (Copenhagen / DK; Suzhou / CN), Maico Lechner (Copenhagen / DK), Ulises H. Guzman (Copenhagen / DK), Christian Beusch (Atlanta, GA / US), Fabiana Izaguirre (Lyon / FR), Anjali Seth (Lyon / FR), Sergey Rodin (Uppsala / SE), Karl-Henrik Grinnemo (Uppsala / SE), Jesper Velgaard Olsen (Copenhagen / DK)
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 pulse stable isotope labelling by amino acids in cell culture (pSILAC) approach to evaluate protein abundance and turnover in single cells (SC-pSILAC). We demonstrate that two SILAC labels are detectable from ~4000 proteins in single HeLa cells, using state-of-the-art SCP workflow, providing known biological information about protein turnover in HeLa cells and making pulsed and multiplex SILAC applicable to single-cell analysis. Then, we investigate drug-altering global and specific proteins turnover and highlight those treatments effects in single cells. Lastly, we perform a large-scale time-series SC-pSILAC analysis of human induced pluripotent stem cells undirected differentiation encompassing six sampling times over two months and > 1000 cells. Abundance measurements highlight cell-specific markers of stem cells and cells from various lineages and organs. Turnover dynamics highlights differentiation-specific turnover of proteins and co-regulation of core members of protein complexes over the differentiation time. Finally, we show that histone turnover can highlight 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, distinguishing SCP from other single-cell omics methods and enhancing its scientific relevance in biological research in a multidimensional manner.