Spatiotemporal dissection of the cell cycle with single-cell proteogenomics.

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.

Citation

Diana Mahdessian, Anthony J Cesnik, Christian Gnann, Frida Danielsson, Lovisa Stenström, Muhammad Arif, Cheng Zhang, Trang Le, Fredric Johansson, Rutger Schutten, Anna Bäckström, Ulrika Axelsson, Peter Thul, Nathan H Cho, Oana Carja, Mathias Uhlén, Adil Mardinoglu, Charlotte Stadler, Cecilia Lindskog, Burcu Ayoglu, Manuel D Leonetti, Fredrik Pontén, Devin P Sullivan, Emma Lundberg. Spatiotemporal dissection of the cell cycle with single-cell proteogenomics. Nature. 2021 Feb;590(7847):649-654

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PMID: 33627808

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