The development of technologies that allow measurement of the cell cycle at the single-cell level has revealed novel insights into the mechanisms that regulate cell cycle commitment and progression through DNA replication and cell division. These studies have also provided evidence of heterogeneity in cell cycle regulation among individual cells, even within a genetically identical population. Cell cycle mapping combines highly multiplexed imaging with manifold learning to visualize the diversity of "paths" that cells can take through the proliferative cell cycle or into various states of cell cycle arrest. In this chapter, we describe a general protocol of the experimental and computational components of cell cycle mapping. We also provide a comprehensive guide for the design and analysis of experiments, discussing key considerations in detail (e.g., antibody library preparation, analysis strategies, etc.) that may vary depending on the research question being addressed.
Keywords: Cell cycle; Immunofluorescence; Machine learning; Multiplexed imaging; Proteomics; Single-cell.
© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.