Ploidy inference from single-cell data: application to human and mouse cell atlases

Ploidy is relevant to numerous biological phenomena, including development, metabolism, and tissue regeneration. Single-cell RNA-seq and other omics studies are revolutionizing our understanding of biology, yet they have largely overlooked ploidy. This is likely due to the additional assay step required for ploidy measurement. Here, we developed a statistical method to infer ploidy from single-cell ATAC-seq data, addressing this gap. When applied to data from human and mouse cell atlases, our method enabled systematic detection of polyploidy across diverse cell types. This method allows for the integration of ploidy analysis into single-cell studies. Additionally, this method can be adapted to detect the proliferating stage in the cell cycle and copy number variations in cancer cells. The software is implemented as the scPloidy package of the R software and is freely available from CRAN. Ploidy plays a crucial role in many biological processes. Though modern studies offer deep insights into biology, they often neglect ploidy due to measurement challenges. In this research, Takeuchi and Kato have developed a new method to identify ploidy levels using single-cell data, which facilitates the detection of polyploid cells across various cell types and bridges a gap in their understanding. This advancement also underscores the potential impact of integrating ploidy analysis with current single-cell genomic studies.