Deconvolution of Single-Organism Omics Resolves Cellular and Tissue Dynamics during Aging

The deconvolution of bulk omics data provides enhanced resolution of cell populations within samples and has been widely adopted, particularly in cancer biology. In this study, we applied deconvolution approaches to both transcriptomic and proteomic datasets at single-organism resolution in C. elegans , enabling us to infer cellular and tissue contributions to the whole transcriptome or proteome. Our results demonstrate that deconvolution-derived cellular and tissue proportions can serve as robust proxy readouts for dynamic changes in cells and tissues during aging.