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

Area of Science:

• * Computational Biology
• * Molecular Biology
• * Aging Research

Background:

• * Bulk omics data analysis lacks cellular resolution.
• * Deconvolution methods enhance understanding of cell populations in complex samples.
• * Widely adopted in cancer biology for cell type inference.

Purpose of the Study:

• * To apply deconvolution approaches to transcriptomic and proteomic datasets in C. elegans.
• * To infer cellular and tissue contributions to whole-organism omics data.
• * To evaluate deconvolution-derived proportions as indicators of aging-related cellular changes.

Main Methods:

• * Applied deconvolution algorithms to C. elegans transcriptomic data.
• * Applied deconvolution algorithms to C. elegans proteomic data.
• * Analyzed data at single-organism resolution.

Main Results:

• * Successfully inferred cellular and tissue contributions from bulk omics data.
• * Demonstrated the utility of deconvolution in a model organism (C. elegans).
• * Identified deconvolution-derived proportions as reliable proxies for dynamic cellular changes during aging.

Conclusions:

• * Deconvolution of omics data provides valuable cellular insights.
• * This approach is applicable to both transcriptomic and proteomic datasets.
• * Deconvolution-derived cellular proportions are robust indicators of aging processes.