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Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

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

Youngjun Park¹, Adam Antebi^1,2

¹Department of Molecular Genetics of Ageing, Max Planck Institute for Biology of Ageing, Cologne, North Rhine-Westphalia, Germany.

Micropublication Biology

|April 30, 2026

Summary

Omics data deconvolution reveals cell populations in bulk samples. This study used deconvolution on transcriptomic and proteomic data in C. elegans to track cellular changes during aging.

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Single Drosophila Ommatidium Dissection and Imaging

Single Drosophila Ommatidium Dissection and Imaging

Published on: August 19, 2011

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.