Cell Tree Rings: the structure of somatic evolution as a human aging timer.

Area of Science:

• Gerontology
• Genetics
• Computational Biology

Background:

• Biological age estimation commonly uses biomarkers correlated with chronological age, but their link to aging mechanisms is unclear.
• Somatic mutations, as both markers and drivers of aging, offer a potential basis for a more fundamental aging clock.
• Technological challenges in detecting somatic variants in single cells have previously limited the development of such clocks.

Purpose of the Study:

• To demonstrate that somatic mutations detected via single-cell RNA sequencing (scRNA-seq) can construct a cell lineage tree correlating with chronological age.
• To establish a novel aging clock based on cell lineage tree structure.
• To investigate if this cell tree age prediction offers advantages over chronological age in predicting clinical biomarkers.

Main Methods:

• Detection of de novo single-nucleotide variants (SNVs) in human peripheral blood mononuclear cells using a modified scRNA-seq protocol.
• Construction of cell lineage trees from inferred somatic mutation patterns.
• Development of a penalized multiple regression model to predict chronological age based on phylogenetic tree metrics.

Main Results:

• The cell lineage tree model achieved a Pearson correlation of 0.81 with chronological age, with a median absolute error of approximately 4 years.
• Validation on an independent dataset yielded a Pearson correlation of 0.85.
• Cell tree age predictions were superior to chronological age in predicting clinical biomarkers such as glucose, albumin, and leukocyte count.

Conclusions:

• Cell lineage trees derived from somatic mutations represent a new modality for aging assessment, termed 'cell tree age'.
• This method provides a numerical age estimate and reveals the temporal history of somatic evolution and clonal structure.
• Cell Tree Rings complement existing aging clocks and may improve the evaluation of interventions in aging research.