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ZENODO
Preprint . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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Transcriptomic noise accumulates within tissue identity across human aging: a systemic signature distinct from cell-composition drift

Authors: Spiro, Theodor;

Transcriptomic noise accumulates within tissue identity across human aging: a systemic signature distinct from cell-composition drift

Abstract

Aging is often described in two competing languages: accumulation of specific damaged or senescent cell populations versus systemic regulatory erosion affecting every cell. We provide a direct quantitative test of this dichotomy using variance-decomposition of bulk transcriptomes across age. In 263 GTEx v8 donors (20-79 years) with matched samples in six tissues, tissue identity accounts for approximately 0.73 of transcriptomic variance and declines by only 0.031 over forty years (variancePartition REML 0.789 to 0.758, PERMANOVA R-squared = 0.858; observed pi is 243-fold above a permutation null). The small decline is absorbed almost entirely by within-tissue, within-donor residual variance (pi_residual 0.168 to 0.194), not by between-donor systemic factors (pi_donor stable at approximately 0.064). The signature is therefore systemic noise accumulating within every cell type, not a shift toward outlier populations. At the single-cell level, the residual growth is partly compositional (cell-type proportions shift) and partly cell-intrinsic: two platforms from the Tabula Muris Senis (Smart-seq2 and 10x Chromium) give complementary reads, with 10x -- which has lower age-related gene-detection bias -- showing Delta_pi approximately -0.07 per cell type in balanced analysis. Per-tissue, noise accumulates at very different rates: hematopoietic tissue grows in variance approximately 3-fold faster than skeletal muscle (Delta_var = +0.079 vs +0.028 over 40 years), but rates do not cleanly track a dividing/post-mitotic axis. A central unexpected result is that left ventricular myocardium shows the largest Delta_var of any tissue tested (+0.121) despite being post-mitotic, implicating non-turnover mechanisms -- age-related immune infiltration, fibroblast activation, and progressive fibrosis -- as the dominant drivers in cardiac aging at the transcriptomic-composition level. Across four mammalian species (mouse, rat, macaque, human), the rate of pi_tissue decline scales inversely with maximum lifespan (alpha = -1.02 +/- 0.24, R-squared = 0.90, Spearman rho = -1.0). Caloric restriction in rat partially reverses aging-associated pi loss in marrow by reducing residual variance rather than restoring tissue-specific variance (87% rescue; bootstrap 95% CI 82-91%; mechanism confirmed in 100% of iterations). Our transcriptomic measurement captures tissue-specific noise accumulation rates that are not represented in methylation clocks, which are themselves largely tissue-invariant; the two signals are therefore complementary. We interpret the results as direct quantitative support for the systemic-noise view of aging, without denying the reality of specific senescent populations, and identify the hematopoietic compartment and left-ventricular myocardium as tissues worth prioritising for mechanistic follow-up.

Keywords

Aging

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Average