DNA methylation-based prediction of differentiation state at cell-type resolution reveals an age-associated epigenetic mesenchymal drift
- 1Chinese Academy of Sciences
Abstract
Quantifying the differentiation state of cells is an important task in regenerative medicine, cellular reprogramming and in elucidating how differentiation hierarchies are altered in aging and disease. For DNA methylation data (DNAm) there is however an accute shortage of methods for quantifying differentiation state. Here we use machine learning to build EpiDiff, a predictor of differentiation that yields a continuous absolute differentiation index (DI) when applied to a DNAm sample. We extensively validate EpiDiff in single-cells, sorted cells and bulk tissue, as well as in in-vitro differentiation, organoid, reprogramming and cellular rejuvenation experiments. In mixed samples, EpiDiff measures the extent of differentiation across all cellular lineages. In sorted cell populations, we find that DI increases with age and, underpinning this increase, we discover that many cell-types undergo a developmental epigenetic drift that is most prominent towards the mesenchymal lineage. When applied to normal-tissues, EpiDiff is predictive of carcinogenic exposure and cancer risk. In summary, EpiDiff is a unique, general and versatile tool that can accurately quantify the global differentiation state of a DNAm sample with broad applicability to many disciplines.
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BibTeX
@misc{teschendorff2026methylationbased,
title = {{DNA methylation-based prediction of differentiation state at cell-type resolution reveals an age-associated epigenetic mesenchymal drift}},
author = {A. Teschendorff and H. Tong and X. Guo},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.10.758162},
url = {https://www.biorxiv.org/content/10.64898/2026.10.10.758162v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - DNA methylation-based prediction of differentiation state at cell-type resolution reveals an age-associated epigenetic mesenchymal drift
AU - A. Teschendorff
AU - H. Tong
AU - X. Guo
PY - 2026
DA - 2026/10/10
PB - bioRxiv
DO - 10.64898/2026.10.10.758162
UR - https://www.biorxiv.org/content/10.64898/2026.10.10.758162v1
AB - Quantifying the differentiation state of cells is an important task in regenerative medicine, cellular reprogramming and in elucidating how differentiation hierarchies are altered in aging and disease. For DNA methylation data (DNAm) there is however an accute shortage of methods for quantifying differentiation state. Here we use machine learning to build EpiDiff, a predictor of differentiation that yields a continuous absolute differentiation index (DI) when applied to a DNAm sample. We extensively validate EpiDiff in single-cells, sorted cells and bulk tissue, as well as in in-vitro differentiation, organoid, reprogramming and cellular rejuvenation experiments. In mixed samples, EpiDiff measures the extent of differentiation across all cellular lineages. In sorted cell populations, we find that DI increases with age and, underpinning this increase, we discover that many cell-types undergo a developmental epigenetic drift that is most prominent towards the mesenchymal lineage. When applied to normal-tissues, EpiDiff is predictive of carcinogenic exposure and cancer risk. In summary, EpiDiff is a unique, general and versatile tool that can accurately quantify the global differentiation state of a DNAm sample with broad applicability to many disciplines.
ER -