DNA methylation meets lineage tracing: History, recent progress, and future directions

Lineage tracing techniques have been developed rapidly in the past decades by employing new genetic engineering tools. However, due to their invasive nature, these are difficult to apply to humans. Although endogenous DNA mutations can be used for in vivo lineage tracing in humans, their extremely low mutation rate presents substantial technical challenges. Epimutations on DNA methylation happen at a rate of about 0.001 per CpG site per division. Such rich and stable information enables high-resolution, noninvasive lineage tracing in humans, as recently achieved with both MethylTree and EPI-Clone. MethylTree is a computational innovation that accurately predicts cell lineages from single-cell DNA methylation data, be it genome-wide or targeted. EPI-Clone is a targeted approach that requires careful CpG panel selection for specific tissues, which has been validated in blood. In this review, we present an overview of related historical studies, discuss the development of both MethylTree and EPI-Clone, and compare these two approaches. Although EPI-Clone is more scalable and cheaper, MethylTree has a higher resolution and works directly across different tissues. We demonstrate here that MethylTree also works well with EPI-Clone data, thus providing a unified solution for epimutation-based lineage tracing. Finally, we highlight the advantages of epimutation-based lineage tracing, discuss future directions for tool development, and touch on considerations in biological applications. Epimutation-based lineage tracing opens up an exciting avenue for noninvasive lineage tracing in humans across many biological processes.

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Publication Details

Journal
Quantitative Biology
Published
2025-09-21
DOI
https://doi.org/10.1002/qub2.70017
Citations
1
Primary Topic
Epigenetics and DNA Methylation
Type
article
Field-Weighted Citation Impact
0.81

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article

DNA methylation meets lineage tracing: History, recent progress, and future directions

Shou‐Wen Wang, Rong Fu, Mengyang Chen
1 citations
Quantitative Biology
Epigenetics and DNA Methylation
0.81
article

DNA methylation meets lineage tracing: History, recent progress, and future directions

Shou‐Wen Wang, Rong Fu, Mengyang Chen
article en
1 citations

Abstract

Lineage tracing techniques have been developed rapidly in the past decades by employing new genetic engineering tools. However, due to their invasive nature, these are difficult to apply to humans. Although endogenous DNA mutations can be used for in vivo lineage tracing in humans, their extremely low mutation rate presents substantial technical challenges. Epimutations on DNA methylation happen at a rate of about 0.001 per CpG site per division. Such rich and stable information enables high-resolution, noninvasive lineage tracing in humans, as recently achieved with both MethylTree and EPI-Clone. MethylTree is a computational innovation that accurately predicts cell lineages from single-cell DNA methylation data, be it genome-wide or targeted. EPI-Clone is a targeted approach that requires careful CpG panel selection for specific tissues, which has been validated in blood. In this review, we present an overview of related historical studies, discuss the development of both MethylTree and EPI-Clone, and compare these two approaches. Although EPI-Clone is more scalable and cheaper, MethylTree has a higher resolution and works directly across different tissues. We demonstrate here that MethylTree also works well with EPI-Clone data, thus providing a unified solution for epimutation-based lineage tracing. Finally, we highlight the advantages of epimutation-based lineage tracing, discuss future directions for tool development, and touch on considerations in biological applications. Epimutation-based lineage tracing opens up an exciting avenue for noninvasive lineage tracing in humans across many biological processes.

Quantitative BiologyVol. 14(1)
Westlake University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 23%
Epigenetics and DNA Methylation
0.81
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