Scalable methylome-transcriptome profiling resolves clonal organization in human hematopoiesis
Abstract
Understanding human tissue maintenance and remodeling requires linking clonal ancestry to molecular state, yet scalable joint measurement remains challenging. Here, we introduce Droplet-MT and an integrated computational workflow for joint transcriptome and methylome profiling at ~10,000 cells per library. Methylation epimutations enable mutation-independent clonal reconstruction from high-coverage methylomes, while reference-based label transfer assigns cells to established clones at ~0.2% CpG coverage, reducing sequencing cost. Clone-aggregated somatic variants provide genetic annotation without additional single-cell genotyping. Across ~21,000 paired profiles from healthy blood and longitudinal acute myeloid leukemia, we resolved small and expanded clones with heterogeneous lineage output. In leukemia, the framework distinguished changes in malignant clonal abundance, state remodeling within persistent clones, and recovery of healthy polyclonal hematopoiesis. Malignant monocytes remained transcriptionally and epigenetically distinct from healthy and recovery-associated cells despite treatment-associated remodeling. Droplet-MT enables scalable analysis of human tissues by linking clonal organization with molecular state and retrospective genetic annotation.
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BibTeX
@misc{shao2026scalable,
title = {{Scalable methylome-transcriptome profiling resolves clonal organization in human hematopoiesis}},
author = {J. Shao and M. Chen and Y. Liu and J. Sun and G. Zhang and Z.-Y. Yan and H. Lin and M. Ren and S. Pei and J. Peng and Y. Gong and S. WANG},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.08.757494},
url = {https://www.biorxiv.org/content/10.64898/2026.10.08.757494v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Scalable methylome-transcriptome profiling resolves clonal organization in human hematopoiesis
AU - J. Shao
AU - M. Chen
AU - Y. Liu
AU - J. Sun
AU - G. Zhang
AU - Z.-Y. Yan
AU - H. Lin
AU - M. Ren
AU - S. Pei
AU - J. Peng
AU - Y. Gong
AU - S. WANG
PY - 2026
DA - 2026/10/08
PB - bioRxiv
DO - 10.64898/2026.10.08.757494
UR - https://www.biorxiv.org/content/10.64898/2026.10.08.757494v1
AB - Understanding human tissue maintenance and remodeling requires linking clonal ancestry to molecular state, yet scalable joint measurement remains challenging. Here, we introduce Droplet-MT and an integrated computational workflow for joint transcriptome and methylome profiling at ~10,000 cells per library. Methylation epimutations enable mutation-independent clonal reconstruction from high-coverage methylomes, while reference-based label transfer assigns cells to established clones at ~0.2% CpG coverage, reducing sequencing cost. Clone-aggregated somatic variants provide genetic annotation without additional single-cell genotyping. Across ~21,000 paired profiles from healthy blood and longitudinal acute myeloid leukemia, we resolved small and expanded clones with heterogeneous lineage output. In leukemia, the framework distinguished changes in malignant clonal abundance, state remodeling within persistent clones, and recovery of healthy polyclonal hematopoiesis. Malignant monocytes remained transcriptionally and epigenetically distinct from healthy and recovery-associated cells despite treatment-associated remodeling. Droplet-MT enables scalable analysis of human tissues by linking clonal organization with molecular state and retrospective genetic annotation.
ER -