A machine learning-based megakaryocyte identification system uncovers resident organs, markers, and functional diversity
Meijuan Xia, Yezi Ma, Yifei Cai, Jingjing Zhao, Yao Zhong, Sibei Guo, Minmin Li, Pei Su, Biao Shen, Huizhen He, Xiaoyuan Chen, Lin Zheng, Le Li, Ziqi Huo, Wen Zhou, Fei Wang, Cuicui Liu, Hongtao Wang
Journal:DEVELOPMENTAL CELL
IF:9.2
DOI:10.1016/j.devcel.2026.04.005
PMID:42119559
Published:2026-05-11
research field:生物医学中的机器学习免疫学血液学转录组学发育生物学系统生物学
Abstract
The organ- and stage-specific diversity of megakaryocytes (MKs) has prompted a reassessment of their distribution and functions. By integrating single-cell transcriptomic data across multiple organs and developmental stages, we identified previously unreported MK and platelet markers, including Tnik , a key regulator of MK function and platelet production. Using these markers alongside established ones, we developed a machine learning-based MK identification system (MKIDS) that enables MK detection in the brain, heart, and placenta in mice and humans. Functional studies demonstrated that brain-resident MKs are essential for neural development, underscoring organ-specific roles of MKs in regulating tissue development and function. Transcriptomic integration of MKs across organs and stages, with functional validation, revealed a developmental shift in platelet production—from a mitochondria-low to a mitochondria-enriched subpopulation. Our findings offer a transformative perspective on the MK system, highlighting its cellular diversity, functional complexity, and developmental dynamics.
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