Work

A machine learning-based megakaryocyte identification system uncovers resident organs, markers, and functional diversity

Year: 2026
Type: article
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.
Language: English
Subfield: Hematology
Field: Medicine
Open Access status: hybrid
Estimated APC paid: $10,400
OpenAlex ID: W7160877605
FWCI: 8.956
Cites: 138
Cited by: 2
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