分子生物学
IVD分子诊断
细胞培养与分析
蛋白研究
细胞因子
重组蛋白
抗体
高通量测序建库
病原检测UCF系列
生物医药
工具酶
抑制剂激活剂与常用试剂
仪器
耗材

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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