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ENTRAIN:将轨迹推断和基因调节网络与空间数据集成在一起,以共同定位确定细胞命运的受体-连接体相互作用

Wunna Kyaw^1,2, Ryan C Chai^2,3, Weng Hua Khoo^2,3

¹Precision Immunology Program, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.

Bioinformatics (Oxford, England)

|December 19, 2023

概括

一种新的计算方法,ENTRAIN,识别了影响细胞分化的微环境信号. 这种方法将细胞命运潜力映射到细胞相互作用中,揭示细胞发育的关键驱动因素.

科学领域:

计算生物学计算生物学
发展生物学发展生物学
单细胞基因组学单细胞基因组学

背景情况:

单细胞基因表达造型是推断细胞分化轨迹的标准.
现有的方法往往无法捕获来自细胞利基的关键微环境信号.
了解这些利基信号对于完整了解细胞命运决定至关重要.

研究的目的:

开发一种计算方法,将微环境信号集成到轨迹推断中.
识别和量化细胞外信号对细胞分化的贡献.
将细胞命运潜力与特定的细胞相互作用联系起来.

主要方法:

引入了环境意识轨迹推理 (ENTRAIN),一种新的计算方法.
集成的轨迹推断与配体-受体对基因调节网络.
应用ENTRAIN对单细胞骨髓和空间解析的胚胎神经发生学数据集.

主要成果:

ENTRAIN成功地识别了驱动分化轨迹的细胞外信号.
该方法评估了已识别的信号的相对贡献.
在各种数据集上得到验证,揭示了细胞分化已知的和新的环境驱动因素.

结论:

ENTRAIN为将微环境背景纳入细胞命运研究提供了一个强大的框架.
该方法使细胞命运潜力与细胞相互作用的空间映射成为可能.
ENTRAIN促进了人们对利基信号如何协调细胞分化的理解.

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