Precision Immunology Program, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.
St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW Sydney, Darlinghurst, NSW 2010, Australia.
Bioinformatics. 2023 Dec 1;39(12). doi: 10.1093/bioinformatics/btad765.
Cell fate is commonly studied by profiling the gene expression of single cells to infer developmental trajectories based on expression similarity, RNA velocity, or statistical mechanical properties. However, current approaches do not recover microenvironmental signals from the cellular niche that drive a differentiation trajectory.
We resolve this with environment-aware trajectory inference (ENTRAIN), a computational method that integrates trajectory inference methods with ligand-receptor pair gene regulatory networks to identify extracellular signals and evaluate their relative contribution towards a differentiation trajectory. The output from ENTRAIN can be superimposed on spatial data to co-localize cells and molecules in space and time to map cell fate potentials to cell-cell interactions. We validate and benchmark our approach on single-cell bone marrow and spatially resolved embryonic neurogenesis datasets to identify known and novel environmental drivers of cellular differentiation.
ENTRAIN is available as a public package at https://github.com/theimagelab/entrain and can be used on both single-cell and spatially resolved datasets.
通常通过分析单细胞的基因表达来研究细胞命运，以根据表达相似性、RNA 速度或统计力学性质推断发育轨迹。然而，目前的方法无法从驱动分化轨迹的细胞生态位中恢复微观环境信号。
我们通过环境感知轨迹推断 (ENTRAIN) 解决了这个问题，这是一种计算方法，它将轨迹推断方法与配体-受体对基因调控网络集成在一起，以识别细胞外信号并评估它们对分化轨迹的相对贡献。ENTRAIN 的输出可以叠加在空间数据上，以在空间和时间上共定位细胞和分子，将细胞命运潜力映射到细胞-细胞相互作用上。我们在单细胞骨髓和空间分辨胚胎神经发生数据集上验证和基准测试了我们的方法，以识别已知和新的细胞分化的环境驱动因素。
ENTRAIN 可在 https://github.com/theimagelab/entrain 上作为公共包获得，并可用于单细胞和空间分辨数据集。
