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通过网络推断和计算机基因扰动解析细胞身份。

Dissecting cell identity via network inference and in silico gene perturbation.

机构信息

Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA.

Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA.

出版信息

Nature. 2023 Feb;614(7949):742-751. doi: 10.1038/s41586-022-05688-9. Epub 2023 Feb 8.

DOI:10.1038/s41586-022-05688-9
PMID:36755098
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9946838/
Abstract

Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks. Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms-mouse and human haematopoiesis, and zebrafish embryogenesis-and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from the loss of noto, an established notochord regulator. Furthermore, we identify an axial mesoderm regulator, lhx1a. Together, these results show that CellOracle can be used to analyse the regulation of cell identity by transcription factors, and can provide mechanistic insights into development and differentiation.

摘要

细胞身份由基因表达的复杂调控决定，表现为基因调控网络。在这里，我们使用从单细胞多组学数据推断出的基因调控网络来进行计算机转录因子扰动，仅使用未受扰的野生型数据模拟随后的细胞身份变化。我们将这种基于机器学习的方法 CellOracle 应用于成熟的范例——小鼠和人类造血以及斑马鱼胚胎发生——我们正确地模拟了由于转录因子扰动而导致的表型报告变化。通过在发育中的斑马鱼中进行系统的计算机转录因子扰动，我们模拟并实验验证了先前未报道的表型，该表型是由 noto 缺失引起的，noto 是一种已建立的脊索调节剂。此外，我们确定了一个轴向中胚层调节剂 lhx1a。总之，这些结果表明 CellOracle 可用于分析转录因子对细胞身份的调控，并可提供对发育和分化的机制见解。

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