小柯机器人

美国圣路易斯华盛顿大学Samantha A. Morris课题组通过网络干扰和计算基因扰动解剖细胞身份。2023年2月8日，《自然》杂志在线发表了这项成果。

研究人员从单细胞多组学数据推断出基因调控网络，并在计算转录因子扰动中执行，并只用未扰动的野生型数据来模拟随之而来的细胞特性变化。研究人员将这种基于机器学习的方法CellOracle应用于已建立的范式——小鼠和人类造血，以及斑马鱼胚胎发生，并且研究人员正确地模拟了由于转录因子扰动而发生的表型变化。通过在发育中的斑马鱼中系统的计算转录因子干扰，研究人员模拟并实验验证了先前未报道的由noto（一个已建立的脊索调节因子）的缺失所引起的表型。此外，研究人员还鉴定了一个轴向中胚层调节因子lhx1a。总之，这些结果表明，CellOracle可以用于分析转录因子对细胞身份的调控，并可以为发育和分化提供机制见解。

据悉，细胞身份是由基因表达的复杂调控所控制的，表现为基因调控网络。

附：英文原文

Title: Dissecting cell identity via network inference and in silico gene perturbation

Author: Kamimoto, Kenji, Stringa, Blerta, Hoffmann, Christy M., Jindal, Kunal, Solnica-Krezel, Lilianna, Morris, Samantha A.

Issue&Volume: 2023-02-08

Abstract: Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks1. 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.

DOI: 10.1038/s41586-022-05688-9

Source: https://www.nature.com/articles/s41586-022-05688-9

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
投稿链接：http://www.nature.com/authors/submit_manuscript.html