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研究利用单细胞表型探索细胞内基因互作
作者:小柯机器人 发布时间:2019/8/23 13:57:17

美国加州大学旧金山分校Jonathan S. Weissman、Luke A. Gilbert及Thomas M. Norman小组,在最新研究中利用丰富的单细胞表型探索细胞内基因互作。该研究于2019年8月23日发表于国际学术期刊《科学》上。

研究人员提出一种用于解释从转录表型到细胞状态(流形)的高维结构构建的新分析方法。利用这种方法,研究人员从Perturb-seq的数据库中发掘了基于生长功能以及获得性功能的基因互作图谱。对这种多样性的探索可以帮助有序的对调节途径,GI的分类原则(例如,鉴定抑制子)和协同作用的机理阐明,如CBL和CNN1可以协调驱动红细胞分化。最后,研究人员提出可以应用系统机器学习来预测相互作用,促进对更大的GI的探索。

如何从基因的组合表达中产生细胞和生物复杂性是生物学中的核心问题。高通量表型分析方法,如Perturb-seq(单细胞RNA测序联合CRISPR筛选),为大规模探索此类基因相互作用(GI)提供了机会。

附:英文原文

Title: Exploring genetic interaction manifolds constructed from rich single-cell phenotypes

Author: Thomas M. Norman, Max A. Horlbeck, Joseph M. Replogle, Alex Y. Ge, Albert Xu, Marco Jost, Luke A. Gilbert, Jonathan S. Weissman

Issue&Volume: Volume 365 Issue 6455

Abstract: How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-sequencing pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g., identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we applied recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.

DOI: 10.1126/science.aax4438

Source:https://science.sciencemag.org/content/365/6455/786

期刊信息
Science:《科学》,创刊于1880年。隶属于美国科学促进会,最新IF:41.037