基于图像的综合表型分析揭示多维的、疾病特异性的变异效应
基于图像的综合表型分析揭示多维的、疾病特异性的变异效应
作者： 小柯机器人 发布时间：2026/5/13 17:31:04
本期文章：《细胞》：Online/在线发表
2026年5月12日出版的《细胞》杂志发表了美国科学家的一项最新研究成果。来自华盛顿大学的Douglas M. Fowler小组的论文发现了基于图像的综合表型分析揭示了多维的、疾病特异性的变异效应。
该课题组研究人员开发了变异原位测序（VIS-seq），这是一种基于图像的混合方法，可测量不同细胞类型中变异对分子和细胞表型的影响。将VIS-seq应用于约3000个LMNA和PTEN变异，获得了高维形态图谱，捕获了蛋白质丰度、定位、活性和细胞结构的变化。VIS-seq鉴定出连接子域LMNA变异的一个子集，该子集增加了核环状度，而聚合或低丰度的杆状子域变异则降低了核环状度。
VIS-seq还发现了自闭症相关的PTEN变异，这些变异错误定位并准确区分了自闭症相关、肿瘤综合征相关和gnomAD控制变异。大多数变异影响了一个多维的表型连续体，而不是由任何单一的功能读数再现。VIS-seq通过将变异与细胞图像联系起来，阐明了变异效应如何从分子级联到亚细胞结构再到细胞，为解决变异功能的复杂性提供了一个框架。
研究人员表示，遗传变异产生复杂的表型效应，混淆了当前的分析和预测模型。
附：英文原文
Title: Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects
Author: Sriram Pendyala, Katie Partington, Nicholas Bradley, Abbye E. McEwen, Gwenneth Straub, Hyeon-Jin Kim, Shawn Fayer, Daniel Lee Holmes, Katherine A. Sitko, Riddhiman K. Garge, Ziyu R. Wang, Melinda K. Wheelock, Allyssa J. Vandi, Rachel L. Powell, Clayton E. Friedman, Evan McDermot, Nishka Kishore, Frederick P. Roth, Alan F. Rubin, Kai-Chun Yang, Lea M. Starita, William S. Noble, Douglas M. Fowler
Issue&Volume: 2026-05-12
Abstract: Genetic variants produce complex phenotypic effects that confound current assays and predictive models. We developed variant in situ sequencing (VIS-seq), a pooled, image-based method measuring variant effects on molecular and cellular phenotypes in diverse cell types. Applying VIS-seq to ～3,000 LMNA and PTEN variants yielded high-dimensional morphological profiles capturing changes in protein abundance, localization, activity, and cell architecture. VIS-seq identified a subset of linker-subdomain LMNA variants that increase nuclear circularity, in contrast to aggregating or low-abundance rod-subdomain variants that decrease circularity. VIS-seq also identified autism-associated PTEN variants that mislocalize and accurately distinguished autism-linked from tumor syndrome-linked and gnomAD control variants. Most variants impacted a multidimensional phenotypic continuum not recapitulated by any single functional readout. By linking variants to cell images at scale, VIS-seq illuminates how variant effects cascade from molecules to subcellular structures to cells, providing a framework for resolving the complexity of variant function.
DOI: 10.1016/j.cell.2026.04.031
Source: https://www.cell.com/cell/abstract/S0092-8674(26)00466-6