凝聚态函数扰动的深度学习
凝聚态函数扰动的深度学习
作者： 小柯机器人 发布时间：2026/6/5 17:32:09
本期文章：《细胞》：Online/在线发表
普林斯顿大学Clifford P. Brangwynne课题组宣布他们提出了凝聚态函数扰动的深度学习。相关论文于2026年6月4日发表于国际顶尖学术期刊《细胞》杂志上。
为了弥补这一差距，课题组人员开发了一个基于神经网络的框架——Deep-Phase（相分离凝聚物的深度学习），该框架将显微镜图像主题化，以直接测量相关生化过程中药理学改变导致的凝聚物形态变化。课题组使用Deep-Phase来精确量化多相核苷的时间和浓度依赖性结构扰动，并表明它们与抑制核糖体RNA （rRNA）转录和加工的药物的效力紧密耦合。在化学筛选中应用Deep-Phase，研究人员鉴定了一种独特的核核形态，并发现了DNA拓扑异构酶在rRNA加工中的作用。这种形态学的机制研究提供了对核仁亚室之间的界面如何维持的见解。小组展示了Deep-Phase对不同细胞系、标记技术和凝聚物的适应性，为连接分子途径和细胞中尺度组织提供了一个强大的平台。
研究人员表示，生物分子凝聚体将细胞内部分隔开来以组织复杂的功能，然而将凝聚体内部的分子相互作用与其中尺度组织联系起来仍然是一个主要挑战。
附：英文原文
Title: Deep learning of functional perturbations from condensate morphology
Author: Anita Donlic, Troy J. Comi, Sofia A. Quinodoz, Nima Jaberi-Lashkari, Krist Antunes Fernandes, Lifei Jiang, Lennard W. Wiesner, Ai Ing Lim, Clifford P. Brangwynne
Issue&Volume: 2026-06-04
Abstract: Biomolecular condensates compartmentalize the interior of cells to organize complex functions, yet linking molecular interactions within condensates to their mesoscale organization remains a major challenge. To bridge this gap, we developed a neural-network-based framework—Deep-Phase (deep learning of phase-separated condensates)—that uses microscopy images to directly measure condensate morphology changes resulting from pharmacological alterations in associated biochemical processes. We use Deep-Phase to precisely quantify time- and concentration-dependent structural perturbations to the multiphase nucleolus and show that they are tightly coupled to potencies of drugs inhibiting ribosomal RNA (rRNA) transcription and processing. Applying Deep-Phase in a chemical screen, we identify a unique nucleolar morphology and discover a role for a DNA topoisomerase in rRNA processing. Mechanistic studies of this morphology provide insights into how the interfaces between nucleolar sub-compartments are maintained. We demonstrate Deep-Phase’s adaptability to diverse cell lines, labeling techniques, and condensates, offering a powerful platform for connecting molecular pathways to cellular mesoscale organization.
DOI: 10.1016/j.cell.2026.05.010
Source: https://www.cell.com/cell/abstract/S0092-8674(26)00569-6