麻省理工学院和哈佛大学的布罗德研究所Aviv Regev课题组揭示了利用高通量集合成像技术对核凝析物的蛋白序列决定因素进行表征。这一研究成果发表在2025年6月16日出版的国际学术期刊《自然—方法学》上。

该研究组开发了CondenSeq，这是一种高通量汇集成像和原位测序方法，用于测量蛋白质序列的需求形成核凝聚物的倾向。利用这些大规模的实验，小组在广泛的序列上下文中评估了数十个序列特征的影响，确定了几个具有高度一致的、与上下文无关的效果的特征，以及其他具有不太一致效果的特征。研究小组还发现了多种类型的凝析油，并发现了驱动它们形成的独特序列特性。他们的结果提供了蛋白质序列和核凝析物形成之间关系的系统概述，并建立了进一步在规模上剖析这些关系的一般方法。

据悉，生物分子凝聚物组织了许多亚细胞过程，并与包括神经变性和癌症在内的疾病有关。蛋白质序列内在地编码它们形成凝聚体的倾向，但调节这种行为的特定序列特征尚未在规模上系统地探索。

附：英文原文

Title: Characterizing protein sequence determinants of nuclear condensates by high-throughput pooled imaging with CondenSeq

Author: Kappel, Kalli, Strebinger, Daniel, Edmonds, KeHuan K., Chau-Duy-Tam VO, Samuel, Vockley, Christopher M., Biswas, Tridib, Farhi, Samouil L., Macrae, Rhiannon, Zhang, Feng, Regev, Aviv

Issue&Volume: 2025-06-16

Abstract: Biomolecular condensates organize numerous subcellular processes and have been implicated in diseases, including neurodegeneration and cancer. Protein sequences intrinsically encode their propensity to form condensates, but specific sequence features that regulate this behavior have not been systematically explored at scale. Here, we develop CondenSeq, a high-throughput pooled imaging with in situ sequencing approach to measure propensities of thousands of protein sequences to form nuclear condensates. Leveraging the large scale of these experiments, we evaluated the impacts of dozens of sequence features across a wide range of sequence contexts, identifying several features with highly consistent, context-independent effects and others with less-consistent effects. We also identified multiple classes of condensates and discovered distinct sequence properties that drive their formation. Our results provide a systematic overview of the relationships between protein sequences and nuclear condensate formation and establish a general approach for further dissecting these relationships at scale.

DOI: 10.1038/s41592-025-02726-y

Source: https://www.nature.com/articles/s41592-025-02726-y