小柯机器人

美国卡耐基梅隆大学Jian Ma研究团队开发出识别单细胞3D基因组亚区的新方法scGHOST。2024年4月8日，《自然—方法学》杂志在线发表了这项成果。

研究人员介绍了一种使用图嵌入和受限随机漫步采样的单细胞亚区室注释方法——scGHOST。将scGHOST应用于单细胞Hi-C（scHi-C）数据和单细胞三维基因组成像得到的接触图，证明了单细胞亚区室的可靠识别，为了解细胞间核子亚区室的变异性提供了思路。利用来自复杂组织的scHi-C数据，scGHOST确定了与各种细胞类型和发育阶段的基因转录相关的，细胞类型特异性或等位基因特异性亚细胞区，表明了单细胞亚细胞区的功能意义。

据悉，scHi-C技术可以从单个细胞的3D基因组组织中探测全基因组细胞间的变异性。目前已开发出基于scHi-C的计算方法来揭示单细胞三维基因组特征，包括A/B区、拓扑关联域和染色质环。然而，目前还没有注释单细胞亚区室的方法，而这对于了解染色体在单细胞中的空间定位非常重要。

附：英文原文

Title: scGHOST: identifying single-cell 3D genome subcompartments

Author: Xiong, Kyle, Zhang, Ruochi, Ma, Jian

Issue&Volume: 2024-04-08

Abstract: Single-cell Hi-C (scHi-C) technologies allow for probing of genome-wide cell-to-cell variability in three-dimensional (3D) genome organization from individual cells. Computational methods have been developed to reveal single-cell 3D genome features based on scHi-C, including A/B compartments, topologically associating domains and chromatin loops. However, no method exists for annotating single-cell subcompartments, which is important for understanding chromosome spatial localization in single cells. Here we present scGHOST, a single-cell subcompartment annotation method using graph embedding with constrained random walk sampling. Applications of scGHOST to scHi-C data and contact maps derived from single-cell 3D genome imaging demonstrate reliable identification of single-cell subcompartments, offering insights into cell-to-cell variability of nuclear subcompartments. Using scHi-C data from complex tissues, scGHOST identifies cell-type-specific or allele-specific subcompartments linked to gene transcription across various cell types and developmental stages, suggesting functional implications of single-cell subcompartments. 

DOI: 10.1038/s41592-024-02230-9

Source: https://www.nature.com/articles/s41592-024-02230-9

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex