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scHi-C和scRNA-seq数据的整合定义了不同的3D调节和生物环境依赖的细胞亚群

Yufan Zhou, Tian Li, Lavanya Choppavarapu

bioRxiv : the preprint server for biology

|October 24, 2023

概括

我们开发了一个新的算法来整合单细胞中的3D基因组结构和基因表达. 这种方法可以识别乳腺癌中的新细胞状态,并提供对药物耐药性的见解.

科学领域:

基因组学就是基因组学.
癌症生物学癌症生物学
计算生物学计算生物学

背景情况:

在单细胞分辨率上整合3D染色体结构和基因表达是一个重大挑战.
了解癌症中的细胞异质性需要多模式单细胞分析.

研究的目的:

开发一种用于整合单细胞Hi-C和RNA-seq数据的计算方法.
根据3D染色体组织和基因表达来识别和描述细胞亚群.
研究3D染色质结构在乳腺癌异质性和药物耐受性中的作用.

主要方法:

开发了多原子数据集成 (MUDI) 算法.
单细胞Hi-C (scHi-C) 和单细胞RNA测序 (scRNA-seq) 数据的整合.
在乳腺癌细胞模型中应用MUDI.

主要成果:

在MUDI算法成功地集成scHi-C和scRNA-seq数据在单细胞分辨率.
确定拓保护的关联域 (CAD) 作为特征单细胞3D染色体结构.
发现了20个拓集成子群 (TISP),定义了3D调节的乳腺癌细胞状态.
TISP与高循环乳腺癌持久细胞的关联以及染色质修饰酶的潜在调节.

结论:

MUDI算法使3D基因组架构和单细胞基因表达的前所未有的整合成为可能.
新型TISP提供了对耐药癌细胞3D调控异质性的机制性见解.
这种方法提高了我们对癌细胞可塑性和潜在治疗点的理解.

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相关概念视频

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...