英国欧洲生物信息学研究所Sarah A. Teichmann和Oliver Stegl研究团队合作取得一项新成果。他们利用Cardelino，计算集成的体细胞克隆亚结构和单细胞转录组。这一研究成果于2020年3月16日发表在《自然-方法学》杂志上。

他们介绍了cardelino（https://github.com/single-cell-genetics/cardelino），这是一种计算方法，用于使用单细胞RNA-测序（scRNA-seq）分析推断单个细胞的克隆树构型和起源克隆。Cardelino可以灵活地整合信息，这些信息来自基于大量外显子组测序数据推断出的不完善克隆树以及来自scRNA-seq数据的稀疏变异等位基因。他们将cardelino应用于已发布的癌症数据集，以及来自32个人类皮肤成纤维细胞系的新生成的匹配scRNA-seq和exome-seq数据，从而确定了来自不同体细胞克隆的细胞之间数百种差异表达的基因。这些基因通常集中在细胞周期和增殖途径中，表明细胞分裂基因在健康皮肤的体细胞进化中的作用。

据悉，批量和单细胞DNA测序已能够从体细胞变异的频率和共现模式重建体细胞组织的克隆亚结构。但是，尚未建立表征克隆之间表型变异的方法。

附：英文原文

Title: Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes

Author: Davis J. McCarthy, Raghd Rostom, Yuanhua Huang, Daniel J. Kunz, Petr Danecek, Marc Jan Bonder, Tzachi Hagai, Ruqian Lyu, Wenyi Wang, Daniel J. Gaffney, Benjamin D. Simons, Oliver Stegle, Sarah A. Teichmann

Issue&Volume: 2020-03-16

Abstract: Bulk and single-cell DNA sequencing has enabled reconstructing clonal substructures of somatic tissues from frequency and cooccurrence patterns of somatic variants. However, approaches to characterize phenotypic variations between clones are not established. Here we present cardelino (https://github.com/single-cell-genetics/cardelino), a computational method for inferring the clonal tree configuration and the clone of origin of individual cells assayed using single-cell RNA-seq (scRNA-seq). Cardelino flexibly integrates information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. We apply cardelino to a published cancer dataset and to newly generated matched scRNA-seq and exome-seq data from 32 human dermal fibroblast lines, identifying hundreds of differentially expressed genes between cells from different somatic clones. These genes are frequently enriched for cell cycle and proliferation pathways, indicating a role for cell division genes in somatic evolution in healthy skin.

DOI: 10.1038/s41592-020-0766-3

Source: https://www.nature.com/articles/s41592-020-0766-3