美国哈佛医学院Peter V. Kharchenko团队近期取得重要工作进展，他们研究开发了单细胞转录组体细胞拷贝数变化的单倍型感知分析方法。相关研究成果2022年9月26日在线发表于《自然—生物技术》杂志上。

研究人员提出了一种计算方法，Numbat，它将基于群体的定相获得的单倍型信息与等位基因和表达信号相结合，以增强对scRNA-seq拷贝数变化的检测。Numbat利用亚克隆之间的进化关系来迭代推断单细胞拷贝数概况和肿瘤克隆系统发育。对包括多发性骨髓瘤、胃癌、乳腺癌和甲状腺癌在内的22个肿瘤样本的分析表明，Numbat可以重建肿瘤拷贝数谱并精确识别肿瘤微环境中的恶性细胞。研究人员确定了与肿瘤进展和治疗耐药性相关的转录特征的遗传亚群。Numbat既不需要样本匹配的DNA数据，也不需要先验基因分型，适用于广泛的实验环境和癌症类型。

据介绍，基因组不稳定和转录程序的异常改变都在癌症中起着重要作用。单细胞RNA测序（scRNA-seq）有潜力在单一试验中研究肿瘤异质性的遗传和非遗传来源。

附：英文原文

Title: Haplotype-aware analysis of somatic copy number variations from single -cell transcriptomes

Author: Gao, Teng, Soldatov, Ruslan, Sarkar, Hirak, Kurkiewicz, Adam, Biederstedt, Evan, Loh, Po-Ru, Kharchenko, Peter V.

Issue&Volume: 2022-09-26

Abstract: Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.

DOI: 10.1038/s41587-022-01468-y

Source: https://www.nature.com/articles/s41587-022-01468-y