小柯机器人

欧洲生物信息学研究所Isidro Cortés-Ciriano研究团队取得一项新突破。他们利用高通量单细胞分析数据集对体细胞突变进行了从头检测。这一研究成果发表在2023年7月6日出版的国际学术期刊《自然—生物技术》上。

研究人员研发了SComatic，这是一种旨在直接检测单细胞转录组和ATAC-seq（转座酶可及染色质序列测定）数据集中体细胞突变的算法，而无需匹配批量或单细胞DNA测序数据。SComatic使用过滤器和在非肿瘤样品上参数化的统计测试将体细胞突变与多态性、RNA编辑和伪影区分开来。使用来自688个单细胞RNA-seq（scRNA-seq）和单细胞ATAC-seq（scATAC-seq）数据集的260万个单细胞、跨癌症和非肿瘤样本，研究表明SComatic可以准确地检测单细胞中的突变，即使是来自多克隆组织的分化细胞，这些细胞不适合使用现有方法进行突变检测。

根据匹配的基因组测序和scRNA-seq数据进行验证，SComatic在不同数据集中的F1得分在0.6到0.7之间，而第二好的方法则为0.2到0.4。总之，SComatic可进行从头突变特征分析，并可以在单细胞分辨率水平研究克隆异质性和突变可能。

据了解，在单细胞分辨率表征体细胞突变对于研究癌症进展、克隆镶嵌和细胞可塑性至关重要。

附：英文原文

Title: De novo detection of somatic mutations in high-throughput single-cell profiling data sets

Author: Muyas, Francesc, Sauer, Carolin M., Valle-Incln, Jose Espejo, Li, Ruoyan, Rahbari, Raheleh, Mitchell, Thomas J., Hormoz, Sahand, Corts-Ciriano, Isidro

Issue&Volume: 2023-07-06

Abstract: Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2–0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.

DOI: 10.1038/s41587-023-01863-z

Source: https://www.nature.com/articles/s41587-023-01863-z

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex