小柯机器人

法国巴黎大学Antonio Rausell研究组开发出新方法，可在单细胞水平提取基因特征并识别细胞身份。这一研究成果于2021年4月29日在线发表在国际学术期刊《自然—生物技术》上。

研究人员报道了Cell-ID，这是一种从单细胞测序数据中可靠提取每细胞基因特征的无聚类多元统计方法。研究人员将Cell-ID应用于来自多个人类和小鼠样本的数据，包括血细胞、胰岛和气道、肠和嗅觉上皮细胞，以及全面的小鼠细胞图集数据集。研究人员证明，Cell-ID特征可在不同的供体、起源组织、物种和单细胞组学技术之间重现，并可用于自动细胞类型注释和跨数据集的细胞匹配。Cell-ID可改善单个细胞水平的生物学解释，从而能够发现以前未表征的稀有细胞类型或细胞状态。Cell-ID已作为开源R软件包发放。

据悉，由于与高通量单细胞测序相关的随机性，当前探索细胞类型多样性的方法依赖于基于聚类的计算方法，这些异质性的表征停留在细胞亚群而不是完整的单细胞分辨率水平。

附：英文原文

Title: Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID

Author: Akira Cortal, Loredana Martignetti, Emmanuelle Six, Antonio Rausell

Issue&Volume: 2021-04-29

Abstract: Because of the stochasticity associated with high-throughput single-cell sequencing, current methods for exploring cell-type diversity rely on clustering-based computational approaches in which heterogeneity is characterized at cell subpopulation rather than at full single-cell resolution. Here we present Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. We applied Cell-ID to data from multiple human and mouse samples, including blood cells, pancreatic islets and airway, intestinal and olfactory epithelium, as well as to comprehensive mouse cell atlas datasets. We demonstrate that Cell-ID signatures are reproducible across different donors, tissues of origin, species and single-cell omics technologies, and can be used for automatic cell-type annotation and cell matching across datasets. Cell-ID improves biological interpretation at individual cell level, enabling discovery of previously uncharacterized rare cell types or cell states. Cell-ID is distributed as an open-source R software package. Cell-ID facilitates the analysis of cell-type heterogeneity and cell identity across multiple samples at the single-cell level.

DOI: 10.1038/s41587-021-00896-6

Source: https://www.nature.com/articles/s41587-021-00896-6

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