小柯机器人

美国加州理工学院Yuki Oka和美国德克萨斯大学西南医学中心Allan-Hermann Pool共同合作，近期取得重要工作进展。他们研究利用优化的转录组参考恢复缺失的单细胞RNA测序数据。相关研究成果2023年9月11日在线发表于《自然—方法学》杂志上。

据介绍，单细胞RNA测序（scRNA-seq）是表征细胞多样性和在整个生物学中产生假设的不可或缺的工具。基于液滴的scRNA-seq数据集通常缺乏可以用其他方法检测的基因表达数据。

研究人员发现，目前观察到的敏感性缺陷源于三个方面：（1）3′基因末端的注释不良；（2） 内含子读取整合问题；和（3）基因重叠引起的阅读损失。研究人员表明，缺失的基因表达数据可以通过优化scRNA-seq的参考转录组来恢复，方法是通过恢复错误的基因间读数、实施混合前mRNA定位策略和解决基因重叠。通过对小鼠和人类组织数据的不同收集，研究人员证明，参考优化可以显著提高细胞图谱的分辨率，并揭示缺失的细胞类型和标记基因。

总之，这一研究结果表明，转录组参考文献需要针对scRNA-seq分析进行优化，并保证对先前发表的数据集和细胞图谱进行重新分析。

附：英文原文

Title: Recovery of missing single-cell RNA-sequencing data with optimized transcriptomic references

Author: Pool, Allan-Hermann, Poldsam, Helen, Chen, Sisi, Thomson, Matt, Oka, Yuki

Issue&Volume: 2023-09-11

Abstract: Single-cell RNA-sequencing (scRNA-seq) is an indispensable tool for characterizing cellular diversity and generating hypotheses throughout biology. Droplet-based scRNA-seq datasets often lack expression data for genes that can be detected with other methods. Here we show that the observed sensitivity deficits stem from three sources: (1) poor annotation of 3′ gene ends; (2) issues with intronic read incorporation; and (3) gene overlap-derived read loss. We show that missing gene expression data can be recovered by optimizing the reference transcriptome for scRNA-seq through recovering false intergenic reads, implementing a hybrid pre-mRNA mapping strategy and resolving gene overlaps. We demonstrate, with a diverse collection of mouse and human tissue data, that reference optimization can substantially improve cellular profiling resolution and reveal missing cell types and marker genes. Our findings argue that transcriptomic references need to be optimized for scRNA-seq analysis and warrant a reanalysis of previously published datasets and cell atlases. This paper presents an improved approach for mapping single-cell RNA-seq reads with optimized transcriptomic references, which markedly recovers previously missing gene expression data.

DOI: 10.1038/s41592-023-02003-w

Source: https://www.nature.com/articles/s41592-023-02003-w

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex