近日，西班牙巴塞罗那科技学院Holger Heyn等研究人员对用于细胞图谱计划的单细胞RNA测序实验方案进行基准测试。该研究于2020年4月6日在线发表于《自然—生物技术》。
研究人员表示，单细胞RNA测序（scRNA-seq）是表征样品中单个细胞的转录组的主要技术。最新的实验方案可扩展到数千个细胞，并被用于绘制组织、器官和生物体的细胞图谱。但是，这些实验方案在RNA捕获效率、偏倚、规模和成本方面有很大不同，并且它们在不同应用中的相对优势尚不清楚。
研究人员生成了基准数据集，可根据其全面描述细胞类型和状态的能力来系统性评估实验方案。研究人员进行了一项多中心研究，比较了13种常用scRNA-seq和单核RNA-seq实验方案。比较分析显示实验方案的性能存在明显差异。这些实验方案在文库的复杂性及其检测细胞类型标记的能力方面有所不同，从而影响了它们的预测价值和与参考细胞图谱整合的适用性。这些结果为独立研究人员和团队研究项目（如人类细胞图谱计划）提供了指导。
附：英文原文
Title: Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
Author: Elisabetta Mereu, Atefeh Lafzi, Catia Moutinho, Christoph Ziegenhain, Davis J. McCarthy, Adrin lvarez-Varela, Eduard Batlle, Sagar, Dominic Grn, Julia K. Lau, Stphane C. Boutet, Chad Sanada, Aik Ooi, Robert C. Jones, Kelly Kaihara, Chris Brampton, Yasha Talaga, Yohei Sasagawa, Kaori Tanaka, Tetsutaro Hayashi, Caroline Braeuning, Cornelius Fischer, Sascha Sauer, Timo Trefzer, Christian Conrad, Xian Adiconis, Lan T. Nguyen, Aviv Regev, Joshua Z. Levin, Swati Parekh, Aleksandar Janjic, Lucas E. Wange, Johannes W. Bagnoli, Wolfgang Enard, Marta Gut, Rickard Sandberg, Itoshi Nikaido, Ivo Gut, Oliver Stegle, Holger Heyn
Issue&Volume: 2020-04-06
Abstract: Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing the transcriptomes of individual cells in a sample. The latest protocols are scalable to thousands of cells and are being used to compile cell atlases of tissues, organs and organisms. However, the protocols differ substantially with respect to their RNA capture efficiency, bias, scale and costs, and their relative advantages for different applications are unclear. In the present study, we generated benchmark datasets to systematically evaluate protocols in terms of their power to comprehensively describe cell types and states. We performed a multicenter study comparing 13 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied to a heterogeneous reference sample resource. Comparative analysis revealed marked differences in protocol performance. The protocols differed in library complexity and their ability to detect cell-type markers, impacting their predictive value and suitability for integration into reference cell atlases. These results provide guidance both for individual researchers and for consortium projects such as the Human Cell Atlas.
DOI: 10.1038/s41587-020-0469-4
Source: https://www.nature.com/articles/s41587-020-0469-4
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
本期文章：《自然—生物技术》：Online/在线发表
