Optimized single-nucleus transcriptional profiling by combinatorial indexing

通过组合索引优化单核转录分析

阅读:5
作者:Beth K Martin, Chengxiang Qiu, Eva Nichols, Melissa Phung, Rula Green-Gladden, Sanjay Srivatsan, Ronnie Blecher-Gonen, Brian J Beliveau, Cole Trapnell, Junyue Cao, Jay Shendure

Abstract

Single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) is a powerful method for recovering gene expression data from an exponentially scalable number of individual cells or nuclei. However, sci-RNA-seq is a complex protocol that has historically exhibited variable performance on different tissues, as well as lower sensitivity than alternative methods. Here, we report a simplified, optimized version of the sci-RNA-seq protocol with three rounds of split-pool indexing that is faster, more robust and more sensitive and has a higher yield than the original protocol, with reagent costs on the order of 1 cent per cell or less. The total hands-on time from nuclei isolation to final library preparation takes 2-3 d, depending on the number of samples sharing the experiment. The improvements also allow RNA profiling from tissues rich in RNases like older mouse embryos or adult tissues that were problematic for the original method. We showcase the optimized protocol via whole-organism analysis of an E16.5 mouse embryo, profiling ~380,000 nuclei in a single experiment. Finally, we introduce a 'Tiny-Sci' protocol for experiments in which input material is very limited.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。