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An optimized protocol for single cell transcriptional profiling by combinatorial indexing

Authors :
Martin, Beth K.
Qiu, Chengxiang
Nichols, Eva
Phung, Melissa
Green-Gladden, Rula
Srivatsan, Sanjay
Blecher-Gonen, Ronnie
Beliveau, Brian J.
Trapnell, Cole
Cao, Junyue
Shendure, Jay
Publication Year :
2021

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 three-level sci-RNA-seq protocol that is faster, higher yield, more robust, and more sensitive, than the original sci-RNA-seq3 protocol, with reagent costs on the order of 1 cent per cell or less. 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 where input is extremely limited.<br />Comment: fixed a couple errors

Subjects

Subjects :
Quantitative Biology - Genomics

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2110.15400
Document Type :
Working Paper