Nucleus

Bioinformatics

Biology

Operating system

Profiling (computer programming)

Information retrieval

Computer science

Computational biology

Search engine indexing

Cell biology

Nature Protocols

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. This protocol presents an optimized version of the single-cell combinatorial indexing RNA sequencing protocol that is faster, less expensive and suitable for profiling tissues rich in RNases, as well as a ‘Tiny-Sci’ protocol for limited input samples.

Optimized single-nucleus transcriptional profiling by combinatorial indexing