Systematic comparison of single-cell and single-nucleus RNA-sequencing methods

The scale and capabilities of single-cell RNA-sequencing methods have expanded rapidly in recent years, enabling major discoveries and large-scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, researchers from the Broad Institute directly compare seven methods for single-cell and/or single-nucleus profiling-selecting representative methods based on their usage and the researchers expertise and resources to prepare libraries-including two low-throughput and five high-throughput methods. They tested the methods on three types of samples: cell lines, peripheral blood mononuclear cells and brain tissue, generating 36 libraries in six separate experiments in a single center. To directly compare the methods and avoid processing differences introduced by the existing pipelines, the researchers developed scumi, a flexible computational pipeline that can be used with any single-cell RNA-sequencing method. They evaluated the methods for both basic performance, such as the structure and alignment of reads, sensitivity and extent of multiplets, and for their ability to recover known biological information in the samples.

a, Samples. b, scRNA-seq methods. c, Computational pipeline summary. Cell line mixtures were tested with all methods. PBMCs were tested with all methods except sci-RNA-seq. Cortex nuclei were tested with Smart-seq2, 10x Chromium, Drop-seq (aka DroNc-seq for nuclei) and sci-RNA-seq.