High sensitivity single cell RNA sequencing with split pool barcoding

Abstract

Single cell RNA sequencing (scRNA-seq) has become a core tool for researchers to understand biology. As scRNA-seq has become more ubiquitous, many applications demand higher scalability and sensitivity. Split-pool combinatorial barcoding makes it possible to scale projects to hundreds of samples and millions of cells, overcoming limitations of previous droplet based technologies. However, there is still a need for increased sensitivity for both droplet and combinatorial barcoding based scRNA-seq technologies. To meet this need, here we introduce an updated combinatorial barcoding method for scRNA-seq with dramatically improved sensitivity. To assess performance, we profile a variety of sample types, including cell lines, human peripheral blood mononuclear cells (PBMCs), mouse brain nuclei, and mouse liver nuclei. When compared to the previously best performing approach, we find up to a 2.6-fold increase in unique transcripts detected per cell and up to a 1.8-fold increase in genes detected per cell. These improvements to transcript and gene detection increase the resolution of the resulting data, making it easier to distinguish cell types and states in heterogeneous samples. Split-pool combinatorial barcoding already enables scaling to millions of cells, the ability to perform scRNA-seq on previously fixed and frozen samples, and access to scRNA-seq without the need to purchase specialized lab equipment. Our hope is that by combining these previous advantages with the dramatic improvements to sensitivity presented here, we will elevate the standards and capabilities of scRNA-seq for the broader community.

HybriSeq: Probe-based Device-free Single-cell RNA Profiling

“…Based on the binned phase scores, clustering the cells into three phases, G1, S, and G2M, shows a more biologically representative clustering than the Leiden clustering (Figure 3E). The proportion of each cell type was similar to a previously published single-cell transcriptome analysis of HEK293 cells (16) when only genes with HybriSeq probes were considered (Supplementary Figure SA-B). Additionally, the expression distribution profiles of cell binned by phase score show a clearer trend compared to the subtle trend seen with Leiden clustering (Supplementary Figure S8, S9) and the pattern of co-expression in the scaled expression profile is much clearer when grouped by G1, S, G2M clusters (Figure 3F).…”

Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases

“…To extend CD4 + T cell profiling to various autoimmune and infectious diseases, we performed a meta-analysis using publicly available single-cell data. 6 , 8 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 We integrated publicly available datasets with two strategies: (1) quantitative evaluation of cell frequencies by mapping to our reference and (2) evaluation of qualitative changes per cell type using NMFproj. We extracted CD4 + T cells from peripheral blood mononuclear cells (PBMCs) using Azimuth 62 and then mapped them to our reference using Symphony 14 ( Figure 3 A, the pipeline is available at https://github.com/yyoshiaki/screfmapping ).…”

Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens

“…The Evercode kit is compatible with fixed samples, enabling us to perturb, stimulate, and fix cells with our five distinct pathway perturbation libraries at different times, and subsequently perform scRNA-seq simultaneously on all samples. This workflow also leverages three levels of combinatorial indexing to increase the scalability and cost-effectiveness of large-scale analysis 42 . Briefly, our modifications included the addition of a guide-specific primer to the cDNA amplification reaction, and the modification of the PCR reaction conditions to optimize guide recovery without adversely affecting the whole transcriptome amplification (Supplementary Figure 1,2).…”