Scaling single cell transcriptomics through split pool barcoding

Abstract

Constructing an atlas of cell types in complex organisms will require a collective effort to characterize billions of individual cells. Single cell RNA sequencing (scRNA-seq) has emerged as the main tool for characterizing cellular diversity, but current methods use custom microfluidics or microwells to compartmentalize single cells, limiting scalability and widespread adoption. Here we present Split Pool Ligation-based Transcriptome sequencing (SPLiT-seq), a scRNA-seq method that labels the cellular origin of RNA through combinatorial indexing. SPLiT-seq is compatible with fixed cells, scales exponentially, uses only basic laboratory equipment, and costs one cent per cell. We used this approach to analyze 109,069 single cell transcriptomes from an entire postnatal day 5 mouse brain, providing the first global snapshot at this stage of development. We identified 13 main populations comprising different types of neurons, glia, immune cells, endothelia, as well as types in the blood-brain-barrier. Moreover, we resolve substructure within these clusters corresponding to cells at different stages of development. As sequencing capacity increases, SPLiT-seq will enable profiling of billions of cells in a single experiment.Over three hundred years have passed since the discovery of the cell, yet we still do not have a complete catalogue of cell types or their functions. While transcriptomic profiling of individual cells has emerged as a promising solution to characterizing cellular diversity (1, 2), increases in throughput are needed before a complete "atlas" of cell types can be generated. Recent single cell RNA-seq (scRNA-seq) methods have profiled tens of thousands of individual cells (3-6), revealing new insights about the immune system (7) and identifying new cell types in the brain (8-11). However, since these methods require cell sorters and custom microfluidics or microwells, throughput is still limited, experiments are costly, and access is limited to a small number of labs.peer-reviewed)

Visualizing and Interpreting Single-Cell Gene Expression Datasets with Similarity Weighted Nonnegative Embedding

“…Recent advances in droplet based single cell RNA-seq technology (Macosko et al 2015; Lake et al 2017) as well as combinatorial indexing techniques (Cao et al, 2017; Rosenberg et al, 2017) have improved throughput to the point where tens of thousands or even millions of single cells can be sequenced in a single experiment, creating an influx of single cell gene expression datasets. In response to this influx of data, computational methods have been developed for latent factor identification (Buettner et al, 2017), clustering (Wang et al, 2017), cell trajectory reconstruction (Qiu et al, 2017; Setty et al, 2016), and differential expression (Kharchenko et al, 2014).…”

Visualizing and interpreting single-cell gene expression datasets with Similarity Weighted Nonnegative Embedding

“…Single cell gene expression profiling has enabled the quantitative analysis of many different cell types and states, such as human brain cell types (Lake et al 2016;Lake et al 2017) and cancer cell states (Tirosh et al 2016;Puram et al 2017), while also enabling the reconstruction of cell state trajectories during reprogramming and development (Trapnell et al 2014;Qiu et al 2017;Setty et al 2016). Recent advances in droplet based single cell RNA-seq technology (Macosko et al 2015;Lake et al 2017) as well as combinatorial indexing techniques (Cao et al 2017;Rosenberg et al 2017) have improved throughput to the point where tens of thousands of single cells can be sequenced in a single experiment, creating an influx of large single cell gene expression datasets. Numerous computational methods have been developed for latent factor identification (Buettner et al 2017), clustering (Wang et al 2017), cell trajectory reconstruction (Qiu et al 2017;Setty et al 2016), and differential expression (Kharchenko et al 2014).…”

Identifying progressive gene network perturbation from single-cell RNA-seq data

“…Recent advances in the field of single cell RNA sequencing (scRNA-seq) have enabled us to ask novel biological questions, which creates needs to develop new statistical methods to address them [1]- [3]. Unlike bulk RNA-seq or microarray measurements, scRNA-seq captures cell-to-cell variability in gene expression programs.…”