Poster
Overview
As single cell RNA sequencing has become more ubiquitous across different research applications, the average number of cells profiled per project has exponentially increased year-over-year. The scope and ambition of these studies is increasing, with researchers routinely including multiple biological replicates, samples across time-course studies, as well as deep profiling of specific cell types to identify unique populations. As the scope and scale of single cell experiment grows, the sequencing infrastructure and computational tools are keeping pace. It’s necessary to take advantage (and test the limits) of scRNA-seq and explore the possibilities of what large single cell datasets can do to elucidate biological insights. Given the limitations of conventional single cell approaches, many researchers are constrained in terms of how big they can scale their experiments in terms of samples and overall input. But the versatility of combinatorial barcoding technology has driven development of scaling at a much faster pace. There have been numerous developmental breakthroughs that offer distinct advantages over other methods. Here we present improvements in sensitivity, mappable reads and lower sample input requirements with large increases in sample multiplexing and the numbers of cells that can be processed in a single experiment. We demonstrate this approach by processing hundreds of samples in concurrence with scaling beyond 1 million cells in a single experiment. The process is compatible with automation using standard liquid handling instruments for even higher throughput applications. This approach democratizes the scaling of single-cell sequencing experiments to make processing 1 million cells the standard, inspires the scientific community to think bigger and position the field for significant advances in single-cell RNA sequencing applications.