The ability to characterize individual cells in a heterogeneous population is becoming increasingly important in biological research and clinical diagnostics. Traditionally, most classification methods have relied on optical detection of cell-surface proteins. Downstream analysis of the sorted cells provides an additional layer of information for cellular phenotyping and characterization. With the decreasing costs of high-throughput sequencing over the past few years, many researchers have started to perform single-cell RNA sequencing (scRNA-Seq) on their FACS-sorted cells to correlate the content of their transcriptomes to the expression of specific cell-surface markers. While effective, FACS/scRNA-Seq approaches suffer from relatively low throughput and from an experimental bias in that only cell types chosen a priori are sorted and sequenced. Clearly these methods are not well suited for discovery of novel cell populations or for characterizing complex tissues that require the analysis of tens of thousands of cells. The recent explosion of droplet-based microfluidic techniques for single-cell sequencing has allowed scRNA- Seq experiments to scale to enormous numbers of cells, alleviating the throughput bottleneck and bypassing the experimental bias encountered by FACS. However, droplet-based scRNA-Seq approaches have yet to become mainstream and ubiquitous owing to two key disadvantages. First, the equipment to perform microfluidic droplet-based single-cell sequencing is expensive and requires specifically trained personnel for assembly and operation, which hinders accessibility of these systems to the average research or clinical laboratory. Second, none of the currently available droplet-based single-cell-sequencing methods are able to capture the cell-surface expression data that is attainable by FACS analysis. To overcome the limitations with current droplet technologies, we plan to accomplish the following two aims. First, we will create an affordable, open-source, and compact/portable microfluidic platform to broadly enable droplet-based single-cell sequencing methods. This device, named ?mini-Drops?, will empower any lab to perform microfluidic single-cell experiments, either at the bench, in the clinic, or in the field. Second, we will develop a technique, termed ?cellular indexing of transcriptome and epitopes by sequencing? or CITE-seq, to simultaneously characterize the transcriptome and a potentially unlimited number of cell-surface markers from the same cell in a high-throughput manner.
We aim to transform the understanding of cellular processes and disease states with high information content single-cell transcriptomic and proteomic profiling by performing CITE-seq on mini-Drops in diverse laboratory settings.
The study of individual cells that make up a complex tissue is becoming increasingly important in biological research and clinical testing, but our ability to analyze single cells is impeded by the high cost of instrumentation and limitations of currently available methods. We address these impediments with an open- source, affordable platform for single-cell analysis and a companion biological technique to examine these cells with unprecedented resolution. Our approach will increase the depth of information obtained from single cells, which in turn will enrich our understanding of cell biology and disease.
