By Derek Vargas, Application Scientist, MedGenome Inc.
Single-cell genomics techniques are revolutionizing our ability to characterize complex tissues. Although bulk RNA sequencing experiments can be insightful, they often mask important biological activity of rare cell types and fail to show the variability in gene expression between individual cells. The rapid development of low-input RNA seq methods has led to an explosion of single-cell RNA-seq platforms, each with their own advantages and limitations. Droplet-based methods (10X Chromium, DropSeq) can be used to analyze thousands of cells in a single prep. In this method, single cells are separated using microfluidics. They are then captured in emulsion, tagged with cell barcodes, and then further processed into a single library. On the other hand, plate-based methods (SMART-seq) for single-cell sequencing require flow sorting cells into individual wells. Each cell is individually lysed and the RNA is used to generate a library. This method requires sorting equipment and has a lower throughput but is far more sensitive than droplet-based methods. Plate-Seq can detect thousands of more genes per cell than droplet-based methods. Additionally, plate-seq generates data from full length mRNA transcripts, whereas droplet-based methods only provide data on the 3’ or 5’ end.
While each method for single-cell sequencing offers benefits, combining these two platforms can provide greater insights into heterogeneous cell populations, and offer a window into the various stages of differentiation and activation states in developing cell populations. Researchers from Xiamen University (Lin et al., 2021) were able to design a computational method which uses both types of data. By analyzing data from both droplet-based and plate-based single-cell experiments, they were able to describe the lineage features and predict the developmental path of mature human pancreatic islet cells. They used mitochondrial genome variants as endogenous lineage-tracking markers, and clustered their data based on these variants. Based on the distinct lineage features between alpha and beta cells, they determined that these cell types develop from different progenitors.
Single-cell RNA sequencing is now widely employed in immunological studies seeking to resolve previously unrecognized cellular heterogeneity, define processes in cell development and differentiation, and understand the gene regulatory networks that predict immune function. In this area also, researchers are finding that an approach combining droplet-based and plate-based single-cell sequencing methods can be beneficial to characterize cytotoxic T lymphocytes. Scientists at the Technical University of Munich (Kanev et al., 2021) tailored a droplet-based approach for high-throughput analysis, and a plate-based method for high depth sequencing. They named these methods tDrop-seq and tSCRB-seq, respectively. They noted that conventional droplet-based methods have inherently low mRNA capture efficiency for cytotoxic T cells and optimized a method for increasing the sensitivity. Although, tDrop-seq allowed them to process large numbers of cells, the low copy number of genes limited the power of analysis for these cells. They then used tSCRP-seq to generate high resolution data necessary for defining the critical mechanisms of T cell differentiation.
These studies highlight the importance of both droplet-based and plate-based single-cell sequencing methods. Though many researchers struggle with the decision between high throughput and low-cost drop-seq, or high-resolution data from plate-seq, there are certainly benefits when combining both methods. A dual method approach has the potential to shed more light on cellular processes involved in health and disease, as well as provide insights into cellular development.
Single-cell transcriptome lineage tracing of human pancreatic development identifies distinct developmental trajectories of alpha and beta cells.
Tailoring the resolution of single-cell RNA sequencing for primary cytotoxic T cells.
#Single-Cell Sequencing, #RNA sequencing, #Single-cell genomics, #RNA seq methods, #cellular heterogeneity, #T cell differentiation, #Single-cell transcriptome