Single-cell analysis allows researchers to tease apart single cells from a population, going beyond the analysis afforded by traditional bulk profiling methods. Single-cell RNA sequencing can uncover rare cell populations, regulatory relationships between genes, and determine cell lineages during development. A team of researchers at Cornell University have developed an improvement to the existing single-cell RNA sequencing methods that makes the technology even more useful.
In 2015, researchers from Harvard University and the Massachusetts Institute of Technology introduced Drop-seq, a method to simultaneously and efficiently characterize the identities of thousands of cells, using nanoliter-scale droplets and attaching a unique identifier to each cell’s RNA. In Drop-seq, individual cells are encapsulated with labeled microparticles that initiate reverse transcription of cellular mRNA. “Those technologies are very popular because they’ve lowered the cost of these types of analyses and sort of democratized them, made them very cheap and easy to do for many labs,” noted Iwijn De Vlaminck, Ph.D., assistant professor in biomedical engineering at Cornell University.
The drawback, however, is that Drop-seq can only identify a certain type of messenger RNA (mRNA) molecule, which limits the potential scope of analyses. Dr. De Vlaminck and his collaborators have devised a simple, inexpensive twist to the existing Drop-seq protocol that enables multiplexed amplicon sequencing and transcriptome profiling in single cells. They call their new method droplet-assisted RNA targeting by single-cell sequencing (DART-seq).
Their work, published recently in Nature Methods in a paper titled, “Simultaneous Multiplexed Amplicon Sequencing and Transcriptome Profiling in Single Cells,” shows an effective method to enzymatically customize the beads prior to performing conventional Drop-seq analysis, which allows for the recovery and analysis of a greater variety of molecules than are available through Drop-seq sequencing.
In addition to serving as an improved single-cell sequencing method, DART-seq may also lead to discoveries in infection and immunology. The researchers applied DART-seq to simultaneously characterize the non-A-tailed transcripts of a segmented dsRNA virus and the transcriptome of the infected cell. In addition, they used DART-seq to simultaneously determine the natively paired, variable region heavy and light chain amplicons and the transcriptome of B lymphocytes. The technology could identify virus-infected cells, and quantify viral and host gene expression, thus enabling examination of the host response to infection at a single-cell level.
“A single virus species can be very diverse, and that diversity permits them to do extraordinary things,” said Philip Burnham, a graduate student in the De Vlaminck lab and co-first author on the paper. “So if you can zoom down to the single-cell level, you can actually see how minor changes in the virus cause a potentially huge change in how the cell reacts to that small mutation.”
Mridu Saikia, Ph.D., a postdoctoral associate in the De Vlaminck lab and co-first author on the paper, thinks DART-seq will also help inform new approaches to cancer therapy. “Cancer cells are a very heterogeneous population,” she said, “and when you don’t look at them at the single-cell level, you often miss important information. So our technology also allows that.”
