Single-cell RNA sequencing is a major focus of intense research today. Now, with the aim of ensuring that this technology leverages the best possible techniques, an international team has benchmarked as many as 13 different techniques.
Image Credit: nobeastsofierce/Shutterstock.com
Headed by Spain-based Holger Heyn of the Centro Nacional de Análisis Genómico (CNAG-CRG), the group discovered that the Quartz-seq2 technique, devised by another team in the RIKEN Center for Biosystems Dynamics Research, was generally the most optimal technique created to sequence single-cell RNA. The study was published in the Nature Biotechnology journal.
The international group initiated this study with respect to the concern that the techniques used for genomic analysis were not benchmarked in the earlier days, which gave rise to problems later in the process. This is because the different teams were utilizing different techniques that had variable standards and arrived at different results.
Keeping this aspect in mind, several groups that focus on the analysis of single-cell RNA collaborated to assess various techniques to make sure that there is excellent reproducibility.
Single-cell DNA sequencing is regarded as the next crucial project in genomic studies. Genomic research was originally represented by the Human Genome Project, which was sought to establish the DNA sequence that exists in all the cells in any organism.
While the cells in an organism share the same DNA code, all the cells are actually phenotypically different because different types of genes are either expressed or not expressed on the basis of epigenetic factors. It is this aspect that makes things complicated.
There are enormous variations in the expression of genetic regions called enhancers and promoters, which act on other genetic regions and do not directly code proteins.
By interpreting the genetic composition of an individual cell, it would be easy to identify how each cell differs in conditions like cancer, and how the cells modify at the time of the development process.
Scientists, who are presently taking part in the Human Cell Atlas, are looking for ways to create a detailed atlas of the expression of genes in different types of cells.
For comparison purpose, the team utilized all the 13 techniques to study a group of about 3,000 cells chosen to meet four conditions—it contained a wide range of cell types, a few of the cells were extremely analogous with just slight variations in gene expression, the cells exhibited trackable markers, and they comprised cells from different species. Most of the cells were human peripheral blood cells as well as mouse colon cells, but they also comprised a negligible set of dog cells.
The techniques were assessed based on the accuracy to identify marker expression and cell profiles. The team then assessed these techniques utilizing six crucial metrics such as the overall level of expression in transcriptional signatures, gene detection, classification probability, cluster accuracy, cluster accuracy following integration, and mixability.
Metrics like these were chosen to compare these techniques in terms of their precision, applicability to numerous cell types; potential to create reproducible profiles; potential to distinguish between closely associated cell types, compatibility with other techniques, potential to identify population markers, and have excellent predictive value for cell mapping.
Consequently, the team discovered that the Quartz-seq2 technique, devised by RIKEN researchers in Japan, to be specifically precise and scored the highest on the benchmark.
We were very happy that our method was selected as overall best, and plan to further improve it so that we can achieve the best results in projects such as the Human Atlas Project.”
Itoshi Nikaido, Group Leader, RIKEN
According to Dr. Heyn, “The protocols showed profound performance differences and we hope that our work contributes to developing standards and guidelines towards the production of high-quality datasets for the Human Cell Atlas and broader single-cell community.”
Mereu, E., et al. (2020) Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nature Biotechnology. doi.org/10.1038/s41587-020-0469-4.