Quartz-seq2 Deemed Best Method to Sequence Single-Cell RNA

According to a paper published today in Nature Biotechnology, Quartz-seq2 is the best overall method for single-cell RNA sequencing. The determination was based on a benchmarking study of 13 different methods.

The paper’s authors, led by Holger Heyn of the Centro Nacional de Análisis Genómico in Spain, began the project based on the concern that in the past, lack of benchmarking on methods used for genomic analysis had given rise to problems later in the process, as different groups were using different methods that had varying standards and came up with different results.

To make the comparison, the group used the 13 methods to analyze a set of approximately 3,000 cells selected to fulfill four conditions: it included a variety of cell types, some of the cells were very similar, with only subtle differences in gene expression, the cells had trackable markers, and they included cells from different species. The cells were mostly human peripheral blood cells and mouse colon cells, but also included a small set of dog cells.

The methods were evaluated based on how precisely they could detect cell profiles and marker expression. The group evaluated the methods using six key metrics: gene detection, overall level of expression in transcriptional signatures, cluster accuracy, classification probability, cluster accuracy after integration, and mixability. These metrics were selected to compare the methods in terms of their accuracy, applicability to various cell types, ability to distinguish between closely related cell types, ability to produce reproducible profiles, ability to detect population markers, compatibility with other methods, and good predictive value for cell mapping.

As a result, they found that the Quartz-seq2 method, developed by researchers at RIKEN in Japan, was particularly accurate, scoring highest on the benchmark.

"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,"Dr. Heyn explained.