MATQ-seq – effective detection of variation in single-cell transcriptomes

The development of single-cell RNA-seq has allowed the detection of gene expression at an anatomical resolution that is not accessible by bulk RNA-seq approaches. While single-cell RNA-seq methods have been successfully used to identify new cell types in complex tissues, technical noise in these methods is still substantial and affects researchers’ ability to detect subtle biological differences in gene expression of single cells. Cells of the same type possess dynamic transcriptional variation that derives from intrinsic and extrinsic sources and is challenging to identify using current methods. A sensitive and quantitative single-cell RNA-seq assay that could detect this transcriptional variation as well as subtle differences in gene expression between related cells (e.g., heterogeneous tumor cells) or cell states (e.g., cell fates in development) is highly desirable.

Baylor College of Medicine researchers have developed a highly sensitive sequencing protocol called MATQ-seq (multiple annealing and dC-tailing-based quantitative single-cell RNA-seq). MATQ-seq can detect transcriptional variation among cells of the same population, and they systematically characterize technical noise in order to demonstrate that the detected transcriptional variation is biologically genuine. In contrast to popular switching mechanism at 5′ end of RNA template (SMART)-chemistry-based methods, MATQ-seq provides whole gene body coverage and allows for the detection of total RNA, including noncoding and nonpolyadenylated RNA. In addition, MATQ-seq removes PCR bias using a molecular barcoding strategy. The researchers sequenced over 90 single cells with MATQ-seq to demonstrate its sensitivity and accuracy.

Experimental scheme of MATQ-seq

‘PolyC tailing’ refers to the tailing reaction by terminal transferase. dT₂₀, 20 consecutive thymine bases. Red indicates common MALBAC primer sequences; blue indicates three consecutive G or T ends; green indicates synthesized cDNAs.

Availability – MATLAB scripts are available at: http://www.nature.com/nmeth/journal/vaop/ncurrent/extref/nmeth.4145-S7.zip