RNA-Seq Technique Provides Method for Unraveling Data from Mixture of People

May 19, 2020 | Biotechnology

Reading time: 2-3 minutes

Today, Lab.Equipment has curated a new Biotechnology article published from GEN – Genetic Engineering and Biotechnology News:

Scientists at the Wellcome Sanger Institute and their collaborators say they have created a new computational method for assigning the donor in single cell RNA sequencing experiments that provide an accurate way to unravel data from a mixture of people. The Souporcell technique, described in an article “Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes” in Nature Methods, could help study how genetic variants in different people affect which genes are expressed during infection or response to drugs, according to the team, which believes the software could increase efficiency of single-cell experiments, assisting research into transplants, personalized medicine, and malaria.

Single-cell RNA sequencing (RNAseq) can reveal exactly which genes are switched on in each individual cell, revealing cell types and what they do. Pooling multiple people’s cells into a single cell RNAseq experiment helps to identify how different genomes affect this gene expression. However it is essential to be able to separate the resulting data by individual, which can be very difficult. The researchers tested Souporcell against three other computational methods using placental cells, pluripotent stem cell lines, and malaria parasites.

“Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs, and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate... More of this in the GEN Blog.

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