![]() Step 1: Absolute scaling of RNA levels is possible using ERCC spike in or UMIs (in vitro setup) ![]() ![]() More supporting data can be found in the preprint. Instead, previously published scRNAseq datasets were used.įor this summary, selected key experiments are described. As a hallmark, genes summarized by the GO terms “chromatin remodelling”, “DNA repair”, “ribosome biogenesis” and “translation” show especially high transcript levels during hypertranscription.įor the study, no new scRNAseq data was generated. Transcription rates and transcript levels are globally increased in hypertranscribing cells, which is accompanied by a global opening of chromatin, high activity of chromatin remodellers and general transcription amplifiers, such as members of the Myc protein family. Hypertranscription has been characterized previously by various groups for specific cell types during development. Instead, they found that it is a rather general biological process, which occurs in cells with a high demand for biomass.ĭefinition of the term “hypertranscription”: When the authors applied their tool to scRNAseq data from adult mouse organs, they were able to show that hypertranscription is not a rare program restricted to specific cell types and periods during development. Instead, such differences can be quantified in existing scRNA-seq data sets using UMIs and/ or ERCC spike ins and have a biological meaning. ![]() By using a well characterized biological example of cells that increase their total transcript amounts during development (referred to as “hypertranscription”), the authors were able to verify that differences in total transcript levels between cells are not mainly due to technical artefacts. aimed to investigate whether current scRNA-seq protocols can be used to measure total transcript levels of single cells. While relative gene expression differences are currently analyzed with great interest in single cell data, differences in total transcript levels were mainly considered technical artefacts and removed during normalization, assuming that single cells contain similar amounts of total RNA. Single cell RNA-seq (scRNA-seq) is a powerful tool to measure gene expression in individual cells. ![]()
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