Dataset Information

TransSynW: A single-cell RNA-sequencing based web application to guide cell conversion experiments.

ABSTRACT: Generation of desired cell types by cell conversion remains a challenge. In particular, derivation of novel cell subtypes identified by single-cell technologies will open up new strategies for cell therapies. The recent increase in the generation of single-cell RNA-sequencing (scRNA-seq) data and the concomitant increase in the interest expressed by researchers in generating a wide range of functional cells prompted us to develop a computational tool for tackling this challenge. Here we introduce a web application, TransSynW, which uses scRNA-seq data for predicting cell conversion transcription factors (TFs) for user-specified cell populations. TransSynW prioritizes pioneer factors among predicted conversion TFs to facilitate chromatin opening often required for cell conversion. In addition, it predicts marker genes for assessing the performance of cell conversion experiments. Furthermore, TransSynW does not require users' knowledge of computer programming and computational resources. We applied TransSynW to different levels of cell conversion specificity, which recapitulated known conversion TFs at each level. We foresee that TransSynW will be a valuable tool for guiding experimentalists to design novel protocols for cell conversion in stem cell research and regenerative medicine.

SUBMITTER: Ribeiro MM

PROVIDER: S-EPMC7848352 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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TransSynW: A single-cell RNA-sequencing based web application to guide cell conversion experiments.

Ribeiro Mariana Messias MM Okawa Satoshi S Del Sol Antonio A

Stem cells translational medicine 20201008 2

Generation of desired cell types by cell conversion remains a challenge. In particular, derivation of novel cell subtypes identified by single-cell technologies will open up new strategies for cell therapies. The recent increase in the generation of single-cell RNA-sequencing (scRNA-seq) data and the concomitant increase in the interest expressed by researchers in generating a wide range of functional cells prompted us to develop a computational tool for tackling this challenge. Here we introduc ...[more]

PMID: 33125830

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Project description:Until recently, high-throughput gene expression technology, such as RNA-Sequencing (RNA-seq) required hundreds of thousands of cells to produce reliable measurements. Recent technical advances permit genome-wide gene expression measurement at the single-cell level. Single-cell RNA-Seq (scRNA-seq) is the most widely used and numerous publications are based on data produced with this technology. However, RNA-seq and scRNA-seq data are markedly different. In particular, unlike RNA-seq, the majority of reported expression levels in scRNA-seq are zeros, which could be either biologically-driven, genes not expressing RNA at the time of measurement, or technically-driven, genes expressing RNA, but not at a sufficient level to be detected by sequencing technology. Another difference is that the proportion of genes reporting the expression level to be zero varies substantially across single cells compared to RNA-seq samples. However, it remains unclear to what extent this cell-to-cell variation is being driven by technical rather than biological variation. Furthermore, while systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies, these issues have received minimal attention in published studies based on scRNA-seq technology. Here, we use an assessment experiment to examine data from published studies and demonstrate that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we present evidence that some of these reported zeros are driven by technical variation by demonstrating that scRNA-seq produces more zeros than expected and that this bias is greater for lower expressed genes. In addition, this missing data problem is exacerbated by the fact that this technical variation varies cell-to-cell. Then, we show how this technical cell-to-cell variability can be confused with novel biological results. Finally, we demonstrate and discuss how batch-effects and confounded experiments can intensify the problem.

Dataset Information

TransSynW: A single-cell RNA-sequencing based web application to guide cell conversion experiments.

Publications

TransSynW: A single-cell RNA-sequencing based web application to guide cell conversion experiments.

Similar Datasets

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