Stem Cells Translational Medicine · 2020 · 19 citations · 45 references
Life SciencesCell BiologyMolecular BiologyGene ExpressionSingle-cell BioinformaticsTranscriptomicsCell-type-specific TranscriptomicsSingle-cell AnalysisSingle-cell GenomicsSingle-cell Rna Sequencing AnalysisSingle-cell TranscriptomicsStem Cell GenomicsCell Fate ConversionCell Identity ConversionCell Type SpecificityCell-type-specific Transcription FactorsPioneer FactorsPioneer Transcription FactorsSingle-cell Rna SequencingSingle-cell Rna Sequencing Cell Type Annotation
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.
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Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
A human liver cell atlas reveals heterogeneity and epithelial progenitors
Nadim Aizarani, Antonio Saviano, Laurent Mailly et al. · Nature · 2019 · 1.2K citations · Full text