Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells

By Q. Deng, Daniel Ramsköld, Björn Reinius, Rickard Sandberg. 2014

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In Molecular Biology

Citations in the corpus

Evolution of resilience in protein interactomes across the tree of life (2018, background): “As more detailed information about functions of individual proteins in the interactome (43), as well as dynamic protein-expression data (44), becomes available, our measure of interactome resilience could be adapted to give a more complex definition of resiliency, which might yield more detailed evo”
Mapping the Mouse Cell Atlas by Microwell-Seq. (2018, methodology): “Figure S7C is based on the 5 datasets including male fetal gonad, pancreas and spleen data fromMCA, as well as hematopoietic stem cells (Zhou et al., 2016) and preimplantation embryo data (Deng et al., 2014; Posfai et al., 2017).”
Spatial reconstruction of single-cell gene expression (2015, methodology): “Single-cell RNA-seq is quickly becoming an established experimental method, with ongoing cost and throughput improvements enabling applications from cell type discovery 6,7 to regulatory network inference 8,9 to the reconstruction of developmental processes 10–12 .”
Classification of low quality cells from single-cell RNA-seq data (2016, background): “Additionally, the pipeline allows allele-specific experiments to be quantified, which is an important application [12, 30, 31].”
An accurate and robust imputation method scImpute for single-cell RNA-seq data (2018, background): “The sensitivity analysis based on the mouse embryo data 22 suggests that scImpute is robust to varying parameter values (Supplementary Fig.”
DrImpute: imputing dropout events in single cell RNA sequencing data (2017, background): “…inference with and without imputing the dropout events on three published temporal scRNA-seq datasets, mouse preimplantation embryonic development data (Deng [6]), human preimplantation embryonic development data (Pet-ropoulos [38]), and mouse early mesodermal development data (Scialdone [37]).”
Single-cell Genomics Unveils Critical Regulators of Th17 cell Pathogenicity (2015, positive): “To account for expressed transcripts that are not detected (false negatives) due to the limitations of single-cell RNA-seq (Deng et al., 2014; Shalek et al., 2014), we down-weighted the contribution of less reliably measured transcripts (Figure S1C, Experimental Procedures).”
Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, methodology): “Smart-seq2 (improved version of Smart-seq) 41 generates full-length transcripts and is thus suitable for the discovery of alternative-splicing events and allele-speci ﬁ c expression using single-nucleotide polymorphisms 42 .”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “Deng et al. (2014) used a series of stringent criteria to ﬁlter the potentially false allelic calls resulted from the technical variability of scRNA-seq in studying allelic expression proﬁle of mouse preimplantation embryos.”
Transcriptional enhancers: from properties to genome-wide predictions (2014, methodology): “…the down-scaling of methods — such as high-throughput RNA sequencing (RNA-seq), ChIP–seq, the mapping of open chromatin (for example, by the assay for transposase-accessible chromatin using sequencing (ATAC-seq)) and Hi-C — for their application to few cells or even to individual cells 133–136 .”