Single-cell RNA-seq: advances and future challenges

By Antoine-Emmanuel Saliba, A. Westermann, S. Gorski, Jörg Vogel. Nucleic Acids Research, 2014

800 citations recorded by the source corpus.

Phenotypically identical cells can dramatically vary with respect to behavior during their lifespan and this variation is reflected in their molecular composition such as the transcriptomic landscape. Single-cell transcriptomics using next-generation transcript sequencing (RNA-seq) is now emerging as a powerful tool to profile cell-to-cell variability on a genomic scale. Its application has already greatly impacted our conceptual understanding of diverse biological processes with broad implications for both basic and clinical research. Different single-cell RNA-seq protocols have been introduced and are reviewed here—each one with its own strengths and current limitations. We further provide an overview of the biological questions single-cell RNA-seq has been used to address, the major findings obtained from such studies, and current challenges and expected future developments in this booming field.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b1575e12995315c26847eae3f4e9647da32eed77

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Citations in the corpus

An accurate and robust imputation method scImpute for single-cell RNA-seq data (2018, methodology): “Fortunately, single-cell RNA sequencing (scRNA-seq) technologies are now emerging as a powerful tool to capture transcriptome-wide cell-to-cell variability 3 – 5 .”
Design and Analysis of Single-Cell Sequencing Experiments. (2015, background): “A more detailed discussion of single-cell isolation methods has been published recently (Saliba et al., 2014).”
Defining cell types and states with single-cell genomics (2015, background): “A recent review provides a thorough discussion of the molecular biology underlying recent protocols (Saliba et al. 2014).”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, methodology): “…dimension reduction approach used for unsupervised clustering of cells Input is typically a correlation or similarity matrix Both Saliba et al. [5] and Kolodziejczyk et al. [6] review single-cell isolation practices in detail, with the latter including details on the more recent droplet…”
Identification of cell types from single-cell transcriptomes using a novel clustering method (2015, methodology): “With the rapid adaption of single-cell RNA-Seq techniques (Saliba et al. , 2014), enormous transcriptome datasets have been generated at single-cell resolution.”
Single-cell transcriptome sequencing: recent advances and remaining challenges (2016, background): “Although micromanipulation or laser dissection techniques can isolate single cells from known locations within a cell population or tissue, these methods are labor-intensive or require specialized equipment 22,33,38 .”
Cell Type-Specific Transcriptome Analysis in the Drosophila Mushroom Body Reveals Memory-Related Changes in Gene Expression (2016, neutral): “We harvested cells for RNAseq from flies (carrying the GAL4 constructs that label the aforementioned MB cell types) demonstrating learned odor avoidance (Figure 1A).”
Comprehensive functional genomic resource and integrative model for the human brain (2018, background): “In addition, current single-cell techniques suffer from low sensitivity and dropouts; thus, it remains challenging to reliably quantify low-abundance transcripts (15, 53).”