Single-cell transcriptome sequencing: recent advances and remaining challenges
By Serena Liu, Cole Trapnell. 2016
235 citations recorded by the source corpus.
Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.
Subfields: Immunology, Molecular Biology
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In Immunology
In Molecular Biology
- A. Haque
- Jingyi Jessica Li
- Wei Vivian Li
- Sarah A. Teichmann
- T. Lönnberg
- Jessica A. Engel
- Fabian J Theis
- Malte D. Luecken
Citations in the corpus
- An accurate and robust imputation method scImpute for single-cell RNA-seq data (2018, background): “However, in the ~300 DEC upregulated genes that are only detected in the raw data, enriched GO terms are general and not characteristic to DECs (Supplementary Tables 6 and 7).”
- A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications (2017, background): “inference of gene-regulatory networks that underlie functional heterogeneity and cell-type specification [30, 31].”