Literature DB >> 30192636

Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues.

Kenneth D Birnbaum1.   

Abstract

The growing scale and declining cost of single-cell RNA-sequencing (RNA-seq) now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms such as cellular variance. The characterization of anatomy and developmental dynamics has not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution obtained by single-cell RNA-seq is a boon to genetics because the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible.

Entities:  

Keywords:  pooled CRISPR; rare cell types; single-cell RNA-seq; single-cell power analysis; trajectory analysis

Mesh:

Substances:

Year:  2018        PMID: 30192636      PMCID: PMC6314027          DOI: 10.1146/annurev-genet-120417-031247

Source DB:  PubMed          Journal:  Annu Rev Genet        ISSN: 0066-4197            Impact factor:   16.830


  81 in total

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2.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

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3.  CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification.

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Journal:  Cell Rep       Date:  2012-08-30       Impact factor: 9.423

Review 4.  Computational methods for trajectory inference from single-cell transcriptomics.

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Journal:  Eur J Immunol       Date:  2016-10-19       Impact factor: 5.532

5.  RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells.

Authors:  Kok Hao Chen; Alistair N Boettiger; Jeffrey R Moffitt; Siyuan Wang; Xiaowei Zhuang
Journal:  Science       Date:  2015-04-09       Impact factor: 47.728

6.  Comprehensive single-cell transcriptional profiling of a multicellular organism.

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Review 7.  Unravelling biology and shifting paradigms in cancer with single-cell sequencing.

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Journal:  Nat Rev Cancer       Date:  2017-08-24       Impact factor: 60.716

8.  Whole-organism lineage tracing by combinatorial and cumulative genome editing.

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9.  Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells.

Authors:  Stefan Semrau; Johanna E Goldmann; Magali Soumillon; Tarjei S Mikkelsen; Rudolf Jaenisch; Alexander van Oudenaarden
Journal:  Nat Commun       Date:  2017-10-23       Impact factor: 14.919

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Journal:  Genome Biol       Date:  2018-12-19       Impact factor: 13.583

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  36 in total

1.  Single-cell transcriptomics from human pancreatic islets: sample preparation matters.

Authors:  Lori L Bonnycastle; Derek E Gildea; Tingfen Yan; Narisu Narisu; Amy J Swift; Tyra G Wolfsberg; Michael R Erdos; Francis S Collins
Journal:  Biol Methods Protoc       Date:  2020-01-16

Review 2.  Cross-species RNA-seq for deciphering host-microbe interactions.

Authors:  Alexander J Westermann; Jörg Vogel
Journal:  Nat Rev Genet       Date:  2021-02-17       Impact factor: 53.242

Review 3.  Design and application of single-cell RNA sequencing to study kidney immune cells in lupus nephritis.

Authors:  Deepak A Rao; Arnon Arazi; David Wofsy; Betty Diamond
Journal:  Nat Rev Nephrol       Date:  2019-12-18       Impact factor: 28.314

4.  Quantitative imaging of RNA polymerase II activity in plants reveals the single-cell basis of tissue-wide transcriptional dynamics.

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5.  Single-cell RNA-seq clustering: datasets, models, and algorithms.

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Journal:  RNA Biol       Date:  2020-03-01       Impact factor: 4.652

Review 6.  A Single-Cell Perspective of the Mammalian Liver in Health and Disease.

Authors:  Xuelian Xiong; Henry Kuang; Tongyu Liu; Jiandie D Lin
Journal:  Hepatology       Date:  2020-04       Impact factor: 17.425

7.  Deciphering Metabolic Heterogeneity by Single-Cell Analysis.

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Journal:  Anal Chem       Date:  2019-10-08       Impact factor: 6.986

8.  Drosophila PDGF/VEGF signaling from muscles to hepatocyte-like cells protects against obesity.

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9.  Single-cell nucleic acid profiling in droplets (SNAPD) enables high-throughput analysis of heterogeneous cell populations.

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10.  Regulation of Cell Type-Specific Immunity Networks in Arabidopsis Roots.

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Journal:  Plant Cell       Date:  2020-07-22       Impact factor: 11.277

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