Literature DB >> 22821589

Cell perturbation screens for target identification by RNAi.

Kubilay Demir1, Michael Boutros.   

Abstract

Over the last decade, cell-based screening has become a powerful method in target identification and plays an important role both in basic research and drug discovery. The availability of whole genome sequences and improvements in cell-based screening techniques opened new avenues for high-throughput experiments. Large libraries of RNA interference reagents available for many organisms allow the dissection of broad spectrum of cellular processes. Here, we describe the current state of the large-scale phenotype screening with a focus on cell-based screens. We underline the importance and provide details of screen design, scalability, performance, data analysis, and hit prioritization. Similar to classical high-throughput in vitro screens with defined-target approaches in the past, cell-based screens depend on a successful establishment of robust phenotypic assays, the ability to quantitatively measure phenotypic changes and bioinformatics methods for data analysis, integration, and interpretation.

Mesh:

Year:  2012        PMID: 22821589     DOI: 10.1007/978-1-61779-965-5_1

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  5 in total

1.  Randomized CRISPR-Cas Transcriptional Perturbation Screening Reveals Protective Genes against Alpha-Synuclein Toxicity.

Authors:  Ying-Chou Chen; Fahim Farzadfard; Nava Gharaei; William C W Chen; Jicong Cao; Timothy K Lu
Journal:  Mol Cell       Date:  2017-10-05       Impact factor: 17.970

2.  siRNA screen of ES cell-derived motor neurons identifies novel regulators of tetanus toxin and neurotrophin receptor trafficking.

Authors:  Marco Terenzio; Matthew Golding; Giampietro Schiavo
Journal:  Front Cell Neurosci       Date:  2014-05-20       Impact factor: 5.505

3.  Discovery of a dicer-independent, cell-type dependent alternate targeting sequence generator: implications in gene silencing & pooled RNAi screens.

Authors:  Bhavneet Bhinder; David Shum; Mu Li; Glorymar Ibáñez; Alexander V Vlassov; Susan Magdaleno; Hakim Djaballah
Journal:  PLoS One       Date:  2014-07-02       Impact factor: 3.240

4.  In silico characterization of cell-cell interactions using a cellular automata model of cell culture.

Authors:  Takanori Kihara; Kosuke Kashitani; Jun Miyake
Journal:  BMC Res Notes       Date:  2017-07-14

5.  Φ-score: A cell-to-cell phenotypic scoring method for sensitive and selective hit discovery in cell-based assays.

Authors:  Laurent Guyon; Christian Lajaunie; Frédéric Fer; Ricky Bhajun; Eric Sulpice; Guillaume Pinna; Anna Campalans; J Pablo Radicella; Philippe Rouillier; Mélissa Mary; Stéphanie Combe; Patricia Obeid; Jean-Philippe Vert; Xavier Gidrol
Journal:  Sci Rep       Date:  2015-09-18       Impact factor: 4.379

  5 in total

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