Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes.
Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes.
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DOI：
10.1083/jcb.202006180
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发表时间：
2021-02-01
期刊：
影响因子：
--
通讯作者：
Youle RJ
中科院分区：
文献类型：
--
作者：
Kanfer G;Sarraf SA;Maman Y;Baldwin H;Dominguez-Martin E;Johnson KR;Ward ME;Kampmann M;Lippincott-Schwartz J;Youle RJ
Kanfer et al. develop a pooled CRISPRi screening method to identify genes regulating intracellular protein localization, organelle morphology or other subcellular phenotypes. The method uses machine learning to identify genetically altered cells, a photoactivated fluorescent protein to label them, and FACS plus deep sequencing to identify the affected gene. Genome-wide CRISPR screens have transformed our ability to systematically interrogate human gene function, but are currently limited to a subset of cellular phenotypes. We report a novel pooled screening approach for a wider range of cellular and subtle subcellular phenotypes. Machine learning and convolutional neural network models are trained on the subcellular phenotype to be queried. Genome-wide screening then utilizes cells stably expressing dCas9-KRAB (CRISPRi), photoactivatable fluorescent protein (PA-mCherry), and a lentiviral guide RNA (gRNA) pool. Cells are screened by using microscopy and classified by artificial intelligence (AI) algorithms, which precisely identify the genetically altered phenotype. Cells with the phenotype of interest are photoactivated and isolated via flow cytometry, and the gRNAs are identified by sequencing. A proof-of-concept screen accurately identified PINK1 as essential for Parkin recruitment to mitochondria. A genome-wide screen identified factors mediating TFEB relocation from the nucleus to the cytosol upon prolonged starvation. Twenty-one of the 64 hits called by the neural network model were independently validated, revealing new effectors of TFEB subcellular localization. This approach, AI-photoswitchable screening (AI-PS), offers a novel screening platform capable of classifying a broad range of mammalian subcellular morphologies, an approach largely unattainable with current methodologies at genome-wide scale.
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影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
48
作者：
Bzdok D;Krzywinski M;Altman N
通讯作者：
Altman N
DOI：
10.1093/bioinformatics/btq046
发表时间：
2010-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Pau G;Fuchs F;Sklyar O;Boutros M;Huber W
通讯作者：
Huber W
影响因子：
64.5
作者：
Feldman, David;Singh, Avtar;Blainey, Paul C.
通讯作者：
Blainey, Paul C.
影响因子：
9.9
作者：
Hasle, Nicholas;Cooke, Anthony;Fowler, Douglas M.
通讯作者：
Fowler, Douglas M.