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Cheng J, Lin G, Wang T, Wang Y, Guo W, Liao J, Yang P, Chen J, Shao X, Lu X, Zhu L, Wang Y, Fan X. Massively Parallel CRISPR-Based Genetic Perturbation Screening at Single-Cell Resolution. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2204484. [PMID: 36504444 PMCID: PMC9896079 DOI: 10.1002/advs.202204484] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/09/2022] [Indexed: 06/17/2023]
Abstract
The clustered regularly interspaced short palindromic repeats (CRISPR)-based genetic screening has been demonstrated as a powerful approach for unbiased functional genomics research. Single-cell CRISPR screening (scCRISPR) techniques, which result from the combination of single-cell toolkits and CRISPR screening, allow dissecting regulatory networks in complex biological systems at unprecedented resolution. These methods allow cells to be perturbed en masse using a pooled CRISPR library, followed by high-content phenotyping. This is technically accomplished by annotating cells with sgRNA-specific barcodes or directly detectable sgRNAs. According to the integration of distinct single-cell technologies, these methods principally fall into four categories: scCRISPR with RNA-seq, scCRISPR with ATAC-seq, scCRISPR with proteome probing, and imaging-based scCRISPR. scCRISPR has deciphered genotype-phenotype relationships, genetic regulations, tumor biological issues, and neuropathological mechanisms. This review provides insight into the technical breakthrough of scCRISPR by systematically summarizing the advancements of various scCRISPR methodologies and analyzing their merits and limitations. In addition, an application-oriented approach guide is offered to meet researchers' individualized demands.
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Affiliation(s)
- Junyun Cheng
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Gaole Lin
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Tianhao Wang
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Yunzhu Wang
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Wenbo Guo
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Jie Liao
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Penghui Yang
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Jie Chen
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Xin Shao
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Xiaoyan Lu
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
- State Key Laboratory of Component‐Based Chinese MedicineInnovation Center in Zhejiang UniversityHangzhou310058China
- Jinhua Institute of Zhejiang UniversityJinhua321016China
| | - Ling Zhu
- The Save Sight InstituteFaculty of Medicine and Healththe University of SydneySydneyNSW2000Australia
| | - Yi Wang
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
- State Key Laboratory of Component‐Based Chinese MedicineInnovation Center in Zhejiang UniversityHangzhou310058China
- Future Health LaboratoryInnovation Center of Yangtze River DeltaZhejiang UniversityJiaxing314100China
| | - Xiaohui Fan
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
- State Key Laboratory of Component‐Based Chinese MedicineInnovation Center in Zhejiang UniversityHangzhou310058China
- Jinhua Institute of Zhejiang UniversityJinhua321016China
- The Save Sight InstituteFaculty of Medicine and Healththe University of SydneySydneyNSW2000Australia
- Future Health LaboratoryInnovation Center of Yangtze River DeltaZhejiang UniversityJiaxing314100China
- Westlake Laboratory of Life Sciences and BiomedicineHangzhou310024China
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Brown M, Greenwood E, Zeng B, Powell JE, Gibson G. Effect of all-but-one conditional analysis for eQTL isolation in peripheral blood. Genetics 2023; 223:iyac162. [PMID: 36321965 PMCID: PMC9836021 DOI: 10.1093/genetics/iyac162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/13/2022] [Indexed: 11/13/2022] Open
Abstract
Expression quantitative trait locus detection has become increasingly important for understanding how noncoding variants contribute to disease susceptibility and complex traits. The major challenges in expression quantitative trait locus fine-mapping and causal variant discovery relate to the impact of linkage disequilibrium on signals due to one or multiple functional variants that lie within a credible set. We perform expression quantitative trait locus fine-mapping using the all-but-one approach, conditioning each signal on all others detected in an interval, on the Consortium for the Architecture of Gene Expression cohorts of microarray-based peripheral blood gene expression in 2,138 European-ancestry human adults. We contrast these results with traditional forward stepwise conditional analysis and a Bayesian localization method. All-but-one conditioning significantly modifies effect-size estimates for 51% of 2,351 expression quantitative trait locus peaks, but only modestly affects credible set size and location. On the other hand, both conditioning approaches result in unexpectedly low overlap with Bayesian credible sets, with just 57% peak concordance and between 50% and 70% SNP sharing, leading us to caution against the assumption that any one localization method is superior to another. We also cross reference our results with ATAC-seq data, cell-type-specific expression quantitative trait locus, and activity-by-contact-enhancers, leading to the proposal of a 5-tier approach to further reduce credible set sizes and prioritize likely causal variants for all known inflammatory bowel disease risk loci active in immune cells.
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Affiliation(s)
- Margaret Brown
- Center for Integrative Genomics, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Emily Greenwood
- Center for Integrative Genomics, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Biao Zeng
- Present address for Biao Zeng: Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joseph E Powell
- Present address for Joseph E Powell: Garvan-Weizmann Center for Cellular Genomics, Sydney, NSW 2010, Australia
| | - Greg Gibson
- Center for Integrative Genomics, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Alsheikh AJ, Wollenhaupt S, King EA, Reeb J, Ghosh S, Stolzenburg LR, Tamim S, Lazar J, Davis JW, Jacob HJ. The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases. BMC Med Genomics 2022; 15:74. [PMID: 35365203 PMCID: PMC8973751 DOI: 10.1186/s12920-022-01216-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 03/17/2022] [Indexed: 02/08/2023] Open
Abstract
Background The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants. Methods To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles. Results We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33). Conclusions This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-022-01216-w.
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Affiliation(s)
- Ammar J Alsheikh
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA.
| | - Sabrina Wollenhaupt
- Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
| | - Emily A King
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - Jonas Reeb
- Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
| | - Sujana Ghosh
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | | | - Saleh Tamim
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - Jozef Lazar
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - J Wade Davis
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - Howard J Jacob
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
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4
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Cooper SE, Schwartzentruber J, Bello E, Coomber EL, Bassett AR. Screening for functional transcriptional and splicing regulatory variants with GenIE. Nucleic Acids Res 2021; 48:e131. [PMID: 33152068 PMCID: PMC7736817 DOI: 10.1093/nar/gkaa960] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 09/22/2020] [Accepted: 10/08/2020] [Indexed: 12/14/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified numerous genetic loci underlying human diseases, but a fundamental challenge remains to accurately identify the underlying causal genes and variants. Here, we describe an arrayed CRISPR screening method, Genome engineering-based Interrogation of Enhancers (GenIE), which assesses the effects of defined alleles on transcription or splicing when introduced in their endogenous genomic locations. We use this sensitive assay to validate the activity of transcriptional enhancers and splice regulatory elements in human induced pluripotent stem cells (hiPSCs), and develop a software package (rgenie) to analyse the data. We screen the 99% credible set of Alzheimer's disease (AD) GWAS variants identified at the clusterin (CLU) locus to identify a subset of likely causal variants, and employ GenIE to understand the impact of specific mutations on splicing efficiency. We thus establish GenIE as an efficient tool to rapidly screen for the role of transcribed variants on gene expression.
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Affiliation(s)
- Sarah E Cooper
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK.,OpenTargets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK
| | - Jeremy Schwartzentruber
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK.,OpenTargets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Erica Bello
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK.,OpenTargets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK
| | - Eve L Coomber
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK
| | - Andrew R Bassett
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK.,OpenTargets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK
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Tian R, Pan Y, Etheridge THA, Deshmukh H, Gulick D, Gibson G, Bao G, Lee CM. Pitfalls in Single Clone CRISPR-Cas9 Mutagenesis to Fine-map Regulatory Intervals. Genes (Basel) 2020; 11:E504. [PMID: 32375333 PMCID: PMC7288657 DOI: 10.3390/genes11050504] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 04/15/2020] [Accepted: 04/22/2020] [Indexed: 12/11/2022] Open
Abstract
The majority of genetic variants affecting complex traits map to regulatory regions of genes, and typically lie in credible intervals of 100 or more SNPs. Fine mapping of the causal variant(s) at a locus depends on assays that are able to discriminate the effects of polymorphisms or mutations on gene expression. Here, we evaluated a moderate-throughput CRISPR-Cas9 mutagenesis approach, based on replicated measurement of transcript abundance in single-cell clones, by deleting candidate regulatory SNPs, affecting four genes known to be affected by large-effect expression Quantitative Trait Loci (eQTL) in leukocytes, and using Fluidigm qRT-PCR to monitor gene expression in HL60 pro-myeloid human cells. We concluded that there were multiple constraints that rendered the approach generally infeasible for fine mapping. These included the non-targetability of many regulatory SNPs, clonal variability of single-cell derivatives, and expense. Power calculations based on the measured variance attributable to major sources of experimental error indicated that typical eQTL explaining 10% of the variation in expression of a gene would usually require at least eight biological replicates of each clone. Scanning across credible intervals with this approach is not recommended.
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Affiliation(s)
- Ruoyu Tian
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA; (R.T.); (D.G.)
| | - Yidan Pan
- Systems, Synthetic, and Physical Biology, Rice University, Houston, TX 77005, USA;
- Department of Bioengineering, Rice University, Houston, TX 77005, USA; (H.D.); (T.H.A.E.)
| | - Thomas H. A. Etheridge
- Department of Bioengineering, Rice University, Houston, TX 77005, USA; (H.D.); (T.H.A.E.)
| | - Harshavardhan Deshmukh
- Department of Bioengineering, Rice University, Houston, TX 77005, USA; (H.D.); (T.H.A.E.)
| | - Dalia Gulick
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA; (R.T.); (D.G.)
| | - Greg Gibson
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA; (R.T.); (D.G.)
| | - Gang Bao
- Systems, Synthetic, and Physical Biology, Rice University, Houston, TX 77005, USA;
- Department of Bioengineering, Rice University, Houston, TX 77005, USA; (H.D.); (T.H.A.E.)
| | - Ciaran M Lee
- APC Microbiome Ireland, University College Cork, Cork T12 YN60, Ireland
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