1
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Rood JE, Hupalowska A, Regev A. Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas. Cell 2024; 187:4520-4545. [PMID: 39178831 DOI: 10.1016/j.cell.2024.07.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 07/15/2024] [Accepted: 07/21/2024] [Indexed: 08/26/2024]
Abstract
Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.
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Affiliation(s)
| | | | - Aviv Regev
- Genentech, South San Francisco, CA, USA.
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2
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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3
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Cooper S, Obolenski S, Waters AJ, Bassett AR, Coelho MA. Analyzing the functional effects of DNA variants with gene editing. CELL REPORTS METHODS 2024; 4:100776. [PMID: 38744287 PMCID: PMC11133854 DOI: 10.1016/j.crmeth.2024.100776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/01/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Continual advancements in genomics have led to an ever-widening disparity between the rate of discovery of genetic variants and our current understanding of their functions and potential roles in disease. Systematic methods for phenotyping DNA variants are required to effectively translate genomics data into improved outcomes for patients with genetic diseases. To make the biggest impact, these approaches must be scalable and accurate, faithfully reflect disease biology, and define complex disease mechanisms. We compare current methods to analyze the function of variants in their endogenous DNA context using genome editing strategies, such as saturation genome editing, base editing and prime editing. We discuss how these technologies can be linked to high-content readouts to gain deep mechanistic insights into variant effects. Finally, we highlight key challenges that need to be addressed to bridge the genotype to phenotype gap, and ultimately improve the diagnosis and treatment of genetic diseases.
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Affiliation(s)
- Sarah Cooper
- Cellular and Gene Editing Research, Wellcome Sanger Institute, Hinxton, UK
| | - Sofia Obolenski
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, UK; Department of Dermatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Andrew J Waters
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Andrew R Bassett
- Cellular and Gene Editing Research, Wellcome Sanger Institute, Hinxton, UK.
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4
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Barry T, Mason K, Roeder K, Katsevich E. Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection. Genome Biol 2024; 25:124. [PMID: 38760839 PMCID: PMC11100084 DOI: 10.1186/s13059-024-03254-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 04/19/2024] [Indexed: 05/19/2024] Open
Abstract
Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.
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Affiliation(s)
- Timothy Barry
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA.
| | - Kaishu Mason
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, USA
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA
| | - Eugene Katsevich
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA.
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5
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Morris JA, Sun JS, Sanjana NE. Next-generation forward genetic screens: uniting high-throughput perturbations with single-cell analysis. Trends Genet 2024; 40:118-133. [PMID: 37989654 PMCID: PMC10872607 DOI: 10.1016/j.tig.2023.10.012] [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: 08/01/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023]
Abstract
Programmable genome-engineering technologies, such as CRISPR (clustered regularly interspaced short palindromic repeats) nucleases and massively parallel CRISPR screens that capitalize on this programmability, have transformed biomedical science. These screens connect genes and noncoding genome elements to disease-relevant phenotypes, but until recently have been limited to individual phenotypes such as growth or fluorescent reporters of gene expression. By pairing massively parallel screens with high-dimensional profiling of single-cell types/states, we can now measure how individual genetic perturbations or combinations of perturbations impact the cellular transcriptome, proteome, and epigenome. We review technologies that pair CRISPR screens with single-cell multiomics and the unique opportunities afforded by extending pooled screens using deep multimodal phenotyping.
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Affiliation(s)
- John A Morris
- New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA
| | - Jennifer S Sun
- New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA
| | - Neville E Sanjana
- New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA.
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6
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Yang C, Lei Y, Ren T, Yao M. The Current Situation and Development Prospect of Whole-Genome Screening. Int J Mol Sci 2024; 25:658. [PMID: 38203828 PMCID: PMC10779205 DOI: 10.3390/ijms25010658] [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: 11/21/2023] [Revised: 12/22/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
High-throughput genetic screening is useful for discovering critical genes or gene sequences that trigger specific cell functions and/or phenotypes. Loss-of-function genetic screening is mainly achieved through RNA interference (RNAi), CRISPR knock-out (CRISPRko), and CRISPR interference (CRISPRi) technologies. Gain-of-function genetic screening mainly depends on the overexpression of a cDNA library and CRISPR activation (CRISPRa). Base editing can perform both gain- and loss-of-function genetic screening. This review discusses genetic screening techniques based on Cas9 nuclease, including Cas9-mediated genome knock-out and dCas9-based gene activation and interference. We compare these methods with previous genetic screening techniques based on RNAi and cDNA library overexpression and propose future prospects and applications for CRISPR screening.
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Affiliation(s)
| | | | | | - Mingze Yao
- Shanxi Provincial Key Laboratory for Medical Molecular Cell Biology, Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education and Institute of Biomedical Sciences, Shanxi University, Taiyuan 030006, China; (C.Y.); (Y.L.); (T.R.)
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7
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Ishikawa M, Sugino S, Masuda Y, Tarumoto Y, Seto Y, Taniyama N, Wagai F, Yamauchi Y, Kojima Y, Kiryu H, Yusa K, Eiraku M, Mochizuki A. RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations. Commun Biol 2023; 6:1290. [PMID: 38155269 PMCID: PMC10754834 DOI: 10.1038/s42003-023-05594-4] [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: 12/08/2022] [Accepted: 11/15/2023] [Indexed: 12/30/2023] Open
Abstract
Single-cell RNA-seq analysis coupled with CRISPR-based perturbation has enabled the inference of gene regulatory networks with causal relationships. However, a snapshot of single-cell CRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a computational method that infers gene regulatory networks using a time-series single-cell CRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its regulatory network. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring gene regulatory networks. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a network consistent with multiple databases and literature. Accurate inference of gene regulatory networks by RENGE would enable the identification of key factors for various biological systems.
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Affiliation(s)
- Masato Ishikawa
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan.
| | - Seiichi Sugino
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yoshie Masuda
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yusuke Tarumoto
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yusuke Seto
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Nobuko Taniyama
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Fumi Wagai
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yuhei Yamauchi
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yasuhiro Kojima
- Laboratory of Computational Life Science, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Hisanori Kiryu
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
| | - Kosuke Yusa
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Mototsugu Eiraku
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, 606-8507, Japan
| | - Atsushi Mochizuki
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
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8
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Song B, Liu D, Dai W, McMyn N, Wang Q, Yang D, Krejci A, Vasilyev A, Untermoser N, Loregger A, Song D, Williams B, Rosen B, Cheng X, Chao L, Kale HT, Zhang H, Diao Y, Bürckstümmer T, Siliciano JM, Li JJ, Siliciano R, Huangfu D, Li W. Decoding Heterogenous Single-cell Perturbation Responses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564796. [PMID: 37961332 PMCID: PMC10635009 DOI: 10.1101/2023.10.30.564796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Understanding diverse responses of individual cells to the same perturbation is central to many biological and biomedical problems. Current methods, however, do not precisely quantify the strength of perturbation responses and, more importantly, reveal new biological insights from heterogeneity in responses. Here we introduce the perturbation-response score (PS), based on constrained quadratic optimization, to quantify diverse perturbation responses at a single-cell level. Applied to single-cell transcriptomes of large-scale genetic perturbation datasets (e.g., Perturb-seq), PS outperforms existing methods for quantifying partial gene perturbation responses. In addition, PS presents two major advances. First, PS enables large-scale, single-cell-resolution dosage analysis of perturbation, without the need to titrate perturbation strength. By analyzing the dose-response patterns of over 2,000 essential genes in Perturb-seq, we identify two distinct patterns, depending on whether a moderate reduction in their expression induces strong downstream expression alterations. Second, PS identifies intrinsic and extrinsic biological determinants of perturbation responses. We demonstrate the application of PS in contexts such as T cell stimulation, latent HIV-1 expression, and pancreatic cell differentiation. Notably, PS unveiled a previously unrecognized, cell-type-specific role of coiled-coil domain containing 6 (CCDC6) in guiding liver and pancreatic lineage decisions, where CCDC6 knockouts drive the endoderm cell differentiation towards liver lineage, rather than pancreatic lineage. The PS approach provides an innovative method for dose-to-function analysis and will enable new biological discoveries from single-cell perturbation datasets. One sentence summary We present a method to quantify diverse perturbation responses and discover novel biological insights in single-cell perturbation datasets.
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9
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Schraivogel D, Steinmetz LM, Parts L. Pooled Genome-Scale CRISPR Screens in Single Cells. Annu Rev Genet 2023; 57:223-244. [PMID: 37562410 DOI: 10.1146/annurev-genet-072920-013842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Assigning functions to genes and learning how to control their expression are part of the foundation of cell biology and therapeutic development. An efficient and unbiased method to accomplish this is genetic screening, which historically required laborious clone generation and phenotyping and is still limited by scale today. The rapid technological progress on modulating gene function with CRISPR-Cas and measuring it in individual cells has now relaxed the major experimental constraints and enabled pooled screening with complex readouts from single cells. Here, we review the principles and practical considerations for pooled single-cell CRISPR screening. We discuss perturbation strategies, experimental model systems, matching the perturbation to the individual cells, reading out cell phenotypes, and data analysis. Our focus is on single-cell RNA sequencing and cell sorting-based readouts, including image-enabled cell sorting. We expect this transformative approach to fuel biomedical research for the next several decades.
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Affiliation(s)
- Daniel Schraivogel
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany;
| | - Lars M Steinmetz
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany;
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
- Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California, USA
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10
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Song B, Li W. Factoring single-cell perturbations. Nat Methods 2023; 20:1629-1630. [PMID: 37770708 DOI: 10.1038/s41592-023-02002-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Affiliation(s)
- Bicna Song
- Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, USA
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA
| | - Wei Li
- Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, USA.
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA.
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11
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Zhou Y, Luo K, Liang L, Chen M, He X. A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening. Nat Methods 2023; 20:1693-1703. [PMID: 37770710 PMCID: PMC10630124 DOI: 10.1038/s41592-023-02017-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 08/18/2023] [Indexed: 09/30/2023]
Abstract
Clustered regularly interspaced short palindromic repeats (CRISPR) screening coupled with single-cell RNA sequencing has emerged as a powerful tool to characterize the effects of genetic perturbations on the whole transcriptome at a single-cell level. However, due to its sparsity and complex structure, analysis of single-cell CRISPR screening data is challenging. In particular, standard differential expression analysis methods are often underpowered to detect genes affected by CRISPR perturbations. We developed a statistical method for such data, called guided sparse factor analysis (GSFA). GSFA infers latent factors that represent coregulated genes or gene modules; by borrowing information from these factors, it infers the effects of genetic perturbations on individual genes. We demonstrated through extensive simulation studies that GSFA detects perturbation effects with much higher power than state-of-the-art methods. Using single-cell CRISPR data from human CD8+ T cells and neural progenitor cells, we showed that GSFA identified biologically relevant gene modules and specific genes affected by CRISPR perturbations, many of which were missed by existing methods, providing new insights into the functions of genes involved in T cell activation and neurodevelopment.
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Affiliation(s)
- Yifan Zhou
- Graduate Program of Biophysical Sciences, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Lifan Liang
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Mengjie Chen
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Department of Medicine, University of Chicago, Chicago, IL, USA.
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
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12
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Dong M, Wang B, Wei J, de O Fonseca AH, Perry CJ, Frey A, Ouerghi F, Foxman EF, Ishizuka JJ, Dhodapkar RM, van Dijk D. Causal identification of single-cell experimental perturbation effects with CINEMA-OT. Nat Methods 2023; 20:1769-1779. [PMID: 37919419 PMCID: PMC10630139 DOI: 10.1038/s41592-023-02040-5] [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: 11/14/2022] [Accepted: 09/08/2023] [Indexed: 11/04/2023]
Abstract
Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. Here we present a causal-inference-based approach to single-cell perturbation analysis, termed CINEMA-OT (causal independent effect module attribution + optimal transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment-effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment-effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly generated datasets: (1) rhinovirus and cigarette-smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette-smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment.
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Affiliation(s)
- Mingze Dong
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Bao Wang
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Jessica Wei
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | | | - Curtis J Perry
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Alexander Frey
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
- Department of Surgery, Yale School of Medicine, New Haven, CT, USA
| | - Feriel Ouerghi
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Ellen F Foxman
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA.
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
| | - Jeffrey J Ishizuka
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA.
| | - Rahul M Dhodapkar
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - David van Dijk
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
- Department of Internal Medicine (Cardiology), Yale School of Medicine, New Haven, CT, USA.
- Department of Computer Science, Yale University, New Haven, CT, USA.
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Liang J, Wei J, Cao J, Qian J, Gao R, Li X, Wang D, Gu Y, Dong L, Yu J, Zhao B, Wang X. In-organoid single-cell CRISPR screening reveals determinants of hepatocyte differentiation and maturation. Genome Biol 2023; 24:251. [PMID: 37907970 PMCID: PMC10617096 DOI: 10.1186/s13059-023-03084-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 10/06/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Harnessing hepatocytes for basic research and regenerative medicine demands a complete understanding of the genetic determinants underlying hepatocyte differentiation and maturation. Single-cell CRISPR screens in organoids could link genetic perturbations with parallel transcriptomic readout in single cells, providing a powerful method to delineate roles of cell fate regulators. However, a big challenge for identifying key regulators during data analysis is the low expression levels of transcription factors (TFs), which are difficult to accurately estimate due to noise and dropouts in single-cell sequencing. Also, it is often the changes in TF activities in the transcriptional cascade rather than the expression levels of TFs that are relevant to the cell fate transition. RESULTS Here, we develop Organoid-based Single-cell CRISPR screening Analyzed with Regulons (OSCAR), a framework using regulon activities as readouts to dissect gene knockout effects in organoids. In adult-stem-cell-derived liver organoids, we map transcriptomes in 80,576 cells upon 246 perturbations associated with transcriptional regulation of hepatocyte formation. Using OSCAR, we identify known and novel positive and negative regulators, among which Fos and Ubr5 are the top-ranked ones. Further single-gene loss-of-function assays demonstrate that Fos depletion in mouse and human liver organoids promote hepatocyte differentiation by specific upregulation of liver metabolic genes and pathways, and conditional knockout of Ubr5 in mouse liver delays hepatocyte maturation. CONCLUSIONS Altogether, we provide a framework to explore lineage specifiers in a rapid and systematic manner, and identify hepatocyte determinators with potential clinical applications.
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Affiliation(s)
- Junbo Liang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Jinsong Wei
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Jun Cao
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
- Institute of Clinical Medicine, Peking Union Medical College and Chinese Academy of Medical Sciences, Translational Medicine Center, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Jun Qian
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Ran Gao
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Xiaoyu Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Dingding Wang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Yani Gu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Lei Dong
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Chemistry and Biomedicine Innovative Center, Nanjing University, Nanjing, 210023, China
| | - Jia Yu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Bing Zhao
- School of Basic Medical Sciences, Jiangxi Medical College, Nanchang University, Nanchang, 330031, China.
- Institute of Respiratory Disease, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China.
- Institute of Organoid Technology, Kunming Medical University, Kunming, 650500, China.
| | - Xiaoyue Wang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China.
- Institute of Clinical Medicine, Peking Union Medical College and Chinese Academy of Medical Sciences, Translational Medicine Center, Peking Union Medical College Hospital, Beijing, 100730, China.
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Meyers S, Demeyer S, Cools J. CRISPR screening in hematology research: from bulk to single-cell level. J Hematol Oncol 2023; 16:107. [PMID: 37875911 PMCID: PMC10594891 DOI: 10.1186/s13045-023-01495-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/21/2023] [Indexed: 10/26/2023] Open
Abstract
The CRISPR genome editing technology has revolutionized the way gene function is studied. Genome editing can be achieved in single genes or for thousands of genes simultaneously in sensitive genetic screens. While conventional genetic screens are limited to bulk measurements of cell behavior, recent developments in single-cell technologies make it possible to combine CRISPR screening with single-cell profiling. In this way, cell behavior and gene expression can be monitored simultaneously, with the additional possibility of including data on chromatin accessibility and protein levels. Moreover, the availability of various Cas proteins leading to inactivation, activation, or other effects on gene function further broadens the scope of such screens. The integration of single-cell multi-omics approaches with CRISPR screening open the path to high-content information on the impact of genetic perturbations at single-cell resolution. Current limitations in cell throughput and data density need to be taken into consideration, but new technologies are rapidly evolving and are likely to easily overcome these limitations. In this review, we discuss the use of bulk CRISPR screening in hematology research, as well as the emergence of single-cell CRISPR screening and its added value to the field.
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Affiliation(s)
- Sarah Meyers
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Sofie Demeyer
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Jan Cools
- Center for Human Genetics, KU Leuven, Leuven, Belgium.
- Center for Cancer Biology, VIB, Leuven, Belgium.
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium.
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15
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Balmas E, Sozza F, Bottini S, Ratto ML, Savorè G, Becca S, Snijders KE, Bertero A. Manipulating and studying gene function in human pluripotent stem cell models. FEBS Lett 2023; 597:2250-2287. [PMID: 37519013 DOI: 10.1002/1873-3468.14709] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 08/01/2023]
Abstract
Human pluripotent stem cells (hPSCs) are uniquely suited to study human development and disease and promise to revolutionize regenerative medicine. These applications rely on robust methods to manipulate gene function in hPSC models. This comprehensive review aims to both empower scientists approaching the field and update experienced stem cell biologists. We begin by highlighting challenges with manipulating gene expression in hPSCs and their differentiated derivatives, and relevant solutions (transfection, transduction, transposition, and genomic safe harbor editing). We then outline how to perform robust constitutive or inducible loss-, gain-, and change-of-function experiments in hPSCs models, both using historical methods (RNA interference, transgenesis, and homologous recombination) and modern programmable nucleases (particularly CRISPR/Cas9 and its derivatives, i.e., CRISPR interference, activation, base editing, and prime editing). We further describe extension of these approaches for arrayed or pooled functional studies, including emerging single-cell genomic methods, and the related design and analytical bioinformatic tools. Finally, we suggest some directions for future advancements in all of these areas. Mastering the combination of these transformative technologies will empower unprecedented advances in human biology and medicine.
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Affiliation(s)
- Elisa Balmas
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center "Guido Tarone", University of Turin, Torino, Italy
| | - Federica Sozza
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center "Guido Tarone", University of Turin, Torino, Italy
| | - Sveva Bottini
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center "Guido Tarone", University of Turin, Torino, Italy
| | - Maria Luisa Ratto
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center "Guido Tarone", University of Turin, Torino, Italy
| | - Giulia Savorè
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center "Guido Tarone", University of Turin, Torino, Italy
| | - Silvia Becca
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center "Guido Tarone", University of Turin, Torino, Italy
| | - Kirsten Esmee Snijders
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center "Guido Tarone", University of Turin, Torino, Italy
| | - Alessandro Bertero
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center "Guido Tarone", University of Turin, Torino, Italy
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16
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Bielczyk-Maczynska E, Sharma D, Blencowe M, Saliba Gustafsson P, Gloudemans MJ, Yang X, Carcamo-Orive I, Wabitsch M, Svensson KJ, Park CY, Quertermous T, Knowles JW, Li J. A single-cell CRISPRi platform for characterizing candidate genes relevant to metabolic disorders in human adipocytes. Am J Physiol Cell Physiol 2023; 325:C648-C660. [PMID: 37486064 PMCID: PMC10635647 DOI: 10.1152/ajpcell.00148.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/07/2023] [Accepted: 07/19/2023] [Indexed: 07/25/2023]
Abstract
CROP-Seq combines gene silencing using CRISPR interference with single-cell RNA sequencing. Here, we applied CROP-Seq to study adipogenesis and adipocyte biology. Human preadipocyte SGBS cell line expressing KRAB-dCas9 was transduced with a sgRNA library. Following selection, individual cells were captured using microfluidics at different timepoints during adipogenesis. Bioinformatic analysis of transcriptomic data was used to determine the knockdown effects, the dysregulated pathways, and to predict cellular phenotypes. Single-cell transcriptomes recapitulated adipogenesis states. For all targets, over 400 differentially expressed genes were identified at least at one timepoint. As a validation of our approach, the knockdown of PPARG and CEBPB (which encode key proadipogenic transcription factors) resulted in the inhibition of adipogenesis. Gene set enrichment analysis generated hypotheses regarding the molecular function of novel genes. MAFF knockdown led to downregulation of transcriptional response to proinflammatory cytokine TNF-α in preadipocytes and to decreased CXCL-16 and IL-6 secretion. TIPARP knockdown resulted in increased expression of adipogenesis markers. In summary, this powerful, hypothesis-free tool can identify novel regulators of adipogenesis, preadipocyte, and adipocyte function associated with metabolic disease.NEW & NOTEWORTHY Genomics efforts led to the identification of many genomic loci that are associated with metabolic traits, many of which are tied to adipose tissue function. However, determination of the causal genes, and their mechanism of action in metabolism, is a time-consuming process. Here, we use an approach to determine the transcriptional outcome of candidate gene knockdown for multiple genes at the same time in a human cell model of adipogenesis.
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Affiliation(s)
- Ewa Bielczyk-Maczynska
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
| | - Disha Sharma
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
| | - Montgomery Blencowe
- Department of Integrative Biology and Physiology, University of California, Los Angeles, California, United States
| | - Peter Saliba Gustafsson
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
- Cardiovascular Medicine Unit, Department of Medicine, Center for Molecular Medicine at BioClinicum, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Michael J Gloudemans
- Department of Pathology, Stanford University School of Medicine, Stanford, California, United States
- Biomedical Informatics Training Program, Stanford, California, United States
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, California, United States
| | - Ivan Carcamo-Orive
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
| | - Martin Wabitsch
- Department of Pediatrics and Adolescent Medicine, Center for Rare Endocrine Diseases, Division of Pediatric Endocrinology and Diabetes, Ulm University Medical Centre, Ulm, Germany
| | - Katrin J Svensson
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
- Department of Pathology, Stanford University School of Medicine, Stanford, California, United States
| | - Chong Y Park
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
| | - Thomas Quertermous
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
| | - Joshua W Knowles
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
- Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, California, United States
| | - Jiehan Li
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States
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17
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Ghamsari R, Rosenbluh J, Menon AV, Lovell NH, Alinejad-Rokny H. Technological Convergence: Highlighting the Power of CRISPR Single-Cell Perturbation Toolkit for Functional Interrogation of Enhancers. Cancers (Basel) 2023; 15:3566. [PMID: 37509229 PMCID: PMC10377346 DOI: 10.3390/cancers15143566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 07/30/2023] Open
Abstract
Higher eukaryotic enhancers, as a major class of regulatory elements, play a crucial role in the regulation of gene expression. Over the last decade, the development of sequencing technologies has flooded researchers with transcriptome-phenotype data alongside emerging candidate regulatory elements. Since most methods can only provide hints about enhancer function, there have been attempts to develop experimental and computational approaches that can bridge the gap in the causal relationship between regulatory regions and phenotypes. The coupling of two state-of-the-art technologies, also referred to as crisprQTL, has emerged as a promising high-throughput toolkit for addressing this question. This review provides an overview of the importance of studying enhancers, the core molecular foundation of crisprQTL, and recent studies utilizing crisprQTL to interrogate enhancer-phenotype correlations. Additionally, we discuss computational methods currently employed for crisprQTL data analysis. We conclude by pointing out common challenges, making recommendations, and looking at future prospects, with the aim of providing researchers with an overview of crisprQTL as an important toolkit for studying enhancers.
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Affiliation(s)
- Reza Ghamsari
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
| | - Joseph Rosenbluh
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - A Vipin Menon
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
| | - Nigel H Lovell
- The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, Sydney, NSW 2052, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
- UNSW Data Science Hub, UNSW Sydney, Sydney, NSW 2052, Australia
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18
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Van de Sande B, Lee JS, Mutasa-Gottgens E, Naughton B, Bacon W, Manning J, Wang Y, Pollard J, Mendez M, Hill J, Kumar N, Cao X, Chen X, Khaladkar M, Wen J, Leach A, Ferran E. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov 2023; 22:496-520. [PMID: 37117846 PMCID: PMC10141847 DOI: 10.1038/s41573-023-00688-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/10/2023] [Indexed: 04/30/2023]
Abstract
Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.
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Affiliation(s)
| | | | | | - Bart Naughton
- Computational Neurobiology, Eisai, Cambridge, MA, USA
| | - Wendi Bacon
- EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
- The Open University, Milton Keynes, UK
| | | | - Yong Wang
- Precision Bioinformatics, Prometheus Biosciences, San Diego, CA, USA
| | | | - Melissa Mendez
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | - Jon Hill
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Namit Kumar
- Informatics & Predictive Sciences, Bristol Myers Squibb, San Diego, CA, USA
| | - Xiaohong Cao
- Genomic Research Center, AbbVie Inc., Cambridge, MA, USA
| | - Xiao Chen
- Magnet Biomedicine, Cambridge, MA, USA
| | - Mugdha Khaladkar
- Human Genetics and Computational Biology, GlaxoSmithKline, Collegeville, PA, USA
| | - Ji Wen
- Oncology Research and Development Unit, Pfizer, La Jolla, CA, USA
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19
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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: 7] [Impact Index Per Article: 7.0] [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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20
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Zhao Y, Zhang M, Yang D. Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens. QUANTITATIVE BIOLOGY 2022; 10:307-320. [PMID: 36937794 PMCID: PMC10019185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2023]
Abstract
Background Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects. Results Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens. Conclusion Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods.
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Affiliation(s)
- Yueshan Zhao
- Center for Pharmacogenetics, Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh PA 15261, USA
| | - Min Zhang
- Center for Pharmacogenetics, Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh PA 15261, USA
| | - Da Yang
- Center for Pharmacogenetics, Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh PA 15261, USA
- UPMC Hillman Cancer Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Correspondence:
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21
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Holcomb EA, Pearson AN, Jungles KM, Tate A, James J, Jiang L, Huber AK, Green MD. High-content CRISPR screening in tumor immunology. Front Immunol 2022; 13:1041451. [PMID: 36479127 PMCID: PMC9721350 DOI: 10.3389/fimmu.2022.1041451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 10/21/2022] [Indexed: 11/22/2022] Open
Abstract
CRISPR screening is a powerful tool that links specific genetic alterations to corresponding phenotypes, thus allowing for high-throughput identification of novel gene functions. Pooled CRISPR screens have enabled discovery of innate and adaptive immune response regulators in the setting of viral infection and cancer. Emerging methods couple pooled CRISPR screens with parallel high-content readouts at the transcriptomic, epigenetic, proteomic, and optical levels. These approaches are illuminating cancer immune evasion mechanisms as well as nominating novel targets that augment T cell activation, increase T cell infiltration into tumors, and promote enhanced T cell cytotoxicity. This review details recent methodological advances in high-content CRISPR screens and highlights the impact this technology is having on tumor immunology.
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Affiliation(s)
- Erin A. Holcomb
- Graduate Program in Immunology, School of Medicine, University of Michigan, Ann Arbor, MI, United States,Department of Radiation Oncology, School of Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Ashley N. Pearson
- Graduate Program in Immunology, School of Medicine, University of Michigan, Ann Arbor, MI, United States,Department of Radiation Oncology, School of Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Kassidy M. Jungles
- Department of Radiation Oncology, School of Medicine, University of Michigan, Ann Arbor, MI, United States,Department of Pharmacology, School of Medicine, University of Michigan, Ann Arbor, MI, United States,Rogel Cancer Center, University of Michigan, Ann Arbor, MI, United States
| | - Akshay Tate
- Department of Radiation Oncology, School of Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Jadyn James
- Department of Radiation Oncology, School of Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Long Jiang
- Department of Radiation Oncology, School of Medicine, University of Michigan, Ann Arbor, MI, United States,Institute of Health and Medicine, Hefei Comprehensive National Science Center, Hefei, China
| | - Amanda K. Huber
- Department of Radiation Oncology, School of Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Michael D. Green
- Graduate Program in Immunology, School of Medicine, University of Michigan, Ann Arbor, MI, United States,Department of Radiation Oncology, School of Medicine, University of Michigan, Ann Arbor, MI, United States,Rogel Cancer Center, University of Michigan, Ann Arbor, MI, United States,Department of Microbiology and Immunology, School of Medicine, University of Michigan, Ann Arbor, MI, United States,Department of Radiation Oncology, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, United States,*Correspondence: Michael D. Green,
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22
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Walton RT, Singh A, Blainey PC. Pooled genetic screens with image‐based profiling. Mol Syst Biol 2022; 18:e10768. [PMID: 36366905 PMCID: PMC9650298 DOI: 10.15252/msb.202110768] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 11/13/2022] Open
Abstract
Spatial structure in biology, spanning molecular, organellular, cellular, tissue, and organismal scales, is encoded through a combination of genetic and epigenetic factors in individual cells. Microscopy remains the most direct approach to exploring the intricate spatial complexity defining biological systems and the structured dynamic responses of these systems to perturbations. Genetic screens with deep single‐cell profiling via image features or gene expression programs have the capacity to show how biological systems work in detail by cataloging many cellular phenotypes with one experimental assay. Microscopy‐based cellular profiling provides information complementary to next‐generation sequencing (NGS) profiling and has only recently become compatible with large‐scale genetic screens. Optical screening now offers the scale needed for systematic characterization and is poised for further scale‐up. We discuss how these methodologies, together with emerging technologies for genetic perturbation and microscopy‐based multiplexed molecular phenotyping, are powering new approaches to reveal genotype–phenotype relationships.
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Affiliation(s)
- Russell T Walton
- Broad Institute of MIT and Harvard Cambridge MA USA
- Department of Biological Engineering MIT Cambridge MA USA
| | - Avtar Singh
- Broad Institute of MIT and Harvard Cambridge MA USA
| | - Paul C Blainey
- Broad Institute of MIT and Harvard Cambridge MA USA
- Department of Biological Engineering MIT Cambridge MA USA
- Koch Institute for Integrative Cancer Research MIT Cambridge MA USA
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23
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Ancos-Pintado R, Bragado-García I, Morales ML, García-Vicente R, Arroyo-Barea A, Rodríguez-García A, Martínez-López J, Linares M, Hernández-Sánchez M. High-Throughput CRISPR Screening in Hematological Neoplasms. Cancers (Basel) 2022; 14:3612. [PMID: 35892871 PMCID: PMC9329962 DOI: 10.3390/cancers14153612] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/16/2022] [Accepted: 07/20/2022] [Indexed: 02/01/2023] Open
Abstract
CRISPR is becoming an indispensable tool in biological research, revolutionizing diverse fields of medical research and biotechnology. In the last few years, several CRISPR-based genome-targeting tools have been translated for the study of hematological neoplasms. However, there is a lack of reviews focused on the wide uses of this technology in hematology. Therefore, in this review, we summarize the main CRISPR-based approaches of high throughput screenings applied to this field. Here we explain several libraries and algorithms for analysis of CRISPR screens used in hematology, accompanied by the most relevant databases. Moreover, we focus on (1) the identification of novel modulator genes of drug resistance and efficacy, which could anticipate relapses in patients and (2) new therapeutic targets and synthetic lethal interactions. We also discuss the approaches to uncover novel biomarkers of malignant transformations and immune evasion mechanisms. We explain the current literature in the most common lymphoid and myeloid neoplasms using this tool. Then, we conclude with future directions, highlighting the importance of further gene candidate validation and the integration and harmonization of the data from CRISPR screening approaches.
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Affiliation(s)
- Raquel Ancos-Pintado
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain; (I.B.-G.); (A.A.-B.)
| | - Irene Bragado-García
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain; (I.B.-G.); (A.A.-B.)
| | - María Luz Morales
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
| | - Roberto García-Vicente
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
| | - Andrés Arroyo-Barea
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain; (I.B.-G.); (A.A.-B.)
| | - Alba Rodríguez-García
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
| | - Joaquín Martínez-López
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
- Department of Medicine, Medicine School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain
| | - María Linares
- Department of Translational Hematology, Instituto de Investigación Hospital 12 de Octubre (imas12), Hematological Malignancies Clinical Research Unit H12O-CNIO, CIBERONC, ES 28041 Madrid, Spain; (R.A.-P.); (M.L.M.); (R.G.-V.); (A.R.-G.); (J.M.-L.); (M.L.)
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain; (I.B.-G.); (A.A.-B.)
| | - María Hernández-Sánchez
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, ES 28040 Madrid, Spain; (I.B.-G.); (A.A.-B.)
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24
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Computational tools and resources for CRISPR/Cas genome editing. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00027-4. [PMID: 35341983 PMCID: PMC10372911 DOI: 10.1016/j.gpb.2022.02.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 02/22/2022] [Accepted: 02/28/2022] [Indexed: 12/21/2022]
Abstract
The past decade has witnessed a rapid evolution in identifying more versatile clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) nucleases and their functional variants as well as in developing precise CRISPR/Cas-derived genome editors. The programmable and robust features of the genomic editors provide an effective RNA-guided platform for fundamental life science research and subsequent applications in diverse scenarios, including biomedical innovation and targeted crop improvement. One of the most essential principles is to guide alterations in genomic sequences or genes in the intended manner without undesired off-target impacts, which strongly depends on the efficiency and specificity of single guide RNA (sgRNA)-directed recognition of targeted DNA sequences. Recent advances in empirical scoring algorithms and machine learning models have facilitated sgRNA design and off-target prediction. In this review, we first briefly introduced the different features of CRISPR/Cas tools that should be taken into consideration to achieve specific purposes. Secondly, we focused on the computer-assisted tools and resources that are widely used in designing sgRNAs and analyzing CRISPR/Cas-induced on- and off-target mutations. Thirdly, we provide insights on the limitations of available computational tools that surely help researchers of this field for further optimization. Lastly, we suggested a simple but effective workflow for choosing and applying web-based resources and tools for CRISPR/Cas genome editing.
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25
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Bock C, Datlinger P, Chardon F, Coelho MA, Dong MB, Lawson KA, Lu T, Maroc L, Norman TM, Song B, Stanley G, Chen S, Garnett M, Li W, Moffat J, Qi LS, Shapiro RS, Shendure J, Weissman JS, Zhuang X. High-content CRISPR screening. NATURE REVIEWS. METHODS PRIMERS 2022; 2:9. [PMID: 37214176 PMCID: PMC10200264 DOI: 10.1038/s43586-022-00098-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
CRISPR screens are a powerful source of biological discovery, enabling the unbiased interrogation of gene function in a wide range of applications and species. In pooled CRISPR screens, various genetically encoded perturbations are introduced into pools of cells. The targeted cells proliferate under a biological challenge such as cell competition, drug treatment or viral infection. Subsequently, the perturbation-induced effects are evaluated by sequencing-based counting of the guide RNAs that specify each perturbation. The typical results of such screens are ranked lists of genes that confer sensitivity or resistance to the biological challenge of interest. Contributing to the broad utility of CRISPR screens, adaptations of the core CRISPR technology make it possible to activate, silence or otherwise manipulate the target genes. Moreover, high-content read-outs such as single-cell RNA sequencing and spatial imaging help characterize screened cells with unprecedented detail. Dedicated software tools facilitate bioinformatic analysis and enhance reproducibility. CRISPR screening has unravelled various molecular mechanisms in basic biology, medical genetics, cancer research, immunology, infectious diseases, microbiology and other fields. This Primer describes the basic and advanced concepts of CRISPR screening and its application as a flexible and reliable method for biological discovery, biomedical research and drug development - with a special emphasis on high-content methods that make it possible to obtain detailed biological insights directly as part of the screen.
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Affiliation(s)
- Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Paul Datlinger
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Florence Chardon
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Matthew B. Dong
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Systems Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Keith A. Lawson
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Tian Lu
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Laetitia Maroc
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada
| | - Thomas M. Norman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, CA, USA
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bicna Song
- Center for Genetic Medicine Research, Children’s National Hospital, Washington, DC, USA
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA
| | - Geoff Stanley
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Sidi Chen
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Systems Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Mathew Garnett
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Wei Li
- Center for Genetic Medicine Research, Children’s National Hospital, Washington, DC, USA
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Lei S. Qi
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
- ChEM-H, Stanford University, Stanford, CA, USA
| | - Rebecca S. Shapiro
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Jonathan S. Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, CA, USA
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
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26
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Barry T, Wang X, Morris JA, Roeder K, Katsevich E. SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis. Genome Biol 2021; 22:344. [PMID: 34930414 PMCID: PMC8686614 DOI: 10.1186/s13059-021-02545-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 11/10/2021] [Indexed: 12/16/2022] Open
Abstract
Single-cell CRISPR screens are a promising biotechnology for mapping regulatory elements to target genes at genome-wide scale. However, technical factors like sequencing depth impact not only expression measurement but also perturbation detection, creating a confounding effect. We demonstrate on two single-cell CRISPR screens how these challenges cause calibration issues. We propose SCEPTRE: analysis of single-cell perturbation screens via conditional resampling, which infers associations between perturbations and expression by resampling the former according to a working model for perturbation detection probability in each cell. SCEPTRE demonstrates very good calibration and sensitivity on CRISPR screen data, yielding hundreds of new regulatory relationships supported by orthogonal biological evidence.
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Affiliation(s)
- Timothy Barry
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, 15213, PA, USA
| | - Xuran Wang
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, 15213, PA, USA
| | - John A Morris
- New York Genome Center, New York, USA
- Department of Biology, New York University, 24 Waverly Pl 6th Floor, New York, 10003, NY, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, 15213, PA, USA
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, 15213, PA, USA
| | - Eugene Katsevich
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, 19104, PA, USA.
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27
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Zhao X, Fu X, Yin C, Lu F. Wheat speciation and adaptation: perspectives from reticulate evolution. ABIOTECH 2021; 2:386-402. [PMID: 36311810 PMCID: PMC9590565 DOI: 10.1007/s42994-021-00047-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 04/27/2021] [Indexed: 12/16/2022]
Abstract
Reticulate evolution through the interchanging of genetic components across organisms can impact significantly on the fitness and adaptation of species. Bread wheat (Triticum aestivum subsp. aestivum) is one of the most important crops in the world. Allopolyploid speciation, frequent hybridization, extensive introgression, and occasional horizontal gene transfer (HGT) have been shaping a typical paradigm of reticulate evolution in bread wheat and its wild relatives, which is likely to have a substantial influence on phenotypic traits and environmental adaptability of bread wheat. In this review, we outlined the evolutionary history of bread wheat and its wild relatives with a highlight on the interspecific hybridization events, demonstrating the reticulate relationship between species/subspecies in the genera Triticum and Aegilops. Furthermore, we discussed the genetic mechanisms and evolutionary significance underlying the introgression of bread wheat and its wild relatives. An in-depth understanding of the evolutionary process of Triticum species should be beneficial to future genetic study and breeding of bread wheat.
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Affiliation(s)
- Xuebo Zhao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiangdong Fu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Changbin Yin
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China
| | - Fei Lu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
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28
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Wang L. Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr. Nat Commun 2021; 12:6395. [PMID: 34737291 PMCID: PMC8568964 DOI: 10.1038/s41467-021-26682-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 10/19/2021] [Indexed: 12/13/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Furthermore, statistical association testing remains difficult for scRNA-seq. Here we present Normalisr, a normalization and statistical association testing framework that unifies single-cell differential expression, co-expression, and CRISPR screen analyses with linear models. By systematically detecting and removing nonlinear confounders arising from library size at mean and variance levels, Normalisr achieves high sensitivity, specificity, speed, and generalizability across multiple scRNA-seq protocols and experimental conditions with unbiased p-value estimation. The superior scalability allows us to reconstruct robust gene regulatory networks from trans-effects of guide RNAs in large-scale single cell CRISPRi screens. On conventional scRNA-seq, Normalisr recovers gene-level co-expression networks that recapitulated known gene functions.
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Affiliation(s)
- Lingfei Wang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA.
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, MA, USA.
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29
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Borch Jensen M, Marblestone A. In vivo Pooled Screening: A Scalable Tool to Study the Complexity of Aging and Age-Related Disease. FRONTIERS IN AGING 2021; 2:714926. [PMID: 35822038 PMCID: PMC9261400 DOI: 10.3389/fragi.2021.714926] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 08/18/2021] [Indexed: 12/12/2022]
Abstract
Biological aging, and the diseases of aging, occur in a complex in vivo environment, driven by multiple interacting processes. A convergence of recently developed technologies has enabled in vivo pooled screening: direct administration of a library of different perturbations to a living animal, with a subsequent readout that distinguishes the identity of each perturbation and its effect on individual cells within the animal. Such screens hold promise for efficiently applying functional genomics to aging processes in the full richness of the in vivo setting. In this review, we describe the technologies behind in vivo pooled screening, including a range of options for delivery, perturbation and readout methods, and outline their potential application to aging and age-related disease. We then suggest how in vivo pooled screening, together with emerging innovations in each of its technological underpinnings, could be extended to shed light on key open questions in aging biology, including the mechanisms and limits of epigenetic reprogramming and identifying cellular mediators of systemic signals in aging.
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Affiliation(s)
| | - Adam Marblestone
- Astera Institute, San Francisco, CA, United States
- Federation of American Scientists, Washington D.C., CA, United States
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30
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Qin J, Hu Y, Yao JC, Leung RWT, Zhou Y, Qin Y, Wang J. Cell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data. Brief Bioinform 2021; 22:6347206. [PMID: 34374760 DOI: 10.1093/bib/bbab311] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/06/2021] [Accepted: 07/19/2021] [Indexed: 01/09/2023] Open
Abstract
Cell fate conversion by overexpressing defined factors is a powerful tool in regenerative medicine. However, identifying key factors for cell fate conversion requires laborious experimental efforts; thus, many of such conversions have not been achieved yet. Nevertheless, cell fate conversions found in many published studies were incomplete as the expression of important gene sets could not be manipulated thoroughly. Therefore, the identification of master transcription factors for complete and efficient conversion is crucial to render this technology more applicable clinically. In the past decade, systematic analyses on various single-cell and bulk OMICs data have uncovered numerous gene regulatory mechanisms, and made it possible to predict master gene regulators during cell fate conversion. By virtue of the sparse structure of master transcription factors and the group structure of their simultaneous regulatory effects on the cell fate conversion process, this study introduces a novel computational method predicting master transcription factors based on group sparse optimization technique integrating data from multi-OMICs levels, which can be applicable to both single-cell and bulk OMICs data with a high tolerance of data sparsity. When it is compared with current prediction methods by cross-referencing published and validated master transcription factors, it possesses superior performance. In short, this method facilitates fast identification of key regulators, give raise to the possibility of higher successful conversion rate and in the hope of reducing experimental cost.
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Affiliation(s)
- Jing Qin
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China
| | - Yaohua Hu
- Shenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China
| | - Jen-Chih Yao
- Research Center for Interneural Computing, China Medical University, Taichung 40402, Taiwan
| | - Ricky Wai Tak Leung
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China
| | - Yongqiang Zhou
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China
| | - Yiming Qin
- Center for Genomic Sciences & School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong
| | - Junwen Wang
- Department of Quantitative Health Sciences and Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA.,Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA
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31
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Rodda M, Niissalo MA. Plastome evolution and organisation in the Hoya group (Apocynaceae). Sci Rep 2021; 11:14520. [PMID: 34267257 PMCID: PMC8282776 DOI: 10.1038/s41598-021-93890-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 07/01/2021] [Indexed: 11/12/2022] Open
Abstract
The genus Hoya is highly diverse and many of its species are popular ornamental plants. However, the relationships between Hoya and related genera (the Hoya group) are not fully resolved. In this study, we report 20 newly sequenced plastomes of species in the Hoya group. The complete plastomes vary in length from 175,405 to 178,525 bp while the LSCs vary from 90,248 to 92,364 bp and the complete SSCs vary from 2,285 to 2,304 bp, making the SSC in the Hoya group one of the shortest known in the angiosperms. The plastome structure in the Hoya group is characterised by a massive increase in the size of the inverted repeats as compared to the outgroups. In all ingroup species, the IR/SSC boundary moved from ycf1 to ndhF while this was not observed in outgroup taxa, making it a synapomorphy for the Hoya group. We have also assembled the mitogenome of Hoya lithophytica, which, at 718,734 bp, is the longest reported in the family. The phylogenetic analysis using exons from 42 taxa in the Hoya group and three outgoups confirms that the earliest divergent genus in the Hoya group is Papuahoya, followed by Dischidia. The relationship between Dischidia and the clade which includes all Hoya and Oreosparte taxa, is not fully supported. Oreosparte is nested in Hoya making it paraphyletic unless Clemensiella is recognised as a separate genus.
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Affiliation(s)
- Michele Rodda
- Singapore Botanic Gardens, National Parks Board, 1 Cluny Road, Singapore, 259569, Singapore.
| | - Matti A Niissalo
- Singapore Botanic Gardens, National Parks Board, 1 Cluny Road, Singapore, 259569, Singapore
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32
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Ji Y, Lotfollahi M, Wolf FA, Theis FJ. Machine learning for perturbational single-cell omics. Cell Syst 2021; 12:522-537. [PMID: 34139164 DOI: 10.1016/j.cels.2021.05.016] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 05/04/2021] [Accepted: 05/19/2021] [Indexed: 12/18/2022]
Abstract
Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.
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Affiliation(s)
- Yuge Ji
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - F Alexander Wolf
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Cellarity, Cambridge, MA, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany; Cellarity, Cambridge, MA, USA.
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33
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Lee S, Kim J, Park JE. Single-Cell Toolkits Opening a New Era for Cell Engineering. Mol Cells 2021; 44:127-135. [PMID: 33795531 PMCID: PMC8019599 DOI: 10.14348/molcells.2021.0002] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 03/04/2021] [Accepted: 03/11/2021] [Indexed: 02/07/2023] Open
Abstract
Since the introduction of RNA sequencing (RNA-seq) as a high-throughput mRNA expression analysis tool, this procedure has been increasingly implemented to identify cell-level transcriptome changes in a myriad of model systems. However, early methods processed cell samples in bulk, and therefore the unique transcriptomic patterns of individual cells would be lost due to data averaging. Nonetheless, the recent and continuous development of new single-cell RNA sequencing (scRNA-seq) toolkits has enabled researchers to compare transcriptomes at a single-cell resolution, thus facilitating the analysis of individual cellular features and a deeper understanding of cellular functions. Nonetheless, the rapid evolution of high throughput single-cell "omics" tools has created the need for effective hypothesis verification strategies. Particularly, this issue could be addressed by coupling cell engineering techniques with single-cell sequencing. This approach has been successfully employed to gain further insights into disease pathogenesis and the dynamics of differentiation trajectories. Therefore, this review will discuss the current status of cell engineering toolkits and their contributions to single-cell and genome-wide data collection and analyses.
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Affiliation(s)
- Sean Lee
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jireh Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jong-Eun Park
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
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34
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Zhang Y, Zhao G, Ahmed FYH, Yi T, Hu S, Cai T, Liao Q. In silico Method in CRISPR/Cas System: An Expedite and Powerful Booster. Front Oncol 2020; 10:584404. [PMID: 33123486 PMCID: PMC7567020 DOI: 10.3389/fonc.2020.584404] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 08/24/2020] [Indexed: 12/11/2022] Open
Abstract
The CRISPR/Cas system has stood in the center of attention in the last few years as a revolutionary gene editing tool with a wide application to investigate gene functions. However, the labor-intensive workflow requires a sophisticated pre-experimental and post-experimental analysis, thus becoming one of the hindrances for the further popularization of practical applications. Recently, the increasing emergence and advancement of the in silico methods play a formidable role to support and boost experimental work. However, various tools based on distinctive design principles and frameworks harbor unique characteristics that are likely to confuse users about how to choose the most appropriate one for their purpose. In this review, we will present a comprehensive overview and comparisons on the in silico methods from the aspects of CRISPR/Cas system identification, guide RNA design, and post-experimental assistance. Furthermore, we establish the hypotheses in light of the new trends around the technical optimization and hope to provide significant clues for future tools development.
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Affiliation(s)
- Yuwei Zhang
- Hwa Mei Hospital, University of Chinese Academy of Science, Ningbo, China.,Zhejiang Key Laboratory of Pathophysiology, Department of Preventative Medicine, Medical School of Ningbo University, Ningbo, China.,Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
| | - Guofang Zhao
- Hwa Mei Hospital, University of Chinese Academy of Science, Ningbo, China.,Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
| | - Fatma Yislam Hadi Ahmed
- Zhejiang Key Laboratory of Pathophysiology, Department of Preventative Medicine, Medical School of Ningbo University, Ningbo, China
| | - Tianfei Yi
- Zhejiang Key Laboratory of Pathophysiology, Department of Preventative Medicine, Medical School of Ningbo University, Ningbo, China
| | - Shiyun Hu
- Zhejiang Key Laboratory of Pathophysiology, Department of Preventative Medicine, Medical School of Ningbo University, Ningbo, China
| | - Ting Cai
- Hwa Mei Hospital, University of Chinese Academy of Science, Ningbo, China.,Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
| | - Qi Liao
- Hwa Mei Hospital, University of Chinese Academy of Science, Ningbo, China.,Zhejiang Key Laboratory of Pathophysiology, Department of Preventative Medicine, Medical School of Ningbo University, Ningbo, China.,Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
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35
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Lalli MA, Avey D, Dougherty JD, Milbrandt J, Mitra RD. High-throughput single-cell functional elucidation of neurodevelopmental disease-associated genes reveals convergent mechanisms altering neuronal differentiation. Genome Res 2020; 30:1317-1331. [PMID: 32887689 PMCID: PMC7545139 DOI: 10.1101/gr.262295.120] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 08/03/2020] [Indexed: 12/16/2022]
Abstract
The overwhelming success of exome- and genome-wide association studies in discovering thousands of disease-associated genes necessitates developing novel high-throughput functional genomics approaches to elucidate the molecular mechanisms of these genes. Here, we have coupled multiplexed repression of neurodevelopmental disease–associated genes to single-cell transcriptional profiling in differentiating human neurons to rapidly assay the functions of multiple genes in a disease-relevant context, assess potentially convergent mechanisms, and prioritize genes for specific functional assays. For a set of 13 autism spectrum disorder (ASD)–associated genes, we show that this approach generated important mechanistic insights, revealing two functionally convergent modules of ASD genes: one that delays neuron differentiation and one that accelerates it. Five genes that delay neuron differentiation (ADNP, ARID1B, ASH1L, CHD2, and DYRK1A) mechanistically converge, as they all dysregulate genes involved in cell-cycle control and progenitor cell proliferation. Live-cell imaging after individual ASD-gene repression validated this functional module, confirming that these genes reduce neural progenitor cell proliferation and neurite growth. Finally, these functionally convergent ASD gene modules predicted shared clinical phenotypes among individuals with mutations in these genes. Altogether, these results show the utility of a novel and simple approach for the rapid functional elucidation of neurodevelopmental disease-associated genes.
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Affiliation(s)
- Matthew A Lalli
- Department of Genetics, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA.,Edison Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA
| | - Denis Avey
- Department of Genetics, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA.,Edison Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA
| | - Joseph D Dougherty
- Department of Genetics, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA.,Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA
| | - Jeffrey Milbrandt
- Department of Genetics, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA
| | - Robi D Mitra
- Department of Genetics, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA.,Edison Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA
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36
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Lin X, Chemparathy A, La Russa M, Daley T, Qi LS. Computational Methods for Analysis of Large-Scale CRISPR Screens. Annu Rev Biomed Data Sci 2020. [DOI: 10.1146/annurev-biodatasci-020520-113523] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Large-scale CRISPR-Cas pooled screens have shown great promise to investigate functional links between genotype and phenotype at the genome-wide scale. In addition to technological advancement, there is a need to develop computational methods to analyze the large datasets obtained from high-throughput CRISPR screens. Many computational methods have been developed to identify reliable gene hits from various screens. In this review, we provide an overview of the technology development of CRISPR screening platforms, with a focus on recent advances in computational methods to identify and model gene effects using CRISPR screen datasets. We also discuss existing challenges and opportunities for future computational methods development.
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Affiliation(s)
- Xueqiu Lin
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA
| | | | - Marie La Russa
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Timothy Daley
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA
- Department of Statistics, Stanford University, Stanford, California 94305, USA
| | - Lei S. Qi
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA
- Department of Chemical and Systems Biology and ChEM-H (Chemistry, Engineering, and Medicine for Human Health), Stanford University, Stanford, California 94305, USA
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37
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Clement K, Hsu JY, Canver MC, Joung JK, Pinello L. Technologies and Computational Analysis Strategies for CRISPR Applications. Mol Cell 2020; 79:11-29. [PMID: 32619467 PMCID: PMC7497852 DOI: 10.1016/j.molcel.2020.06.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Revised: 03/12/2020] [Accepted: 06/05/2020] [Indexed: 12/21/2022]
Abstract
The CRISPR-Cas system offers a programmable platform for eukaryotic genome and epigenome editing. The ability to perform targeted genetic and epigenetic perturbations enables researchers to perform a variety of tasks, ranging from investigating questions in basic biology to potentially developing novel therapeutics for the treatment of disease. While CRISPR systems have been engineered to target DNA and RNA with increased precision, efficiency, and flexibility, assays to identify off-target editing are becoming more comprehensive and sensitive. Furthermore, techniques to perform high-throughput genome and epigenome editing can be paired with a variety of readouts and are uncovering important cellular functions and mechanisms. These technological advances drive and are driven by accompanying computational approaches. Here, we briefly present available CRISPR technologies and review key computational advances and considerations for various CRISPR applications. In particular, we focus on the analysis of on- and off-target editing and CRISPR pooled screen data.
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Affiliation(s)
- Kendell Clement
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA; Department of Pathology, Harvard Medical School, Boston, MA, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan Y Hsu
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA; Department of Pathology, Harvard Medical School, Boston, MA, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew C Canver
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA; Department of Pathology, Harvard Medical School, Boston, MA, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Pathology and Laboratory Medicine, New York-Presbyterian Hospital, Weill Cornell Medicine, New York, NY, USA
| | - J Keith Joung
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA; Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA; Department of Pathology, Harvard Medical School, Boston, MA, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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38
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Hanna RE, Doench JG. Design and analysis of CRISPR-Cas experiments. Nat Biotechnol 2020; 38:813-823. [PMID: 32284587 DOI: 10.1038/s41587-020-0490-7] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 03/06/2020] [Indexed: 02/08/2023]
Abstract
A large and ever-expanding set of CRISPR-Cas systems now enables the rapid and flexible manipulation of genomes in both targeted and large-scale experiments. Numerous software tools and analytical methods have been developed for the design and analysis of CRISPR-Cas experiments, including resources to design optimal guide RNAs for various modes of manipulation and to analyze the results of such experiments. A major recent focus has been the development of comprehensive tools for use on data from large-scale CRISPR-based genetic screens. As this field continues to progress, a clear ongoing challenge is not only to innovate, but to actively maintain and improve existing tools so that researchers across disciplines can rely on a stable set of excellent computational resources for CRISPR-Cas experiments.
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Affiliation(s)
- Ruth E Hanna
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - John G Doench
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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39
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Höllbacher B, Balázs K, Heinig M, Uhlenhaut NH. Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation. Comput Struct Biotechnol J 2020; 18:1330-1341. [PMID: 32612756 PMCID: PMC7306512 DOI: 10.1016/j.csbj.2020.05.018] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 05/21/2020] [Accepted: 05/23/2020] [Indexed: 02/06/2023] Open
Abstract
Advancements in the field of next generation sequencing lead to the generation of ever-more data, with the challenge often being how to combine and reconcile results from different OMICs studies such as genome, epigenome and transcriptome. Here we provide an overview of the standard processing pipelines for ChIP-seq and RNA-seq as well as common downstream analyses. We describe popular multi-omics data integration approaches used to identify target genes and co-factors, and we discuss how machine learning techniques may predict transcriptional regulators and gene expression.
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Affiliation(s)
- Barbara Höllbacher
- Institute for Diabetes and Cancer IDC, Helmholtz Zentrum Muenchen (HMGU) and German Center for Diabetes Research (DZD), Munich 85764, Neuherberg, Germany.,Institute of Computational Biology ICB, Helmholtz Zentrum Muenchen (HMGU) and German Center for Diabetes Research (DZD), Munich 85764, Neuherberg, Germany.,Department of Informatics, TUM, Munich 85748, Garching, Germany
| | - Kinga Balázs
- Institute for Diabetes and Cancer IDC, Helmholtz Zentrum Muenchen (HMGU) and German Center for Diabetes Research (DZD), Munich 85764, Neuherberg, Germany
| | - Matthias Heinig
- Institute of Computational Biology ICB, Helmholtz Zentrum Muenchen (HMGU) and German Center for Diabetes Research (DZD), Munich 85764, Neuherberg, Germany.,Department of Informatics, TUM, Munich 85748, Garching, Germany
| | - N Henriette Uhlenhaut
- Institute for Diabetes and Cancer IDC, Helmholtz Zentrum Muenchen (HMGU) and German Center for Diabetes Research (DZD), Munich 85764, Neuherberg, Germany.,Metabolic Programming, TUM School of Life Sciences Weihenstephan, Munich 85354, Freising, Germany
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