1
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Southard KM, Ardy RC, Tang A, O'Sullivan DD, Metzner E, Guruvayurappan K, Norman TM. Comprehensive transcription factor perturbations recapitulate fibroblast transcriptional states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.606073. [PMID: 39131349 PMCID: PMC11312553 DOI: 10.1101/2024.07.31.606073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Cell atlas projects have nominated recurrent transcriptional states as drivers of biological processes and disease, but their origins, regulation, and properties remain unclear. To enable complementary functional studies, we developed a scalable approach for recapitulating cell states in vitro using CRISPR activation (CRISPRa) Perturb-seq. Aided by a novel multiplexing method, we activated 1,836 transcription factors in two cell types. Measuring 21,958 perturbations showed that CRISPRa activated targets within physiological ranges, that epigenetic features predicted activatable genes, and that the protospacer seed region drove an off-target effect. Perturbations recapitulated in vivo fibroblast states, including universal and inflammatory states, and identified KLF4 and KLF5 as key regulators of the universal state. Inducing the universal state suppressed disease-associated states, highlighting its therapeutic potential. Our findings cement CRISPRa as a tool for perturbing differentiated cells and indicate that in vivo states can be elicited via perturbation, enabling studies of clinically relevant states ex vivo .
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2
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Chow A, Lareau CA. Concepts and new developments in droplet-based single cell multi-omics. Trends Biotechnol 2024:S0167-7799(24)00184-7. [PMID: 39095258 DOI: 10.1016/j.tibtech.2024.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/31/2024] [Accepted: 07/12/2024] [Indexed: 08/04/2024]
Abstract
Single cell sequencing technologies have become a fixture in the molecular profiling of cells due to their ease, flexibility, and commercial availability. In particular, partitioning individual cells inside oil droplets via microfluidic reactions enables transcriptomic or multi-omic measurements for thousands of cells in parallel. Complementing the multitude of biological discoveries from genomics analyses, the past decade has brought new capabilities from assay baselines to enable a deeper understanding of the complex data from single cell multi-omics. Here, we highlight four innovations that have improved the reliability and understanding of droplet microfluidic assays. We emphasize new developments that further orient principles of technology development and guidelines for the design, benchmarking, and implementation of new droplet-based methodologies.
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Affiliation(s)
- Arthur Chow
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Caleb A Lareau
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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3
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Metzner E, Southard KM, Norman TM. Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.26.605307. [PMID: 39091800 PMCID: PMC11291144 DOI: 10.1101/2024.07.26.605307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Pseudotime analysis of perturbations connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state.
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Affiliation(s)
- Eli Metzner
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Kaden M. Southard
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Thomas M. Norman
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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4
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Frenkel M, Corban JE, Hujoel MLA, Morris Z, Raman S. Large-scale discovery of chromatin dysregulation induced by oncofusions and other protein-coding variants. Nat Biotechnol 2024:10.1038/s41587-024-02347-4. [PMID: 39048711 DOI: 10.1038/s41587-024-02347-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 07/02/2024] [Indexed: 07/27/2024]
Abstract
Population-scale databases have expanded to millions of protein-coding variants, yet insight into their mechanistic consequences has lagged. Here we present PROD-ATAC, a high-throughput method for discovering the effects of protein-coding variants on chromatin regulation. A pooled variant library is expressed in a disease-agnostic cell line, and single-cell assay for transposase-accessible chromatin resolves each variant's effect on the chromatin landscape. Using PROD-ATAC, we characterized the effects of more than 100 oncofusions (cancer-causing chimeric proteins) and controls and revealed that chromatin remodeling is common to fusions spanning an enormous range of fusion frequencies. Furthermore, fusion-induced dysregulation can be context agnostic, as observed mechanisms often overlapped with cancer and cell-type-specific prior knowledge. We also showed that gain-of-function activity is common among oncofusions. This work begins to outline a global map of fusion-induced chromatin alterations. We suggest that there might be convergent mechanisms among disparate oncofusions and shared modes of dysregulation among fusions present in tumors at different frequencies. PROD-ATAC is generalizable to any set of protein-coding variants.
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Affiliation(s)
- Max Frenkel
- Cellular and Molecular Biology Graduate Program, University of Wisconsin-Madison, Madison, WI, USA
- Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - James E Corban
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Margaux L A Hujoel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zachary Morris
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.
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5
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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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6
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Yun S, Noh M, Yu J, Kim HJ, Hui CC, Lee H, Son JE. Unlocking biological mechanisms with integrative functional genomics approaches. Mol Cells 2024; 47:100092. [PMID: 39019219 DOI: 10.1016/j.mocell.2024.100092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 07/01/2024] [Accepted: 07/08/2024] [Indexed: 07/19/2024] Open
Abstract
Reverse genetics offers precise functional insights into genes through the targeted manipulation of gene expression followed by phenotypic assessment. While these approaches have proven effective in model organisms such as Saccharomyces cerevisiae, large-scale genetic manipulations in human cells were historically unfeasible due to methodological limitations. However, recent advancements in functional genomics, particularly clustered regularly interspaced short palindromic repeats (CRISPR)-based screening technologies and next-generation sequencing platforms, have enabled pooled screening technologies that allow massively parallel, unbiased assessments of biological phenomena in human cells. This review provides a comprehensive overview of cutting-edge functional genomic screening technologies applicable to human cells, ranging from short hairpin RNA screens to modern CRISPR screens. Additionally, we explore the integration of CRISPR platforms with single-cell approaches to monitor gene expression, chromatin accessibility, epigenetic regulation, and chromatin architecture following genetic perturbations at the omics level. By offering an in-depth understanding of these genomic screening methods, this review aims to provide insights into more targeted and effective strategies for genomic research and personalized medicine.
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Affiliation(s)
- Sehee Yun
- Department of Life Sciences, Korea University, Seoul 02841, Korea
| | - Minsoo Noh
- Department of Life Sciences, Korea University, Seoul 02841, Korea; Department of Internal Medicine and Laboratory of Genomics and Translational Medicine, Gachon University College of Medicine, Incheon 21565, Korea
| | - Jivin Yu
- Department of Life Sciences, Korea University, Seoul 02841, Korea
| | - Hyeon-Jai Kim
- Department of Life Sciences, Korea University, Seoul 02841, Korea
| | - Chi-Chung Hui
- Program in Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Hunsang Lee
- Department of Life Sciences, Korea University, Seoul 02841, Korea.
| | - Joe Eun Son
- School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Korea.
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7
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Frenkel M, Raman S. Discovering mechanisms of human genetic variation and controlling cell states at scale. Trends Genet 2024; 40:587-600. [PMID: 38658256 DOI: 10.1016/j.tig.2024.03.010] [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: 02/24/2024] [Revised: 03/29/2024] [Accepted: 03/29/2024] [Indexed: 04/26/2024]
Abstract
Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.
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Affiliation(s)
- Max Frenkel
- Cellular and Molecular Biology Graduate Program, University of Wisconsin, Madison, WI, USA; Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Biochemistry, University of Wisconsin, Madison, WI, USA.
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA; Department of Bacteriology, University of Wisconsin, Madison, WI, USA; Department of Chemical and Biological Engineering, University of Wisconsin, Madison, WI, USA.
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8
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Do BT, Hsu PP, Vermeulen SY, Wang Z, Hirz T, Abbott KL, Aziz N, Replogle JM, Bjelosevic S, Paolino J, Nelson SA, Block S, Darnell AM, Ferreira R, Zhang H, Milosevic J, Schmidt DR, Chidley C, Harris IS, Weissman JS, Pikman Y, Stegmaier K, Cheloufi S, Su XA, Sykes DB, Vander Heiden MG. Nucleotide depletion promotes cell fate transitions by inducing DNA replication stress. Dev Cell 2024:S1534-5807(24)00327-7. [PMID: 38823395 DOI: 10.1016/j.devcel.2024.05.010] [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: 01/30/2024] [Revised: 04/14/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
Control of cellular identity requires coordination of developmental programs with environmental factors such as nutrient availability, suggesting that perturbing metabolism can alter cell state. Here, we find that nucleotide depletion and DNA replication stress drive differentiation in human and murine normal and transformed hematopoietic systems, including patient-derived acute myeloid leukemia (AML) xenografts. These cell state transitions begin during S phase and are independent of ATR/ATM checkpoint signaling, double-stranded DNA break formation, and changes in cell cycle length. In systems where differentiation is blocked by oncogenic transcription factor expression, replication stress activates primed regulatory loci and induces lineage-appropriate maturation genes despite the persistence of progenitor programs. Altering the baseline cell state by manipulating transcription factor expression causes replication stress to induce genes specific for alternative lineages. The ability of replication stress to selectively activate primed maturation programs across different contexts suggests a general mechanism by which changes in metabolism can promote lineage-appropriate cell state transitions.
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Affiliation(s)
- Brian T Do
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Harvard-MIT Health Sciences and Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Peggy P Hsu
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Dana-Farber Cancer Institute, Boston, MA 02115, USA; Massachusetts General Hospital Cancer Center, Boston, MA 02113, USA; Rogel Cancer Center and Division of Hematology and Oncology, Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Sidney Y Vermeulen
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Zhishan Wang
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Taghreed Hirz
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02113, USA; Harvard Stem Cell Institute, Cambridge, MA 02139, USA
| | - Keene L Abbott
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Najihah Aziz
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02113, USA; Harvard Stem Cell Institute, Cambridge, MA 02139, USA
| | - Joseph M Replogle
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA; Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Stefan Bjelosevic
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jonathan Paolino
- Department of Pediatric Oncology, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Samantha A Nelson
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Samuel Block
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Alicia M Darnell
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Raphael Ferreira
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Hanyu Zhang
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02113, USA; Harvard Stem Cell Institute, Cambridge, MA 02139, USA
| | - Jelena Milosevic
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02113, USA; Harvard Stem Cell Institute, Cambridge, MA 02139, USA
| | - Daniel R Schmidt
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Christopher Chidley
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Isaac S Harris
- Wilmot Cancer Institute, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Jonathan S Weissman
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Yana Pikman
- Department of Pediatric Oncology, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Kimberly Stegmaier
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Sihem Cheloufi
- Department of Biochemistry, University of California, Riverside, Riverside, CA 92521, USA; Stem Cell Center, University of California, Riverside, Riverside, CA 92521, USA; Center for RNA Biology and Medicine, Riverside, CA 92521, USA
| | - Xiaofeng A Su
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - David B Sykes
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02113, USA; Harvard Stem Cell Institute, Cambridge, MA 02139, USA
| | - Matthew G Vander Heiden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Dana-Farber Cancer Institute, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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9
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Gerbaldo F, Sonder E, Fischer V, Frei S, Wang J, Gapp K, Robinson MD, Germain PL. On the identification of differentially-active transcription factors from ATAC-seq data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583825. [PMID: 38496482 PMCID: PMC10942475 DOI: 10.1101/2024.03.06.583825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
ATAC-seq has emerged as a rich epigenome profiling technique, and is commonly used to identify Transcription Factors (TFs) underlying given phenomena. A number of methods can be used to identify differentially-active TFs through the accessibility of their DNA-binding motif, however little is known on the best approaches for doing so. Here we benchmark several such methods using a combination of curated datasets with various forms of short-term perturbations on known TFs, as well as semi-simulations. We include both methods specifically designed for this type of data as well as some that can be repurposed for it. We also investigate variations to these methods, and identify three particularly promising approaches (chromVAR-limma with critical adjustments, monaLisa and a combination of GC smooth quantile normalization and multivariate modeling). We further investigate the specific use of nucleosome-free fragments, the combination of top methods, and the impact of technical variation. Finally, we illustrate the use of the top methods on a novel dataset to characterize the impact on DNA accessibility of TRAnscription Factor TArgeting Chimeras (TRAFTAC), which can deplete TFs - in our case NFkB - at the protein level.
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Affiliation(s)
- Felix Gerbaldo
- Computational Neurogenomics, D-HEST Institute for Neurosciences, Zürich, Switzerland
- Systems Neuroscience, D-HEST Institute for Neurosciences, Zürich, Switzerland
| | - Emanuel Sonder
- Computational Neurogenomics, D-HEST Institute for Neurosciences, Zürich, Switzerland
- Systems Neuroscience, D-HEST Institute for Neurosciences, Zürich, Switzerland
- Department of Molecular Life Sciences, University of Zürich, Zürich, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Switzerland
| | - Vincent Fischer
- Epigenetics and Neuroendocrinology, D-HEST Institute for Neurosciences, Zürich, Switzerland
| | - Selina Frei
- Epigenetics and Neuroendocrinology, D-HEST Institute for Neurosciences, Zürich, Switzerland
| | - Jiayi Wang
- Department of Molecular Life Sciences, University of Zürich, Zürich, Switzerland
| | - Katharina Gapp
- Epigenetics and Neuroendocrinology, D-HEST Institute for Neurosciences, Zürich, Switzerland
| | - Mark D Robinson
- Department of Molecular Life Sciences, University of Zürich, Zürich, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Switzerland
| | - Pierre-Luc Germain
- Computational Neurogenomics, D-HEST Institute for Neurosciences, Zürich, Switzerland
- Department of Molecular Life Sciences, University of Zürich, Zürich, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Switzerland
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10
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Peidli S, Green TD, Shen C, Gross T, Min J, Garda S, Yuan B, Schumacher LJ, Taylor-King JP, Marks DS, Luna A, Blüthgen N, Sander C. scPerturb: harmonized single-cell perturbation data. Nat Methods 2024; 21:531-540. [PMID: 38279009 DOI: 10.1038/s41592-023-02144-y] [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: 01/28/2023] [Accepted: 12/04/2023] [Indexed: 01/28/2024]
Abstract
Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.
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Affiliation(s)
- Stefan Peidli
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Tessa D Green
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Ciyue Shen
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | | | - Joseph Min
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Samuele Garda
- Institute of Biology, Humboldt-Universität, Berlin, Germany
- Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bo Yuan
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Linus J Schumacher
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Augustin Luna
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
- Computational Biology Branch, National Library of Medicine and Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD, USA.
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Chris Sander
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
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11
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Proteins shaping global chromatin accessibility. Nat Genet 2024; 56:367-368. [PMID: 38418745 DOI: 10.1038/s41588-024-01667-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
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12
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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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13
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Mihai IS, Chafle S, Henriksson J. Representing and extracting knowledge from single-cell data. Biophys Rev 2024; 16:29-56. [PMID: 38495441 PMCID: PMC10937862 DOI: 10.1007/s12551-023-01091-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 06/28/2023] [Indexed: 03/19/2024] Open
Abstract
Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.
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Affiliation(s)
- Ionut Sebastian Mihai
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
- Industrial Doctoral School, Umeå University, Umeå, Sweden
| | - Sarang Chafle
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
| | - Johan Henriksson
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
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14
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Meng Q, Wei L, Ma K, Shi M, Lin X, Ho JWK, Li Y, Zhang X. scDecouple: decoupling cellular response from infected proportion bias in scCRISPR-seq. Brief Bioinform 2024; 25:bbae011. [PMID: 38324621 PMCID: PMC10849189 DOI: 10.1093/bib/bbae011] [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: 10/31/2023] [Revised: 12/18/2023] [Accepted: 01/05/2024] [Indexed: 02/09/2024] Open
Abstract
Single-cell clustered regularly interspaced short palindromic repeats-sequencing (scCRISPR-seq) is an emerging high-throughput CRISPR screening technology where the true cellular response to perturbation is coupled with infected proportion bias of guide RNAs (gRNAs) across different cell clusters. The mixing of these effects introduces noise into scCRISPR-seq data analysis and thus obstacles to relevant studies. We developed scDecouple to decouple true cellular response of perturbation from the influence of infected proportion bias. scDecouple first models the distribution of gene expression profiles in perturbed cells and then iteratively finds the maximum likelihood of cell cluster proportions as well as the cellular response for each gRNA. We demonstrated its performance in a series of simulation experiments. By applying scDecouple to real scCRISPR-seq data, we found that scDecouple enhances the identification of biologically perturbation-related genes. scDecouple can benefit scCRISPR-seq data analysis, especially in the case of heterogeneous samples or complex gRNA libraries.
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Affiliation(s)
- Qiuchen Meng
- MOE Key Lab of Bioinformatics & Bioinformatics Division BRNIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Lei Wei
- MOE Key Lab of Bioinformatics & Bioinformatics Division BRNIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Kun Ma
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Ming Shi
- MOE Key Lab of Bioinformatics & Bioinformatics Division BRNIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xinyi Lin
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Yinqing Li
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
- IDG-McGovern Institute for Brain Research, Center for Synthetic and Systems Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- MOE Key Lab of Bioinformatics & Bioinformatics Division BRNIST, Department of Automation, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, School of Life Sciences and School of Medicine, Tsinghua University, Beijing 100084, China
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15
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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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16
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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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17
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Paas-Oliveros E, Hernández-Lemus E, de Anda-Jáuregui G. Computational single cell oncology: state of the art. Front Genet 2023; 14:1256991. [PMID: 38028624 PMCID: PMC10663273 DOI: 10.3389/fgene.2023.1256991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.
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Affiliation(s)
- Ernesto Paas-Oliveros
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Investigadores por Mexico, Conahcyt, Mexico City, Mexico
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18
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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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19
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Shi Q, Chen X, Zhang Z. Decoding Human Biology and Disease Using Single-cell Omics Technologies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:926-949. [PMID: 37739168 PMCID: PMC10928380 DOI: 10.1016/j.gpb.2023.06.003] [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: 01/19/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 09/24/2023]
Abstract
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
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Affiliation(s)
- Qiang Shi
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing 102206, China.
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20
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Baysoy A, Bai Z, Satija R, Fan R. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol 2023; 24:695-713. [PMID: 37280296 PMCID: PMC10242609 DOI: 10.1038/s41580-023-00615-w] [Citation(s) in RCA: 114] [Impact Index Per Article: 114.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2023] [Indexed: 06/08/2023]
Abstract
Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.
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Affiliation(s)
- Alev Baysoy
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Zhiliang Bai
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Rahul Satija
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
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21
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Frenkel M, Hujoel ML, Morris Z, Raman S. Discovering chromatin dysregulation induced by protein-coding perturbations at scale. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.20.555752. [PMID: 37781603 PMCID: PMC10541138 DOI: 10.1101/2023.09.20.555752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Although population-scale databases have expanded to millions of protein-coding variants, insight into variant mechanisms has not kept pace. We present PROD-ATAC, a high-throughput method for discovering the effects of protein-coding variants on chromatin. A pooled library of variants is expressed in a disease-agnostic cell line, and single-cell ATAC resolves each variant's effect on chromatin. Using PROD-ATAC, we characterized the effects of >100 oncofusions (a class of cancer-causing chimeric proteins) and controls and revealed that pioneer activity is a common feature of fusions spanning an enormous range of fusion frequencies. Further, fusion-induced dysregulation can be context-agnostic as observed mechanisms often overlapped with cancer and cell-type specific prior knowledge. We also showed that gain-of-function pioneering is common among oncofusions. This work provides a global view of fusion-induced chromatin. We uncovered convergent mechanisms among disparate oncofusions and shared modes of dysregulation across different cancers. PROD-ATAC is generalizable to any set of protein-coding variants.
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Affiliation(s)
- Max Frenkel
- Cellular and Molecular Biology Graduate Program, University of Wisconsin, Madison, Wisconsin, USA
- Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Biochemistry, University of Wisconsin, Madison, Wisconsin, USA
| | - Margaux L.A. Hujoel
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zachary Morris
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin, Madison, Wisconsin, USA
- Department of Bacteriology, University of Wisconsin, Madison, Wisconsin, USA
- Department of Chemical and Biological Engineering, University of Wisconsin, Madison, Wisconsin, USA
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22
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Athaya T, Ripan RC, Li X, Hu H. Multimodal deep learning approaches for single-cell multi-omics data integration. Brief Bioinform 2023; 24:bbad313. [PMID: 37651607 PMCID: PMC10516349 DOI: 10.1093/bib/bbad313] [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: 05/08/2023] [Revised: 06/23/2023] [Accepted: 07/18/2023] [Indexed: 09/02/2023] Open
Abstract
Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively integrate these rapidly accumulating datasets, including deep learning. However, despite the proven success of deep learning in integrating multi-omics data and its better performance over classical computational methods, there has been no systematic study of its application to single-cell multi-omics data integration. To fill this gap, we conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration, taking into account recent studies from multiple perspectives. Specifically, we first summarized different modalities found in single-cell multi-omics data. We then reviewed current deep learning techniques for processing multimodal data and categorized deep learning-based integration methods for single-cell multi-omics data according to data modality, deep learning architecture, fusion strategy, key tasks and downstream analysis. Finally, we provided insights into using these deep learning models to integrate multi-omics data and better understand single-cell biological mechanisms.
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Affiliation(s)
- Tasbiraha Athaya
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
| | - Rony Chowdhury Ripan
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
| | - Xiaoman Li
- Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, Florida, United States of America
| | - Haiyan Hu
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
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23
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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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24
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Jassinskaja M, Gonka M, Kent DG. Resolving the hematopoietic stem cell state by linking functional and molecular assays. Blood 2023; 142:543-552. [PMID: 36735913 PMCID: PMC10644060 DOI: 10.1182/blood.2022017864] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/17/2023] [Accepted: 01/17/2023] [Indexed: 02/05/2023] Open
Abstract
One of the most challenging aspects of stem cell research is the reliance on retrospective assays for ascribing function. This is especially problematic for hematopoietic stem cell (HSC) research in which the current functional assay that formally establishes its HSC identity involves long-term serial transplantation assays that necessitate the destruction of the initial cell state many months before knowing that it was, in fact, an HSC. In combination with the explosion of equally destructive single-cell molecular assays, the paradox facing researchers is how to determine the molecular state of a functional HSC when you cannot concomitantly assess its functional and molecular properties. In this review, we will give a historical overview of the functional and molecular assays in the field, identify new tools that combine molecular and functional readouts in populations of HSCs, and imagine the next generation of computational and molecular profiling tools that may help us better link cell function with molecular state.
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Affiliation(s)
- Maria Jassinskaja
- Department of Biology, York Biomedical Research Institute, University of York, York, United Kingdom
| | - Monika Gonka
- Department of Biology, York Biomedical Research Institute, University of York, York, United Kingdom
| | - David G. Kent
- Department of Biology, York Biomedical Research Institute, University of York, York, United Kingdom
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25
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Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet 2023; 24:494-515. [PMID: 36864178 PMCID: PMC9979144 DOI: 10.1038/s41576-023-00580-2] [Citation(s) in RCA: 211] [Impact Index Per Article: 211.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/20/2023] [Indexed: 03/04/2023]
Abstract
The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.
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Affiliation(s)
- Katy Vandereyken
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Alejandro Sifrim
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Bernard Thienpont
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Thierry Voet
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium.
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA.
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26
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Awwad SW, Serrano-Benitez A, Thomas JC, Gupta V, Jackson SP. Revolutionizing DNA repair research and cancer therapy with CRISPR-Cas screens. Nat Rev Mol Cell Biol 2023; 24:477-494. [PMID: 36781955 DOI: 10.1038/s41580-022-00571-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2022] [Indexed: 02/15/2023]
Abstract
All organisms possess molecular mechanisms that govern DNA repair and associated DNA damage response (DDR) processes. Owing to their relevance to human disease, most notably cancer, these mechanisms have been studied extensively, yet new DNA repair and/or DDR factors and functional interactions between them are still being uncovered. The emergence of CRISPR technologies and CRISPR-based genetic screens has enabled genome-scale analyses of gene-gene and gene-drug interactions, thereby providing new insights into cellular processes in distinct DDR-deficiency genetic backgrounds and conditions. In this Review, we discuss the mechanistic basis of CRISPR-Cas genetic screening approaches and describe how they have contributed to our understanding of DNA repair and DDR pathways. We discuss how DNA repair pathways are regulated, and identify and characterize crosstalk between them. We also highlight the impacts of CRISPR-based studies in identifying novel strategies for cancer therapy, and in understanding, overcoming and even exploiting cancer-drug resistance, for example in the contexts of PARP inhibition, homologous recombination deficiencies and/or replication stress. Lastly, we present the DDR CRISPR screen (DDRcs) portal , in which we have collected and reanalysed data from CRISPR screen studies and provide a tool for systematically exploring them.
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Affiliation(s)
- Samah W Awwad
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- The Gurdon Institute and Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Almudena Serrano-Benitez
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
- The Gurdon Institute and Department of Biochemistry, University of Cambridge, Cambridge, UK.
| | - John C Thomas
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
- The Gurdon Institute and Department of Biochemistry, University of Cambridge, Cambridge, UK.
| | - Vipul Gupta
- The Gurdon Institute and Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Stephen P Jackson
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
- The Gurdon Institute and Department of Biochemistry, University of Cambridge, Cambridge, UK.
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27
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Turkalj S, Jakobsen NA, Groom A, Metzner M, Riva SG, Gür ER, Usukhbayar B, Salazar MA, Hentges LD, Mickute G, Clark K, Sopp P, Davies JOJ, Hughes JR, Vyas P. GTAC enables parallel genotyping of multiple genomic loci with chromatin accessibility profiling in single cells. Cell Stem Cell 2023; 30:722-740.e11. [PMID: 37146586 DOI: 10.1016/j.stem.2023.04.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 02/23/2023] [Accepted: 04/12/2023] [Indexed: 05/07/2023]
Abstract
Understanding clonal evolution and cancer development requires experimental approaches for characterizing the consequences of somatic mutations on gene regulation. However, no methods currently exist that efficiently link high-content chromatin accessibility with high-confidence genotyping in single cells. To address this, we developed Genotyping with the Assay for Transposase-Accessible Chromatin (GTAC), enabling accurate mutation detection at multiple amplified loci, coupled with robust chromatin accessibility readout. We applied GTAC to primary acute myeloid leukemia, obtaining high-quality chromatin accessibility profiles and clonal identities for multiple mutations in 88% of cells. We traced chromatin variation throughout clonal evolution, showing the restriction of different clones to distinct differentiation stages. Furthermore, we identified switches in transcription factor motif accessibility associated with a specific combination of driver mutations, which biased transformed progenitors toward a leukemia stem cell-like chromatin state. GTAC is a powerful tool to study clonal heterogeneity across a wide spectrum of pre-malignant and neoplastic conditions.
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Affiliation(s)
- Sven Turkalj
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Niels Asger Jakobsen
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK; Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Angus Groom
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Marlen Metzner
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Simone G Riva
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - E Ravza Gür
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Batchimeg Usukhbayar
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Mirian Angulo Salazar
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Lance D Hentges
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Gerda Mickute
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Kevin Clark
- Flow Cytometry Facility, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Paul Sopp
- Flow Cytometry Facility, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - James O J Davies
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK; Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Jim R Hughes
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Paresh Vyas
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK; Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
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28
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Huang S, Baskin JM. Adding a Chemical Biology Twist to CRISPR Screening. Isr J Chem 2023; 63:e202200056. [PMID: 37588264 PMCID: PMC10427134 DOI: 10.1002/ijch.202200056] [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: 08/21/2022] [Indexed: 11/09/2022]
Abstract
In less than a decade, CRISPR screening has revolutionized forward genetics and cell and molecular biology. Advances in screening technologies, including sgRNA libraries, Cas9-expressing cell lines, and streamlined sequencing pipelines, have democratized pooled CRISPR screens at genome-wide scale. Initially, many such screens were survival-based, identifying essential genes in physiological or perturbed processes. With the application of new chemical biology tools to CRISPR screening, the phenotypic space is no longer limited to live/dead selection or screening for levels of conventional fluorescent protein reporters. Further, the resolution has been increased from cell populations to single cells or even the subcellular level. We highlight advances in pooled CRISPR screening, powered by chemical biology, that have expanded phenotypic space, resolution, scope, and scalability as well as strengthened the CRISPR/Cas enzyme toolkit to enable biological hypothesis generation and discovery.
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Affiliation(s)
- Shiying Huang
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853 USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853 USA
| | - Jeremy M Baskin
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853 USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853 USA
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29
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Cheng J, Lin G, Wang T, Wang Y, Guo W, Liao J, Yang P, Chen J, Shao X, Lu X, Zhu L, Wang Y, Fan X. Massively Parallel CRISPR-Based Genetic Perturbation Screening at Single-Cell Resolution. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2204484. [PMID: 36504444 PMCID: PMC9896079 DOI: 10.1002/advs.202204484] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/09/2022] [Indexed: 06/17/2023]
Abstract
The clustered regularly interspaced short palindromic repeats (CRISPR)-based genetic screening has been demonstrated as a powerful approach for unbiased functional genomics research. Single-cell CRISPR screening (scCRISPR) techniques, which result from the combination of single-cell toolkits and CRISPR screening, allow dissecting regulatory networks in complex biological systems at unprecedented resolution. These methods allow cells to be perturbed en masse using a pooled CRISPR library, followed by high-content phenotyping. This is technically accomplished by annotating cells with sgRNA-specific barcodes or directly detectable sgRNAs. According to the integration of distinct single-cell technologies, these methods principally fall into four categories: scCRISPR with RNA-seq, scCRISPR with ATAC-seq, scCRISPR with proteome probing, and imaging-based scCRISPR. scCRISPR has deciphered genotype-phenotype relationships, genetic regulations, tumor biological issues, and neuropathological mechanisms. This review provides insight into the technical breakthrough of scCRISPR by systematically summarizing the advancements of various scCRISPR methodologies and analyzing their merits and limitations. In addition, an application-oriented approach guide is offered to meet researchers' individualized demands.
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Affiliation(s)
- Junyun Cheng
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Gaole Lin
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Tianhao Wang
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Yunzhu Wang
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Wenbo Guo
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Jie Liao
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Penghui Yang
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Jie Chen
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Xin Shao
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Xiaoyan Lu
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
- State Key Laboratory of Component‐Based Chinese MedicineInnovation Center in Zhejiang UniversityHangzhou310058China
- Jinhua Institute of Zhejiang UniversityJinhua321016China
| | - Ling Zhu
- The Save Sight InstituteFaculty of Medicine and Healththe University of SydneySydneyNSW2000Australia
| | - Yi Wang
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
- State Key Laboratory of Component‐Based Chinese MedicineInnovation Center in Zhejiang UniversityHangzhou310058China
- Future Health LaboratoryInnovation Center of Yangtze River DeltaZhejiang UniversityJiaxing314100China
| | - Xiaohui Fan
- Pharmaceutical Informatics InstituteCollege of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
- State Key Laboratory of Component‐Based Chinese MedicineInnovation Center in Zhejiang UniversityHangzhou310058China
- Jinhua Institute of Zhejiang UniversityJinhua321016China
- The Save Sight InstituteFaculty of Medicine and Healththe University of SydneySydneyNSW2000Australia
- Future Health LaboratoryInnovation Center of Yangtze River DeltaZhejiang UniversityJiaxing314100China
- Westlake Laboratory of Life Sciences and BiomedicineHangzhou310024China
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30
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Preissl S, Gaulton KJ, Ren B. Characterizing cis-regulatory elements using single-cell epigenomics. Nat Rev Genet 2023; 24:21-43. [PMID: 35840754 PMCID: PMC9771884 DOI: 10.1038/s41576-022-00509-1] [Citation(s) in RCA: 65] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/24/2022] [Indexed: 12/24/2022]
Abstract
Cell type-specific gene expression patterns and dynamics during development or in disease are controlled by cis-regulatory elements (CREs), such as promoters and enhancers. Distinct classes of CREs can be characterized by their epigenomic features, including DNA methylation, chromatin accessibility, combinations of histone modifications and conformation of local chromatin. Tremendous progress has been made in cataloguing CREs in the human genome using bulk transcriptomic and epigenomic methods. However, single-cell epigenomic and multi-omic technologies have the potential to provide deeper insight into cell type-specific gene regulatory programmes as well as into how they change during development, in response to environmental cues and through disease pathogenesis. Here, we highlight recent advances in single-cell epigenomic methods and analytical tools and discuss their readiness for human tissue profiling.
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Affiliation(s)
- Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Kyle J Gaulton
- Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA.
| | - Bing Ren
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
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31
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Galindez G, Sadegh S, Baumbach J, Kacprowski T, List M. Network-based approaches for modeling disease regulation and progression. Comput Struct Biotechnol J 2022; 21:780-795. [PMID: 36698974 PMCID: PMC9841310 DOI: 10.1016/j.csbj.2022.12.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
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Affiliation(s)
- Gihanna Galindez
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.,Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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32
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Shi H, Doench JG, Chi H. CRISPR screens for functional interrogation of immunity. Nat Rev Immunol 2022:10.1038/s41577-022-00802-4. [DOI: 10.1038/s41577-022-00802-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 12/13/2022]
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33
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Doebley AL, Ko M, Liao H, Cruikshank AE, Santos K, Kikawa C, Hiatt JB, Patton RD, De Sarkar N, Collier KA, Hoge ACH, Chen K, Zimmer A, Weber ZT, Adil M, Reichel JB, Polak P, Adalsteinsson VA, Nelson PS, MacPherson D, Parsons HA, Stover DG, Ha G. A framework for clinical cancer subtyping from nucleosome profiling of cell-free DNA. Nat Commun 2022; 13:7475. [PMID: 36463275 PMCID: PMC9719521 DOI: 10.1038/s41467-022-35076-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/17/2022] [Indexed: 12/05/2022] Open
Abstract
Cell-free DNA (cfDNA) has the potential to inform tumor subtype classification and help guide clinical precision oncology. Here we develop Griffin, a framework for profiling nucleosome protection and accessibility from cfDNA to study the phenotype of tumors using as low as 0.1x coverage whole genome sequencing data. Griffin employs a GC correction procedure tailored to variable cfDNA fragment sizes, which generates a better representation of chromatin accessibility and improves the accuracy of cancer detection and tumor subtype classification. We demonstrate estrogen receptor subtyping from cfDNA in metastatic breast cancer. We predict estrogen receptor subtype in 139 patients with at least 5% detectable circulating tumor DNA with an area under the receive operator characteristic curve (AUC) of 0.89 and validate performance in independent cohorts (AUC = 0.96). In summary, Griffin is a framework for accurate tumor subtyping and can be generalizable to other cancer types for precision oncology applications.
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Affiliation(s)
- Anna-Lisa Doebley
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Minjeong Ko
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Hanna Liao
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - A Eden Cruikshank
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA
| | | | - Caroline Kikawa
- Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Joseph B Hiatt
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Robert D Patton
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Navonil De Sarkar
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | | | - Anna C H Hoge
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Katharine Chen
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA
| | - Anat Zimmer
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Zachary T Weber
- Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Mohamed Adil
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Jonathan B Reichel
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Paz Polak
- Department of Oncological Sciences, Icahn School of Medicine, Mount Sinai, New York, NY, USA
| | | | - Peter S Nelson
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
- Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - David MacPherson
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Daniel G Stover
- Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Gavin Ha
- Division of Public Health Sciences and Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.
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34
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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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Integration of CRISPR/Cas9 with artificial intelligence for improved cancer therapeutics. J Transl Med 2022; 20:534. [PMID: 36401282 PMCID: PMC9673220 DOI: 10.1186/s12967-022-03765-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/08/2022] [Indexed: 11/19/2022] Open
Abstract
Gene editing has great potential in treating diseases caused by well-characterized molecular alterations. The introduction of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)–based gene-editing tools has substantially improved the precision and efficiency of gene editing. The CRISPR/Cas9 system offers several advantages over the existing gene-editing approaches, such as its ability to target practically any genomic sequence, enabling the rapid development and deployment of novel CRISPR-mediated knock-out/knock-in methods. CRISPR/Cas9 has been widely used to develop cancer models, validate essential genes as druggable targets, study drug-resistance mechanisms, explore gene non-coding areas, and develop biomarkers. CRISPR gene editing can create more-effective chimeric antigen receptor (CAR)-T cells that are durable, cost-effective, and more readily available. However, further research is needed to define the CRISPR/Cas9 system’s pros and cons, establish best practices, and determine social and ethical implications. This review summarizes recent CRISPR/Cas9 developments, particularly in cancer research and immunotherapy, and the potential of CRISPR/Cas9-based screening in developing cancer precision medicine and engineering models for targeted cancer therapy, highlighting the existing challenges and future directions. Lastly, we highlight the role of artificial intelligence in refining the CRISPR system's on-target and off-target effects, a critical factor for the broader application in cancer therapeutics.
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Donohue LK, Guo MG, Zhao Y, Jung N, Bussat RT, Kim DS, Neela PH, Kellman LN, Garcia OS, Meyers RM, Altman RB, Khavari PA. A cis-regulatory lexicon of DNA motif combinations mediating cell-type-specific gene regulation. CELL GENOMICS 2022; 2:100191. [PMID: 36742369 PMCID: PMC9894309 DOI: 10.1016/j.xgen.2022.100191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Gene expression is controlled by transcription factors (TFs) that bind cognate DNA motif sequences in cis-regulatory elements (CREs). The combinations of DNA motifs acting within homeostasis and disease, however, are unclear. Gene expression, chromatin accessibility, TF footprinting, and H3K27ac-dependent DNA looping data were generated and a random-forest-based model was applied to identify 7,531 cell-type-specific cis-regulatory modules (CRMs) across 15 diploid human cell types. A co-enrichment framework within CRMs nominated 838 cell-type-specific, recurrent heterotypic DNA motif combinations (DMCs), which were functionally validated using massively parallel reporter assays. Cancer cells engaged DMCs linked to neoplasia-enabling processes operative in normal cells while also activating new DMCs only seen in the neoplastic state. This integrative approach identifies cell-type-specific cis-regulatory combinatorial DNA motifs in diverse normal and diseased human cells and represents a general framework for deciphering cis-regulatory sequence logic in gene regulation.
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Affiliation(s)
- Laura K.H. Donohue
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA,Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA,Synthego, Redwood City, CA, USA,These authors contributed equally
| | - Margaret G. Guo
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA,Stanford Program in Biomedical Informatics, Stanford University, Stanford, CA, USA,These authors contributed equally
| | - Yang Zhao
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA,Synthego, Redwood City, CA, USA
| | - Namyoung Jung
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA,Department of Life Science, Pohang University of Science and Technology, Pohang, Korea
| | - Rose T. Bussat
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA,23andMe, Inc., Sunnyvale, CA, USA
| | - Daniel S. Kim
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA,Stanford Program in Biomedical Informatics, Stanford University, Stanford, CA, USA
| | - Poornima H. Neela
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA,Fauna Bio, Emeryville, CA, USA
| | - Laura N. Kellman
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA,Stanford Program in Cancer Biology, Stanford University, Stanford, CA, USA
| | - Omar S. Garcia
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Robin M. Meyers
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA,Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Russ B. Altman
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA,Stanford Program in Biomedical Informatics, Stanford University, Stanford, CA, USA,Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Paul A. Khavari
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA,Stanford Program in Cancer Biology, Stanford University, Stanford, CA, USA,Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA,Lead contact,Correspondence:
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37
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Jones IR, Ren X, Shen Y. High-throughput CRISPRi and CRISPRa technologies in 3D genome regulation for neuropsychiatric diseases. Hum Mol Genet 2022; 31:R47-R53. [PMID: 35972825 PMCID: PMC9585669 DOI: 10.1093/hmg/ddac193] [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: 07/14/2022] [Revised: 08/05/2022] [Accepted: 08/09/2022] [Indexed: 11/13/2022] Open
Abstract
Advances in genomics have led to the identification of many risk loci with hundreds of genes and thousands of DNA variants associated with neuropsychiatric disorders. A significant barrier to understanding the genetic underpinnings of complex diseases is the lack of functional characterization of risk genes and variants in biological systems relevant to human health and connecting disease-associated variants to pathological phenotypes. Characterizing gene and DNA variant functions requires genetic perturbations followed by molecular and cellular assays of neurobiological phenotypes. However, generating null or mutant alleles is low throughput, making it impossible to characterize disease-associated variants in large quantities efficiently. CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) screens can be leveraged to dissect the biological consequences of the tested genes and variants in their native context. Nevertheless, testing non-coding variants associated with complex diseases remains non-trivial. In this review, we first discuss the current challenges of interpreting the function of the non-coding genome and approaches to prioritizing disease-associated variants in the context of the 3D epigenome. Second, we provide a brief overview of high-throughput CRISPRi and CRISPRa screening strategies applicable for characterizing non-coding sequences in appropriate biological systems. Lastly, we discuss the promising prospects of using CRISPR-based technologies to dissect DNA sequences associated with neuropsychiatric diseases.
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Affiliation(s)
- Ian R Jones
- Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Xingjie Ren
- Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
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38
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Farheen J, Hosmane NS, Zhao R, Zhao Q, Iqbal MZ, Kong X. Nanomaterial-assisted CRISPR gene-engineering - A hallmark for triple-negative breast cancer therapeutics advancement. Mater Today Bio 2022; 16:100450. [PMID: 36267139 PMCID: PMC9576993 DOI: 10.1016/j.mtbio.2022.100450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 09/16/2022] [Accepted: 10/02/2022] [Indexed: 11/05/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is the most violent class of tumor and accounts for 20–24% of total breast carcinoma, in which frequently rare mutation occurs in high frequency. The poor prognosis, recurrence, and metastasis in the brain, heart, liver and lungs decline the lifespan of patients by about 21 months, emphasizing the need for advanced treatment. Recently, the adaptive immunity mechanism of archaea and bacteria, called clustered regularly interspaced short palindromic repeats (CRISPR) combined with nanotechnology, has been utilized as a potent gene manipulating tool with an extensive clinical application in cancer genomics due to its easeful usage and cost-effectiveness. However, CRISPR/Cas are arguably the efficient technology that can be made efficient via organic material-assisted approaches. Despite the efficacy of the CRISPR/Cas@nano complex, problems regarding successful delivery, biodegradability, and toxicity remain to render its medical implications. Therefore, this review is different in focus from past reviews by (i) detailing all possible genetic mechanisms of TNBC occurrence; (ii) available treatments and gene therapies for TNBC; (iii) overview of the delivery system and utilization of CRISPR-nano complex in TNBC, and (iv) recent advances and related toxicity of CRISPR-nano complex towards clinical trials for TNBC.
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Affiliation(s)
- Jabeen Farheen
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China,Zhejiang-Mauritius Joint Research Centre for Biomaterials and Tissue Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China
| | - Narayan S. Hosmane
- Department of Chemistry & Biochemistry, Northern Illinois University, DeKalb, IL, 60115, USA
| | - Ruibo Zhao
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China,Zhejiang-Mauritius Joint Research Centre for Biomaterials and Tissue Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China,Department of Materials, Imperial College London, London, SW7 2AZ, UK
| | - Qingwei Zhao
- Research Center for Clinical Pharmacy & Key Laboratory for Drug Evaluation and Clinical Research of Zhejiang Province, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, PR China
| | - M. Zubair Iqbal
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China,Zhejiang-Mauritius Joint Research Centre for Biomaterials and Tissue Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China,Corresponding author. Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China.
| | - Xiangdong Kong
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China,Zhejiang-Mauritius Joint Research Centre for Biomaterials and Tissue Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China,Corresponding author. Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China
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Xu Y, Begoli E, McCord RP. sciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial network. NPJ Syst Biol Appl 2022; 8:33. [PMID: 36089620 PMCID: PMC9464763 DOI: 10.1038/s41540-022-00245-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 09/01/2022] [Indexed: 11/09/2022] Open
Abstract
The boom in single-cell technologies has brought a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in these single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge. Here, we present an adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 5 existing methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with consistent performance across datasets and better balance of mutual transferring between modalities than the other 5 existing methods. We further applied sciCAN to 10X Multiome data and confirmed that the integrated representation preserves biological relationships within the hematopoietic hierarchy. Finally, we investigated CRISPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities.
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Affiliation(s)
- Yang Xu
- grid.411461.70000 0001 2315 1184UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN USA
| | - Edmon Begoli
- grid.135519.a0000 0004 0446 2659Oak Ridge National Laboratory, Oak Ridge, TN USA ,grid.411461.70000 0001 2315 1184Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN USA
| | - Rachel Patton McCord
- Biochemistry & Cellular and Molecular Biology Department, University of Tennessee, Knoxville, TN, USA.
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40
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scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks. Nat Methods 2022; 19:1088-1096. [PMID: 35941239 DOI: 10.1038/s41592-022-01562-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 06/27/2022] [Indexed: 12/25/2022]
Abstract
Single-cell assay for transposase-accessible chromatin using sequencing (scATAC) shows great promise for studying cellular heterogeneity in epigenetic landscapes, but there remain important challenges in the analysis of scATAC data due to the inherent high dimensionality and sparsity. Here we introduce scBasset, a sequence-based convolutional neural network method to model scATAC data. We show that by leveraging the DNA sequence information underlying accessibility peaks and the expressiveness of a neural network model, scBasset achieves state-of-the-art performance across a variety of tasks on scATAC and single-cell multiome datasets, including cell clustering, scATAC profile denoising, data integration across assays and transcription factor activity inference.
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41
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Nishiga M, Liu C, Qi LS, Wu JC. The use of new CRISPR tools in cardiovascular research and medicine. Nat Rev Cardiol 2022; 19:505-521. [PMID: 35145236 PMCID: PMC10283450 DOI: 10.1038/s41569-021-00669-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/21/2021] [Indexed: 02/07/2023]
Abstract
Many novel CRISPR-based genome-editing tools, with a wide variety of applications, have been developed in the past few years. The original CRISPR-Cas9 system was developed as a tool to alter genomic sequences in living organisms in a simple way. However, the functions of new CRISPR tools are not limited to conventional genome editing mediated by non-homologous end-joining or homology-directed repair but expand into gene-expression control, epigenome editing, single-nucleotide editing, RNA editing and live-cell imaging. Furthermore, genetic perturbation screening by multiplexing guide RNAs is gaining popularity as a method to identify causative genes and pathways in an unbiased manner. New CRISPR tools can also be applied to ex vivo or in vivo therapeutic genome editing for the treatment of conditions such as hyperlipidaemia. In this Review, we first provide an overview of the diverse new CRISPR tools that have been developed to date. Second, we summarize how these new CRISPR tools are being used to study biological processes and disease mechanisms in cardiovascular research and medicine. Finally, we discuss the prospect of therapeutic genome editing by CRISPR tools to cure genetic cardiovascular diseases.
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Affiliation(s)
- Masataka Nishiga
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Chun Liu
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Lei S Qi
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Chemical & Systems Biology, Stanford University, Stanford, CA, USA
- ChEM-H Institute, Stanford University, Stanford, CA, USA
| | - Joseph C Wu
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
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42
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Ogbeide S, Giannese F, Mincarelli L, Macaulay IC. Into the multiverse: advances in single-cell multiomic profiling. Trends Genet 2022; 38:831-843. [PMID: 35537880 DOI: 10.1016/j.tig.2022.03.015] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/23/2022] [Accepted: 03/25/2022] [Indexed: 10/18/2022]
Abstract
Single-cell transcriptomic approaches have revolutionised the study of complex biological systems, with the routine measurement of gene expression in thousands of cells enabling construction of whole-organism cell atlases. However, the transcriptome is just one layer amongst many that coordinate to define cell type and state and, ultimately, function. In parallel with the widespread uptake of single-cell RNA-seq (scRNA-seq), there has been a rapid emergence of methods that enable multiomic profiling of individual cells, enabling parallel measurement of intercellular heterogeneity in the genome, epigenome, transcriptome, and proteomes. Linking measurements from each of these layers has the potential to reveal regulatory and functional mechanisms underlying cell behaviour in healthy development and disease.
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43
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Isbel L, Grand RS, Schübeler D. Generating specificity in genome regulation through transcription factor sensitivity to chromatin. Nat Rev Genet 2022; 23:728-740. [PMID: 35831531 DOI: 10.1038/s41576-022-00512-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2022] [Indexed: 12/11/2022]
Abstract
Cell type-specific gene expression relies on transcription factors (TFs) binding DNA sequence motifs embedded in chromatin. Understanding how motifs are accessed in chromatin is crucial to comprehend differential transcriptional responses and the phenotypic impact of sequence variation. Chromatin obstacles to TF binding range from DNA methylation to restriction of DNA access by nucleosomes depending on their position, composition and modification. In vivo and in vitro approaches now enable the study of TF binding in chromatin at unprecedented resolution. Emerging insights suggest that TFs vary in their ability to navigate chromatin states. However, it remains challenging to link binding and transcriptional outcomes to molecular characteristics of TFs or the local chromatin substrate. Here, we discuss our current understanding of how TFs access DNA in chromatin and novel techniques and directions towards a better understanding of this critical step in genome regulation.
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Affiliation(s)
- Luke Isbel
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.,School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Ralph S Grand
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.,Zentrum für Molekulare Biologie der Universität Heidelberg, Heidelberg, Germany
| | - Dirk Schübeler
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. .,Faculty of Sciences, University of Basel, Basel, Switzerland.
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44
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Casado-Pelaez M, Bueno-Costa A, Esteller M. Single cell cancer epigenetics. Trends Cancer 2022; 8:820-838. [PMID: 35821003 DOI: 10.1016/j.trecan.2022.06.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 05/02/2022] [Accepted: 06/08/2022] [Indexed: 10/17/2022]
Abstract
Bulk sequencing methodologies have allowed us to make great progress in cancer research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic mechanisms that govern tumor heterogeneity. Consequently, many novel single cell-sequencing methodologies have been developed over the past decade, allowing us to explore the epigenetic components that regulate different aspects of cancer heterogeneity, namely: clonal heterogeneity, tumor microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, and resistance mechanisms. In this review, we explore the different sequencing techniques that enable researchers to study different aspects of epigenetics (DNA methylation, chromatin accessibility, histone modifications, DNA-protein interactions, and chromatin 3D architecture) at the single cell level, their potential applications in cancer, and their current technical limitations.
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Affiliation(s)
- Marta Casado-Pelaez
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain
| | - Alberto Bueno-Costa
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain
| | - Manel Esteller
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain; Centro de Investigacion Biomedica en Red Cancer (CIBERONC), 28029 Madrid, Spain; Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain; Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), Barcelona, Catalonia, Spain.
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45
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Belk JA, Daniel B, Satpathy AT. Epigenetic regulation of T cell exhaustion. Nat Immunol 2022; 23:848-860. [PMID: 35624210 PMCID: PMC10439681 DOI: 10.1038/s41590-022-01224-z] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 04/06/2022] [Indexed: 12/15/2022]
Abstract
Chronic antigen stimulation during viral infections and cancer can lead to T cell exhaustion, which is characterized by reduced effector function and proliferation, and the expression of inhibitory immune checkpoint receptors. Recent studies have demonstrated that T cell exhaustion results in wholescale epigenetic remodeling that confers phenotypic stability to these cells and prevents T cell reinvigoration by checkpoint blockade. Here, we review foundational technologies to profile the epigenome at multiple scales, including mapping the locations of transcription factors and histone modifications, DNA methylation and three-dimensional genome conformation. We discuss how these technologies have elucidated the development and epigenetic regulation of exhausted T cells and functional implications across viral infection, cancer, autoimmunity and engineered T cell therapies. Finally, we cover emerging multi-omic and genome engineering technologies, current and upcoming opportunities to apply these to T cell exhaustion, and therapeutic opportunities for T cell engineering in the clinic.
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Affiliation(s)
- Julia A Belk
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
| | - Bence Daniel
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Ansuman T Satpathy
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA.
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Parker Institute for Cancer Immunotherapy, Stanford University, Stanford, CA, USA.
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46
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Vera CD, Mullen M, Minhas N, Wu JC. Intersectionality and genetic ancestry: New methods to solve old problems. EBioMedicine 2022; 80:104049. [PMID: 35561454 PMCID: PMC9108864 DOI: 10.1016/j.ebiom.2022.104049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/12/2022] [Accepted: 04/24/2022] [Indexed: 12/26/2022] Open
Affiliation(s)
- Carlos D Vera
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - McKay Mullen
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | | | - Joseph C Wu
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA; Department of Radiology, Stanford University, Stanford, CA, USA.
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47
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E Yan R, P Greenfield J, Dahmane N. Developments in high-throughput functional epigenomics: CRISPR-single-cell assay for transposase-accessible chromatin using sequencing screens. Epigenomics 2022; 14:645-649. [PMID: 35574596 DOI: 10.2217/epi-2022-0093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Tweetable abstract CRISPR-scATAC-seq screens pave the way for high-throughput functional epigenomics by linking perturbations to a broad view of epigenetic state and messages hidden within accessible sequences.
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Affiliation(s)
- Rachel E Yan
- Weill Cornell Medical College, Department of Neurological Surgery, New York, NY 10021, USA
| | - Jeffrey P Greenfield
- Weill Cornell Medical College, Department of Neurological Surgery, New York, NY 10021, USA
| | - Nadia Dahmane
- Weill Cornell Medical College, Department of Neurological Surgery, New York, NY 10021, USA
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48
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Das S, Bano S, Kapse P, Kundu GC. CRISPR based therapeutics: a new paradigm in cancer precision medicine. Mol Cancer 2022; 21:85. [PMID: 35337340 PMCID: PMC8953071 DOI: 10.1186/s12943-022-01552-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/24/2022] [Indexed: 02/08/2023] Open
Abstract
Background Clustered regularly interspaced short palindromic repeat (CRISPR)-CRISPR-associated protein (Cas) systems are the latest addition to the plethora of gene-editing tools. These systems have been repurposed from their natural counterparts by means of both guide RNA and Cas nuclease engineering. These RNA-guided systems offer greater programmability and multiplexing capacity than previous generation gene editing tools based on zinc finger nucleases and transcription activator like effector nucleases. CRISPR-Cas systems show great promise for individualization of cancer precision medicine. Main body The biology of Cas nucleases and dead Cas based systems relevant for in vivo gene therapy applications has been discussed. The CRISPR knockout, CRISPR activation and CRISPR interference based genetic screens which offer opportunity to assess functions of thousands of genes in massively parallel assays have been also highlighted. Single and combinatorial gene knockout screens lead to identification of drug targets and synthetic lethal genetic interactions across different cancer phenotypes. There are different viral and non-viral (nanoformulation based) modalities that can carry CRISPR-Cas components to different target organs in vivo. Conclusion The latest developments in the field in terms of optimization of performance of the CRISPR-Cas elements should fuel greater application of the latter in the realm of precision medicine. Lastly, how the already available knowledge can help in furtherance of use of CRISPR based tools in personalized medicine has been discussed.
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Affiliation(s)
- Sumit Das
- National Centre for Cell Science, S P Pune University Campus, Pune, 411007, India
| | - Shehnaz Bano
- National Centre for Cell Science, S P Pune University Campus, Pune, 411007, India
| | - Prachi Kapse
- School of Basic Medical Sciences, S P Pune University, Pune, 411007, India
| | - Gopal C Kundu
- Kalinga Institute of Medical Sciences (KIMS), KIIT Deemed To Be University, Bhubaneswar, 751024, India. .,School of Biotechnology, KIIT Deemed To Be University, Bhubaneswar, 751024, India.
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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: 26] [Impact Index Per Article: 13.0] [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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Saini A, Ghoneim HE, Lio CWJ, Collins PL, Oltz EM. Gene Regulatory Circuits in Innate and Adaptive Immune Cells. Annu Rev Immunol 2022; 40:387-411. [PMID: 35119910 DOI: 10.1146/annurev-immunol-101320-025949] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Cell identity and function largely rely on the programming of transcriptomes during development and differentiation. Signature gene expression programs are orchestrated by regulatory circuits consisting of cis-acting promoters and enhancers, which respond to a plethora of cues via the action of transcription factors. In turn, transcription factors direct epigenetic modifications to revise chromatin landscapes, and drive contacts between distal promoter-enhancer combinations. In immune cells, regulatory circuits for effector genes are especially complex and flexible, utilizing distinct sets of transcription factors and enhancers, depending on the cues each cell type receives during an infection, after sensing cellular damage, or upon encountering a tumor. Here, we review major players in the coordination of gene regulatory programs within innate and adaptive immune cells, as well as integrative omics approaches that can be leveraged to decipher their underlying circuitry. Expected final online publication date for the Annual Review of Immunology, Volume 40 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Ankita Saini
- Department of Microbial Infection and Immunity and Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio, USA; ,
| | - Hazem E Ghoneim
- Department of Microbial Infection and Immunity and Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio, USA; ,
| | - Chan-Wang Jerry Lio
- Department of Microbial Infection and Immunity and Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio, USA; ,
| | - Patrick L Collins
- Department of Microbial Infection and Immunity and Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio, USA; ,
| | - Eugene M Oltz
- Department of Microbial Infection and Immunity and Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio, USA; ,
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