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Engreitz JM, Lawson HA, Singh H, Starita LM, Hon GC, Carter H, Sahni N, Reddy TE, Lin X, Li Y, Munshi NV, Chahrour MH, Boyle AP, Hitz BC, Mortazavi A, Craven M, Mohlke KL, Pinello L, Wang T, Kundaje A, Yue F, Cody S, Farrell NP, Love MI, Muffley LA, Pazin MJ, Reese F, Van Buren E, Dey KK, Kircher M, Ma J, Radivojac P, Balliu B, Williams BA, Huangfu D, Park CY, Quertermous T, Das J, Calderwood MA, Fowler DM, Vidal M, Ferreira L, Mooney SD, Pejaver V, Zhao J, Gazal S, Koch E, Reilly SK, Sunyaev S, Carpenter AE, Buenrostro JD, Leslie CS, Savage RE, Giric S, Luo C, Plath K, Barrera A, Schubach M, Gschwind AR, Moore JE, Ahituv N, Yi SS, Hallgrimsdottir I, Gaulton KJ, Sakaue S, Booeshaghi S, Mattei E, Nair S, Pachter L, Wang AT, Shendure J, Agarwal V, Blair A, Chalkiadakis T, Chardon FM, Dash PM, Deng C, Hamazaki N, Keukeleire P, Kubo C, Lalanne JB, Maass T, Martin B, McDiarmid TA, Nobuhara M, Page NF, Regalado S, Sims J, Ushiki A, Best SM, Boyle G, Camp N, Casadei S, Da EY, Dawood M, Dawson SC, Fayer S, Hamm A, James RG, Jarvik GP, McEwen AE, Moore N, Pendyala S, Popp NA, Post M, Rubin AF, Smith NT, Stone J, Tejura M, Wang ZR, Wheelock MK, Woo I, Zapp BD, Amgalan D, Aradhana A, Arana SM, Bassik MC, Bauman JR, Bhattacharya A, Cai XS, Chen Z, Conley S, Deshpande S, Doughty BR, Du PP, Galante JA, Gifford C, Greenleaf WJ, Guo K, Gupta R, Isobe S, Jagoda E, Jain N, Jones H, Kang HY, Kim SH, Kim Y, Klemm S, Kundu R, Kundu S, Lago-Docampo M, Lee-Yow YC, Levin-Konigsberg R, Li DY, Lindenhofer D, Ma XR, Marinov GK, Martyn GE, McCreery CV, Metzl-Raz E, Monteiro JP, Montgomery MT, Mualim KS, Munger C, Munson G, Nguyen TC, Nguyen T, Palmisano BT, Pampari A, Rabinovitch M, Ramste M, Ray J, Roy KR, Rubio OM, Schaepe JM, Schnitzler G, Schreiber J, Sharma D, Sheth MU, Shi H, Singh V, Sinha R, Steinmetz LM, Tan J, Tan A, Tycko J, Valbuena RC, Amiri VVP, van Kooten MJFM, Vaughan-Jackson A, Venida A, Weldy CS, Worssam MD, Xia F, Yao D, Zeng T, Zhao Q, Zhou R, Chen ZS, Cimini BA, Coppin G, Coté AG, Haghighi M, Hao T, Hill DE, Lacoste J, Laval F, Reno C, Roth FP, Singh S, Spirohn-Fitzgerald K, Taipale M, Teelucksingh T, Tixhon M, Yadav A, Yang Z, Kraus WL, Armendariz DA, Dederich AE, Gogate A, El Hayek L, Goetsch SC, Kaur K, Kim HB, McCoy MK, Nzima MZ, Pinzón-Arteaga CA, Posner BA, Schmitz DA, Sivakumar S, Sundarrajan A, Wang L, Wang Y, Wu J, Xu L, Xu J, Yu L, Zhang Y, Zhao H, Zhou Q, Won H, Bell JL, Broadaway KA, Degner KN, Etheridge AS, Koller BH, Mah W, Mu W, Ritola KD, Rosen JD, Schoenrock SA, Sharp RA, Bauer D, Lettre G, Sherwood R, Becerra B, Blaine LJ, Che E, Francoeur MJ, Gibbs EN, Kim N, King EM, Kleinstiver BP, Lecluze E, Li Z, Patel ZM, Phan QV, Ryu J, Starr ML, Wu T, Gersbach CA, Crawford GE, Allen AS, Majoros WH, Iglesias N, Rai R, Venukuttan R, Li B, Anglen T, Bounds LR, Hamilton MC, Liu S, McCutcheon SR, McRoberts Amador CD, Reisman SJ, ter Weele MA, Bodle JC, Streff HL, Siklenka K, Strouse K, Bernstein BE, Babu J, Corona GB, Dong K, Duarte FM, Durand NC, Epstein CB, Fan K, Gaskell E, Hall AW, Ham AM, Knudson MK, Shoresh N, Wekhande S, White CM, Xi W, Satpathy AT, Corces MR, Chang SH, Chin IM, Gardner JM, Gardell ZA, Gutierrez JC, Johnson AW, Kampman L, Kasowski M, Lareau CA, Liu V, Ludwig LS, McGinnis CS, Menon S, Qualls A, Sandor K, Turner AW, Ye CJ, Yin Y, Zhang W, Wold BJ, Carilli M, Cheong D, Filibam G, Green K, Kawauchi S, Kim C, Liang H, Loving R, Luebbert L, MacGregor G, Merchan AG, Rebboah E, Rezaie N, Sakr J, Sullivan DK, Swarna N, Trout D, Upchurch S, Weber R, Castro CP, Chou E, Feng F, Guerra A, Huang Y, Jiang L, Liu J, Mills RE, Qian W, Qin T, Sartor MA, Sherpa RN, Wang J, Wang Y, Welch JD, Zhang Z, Zhao N, Mukherjee S, Page CD, Clarke S, Doty RW, Duan Y, Gordan R, Ko KY, Li S, Li B, Thomson A, Raychaudhuri S, Price A, Ali TA, Dey KK, Durvasula A, Kellis M, Iakoucheva LM, Kakati T, Chen Y, Benazouz M, Jain S, Zeiberg D, De Paolis Kaluza MC, Velyunskiy M, Gasch A, Huang K, Jin Y, Lu Q, Miao J, Ohtake M, Scopel E, Steiner RD, Sverchkov Y, Weng Z, Garber M, Fu Y, Haas N, Li X, Phalke N, Shan SC, Shedd N, Yu T, Zhang Y, Zhou H, Battle A, Jerby L, Kotler E, Kundu S, Marderstein AR, Montgomery SB, Nigam A, Padhi EM, Patel A, Pritchard J, Raine I, Ramalingam V, Rodrigues KB, Schreiber JM, Singhal A, Sinha R, Wang AT, Abundis M, Bisht D, Chakraborty T, Fan J, Hall DR, Rarani ZH, Jain AK, Kaundal B, Keshari S, McGrail D, Pease NA, Yi VF, Wu H, Kannan S, Song H, Cai J, Gao Z, Kurzion R, Leu JI, Li F, Liang D, Ming GL, Musunuru K, Qiu Q, Shi J, Su Y, Tishkoff S, Xie N, Yang Q, Yang W, Zhang H, Zhang Z, Beer MA, Hadjantonakis AK, Adeniyi S, Cho H, Cutler R, Glenn RA, Godovich D, Hu N, Jovanic S, Luo R, Oh JW, Razavi-Mohseni M, Shigaki D, Sidoli S, Vierbuchen T, Wang X, Williams B, Yan J, Yang D, Yang Y, Sander M, Gaulton KJ, Ren B, Bartosik W, Indralingam HS, Klie A, Mummey H, Okino ML, Wang G, Zemke NR, Zhang K, Zhu H, Zaitlen N, Ernst J, Langerman J, Li T, Sun Y, Rudensky AY, Periyakoil PK, Gao VR, Smith MH, Thomas NM, Donlin LT, Lakhanpal A, Southard KM, Ardy RC, Cherry JM, Gerstein MB, Andreeva K, Assis PR, Borsari B, Douglass E, Dong S, Gabdank I, Graham K, Jolanki O, Jou J, Kagda MS, Lee JW, Li M, Lin K, Miyasato SR, Rozowsky J, Small C, Spragins E, Tanaka FY, Whaling IM, Youngworth IA, Sloan CA, Belter E, Chen X, Chisholm RL, Dickson P, Fan C, Fulton L, Li D, Lindsay T, Luan Y, Luo Y, Lyu H, Ma X, Macias-Velasco J, Miga KH, Quaid K, Stitziel N, Stranger BE, Tomlinson C, Wang J, Zhang W, Zhang B, Zhao G, Zhuo X, Brennand K, Ciccia A, Hayward SB, Huang JW, Leuzzi G, Taglialatela A, Thakar T, Vaitsiankova A, Dey KK, Ali TA, Kim A, Grimes HL, Salomonis N, Gupta R, Fang S, Lee-Kim V, Heinig M, Losert C, Jones TR, Donnard E, Murphy M, Roberts E, Song S, Mostafavi S, Sasse A, Spiro A, Pennacchio LA, Kato M, Kosicki M, Mannion B, Slaven N, Visel A, Pollard KS, Drusinsky S, Whalen S, Ray J, Harten IA, Ho CH, Sanjana NE, Caragine C, Morris JA, Seruggia D, Kutschat AP, Wittibschlager S, Xu H, Fu R, He W, Zhang L, Osorio D, Bly Z, Calluori S, Gilchrist DA, Hutter CM, Morris SA, Samer EK. Deciphering the impact of genomic variation on function. Nature 2024; 633:47-57. [PMID: 39232149 DOI: 10.1038/s41586-024-07510-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 05/02/2024] [Indexed: 09/06/2024]
Abstract
Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.
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Rood JE, Hupalowska A, Regev A. Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas. Cell 2024; 187:4520-4545. [PMID: 39178831 DOI: 10.1016/j.cell.2024.07.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 07/15/2024] [Accepted: 07/21/2024] [Indexed: 08/26/2024]
Abstract
Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.
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Affiliation(s)
| | | | - Aviv Regev
- Genentech, South San Francisco, CA, USA.
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Zhao J, Zhou Y, Tzelepis I, Burget NG, Shi J, Faryabi RB. Oncogenic transcription factors instruct promoter-enhancer hubs in individual triple negative breast cancer cells. SCIENCE ADVANCES 2024; 10:eadl4043. [PMID: 39110799 PMCID: PMC11305386 DOI: 10.1126/sciadv.adl4043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 06/28/2024] [Indexed: 08/10/2024]
Abstract
Sequencing-based mapping of ensemble pairwise interactions among regulatory elements support the existence of topological assemblies known as promoter-enhancer hubs or cliques in cancer. Yet, prevalence, regulators, and functions of promoter-enhancer hubs in individual cancer cells remain unclear. Here, we systematically integrated functional genomics, transcription factor screening, and optical mapping of promoter-enhancer interactions to identify key promoter-enhancer hubs, examine heterogeneity of their assembly, determine their regulators, and elucidate their role in gene expression control in individual triple negative breast cancer (TNBC) cells. Optical mapping of individual SOX9 and MYC alleles revealed the existence of frequent multiway interactions among promoters and enhancers within spatial hubs. Our single-allele studies further demonstrated that lineage-determining SOX9 and signaling-dependent NOTCH1 transcription factors compact MYC and SOX9 hubs. Together, our findings suggest that promoter-enhancer hubs are dynamic and heterogeneous topological assemblies, which are controlled by oncogenic transcription factors and facilitate subtype-restricted gene expression in cancer.
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Affiliation(s)
- Jingru Zhao
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Penn Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yeqiao Zhou
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Penn Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ilias Tzelepis
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Penn Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Noah G. Burget
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Penn Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Junwei Shi
- Penn Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Robert B. Faryabi
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Penn Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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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] [Grants] [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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Affiliation(s)
- Kaden M. Southard
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rico C. Ardy
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anran Tang
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Deirdre D. O’Sullivan
- 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
| | - 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
| | - Karthik Guruvayurappan
- 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
| | - Thomas M. Norman
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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5
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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 PMCID: PMC11345568 DOI: 10.1016/j.mocell.2024.100092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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Xu Z, Sziraki A, Lee J, Zhou W, Cao J. Dissecting key regulators of transcriptome kinetics through scalable single-cell RNA profiling of pooled CRISPR screens. Nat Biotechnol 2024; 42:1218-1223. [PMID: 37749268 PMCID: PMC10961254 DOI: 10.1038/s41587-023-01948-9] [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: 12/05/2022] [Accepted: 08/15/2023] [Indexed: 09/27/2023]
Abstract
We present a combinatorial indexing method, PerturbSci-Kinetics, for capturing whole transcriptomes, nascent transcriptomes and single guide RNA (sgRNA) identities across hundreds of genetic perturbations at the single-cell level. Profiling a pooled CRISPR screen targeting various biological processes, we show the gene expression regulation during RNA synthesis, processing and degradation, miRNA biogenesis and mitochondrial mRNA processing, systematically decoding the genome-wide regulatory network that underlies RNA temporal dynamics at scale.
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Affiliation(s)
- Zihan Xu
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Andras Sziraki
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Jasper Lee
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Wei Zhou
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Junyue Cao
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA.
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7
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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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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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9
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Guckelberger P, Doughty BR, Munson G, Rao SSP, Tan Y, Cai XS, Fulco CP, Nasser J, Mualim KS, Bergman DT, Ray J, Jagoda E, Munger CJ, Gschwind AR, Sheth MU, Tan AS, Pulido SG, Mitra N, Weisz D, Shamim MS, Durand NC, Mahajan R, Khan R, Steinmetz LM, Kanemaki MT, Lander ES, Meissner A, Aiden EL, Engreitz JM. Cohesin-mediated 3D contacts tune enhancer-promoter regulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.12.603288. [PMID: 39026740 PMCID: PMC11257546 DOI: 10.1101/2024.07.12.603288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Enhancers are key drivers of gene regulation thought to act via 3D physical interactions with the promoters of their target genes. However, genome-wide depletions of architectural proteins such as cohesin result in only limited changes in gene expression, despite a loss of contact domains and loops. Consequently, the role of cohesin and 3D contacts in enhancer function remains debated. Here, we developed CRISPRi of regulatory elements upon degron operation (CRUDO), a novel approach to measure how changes in contact frequency impact enhancer effects on target genes by perturbing enhancers with CRISPRi and measuring gene expression in the presence or absence of cohesin. We systematically perturbed all 1,039 candidate enhancers near five cohesin-dependent genes and identified 34 enhancer-gene regulatory interactions. Of 26 regulatory interactions with sufficient statistical power to evaluate cohesin dependence, 18 show cohesin-dependent effects. A decrease in enhancer-promoter contact frequency upon removal of cohesin is frequently accompanied by a decrease in the regulatory effect of the enhancer on gene expression, consistent with a contact-based model for enhancer function. However, changes in contact frequency and regulatory effects on gene expression vary as a function of distance, with distal enhancers (e.g., >50Kb) experiencing much larger changes than proximal ones (e.g., <50Kb). Because most enhancers are located close to their target genes, these observations can explain how only a small subset of genes - those with strong distal enhancers - are sensitive to cohesin. Together, our results illuminate how 3D contacts, influenced by both cohesin and genomic distance, tune enhancer effects on gene expression.
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10
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Yao Y, Zhou Z, Wang X, Liu Z, Zhai Y, Chi X, Du J, Luo L, Zhao Z, Wang X, Xue C, Rao S. SpRY-mediated screens facilitate functional dissection of non-coding sequences at single-base resolution. CELL GENOMICS 2024; 4:100583. [PMID: 38889719 PMCID: PMC11293580 DOI: 10.1016/j.xgen.2024.100583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/28/2024] [Accepted: 05/16/2024] [Indexed: 06/20/2024]
Abstract
CRISPR mutagenesis screens conducted with SpCas9 and other nucleases have identified certain cis-regulatory elements and genetic variants but at a limited resolution due to the absence of protospacer adjacent motif (PAM) sequences. Here, leveraging the broad targeting scope of the near-PAMless SpRY variant, we have demonstrated that saturated SpRY mutagenesis and base editing screens can faithfully identify functional regulatory elements and essential genetic variants for target gene expression at single-base resolution. We further extended this methodology to investigate a genome-wide association study (GWAS) locus at 10q22.1 associated with a red blood cell trait, where we identified potential enhancers regulating HK1 gene expression, despite not all of these enhancers exhibiting typical chromatin signatures. More importantly, our saturated base editing screens pinpoint multiple causal variants within this locus that would otherwise be missed by Bayesian statistical fine-mapping. Our approach is generally applicable to functional interrogation of all non-coding genomic elements while complementing other high-coverage CRISPR screens.
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Affiliation(s)
- Yao Yao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China.
| | - Zhiwei Zhou
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Xiaoling Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Zhirui Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Yixin Zhai
- Department of Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Xiaolin Chi
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Jingyi Du
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Liheng Luo
- Center for Bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine & Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China
| | - Zhigang Zhao
- Department of Medical Oncology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin 300192, China
| | - Xiaoyue Wang
- Center for Bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine & Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China
| | - Chaoyou Xue
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
| | - Shuquan Rao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China.
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11
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Zhou J, Guruvayurappan K, Toneyan S, Chen HV, Chen AR, Koo P, McVicker G. Analysis of single-cell CRISPR perturbations indicates that enhancers act multiplicatively and provides limited evidence for epistatic-like interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.26.538501. [PMID: 37163096 PMCID: PMC10168320 DOI: 10.1101/2023.04.26.538501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
A single gene may have multiple enhancers, but how they work in concert to regulate transcription is poorly understood. To analyze enhancer interactions throughout the genome, we developed a generalized linear modeling framework, GLiMMIRS, for interrogating enhancer effects from single-cell CRISPR experiments. We applied GLiMMIRS to a published dataset and tested for interactions between 46,166 enhancer pairs and corresponding genes, including 264 'high-confidence' enhancer pairs. We found that enhancer effects combine multiplicatively but with limited evidence for further interactions. Only 31 enhancer pairs exhibited significant interactions (FDR < 0.1), of which none came from the high confidence subset and 20 were driven by outlier expression values. Additional analyses of a second CRISPR dataset and in silico enhancer perturbations with Enformer both support a multiplicative model of enhancer effects without interactions. Altogether, our results indicate that enhancer interactions are uncommon or have small effects that are difficult to detect.
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Affiliation(s)
- Jessica Zhou
- Integrative Biology Laboratory, Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, NY 11724
| | - Karthik Guruvayurappan
- Integrative Biology Laboratory, Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA
- School of Biological Sciences, University of California San Diego, La Jolla, CA, 92037, USA
- Halicioglu Data Science Institute, University of California San Diego, La Jolla, CA, 92093, USA
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue New York NY 10065
| | - Shushan Toneyan
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, NY 11724
| | - Hsiuyi V. Chen
- Integrative Biology Laboratory, Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA
- Current address: A*STAR Infectious Diseases Labs, Agency for Science, Technology and Research (A*STAR), Singapore 138648, Singapore
| | - Aaron R. Chen
- Integrative Biology Laboratory, Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA
- Current address: Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, 11794, USA
| | - Peter Koo
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, NY 11724
| | - Graham McVicker
- Integrative Biology Laboratory, Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA
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12
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Moeckel C, Mouratidis I, Chantzi N, Uzun Y, Georgakopoulos-Soares I. Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights. Bioessays 2024; 46:e2300210. [PMID: 38715516 DOI: 10.1002/bies.202300210] [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: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.
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Affiliation(s)
- Camille Moeckel
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Ioannis Mouratidis
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Nikol Chantzi
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Yasin Uzun
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
- Department of Pediatrics, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Ilias Georgakopoulos-Soares
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
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13
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Chapman G, Determan J, Jetter H, Kaushik K, Prakasam R, Kroll KL. Defining cis-regulatory elements and transcription factors that control human cortical interneuron development. iScience 2024; 27:109967. [PMID: 38827400 PMCID: PMC11140214 DOI: 10.1016/j.isci.2024.109967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/08/2024] [Accepted: 05/09/2024] [Indexed: 06/04/2024] Open
Abstract
Although human cortical interneurons (cINs) are a minority population in the cerebral cortex, disruption of interneuron development is a frequent contributor to neurodevelopmental disorders. Here, we utilized a model for deriving cINs from human embryonic stem cells to profile chromatin state changes and generate an atlas of cis-regulatory elements (CREs) controlling human cIN development. We used these data to define candidate transcription factors (TFs) that may bind these CREs to regulate interneuron progenitor specification. Among these were RFX3 and RFX4, risk genes for autism spectrum disorder (ASD) with uncharacterized roles in human neuronal development. Using RFX3 and RFX4 knockdown models, we demonstrated new requirements for both genes in interneuron progenitor specification, with RFX3 deficiency causing precocious neuronal differentiation while RFX4 deficiency instead resulted in cessation of progenitor cell proliferation. Together, this work both defined central features of cis-regulatory control and identified new TF requirements for human interneuron development.
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Affiliation(s)
- Gareth Chapman
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Julianna Determan
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Haley Jetter
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Komal Kaushik
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Ramachandran Prakasam
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Kristen L. Kroll
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
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14
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Enk LUB, Hellmig M, Riecken K, Kilian C, Datlinger P, Jauch-Speer SL, Neben T, Sultana Z, Sivayoganathan V, Borchers A, Paust HJ, Zhao Y, Asada N, Liu S, Agalioti T, Pelczar P, Wiech T, Bock C, Huber TB, Huber S, Bonn S, Gagliani N, Fehse B, Panzer U, Krebs CF. Targeting T cell plasticity in kidney and gut inflammation by pooled single-cell CRISPR screening. Sci Immunol 2024; 9:eadd6774. [PMID: 38875317 DOI: 10.1126/sciimmunol.add6774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 05/23/2024] [Indexed: 06/16/2024]
Abstract
Pro-inflammatory CD4+ T cells are major drivers of autoimmune diseases, yet therapies modulating T cell phenotypes to promote an anti-inflammatory state are lacking. Here, we identify T helper 17 (TH17) cell plasticity in the kidneys of patients with antineutrophil cytoplasmic antibody-associated glomerulonephritis on the basis of single-cell (sc) T cell receptor analysis and scRNA velocity. To uncover molecules driving T cell polarization and plasticity, we established an in vivo pooled scCRISPR droplet sequencing (iCROP-seq) screen and applied it to mouse models of glomerulonephritis and colitis. CRISPR-based gene targeting in TH17 cells could be ranked according to the resulting transcriptional perturbations, and polarization biases into T helper 1 (TH1) and regulatory T cells could be quantified. Furthermore, we show that iCROP-seq can facilitate the identification of therapeutic targets by efficient functional stratification of genes and pathways in a disease- and tissue-specific manner. These findings uncover TH17 to TH1 cell plasticity in the human kidney in the context of renal autoimmunity.
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Affiliation(s)
- Leon U B Enk
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Malte Hellmig
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kristoffer Riecken
- Department of Stem Cell Transplantation, Research Department Cell and Gene Therapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Kilian
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Paul Datlinger
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Saskia L Jauch-Speer
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias Neben
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Zeba Sultana
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Varshi Sivayoganathan
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alina Borchers
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hans-Joachim Paust
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Yu Zhao
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nariaki Asada
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Shuya Liu
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Theodora Agalioti
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Penelope Pelczar
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thorsten Wiech
- Institute of Pathology, Division of Nephropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Tobias B Huber
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Samuel Huber
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nicola Gagliani
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department for General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Boris Fehse
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Stem Cell Transplantation, Research Department Cell and Gene Therapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ulf Panzer
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian F Krebs
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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15
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Chin IM, Gardell ZA, Corces MR. Decoding polygenic diseases: advances in noncoding variant prioritization and validation. Trends Cell Biol 2024; 34:465-483. [PMID: 38719704 DOI: 10.1016/j.tcb.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/12/2024] [Accepted: 03/21/2024] [Indexed: 06/09/2024]
Abstract
Genome-wide association studies (GWASs) provide a key foundation for elucidating the genetic underpinnings of common polygenic diseases. However, these studies have limitations in their ability to assign causality to particular genetic variants, especially those residing in the noncoding genome. Over the past decade, technological and methodological advances in both analytical and empirical prioritization of noncoding variants have enabled the identification of causative variants by leveraging orthogonal functional evidence at increasing scale. In this review, we present an overview of these approaches and describe how this workflow provides the groundwork necessary to move beyond associations toward genetically informed studies on the molecular and cellular mechanisms of polygenic disease.
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Affiliation(s)
- Iris M Chin
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Zachary A Gardell
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - M Ryan Corces
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
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16
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Fagnan A, Aid Z, Baille M, Drakul A, Robert E, Lopez CK, Thirant C, Lecluse Y, Rivière J, Ignacimouttou C, Salmoiraghi S, Anguita E, Naimo A, Marzac C, Pflumio F, Malinge S, Wichmann C, Huang Y, Lobry C, Chaumeil J, Soler E, Bourquin J, Nerlov C, Bernard OA, Schwaller J, Mercher T. The ETO2 transcriptional cofactor maintains acute leukemia by driving a MYB/EP300-dependent stemness program. Hemasphere 2024; 8:e90. [PMID: 38903535 PMCID: PMC11187848 DOI: 10.1002/hem3.90] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/13/2024] [Accepted: 05/01/2024] [Indexed: 06/22/2024] Open
Abstract
Transcriptional cofactors of the ETO family are recurrent fusion partners in acute leukemia. We characterized the ETO2 regulome by integrating transcriptomic and chromatin binding analyses in human erythroleukemia xenografts and controlled ETO2 depletion models. We demonstrate that beyond its well-established repressive activity, ETO2 directly activates transcription of MYB, among other genes. The ETO2-activated signature is associated with a poorer prognosis in erythroleukemia but also in other acute myeloid and lymphoid leukemia subtypes. Mechanistically, ETO2 colocalizes with EP300 and MYB at enhancers supporting the existence of an ETO2/MYB feedforward transcription activation loop (e.g., on MYB itself). Both small-molecule and PROTAC-mediated inhibition of EP300 acetyltransferases strongly reduced ETO2 protein, chromatin binding, and ETO2-activated transcripts. Taken together, our data show that ETO2 positively enforces a leukemia maintenance program that is mediated in part by the MYB transcription factor and that relies on acetyltransferase cofactors to stabilize ETO2 scaffolding activity.
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Affiliation(s)
- Alexandre Fagnan
- Gustave Roussy, INSERM U1170Université Paris‐SaclayVillejuifFrance
- Equipe Labellisée Ligue Contre le CancerParisFrance
- MRC Molecular Hematology Unit, MRC Weatherall Institute of Molecular Medicine, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Zakia Aid
- Gustave Roussy, INSERM U1170Université Paris‐SaclayVillejuifFrance
- Equipe Labellisée Ligue Contre le CancerParisFrance
| | - Marie Baille
- Gustave Roussy, INSERM U1170Université Paris‐SaclayVillejuifFrance
- Equipe Labellisée Ligue Contre le CancerParisFrance
| | - Aneta Drakul
- Division of Oncology and Children's Research CentreUniversity Children's Hospital ZurichZurichSwitzerland
| | - Elie Robert
- Gustave Roussy, INSERM U1170Université Paris‐SaclayVillejuifFrance
- Equipe Labellisée Ligue Contre le CancerParisFrance
| | - Cécile K. Lopez
- Gustave Roussy, INSERM U1170Université Paris‐SaclayVillejuifFrance
- Equipe Labellisée Ligue Contre le CancerParisFrance
| | - Cécile Thirant
- Gustave Roussy, INSERM U1170Université Paris‐SaclayVillejuifFrance
- Equipe Labellisée Ligue Contre le CancerParisFrance
| | - Yann Lecluse
- Gustave Roussy, Plateforme Imagerie et Cytométrie, Université Paris‐Saclay, UMS AMMICA, INSERM US23, CNRS UMS 3655VillejuifFrance
| | - Julie Rivière
- Gustave Roussy, INSERM U1170Université Paris‐SaclayVillejuifFrance
- Equipe Labellisée Ligue Contre le CancerParisFrance
| | - Cathy Ignacimouttou
- Gustave Roussy, INSERM U1170Université Paris‐SaclayVillejuifFrance
- Equipe Labellisée Ligue Contre le CancerParisFrance
| | - Silvia Salmoiraghi
- Department of Oncology and HematologyAzienda Socio Sanitaria Territoriale Papa Giovanni XXIII, FROM Research Foundation, Papa Giovanni XXIII HospitalBergamoItaly
| | - Eduardo Anguita
- Hematology Department, Hospital Clínico San Carlos (HCSC), IML, IdISSC, Department of MedicineUniversidad Complutense de Madrid (UCM)MadridSpain
| | - Audrey Naimo
- Gustave Roussy, Genomic PlatformUniversité Paris‐Saclay, UMS AMMICA, INSERM US23, CNRS UMS 3655VillejuifFrance
| | - Christophe Marzac
- Department of HematologyLeukemia Interception Program, Personalized Cancer Prevention Center, Gustave RoussyVillejuifFrance
| | - Françoise Pflumio
- Equipe Labellisée Ligue Contre le CancerParisFrance
- Unité de Recherche (UMR)‐E008 Stabilité Génétique, Cellules Souches et Radiations, Team Niche and Cancer in Hematopoiesis, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA)Université de Paris‐Université Paris‐SaclayFontenay‐aux‐RosesFrance
- OPALE Carnot Institute, The Organization for Partnerships in LeukemiaParisFrance
| | - Sébastien Malinge
- Gustave Roussy, INSERM U1170Université Paris‐SaclayVillejuifFrance
- Telethon Kids Institute, Perth Children's HospitalNedlandsAustralia
| | - Christian Wichmann
- Department of Transfusion Medicine, Cell Therapeutics and HaemostasisLudwig‐Maximilians‐University of MunichMunichGermany
| | - Yun Huang
- Division of Oncology and Children's Research CentreUniversity Children's Hospital ZurichZurichSwitzerland
| | - Camille Lobry
- Gustave Roussy, INSERM U1170Université Paris‐SaclayVillejuifFrance
- INSERM U944, CNRS UMR7212Institut de Recherche Saint Louis and Université de ParisParisFrance
| | - Julie Chaumeil
- Université de Paris, Institut Cochin, INSERM, CNRSParisFrance
| | - Eric Soler
- IGMM, University of Montpellier, CNRS, Montpellier, France & Université de Paris, Laboratory of Excellence GR‐ExParisFrance
| | - Jean‐Pierre Bourquin
- Division of Oncology and Children's Research CentreUniversity Children's Hospital ZurichZurichSwitzerland
| | - Claus Nerlov
- MRC Molecular Hematology Unit, MRC Weatherall Institute of Molecular Medicine, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | | | - Juerg Schwaller
- Department of BiomedicineUniversity Children's Hospital Beider Basel (UKBB), University of BaselBaselSwitzerland
| | - Thomas Mercher
- Gustave Roussy, INSERM U1170Université Paris‐SaclayVillejuifFrance
- Equipe Labellisée Ligue Contre le CancerParisFrance
- OPALE Carnot Institute, The Organization for Partnerships in LeukemiaParisFrance
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17
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Pacalin NM, Steinhart Z, Shi Q, Belk JA, Dorovskyi D, Kraft K, Parker KR, Shy BR, Marson A, Chang HY. Bidirectional epigenetic editing reveals hierarchies in gene regulation. Nat Biotechnol 2024:10.1038/s41587-024-02213-3. [PMID: 38760566 DOI: 10.1038/s41587-024-02213-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/19/2024] [Indexed: 05/19/2024]
Abstract
CRISPR perturbation methods are limited in their ability to study non-coding elements and genetic interactions. In this study, we developed a system for bidirectional epigenetic editing, called CRISPRai, in which we apply activating (CRISPRa) and repressive (CRISPRi) perturbations to two loci simultaneously in the same cell. We developed CRISPRai Perturb-seq by coupling dual perturbation gRNA detection with single-cell RNA sequencing, enabling study of pooled perturbations in a mixed single-cell population. We applied this platform to study the genetic interaction between two hematopoietic lineage transcription factors, SPI1 and GATA1, and discovered novel characteristics of their co-regulation on downstream target genes, including differences in SPI1 and GATA1 occupancy at genes that are regulated through different modes. We also studied the regulatory landscape of IL2 (interleukin-2) in Jurkat T cells, primary T cells and chimeric antigen receptor (CAR) T cells and elucidated mechanisms of enhancer-mediated IL2 gene regulation. CRISPRai facilitates investigation of context-specific genetic interactions, provides new insights into gene regulation and will enable exploration of non-coding disease-associated variants.
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Affiliation(s)
- Naomi M Pacalin
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Zachary Steinhart
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
| | - Quanming Shi
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Julia A Belk
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Dmytro Dorovskyi
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Katerina Kraft
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Kevin R Parker
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA
- Cartography Biosciences, Inc., South San Francisco, CA, USA
| | - Brian R Shy
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Alexander Marson
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
- Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
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18
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Barry T, Mason K, Roeder K, Katsevich E. Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection. Genome Biol 2024; 25:124. [PMID: 38760839 PMCID: PMC11100084 DOI: 10.1186/s13059-024-03254-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 04/19/2024] [Indexed: 05/19/2024] Open
Abstract
Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.
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Affiliation(s)
- Timothy Barry
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA.
| | - Kaishu Mason
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, USA
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA
| | - Eugene Katsevich
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA.
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19
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Chardon FM, McDiarmid TA, Page NF, Daza RM, Martin B, Domcke S, Regalado SG, Lalanne JB, Calderon D, Li X, Starita LM, Sanders SJ, Ahituv N, Shendure J. Multiplex, single-cell CRISPRa screening for cell type specific regulatory elements. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.28.534017. [PMID: 37034704 PMCID: PMC10081248 DOI: 10.1101/2023.03.28.534017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
CRISPR-based gene activation (CRISPRa) is a promising therapeutic approach for gene therapy, upregulating gene expression by targeting promoters or enhancers in a tissue/cell-type specific manner. Here, we describe an experimental framework that combines highly multiplexed perturbations with single-cell RNA sequencing (sc-RNA-seq) to identify cell-type-specific, CRISPRa-responsive cis- regulatory elements and the gene(s) they regulate. Random combinations of many gRNAs are introduced to each of many cells, which are then profiled and partitioned into test and control groups to test for effect(s) of CRISPRa perturbations of both enhancers and promoters on the expression of neighboring genes. Applying this method to a library of 493 gRNAs targeting candidate cis- regulatory elements in both K562 cells and iPSC-derived excitatory neurons, we identify gRNAs capable of specifically upregulating intended target genes and no other neighboring genes within 1 Mb, including gRNAs yielding upregulation of six autism spectrum disorder (ASD) and neurodevelopmental disorder (NDD) risk genes in neurons. A consistent pattern is that the responsiveness of individual enhancers to CRISPRa is restricted by cell type, implying a dependency on either chromatin landscape and/or additional trans- acting factors for successful gene activation. The approach outlined here may facilitate large-scale screens for gRNAs that activate therapeutically relevant genes in a cell type-specific manner.
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20
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Barry T, Mason K, Roeder K, Katsevich E. Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.15.540875. [PMID: 38659821 PMCID: PMC11042176 DOI: 10.1101/2023.05.15.540875] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.
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21
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Zhong Z, Virshup DM. Recurrent mutations in tumor suppressor FBXW7 bypass Wnt/β-catenin addiction in cancer. SCIENCE ADVANCES 2024; 10:eadk1031. [PMID: 38569029 PMCID: PMC10990278 DOI: 10.1126/sciadv.adk1031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 02/27/2024] [Indexed: 04/05/2024]
Abstract
Pathologic Wnt/β-catenin signaling drives various cancers, leading to multiple approaches to drug this pathway. Appropriate patient selection can maximize success of these interventions. Wnt ligand addiction is a druggable vulnerability in RNF43-mutant/RSPO-fusion cancers. However, pharmacologically targeting the biogenesis of Wnt ligands, e.g., with PORCN inhibitors, has shown mixed therapeutic responses, possibly due to tumor heterogeneity. Here, we show that the tumor suppressor FBXW7 is frequently mutated in RNF43-mutant/RSPO-fusion tumors, and FBXW7 mutations cause intrinsic resistance to anti-Wnt therapies. Mechanistically, FBXW7 inactivation stabilizes multiple oncoproteins including Cyclin E and MYC and antagonizes the cytostatic effect of Wnt inhibitors. Moreover, although FBXW7 mutations do not mitigate β-catenin degradation upon Wnt inhibition, FBXW7-mutant RNF43-mutant/RSPO-fusion cancers instead lose dependence on β-catenin signaling, accompanied by dedifferentiation and loss of lineage specificity. These FBXW7-mutant Wnt/β-catenin-independent tumors are susceptible to multi-cyclin-dependent kinase inhibition. An in-depth understanding of primary resistance to anti-Wnt/β-catenin therapies allows for more appropriate patient selection and use of alternative mechanism-based therapies.
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Affiliation(s)
- Zheng Zhong
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - David M. Virshup
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
- Department of Pediatrics, Duke University, Durham, NC 27710, USA
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22
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Nadal-Ribelles M, Solé C, de Nadal E, Posas F. The rise of single-cell transcriptomics in yeast. Yeast 2024; 41:158-170. [PMID: 38403881 DOI: 10.1002/yea.3934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/24/2024] [Accepted: 02/15/2024] [Indexed: 02/27/2024] Open
Abstract
The field of single-cell omics has transformed our understanding of biological processes and is constantly advancing both experimentally and computationally. One of the most significant developments is the ability to measure the transcriptome of individual cells by single-cell RNA-seq (scRNA-seq), which was pioneered in higher eukaryotes. While yeast has served as a powerful model organism in which to test and develop transcriptomic technologies, the implementation of scRNA-seq has been significantly delayed in this organism, mainly because of technical constraints associated with its intrinsic characteristics, namely the presence of a cell wall, a small cell size and little amounts of RNA. In this review, we examine the current technologies for scRNA-seq in yeast and highlight their strengths and weaknesses. Additionally, we explore opportunities for developing novel technologies and the potential outcomes of implementing single-cell transcriptomics and extension to other modalities. Undoubtedly, scRNA-seq will be invaluable for both basic and applied yeast research, providing unique insights into fundamental biological processes.
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Affiliation(s)
- Mariona Nadal-Ribelles
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Carme Solé
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Eulalia de Nadal
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Francesc Posas
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
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23
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Ren L, Huang D, Liu H, Ning L, Cai P, Yu X, Zhang Y, Luo N, Lin H, Su J, Zhang Y. Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review). Oncol Lett 2024; 27:152. [PMID: 38406595 PMCID: PMC10885005 DOI: 10.3892/ol.2024.14285] [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: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.
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Affiliation(s)
- Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Danni Huang
- Department of Radiology, Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208, P.R. China
| | - Hongjiang Liu
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, Sichuan 610106, P.R. China
| | - Xiaolong Yu
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Research Institute, Material Science and Engineering Institute of Hainan University, Sanya, Hainan 572025, P.R. China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China
| | - Jinsong Su
- Research Institute of Integrated Traditional Chinese Medicine and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Yinghui Zhang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
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24
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Friedman MJ, Wagner T, Lee H, Rosenfeld MG, Oh S. Enhancer-promoter specificity in gene transcription: molecular mechanisms and disease associations. Exp Mol Med 2024; 56:772-787. [PMID: 38658702 PMCID: PMC11058250 DOI: 10.1038/s12276-024-01233-y] [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: 11/26/2023] [Revised: 02/28/2024] [Accepted: 03/05/2024] [Indexed: 04/26/2024] Open
Abstract
Although often located at a distance from their target gene promoters, enhancers are the primary genomic determinants of temporal and spatial transcriptional specificity in metazoans. Since the discovery of the first enhancer element in simian virus 40, there has been substantial interest in unraveling the mechanism(s) by which enhancers communicate with their partner promoters to ensure proper gene expression. These research efforts have benefited considerably from the application of increasingly sophisticated sequencing- and imaging-based approaches in conjunction with innovative (epi)genome-editing technologies; however, despite various proposed models, the principles of enhancer-promoter interaction have still not been fully elucidated. In this review, we provide an overview of recent progress in the eukaryotic gene transcription field pertaining to enhancer-promoter specificity. A better understanding of the mechanistic basis of lineage- and context-dependent enhancer-promoter engagement, along with the continued identification of functional enhancers, will provide key insights into the spatiotemporal control of gene expression that can reveal therapeutic opportunities for a range of enhancer-related diseases.
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Affiliation(s)
- Meyer J Friedman
- Department and School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Tobias Wagner
- Department and School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Haram Lee
- College of Pharmacy Korea University, 2511 Sejong-ro, Sejong, 30019, Republic of Korea
| | - Michael G Rosenfeld
- Department and School of Medicine, University of California, San Diego, La Jolla, CA, USA.
| | - Soohwan Oh
- College of Pharmacy Korea University, 2511 Sejong-ro, Sejong, 30019, Republic of Korea.
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25
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Conery M, Pippin JA, Wagley Y, Trang K, Pahl MC, Villani DA, Favazzo LJ, Ackert-Bicknell CL, Zuscik MJ, Katsevich E, Wells AD, Zemel BS, Voight BF, Hankenson KD, Chesi A, Grant SF. GWAS-informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585778. [PMID: 38562830 PMCID: PMC10983984 DOI: 10.1101/2024.03.19.585778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Over 1,100 independent signals have been identified with genome-wide association studies (GWAS) for bone mineral density (BMD), a key risk factor for mortality-increasing fragility fractures; however, the effector gene(s) for most remain unknown. Informed by a variant-to-gene mapping strategy implicating 89 non-coding elements predicted to regulate osteoblast gene expression at BMD GWAS loci, we executed a single-cell CRISPRi screen in human fetal osteoblast 1.19 cells (hFOBs). The BMD relevance of hFOBs was supported by heritability enrichment from cross-cell type stratified LD-score regression involving 98 cell types grouped into 15 tissues. 24 genes showed perturbation in the screen, with four (ARID5B, CC2D1B, EIF4G2, and NCOA3) exhibiting consistent effects upon siRNA knockdown on three measures of osteoblast maturation and mineralization. Lastly, additional heritability enrichments, genetic correlations, and multi-trait fine-mapping revealed that many BMD GWAS signals are pleiotropic and likely mediate their effects via non-bone tissues that warrant attention in future screens.
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Affiliation(s)
- Mitchell Conery
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - James A. Pippin
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Yadav Wagley
- Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, MI 48109
| | - Khanh Trang
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Matthew C. Pahl
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - David A. Villani
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Cell Biology, Stems Cells and Development Ph.D. Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Lacey J. Favazzo
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- University of Colorado Interdisciplinary Joint Biology Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Cheryl L. Ackert-Bicknell
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- University of Colorado Interdisciplinary Joint Biology Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Michael J. Zuscik
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- University of Colorado Interdisciplinary Joint Biology Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Eugene Katsevich
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew D. Wells
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Babette S. Zemel
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Benjamin F. Voight
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kurt D. Hankenson
- Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, MI 48109
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Struan F.A. Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Endocrinology and Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
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26
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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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27
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Pacesa M, Pelea O, Jinek M. Past, present, and future of CRISPR genome editing technologies. Cell 2024; 187:1076-1100. [PMID: 38428389 DOI: 10.1016/j.cell.2024.01.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 03/03/2024]
Abstract
Genome editing has been a transformative force in the life sciences and human medicine, offering unprecedented opportunities to dissect complex biological processes and treat the underlying causes of many genetic diseases. CRISPR-based technologies, with their remarkable efficiency and easy programmability, stand at the forefront of this revolution. In this Review, we discuss the current state of CRISPR gene editing technologies in both research and therapy, highlighting limitations that constrain them and the technological innovations that have been developed in recent years to address them. Additionally, we examine and summarize the current landscape of gene editing applications in the context of human health and therapeutics. Finally, we outline potential future developments that could shape gene editing technologies and their applications in the coming years.
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Affiliation(s)
- Martin Pacesa
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Station 19, CH-1015 Lausanne, Switzerland
| | - Oana Pelea
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Martin Jinek
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
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Valdivia-Francia F, Sendoel A. No country for old methods: New tools for studying microproteins. iScience 2024; 27:108972. [PMID: 38333695 PMCID: PMC10850755 DOI: 10.1016/j.isci.2024.108972] [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] [Indexed: 02/10/2024] Open
Abstract
Microproteins encoded by small open reading frames (sORFs) have emerged as a fascinating frontier in genomics. Traditionally overlooked due to their small size, recent technological advancements such as ribosome profiling, mass spectrometry-based strategies and advanced computational approaches have led to the annotation of more than 7000 sORFs in the human genome. Despite the vast progress, only a tiny portion of these microproteins have been characterized and an important challenge in the field lies in identifying functionally relevant microproteins and understanding their role in different cellular contexts. In this review, we explore the recent advancements in sORF research, focusing on the new methodologies and computational approaches that have facilitated their identification and functional characterization. Leveraging these new tools hold great promise for dissecting the diverse cellular roles of microproteins and will ultimately pave the way for understanding their role in the pathogenesis of diseases and identifying new therapeutic targets.
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Affiliation(s)
- Fabiola Valdivia-Francia
- University of Zurich, Institute for Regenerative Medicine (IREM), Wagistrasse 12, 8952 Schlieren-Zurich, Switzerland
- Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich/ ETH Zurich, Schlieren-Zurich, Switzerland
| | - Ataman Sendoel
- University of Zurich, Institute for Regenerative Medicine (IREM), Wagistrasse 12, 8952 Schlieren-Zurich, Switzerland
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29
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Hsiung CC, Wilson CM, Sambold NA, Dai R, Chen Q, Misiukiewicz S, Arab A, Teyssier N, O'Loughlin T, Cofsky JC, Shi J, Gilbert LA. Higher-order combinatorial chromatin perturbations by engineered CRISPR-Cas12a for functional genomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.18.558350. [PMID: 37781594 PMCID: PMC10541102 DOI: 10.1101/2023.09.18.558350] [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
Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting 1-3 genomic sites per cell. To develop a tool for higher-order ( > 3) combinatorial targeting of genomic sites with CRISPRi in functional genomics screens, we engineered an Acidaminococcus Cas12a variant -- referred to as mul tiplexed transcriptional interference AsCas12a (multiAsCas12a). multiAsCas12a incorporates a key mutation, R1226A, motivated by the hypothesis of nicking-induced stabilization of the ribonucleoprotein:DNA complex for improving CRISPRi activity. multiAsCas12a significantly outperforms prior state-of-the-art Cas12a variants in combinatorial CRISPRi targeting using high-order multiplexed arrays of lentivirally transduced CRISPR RNAs (crRNA), including in high-throughput pooled screens using 6-plex crRNA array libraries. Using multiAsCas12a CRISPRi, we discover new enhancer elements and dissect the combinatorial function of cis-regulatory elements. These results instantiate a group testing framework for efficiently surveying potentially numerous combinations of chromatin perturbations for biological discovery and engineering.
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30
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Alda-Catalinas C, Ibarra-Soria X, Flouri C, Gordillo JE, Cousminer D, Hutchinson A, Sun B, Pembroke W, Ullrich S, Krejci A, Cortes A, Acevedo A, Malla S, Fishwick C, Drewes G, Rapiteanu R. Mapping the functional impact of non-coding regulatory elements in primary T cells through single-cell CRISPR screens. Genome Biol 2024; 25:42. [PMID: 38308274 PMCID: PMC10835965 DOI: 10.1186/s13059-024-03176-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 01/18/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Drug targets with genetic evidence are expected to increase clinical success by at least twofold. Yet, translating disease-associated genetic variants into functional knowledge remains a fundamental challenge of drug discovery. A key issue is that the vast majority of complex disease associations cannot be cleanly mapped to a gene. Immune disease-associated variants are enriched within regulatory elements found in T-cell-specific open chromatin regions. RESULTS To identify genes and molecular programs modulated by these regulatory elements, we develop a CRISPRi-based single-cell functional screening approach in primary human T cells. Our pipeline enables the interrogation of transcriptomic changes induced by the perturbation of regulatory elements at scale. We first optimize an efficient CRISPRi protocol in primary CD4+ T cells via CROPseq vectors. Subsequently, we perform a screen targeting 45 non-coding regulatory elements and 35 transcription start sites and profile approximately 250,000 T -cell single-cell transcriptomes. We develop a bespoke analytical pipeline for element-to-gene (E2G) mapping and demonstrate that our method can identify both previously annotated and novel E2G links. Lastly, we integrate genetic association data for immune-related traits and demonstrate how our platform can aid in the identification of effector genes for GWAS loci. CONCLUSIONS We describe "primary T cell crisprQTL" - a scalable, single-cell functional genomics approach for mapping regulatory elements to genes in primary human T cells. We show how this framework can facilitate the interrogation of immune disease GWAS hits and propose that the combination of experimental and QTL-based techniques is likely to address the variant-to-function problem.
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Affiliation(s)
| | | | | | | | | | | | - Bin Sun
- Genomic Sciences, GSK, Stevenage, UK
| | | | | | | | | | | | | | | | - Gerard Drewes
- Genomic Sciences, GSK, Stevenage, UK
- Genomic Sciences, GSK, Collegeville, PA, USA
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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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32
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Lin H, Zhang M, Hu M, Zhang Y, Jiang W, Tang W, Ouyang Y, Jiang L, Mi Y, Chen Z, He P, Zhao G, Ouyang X. Emerging applications of single-cell profiling in precision medicine of atherosclerosis. J Transl Med 2024; 22:97. [PMID: 38263066 PMCID: PMC10804726 DOI: 10.1186/s12967-023-04629-y] [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: 06/12/2023] [Accepted: 10/14/2023] [Indexed: 01/25/2024] Open
Abstract
Atherosclerosis is a chronic, progressive, inflammatory disease that occurs in the arterial wall. Despite recent advancements in treatment aimed at improving efficacy and prolonging survival, atherosclerosis remains largely incurable. In this review, we discuss emerging single-cell sequencing techniques and their novel insights into atherosclerosis. We provide examples of single-cell profiling studies that reveal phenotypic characteristics of atherosclerosis plaques, blood, liver, and the intestinal tract. Additionally, we highlight the potential clinical applications of single-cell analysis and propose that combining this approach with other techniques can facilitate early diagnosis and treatment, leading to more accurate medical interventions.
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Affiliation(s)
- Huiling Lin
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China
- Department of Physiology, School of Medicine, Hunan Normal University, Changsha, 410081, Hunan, China
| | - Ming Zhang
- Affiliated Qingyuan Hospital, Guangzhou Medical University (Qingyuan People's Hospital), Qingyuan, 511518, Guangdong, China
| | - Mi Hu
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China
| | - Yangkai Zhang
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China
| | - WeiWei Jiang
- Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Wanying Tang
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China
| | - Yuxin Ouyang
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China
| | - Liping Jiang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yali Mi
- Affiliated Qingyuan Hospital, Guangzhou Medical University (Qingyuan People's Hospital), Qingyuan, 511518, Guangdong, China
| | - Zhi Chen
- College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China
| | - Pingping He
- Department of Nursing, School of Medicine, Hunan Normal University, Changsha, 410081, Hunan, China.
| | - Guojun Zhao
- Affiliated Qingyuan Hospital, Guangzhou Medical University (Qingyuan People's Hospital), Qingyuan, 511518, Guangdong, China.
| | - Xinping Ouyang
- Department of Physiology, Medical College, Institute of Neuroscience Research, Hengyang Key Laboratory of Neurodegeneration and Cognitive Impairment, University of South China, Hengyang, 421001, Hunan, China.
- Department of Physiology, School of Medicine, Hunan Normal University, Changsha, 410081, Hunan, China.
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, 410081, Hunan, Changsha, China.
- The Engineering Research Center of Reproduction and Translational Medicine of Hunan Province, School of Medicine, Hunan Normal University, 410081, Hunan, Changsha, China.
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33
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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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34
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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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35
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Li YR, Lyu Z, Tian Y, Fang Y, Zhu Y, Chen Y, Yang L. Advancements in CRISPR screens for the development of cancer immunotherapy strategies. Mol Ther Oncolytics 2023; 31:100733. [PMID: 37876793 PMCID: PMC10591018 DOI: 10.1016/j.omto.2023.100733] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023] Open
Abstract
CRISPR screen technology enables systematic and scalable interrogation of gene function by using the CRISPR-Cas9 system to perturb gene expression. In the field of cancer immunotherapy, this technology has empowered the discovery of genes, biomarkers, and pathways that regulate tumor development and progression, immune reactivity, and the effectiveness of immunotherapeutic interventions. By conducting large-scale genetic screens, researchers have successfully identified novel targets to impede tumor growth, enhance anti-tumor immune responses, and surmount immunosuppression within the tumor microenvironment (TME). Here, we present an overview of CRISPR screens conducted in tumor cells for the purpose of identifying novel therapeutic targets. We also explore the application of CRISPR screens in immune cells to propel the advancement of cell-based therapies, encompassing T cells, natural killer cells, dendritic cells, and macrophages. Furthermore, we outline the crucial components necessary for the successful implementation of immune-specific CRISPR screens and explore potential directions for future research.
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Affiliation(s)
- Yan-Ruide Li
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Zibai Lyu
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yanxin Tian
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ying Fang
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yichen Zhu
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yuning Chen
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Lili Yang
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
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36
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Song B, Liu D, Dai W, McMyn N, Wang Q, Yang D, Krejci A, Vasilyev A, Untermoser N, Loregger A, Song D, Williams B, Rosen B, Cheng X, Chao L, Kale HT, Zhang H, Diao Y, Bürckstümmer T, Siliciano JM, Li JJ, Siliciano R, Huangfu D, Li W. Decoding Heterogenous Single-cell Perturbation Responses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564796. [PMID: 37961332 PMCID: PMC10635009 DOI: 10.1101/2023.10.30.564796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Understanding diverse responses of individual cells to the same perturbation is central to many biological and biomedical problems. Current methods, however, do not precisely quantify the strength of perturbation responses and, more importantly, reveal new biological insights from heterogeneity in responses. Here we introduce the perturbation-response score (PS), based on constrained quadratic optimization, to quantify diverse perturbation responses at a single-cell level. Applied to single-cell transcriptomes of large-scale genetic perturbation datasets (e.g., Perturb-seq), PS outperforms existing methods for quantifying partial gene perturbation responses. In addition, PS presents two major advances. First, PS enables large-scale, single-cell-resolution dosage analysis of perturbation, without the need to titrate perturbation strength. By analyzing the dose-response patterns of over 2,000 essential genes in Perturb-seq, we identify two distinct patterns, depending on whether a moderate reduction in their expression induces strong downstream expression alterations. Second, PS identifies intrinsic and extrinsic biological determinants of perturbation responses. We demonstrate the application of PS in contexts such as T cell stimulation, latent HIV-1 expression, and pancreatic cell differentiation. Notably, PS unveiled a previously unrecognized, cell-type-specific role of coiled-coil domain containing 6 (CCDC6) in guiding liver and pancreatic lineage decisions, where CCDC6 knockouts drive the endoderm cell differentiation towards liver lineage, rather than pancreatic lineage. The PS approach provides an innovative method for dose-to-function analysis and will enable new biological discoveries from single-cell perturbation datasets. One sentence summary We present a method to quantify diverse perturbation responses and discover novel biological insights in single-cell perturbation datasets.
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37
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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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38
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Wang Y, Armendariz D, Wang L, Zhao H, Xie S, Hon GC. Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.20.567880. [PMID: 38045327 PMCID: PMC10690208 DOI: 10.1101/2023.11.20.567880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Genetic studies have associated thousands of enhancers with breast cancer. However, the vast majority have not been functionally characterized. Thus, it remains unclear how variant-associated enhancers contribute to cancer. Here, we perform single-cell CRISPRi screens of 3,512 regulatory elements associated with breast cancer to measure the impact of these regions on transcriptional phenotypes. Analysis of >500,000 single-cell transcriptomes in two breast cancer cell lines shows that perturbation of variant-associated enhancers disrupts breast cancer gene programs. We observe variant-associated enhancers that directly or indirectly regulate the expression of cancer genes. We also find one-to-multiple and multiple-to-one network motifs where enhancers indirectly regulate cancer genes. Notably, multiple variant-associated enhancers indirectly regulate TP53. Comparative studies illustrate sub-type specific functions between enhancers in ER+ and ER- cells. Finally, we developed the pySpade package to facilitate analysis of single-cell enhancer screens. Overall, we demonstrate that enhancers form regulatory networks that link cancer genes in the genome, providing a more comprehensive understanding of the contribution of enhancers to breast cancer development.
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Affiliation(s)
- Yihan Wang
- Cecil H. and Ida Green Center for Reproductive Biology Sciences
| | | | - Lei Wang
- Cecil H. and Ida Green Center for Reproductive Biology Sciences
| | - Huan Zhao
- Cecil H. and Ida Green Center for Reproductive Biology Sciences
| | - Shiqi Xie
- Cecil H. and Ida Green Center for Reproductive Biology Sciences
- Current address: Genentech, 1 DNA Way, South San Francisco, CA 94080, USA
| | - Gary C Hon
- Cecil H. and Ida Green Center for Reproductive Biology Sciences
- Division of Basic Reproductive Biology Research, Department of Obstetrics and Gynecology, Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390
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39
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Fadul SM, Arshad A, Mehmood R. CRISPR-based epigenome editing: mechanisms and applications. Epigenomics 2023; 15:1137-1155. [PMID: 37990877 DOI: 10.2217/epi-2023-0281] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023] Open
Abstract
Epigenomic anomalies contribute significantly to the development of numerous human disorders. The development of epigenetic research tools is essential for understanding how epigenetic marks contribute to gene expression. A gene-editing technique known as CRISPR (clustered regularly interspaced short palindromic repeats) typically targets a particular DNA sequence using a guide RNA (gRNA). CRISPR/Cas9 technology has been remodeled for epigenome editing by generating a 'dead' Cas9 protein (dCas9) that lacks nuclease activity and juxtaposing it with an epigenetic effector domain. Based on fusion partners of dCas9, a specific epigenetic state can be achieved. CRISPR-based epigenome editing has widespread application in drug screening, cancer treatment and regenerative medicine. This paper discusses the tools developed for CRISPR-based epigenome editing and their applications.
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Affiliation(s)
- Shaima M Fadul
- Department of Life Sciences, College of Science & General Studies, Alfaisal University, Riyadh, 11533, Kingdom of Saudi Arabia
| | - Aleeza Arshad
- Medical Teaching Insitute, Ayub Teaching Hospital, Abbottabad, 22020, Pakistan
| | - Rashid Mehmood
- Department of Life Sciences, College of Science & General Studies, Alfaisal University, Riyadh, 11533, Kingdom of Saudi Arabia
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40
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Zhou X, Renauer PA, Zhou L, Fang SY, Chen S. Applications of CRISPR technology in cellular immunotherapy. Immunol Rev 2023; 320:199-216. [PMID: 37449673 PMCID: PMC10787818 DOI: 10.1111/imr.13241] [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: 05/17/2023] [Accepted: 06/07/2023] [Indexed: 07/18/2023]
Abstract
CRISPR technology has transformed multiple fields, including cancer and immunology. CRISPR-based gene editing and screening empowers direct genomic manipulation of immune cells, opening doors to unbiased functional genetic screens. These screens aid in the discovery of novel factors that regulate and reprogram immune responses, offering novel drug targets. The engineering of immune cells using CRISPR has sparked a transformation in the cellular immunotherapy field, resulting in a multitude of ongoing clinical trials. In this review, we discuss the development and applications of CRISPR and related gene editing technologies in immune cells, focusing on functional genomics screening, gene editing-based cell therapies, as well as future directions in this rapidly advancing field.
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Affiliation(s)
- Xiaoyu Zhou
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Paul A. Renauer
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Liqun Zhou
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
- Immunobiology Program, Yale University, New Haven, CT, USA
| | - Shao-Yu Fang
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
| | - Sidi Chen
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- System Biology Institute, Yale University, West Haven, CT, USA
- Center for Cancer Systems Biology, Yale University, West Haven, CT, USA
- Immunobiology Program, Yale University, New Haven, CT, USA
- Department of Immunobiology, Yale University, New Haven, CT, USA
- Molecular Cell Biology, Genetics, and Development Program, Yale University, New Haven, CT, USA
- Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
- Stem Cell Center, Yale University School of Medicine, New Haven, CT, USA
- Center for Biomedical Data Science, Yale University School of Medicine, New Haven, CT, USA
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41
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Badia-I-Mompel P, Wessels L, Müller-Dott S, Trimbour R, Ramirez Flores RO, Argelaguet R, Saez-Rodriguez J. Gene regulatory network inference in the era of single-cell multi-omics. Nat Rev Genet 2023; 24:739-754. [PMID: 37365273 DOI: 10.1038/s41576-023-00618-5] [Citation(s) in RCA: 66] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 06/28/2023]
Abstract
The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.
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Affiliation(s)
- Pau Badia-I-Mompel
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Lorna Wessels
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Department of Vascular Biology and Tumor Angiogenesis, European Center for Angioscience, Medical Faculty, MannHeim Heidelberg University, Mannheim, Germany
| | - Sophia Müller-Dott
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Rémi Trimbour
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, Paris, France
| | - Ricardo O Ramirez Flores
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | | | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.
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42
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Mulet-Lazaro R, Delwel R. From Genotype to Phenotype: How Enhancers Control Gene Expression and Cell Identity in Hematopoiesis. Hemasphere 2023; 7:e969. [PMID: 37953829 PMCID: PMC10635615 DOI: 10.1097/hs9.0000000000000969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/11/2023] [Indexed: 11/14/2023] Open
Abstract
Blood comprises a wide array of specialized cells, all of which share the same genetic information and ultimately derive from the same precursor, the hematopoietic stem cell (HSC). This diversity of phenotypes is underpinned by unique transcriptional programs gradually acquired in the process known as hematopoiesis. Spatiotemporal regulation of gene expression depends on many factors, but critical among them are enhancers-sequences of DNA that bind transcription factors and increase transcription of genes under their control. Thus, hematopoiesis involves the activation of specific enhancer repertoires in HSCs and their progeny, driving the expression of sets of genes that collectively determine morphology and function. Disruption of this tightly regulated process can have catastrophic consequences: in hematopoietic malignancies, dysregulation of transcriptional control by enhancers leads to misexpression of oncogenes that ultimately drive transformation. This review attempts to provide a basic understanding of enhancers and their role in transcriptional regulation, with a focus on normal and malignant hematopoiesis. We present examples of enhancers controlling master regulators of hematopoiesis and discuss the main mechanisms leading to enhancer dysregulation in leukemia and lymphoma.
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Affiliation(s)
- Roger Mulet-Lazaro
- Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Ruud Delwel
- Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
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43
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Liang J, Wei J, Cao J, Qian J, Gao R, Li X, Wang D, Gu Y, Dong L, Yu J, Zhao B, Wang X. In-organoid single-cell CRISPR screening reveals determinants of hepatocyte differentiation and maturation. Genome Biol 2023; 24:251. [PMID: 37907970 PMCID: PMC10617096 DOI: 10.1186/s13059-023-03084-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 10/06/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Harnessing hepatocytes for basic research and regenerative medicine demands a complete understanding of the genetic determinants underlying hepatocyte differentiation and maturation. Single-cell CRISPR screens in organoids could link genetic perturbations with parallel transcriptomic readout in single cells, providing a powerful method to delineate roles of cell fate regulators. However, a big challenge for identifying key regulators during data analysis is the low expression levels of transcription factors (TFs), which are difficult to accurately estimate due to noise and dropouts in single-cell sequencing. Also, it is often the changes in TF activities in the transcriptional cascade rather than the expression levels of TFs that are relevant to the cell fate transition. RESULTS Here, we develop Organoid-based Single-cell CRISPR screening Analyzed with Regulons (OSCAR), a framework using regulon activities as readouts to dissect gene knockout effects in organoids. In adult-stem-cell-derived liver organoids, we map transcriptomes in 80,576 cells upon 246 perturbations associated with transcriptional regulation of hepatocyte formation. Using OSCAR, we identify known and novel positive and negative regulators, among which Fos and Ubr5 are the top-ranked ones. Further single-gene loss-of-function assays demonstrate that Fos depletion in mouse and human liver organoids promote hepatocyte differentiation by specific upregulation of liver metabolic genes and pathways, and conditional knockout of Ubr5 in mouse liver delays hepatocyte maturation. CONCLUSIONS Altogether, we provide a framework to explore lineage specifiers in a rapid and systematic manner, and identify hepatocyte determinators with potential clinical applications.
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Affiliation(s)
- Junbo Liang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Jinsong Wei
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Jun Cao
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
- Institute of Clinical Medicine, Peking Union Medical College and Chinese Academy of Medical Sciences, Translational Medicine Center, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Jun Qian
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Ran Gao
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Xiaoyu Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Dingding Wang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Yani Gu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Lei Dong
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Chemistry and Biomedicine Innovative Center, Nanjing University, Nanjing, 210023, China
| | - Jia Yu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China
| | - Bing Zhao
- School of Basic Medical Sciences, Jiangxi Medical College, Nanchang University, Nanchang, 330031, China.
- Institute of Respiratory Disease, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China.
- Institute of Organoid Technology, Kunming Medical University, Kunming, 650500, China.
| | - Xiaoyue Wang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, Beijing, 100005, China.
- Institute of Clinical Medicine, Peking Union Medical College and Chinese Academy of Medical Sciences, Translational Medicine Center, Peking Union Medical College Hospital, Beijing, 100730, China.
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44
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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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45
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Kim D, Tran A, Kim HJ, Lin Y, Yang JYH, Yang P. Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data. NPJ Syst Biol Appl 2023; 9:51. [PMID: 37857632 PMCID: PMC10587078 DOI: 10.1038/s41540-023-00312-6] [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: 08/14/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023] Open
Abstract
Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.
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Affiliation(s)
- Daniel Kim
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
| | - Andy Tran
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia
| | - Hani Jieun Kim
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
| | - Yingxin Lin
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia
| | - Jean Yee Hwa Yang
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia.
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia.
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia.
| | - Pengyi Yang
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia.
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia.
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia.
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia.
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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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47
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Zhang S, Zhang H, Forrest MP, Zhou Y, Sun X, Bagchi VA, Kozlova A, Santos MD, Piguel NH, Dionisio LE, Sanders AR, Pang ZP, He X, Penzes P, Duan J. Multiple genes in a single GWAS risk locus synergistically mediate aberrant synaptic development and function in human neurons. CELL GENOMICS 2023; 3:100399. [PMID: 37719141 PMCID: PMC10504676 DOI: 10.1016/j.xgen.2023.100399] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/22/2023] [Accepted: 08/07/2023] [Indexed: 09/19/2023]
Abstract
The mechanistic tie between genome-wide association study (GWAS)-implicated risk variants and disease-relevant cellular phenotypes remains largely unknown. Here, using human induced pluripotent stem cell (hiPSC)-derived neurons as a neurodevelopmental model, we identify multiple schizophrenia (SZ) risk variants that display allele-specific open chromatin (ASoC) and are likely to be functional. Editing the strongest ASoC SNP, rs2027349, near vacuolar protein sorting 45 homolog (VPS45) alters the expression of VPS45, lncRNA AC244033.2, and a distal gene, C1orf54. Notably, the transcriptomic changes in neurons are associated with SZ and other neuropsychiatric disorders. Neurons carrying the risk allele exhibit increased dendritic complexity and hyperactivity. Interestingly, individual/combinatorial gene knockdown shows that these genes alter cellular phenotypes in a non-additive synergistic manner. Our study reveals that multiple genes at a single GWAS risk locus mediate a compound effect on neural function, providing a mechanistic link between a non-coding risk variant and disease-related cellular phenotypes.
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Affiliation(s)
- Siwei Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
| | - Hanwen Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Marc P. Forrest
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Yifan Zhou
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Xiaotong Sun
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Vikram A. Bagchi
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Alena Kozlova
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Marc Dos Santos
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Nicolas H. Piguel
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Leonardo E. Dionisio
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Alan R. Sanders
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
| | - Zhiping P. Pang
- Department of Neuroscience and Cell Biology, Child Health Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Peter Penzes
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
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48
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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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49
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Luo R, Yan J, Oh JW, Xi W, Shigaki D, Wong W, Cho HS, Murphy D, Cutler R, Rosen BP, Pulecio J, Yang D, Glenn RA, Chen T, Li QV, Vierbuchen T, Sidoli S, Apostolou E, Huangfu D, Beer MA. Dynamic network-guided CRISPRi screen identifies CTCF-loop-constrained nonlinear enhancer gene regulatory activity during cell state transitions. Nat Genet 2023; 55:1336-1346. [PMID: 37488417 PMCID: PMC11012226 DOI: 10.1038/s41588-023-01450-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 06/20/2023] [Indexed: 07/26/2023]
Abstract
Comprehensive enhancer discovery is challenging because most enhancers, especially those contributing to complex diseases, have weak effects on gene expression. Our gene regulatory network modeling identified that nonlinear enhancer gene regulation during cell state transitions can be leveraged to improve the sensitivity of enhancer discovery. Using human embryonic stem cell definitive endoderm differentiation as a dynamic transition system, we conducted a mid-transition CRISPRi-based enhancer screen. We discovered a comprehensive set of enhancers for each of the core endoderm-specifying transcription factors. Many enhancers had strong effects mid-transition but weak effects post-transition, consistent with the nonlinear temporal responses to enhancer perturbation predicted by the modeling. Integrating three-dimensional genomic information, we were able to develop a CTCF-loop-constrained Interaction Activity model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Our study provides generalizable strategies for sensitive and systematic enhancer discovery in both normal and pathological cell state transitions.
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Affiliation(s)
- Renhe Luo
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Jielin Yan
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Jin Woo Oh
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Wang Xi
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Dustin Shigaki
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Wilfred Wong
- Computational & Systems Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
| | - Hyein S Cho
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Dylan Murphy
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York City, NY, USA
| | - Ronald Cutler
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Bess P Rosen
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
| | - Julian Pulecio
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Dapeng Yang
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Rachel A Glenn
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
| | - Tingxu Chen
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Qing V Li
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Thomas Vierbuchen
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Simone Sidoli
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Effie Apostolou
- Department of Medicine, Weill Cornell Medicine, New York City, NY, USA
| | - Danwei Huangfu
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA.
| | - Michael A Beer
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
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50
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Armendariz DA, Sundarrajan A, Hon GC. Breaking enhancers to gain insights into developmental defects. eLife 2023; 12:e88187. [PMID: 37497775 PMCID: PMC10374278 DOI: 10.7554/elife.88187] [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: 03/29/2023] [Accepted: 07/19/2023] [Indexed: 07/28/2023] Open
Abstract
Despite ground-breaking genetic studies that have identified thousands of risk variants for developmental diseases, how these variants lead to molecular and cellular phenotypes remains a gap in knowledge. Many of these variants are non-coding and occur at enhancers, which orchestrate key regulatory programs during development. The prevailing paradigm is that non-coding variants alter the activity of enhancers, impacting gene expression programs, and ultimately contributing to disease risk. A key obstacle to progress is the systematic functional characterization of non-coding variants at scale, especially since enhancer activity is highly specific to cell type and developmental stage. Here, we review the foundational studies of enhancers in developmental disease and current genomic approaches to functionally characterize developmental enhancers and their variants at scale. In the coming decade, we anticipate systematic enhancer perturbation studies to link non-coding variants to molecular mechanisms, changes in cell state, and disease phenotypes.
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Affiliation(s)
- Daniel A Armendariz
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, United States
| | - Anjana Sundarrajan
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, United States
| | - Gary C Hon
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, United States
- Hamon Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
- Lyda Hill Department of Bioinformatics, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, United States
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