1
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Yao D, Tycko J, Oh JW, Bounds LR, Gosai SJ, Lataniotis L, Mackay-Smith A, Doughty BR, Gabdank I, Schmidt H, Guerrero-Altamirano T, Siklenka K, Guo K, White AD, Youngworth I, Andreeva K, Ren X, Barrera A, Luo Y, Yardımcı GG, Tewhey R, Kundaje A, Greenleaf WJ, Sabeti PC, Leslie C, Pritykin Y, Moore JE, Beer MA, Gersbach CA, Reddy TE, Shen Y, Engreitz JM, Bassik MC, Reilly SK. Multicenter integrated analysis of noncoding CRISPRi screens. Nat Methods 2024; 21:723-734. [PMID: 38504114 PMCID: PMC11009116 DOI: 10.1038/s41592-024-02216-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 02/18/2024] [Indexed: 03/21/2024]
Abstract
The ENCODE Consortium's efforts to annotate noncoding cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes. Pooled, noncoding CRISPR screens offer a systematic approach to investigate cis-regulatory mechanisms. The ENCODE4 Functional Characterization Centers conducted 108 screens in human cell lines, comprising >540,000 perturbations across 24.85 megabases of the genome. Using 332 functionally confirmed CRE-gene links in K562 cells, we established guidelines for screening endogenous noncoding elements with CRISPR interference (CRISPRi), including accurate detection of CREs that exhibit variable, often low, transcriptional effects. Benchmarking five screen analysis tools, we find that CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity single guide RNAs. We uncover a subtle DNA strand bias for CRISPRi in transcribed regions with implications for screen design and analysis. Together, we provide an accessible data resource, predesigned single guide RNAs for targeting 3,275,697 ENCODE SCREEN candidate CREs with CRISPRi and screening guidelines to accelerate functional characterization of the noncoding genome.
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Affiliation(s)
- David Yao
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Josh Tycko
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
| | - Jin Woo Oh
- Departments of Biomedical Engineering and Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Lexi R Bounds
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
| | - Sager J Gosai
- Broad Institute of Harvard & MIT, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Center for System Biology, Harvard University, Cambridge, MA, USA
- Harvard Graduate Program in Biological and Biomedical Science, Boston, MA, USA
| | - Lazaros Lataniotis
- Department of Neurology, Institute for Human Genetics, University of California, San Franscisco, San Francisco, CA, USA
| | - Ava Mackay-Smith
- University Program in Genetics and Genomics, Duke University School of Medicine, Durham, NC, USA
| | | | - Idan Gabdank
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Henri Schmidt
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tania Guerrero-Altamirano
- University Program in Genetics and Genomics, Duke University School of Medicine, Durham, NC, USA
- Department of Biology, Duke University, Durham, NC, USA
| | - Keith Siklenka
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA
| | - Katherine Guo
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Alexander D White
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | | | - Kalina Andreeva
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Xingjie Ren
- Department of Neurology, Institute for Human Genetics, University of California, San Franscisco, San Francisco, CA, USA
| | - Alejandro Barrera
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA
| | - Yunhai Luo
- Department of Genetics, Stanford University, Stanford, CA, USA
| | | | | | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - William J Greenleaf
- Department of Genetics, Stanford University, Stanford, CA, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Applied Physics, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Pardis C Sabeti
- Broad Institute of Harvard & MIT, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Center for System Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Christina Leslie
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yuri Pritykin
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Jill E Moore
- Program in Bioinformatics and Integrative Biology, RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Michael A Beer
- Departments of Biomedical Engineering and Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Charles A Gersbach
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
| | - Timothy E Reddy
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA
| | - Yin Shen
- Department of Neurology, Institute for Human Genetics, University of California, San Franscisco, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jesse M Engreitz
- Department of Genetics, Stanford University, Stanford, CA, USA
- BASE Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford, CA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Steven K Reilly
- Department of Genetics, Yale University, New Haven, CT, USA.
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2
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Doughty BR, Hinks MM, Schaepe JM, Marinov GK, Thurm AR, Rios-Martinez C, Parks BE, Tan Y, Marklund E, Dubocanin D, Bintu L, Greenleaf WJ. Single-molecule chromatin configurations link transcription factor binding to expression in human cells. bioRxiv 2024:2024.02.02.578660. [PMID: 38352517 PMCID: PMC10862896 DOI: 10.1101/2024.02.02.578660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
The binding of multiple transcription factors (TFs) to genomic enhancers activates gene expression in mammalian cells. However, the molecular details that link enhancer sequence to TF binding, promoter state, and gene expression levels remain opaque. We applied single-molecule footprinting (SMF) to measure the simultaneous occupancy of TFs, nucleosomes, and components of the transcription machinery on engineered enhancer/promoter constructs with variable numbers of TF binding sites for both a synthetic and an endogenous TF. We find that activation domains enhance a TF's capacity to compete with nucleosomes for binding to DNA in a BAF-dependent manner, TF binding on nucleosome-free DNA is consistent with independent binding between TFs, and average TF occupancy linearly contributes to promoter activation rates. We also decompose TF strength into separable binding and activation terms, which can be tuned and perturbed independently. Finally, we develop thermodynamic and kinetic models that quantitatively predict both the binding microstates observed at the enhancer and subsequent time-dependent gene expression. This work provides a template for quantitative dissection of distinct contributors to gene activation, including the activity of chromatin remodelers, TF activation domains, chromatin acetylation, TF concentration, TF binding affinity, and TF binding site configuration.
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Affiliation(s)
| | - Michaela M Hinks
- Bioengineering Department, Stanford University, Stanford, CA 94305, USA
| | - Julia M Schaepe
- Bioengineering Department, Stanford University, Stanford, CA 94305, USA
| | - Georgi K Marinov
- Genetics Department, Stanford University, Stanford, CA 94305, USA
| | - Abby R Thurm
- Biophysics Graduate Program, Stanford University, Stanford, CA 94305, USA
| | | | - Benjamin E Parks
- Computer Science Department, Stanford University, Stanford, CA 94305, USA
| | - Yingxuan Tan
- Computer Science Department, Stanford University, Stanford, CA 94305, USA
| | - Emil Marklund
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Danilo Dubocanin
- Genetics Department, Stanford University, Stanford, CA 94305, USA
| | - Lacramioara Bintu
- Bioengineering Department, Stanford University, Stanford, CA 94305, USA
| | - William J Greenleaf
- Genetics Department, Stanford University, Stanford, CA 94305, USA
- Department of Applied Physics, Stanford University, Stanford, CA 94205, USA
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3
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Martyn GE, Montgomery MT, Jones H, Guo K, Doughty BR, Linder J, Chen Z, Cochran K, Lawrence KA, Munson G, Pampari A, Fulco CP, Kelley DR, Lander ES, Kundaje A, Engreitz JM. Rewriting regulatory DNA to dissect and reprogram gene expression. bioRxiv 2023:2023.12.20.572268. [PMID: 38187584 PMCID: PMC10769263 DOI: 10.1101/2023.12.20.572268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Regulatory DNA sequences within enhancers and promoters bind transcription factors to encode cell type-specific patterns of gene expression. However, the regulatory effects and programmability of such DNA sequences remain difficult to map or predict because we have lacked scalable methods to precisely edit regulatory DNA and quantify the effects in an endogenous genomic context. Here we present an approach to measure the quantitative effects of hundreds of designed DNA sequence variants on gene expression, by combining pooled CRISPR prime editing with RNA fluorescence in situ hybridization and cell sorting (Variant-FlowFISH). We apply this method to mutagenize and rewrite regulatory DNA sequences in an enhancer and the promoter of PPIF in two immune cell lines. Of 672 variant-cell type pairs, we identify 497 that affect PPIF expression. These variants appear to act through a variety of mechanisms including disruption or optimization of existing transcription factor binding sites, as well as creation of de novo sites. Disrupting a single endogenous transcription factor binding site often led to large changes in expression (up to -40% in the enhancer, and -50% in the promoter). The same variant often had different effects across cell types and states, demonstrating a highly tunable regulatory landscape. We use these data to benchmark performance of sequence-based predictive models of gene regulation, and find that certain types of variants are not accurately predicted by existing models. Finally, we computationally design 185 small sequence variants (≤10 bp) and optimize them for specific effects on expression in silico. 84% of these rationally designed edits showed the intended direction of effect, and some had dramatic effects on expression (-100% to +202%). Variant-FlowFISH thus provides a powerful tool to map the effects of variants and transcription factor binding sites on gene expression, test and improve computational models of gene regulation, and reprogram regulatory DNA.
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Affiliation(s)
- Gabriella E Martyn
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Michael T Montgomery
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Hank Jones
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Katherine Guo
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Benjamin R Doughty
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ziwei Chen
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Kelly Cochran
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Kathryn A Lawrence
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Glen Munson
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anusri Pampari
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Charles P Fulco
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Present Address: Sanofi, Cambridge, MA, USA
| | | | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jesse M Engreitz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
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4
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Gschwind AR, Mualim KS, Karbalayghareh A, Sheth MU, Dey KK, Jagoda E, Nurtdinov RN, Xi W, Tan AS, Jones H, Ma XR, Yao D, Nasser J, Avsec Ž, James BT, Shamim MS, Durand NC, Rao SSP, Mahajan R, Doughty BR, Andreeva K, Ulirsch JC, Fan K, Perez EM, Nguyen TC, Kelley DR, Finucane HK, Moore JE, Weng Z, Kellis M, Bassik MC, Price AL, Beer MA, Guigó R, Stamatoyannopoulos JA, Lieberman Aiden E, Greenleaf WJ, Leslie CS, Steinmetz LM, Kundaje A, Engreitz JM. An encyclopedia of enhancer-gene regulatory interactions in the human genome. bioRxiv 2023:2023.11.09.563812. [PMID: 38014075 PMCID: PMC10680627 DOI: 10.1101/2023.11.09.563812] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1-6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and largescale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 elementgene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancerpromoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.
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Affiliation(s)
- Andreas R. Gschwind
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Sciences and Engineering Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford, CA, USA
| | - Kristy S. Mualim
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Plant Biology, Carnegie Institute of Science, Stanford, CA, USA
| | - Alireza Karbalayghareh
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maya U. Sheth
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Sciences and Engineering Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Kushal K. Dey
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Evelyn Jagoda
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ramil N. Nurtdinov
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Wang Xi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anthony S. Tan
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Sciences and Engineering Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford, CA, USA
| | - Hank Jones
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Sciences and Engineering Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford, CA, USA
| | - X. Rosa Ma
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Sciences and Engineering Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford, CA, USA
| | - David Yao
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Present Address: Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | | | - Benjamin T. James
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Muhammad S. Shamim
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
- Department of Bioengineering, Rice University, Houston, TX, USA
- Medical Scientist Training Program, Baylor College of Medicine, Houston, Texas, USA
| | - Neva C. Durand
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Suhas S. P. Rao
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Department of Structural Biology, Stanford University, Stanford, CA, USA
| | - Ragini Mahajan
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
- Department of Biosciences, Rice University, Houston, TX, USA
| | - Benjamin R. Doughty
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Kalina Andreeva
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jacob C. Ulirsch
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Present Address: Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA
| | - Kaili Fan
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Present Address: Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | | | - Tri C. Nguyen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Sciences and Engineering Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford, CA, USA
| | | | - Hilary K. Finucane
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jill E. Moore
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Manolis Kellis
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Michael C. Bassik
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Michael A. Beer
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - John A. Stamatoyannopoulos
- Altius Institute for Biomedical Sciences, Seattle, WA, USA
- Clinical Research Division, Fred Hutch Cancer Center, Seattle, WA, USA
| | - Erez Lieberman Aiden
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
- Department of Computer Science, Rice University, Houston, TX, USA
| | - William J. Greenleaf
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Applied Physics, Stanford University, Stanford, CA, USA
| | | | - Lars M. Steinmetz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Genome Technology Center, Palo Alto, CA, USA
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Anshul Kundaje
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jesse M. Engreitz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Sciences and Engineering Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford, CA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
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5
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Fujimori T, Rios-Martinez C, Thurm AR, Hinks MM, Doughty BR, Sinha J, Le D, Hafner A, Greenleaf WJ, Boettiger AN, Bintu L. Single-cell chromatin state transitions during epigenetic memory formation. bioRxiv 2023:2023.10.03.560616. [PMID: 37873344 PMCID: PMC10592931 DOI: 10.1101/2023.10.03.560616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Repressive chromatin modifications are thought to compact chromatin to silence transcription. However, it is unclear how chromatin structure changes during silencing and epigenetic memory formation. We measured gene expression and chromatin structure in single cells after recruitment and release of repressors at a reporter gene. Chromatin structure is heterogeneous, with open and compact conformations present in both active and silent states. Recruitment of repressors associated with epigenetic memory produces chromatin compaction across 10-20 kilobases, while reversible silencing does not cause compaction at this scale. Chromatin compaction is inherited, but changes molecularly over time from histone methylation (H3K9me3) to DNA methylation. The level of compaction at the end of silencing quantitatively predicts epigenetic memory weeks later. Similarly, chromatin compaction at the Nanog locus predicts the degree of stem-cell fate commitment. These findings suggest that the chromatin state across tens of kilobases, beyond the gene itself, is important for epigenetic memory formation.
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Affiliation(s)
- Taihei Fujimori
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | | | - Abby R. Thurm
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Michaela M. Hinks
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | | | - Joydeb Sinha
- Department of Chemical & Systems Biology, Stanford University, Stanford, CA, USA
| | - Derek Le
- Department of Dermatology, Program in Epithelial Biology, Stanford University, Stanford, CA, USA
- Program in Cancer Biology, Stanford University, Stanford, CA, USA
| | - Antonina Hafner
- Department of Developmental Biology, Stanford University, Stanford, CA, USA
- Current address: Department of Discovery Oncology, Genentech, CA, USA
| | - William J. Greenleaf
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Applied Physics, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | | | - Lacramioara Bintu
- Department of Bioengineering, Stanford University, Stanford, CA, USA
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6
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Marshall JL, Doughty BR, Subramanian V, Guckelberger P, Wang Q, Chen LM, Rodriques SG, Zhang K, Fulco CP, Nasser J, Grinkevich EJ, Noel T, Mangiameli S, Bergman DT, Greka A, Lander ES, Chen F, Engreitz JM. HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes. Proc Natl Acad Sci U S A 2020; 117:33404-33413. [PMID: 33376219 PMCID: PMC7776864 DOI: 10.1073/pnas.2010738117] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Single-cell quantification of RNAs is important for understanding cellular heterogeneity and gene regulation, yet current approaches suffer from low sensitivity for individual transcripts, limiting their utility for many applications. Here we present Hybridization of Probes to RNA for sequencing (HyPR-seq), a method to sensitively quantify the expression of hundreds of chosen genes in single cells. HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and sequencing the amplicons to quantify the expression of chosen genes. HyPR-seq achieves high sensitivity for individual transcripts, detects nonpolyadenylated and low-abundance transcripts, and can profile more than 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell-type frequencies in tissue using low-abundance marker genes. By directing sequencing power to genes of interest and sensitively quantifying individual transcripts, HyPR-seq reduces costs by up to 100-fold compared to whole-transcriptome single-cell RNA-sequencing, making HyPR-seq a powerful method for targeted RNA profiling in single cells.
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Affiliation(s)
| | | | | | - Philine Guckelberger
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biology, Chemistry, and Pharmacy, Freie Universität Berlin, 14195 Berlin, Germany
| | - Qingbo Wang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA 02115
| | - Linlin M Chen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Samuel G Rodriques
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139
- MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Kaite Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Teia Noel
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | | | - Anna Greka
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA 02142;
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Fei Chen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142;
| | - Jesse M Engreitz
- Broad Institute of MIT and Harvard, Cambridge, MA 02142;
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305
- Basic Science and Engineering Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA 94305
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Fulco CP, Nasser J, Jones TR, Munson G, Bergman DT, Subramanian V, Grossman SR, Anyoha R, Doughty BR, Patwardhan TA, Nguyen TH, Kane M, Perez EM, Durand NC, Lareau CA, Stamenova EK, Aiden EL, Lander ES, Engreitz JM. Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations. Nat Genet 2019; 51:1664-1669. [PMID: 31784727 PMCID: PMC6886585 DOI: 10.1038/s41588-019-0538-0] [Citation(s) in RCA: 434] [Impact Index Per Article: 86.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 10/25/2019] [Indexed: 12/31/2022]
Abstract
Enhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases1-4. Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer-gene connections across cell types5,6. We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome, and we applied it to test >3,500 potential enhancer-gene connections for 30 genes. We found that a simple activity-by-contact model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This activity-by-contact model allows us to construct genome-wide maps of enhancer-gene connections in a given cell type, on the basis of chromatin state measurements. Together, CRISPRi-FlowFISH and the activity-by-contact model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome.
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Affiliation(s)
- Charles P Fulco
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Glen Munson
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Sharon R Grossman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
| | | | | | | | - Tung H Nguyen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael Kane
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Neva C Durand
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Center for Genome Architecture, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Computer Science and Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
| | | | | | - Erez Lieberman Aiden
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Center for Genome Architecture, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Computer Science and Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
| | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Biology, MIT, Cambridge, MA, USA.
| | - Jesse M Engreitz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Society of Fellows, Harvard University, Cambridge, MA, USA.
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Doughty BR. The changes in ABO blood group frequency within a mediaeval English population. Med Lab Sci 1977; 34:351-4. [PMID: 340834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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9
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Doughty BR. A modification of the surface-fixation technique for the selection of high titre donors. J Med Lab Technol 1968; 25:125. [PMID: 5650120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
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