1
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Goldberg GW, Kogenaru M, Keegan S, Haase MAB, Kagermazova L, Arias MA, Onyebeke K, Adams S, Beyer DK, Fenyö D, Noyes MB, Boeke JD. Engineered transcription-associated Cas9 targeting in eukaryotic cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.18.558319. [PMID: 37781609 PMCID: PMC10541143 DOI: 10.1101/2023.09.18.558319] [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
DNA targeting Class 2 CRISPR-Cas effector nucleases, including the well-studied Cas9 proteins, evolved protospacer-adjacent motif (PAM) and guide RNA interactions that sequentially license their binding and cleavage activities at protospacer target sites. Both interactions are nucleic acid sequence specific but function constitutively; thus, they provide intrinsic spatial control over DNA targeting activities but naturally lack temporal control. Here we show that engineered Cas9 fusion proteins which bind to nascent RNAs near a protospacer can facilitate spatiotemporal coupling between transcription and DNA targeting at that protospacer: Transcription-associated Cas9 Targeting (TraCT). Engineered TraCT is enabled in eukaryotic yeast or human cells when suboptimal PAM interactions limit basal activity and when one or more nascent RNA substrates are still tethered to the actively transcribed target DNA in cis. Using yeast, we further show that this phenomenon can be applied for selective editing at one of two identical targets in distinct gene loci, or, in diploid allelic loci that are differentially transcribed. Our work demonstrates that temporal control over Cas9's targeting activity at specific DNA sites may be engineered without modifying Cas9's core domains and guide RNA components or their expression levels. More broadly, it establishes co-transcriptional RNA binding as a cis-acting mechanism that can conditionally stimulate CRISPR-Cas DNA targeting in eukaryotic cells.
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Affiliation(s)
- Gregory W. Goldberg
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Manjunatha Kogenaru
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Sarah Keegan
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Max A. B. Haase
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Larisa Kagermazova
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Mauricio A. Arias
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
| | - Kenenna Onyebeke
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Samantha Adams
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Daniel K. Beyer
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - David Fenyö
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Marcus B. Noyes
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Jef D. Boeke
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn NY 11201
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2
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Mellis IA, Melzer ME, Bodkin N, Goyal Y. Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells. Genome Biol 2024; 25:217. [PMID: 39135102 PMCID: PMC11320884 DOI: 10.1186/s13059-024-03351-2] [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: 12/13/2023] [Accepted: 07/25/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. RESULTS We analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation. CONCLUSIONS Our integrative approach identifies several putative hits-genes demonstrating possible transcriptional adaptation-to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.
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Affiliation(s)
- Ian A Mellis
- Department of Pathology and Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
| | - Madeline E Melzer
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Nicholas Bodkin
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- CZ Biohub Chicago, LLC, Chicago, IL, USA.
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3
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Mellis IA, Bodkin N, Melzer ME, Goyal Y. Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.14.553318. [PMID: 37645989 PMCID: PMC10462021 DOI: 10.1101/2023.08.14.553318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates important model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.
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Affiliation(s)
- Ian A. Mellis
- Department of Pathology and Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Nicholas Bodkin
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Madeline E. Melzer
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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4
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Ballouz S, Kawaguchi RK, Pena MT, Fischer S, Crow M, French L, Knight FM, Adams LB, Gillis J. The transcriptional legacy of developmental stochasticity. Nat Commun 2023; 14:7226. [PMID: 37940702 PMCID: PMC10632366 DOI: 10.1038/s41467-023-43024-5] [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: 01/06/2022] [Accepted: 10/30/2023] [Indexed: 11/10/2023] Open
Abstract
Genetic and environmental variation are key contributors during organism development, but the influence of minor perturbations or noise is difficult to assess. This study focuses on the stochastic variation in allele-specific expression that persists through cell divisions in the nine-banded armadillo (Dasypus novemcinctus). We investigated the blood transcriptome of five wild monozygotic quadruplets over time to explore the influence of developmental stochasticity on gene expression. We identify an enduring signal of autosomal allelic variability that distinguishes individuals within a quadruplet despite their genetic similarity. This stochastic allelic variation, akin to X-inactivation but broader, provides insight into non-genetic influences on phenotype. The presence of stochastically canalized allelic signatures represents a novel axis for characterizing organismal variability, complementing traditional approaches based on genetic and environmental factors. We also developed a model to explain the inconsistent penetrance associated with these stochastically canalized allelic expressions. By elucidating mechanisms underlying the persistence of allele-specific expression, we enhance understanding of development's role in shaping organismal diversity.
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Affiliation(s)
- Sara Ballouz
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales Sydney, Sydney, NSW, Australia
| | - Risa Karakida Kawaguchi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- Center for iPS Cell Research and Application, Kyoto University, Kyoto, Japan
| | - Maria T Pena
- US Department of Health and Human Services, Health Resources and Services Administration, Healthcare System Bureau, National Hansen's Disease Program, Baton Rouge, LA, 70803, USA
| | - Stephan Fischer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, F-75015, France
| | - Megan Crow
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- Genentech, Inc., South San Francisco, CA, USA
| | - Leon French
- Physiology Department and Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | | | - Linda B Adams
- US Department of Health and Human Services, Health Resources and Services Administration, Healthcare System Bureau, National Hansen's Disease Program, Baton Rouge, LA, 70803, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.
- Physiology Department and Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.
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5
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Goyal Y, Busch GT, Pillai M, Li J, Boe RH, Grody EI, Chelvanambi M, Dardani IP, Emert B, Bodkin N, Braun J, Fingerman D, Kaur A, Jain N, Ravindran PT, Mellis IA, Kiani K, Alicea GM, Fane ME, Ahmed SS, Li H, Chen Y, Chai C, Kaster J, Witt RG, Lazcano R, Ingram DR, Johnson SB, Wani K, Dunagin MC, Lazar AJ, Weeraratna AT, Wargo JA, Herlyn M, Raj A. Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells. Nature 2023; 620:651-659. [PMID: 37468627 PMCID: PMC10628994 DOI: 10.1038/s41586-023-06342-8] [Citation(s) in RCA: 61] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Even among genetically identical cancer cells, resistance to therapy frequently emerges from a small subset of those cells1-7. Molecular differences in rare individual cells in the initial population enable certain cells to become resistant to therapy7-9; however, comparatively little is known about the variability in the resistance outcomes. Here we develop and apply FateMap, a framework that combines DNA barcoding with single-cell RNA sequencing, to reveal the fates of hundreds of thousands of clones exposed to anti-cancer therapies. We show that resistant clones emerging from single-cell-derived cancer cells adopt molecularly, morphologically and functionally distinct resistant types. These resistant types are largely predetermined by molecular differences between cells before drug addition and not by extrinsic factors. Changes in the dose and type of drug can switch the resistant type of an initial cell, resulting in the generation and elimination of certain resistant types. Samples from patients show evidence for the existence of these resistant types in a clinical context. We observed diversity in resistant types across several single-cell-derived cancer cell lines and cell types treated with a variety of drugs. The diversity of resistant types as a result of the variability in intrinsic cell states may be a generic feature of responses to external cues.
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Affiliation(s)
- Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.
| | - Gianna T Busch
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Maalavika Pillai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jingxin Li
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ryan H Boe
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emanuelle I Grody
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Manoj Chelvanambi
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ian P Dardani
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin Emert
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicholas Bodkin
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jonas Braun
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Amanpreet Kaur
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Naveen Jain
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pavithran T Ravindran
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian A Mellis
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Karun Kiani
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gretchen M Alicea
- Department of Biochemistry and Molecular Biology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Mitchell E Fane
- Department of Biochemistry and Molecular Biology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Syeda Subia Ahmed
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Haiyin Li
- The Wistar Institute, Philadelphia, PA, USA
| | | | - Cedric Chai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Reproductive Science, Northwestern University, Chicago, IL, USA
| | | | - Russell G Witt
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rossana Lazcano
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Davis R Ingram
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sarah B Johnson
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Khalida Wani
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Margaret C Dunagin
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexander J Lazar
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ashani T Weeraratna
- Department of Biochemistry and Molecular Biology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jennifer A Wargo
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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6
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Boe RH, Ayyappan V, Schuh L, Raj A. Allelic correlation is a marker of trade-offs between barriers to transmission of expression variability and signal responsiveness in genetic networks. Cell Syst 2022; 13:1016-1032.e6. [PMID: 36450286 PMCID: PMC9811561 DOI: 10.1016/j.cels.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/28/2022] [Accepted: 10/28/2022] [Indexed: 12/03/2022]
Abstract
Genetic networks should respond to signals but prevent the transmission of spontaneous fluctuations. Limited data from mammalian cells suggest that noise transmission is uncommon, but systematic claims about noise transmission have been limited by the inability to directly measure it. Here, we build a mathematical framework modeling allelic correlation and noise transmission, showing that allelic correlation and noise transmission correspond across model parameters and network architectures. Limiting noise transmission comes with the trade-off of being unresponsive to signals, and within responsive regimes, there is a further trade-off between response time and basal noise transmission. Analysis of allele-specific single-cell RNA-sequencing data revealed that genes encoding upstream factors in signaling pathways and cell-type-specific factors have higher allelic correlation than downstream factors, suggesting they are more subject to regulation. Overall, our findings suggest that some noise transmission must result from signal responsiveness, but it can be minimized by trading off for a slower response. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Ryan H Boe
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vinay Ayyappan
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Lea Schuh
- Institute of AI for Health, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany; Department of Mathematics, Technical University of Munich, Garching 85748, Germany
| | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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7
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Burkart V, Kowalski K, Aldag-Niebling D, Beck J, Frick DA, Holler T, Radocaj A, Piep B, Zeug A, Hilfiker-Kleiner D, dos Remedios CG, van der Velden J, Montag J, Kraft T. Transcriptional bursts and heterogeneity among cardiomyocytes in hypertrophic cardiomyopathy. Front Cardiovasc Med 2022; 9:987889. [PMID: 36082122 PMCID: PMC9445301 DOI: 10.3389/fcvm.2022.987889] [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: 07/06/2022] [Accepted: 08/02/2022] [Indexed: 12/01/2022] Open
Abstract
Transcriptional bursting is a common expression mode for most genes where independent transcription of alleles leads to different ratios of allelic mRNA from cell to cell. Here we investigated burst-like transcription and its consequences in cardiac tissue from Hypertrophic Cardiomyopathy (HCM) patients with heterozygous mutations in the sarcomeric proteins cardiac myosin binding protein C (cMyBP-C, MYBPC3) and cardiac troponin I (cTnI, TNNI3). Using fluorescence in situ hybridization (RNA-FISH) we found that both, MYBPC3 and TNNI3 are transcribed burst-like. Along with that, we show unequal allelic ratios of TNNI3-mRNA among single cardiomyocytes and unequally distributed wildtype cMyBP-C protein across tissue sections from heterozygous HCM-patients. The mutations led to opposing functional alterations, namely increasing (cMyBP-Cc.927−2A>G) or decreasing (cTnIR145W) calcium sensitivity. Regardless, all patients revealed highly variable calcium-dependent force generation between individual cardiomyocytes, indicating contractile imbalance, which appears widespread in HCM-patients. Altogether, we provide strong evidence that burst-like transcription of sarcomeric genes can lead to an allelic mosaic among neighboring cardiomyocytes at mRNA and protein level. In HCM-patients, this presumably induces the observed contractile imbalance among individual cardiomyocytes and promotes HCM-development.
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Affiliation(s)
- Valentin Burkart
- Institute for Molecular and Cell Physiology, Hannover Medical School, Hannover, Germany
- Valentin Burkart
| | - Kathrin Kowalski
- Institute for Molecular and Cell Physiology, Hannover Medical School, Hannover, Germany
| | - David Aldag-Niebling
- Institute for Molecular and Cell Physiology, Hannover Medical School, Hannover, Germany
| | - Julia Beck
- Institute for Molecular and Cell Physiology, Hannover Medical School, Hannover, Germany
| | - Dirk Alexander Frick
- Institute for Molecular and Cell Physiology, Hannover Medical School, Hannover, Germany
| | - Tim Holler
- Institute for Molecular and Cell Physiology, Hannover Medical School, Hannover, Germany
| | - Ante Radocaj
- Institute for Molecular and Cell Physiology, Hannover Medical School, Hannover, Germany
| | - Birgit Piep
- Institute for Molecular and Cell Physiology, Hannover Medical School, Hannover, Germany
| | - Andre Zeug
- Institute for Cellular Neurophysiology, Hannover Medical School, Hannover, Germany
| | | | - Cristobal G. dos Remedios
- Mechanosensory Biophysics Laboratory, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
| | | | - Judith Montag
- Institute for Molecular and Cell Physiology, Hannover Medical School, Hannover, Germany
- *Correspondence: Judith Montag
| | - Theresia Kraft
- Institute for Molecular and Cell Physiology, Hannover Medical School, Hannover, Germany
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8
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Anderson DJ, Pauler FM, McKenna A, Shendure J, Hippenmeyer S, Horwitz MS. Simultaneous brain cell type and lineage determined by scRNA-seq reveals stereotyped cortical development. Cell Syst 2022; 13:438-453.e5. [PMID: 35452605 DOI: 10.1016/j.cels.2022.03.006] [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: 05/14/2021] [Revised: 01/21/2022] [Accepted: 03/30/2022] [Indexed: 11/30/2022]
Abstract
Mutations are acquired frequently, such that each cell's genome inscribes its history of cell divisions. Common genomic alterations involve loss of heterozygosity (LOH). LOH accumulates throughout the genome, offering large encoding capacity for inferring cell lineage. Using only single-cell RNA sequencing (scRNA-seq) of mouse brain cells, we found that LOH events spanning multiple genes are revealed as tracts of monoallelically expressed, constitutionally heterozygous single-nucleotide variants (SNVs). We simultaneously inferred cell lineage and marked developmental time points based on X chromosome inactivation and the total number of LOH events while identifying cell types from gene expression patterns. Our results are consistent with progenitor cells giving rise to multiple cortical cell types through stereotyped expansion and distinct waves of neurogenesis. This type of retrospective analysis could be incorporated into scRNA-seq pipelines and, compared with experimental approaches for determining lineage in model organisms, is applicable where genetic engineering is prohibited, such as humans.
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Affiliation(s)
- Donovan J Anderson
- Allen Discovery Center for Lineage Tracing and Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA 98109, USA
| | - Florian M Pauler
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
| | | | - Jay Shendure
- Allen Discovery Center for Lineage Tracing, Department of Genome Sciences, and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98109, USA
| | - Simon Hippenmeyer
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
| | - Marshall S Horwitz
- Allen Discovery Center for Lineage Tracing and Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA 98109, USA.
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9
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Sood V, Misteli T. The stochastic nature of genome organization and function. Curr Opin Genet Dev 2022; 72:45-52. [PMID: 34808408 PMCID: PMC9014486 DOI: 10.1016/j.gde.2021.10.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/11/2021] [Accepted: 10/29/2021] [Indexed: 02/03/2023]
Abstract
Genomes have complex three-dimensional structures. High-resolution population-based biochemical studies over the last decade have painted a mostly static picture of the genome characterized by universal organizational features, such as chromatin domains and compartments. Yet, when analyzed at the single cell level, these architectural elements are highly variable. The heterogeneity in genome organization is in line with the inherent stochasticity of transcription that shows high variation between individual cells. We highlight recent findings on single-cell variability in genome organization and describe a framework for how the stochastic nature of chromatin organization may relate to transcription dynamics.
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Affiliation(s)
- Varun Sood
- National Cancer Institute, NIH, Bethesda, MD 20892, United States.
| | - Tom Misteli
- National Cancer Institute, NIH, Bethesda, MD 20892, United States.
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10
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Modi S, Dey S, Singh A. Noise suppression in stochastic genetic circuits using PID controllers. PLoS Comput Biol 2021; 17:e1009249. [PMID: 34319990 PMCID: PMC8360635 DOI: 10.1371/journal.pcbi.1009249] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 08/12/2021] [Accepted: 07/05/2021] [Indexed: 01/01/2023] Open
Abstract
Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as PID controllers are discussed, with particular focus on individual controllers separately, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. In contrast, integral feedback has no effect on the protein noise level from stochastic expression, but significantly minimizes the impact of external disturbances, particularly when the disturbance comes at low frequencies. Counter-intuitively, integral feedback is found to amplify external disturbances at intermediate frequencies. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis using both analytical methods and Monte Carlo simulations reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static input-output sensitivity of the open-loop system. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels. In the noisy cellular environment, biochemical species such as genes, RNAs and proteins that often occur at low molecular counts, are subject to considerable stochastic fluctuations in copy numbers over time. How cellular biochemical processes function reliably in the face of such randomness is an intriguing fundamental problem. Increasing evidence suggests that random fluctuations (noise) in protein copy numbers play important functional roles, such as driving genetically identical cells to different cell fates. Moreover, many disease states have been attributed to elevated noise levels in specific proteins. Here we systematically investigate design of biochemical systems that function as proportional, integral and derivative-based feedback controllers to suppress protein count fluctuations arising from bursty expression of the protein and external disturbance in protein synthesis. Our results show that different controllers are effective in buffering different noise components, and identify ranges of feedback gain for minimizing deleterious fluctuations in protein levels.
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Affiliation(s)
- Saurabh Modi
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America
| | - Supravat Dey
- Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, United States of America
| | - Abhyudai Singh
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America
- Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, United States of America
- * E-mail:
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11
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Gregg C. Starvation and Climate Change—How to Constrain Cancer Cell Epigenetic Diversity and Adaptability to Enhance Treatment Efficacy. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.693781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Advanced metastatic cancer is currently not curable and the major barrier to eliminating the disease in patients is the resistance of subpopulations of tumor cells to drug treatments. These resistant subpopulations can arise stochastically among the billions of tumor cells in a patient or emerge over time during therapy due to adaptive mechanisms and the selective pressures of drug therapies. Epigenetic mechanisms play important roles in tumor cell diversity and adaptability, and are regulated by metabolic pathways. Here, I discuss knowledge from ecology, evolution, infectious disease, species extinction, metabolism and epigenetics to synthesize a roadmap to a clinically feasible approach to help homogenize tumor cells and, in combination with drug treatments, drive their extinction. Specifically, cycles of starvation and hyperthermia could help synchronize tumor cells and constrain epigenetic diversity and adaptability by limiting substrates and impairing the activity of chromatin modifying enzymes. Hyperthermia could also help prevent cancer cells from entering dangerous hibernation-like states. I propose steps to a treatment paradigm to help drive cancer extinction that builds on the successes of fasting, hyperthermia and immunotherapy and is achievable in patients. Finally, I highlight the many unknowns, opportunities for discovery and that stochastic gene and allele level epigenetic mechanisms pose a major barrier to cancer extinction that warrants deeper investigation.
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12
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Larsson AJM, Ziegenhain C, Hagemann-Jensen M, Reinius B, Jacob T, Dalessandri T, Hendriks GJ, Kasper M, Sandberg R. Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance. PLoS Comput Biol 2021; 17:e1008772. [PMID: 33690599 PMCID: PMC7978379 DOI: 10.1371/journal.pcbi.1008772] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/19/2021] [Accepted: 02/03/2021] [Indexed: 12/02/2022] Open
Abstract
Transcriptional bursts render substantial biological noise in cellular transcriptomes. Here, we investigated the theoretical extent of allelic expression resulting from transcriptional bursting and how it compared to the amount biallelic, monoallelic and allele-biased expression observed in single-cell RNA-sequencing (scRNA-seq) data. We found that transcriptional bursting can explain the allelic expression patterns observed in single cells, including the frequent observations of autosomal monoallelic gene expression. Importantly, we identified that the burst frequency largely determined the fraction of cells with monoallelic expression, whereas the burst size had little effect on monoallelic observations. The high consistency between the bursting model predictions and scRNA-seq observations made it possible to assess the heterogeneity of a group of cells as their deviation in allelic observations from the expected. Finally, both burst frequency and size contributed to allelic imbalance observations and reinforced that studies of allelic imbalance can be confounded from the inherent noise in transcriptional bursting. Altogether, we demonstrate that allele-level transcriptional bursting renders widespread, although predictable, amounts of monoallelic and biallelic expression in single cells and cell populations. Genes are transcribed into RNA and further translated into proteins. The maternal and paternal copy of each gene are typically transcribed independently, and transcription itself occur in discrete stochastic bursts (transcriptional bursts). Pioneering single-cell analysis of RNA across cells revealed abundant fluctuations in the amounts of maternal and paternal RNA in cells, with frequent observations of RNA from only the maternal or paternal gene copy (monoallelic expression). In this study, we investigated to which extent the observed monoallelic expression across single cells can be explained by transcriptional bursting. We demonstrate that the process of transcriptional bursting is sufficient to explain the amount of monoallelic expression, and we further demonstrate that the frequency of bursts mainly determines the frequency of monoallelic observations. Furthermore, we show that transcriptional bursts may lead to false positive observations of monoallelic expression across cell populations. Therefore, stochastic transcription renders large fluctuations in allelic origin of RNA in cells over time, including frequent monoallelic observations when profiling single cells.
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Affiliation(s)
- Anton J. M. Larsson
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Christoph Ziegenhain
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | | | - Björn Reinius
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Tina Jacob
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
| | - Tim Dalessandri
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Gert-Jan Hendriks
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Maria Kasper
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
- * E-mail:
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13
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Wotherspoon D, Rogerson C, O’Shaughnessy RF. Perspective: Controlling Epidermal Terminal Differentiation with Transcriptional Bursting and RNA Bodies. J Dev Biol 2020; 8:E29. [PMID: 33291764 PMCID: PMC7768391 DOI: 10.3390/jdb8040029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/20/2020] [Accepted: 12/02/2020] [Indexed: 12/21/2022] Open
Abstract
The outer layer of the skin, the epidermis, is the principal barrier to the external environment: post-mitotic cells terminally differentiate to form a tough outer cornified layer of enucleate and flattened cells that confer the majority of skin barrier function. Nuclear degradation is required for correct cornified envelope formation. This process requires mRNA translation during the process of nuclear destruction. In this review and perspective, we address the biology of transcriptional bursting and the formation of ribonuclear particles in model organisms including mammals, and then examine the evidence that these phenomena occur as part of epidermal terminal differentiation.
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Affiliation(s)
- Duncan Wotherspoon
- Centre for Cell Biology and Cutaneous Research, Blizard Institute, Queen Mary University of London, London E1 2AT, UK;
| | | | - Ryan F.L. O’Shaughnessy
- Centre for Cell Biology and Cutaneous Research, Blizard Institute, Queen Mary University of London, London E1 2AT, UK;
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14
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Rv P, Sundaresh A, Karunyaa M, Arun A, Gayen S. Autosomal Clonal Monoallelic Expression: Natural or Artifactual? Trends Genet 2020; 37:206-211. [PMID: 33234351 DOI: 10.1016/j.tig.2020.10.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 10/25/2020] [Accepted: 10/27/2020] [Indexed: 01/08/2023]
Abstract
The prevalence of mitotically heritable clonal random monoallelic expression of autosomal genes (aRME) remains controversial. Specifically, presence of clonal aRME is well supported in vitro but remains elusive in vivo. Here, we provide critical insights into this matter and discuss whether prevalent clonal aRME is natural or artifactual.
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Affiliation(s)
- P Rv
- Department of Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bangalore-560012, India
| | - A Sundaresh
- Department of Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bangalore-560012, India
| | - M Karunyaa
- Department of Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bangalore-560012, India
| | - A Arun
- Department of Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bangalore-560012, India
| | - S Gayen
- Department of Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bangalore-560012, India.
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15
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Zhang K, Deng R, Gao H, Teng X, Li J. Lighting up single-nucleotide variation in situ in single cells and tissues. Chem Soc Rev 2020; 49:1932-1954. [PMID: 32108196 DOI: 10.1039/c9cs00438f] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The ability to 'see' genetic information directly in single cells can provide invaluable insights into complex biological systems. In this review, we discuss recent advances of in situ imaging technologies for visualizing the subtlest sequence alteration, single-nucleotide variation (SNV), at single-cell level. The mechanism of recently developed methods for SNV discrimination are summarized in detail. With recent developments, single-cell SNV imaging methods have opened a new door for studying the heterogenous and stochastic genetic information in individual cells. Furthermore, SNV imaging can be used on morphologically preserved tissue, which can provide information on histological context for gene expression profiling in basic research and genetic diagnosis. Moreover, the ability to visualize SNVs in situ can be further developed into in situ sequencing technology. We expect this review to inspire more research work into in situ SNV imaging technologies for investigating cellular phenotypes and gene regulation at single-nucleotide resolution, and developing new clinical and biomedical applications.
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Affiliation(s)
- Kaixiang Zhang
- Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China. and School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Ruijie Deng
- Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China.
| | - Hua Gao
- Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China. and Department of Pathogeny Biology, Medical College, Zhengzhou University, Zhengzhou 450001, China
| | - Xucong Teng
- Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China.
| | - Jinghong Li
- Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China.
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16
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Varrault A, Dubois E, Le Digarcher A, Bouschet T. Quantifying Genomic Imprinting at Tissue and Cell Resolution in the Brain. EPIGENOMES 2020; 4:21. [PMID: 34968292 PMCID: PMC8594728 DOI: 10.3390/epigenomes4030021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/24/2020] [Accepted: 09/01/2020] [Indexed: 02/06/2023] Open
Abstract
Imprinted genes are a group of ~150 genes that are preferentially expressed from one parental allele owing to epigenetic marks asymmetrically distributed on inherited maternal and paternal chromosomes. Altered imprinted gene expression causes human brain disorders such as Prader-Willi and Angelman syndromes and additional rare brain diseases. Research data principally obtained from the mouse model revealed how imprinted genes act in the normal and pathological brain. However, a better understanding of imprinted gene functions calls for building detailed maps of their parent-of-origin-dependent expression and of associated epigenetic signatures. Here we review current methods for quantifying genomic imprinting at tissue and cell resolutions, with a special emphasis on methods to detect parent-of-origin dependent expression and their applications to the brain. We first focus on bulk RNA-sequencing, the main method to detect parent-of-origin-dependent expression transcriptome-wide. We discuss the benefits and caveats of bulk RNA-sequencing and provide a guideline to use it on F1 hybrid mice. We then review methods for detecting parent-of-origin-dependent expression at cell resolution, including single-cell RNA-seq, genetic reporters, and molecular probes. Finally, we provide an overview of single-cell epigenomics technologies that profile additional features of genomic imprinting, including DNA methylation, histone modifications and chromatin conformation and their combination into sc-multimodal omics approaches, which are expected to yield important insights into genomic imprinting in individual brain cells.
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Affiliation(s)
- Annie Varrault
- Institut de Génomique Fonctionnelle (IGF), Univ. Montpellier, CNRS, INSERM, 34094 Montpellier, France; (A.V.); (A.L.D.)
| | - Emeric Dubois
- Montpellier GenomiX (MGX), Univ. Montpellier, CNRS, INSERM, 34094 Montpellier, France;
| | - Anne Le Digarcher
- Institut de Génomique Fonctionnelle (IGF), Univ. Montpellier, CNRS, INSERM, 34094 Montpellier, France; (A.V.); (A.L.D.)
| | - Tristan Bouschet
- Institut de Génomique Fonctionnelle (IGF), Univ. Montpellier, CNRS, INSERM, 34094 Montpellier, France; (A.V.); (A.L.D.)
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17
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Stochastic allelic expression as trigger for contractile imbalance in hypertrophic cardiomyopathy. Biophys Rev 2020; 12:1055-1064. [PMID: 32661905 PMCID: PMC7429642 DOI: 10.1007/s12551-020-00719-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 07/02/2020] [Indexed: 12/17/2022] Open
Abstract
Hypertrophic cardiomyopathy (HCM), the most common inherited cardiac disease, is caused by several mostly heterozygous mutations in sarcomeric genes. Hallmarks of HCM are cardiomyocyte and myofibrillar disarray and hypertrophy and fibrosis of the septum and the left ventricle. To date, a pathomechanism common to all mutations remains elusive. We have proposed that contractile imbalance, an unequal force generation of neighboring cardiomyocytes, may contribute to development of HCM hallmarks. At the same calcium concentration, we found substantial differences in force generation between individual cardiomyocytes from HCM patients with mutations in β-MyHC (β-myosin heavy chain). Variability among cardiomyocytes was significantly larger in HCM patients as compared with donor controls. We assume that this heterogeneity in force generation among cardiomyocytes may lead to myocardial disarray and trigger hypertrophy and fibrosis. We provided evidence that burst-like transcription of the MYH7-gene, encoding for β-MyHC, is associated with unequal fractions of mutant per wild-type mRNA from cell to cell (cell-to-cell allelic imbalance). This will presumably lead to unequal fractions of mutant per wild-type protein from cell to cell which may underlie contractile imbalance. In this review, we discuss molecular mechanisms of burst-like transcription with regard to contractile imbalance and disease development in HCM.
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18
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Askary A, Sanchez-Guardado L, Linton JM, Chadly DM, Budde MW, Cai L, Lois C, Elowitz MB. In situ readout of DNA barcodes and single base edits facilitated by in vitro transcription. Nat Biotechnol 2020; 38:66-75. [PMID: 31740838 PMCID: PMC6954335 DOI: 10.1038/s41587-019-0299-4] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 09/23/2019] [Accepted: 09/28/2019] [Indexed: 12/12/2022]
Abstract
Molecular barcoding technologies that uniquely identify single cells are hampered by limitations in barcode measurement. Readout by sequencing does not preserve the spatial organization of cells in tissues, whereas imaging methods preserve spatial structure but are less sensitive to barcode sequence. Here we introduce a system for image-based readout of short (20-base-pair) DNA barcodes. In this system, called Zombie, phage RNA polymerases transcribe engineered barcodes in fixed cells. The resulting RNA is subsequently detected by fluorescent in situ hybridization. Using competing match and mismatch probes, Zombie can accurately discriminate single-nucleotide differences in the barcodes. This method allows in situ readout of dense combinatorial barcode libraries and single-base mutations produced by CRISPR base editors without requiring barcode expression in live cells. Zombie functions across diverse contexts, including cell culture, chick embryos and adult mouse brain tissue. The ability to sensitively read out compact and diverse DNA barcodes by imaging will facilitate a broad range of barcoding and genomic recording strategies.
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Affiliation(s)
- Amjad Askary
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Luis Sanchez-Guardado
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - James M Linton
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Duncan M Chadly
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Mark W Budde
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Long Cai
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Carlos Lois
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Michael B Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Howard Hughes Medical Institute and Department of Applied Physics, California Institute of Technology, Pasadena, CA, USA.
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19
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Abstract
The premise of this book is the importance of the tumor microenvironment (TME). Until recently, most research on and clinical attention to cancer biology, diagnosis, and prognosis were focused on the malignant (or premalignant) cellular compartment that could be readily appreciated using standard morphology-based imaging.
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20
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Kravitz SN, Gregg C. New subtypes of allele-specific epigenetic effects: implications for brain development, function and disease. Curr Opin Neurobiol 2019; 59:69-78. [PMID: 31153086 PMCID: PMC7476552 DOI: 10.1016/j.conb.2019.04.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 04/24/2019] [Indexed: 01/15/2023]
Abstract
Typically, it is assumed that the maternal and paternal alleles for most genes are equally expressed. Known exceptions include canonical imprinted genes, random X-chromosome inactivation, olfactory receptors and clustered protocadherins. Here, we highlight recent studies showing that allele-specific expression is frequent in the genome and involves subtypes of epigenetic allelic effects that differ in terms of heritability, clonality and stability over time. Different forms of epigenetic allele regulation could have different roles in brain development, function, and disease. An emerging area involves understanding allelic effects in a cell-type and developmental stage-specific manner and determining how these effects influence the impact of genetic variants and mutations on the brain. A deeper understanding of epigenetics at the allele and cellular level in the brain could help clarify the mechanisms underlying phenotypic variance.
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Affiliation(s)
- Stephanie N Kravitz
- Department of Neurobiology & Anatomy, University of Utah, Salt Lake City, UT 84132-3401, USA; Department of Human Genetics, University of Utah, Salt Lake City, UT 84132-3401, USA
| | - Christopher Gregg
- Department of Neurobiology & Anatomy, University of Utah, Salt Lake City, UT 84132-3401, USA; Department of Human Genetics, University of Utah, Salt Lake City, UT 84132-3401, USA.
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21
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da Silva Francisco Junior R, Dos Santos Ferreira C, Santos E Silva JC, Terra Machado D, Côrtes Martins Y, Ramos V, Simões Carnivali G, Garcia AB, Medina-Acosta E. Pervasive Inter-Individual Variation in Allele-Specific Expression in Monozygotic Twins. Front Genet 2019; 10:1178. [PMID: 31850058 PMCID: PMC6887657 DOI: 10.3389/fgene.2019.01178] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 10/24/2019] [Indexed: 01/19/2023] Open
Abstract
Despite being developed from one zygote, heterokaryotypic monozygotic (MZ) co-twins exhibit discordant karyotypes. Epigenomic studies in biological samples from heterokaryotypic MZ co-twins are of the most significant value for assessing the effects on gene- and allele-specific expression of an extranumerary chromosomal copy or structural chromosomal disparities in otherwise nearly identical germline genetic contributions. Here, we use RNA-Seq data from existing repositories to establish within-pair correlations for the breadth and magnitude of allele-specific expression (ASE) in heterokaryotypic MZ co-twins discordant for trisomy 21 and maternal 21q inheritance, as well as homokaryotypic co-twins. We show that there is a genome-wide disparity at ASE sites between the heterokaryotypic MZ co-twins. Although most of the disparity corresponds to changes in the magnitude of biallelic imbalance, ASE sites switching from either strictly monoallelic to biallelic imbalance or the reverse occur in few genes that are known or predicted to be imprinted, subject to X-chromosome inactivation or A-to-I(G) RNA edited. We also uncovered comparable ASE differences between homokaryotypic MZ twins. The extent of ASE discordance in MZ twins (2.7%) was about 10-fold lower than the expected between pairs of unrelated, non-twin males or females. The results indicate that the observed within-pair dissimilarities in breadth and magnitude of ASE sites in the heterokaryotypic MZ co-twins could not solely be attributable to the aneuploidy and the missing allelic heritability at 21q.
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Affiliation(s)
| | - Cristina Dos Santos Ferreira
- Laboratório de Biotecnologia, Núcleo de Diagnóstico e Investigação Molecular, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, Brazil
| | - Juan Carlo Santos E Silva
- Laboratório de Biotecnologia, Núcleo de Diagnóstico e Investigação Molecular, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, Brazil
| | - Douglas Terra Machado
- Laboratório de Biotecnologia, Núcleo de Diagnóstico e Investigação Molecular, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, Brazil
| | - Yasmmin Côrtes Martins
- Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Victor Ramos
- Department of Genetics, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil
| | - Gustavo Simões Carnivali
- Department of Computational Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Ana Beatriz Garcia
- Laboratório de Biotecnologia, Núcleo de Diagnóstico e Investigação Molecular, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, Brazil
| | - Enrique Medina-Acosta
- Laboratório de Biotecnologia, Núcleo de Diagnóstico e Investigação Molecular, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, Brazil
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22
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Stamoulis G, Garieri M, Makrythanasis P, Letourneau A, Guipponi M, Panousis N, Sloan-Béna F, Falconnet E, Ribaux P, Borel C, Santoni F, Antonarakis SE. Single cell transcriptome in aneuploidies reveals mechanisms of gene dosage imbalance. Nat Commun 2019; 10:4495. [PMID: 31582743 PMCID: PMC6776538 DOI: 10.1038/s41467-019-12273-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 08/16/2019] [Indexed: 12/13/2022] Open
Abstract
Aneuploidy is a major source of gene dosage imbalance due to copy number alterations (CNA), and viable human trisomies are model disorders of altered gene expression. We study gene and allele-specific expression (ASE) of 9668 single-cell fibroblasts from trisomy 21 (T21) discordant twins and from mosaic T21, T18, T13 and T8. We examine 928 single cells with deep scRNAseq. Expected and observed overexpression of trisomic genes in trisomic vs. diploid bulk RNAseq is not detectable in trisomic vs. diploid single cells. Instead, for trisomic genes with low-to-average expression, their altered gene dosage is mainly due to the higher fraction of trisomic cells simultaneously expressing these genes, in agreement with a stochastic 2-state burst-like model of transcription. These results, confirmed in a further analysis of 8740 single fibroblasts with shallow scRNAseq, suggest that the specific transcriptional profile of each gene contributes to the phenotypic variability of trisomies. We propose an improved model to understand the effects of CNA and, generally, of gene regulation on gene dosage imbalance.
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Affiliation(s)
- Georgios Stamoulis
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva 4, Geneva, Switzerland
| | - Marco Garieri
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva 4, Geneva, Switzerland
| | - Periklis Makrythanasis
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva 4, Geneva, Switzerland
- Biomedical Research Institute Academy of Athens, Athens, Greece
| | - Audrey Letourneau
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva 4, Geneva, Switzerland
| | - Michel Guipponi
- Geneva University Hospitals, Service of Genetic Medicine, 1211 Geneva 4, Geneva, Switzerland
| | - Nikolaos Panousis
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva 4, Geneva, Switzerland
| | - Frédérique Sloan-Béna
- Geneva University Hospitals, Service of Genetic Medicine, 1211 Geneva 4, Geneva, Switzerland
| | - Emilie Falconnet
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva 4, Geneva, Switzerland
| | - Pascale Ribaux
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva 4, Geneva, Switzerland
| | - Christelle Borel
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva 4, Geneva, Switzerland
| | - Federico Santoni
- Service of Endocrinology, Diabetes and Metabolism, University Hospital of Lausanne - CHUV, Lausanne, 1011, Switzerland.
| | - Stylianos E Antonarakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva 4, Geneva, Switzerland.
- Geneva University Hospitals, Service of Genetic Medicine, 1211 Geneva 4, Geneva, Switzerland.
- iGE3 Institute of Genetics and Genomics of Geneva, University of Geneva, 1211 Geneva 4, Geneva, Switzerland.
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Larsson AJM, Coucoravas C, Sandberg R, Reinius B. X-chromosome upregulation is driven by increased burst frequency. Nat Struct Mol Biol 2019; 26:963-969. [PMID: 31582851 DOI: 10.1038/s41594-019-0306-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 08/20/2019] [Indexed: 12/14/2022]
Abstract
Ohno's hypothesis postulates that upregulation of X-linked genes rectifies their dosage imbalance relative to autosomal genes, which are present in two active copies per cell. Here we have dissected X-chromosome upregulation into the kinetics of transcription, inferred from allele-specific single-cell RNA sequencing data from somatic and embryonic mouse cells. We confirmed increased X-chromosome expression levels in female and male cells and found that the X chromosome achieved upregulation by elevated burst frequencies. By monitoring transcriptional kinetics in differentiating female mouse embryonic stem cells, we found that increased burst frequency was established on the active X chromosome when X inactivation took place on the other allele. Thus, our study provides mechanistic insights into X-chromosome upregulation.
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Affiliation(s)
- Anton J M Larsson
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Christos Coucoravas
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Björn Reinius
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
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