1
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Chadha Y, Khurana A, Schmoller KM. Eukaryotic cell size regulation and its implications for cellular function and dysfunction. Physiol Rev 2024; 104:1679-1717. [PMID: 38900644 DOI: 10.1152/physrev.00046.2023] [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: 12/26/2023] [Revised: 05/24/2024] [Accepted: 06/19/2024] [Indexed: 06/22/2024] Open
Abstract
Depending on cell type, environmental inputs, and disease, the cells in the human body can have widely different sizes. In recent years, it has become clear that cell size is a major regulator of cell function. However, we are only beginning to understand how the optimization of cell function determines a given cell's optimal size. Here, we review currently known size control strategies of eukaryotic cells and the intricate link of cell size to intracellular biomolecular scaling, organelle homeostasis, and cell cycle progression. We detail the cell size-dependent regulation of early development and the impact of cell size on cell differentiation. Given the importance of cell size for normal cellular physiology, cell size control must account for changing environmental conditions. We describe how cells sense environmental stimuli, such as nutrient availability, and accordingly adapt their size by regulating cell growth and cell cycle progression. Moreover, we discuss the correlation of pathological states with misregulation of cell size and how for a long time this was considered a downstream consequence of cellular dysfunction. We review newer studies that reveal a reversed causality, with misregulated cell size leading to pathophysiological phenotypes such as senescence and aging. In summary, we highlight the important roles of cell size in cellular function and dysfunction, which could have major implications for both diagnostics and treatment in the clinic.
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Affiliation(s)
- Yagya Chadha
- Institute of Functional Epigenetics, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Neuherberg, Germany
| | - Arohi Khurana
- Institute of Functional Epigenetics, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Neuherberg, Germany
| | - Kurt M Schmoller
- Institute of Functional Epigenetics, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Neuherberg, Germany
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2
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Volteras D, Shahrezaei V, Thomas P. Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA sequencing. Cell Syst 2024; 15:694-708.e12. [PMID: 39121860 DOI: 10.1016/j.cels.2024.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/29/2024] [Accepted: 07/11/2024] [Indexed: 08/12/2024]
Abstract
Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Dimitris Volteras
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, SW7 2AZ, UK.
| | - Philipp Thomas
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, SW7 2AZ, UK.
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3
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Ietswaart R, Smalec BM, Xu A, Choquet K, McShane E, Jowhar ZM, Guegler CK, Baxter-Koenigs AR, West ER, Fu BXH, Gilbert L, Floor SN, Churchman LS. Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle. Mol Cell 2024; 84:2765-2784.e16. [PMID: 38964322 PMCID: PMC11315470 DOI: 10.1016/j.molcel.2024.06.008] [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: 09/23/2022] [Revised: 05/15/2024] [Accepted: 06/11/2024] [Indexed: 07/06/2024]
Abstract
Dissecting the regulatory mechanisms controlling mammalian transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm in human and mouse cells. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA-binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered a link between DDX3X and nuclear export. For hundreds of RNA metabolism genes, most transcripts with retained introns were degraded by the nuclear exosome, while the remaining molecules were exported with stable cytoplasmic lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, machine learning identified molecular features that predicted the diverse life cycles of mRNAs.
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Affiliation(s)
- Robert Ietswaart
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA.
| | - Brendan M Smalec
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Albert Xu
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Karine Choquet
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Erik McShane
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Ziad Mohamoud Jowhar
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Chantal K Guegler
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Autum R Baxter-Koenigs
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Emma R West
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | | | - Luke Gilbert
- Arc Institute, Palo Alto, CA 94305, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Urology, University of California, San Francisco, San Francisco, CA 94518, USA
| | - Stephen N Floor
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA.
| | - L Stirling Churchman
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA.
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4
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Pal S, Dhar R. Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making. FEBS Lett 2024; 598:1673-1691. [PMID: 38724715 DOI: 10.1002/1873-3468.14898] [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: 12/21/2023] [Revised: 03/23/2024] [Accepted: 03/27/2024] [Indexed: 07/23/2024]
Abstract
The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.
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Affiliation(s)
- Sampriti Pal
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
| | - Riddhiman Dhar
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
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5
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Fonseca A, Riveras E, Moyano TC, Alvarez JM, Rosa S, Gutiérrez RA. Dynamic changes in mRNA nucleocytoplasmic localization in the nitrate response of Arabidopsis roots. PLANT, CELL & ENVIRONMENT 2024. [PMID: 38950037 DOI: 10.1111/pce.15018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 05/23/2024] [Accepted: 06/14/2024] [Indexed: 07/03/2024]
Abstract
Nitrate is a nutrient and signal that regulates gene expression. The nitrate response has been extensively characterized at the organism, organ, and cell-type-specific levels, but intracellular mRNA dynamics remain unexplored. To characterize nuclear and cytoplasmic transcriptome dynamics in response to nitrate, we performed a time-course expression analysis after nitrate treatment in isolated nuclei, cytoplasm, and whole roots. We identified 402 differentially localized transcripts (DLTs) in response to nitrate treatment. Induced DLT genes showed rapid and transient recruitment of the RNA polymerase II, together with an increase in the mRNA turnover rates. DLTs code for genes involved in metabolic processes, localization, and response to stimulus indicating DLTs include genes with relevant functions for the nitrate response that have not been previously identified. Using single-molecule RNA FISH, we observed early nuclear accumulation of the NITRATE REDUCTASE 1 (NIA1) transcripts in their transcription sites. We found that transcription of NIA1, a gene showing delayed cytoplasmic accumulation, is rapidly and transiently activated; however, its transcripts become unstable when they reach the cytoplasm. Our study reveals the dynamic localization of mRNAs between the nucleus and cytoplasm as an emerging feature in the temporal control of gene expression in response to nitrate treatment in Arabidopsis roots.
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Affiliation(s)
- Alejandro Fonseca
- Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- Center for Genome Regulation, Millennium Institute Center for Genome Regulation (CRG), Santiago, Chile
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Plant Biology, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
| | - Eleodoro Riveras
- Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- Center for Genome Regulation, Millennium Institute Center for Genome Regulation (CRG), Santiago, Chile
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Tomás C Moyano
- Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- Center for Genome Regulation, Millennium Institute Center for Genome Regulation (CRG), Santiago, Chile
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
- Centro de Biotecnología Vegetal, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile
| | - José M Alvarez
- Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- Centro de Biotecnología Vegetal, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile
| | - Stefanie Rosa
- Department of Plant Biology, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
| | - Rodrigo A Gutiérrez
- Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- Center for Genome Regulation, Millennium Institute Center for Genome Regulation (CRG), Santiago, Chile
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
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6
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Ma M, Szavits-Nossan J, Singh A, Grima R. Analysis of a detailed multi-stage model of stochastic gene expression using queueing theory and model reduction. Math Biosci 2024; 373:109204. [PMID: 38710441 DOI: 10.1016/j.mbs.2024.109204] [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: 01/23/2024] [Revised: 04/03/2024] [Accepted: 04/29/2024] [Indexed: 05/08/2024]
Abstract
We introduce a biologically detailed, stochastic model of gene expression describing the multiple rate-limiting steps of transcription, nuclear pre-mRNA processing, nuclear mRNA export, cytoplasmic mRNA degradation and translation of mRNA into protein. The processes in sub-cellular compartments are described by an arbitrary number of processing stages, thus accounting for a significantly finer molecular description of gene expression than conventional models such as the telegraph, two-stage and three-stage models of gene expression. We use two distinct tools, queueing theory and model reduction using the slow-scale linear-noise approximation, to derive exact or approximate analytic expressions for the moments or distributions of nuclear mRNA, cytoplasmic mRNA and protein fluctuations, as well as lower bounds for their Fano factors in steady-state conditions. We use these to study the phase diagram of the stochastic model; in particular we derive parametric conditions determining three types of transitions in the properties of mRNA fluctuations: from sub-Poissonian to super-Poissonian noise, from high noise in the nucleus to high noise in the cytoplasm, and from a monotonic increase to a monotonic decrease of the Fano factor with the number of processing stages. In contrast, protein fluctuations are always super-Poissonian and show weak dependence on the number of mRNA processing stages. Our results delineate the region of parameter space where conventional models give qualitatively incorrect results and provide insight into how the number of processing stages, e.g. the number of rate-limiting steps in initiation, splicing and mRNA degradation, shape stochastic gene expression by modulation of molecular memory.
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Affiliation(s)
- Muhan Ma
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | | | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark DE 19716, USA
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK.
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7
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Guha M, Singh A, Butzin NC. Gram-positive bacteria are primed for surviving lethal doses of antibiotics and chemical stress. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596288. [PMID: 38895422 PMCID: PMC11185512 DOI: 10.1101/2024.05.28.596288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Antibiotic resistance kills millions worldwide yearly. However, a major contributor to recurrent infections lies in a small fraction of bacterial cells, known as persisters. These cells are not inherently antibiotic-resistant, yet they lead to increased antibiotic usage, raising the risk of developing resistant progenies. In a bacterial population, individual cells exhibit considerable fluctuations in their gene expression levels despite being cultivated under identical, stable conditions. This variability in cell-to-cell characteristics (phenotypic diversity) within an isogenic population enables persister cells to withstand antibiotic exposure by entering a non-dividing state. We recently showed the existence of "primed cells" in E. coli. Primed cells are dividing cells prepared for antibiotic stress before encountering it and are more prone to form persisters. They also pass their "prepared state" down for several generations through epigenetic memory. Here, we show that primed cells are common among distant bacterial lineages, allowing for survival against antibiotics and other chemical stress, and form in different growth phases. They are also responsible for increased persister levels in transition and stationary phases compared to the log phase. We tested and showed that the Gram-positive bacterium Bacillus megaterium, evolutionarily very distant from E. coli, forms primed cells and has a transient epigenetic memory that is maintained for 7 generations or more. We showed this using ciprofloxacin and the non-antibiotic chemical stress fluoride. It is well established that persister levels are higher in the stationary phase than in the log phase, and B. megaterium persisters levels are nearly identical from the early to late-log phase but are ~2-fold and ~4-fold higher in the transition and stationary phase, respectively. It was previously proposed that there are two distinct types of persisters: Type II forms in the log phase, while Type I forms in the stationary phase. However, we show that primed cells lead to increased persisters in the transition and stationary phase and found no evidence of Type I or II persisters with distant phenotypes. Overall, we have provided substantial evidence of the importance of primed cells and their transitory epigenetic memories to surviving stress.
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Affiliation(s)
- Manisha Guha
- Department of Biology and Microbiology; South Dakota State University; Brookings, SD, 57006; USA
| | - Abhyudai Singh
- Electrical & Computer Engineering; University of Delaware; Newark, DE 19716; USA
| | - Nicholas C. Butzin
- Department of Biology and Microbiology; South Dakota State University; Brookings, SD, 57006; USA
- Department of Chemistry and Biochemistry; South Dakota State University; Brookings, SD, 57006; USA
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8
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Tarkhov AE, Lindstrom-Vautrin T, Zhang S, Ying K, Moqri M, Zhang B, Tyshkovskiy A, Levy O, Gladyshev VN. Nature of epigenetic aging from a single-cell perspective. NATURE AGING 2024; 4:854-870. [PMID: 38724733 DOI: 10.1038/s43587-024-00616-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/26/2024] [Indexed: 05/15/2024]
Abstract
Age-related changes in DNA methylation (DNAm) form the basis of the most robust predictors of age-epigenetic clocks-but a clear mechanistic understanding of exactly which aspects of aging are quantified by these clocks is lacking. Here, to clarify the nature of epigenetic aging, we juxtapose the dynamics of tissue and single-cell DNAm in mice. We compare these changes during early development with those observed during adult aging in mice, and corroborate our analyses with a single-cell RNA sequencing analysis within the same multiomics dataset. We show that epigenetic aging involves co-regulated changes as well as a major stochastic component, and this is consistent with transcriptional patterns. We further support the finding of stochastic epigenetic aging by direct tissue and single-cell DNAm analyses and modeling of aging DNAm trajectories with a stochastic process akin to radiocarbon decay. Finally, we describe a single-cell algorithm for the identification of co-regulated and stochastic CpG clusters showing consistent transcriptomic coordination patterns. Together, our analyses increase our understanding of the basis of epigenetic clocks and highlight potential opportunities for targeting aging and evaluating longevity interventions.
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Affiliation(s)
- Andrei E Tarkhov
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Retro Biosciences Inc., Redwood City, CA, USA.
| | - Thomas Lindstrom-Vautrin
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sirui Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kejun Ying
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Obstetrics & Gynecology, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Bohan Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alexander Tyshkovskiy
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Orr Levy
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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9
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Lenz G. Heterogeneity generating capacity in tumorigenesis and cancer therapeutics. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167226. [PMID: 38734320 DOI: 10.1016/j.bbadis.2024.167226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 04/26/2024] [Accepted: 05/06/2024] [Indexed: 05/13/2024]
Abstract
Cells of multicellular organisms generate heterogeneity in a controlled and transient fashion during embryogenesis, which can be reactivated in pathologies such as cancer. Although genomic heterogeneity is an important part of tumorigenesis, continuous generation of phenotypic heterogeneity is central for the adaptation of cancer cells to the challenges of tumorigenesis and response to therapy. Here I discuss the capacity of generating heterogeneity, hereafter called cell hetness, in cancer cells both as the activation of hetness oncogenes and inactivation of hetness tumor suppressor genes, which increase the generation of heterogeneity, ultimately producing an increase in adaptability and cell fitness. Transcriptomic high hetness states in therapy-tolerant cell states denote its importance in cancer resistance to therapy. The definition of the concept of hetness will allow the understanding of its origins, its control during embryogenesis, its loss of control in tumorigenesis and cancer therapeutics and its active targeting.
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Affiliation(s)
- Guido Lenz
- Departamento de Biofísica, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.
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10
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Rafelski SM, Theriot JA. Establishing a conceptual framework for holistic cell states and state transitions. Cell 2024; 187:2633-2651. [PMID: 38788687 DOI: 10.1016/j.cell.2024.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/10/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024]
Abstract
Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types.
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Affiliation(s)
- Susanne M Rafelski
- Allen Institute for Cell Science, 615 Westlake Avenue N, Seattle, WA 98125, USA.
| | - Julie A Theriot
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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11
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Müller JM, Moos K, Baar T, Maier KC, Zumer K, Tresch A. Nuclear export is a limiting factor in eukaryotic mRNA metabolism. PLoS Comput Biol 2024; 20:e1012059. [PMID: 38753883 PMCID: PMC11135743 DOI: 10.1371/journal.pcbi.1012059] [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: 10/20/2023] [Revised: 05/29/2024] [Accepted: 04/09/2024] [Indexed: 05/18/2024] Open
Abstract
The eukaryotic mRNA life cycle includes transcription, nuclear mRNA export and degradation. To quantify all these processes simultaneously, we perform thiol-linked alkylation after metabolic labeling of RNA with 4-thiouridine (4sU), followed by sequencing of RNA (SLAM-seq) in the nuclear and cytosolic compartments of human cancer cells. We develop a model that reliably quantifies mRNA-specific synthesis, nuclear export, and nuclear and cytosolic degradation rates on a genome-wide scale. We find that nuclear degradation of polyadenylated mRNA is negligible and nuclear mRNA export is slow, while cytosolic mRNA degradation is comparatively fast. Consequently, an mRNA molecule generally spends most of its life in the nucleus. We also observe large differences in the nuclear export rates of different 3'UTR transcript isoforms. Furthermore, we identify genes whose expression is abruptly induced upon metabolic labeling. These transcripts are exported substantially faster than average mRNAs, suggesting the existence of alternative export pathways. Our results highlight nuclear mRNA export as a limiting factor in mRNA metabolism and gene regulation.
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Affiliation(s)
- Jason M. Müller
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Institute of Medical Statistics and Computational Biology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Katharina Moos
- Institute of Medical Statistics and Computational Biology, Faculty of Medicine, University of Cologne, Cologne, Germany
- Institute of Medical Bioinformatics and Systems Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Till Baar
- Institute of Medical Statistics and Computational Biology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Kerstin C. Maier
- Department of Molecular Biology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Kristina Zumer
- Department of Molecular Biology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Achim Tresch
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Institute of Medical Statistics and Computational Biology, Faculty of Medicine, University of Cologne, Cologne, Germany
- Center for Data and Simulation Science, University of Cologne, Cologne, Germany
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12
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Ramirez Flores RO, Schäfer PSL, Küchenhoff L, Saez-Rodriguez J. Complementing Cell Taxonomies with a Multicellular Analysis of Tissues. Physiology (Bethesda) 2024; 39:0. [PMID: 38319138 DOI: 10.1152/physiol.00001.2024] [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: 01/03/2024] [Accepted: 01/31/2024] [Indexed: 02/07/2024] Open
Abstract
The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.
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Affiliation(s)
- Ricardo Omar Ramirez Flores
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Sven Lars Schäfer
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Leonie Küchenhoff
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
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13
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Majchrzak M, Stojanović O, Ajjaji D, Ben M'barek K, Omrane M, Thiam AR, Klemm RW. Perilipin membrane integration determines lipid droplet heterogeneity in differentiating adipocytes. Cell Rep 2024; 43:114093. [PMID: 38602875 DOI: 10.1016/j.celrep.2024.114093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 03/12/2024] [Accepted: 03/27/2024] [Indexed: 04/13/2024] Open
Abstract
The storage of fat within lipid droplets (LDs) of adipocytes is critical for whole-body health. Acute fatty acid (FA) uptake by differentiating adipocytes leads to the formation of at least two LD classes marked by distinct perilipins (PLINs). How this LD heterogeneity arises is an important yet unresolved cell biological problem. Here, we show that an unconventional integral membrane segment (iMS) targets the adipocyte specific LD surface factor PLIN1 to the endoplasmic reticulum (ER) and facilitates high-affinity binding to the first LD class. The other PLINs remain largely excluded from these LDs until FA influx recruits them to a second LD population. Preventing ER targeting turns PLIN1 into a soluble, cytoplasmic LD protein, reduces its LD affinity, and switches its LD class specificity. Conversely, moving the iMS to PLIN2 leads to ER insertion and formation of a separate LD class. Our results shed light on how differences in organelle targeting and disparities in lipid affinity of LD surface factors contribute to formation of LD heterogeneity.
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Affiliation(s)
- Mario Majchrzak
- Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
| | - Ozren Stojanović
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, UK
| | - Dalila Ajjaji
- Laboratoire de Physique de l'École Normale Supérieure (ENS), Université PSL, CNRS, Sorbonne Université, Université de Paris, 75005 Paris, France
| | - Kalthoum Ben M'barek
- Laboratoire de Physique de l'École Normale Supérieure (ENS), Université PSL, CNRS, Sorbonne Université, Université de Paris, 75005 Paris, France
| | - Mohyeddine Omrane
- Laboratoire de Physique de l'École Normale Supérieure (ENS), Université PSL, CNRS, Sorbonne Université, Université de Paris, 75005 Paris, France
| | - Abdou Rachid Thiam
- Laboratoire de Physique de l'École Normale Supérieure (ENS), Université PSL, CNRS, Sorbonne Université, Université de Paris, 75005 Paris, France
| | - Robin W Klemm
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, UK; Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.
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14
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Ntasis VF, Guigó R. Studying relative RNA localization From nucleus to the cytosol. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583744. [PMID: 38559161 PMCID: PMC10979850 DOI: 10.1101/2024.03.06.583744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The precise coordination of important biological processes, such as differentiation and development, is highly dependent on the regulation of expression of the genetic information. The flow of the genetic information is tightly regulated on multiple levels. Among them, RNA export to cytosol is an essential step for the production of proteins in eukaryotic cells. Hence, estimating the relative concentration of RNA molecules of a given transcript species in the nucleus and in the cytosol is of major significance as it contributes to the understanding of the dynamics of RNA trafficking between the nucleus and the cytosol. The most efficient way to estimate the levels of RNA species genome-wide is through RNA sequencing (RNAseq). While RNAseq can be performed separately in the nucleus and in the cytosol, because measured transcript levels are relative to the total volume of RNA in these compartments, and because this volume is usually unknown, the transcript levels in the nucleus and in the cytosol cannot be directly compared. Here we show theoretically that if, in addition to nuclear and cytosolic RNA-seq, whole cell RNA-seq is also performed, then accurate estimations of the localization of transcripts can be obtained. Based on this, we designed a method that estimates, first the fraction of the total RNA volume in the cytosol (nucleus), and then, this fraction for every transcript. We evaluate our methodology on simulated data and nuclear and cytosolic single cell data available. Finally, we use our method to investigate the cellular localization of transcripts using bulk RNAseq data from the ENCODE project.
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Affiliation(s)
- Vasilis F. Ntasis
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
- Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
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15
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Pérez-Ortín JE, García-Marcelo MJ, Delgado-Román I, Muñoz-Centeno MC, Chávez S. Influence of cell volume on the gene transcription rate. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2024; 1867:195008. [PMID: 38246270 DOI: 10.1016/j.bbagrm.2024.195008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024]
Abstract
Cells vary in volume throughout their life cycle and in many other circumstances, while their genome remains identical. Hence, the RNA production factory must adapt to changing needs, while maintaining the same production lines. This paradox is resolved by different mechanisms in distinct cells and circumstances. RNA polymerases have evolved to cope with the particular circumstances of each case and the different characteristics of the several RNA molecule types, especially their stabilities. Here we review current knowledge on these issues. We focus on the yeast Saccharomyces cerevisiae, where many of the studies have been performed, although we compare and discuss the results obtained in other eukaryotes and propose several ideas and questions to be tested and solved in the future. TAKE AWAY.
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Affiliation(s)
- José E Pérez-Ortín
- Instituto de Biotecnología y Biomedicina (BIOTECMED), Facultad de Biológicas, Universitat de València, C/ Dr. Moliner 50, E46100 Burjassot, Spain.
| | - María J García-Marcelo
- Instituto de Biotecnología y Biomedicina (BIOTECMED), Facultad de Biológicas, Universitat de València, C/ Dr. Moliner 50, E46100 Burjassot, Spain; Instituto de Biomedicina de Sevilla, Universidad de Sevilla-CSIC-Hospital Universitario V. del Rocío, Seville 41012, Spain; Departamento de Genética, Facultad de Biología, Universidad de Sevilla, Seville, Spain
| | - Irene Delgado-Román
- Instituto de Biomedicina de Sevilla, Universidad de Sevilla-CSIC-Hospital Universitario V. del Rocío, Seville 41012, Spain; Departamento de Genética, Facultad de Biología, Universidad de Sevilla, Seville, Spain
| | - María C Muñoz-Centeno
- Instituto de Biomedicina de Sevilla, Universidad de Sevilla-CSIC-Hospital Universitario V. del Rocío, Seville 41012, Spain; Departamento de Genética, Facultad de Biología, Universidad de Sevilla, Seville, Spain
| | - Sebastián Chávez
- Instituto de Biomedicina de Sevilla, Universidad de Sevilla-CSIC-Hospital Universitario V. del Rocío, Seville 41012, Spain; Departamento de Genética, Facultad de Biología, Universidad de Sevilla, Seville, Spain
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16
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Baghdassarian HM, Lewis NE. Resource allocation in mammalian systems. Biotechnol Adv 2024; 71:108305. [PMID: 38215956 PMCID: PMC11182366 DOI: 10.1016/j.biotechadv.2023.108305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/14/2024]
Abstract
Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.
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Affiliation(s)
- Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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17
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Cui H, Maan H, Vladoiu MC, Zhang J, Taylor MD, Wang B. DeepVelo: deep learning extends RNA velocity to multi-lineage systems with cell-specific kinetics. Genome Biol 2024; 25:27. [PMID: 38243313 PMCID: PMC10799431 DOI: 10.1186/s13059-023-03148-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024] Open
Abstract
Existing RNA velocity estimation methods strongly rely on predefined dynamics and cell-agnostic constant transcriptional kinetic rates, assumptions often violated in complex and heterogeneous single-cell RNA sequencing (scRNA-seq) data. Using a graph convolution network, DeepVelo overcomes these limitations by generalizing RNA velocity to cell populations containing time-dependent kinetics and multiple lineages. DeepVelo infers time-varying cellular rates of transcription, splicing, and degradation, recovers each cell's stage in the differentiation process, and detects functionally relevant driver genes regulating these processes. Application to various developmental and pathogenic processes demonstrates DeepVelo's capacity to study complex differentiation and lineage decision events in heterogeneous scRNA-seq data.
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Affiliation(s)
- Haotian Cui
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Hassaan Maan
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Maria C Vladoiu
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Jiao Zhang
- The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Michael D Taylor
- The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
- Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
| | - Bo Wang
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
- Vector Institute, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
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18
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Grima R, Esmenjaud PM. Quantifying and correcting bias in transcriptional parameter inference from single-cell data. Biophys J 2024; 123:4-30. [PMID: 37885177 PMCID: PMC10808030 DOI: 10.1016/j.bpj.2023.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/12/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
The snapshot distribution of mRNA counts per cell can be measured using single-molecule fluorescence in situ hybridization or single-cell RNA sequencing. These distributions are often fit to the steady-state distribution of the two-state telegraph model to estimate the three transcriptional parameters for a gene of interest: mRNA synthesis rate, the switching on rate (the on state being the active transcriptional state), and the switching off rate. This model assumes no extrinsic noise, i.e., parameters do not vary between cells, and thus estimated parameters are to be understood as approximating the average values in a population. The accuracy of this approximation is currently unclear. Here, we develop a theory that explains the size and sign of estimation bias when inferring parameters from single-cell data using the standard telegraph model. We find specific bias signatures depending on the source of extrinsic noise (which parameter is most variable across cells) and the mode of transcriptional activity. If gene expression is not bursty then the population averages of all three parameters are overestimated if extrinsic noise is in the synthesis rate; underestimation occurs if extrinsic noise is in the switching on rate; both underestimation and overestimation can occur if extrinsic noise is in the switching off rate. We find that some estimated parameters tend to infinity as the size of extrinsic noise approaches a critical threshold. In contrast when gene expression is bursty, we find that in all cases the mean burst size (ratio of the synthesis rate to the switching off rate) is overestimated while the mean burst frequency (the switching on rate) is underestimated. We estimate the size of extrinsic noise from the covariance matrix of sequencing data and use this together with our theory to correct published estimates of transcriptional parameters for mammalian genes.
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Affiliation(s)
- Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.
| | - Pierre-Marie Esmenjaud
- Biology Department, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
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19
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Stossi F, Rivera Tostado A, Johnson HL, Mistry RM, Mancini MG, Mancini MA. Gene transcription regulation by ER at the single cell and allele level. Steroids 2023; 200:109313. [PMID: 37758052 PMCID: PMC10842394 DOI: 10.1016/j.steroids.2023.109313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/12/2023] [Accepted: 09/23/2023] [Indexed: 10/03/2023]
Abstract
In this short review we discuss the current view of how the estrogen receptor (ER), a pivotal member of the nuclear receptor superfamily of transcription factors, regulates gene transcription at the single cell and allele level, focusing on in vitro cell line models. We discuss central topics and new trends in molecular biology including phenotypic heterogeneity, single cell sequencing, nuclear phase separated condensates, single cell imaging, and image analysis methods, with particular focus on the methodologies and results that have been reported in the last few years using microscopy-based techniques. These observations augment the results from biochemical assays that lead to a much more complex and dynamic view of how ER, and arguably most transcription factors, act to regulate gene transcription.
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Affiliation(s)
- Fabio Stossi
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States.
| | | | - Hannah L Johnson
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States
| | - Ragini M Mistry
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States
| | - Maureen G Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States
| | - Michael A Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States; Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX, United States.
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20
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El Marrahi A, Lipreri F, Kang Z, Gsell L, Eroglu A, Alber D, Hausser J. NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology. Nat Commun 2023; 14:7182. [PMID: 37935691 PMCID: PMC10630431 DOI: 10.1038/s41467-023-42878-z] [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: 03/02/2023] [Accepted: 10/24/2023] [Indexed: 11/09/2023] Open
Abstract
Advances in multiplex histology allow surveying millions of cells, dozens of cell types, and up to thousands of phenotypes within the spatial context of tissue sections. This leads to a combinatorial challenge in (a) summarizing the cellular and phenotypic architecture of tissues and (b) identifying phenotypes with interesting spatial architecture. To address this, we combine ideas from community ecology and machine learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage of geometric constraints on local cellular composition imposed by the niche structure of tissues in order to automatically segment tissue sections into niches and their interfaces. Projecting phenotypes on niches and their interfaces identifies previously-reported and previously-unreported spatially-driven phenotypes, concisely summarizes the phenotypic architecture of tissues, and reveals fundamental properties of tissue architecture. NIPMAP is applicable to both protein and RNA multiplex histology of healthy and diseased tissue. An open-source R/Python package implements NIPMAP.
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Affiliation(s)
- Anissa El Marrahi
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Fabio Lipreri
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Ziqi Kang
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Louise Gsell
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Alper Eroglu
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - David Alber
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Jean Hausser
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden.
- SciLifeLab; Solna, Stockholm, 171 65, Sweden.
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21
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Bunne C, Stark SG, Gut G, Del Castillo JS, Levesque M, Lehmann KV, Pelkmans L, Krause A, Rätsch G. Learning single-cell perturbation responses using neural optimal transport. Nat Methods 2023; 20:1759-1768. [PMID: 37770709 PMCID: PMC10630137 DOI: 10.1038/s41592-023-01969-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 06/23/2023] [Indexed: 09/30/2023]
Abstract
Understanding and predicting molecular responses in single cells upon chemical, genetic or mechanical perturbations is a core question in biology. Obtaining single-cell measurements typically requires the cells to be destroyed. This makes learning heterogeneous perturbation responses challenging as we only observe unpaired distributions of perturbed or non-perturbed cells. Here we leverage the theory of optimal transport and the recent advent of input convex neural architectures to present CellOT, a framework for learning the response of individual cells to a given perturbation by mapping these unpaired distributions. CellOT outperforms current methods at predicting single-cell drug responses, as profiled by scRNA-seq and a multiplexed protein-imaging technology. Further, we illustrate that CellOT generalizes well on unseen settings by (1) predicting the scRNA-seq responses of holdout patients with lupus exposed to interferon-β and patients with glioblastoma to panobinostat; (2) inferring lipopolysaccharide responses across different species; and (3) modeling the hematopoietic developmental trajectories of different subpopulations.
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Affiliation(s)
- Charlotte Bunne
- Department of Computer Science, ETH Zurich, Zürich, Switzerland
- AI Center, ETH Zurich, Zürich, Switzerland
| | - Stefan G Stark
- Department of Computer Science, ETH Zurich, Zürich, Switzerland
- AI Center, ETH Zurich, Zürich, Switzerland
- Medical Informatics Unit, University of Zurich Hospital, Zürich, Switzerland
- Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Gabriele Gut
- Department of Molecular Life Sciences, University of Zurich, Zürich, Switzerland
| | | | - Mitch Levesque
- Department of Dermatology, University of Zurich Hospital, University of Zurich, Zürich, Switzerland
| | - Kjong-Van Lehmann
- Department of Computer Science, ETH Zurich, Zürich, Switzerland.
- Cancer Research Center Cologne-Essen, Site: Center Integrated Oncology Aachen, Aachen, Germany.
| | - Lucas Pelkmans
- Department of Molecular Life Sciences, University of Zurich, Zürich, Switzerland.
| | - Andreas Krause
- Department of Computer Science, ETH Zurich, Zürich, Switzerland.
- AI Center, ETH Zurich, Zürich, Switzerland.
| | - Gunnar Rätsch
- Department of Computer Science, ETH Zurich, Zürich, Switzerland.
- AI Center, ETH Zurich, Zürich, Switzerland.
- Medical Informatics Unit, University of Zurich Hospital, Zürich, Switzerland.
- Swiss Institute of Bioinformatics, Zurich, Switzerland.
- Department of Biology, ETH Zurich, Zürich, Switzerland.
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22
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Shi C, Yang X, Zhang J, Zhou T. Stochastic modeling of the mRNA life process: A generalized master equation. Biophys J 2023; 122:4023-4041. [PMID: 37653725 PMCID: PMC10598292 DOI: 10.1016/j.bpj.2023.08.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/29/2023] [Accepted: 08/29/2023] [Indexed: 09/02/2023] Open
Abstract
The mRNA life cycle is a complex biochemical process, involving transcription initiation, elongation, termination, splicing, and degradation. Each of these molecular events is multistep and can create a memory. The effect of this molecular memory on gene expression is not clear, although there are many related yet scattered experimental reports. To address this important issue, we develop a general theoretical framework formulated as a master equation in the sense of queue theory, which can reduce to multiple previously studied gene models in limiting cases. This framework allows us to interpret experimental observations, extract kinetic parameters from experimental data, and identify how the mRNA kinetics vary under regulatory influences. Notably, it allows us to evaluate the influences of elongation processes on mature RNA distribution; e.g., we find that the non-exponential elongation time can induce the bimodal mRNA expression and there is an optimal elongation noise intensity such that the mature RNA noise achieves the lowest level. In a word, our framework can not only provide insight into complex mRNA life processes but also bridge a dialogue between theoretical studies and experimental data.
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Affiliation(s)
- Changhong Shi
- State Key Laboratory of Respiratory Disease, School of Public Health, Guangzhou Medical University, Guangzhou, China
| | - Xiyan Yang
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou, China
| | - Jiajun Zhang
- School of Mathematics and Computational Science and Guangdong Province Key Laboratory of Computational Science, Sun Yat-Sen University, Guangzhou, China.
| | - Tianshou Zhou
- School of Mathematics and Computational Science and Guangdong Province Key Laboratory of Computational Science, Sun Yat-Sen University, Guangzhou, China.
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23
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Zhang J, Wu Q, Hu X, Wang Y, Lu J, Chakraborty R, Martin KA, Guo S. Serum Response Factor Reduces Gene Expression Noise and Confers Cell State Stability. Stem Cells 2023; 41:907-915. [PMID: 37386941 PMCID: PMC11009695 DOI: 10.1093/stmcls/sxad051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 06/09/2023] [Indexed: 07/01/2023]
Abstract
The role of serum response factor (Srf), a central mediator of actin dynamics and mechanical signaling, in cell identity regulation is debated to be either a stabilizer or a destabilizer. We investigated the role of Srf in cell fate stability using mouse pluripotent stem cells. Despite the fact that serum-containing cultures yield heterogeneous gene expression, deletion of Srf in mouse pluripotent stem cells leads to further exacerbated cell state heterogeneity. The exaggerated heterogeneity is detectible not only as increased lineage priming but also as the developmentally earlier 2C-like cell state. Thus, pluripotent cells explore more variety of cellular states in both directions of development surrounding naïve pluripotency, a behavior that is constrained by Srf. These results support that Srf functions as a cell state stabilizer, providing rationale for its functional modulation in cell fate intervention and engineering.
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Affiliation(s)
- Jian Zhang
- Department of Cell Biology, Yale University, New Haven, CT, USA
- Yale Stem Cell Center, Yale University, New Haven, CT, USA
| | - Qiao Wu
- Department of Cell Biology, Yale University, New Haven, CT, USA
- Yale Stem Cell Center, Yale University, New Haven, CT, USA
| | - Xiao Hu
- Department of Cell Biology, Yale University, New Haven, CT, USA
- Yale Stem Cell Center, Yale University, New Haven, CT, USA
| | - Yadong Wang
- Yale Stem Cell Center, Yale University, New Haven, CT, USA
- Department of Genetics, Yale University, New Haven, CT, USA
| | - Jun Lu
- Yale Stem Cell Center, Yale University, New Haven, CT, USA
- Department of Genetics, Yale University, New Haven, CT, USA
| | - Raja Chakraborty
- Department of Medicine, Section of Cardiovascular Medicine, Yale University, New Haven, CT, USA
| | - Kathleen A Martin
- Department of Medicine, Section of Cardiovascular Medicine, Yale University, New Haven, CT, USA
| | - Shangqin Guo
- Department of Cell Biology, Yale University, New Haven, CT, USA
- Yale Stem Cell Center, Yale University, New Haven, CT, USA
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24
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Stossi F, Singh PK, Safari K, Marini M, Labate D, Mancini MA. High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery. Biochem Pharmacol 2023; 216:115770. [PMID: 37660829 DOI: 10.1016/j.bcp.2023.115770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/05/2023]
Abstract
Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.
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Affiliation(s)
- Fabio Stossi
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA.
| | - Pankaj K Singh
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Kazem Safari
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Michela Marini
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Demetrio Labate
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Michael A Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
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25
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Ghasemi SM, Singh PK, Johnson HL, Koksoy A, Mancini MA, Stossi F, Azencott R. Analysis and Modeling of Early Estradiol-induced GREB1 Single Allele Gene Transcription at the Population Level. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.30.555527. [PMID: 37693572 PMCID: PMC10491237 DOI: 10.1101/2023.08.30.555527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Single molecule fluorescence in situ hybridization (smFISH) can be used to visualize transcriptional activation at the single allele level. We and others have applied this approach to better understand the mechanisms of activation by steroid nuclear receptors. However, there is limited understanding of the interconnection between the activation of target gene alleles inside the same nucleus and within large cell populations. Using the GREB1 gene as an early estrogen receptor (ER) response target, we applied smFISH to track E2-activated GREB1 allelic transcription over early time points to evaluate potential dependencies between alleles within the same nucleus. We compared two types of experiments where we altered the initial status of GREB1 basal transcription by treating cells with and without the elongation inhibitor flavopiridol (FV). E2 stimulation changed the frequencies of active GREB1 alleles in the cell population independently of FV pre-treatment. In FV treated cells, the response time to hormone was delayed, albeit still reaching at 90 minutes the same levels as in cells not treated by FV. We show that the joint frequencies of GREB1 activated alleles observed at the cell population level imply significant dependency between pairs of alleles within the same nucleus. We identify probabilistic models of joint alleles activations by applying a principle of maximum entropy. For pairs of alleles, we have then quantified statistical dependency by computing their mutual information. We have then introduced a stochastic model compatible with allelic statistical dependencies, and we have fitted this model to our data by intensive simulations. This provided estimates of the average lifetime for degradation of GREB1 introns and of the mean time between two successive transcription rounds. Our approach informs on how to extract information on single allele regulation by ER from within a large population of cells, and should be applicable to many other genes. AUTHOR SUMMARY After application of a gene transcription stimulus, in this case the hormone 17 β -estradiol, on large populations of cells over a short time period, we focused on quantifying and modeling the frequencies of GREB1 single allele activations. We have established an experimental and computational pipeline to analyze large numbers of high resolution smFISH images to detect and monitor active GREB1 alleles, that can be translatable to any target gene of interest. A key result is that, at the population level, activation of individual GREB1 alleles within the same nucleus do exhibit statistically significant dependencies which we quantify by the mutual information between activation states of pairs of alleles. After noticing that frequencies of joint alleles activations observed over our large cell populations evolve smoothly in time, we have defined a population level stochastic model which we fit to the observed time course of GREB1 activation frequencies. This provided coherent estimates of the mean time between rounds of GREB1 transcription and the mean lifetime of nascent mRNAs. Our algorithmic approach and experimental methods are applicable to many other genes.
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26
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Haerinck J, Goossens S, Berx G. The epithelial-mesenchymal plasticity landscape: principles of design and mechanisms of regulation. Nat Rev Genet 2023; 24:590-609. [PMID: 37169858 DOI: 10.1038/s41576-023-00601-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2023] [Indexed: 05/13/2023]
Abstract
Epithelial-mesenchymal plasticity (EMP) enables cells to interconvert between several states across the epithelial-mesenchymal landscape, thereby acquiring hybrid epithelial/mesenchymal phenotypic features. This plasticity is crucial for embryonic development and wound healing, but also underlies the acquisition of several malignant traits during cancer progression. Recent research using systems biology and single-cell profiling methods has provided novel insights into the main forces that shape EMP, which include the microenvironment, lineage specification and cell identity, and the genome. Additionally, key roles have emerged for hysteresis (cell memory) and cellular noise, which can drive stochastic transitions between cell states. Here, we review these forces and the distinct but interwoven layers of regulatory control that stabilize EMP states or facilitate epithelial-mesenchymal transitions (EMTs) and discuss the therapeutic potential of manipulating the EMP landscape.
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Affiliation(s)
- Jef Haerinck
- Molecular and Cellular Oncology Laboratory, Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Steven Goossens
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Unit for Translational Research in Oncology, Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Geert Berx
- Molecular and Cellular Oncology Laboratory, Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium.
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium.
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27
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Weidemann DE, Holehouse J, Singh A, Grima R, Hauf S. The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian. SCIENCE ADVANCES 2023; 9:eadh5138. [PMID: 37556551 PMCID: PMC10411910 DOI: 10.1126/sciadv.adh5138] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 07/07/2023] [Indexed: 08/11/2023]
Abstract
Gene expression inherently gives rise to stochastic variation ("noise") in the production of gene products. Minimizing noise is crucial for ensuring reliable cellular functions. However, noise cannot be suppressed below a certain intrinsic limit. For constitutively expressed genes, this limit is typically assumed to be Poissonian noise, wherein the variance in mRNA numbers is equal to their mean. Here, we demonstrate that several cell division genes in fission yeast exhibit mRNA variances significantly below this limit. The reduced variance can be explained by a gene expression model incorporating multiple transcription and mRNA degradation steps. Notably, in this sub-Poissonian regime, distinct from Poissonian or super-Poissonian regimes, cytoplasmic noise is effectively suppressed through a higher mRNA export rate. Our findings redefine the lower limit of eukaryotic gene expression noise and uncover molecular requirements for achieving ultralow noise, which is expected to be important for vital cellular functions.
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Affiliation(s)
- Douglas E. Weidemann
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
| | - James Holehouse
- The Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87510, USA
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK
| | - Silke Hauf
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
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28
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Pietilä MK, Bachmann JJ, Ravantti J, Pelkmans L, Fraefel C. Cellular state landscape and herpes simplex virus type 1 infection progression are connected. Nat Commun 2023; 14:4515. [PMID: 37500668 PMCID: PMC10374626 DOI: 10.1038/s41467-023-40148-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 07/14/2023] [Indexed: 07/29/2023] Open
Abstract
Prediction, prevention and treatment of virus infections require understanding of cell-to-cell variability that leads to heterogenous disease outcomes, but the source of this heterogeneity has yet to be clarified. To study the multimodal response of single human cells to herpes simplex virus type 1 (HSV-1) infection, we mapped high-dimensional viral and cellular state spaces throughout the infection using multiplexed imaging and quantitative single-cell measurements of viral and cellular mRNAs and proteins. Here we show that the high-dimensional cellular state scape can predict heterogenous infections, and cells move through the cellular state landscape according to infection progression. Spatial information reveals that infection changes the cellular state of both infected cells and of their neighbors. The multiplexed imaging of HSV-1-induced cellular modifications links infection progression to changes in signaling responses, transcriptional activity, and processing bodies. Our data show that multiplexed quantification of responses at the single-cell level, across thousands of cells helps predict infections and identify new targets for antivirals.
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Affiliation(s)
- Maija K Pietilä
- Institute of Virology, University of Zurich, Zurich, Switzerland.
| | - Jana J Bachmann
- Institute of Virology, University of Zurich, Zurich, Switzerland
| | - Janne Ravantti
- Molecular and Integrative Biosciences Research Programme, University of Helsinki, Helsinki, Finland
| | - Lucas Pelkmans
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Cornel Fraefel
- Institute of Virology, University of Zurich, Zurich, Switzerland.
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29
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Tang W, Jørgensen ACS, Marguerat S, Thomas P, Shahrezaei V. Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics. Bioinformatics 2023; 39:btad395. [PMID: 37354494 PMCID: PMC10318389 DOI: 10.1093/bioinformatics/btad395] [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: 03/06/2023] [Revised: 05/18/2023] [Accepted: 06/22/2023] [Indexed: 06/26/2023] Open
Abstract
MOTIVATION Gene expression is characterized by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data are prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. RESULTS Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and nonallele-specific scRNA-seq data. AVAILABILITY AND IMPLEMENTATION The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC, respectively.
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Affiliation(s)
- Wenhao Tang
- Department of Mathematics, Imperial College London, London SW7 2BX, United Kingdom
| | - Andreas Christ Sølvsten Jørgensen
- Department of Mathematics, Imperial College London, London SW7 2BX, United Kingdom
- I-X Centre for AI in Science, Imperial College London, White City Campus, London W12 0BZ, United Kingdom
| | - Samuel Marguerat
- MRC London Institute of Medical Sciences (LMS), London W12 0NN, United Kingdom
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
| | - Philipp Thomas
- Department of Mathematics, Imperial College London, London SW7 2BX, United Kingdom
| | - Vahid Shahrezaei
- Department of Mathematics, Imperial College London, London SW7 2BX, United Kingdom
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30
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Rosales-Alvarez RE, Rettkowski J, Herman JS, Dumbović G, Cabezas-Wallscheid N, Grün D. VarID2 quantifies gene expression noise dynamics and unveils functional heterogeneity of ageing hematopoietic stem cells. Genome Biol 2023; 24:148. [PMID: 37353813 PMCID: PMC10290360 DOI: 10.1186/s13059-023-02974-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 05/18/2023] [Indexed: 06/25/2023] Open
Abstract
Variability of gene expression due to stochasticity of transcription or variation of extrinsic signals, termed biological noise, is a potential driving force of cellular differentiation. Utilizing single-cell RNA-sequencing, we develop VarID2 for the quantification of biological noise at single-cell resolution. VarID2 reveals enhanced nuclear versus cytoplasmic noise, and distinct regulatory modes stratified by correlation between noise, expression, and chromatin accessibility. Noise levels are minimal in murine hematopoietic stem cells (HSCs) and increase during differentiation and ageing. Differential noise identifies myeloid-biased Dlk1+ long-term HSCs in aged mice with enhanced quiescence and self-renewal capacity. VarID2 reveals noise dynamics invisible to conventional single-cell transcriptome analysis.
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Affiliation(s)
- Reyna Edith Rosales-Alvarez
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
- International Max Planck Research School for Immunobiology, Epigenetics, and Metabolism (IMPRS-IEM), Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Jasmin Rettkowski
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), Freiburg, Germany
| | - Josip Stefan Herman
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Gabrijela Dumbović
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Nina Cabezas-Wallscheid
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- CIBSS-Centre for Integrative Biological Signaling Studies, University of Freiburg, Freiburg, Germany
| | - Dominic Grün
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
- Helmholtz Institute for RNA-Based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI), Würzburg, Germany.
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31
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Spitzer H, Berry S, Donoghoe M, Pelkmans L, Theis FJ. Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps. Nat Methods 2023:10.1038/s41592-023-01894-z. [PMID: 37248388 PMCID: PMC10333128 DOI: 10.1038/s41592-023-01894-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 04/25/2023] [Indexed: 05/31/2023]
Abstract
Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of the genome and its products at multiple length scales. Here, we introduce a deep learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a conditional variational autoencoder to learn representations of molecular pixel profiles that are consistent across heterogeneous cell populations and experimental perturbations. Clustering these pixel-level representations identifies consistent subcellular landmarks, which can be quantitatively compared in terms of their size, shape, molecular composition and relative spatial organization. Using high-resolution multiplexed immunofluorescence, this reveals how subcellular organization changes upon perturbation of RNA synthesis, RNA processing or cell size, and uncovers links between the molecular composition of membraneless organelles and cell-to-cell variability in bulk RNA synthesis rates. By capturing interpretable cellular phenotypes, we anticipate that CAMPA will greatly accelerate the systematic mapping of multiscale atlases of biological organization to identify the rules by which context shapes physiology and disease.
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Affiliation(s)
- Hannah Spitzer
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
| | - Scott Berry
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- EMBL Australia Node in Single Molecule Science, School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Mark Donoghoe
- Stats Central, Mark Wainwright Analytical Centre, University of New South Wales, Sydney, New South Wales, Australia
| | - Lucas Pelkmans
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- School of Computation, Information and Technology CIT, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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32
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Carilli M, Gorin G, Choi Y, Chari T, Pachter L. Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.13.523995. [PMID: 36712140 PMCID: PMC9882246 DOI: 10.1101/2023.01.13.523995] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
We motivate and present biVI, which combines the variational autoencoder framework of scVI with biophysically motivated, bivariate models for nascent and mature RNA distributions. While previous approaches to integrate bimodal data via the variational autoencoder framework ignore the causal relationship between measurements, biVI models the biophysical processes that give rise to observations. We demonstrate through simulated benchmarking that biVI captures cell type structure in a low-dimensional space and accurately recapitulates parameter values and copy number distributions. On biological data, biVI provides a scalable route for identifying the biophysical mechanisms underlying gene expression. This analytical approach outlines a generalizable strategy for treating multimodal datasets generated by high-throughput, single-cell genomic assays.
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Affiliation(s)
- Maria Carilli
- Division of Biology and Biological Engineering, California Institute of Technology
| | - Gennady Gorin
- Division of Chemistry and Chemical Engineering, California Institute of Technology
| | - Yongin Choi
- Biomedical Engineering Graduate Group, University of California, Davis
- Genome Center, University of California, Davis
| | - Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology
- Department of Computing and Mathematical Sciences, California Institute of Technology
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33
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Ren J, Zhou H, Zeng H, Wang CK, Huang J, Qiu X, Sui X, Li Q, Wu X, Lin Z, Lo JA, Maher K, He Y, Tang X, Lam J, Chen H, Li B, Fisher DE, Liu J, Wang X. Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape. Nat Methods 2023; 20:695-705. [PMID: 37038000 PMCID: PMC10172111 DOI: 10.1038/s41592-023-01829-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 02/22/2023] [Indexed: 04/12/2023]
Abstract
Spatiotemporal regulation of the cellular transcriptome is crucial for proper protein expression and cellular function. However, the intricate subcellular dynamics of RNA remain obscured due to the limitations of existing transcriptomics methods. Here, we report TEMPOmap-a method that uncovers subcellular RNA profiles across time and space at the single-cell level. TEMPOmap integrates pulse-chase metabolic labeling with highly multiplexed three-dimensional in situ sequencing to simultaneously profile the age and location of individual RNA molecules. Using TEMPOmap, we constructed the subcellular RNA kinetic landscape in various human cells from transcription and translocation to degradation. Clustering analysis of RNA kinetic parameters across single cells revealed 'kinetic gene clusters' whose expression patterns were shaped by multistep kinetic sculpting. Importantly, these kinetic gene clusters are functionally segregated, suggesting that subcellular RNA kinetics are differentially regulated in a cell-state- and cell-type-dependent manner. Spatiotemporally resolved transcriptomics provides a gateway to uncovering new spatiotemporal gene regulation principles.
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Affiliation(s)
- Jingyi Ren
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Haowen Zhou
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hu Zeng
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Jiahao Huang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xiaojie Qiu
- Whitehead Institute for Biomedical Research Cambridge, Cambridge, MA, USA
| | - Xin Sui
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Qiang Li
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Xunwei Wu
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Zuwan Lin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Jennifer A Lo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kamal Maher
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yichun He
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Xin Tang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Judson Lam
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hongyu Chen
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Brian Li
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - David E Fisher
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Xiao Wang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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34
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Weidemann DE, Singh A, Grima R, Hauf S. The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.06.531283. [PMID: 36945401 PMCID: PMC10028819 DOI: 10.1101/2023.03.06.531283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Stochastic variation in gene products ("noise") is an inescapable by-product of gene expression. Noise must be minimized to allow for the reliable execution of cellular functions. However, noise cannot be suppressed beyond an intrinsic lower limit. For constitutively expressed genes, this limit is believed to be Poissonian, meaning that the variance in mRNA numbers cannot be lower than their mean. Here, we show that several cell division genes in fission yeast have mRNA variances significantly below this limit, which cannot be explained by the classical gene expression model for low-noise genes. Our analysis reveals that multiple steps in both transcription and mRNA degradation are essential to explain this sub-Poissonian variance. The sub-Poissonian regime differs qualitatively from previously characterized noise regimes, a hallmark being that cytoplasmic noise is reduced when the mRNA export rate increases. Our study re-defines the lower limit of eukaryotic gene expression noise and identifies molecular requirements for ultra-low noise which are expected to support essential cell functions.
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Affiliation(s)
- Douglas E Weidemann
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3JR, Scotland, UK
| | - Silke Hauf
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
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35
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Garner RM, Molines AT, Theriot JA, Chang F. Vast heterogeneity in cytoplasmic diffusion rates revealed by nanorheology and Doppelgänger simulations. Biophys J 2023; 122:767-783. [PMID: 36739478 PMCID: PMC10027447 DOI: 10.1016/j.bpj.2023.01.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 12/22/2022] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
The cytoplasm is a complex, crowded, actively driven environment whose biophysical characteristics modulate critical cellular processes such as cytoskeletal dynamics, phase separation, and stem cell fate. Little is known about the variance in these cytoplasmic properties. Here, we employed particle-tracking nanorheology on genetically encoded multimeric 40 nm nanoparticles (GEMs) to measure diffusion within the cytoplasm of individual fission yeast (Schizosaccharomyces pombe) cellscells. We found that the apparent diffusion coefficients of individual GEM particles varied over a 400-fold range, while the differences in average particle diffusivity among individual cells spanned a 10-fold range. To determine the origin of this heterogeneity, we developed a Doppelgänger simulation approach that uses stochastic simulations of GEM diffusion that replicate the experimental statistics on a particle-by-particle basis, such that each experimental track and cell had a one-to-one correspondence with their simulated counterpart. These simulations showed that the large intra- and inter-cellular variations in diffusivity could not be explained by experimental variability but could only be reproduced with stochastic models that assume a wide intra- and inter-cellular variation in cytoplasmic viscosity. The simulation combining intra- and inter-cellular variation in viscosity also predicted weak nonergodicity in GEM diffusion, consistent with the experimental data. To probe the origin of this variation, we found that the variance in GEM diffusivity was largely independent of factors such as temperature, the actin and microtubule cytoskeletons, cell-cyle stage, and spatial locations, but was magnified by hyperosmotic shocks. Taken together, our results provide a striking demonstration that the cytoplasm is not "well-mixed" but represents a highly heterogeneous environment in which subcellular components at the 40 nm size scale experience dramatically different effective viscosities within an individual cell, as well as in different cells in a genetically identical population. These findings carry significant implications for the origins and regulation of biological noise at cellular and subcellular levels.
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Affiliation(s)
- Rikki M Garner
- Biophysics Program, Stanford University School of Medicine, Stanford, California; Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, Washington; Marine Biological Laboratory, Woods Hole, Massachusetts.
| | - Arthur T Molines
- Department of Cell and Tissue Biology, University of California San Francisco, San Francisco, California; Marine Biological Laboratory, Woods Hole, Massachusetts.
| | - Julie A Theriot
- Biophysics Program, Stanford University School of Medicine, Stanford, California; Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, Washington; Marine Biological Laboratory, Woods Hole, Massachusetts
| | - Fred Chang
- Department of Cell and Tissue Biology, University of California San Francisco, San Francisco, California; Marine Biological Laboratory, Woods Hole, Massachusetts
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36
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Borsi G, Motheramgari K, Dhiman H, Baumann M, Sinkala E, Sauerland M, Riba J, Borth N. Single-cell RNA sequencing reveals homogeneous transcriptome patterns and low variance in a suspension CHO-K1 and an adherent HEK293FT cell line in culture conditions. J Biotechnol 2023; 364:13-22. [PMID: 36708997 DOI: 10.1016/j.jbiotec.2023.01.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 01/15/2023] [Accepted: 01/21/2023] [Indexed: 01/27/2023]
Abstract
Recombinant mammalian host cell lines, in particular CHO and HEK293 cells, are used for the industrial production of therapeutic proteins. Despite their well-known genomic instability, the control mechanisms that enable cells to respond to changes in the environmental conditions are not yet fully understood, nor do we have a good understanding of the factors that lead to phenotypic shifts in long-term cultures. A contributing factor could be inherent diversity in transcriptomes within a population. In this study, we used a full-length coverage single-cell RNA sequencing (scRNA-seq) approach to investigate and compare cell-to-cell variability and the impact of standardized and homogenous culture conditions on the diversity of individual cell transcriptomes, comparing suspension CHO-K1 and adherent HEK293FT cells. Our data showed a critical batch effect from the sequencing of four 96-well plates of CHO-K1 single cells stored for different periods of time, which was and may be therefore identified as a technical variable to consider in experimental planning. Besides, in an artificial and controlled culture environment such as used in routine cell culture technology, the gene expression pattern of a given population does not reveal any marker gene capable to disclose relevant cell population substructures, both for CHO-K1 cells and for HEK293FT cells. The variation observed is primarily driven by the cell cycle.
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Affiliation(s)
- Giulia Borsi
- BOKU University of Natural Resources and Life Sciences, Institute of Animal Cell Technology and Systems Biology, Muthgasse 18, 1190, Vienna, Austria
| | - Krishna Motheramgari
- Austrian Centre of Industrial Biotechnology (acib GmbH), Muthgasse 11, 1190, Vienna, Austria
| | - Heena Dhiman
- Austrian Centre of Industrial Biotechnology (acib GmbH), Muthgasse 11, 1190, Vienna, Austria
| | - Martina Baumann
- Austrian Centre of Industrial Biotechnology (acib GmbH), Muthgasse 11, 1190, Vienna, Austria
| | | | | | | | - Nicole Borth
- BOKU University of Natural Resources and Life Sciences, Institute of Animal Cell Technology and Systems Biology, Muthgasse 18, 1190, Vienna, Austria.
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37
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Singh A, Saint-Antoine M. Probing transient memory of cellular states using single-cell lineages. Front Microbiol 2023; 13:1050516. [PMID: 36824587 PMCID: PMC9942930 DOI: 10.3389/fmicb.2022.1050516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 12/22/2022] [Indexed: 02/10/2023] Open
Abstract
The inherent stochasticity in the gene product levels can drive single cells within an isoclonal population to different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time, makes it a particularly hard phenomenon to characterize. We reviewed recent progress in leveraging the classical Luria-Delbrück experiment to infer the transient heritability of the cellular states. Similar to the original experiment, individual cells were first grown into cell colonies, and then, the fraction of cells residing in different states was assayed for each colony. We discuss modeling approaches for capturing dynamic state transitions in a growing cell population and highlight formulas that identify the kinetics of state switching from the extent of colony-to-colony fluctuations. The utility of this method in identifying multi-generational memory of the both expression and phenotypic states is illustrated across diverse biological systems from cancer drug resistance, reactivation of human viruses, and cellular immune responses. In summary, this fluctuation-based methodology provides a powerful approach for elucidating cell-state transitions from a single time point measurement, which is particularly relevant in situations where measurements lead to cell death (as in single-cell RNA-seq or drug treatment) or cause an irreversible change in cell physiology.
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Affiliation(s)
- Abhyudai Singh
- Departments of Electrical and Computer Engineering, Biomedical Engineering, Mathematical Sciences University of Delaware, Newark, DE, United States
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38
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Structural landscape inside cells mapped in detail. Nature 2023:10.1038/d41586-022-04227-w. [PMID: 36599996 DOI: 10.1038/d41586-022-04227-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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39
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Sarma U, Ripka L, Anyaegbunam UA, Legewie S. Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway. Methods Mol Biol 2023; 2634:215-251. [PMID: 37074581 DOI: 10.1007/978-1-0716-3008-2_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.
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Affiliation(s)
- Uddipan Sarma
- Institute of Molecular Biology (IMB), Mainz, Germany
| | - Lorenz Ripka
- Institute of Molecular Biology (IMB), Mainz, Germany
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany
| | - Uchenna Alex Anyaegbunam
- Institute of Molecular Biology (IMB), Mainz, Germany
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany
| | - Stefan Legewie
- Institute of Molecular Biology (IMB), Mainz, Germany.
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany.
- Stuttgart Research Center for Systems Biology, University of Stuttgart, Stuttgart, Germany.
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40
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Gorin G, Vastola JJ, Fang M, Pachter L. Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments. Nat Commun 2022; 13:7620. [PMID: 36494337 PMCID: PMC9734650 DOI: 10.1038/s41467-022-34857-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 11/09/2022] [Indexed: 12/13/2022] Open
Abstract
The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. Answering this question requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis). This enables the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a continuous stochastic transcription rate driving a discrete RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about transcription rate variation. One assumes variation is due to DNA experiencing mechanical strain, while the other assumes it is due to regulator number fluctuations. We introduce a framework for numerically and analytically studying such models, and apply Bayesian model selection to identify candidate genes that show signatures of each model in single-cell transcriptomic data from mouse glutamatergic neurons.
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Affiliation(s)
- Gennady Gorin
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - John J Vastola
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Meichen Fang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125, USA.
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41
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Jia C, Grima R. Coupling gene expression dynamics to cell size dynamics and cell cycle events: Exact and approximate solutions of the extended telegraph model. iScience 2022; 26:105746. [PMID: 36619980 PMCID: PMC9813732 DOI: 10.1016/j.isci.2022.105746] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/02/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
The standard model describing the fluctuations of mRNA numbers in single cells is the telegraph model which includes synthesis and degradation of mRNA, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by the cell cycle phase, cellular growth and division, and other crucial aspects of cellular biology. Here, we derive the analytical time-dependent solution of an extended telegraph model that explicitly considers the doubling of gene copy numbers upon DNA replication, dependence of the mRNA synthesis rate on cellular volume, gene dosage compensation, partitioning of molecules during cell division, cell-cycle duration variability, and cell-size control strategies. Based on the time-dependent solution, we obtain the analytical distributions of transcript numbers for lineage and population measurements in steady-state growth and also find a linear relation between the Fano factor of mRNA fluctuations and cell volume fluctuations. We show that generally the lineage and population distributions in steady-state growth cannot be accurately approximated by the steady-state solution of extrinsic noise models, i.e. a telegraph model with parameters drawn from probability distributions. This is because the mRNA lifetime is often not small enough compared to the cell cycle duration to erase the memory of division and replication. Accurate approximations are possible when this memory is weak, e.g. for genes with bursty expression and for which there is sufficient gene dosage compensation when replication occurs.
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Affiliation(s)
- Chen Jia
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing 100193, China
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK,Corresponding author
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42
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Matabishi-Bibi L, Challal D, Barucco M, Libri D, Babour A. Termination of the unfolded protein response is guided by ER stress-induced HAC1 mRNA nuclear retention. Nat Commun 2022; 13:6331. [PMID: 36284099 PMCID: PMC9596429 DOI: 10.1038/s41467-022-34133-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 10/14/2022] [Indexed: 12/25/2022] Open
Abstract
Cellular homeostasis is maintained by surveillance mechanisms that intervene at virtually every step of gene expression. In the nucleus, the yeast chromatin remodeler Isw1 holds back maturing mRNA ribonucleoparticles to prevent their untimely export, but whether this activity operates beyond quality control of mRNA biogenesis to regulate gene expression is unknown. Here, we identify the mRNA encoding the central effector of the unfolded protein response (UPR) HAC1, as an Isw1 RNA target. The direct binding of Isw1 to the 3' untranslated region of HAC1 mRNA restricts its nuclear export and is required for accurate UPR abatement. Accordingly, ISW1 inactivation sensitizes cells to endoplasmic reticulum (ER) stress while its overexpression reduces UPR induction. Our results reveal an unsuspected mechanism, in which binding of ER-stress induced Isw1 to HAC1 mRNA limits its nuclear export, providing a feedback loop that fine-tunes UPR attenuation to guarantee homeostatic adaptation to ER stress.
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Affiliation(s)
- Laura Matabishi-Bibi
- grid.508487.60000 0004 7885 7602Univ Paris Diderot, Sorbonne Paris Cité, INSERM U944, CNRS UMR7212, Hôpital St. Louis 1, Avenue Claude Vellefaux, 75475 Paris Cedex 10, France
| | - Drice Challal
- grid.457334.20000 0001 0667 2738Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Mara Barucco
- grid.461913.80000 0001 0676 2143Institut Jacques Monod, Univ Paris Diderot, Sorbonne Paris Cité, CNRS, Bâtiment Buffon, 15 rue Hélène Brion, 75205 Paris Cedex 13, France
| | - Domenico Libri
- grid.429192.50000 0004 0599 0285Institut de Génétique Moléculaire de Montpellier, Univ Montpellier, CNRS, Montpellier, France
| | - Anna Babour
- grid.508487.60000 0004 7885 7602Univ Paris Diderot, Sorbonne Paris Cité, INSERM U944, CNRS UMR7212, Hôpital St. Louis 1, Avenue Claude Vellefaux, 75475 Paris Cedex 10, France
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43
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Fu X, Patel HP, Coppola S, Xu L, Cao Z, Lenstra TL, Grima R. Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions. eLife 2022; 11:e82493. [PMID: 36250630 PMCID: PMC9648968 DOI: 10.7554/elife.82493] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 10/14/2022] [Indexed: 11/13/2022] Open
Abstract
Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy but in experiments, cells may have two gene copies as cells replicate their genome during the cell cycle. While it is often presumed that post-transcriptional noise and gene copy number variation affect transcriptional parameter estimation, the size of the error introduced remains unclear. To address this issue, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle phase and compare the results to live-cell transcription measurements of the same gene. We find that: (i) correcting for cell cycle dynamics decreases the promoter switching rates and the initiation rate, and increases the fraction of time spent in the active state, as well as the burst size; (ii) additional correction for post-transcriptional noise leads to further increases in the burst size and to a large reduction in the errors in parameter estimation. Furthermore, we outline how to correctly adjust for measurement noise in smFISH due to uncertainty in transcription site localisation when introns cannot be labelled. Simulations with parameters estimated from nascent smFISH data, which is corrected for cell cycle phases and measurement noise, leads to autocorrelation functions that agree with those obtained from live-cell imaging.
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Affiliation(s)
- Xiaoming Fu
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and TechnologyShanghaiChina
- School of Biological Sciences, University of EdinburghEdinburghUnited Kingdom
- Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-RossendorfGörlitzGermany
| | - Heta P Patel
- The Netherlands Cancer Institute, Oncode Institute, Division of Gene RegulationAmsterdamNetherlands
| | - Stefano Coppola
- The Netherlands Cancer Institute, Oncode Institute, Division of Gene RegulationAmsterdamNetherlands
| | - Libin Xu
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and TechnologyShanghaiChina
| | - Zhixing Cao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and TechnologyShanghaiChina
| | - Tineke L Lenstra
- The Netherlands Cancer Institute, Oncode Institute, Division of Gene RegulationAmsterdamNetherlands
| | - Ramon Grima
- School of Biological Sciences, University of EdinburghEdinburghUnited Kingdom
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44
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Abstract
The most fundamental feature of cellular form is size, which sets the scale of all cell biological processes. Growth, form, and function are all necessarily linked in cell biology, but we often do not understand the underlying molecular mechanisms nor their specific functions. Here, we review progress toward determining the molecular mechanisms that regulate cell size in yeast, animals, and plants, as well as progress toward understanding the function of cell size regulation. It has become increasingly clear that the mechanism of cell size regulation is deeply intertwined with basic mechanisms of biosynthesis, and how biosynthesis can be scaled (or not) in proportion to cell size. Finally, we highlight recent findings causally linking aberrant cell size regulation to cellular senescence and their implications for cancer therapies.
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Affiliation(s)
- Shicong Xie
- Department of Biology, Stanford University, Stanford, California, USA;
| | - Matthew Swaffer
- Department of Biology, Stanford University, Stanford, California, USA;
| | - Jan M Skotheim
- Department of Biology, Stanford University, Stanford, California, USA;
- Chan Zuckerberg Biohub, San Francisco, California, USA
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45
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Mazille M, Buczak K, Scheiffele P, Mauger O. Stimulus-specific remodeling of the neuronal transcriptome through nuclear intron-retaining transcripts. EMBO J 2022; 41:e110192. [PMID: 36149731 DOI: 10.15252/embj.2021110192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 08/20/2022] [Accepted: 08/30/2022] [Indexed: 11/09/2022] Open
Abstract
The nuclear envelope has long been considered primarily a physical barrier separating nuclear and cytosolic contents. More recently, nuclear compartmentalization has been shown to have additional regulatory functions in controlling gene expression. A sizeable proportion of protein-coding mRNAs is more prevalent in the nucleus than in the cytosol, suggesting regulated mRNA trafficking to the cytosol, but the mechanisms underlying controlled nuclear mRNA retention remain unclear. Here, we provide a comprehensive map of the subcellular localization of mRNAs in mature mouse cortical neurons, and reveal that transcripts retained in the nucleus comprise the majority of stable intron-retaining mRNAs. Systematically probing the fate of nuclear transcripts upon neuronal stimulation, we found opposite effects on sub-populations of transcripts: while some are targeted for degradation, others complete splicing to generate fully mature mRNAs that are exported to the cytosol and mediate rapid increases in protein levels. Finally, different forms of stimulation mobilize distinct groups of intron-retaining transcripts, with this selectivity arising from the activation of specific signaling pathways. Overall, our findings uncover a cue-specific control of intron retention as a major regulator of acute remodeling of the neuronal transcriptome.
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Affiliation(s)
- Maxime Mazille
- Biozentrum of the University of Basel, Basel, Switzerland
| | | | | | - Oriane Mauger
- Biozentrum of the University of Basel, Basel, Switzerland
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46
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Zatulovskiy E, Lanz MC, Zhang S, McCarthy F, Elias JE, Skotheim JM. Delineation of proteome changes driven by cell size and growth rate. Front Cell Dev Biol 2022; 10:980721. [PMID: 36133920 PMCID: PMC9483106 DOI: 10.3389/fcell.2022.980721] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/09/2022] [Indexed: 01/10/2023] Open
Abstract
Increasing cell size drives changes to the proteome, which affects cell physiology. As cell size increases, some proteins become more concentrated while others are diluted. As a result, the state of the cell changes continuously with increasing size. In addition to these proteomic changes, large cells have a lower growth rate (protein synthesis rate per unit volume). That both the cell's proteome and growth rate change with cell size suggests they may be interdependent. To test this, we used quantitative mass spectrometry to measure how the proteome changes in response to the mTOR inhibitor rapamycin, which decreases the cellular growth rate and has only a minimal effect on cell size. We found that large cell size and mTOR inhibition, both of which lower the growth rate of a cell, remodel the proteome in similar ways. This suggests that many of the effects of cell size are mediated by the size-dependent slowdown of the cellular growth rate. For example, the previously reported size-dependent expression of some senescence markers could reflect a cell's declining growth rate rather than its size per se. In contrast, histones and other chromatin components are diluted in large cells independently of the growth rate, likely so that they remain in proportion with the genome. Finally, size-dependent changes to the cell's growth rate and proteome composition are still apparent in cells continually exposed to a saturating dose of rapamycin, which indicates that cell size can affect the proteome independently of mTORC1 signaling. Taken together, our results clarify the dependencies between cell size, growth, mTOR activity, and the proteome remodeling that ultimately controls many aspects of cell physiology.
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Affiliation(s)
| | - Michael C. Lanz
- Department of Biology, Stanford University, Stanford, CA, United States
- Chan Zuckerberg Biohub, Stanford, CA, United States
| | - Shuyuan Zhang
- Department of Biology, Stanford University, Stanford, CA, United States
| | | | | | - Jan M. Skotheim
- Department of Biology, Stanford University, Stanford, CA, United States
- Chan Zuckerberg Biohub, Stanford, CA, United States
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47
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Gorin G, Fang M, Chari T, Pachter L. RNA velocity unraveled. PLoS Comput Biol 2022; 18:e1010492. [PMID: 36094956 PMCID: PMC9499228 DOI: 10.1371/journal.pcbi.1010492] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 09/22/2022] [Accepted: 08/14/2022] [Indexed: 11/24/2022] Open
Abstract
We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.
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Affiliation(s)
- Gennady Gorin
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Meichen Fang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, United States of America
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48
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Gupta A, Martin-Rufino JD, Jones TR, Subramanian V, Qiu X, Grody EI, Bloemendal A, Weng C, Niu SY, Min KH, Mehta A, Zhang K, Siraj L, Al' Khafaji A, Sankaran VG, Raychaudhuri S, Cleary B, Grossman S, Lander ES. Inferring gene regulation from stochastic transcriptional variation across single cells at steady state. Proc Natl Acad Sci U S A 2022; 119:e2207392119. [PMID: 35969771 PMCID: PMC9407670 DOI: 10.1073/pnas.2207392119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/20/2022] [Indexed: 12/24/2022] Open
Abstract
Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.
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Affiliation(s)
- Anika Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
| | - Jorge D. Martin-Rufino
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
| | | | | | - Xiaojie Qiu
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
- HHMI, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | | | - Chen Weng
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
| | | | - Kyung Hoi Min
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Arnav Mehta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Dana-Farber Cancer Institute, Boston, MA 02215
- Department of Medicine, Massachusetts General Hospital, Boston, MA 02114
| | - Kaite Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Layla Siraj
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Vijay G. Sankaran
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
| | - Soumya Raychaudhuri
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA 02115
| | - Brian Cleary
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Eric S. Lander
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115
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49
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Comprehensive analysis of the circadian nuclear and cytoplasmic transcriptome in mouse liver. PLoS Genet 2022; 18:e1009903. [PMID: 35921362 PMCID: PMC9377612 DOI: 10.1371/journal.pgen.1009903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 08/15/2022] [Accepted: 07/06/2022] [Indexed: 11/19/2022] Open
Abstract
In eukaryotes, RNA is synthesised in the nucleus, spliced, and exported to the cytoplasm where it is translated and finally degraded. Any of these steps could be subject to temporal regulation during the circadian cycle, resulting in daily fluctuations of RNA accumulation and affecting the distribution of transcripts in different subcellular compartments. Our study analysed the nuclear and cytoplasmic, poly(A) and total transcriptomes of mouse livers collected over the course of a day. These data provide a genome-wide temporal inventory of enrichment in subcellular RNA, and revealed specific signatures of splicing, nuclear export and cytoplasmic mRNA stability related to transcript and gene lengths. Combined with a mathematical model describing rhythmic RNA profiles, we could test the rhythmicity of export rates and cytoplasmic degradation rates of approximately 1400 genes. With nuclear export times usually much shorter than cytoplasmic half-lives, we found that nuclear export contributes to the modulation and generation of rhythmic profiles of 10% of the cycling nuclear mRNAs. This study contributes to a better understanding of the dynamic regulation of the transcriptome during the day-night cycle.
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50
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Dionisi S, Piera K, Baumschlager A, Khammash M. Implementation of a Novel Optogenetic Tool in Mammalian Cells Based on a Split T7 RNA Polymerase. ACS Synth Biol 2022; 11:2650-2661. [PMID: 35921263 PMCID: PMC9396705 DOI: 10.1021/acssynbio.2c00067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Optogenetic tools are widely used to control gene expression
dynamics
both in prokaryotic and eukaryotic cells. These tools are used in
a variety of biological applications from stem cell differentiation
to metabolic engineering. Despite some tools already available in
bacteria, no light-inducible system currently exists to control gene
expression independently from mammalian transcriptional and/or translational
machineries thus working orthogonally to endogenous regulatory mechanisms.
Such a tool would be particularly important in synthetic biology,
where orthogonality is advantageous to achieve robust activation of
synthetic networks. Here we implement, characterize, and optimize
a new optogenetic tool in mammalian cells based on a previously published
system in bacteria called Opto-T7RNAPs. The tool is orthogonal to
the cellular machinery for transcription and consists of a split T7
RNA polymerase coupled with the blue light-inducible magnets system
(mammalian OptoT7–mOptoT7). In our study we exploited the T7
polymerase’s viral origins to tune our system’s expression
level, reaching up to an almost 20-fold change activation over the
dark control. mOptoT7 is used here to generate mRNA for protein expression,
shRNA for protein inhibition, and Pepper aptamer for RNA visualization.
Moreover, we show that mOptoT7 can mitigate the gene expression burden
when compared to another optogenetic construct. These properties make
mOptoT7 a powerful new tool to use when orthogonality and viral RNA
species (that lack endogenous RNA modifications) are desired.
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Affiliation(s)
- Sara Dionisi
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Karol Piera
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Armin Baumschlager
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Mustafa Khammash
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058 Basel, Switzerland
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