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Kanazawa M, Ninomiya I, Otsu Y, Hatakeyama M. Next-generation regenerative therapy for ischemic stroke using peripheral blood mononuclear cells. Neural Regen Res 2024; 19:2341-2342. [PMID: 38526265 PMCID: PMC11090419 DOI: 10.4103/nrr.nrr-d-23-01784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/01/2023] [Accepted: 12/21/2023] [Indexed: 03/26/2024] Open
Affiliation(s)
- Masato Kanazawa
- Department of Neurology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Itaru Ninomiya
- Department of Neurology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Yutaka Otsu
- Department of Neurology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Masahiro Hatakeyama
- Department of Neurology, Brain Research Institute, Niigata University, Niigata, Japan
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2
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Sadria M, Bury TM. FateNet: an integration of dynamical systems and deep learning for cell fate prediction. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae525. [PMID: 39177093 DOI: 10.1093/bioinformatics/btae525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/28/2024] [Accepted: 08/21/2024] [Indexed: 08/24/2024]
Abstract
MOTIVATION Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and cellular state dynamics from single-cell RNA sequencing data lack precision regarding decision points and broader tissue implications. Addressing this gap, we present FateNet, a computational approach integrating dynamical systems theory and deep learning to probe the cell decision-making process using scRNA-seq data. RESULTS By leveraging information about normal forms and scaling behavior near bifurcations common to many dynamical systems, FateNet predicts cell decision occurrence with higher accuracy than conventional methods and offers qualitative insights into the new state of the biological system. Also, through in-silico perturbation experiments, FateNet identifies key genes and pathways governing the differentiation process in hematopoiesis. Validated using different scRNA-seq data, FateNet emerges as a user-friendly and valuable tool for predicting critical points in biological processes, providing insights into complex trajectories. AVAILABILITY AND IMPLEMENTATION github.com/ThomasMBury/fatenet.
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Affiliation(s)
- Mehrshad Sadria
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Thomas M Bury
- Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
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3
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Zhang J, Larschan E, Bigness J, Singh R. scNODE : generative model for temporal single cell transcriptomic data prediction. Bioinformatics 2024; 40:ii146-ii154. [PMID: 39230694 PMCID: PMC11373355 DOI: 10.1093/bioinformatics/btae393] [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] [Indexed: 09/05/2024] Open
Abstract
SUMMARY Measurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODE's predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path. AVAILABILITY AND IMPLEMENTATION The data and code are publicly available at https://github.com/rsinghlab/scNODE.
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Affiliation(s)
- Jiaqi Zhang
- Department of Computer Science, Brown University, Providence, RI 02906, United States
| | - Erica Larschan
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, United States
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, United States
| | - Jeremy Bigness
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, United States
| | - Ritambhara Singh
- Department of Computer Science, Brown University, Providence, RI 02906, United States
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, United States
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4
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Alicea B, Bastani S, Gordon NK, Crawford-Young S, Gordon R. The Molecular Basis of Differentiation Wave Activity in Embryogenesis. Biosystems 2024; 243:105272. [PMID: 39033973 DOI: 10.1016/j.biosystems.2024.105272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 07/23/2024]
Abstract
As development varies greatly across the tree of life, it may seem difficult to suggest a model that proposes a single mechanism for understanding collective cell behaviors and the coordination of tissue formation. Here we propose a mechanism called differentiation waves, which unify many disparate results involving developmental systems from across the tree of life. We demonstrate how a relatively simple model of differentiation proceeds not from function-related molecular mechanisms, but from so-called differentiation waves. A phenotypic model of differentiation waves is introduced, and its relation to molecular mechanisms is proposed. These waves contribute to a differentiation tree, which is an alternate way of viewing cell lineage and local action of the molecular factors. We construct a model of differentiation wave-related molecular mechanisms (genome, epigenome, and proteome) based on bioinformatic data from the nematode Caenorhabditis elegans. To validate this approach across different modes of development, we evaluate protein expression across different types of development by comparing Caenorhabditis elegans with several model organisms: fruit flies (Drosophila melanogaster), yeast (Saccharomyces cerevisiae), and mouse (Mus musculus). Inspired by gene regulatory networks, two Models of Interactive Contributions (fully-connected MICs and ordered MICs) are used to suggest potential genomic contributions to differentiation wave-related proteins. This, in turn, provides a framework for understanding differentiation and development.
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Affiliation(s)
- Bradly Alicea
- Orthogonal Research and Education Lab, Champaign-Urbana, IL, USA; OpenWorm Foundation, Boston, MA, USA; University of Illinois Urbana-Champaign, USA.
| | - Suroush Bastani
- Orthogonal Research and Education Lab, Champaign-Urbana, IL, USA.
| | | | | | - Richard Gordon
- Gulf Specimen Marine Laboratory & Aquarium, Panacea, FL, USA.
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5
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Zhu L, Yang S, Zhang K, Wang H, Fang X, Wang J. Uncovering underlying physical principles and driving forces of cell differentiation and reprogramming from single-cell transcriptomics. Proc Natl Acad Sci U S A 2024; 121:e2401540121. [PMID: 39150785 PMCID: PMC11348339 DOI: 10.1073/pnas.2401540121] [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: 01/25/2024] [Accepted: 06/28/2024] [Indexed: 08/18/2024] Open
Abstract
Recent advances in single-cell sequencing technology have revolutionized our ability to acquire whole transcriptome data. However, uncovering the underlying transcriptional drivers and nonequilibrium driving forces of cell function directly from these data remains challenging. We address this by learning cell state vector fields from discrete single-cell RNA velocity to quantify the single-cell global nonequilibrium driving forces as landscape and flux. From single-cell data, we quantified the Waddington landscape, showing that optimal paths for differentiation and reprogramming deviate from the naively expected landscape gradient paths and may not pass through landscape saddles at finite fluctuations, challenging conventional transition state estimation of kinetic rate for cell fate decisions due to the presence of the flux. A key insight from our study is that stem/progenitor cells necessitate greater energy dissipation for rapid cell cycles and self-renewal, maintaining pluripotency. We predict optimal developmental pathways and elucidate the nucleation mechanism of cell fate decisions, with transition states as nucleation sites and pioneer genes as nucleation seeds. The concept of loop flux quantifies the contributions of each cycle flux to cell state transitions, facilitating the understanding of cell dynamics and thermodynamic cost, and providing insights into optimizing biological functions. We also infer cell-cell interactions and cell-type-specific gene regulatory networks, encompassing feedback mechanisms and interaction intensities, predicting genetic perturbation effects on cell fate decisions from single-cell omics data. Essentially, our methodology validates the landscape and flux theory, along with its associated quantifications, offering a framework for exploring the physical principles underlying cellular differentiation and reprogramming and broader biological processes through high-throughput single-cell sequencing experiments.
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Affiliation(s)
- Ligang Zhu
- College of Physics, Jilin University, Changchun130021, China
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun130022, China
| | - Songlin Yang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun130022, China
| | - Kun Zhang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun130022, China
| | - Hong Wang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun130022, China
| | - Xiaona Fang
- College of Chemistry, Northeast Normal University, Changchun130024, China
| | - Jin Wang
- Center for Theoretical Interdisciplinary Sciences, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou325001, China
- Department of Chemistry, Physics and Astronomy, Stony Brook University, Stony Brook, NY11794
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6
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Zitnik M, Li MM, Wells A, Glass K, Morselli Gysi D, Krishnan A, Murali TM, Radivojac P, Roy S, Baudot A, Bozdag S, Chen DZ, Cowen L, Devkota K, Gitter A, Gosline SJC, Gu P, Guzzi PH, Huang H, Jiang M, Kesimoglu ZN, Koyuturk M, Ma J, Pico AR, Pržulj N, Przytycka TM, Raphael BJ, Ritz A, Sharan R, Shen Y, Singh M, Slonim DK, Tong H, Yang XH, Yoon BJ, Yu H, Milenković T. Current and future directions in network biology. BIOINFORMATICS ADVANCES 2024; 4:vbae099. [PMID: 39143982 PMCID: PMC11321866 DOI: 10.1093/bioadv/vbae099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 05/31/2024] [Accepted: 07/08/2024] [Indexed: 08/16/2024]
Abstract
Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation Not applicable.
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Affiliation(s)
- Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Michelle M Li
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Aydin Wells
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Deisy Morselli Gysi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Statistics, Federal University of Paraná, Curitiba, Paraná 81530-015, Brazil
- Department of Physics, Northeastern University, Boston, MA 02115, United States
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
| | - Sushmita Roy
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Wisconsin Institute for Discovery, Madison, WI 53715, United States
| | - Anaïs Baudot
- Aix Marseille Université, INSERM, MMG, Marseille, France
| | - Serdar Bozdag
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- Department of Mathematics, University of North Texas, Denton, TX 76203, United States
| | - Danny Z Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Kapil Devkota
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Morgridge Institute for Research, Madison, WI 53715, United States
| | - Sara J C Gosline
- Biological Sciences Division, Pacific Northwest National Laboratory, Seattle, WA 98109, United States
| | - Pengfei Gu
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Pietro H Guzzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, 88100, Italy
| | - Heng Huang
- Department of Computer Science, University of Maryland College Park, College Park, MD 20742, United States
| | - Meng Jiang
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Ziynet Nesibe Kesimoglu
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Mehmet Koyuturk
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, United States
| | - Nataša Pržulj
- Department of Computer Science, University College London, London, WC1E 6BT, England
- ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, 08010, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
| | - Anna Ritz
- Department of Biology, Reed College, Portland, OR 97202, United States
| | - Roded Sharan
- School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, United States
| | - Donna K Slonim
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Hanghang Tong
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Xinan Holly Yang
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, United States
| | - Byung-Jun Yoon
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, United States
| | - Haiyuan Yu
- Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, United States
| | - Tijana Milenković
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
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7
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Jeong Y, Ronen J, Kopp W, Lutsik P, Akalin A. scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data. BMC Bioinformatics 2024; 25:257. [PMID: 39107690 PMCID: PMC11304929 DOI: 10.1186/s12859-024-05880-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 07/23/2024] [Indexed: 08/10/2024] Open
Abstract
The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.
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Affiliation(s)
- Yunhee Jeong
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, Germany
- Faculty of Mathematics and Informatics, Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, Germany
| | - Jonathan Ronen
- Bioinformatics and Omics Data Science Platform, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Inceptive Nucleics, Inc., Palo Alto, CA, USA
| | - Wolfgang Kopp
- Bioinformatics and Omics Data Science Platform, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Roche Diagnostics GmbH, Penzberg, Germany
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, Germany.
- Department of Oncology, Catholic University (KU) Leuven, Leuven, Belgium.
| | - Altuna Akalin
- Bioinformatics and Omics Data Science Platform, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany.
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8
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Aggarwal A, Nasreen A, Sharma B, Sahoo S, Aswin K, Faruq M, Pandey R, Jolly MK, Singh A, Gokhale RS, Natarajan VT. Distinct melanocyte subpopulations defined by stochastic expression of proliferation or maturation programs enable a rapid and sustainable pigmentation response. PLoS Biol 2024; 22:e3002776. [PMID: 39163475 PMCID: PMC11364419 DOI: 10.1371/journal.pbio.3002776] [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: 09/12/2023] [Revised: 08/30/2024] [Accepted: 07/30/2024] [Indexed: 08/22/2024] Open
Abstract
The ultraviolet (UV) radiation triggers a pigmentation response in human skin, wherein, melanocytes rapidly activate divergent maturation and proliferation programs. Using single-cell sequencing, we demonstrate that these 2 programs are segregated in distinct subpopulations in melanocytes of human and zebrafish skin. The coexistence of these 2 cell states in cultured melanocytes suggests possible cell autonomy. Luria-Delbrück fluctuation test reveals that the initial establishment of these states is stochastic. Tracking of pigmenting cells ascertains that the stochastically acquired state is faithfully propagated in the progeny. A systemic approach combining single-cell multi-omics (RNA+ATAC) coupled to enhancer mapping with H3K27 acetylation successfully identified state-specific transcriptional networks. This comprehensive analysis led to the construction of a gene regulatory network (GRN) that under the influence of noise, establishes a bistable system of pigmentation and proliferation at the population level. This GRN recapitulates melanocyte behaviour in response to external cues that reinforce either of the states. Our work highlights that inherent stochasticity within melanocytes establishes dedicated states, and the mature state is sustained by selective enhancers mark through histone acetylation. While the initial cue triggers a proliferation response, the continued signal activates and maintains the pigmenting subpopulation via epigenetic imprinting. Thereby our study provides the basis of coexistence of distinct populations which ensures effective pigmentation response while preserving the self-renewal capacity.
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Affiliation(s)
- Ayush Aggarwal
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Ayesha Nasreen
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Babita Sharma
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Sarthak Sahoo
- Department of Bioengineering, Indian Institute of Science, Bangalore, India
| | - Keerthic Aswin
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Mohammed Faruq
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Rajesh Pandey
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Mohit K. Jolly
- Department of Bioengineering, Indian Institute of Science, Bangalore, India
| | - Abhyudai Singh
- Electrical and Computer Engineering, University of Delaware, Newark, Delaware, United States of America
- Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America
| | - Rajesh S. Gokhale
- National Institute of Immunology, New Delhi, India
- Indian Institute of Science Education and Research Pune, Pune, India
| | - Vivek T. Natarajan
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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9
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Liu A, Mair A, Matos JL, Vollbrecht M, Xu SL, Bergmann DC. bHLH transcription factors cooperate with chromatin remodelers to regulate cell fate decisions during Arabidopsis stomatal development. PLoS Biol 2024; 22:e3002770. [PMID: 39150946 DOI: 10.1371/journal.pbio.3002770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 08/28/2024] [Accepted: 07/26/2024] [Indexed: 08/18/2024] Open
Abstract
The development of multicellular organisms requires coordinated changes in gene expression that are often mediated by the interaction between transcription factors (TFs) and their corresponding cis-regulatory elements (CREs). During development and differentiation, the accessibility of CREs is dynamically modulated by the epigenome. How the epigenome, CREs, and TFs together exert control over cell fate commitment remains to be fully understood. In the Arabidopsis leaf epidermis, meristemoids undergo a series of stereotyped cell divisions, then switch fate to commit to stomatal differentiation. Newly created or reanalyzed scRNA-seq and ChIP-seq data confirm that stomatal development involves distinctive phases of transcriptional regulation and that differentially regulated genes are bound by the stomatal basic helix-loop-helix (bHLH) TFs. Targets of the bHLHs often reside in repressive chromatin before activation. MNase-seq evidence further suggests that the repressive state can be overcome and remodeled upon activation by specific stomatal bHLHs. We propose that chromatin remodeling is mediated through the recruitment of a set of physical interactors that we identified through proximity labeling-the ATPase-dependent chromatin remodeling SWI/SNF complex and the histone acetyltransferase HAC1. The bHLHs and chromatin remodelers localize to overlapping genomic regions in a hierarchical order. Furthermore, plants with stage-specific knockdown of the SWI/SNF components or HAC1 fail to activate specific bHLH targets and display stomatal development defects. Together, these data converge on a model for how stomatal TFs and epigenetic machinery cooperatively regulate transcription and chromatin remodeling during progressive fate specification.
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Affiliation(s)
- Ao Liu
- Howard Hughes Medical Institute, Stanford, California, United States of America
| | - Andrea Mair
- Howard Hughes Medical Institute, Stanford, California, United States of America
| | - Juliana L Matos
- Department of Biology, Stanford University, Stanford, California, United States of America
| | - Macy Vollbrecht
- Department of Biology, Stanford University, Stanford, California, United States of America
| | - Shou-Ling Xu
- Carnegie Institution for Science, Stanford, California, United States of America
- Carnegie Mass Spectrometry Facility, Carnegie Institution for Science, Stanford, California, United States of America
| | - Dominique C Bergmann
- Howard Hughes Medical Institute, Stanford, California, United States of America
- Department of Biology, Stanford University, Stanford, California, United States of America
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10
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Bernadskaya YY, Kuan A, Tjärnberg A, Brandenburg J, Zheng P, Wiechecki K, Kaplan N, Failla M, Bikou M, Madilian O, Wang W, Christiaen L. Cell cycle-driven transcriptome maturation confers multilineage competence to cardiopharyngeal progenitors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.23.604718. [PMID: 39091743 PMCID: PMC11291048 DOI: 10.1101/2024.07.23.604718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
During development, stem and progenitor cells divide and transition through germ layer- and lineage-specific multipotent states to generate the diverse cell types that compose an animal. Defined changes in biomolecular composition underlie the progressive loss of potency and acquisition of lineage-specific characteristics. For example, multipotent cardiopharyngeal progenitors display multilineage transcriptional priming, whereby both the cardiac and pharyngeal muscle programs are partially active and coexist in the same progenitor cells, while their daughter cells engage in a cardiac or pharyngeal muscle differentiation path only after cell division. Here, using the tunicate Ciona, we studied the acquisition of multilineage competence and the coupling between fate decisions and cell cycle progression. We showed that multipotent cardiopharyngeal progenitors acquire the competence to produce distinct Tbx1/10 (+) and (-) daughter cells shortly before mitosis, which is necessary for Tbx1/10 activation. By combining transgene-based sample barcoding with single cell RNA-seq (scRNA-seq), we uncovered transcriptome-wide dynamics in migrating cardiopharyngeal progenitors as cells progress through G1, S and G2 phases. We termed this process "transcriptome maturation", and identified candidate "mature genes", including the Rho GAP-coding gene Depdc1 , which peak in late G2. Functional assays indicated that transcriptome maturation fosters cardiopharyngeal competence, in part through multilineage priming and proper oriented and asymmetric division that influences subsequent fate decisions, illustrating the concept of "behavioral competence". Both classic feedforward circuits and coupling with cell cycle progression drive transcriptome maturation, uncovering distinct levels of coupling between cell cycle progression and fateful molecular transitions. We propose that coupling competence and fate decision with the G2 and G1 phases, respectively, ensures the timely deployment of lineage-specific programs.
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11
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Liu Y, Zhang SY, Kleijn IT, Stumpf MPH. Approximate Bayesian computation for inferring Waddington landscapes from single-cell data. ROYAL SOCIETY OPEN SCIENCE 2024; 11:231697. [PMID: 39076359 PMCID: PMC11285904 DOI: 10.1098/rsos.231697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 05/01/2024] [Indexed: 07/31/2024]
Abstract
Single-cell technologies allow us to gain insights into cellular processes at unprecedented resolution. In stem cell and developmental biology snapshot data allow us to characterize how the transcriptional states of cells change between successive cell types. Here, we show how approximate Bayesian computation (ABC) can be employed to calibrate mathematical models against single-cell data. In our simulation study, we demonstrate the pivotal role of the adequate choice of distance measures appropriate for single-cell data. We show that for good distance measures, notably optimal transport with the Sinkhorn divergence, we can infer parameters for mathematical models from simulated single-cell data. We show that the ABC posteriors can be used (i) to characterize parameter sensitivity and identify dependencies between different parameters and (ii) to construct representations of the Waddington or epigenetic landscape, which forms a popular and interpretable representation of the developmental dynamics. In summary, these results pave the way for fitting mechanistic models of stem cell differentiation to single-cell data.
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Affiliation(s)
- Yujing Liu
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Stephen Y. Zhang
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | | | - Michael P. H. Stumpf
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
- School of BioScience, University of Melbourne, Melbourne, Australia
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12
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Rukhlenko OS, Imoto H, Tambde A, McGillycuddy A, Junk P, Tuliakova A, Kolch W, Kholodenko BN. Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets. Cancers (Basel) 2024; 16:2354. [PMID: 39001416 PMCID: PMC11240448 DOI: 10.3390/cancers16132354] [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: 05/07/2024] [Revised: 06/17/2024] [Accepted: 06/23/2024] [Indexed: 07/16/2024] Open
Abstract
Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.
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Affiliation(s)
- Oleksii S Rukhlenko
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Hiroaki Imoto
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Ayush Tambde
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- Stratford College, D06 T9V3 Dublin, Ireland
| | - Amy McGillycuddy
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- School of Biological, Health and Sports Sciences, Technological University, D07 H6K8 Dublin, Ireland
| | - Philipp Junk
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Anna Tuliakova
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Walter Kolch
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Boris N Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, D04 V1W8 Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
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13
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Heydari Z, Moeinvaziri F, Mirazimi SMA, Dashti F, Smirnova O, Shpichka A, Mirzaei H, Timashev P, Vosough M. Alteration in DNA methylation patterns: Epigenetic signatures in gastrointestinal cancers. Eur J Pharmacol 2024; 973:176563. [PMID: 38593929 DOI: 10.1016/j.ejphar.2024.176563] [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: 01/22/2024] [Revised: 03/20/2024] [Accepted: 04/03/2024] [Indexed: 04/11/2024]
Abstract
Abnormalities in epigenetic modifications can cause malignant transformations in cells, leading to cancers of the gastrointestinal (GI) tract, which accounts for 20% of all cancers worldwide. Among the epigenetic alterations, DNA hypomethylation is associated with genomic instability. In addition, CpG methylation and promoter hypermethylation have been recognized as biomarkers for different malignancies. In GI cancers, epigenetic alterations affect genes responsible for cell cycle control, DNA repair, apoptosis, and tumorigenic-specific signaling pathways. Understanding the pattern of alterations in DNA methylation in GI cancers could help scientists discover new molecular-based pharmaceutical treatments. This study highlights alterations in DNA methylation in GI cancers. Understanding epigenetic differences among GI cancers may improve targeted therapies and lead to the discovery of new diagnostic biomarkers.
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Affiliation(s)
- Zahra Heydari
- Institute for Regenerative Medicine, Sechenov University, Moscow, Russia
| | - Farideh Moeinvaziri
- Department of Regenerative Medicine, Cell Science Research Centre, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Seyed Mohammad Ali Mirazimi
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Kashan University of Medical Sciences, Kashan, Iran
| | - Fatemeh Dashti
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Kashan University of Medical Sciences, Kashan, Iran
| | - Olga Smirnova
- Institute for Regenerative Medicine, Sechenov University, Moscow, Russia
| | - Anastasia Shpichka
- Institute for Regenerative Medicine, Sechenov University, Moscow, Russia
| | - Hamed Mirzaei
- Research Center for Biochemistry and Nutrition in Metabolic Diseases, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran.
| | - Peter Timashev
- Institute for Regenerative Medicine, Sechenov University, Moscow, Russia; World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov University, Moscow, Russia; Chemistry Department, Lomonosov Moscow State University, Moscow, Russia.
| | - Massoud Vosough
- Department of Regenerative Medicine, Cell Science Research Centre, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.
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14
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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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15
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Lee J, Kim N, Cho KH. Decoding the principle of cell-fate determination for its reverse control. NPJ Syst Biol Appl 2024; 10:47. [PMID: 38710700 PMCID: PMC11074314 DOI: 10.1038/s41540-024-00372-2] [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: 12/11/2023] [Accepted: 04/16/2024] [Indexed: 05/08/2024] Open
Abstract
Understanding and manipulating cell fate determination is pivotal in biology. Cell fate is determined by intricate and nonlinear interactions among molecules, making mathematical model-based quantitative analysis indispensable for its elucidation. Nevertheless, obtaining the essential dynamic experimental data for model development has been a significant obstacle. However, recent advancements in large-scale omics data technology are providing the necessary foundation for developing such models. Based on accumulated experimental evidence, we can postulate that cell fate is governed by a limited number of core regulatory circuits. Following this concept, we present a conceptual control framework that leverages single-cell RNA-seq data for dynamic molecular regulatory network modeling, aiming to identify and manipulate core regulatory circuits and their master regulators to drive desired cellular state transitions. We illustrate the proposed framework by applying it to the reversion of lung cancer cell states, although it is more broadly applicable to understanding and controlling a wide range of cell-fate determination processes.
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Affiliation(s)
- Jonghoon Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Namhee Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- biorevert, Inc., Daejeon, Republic of Korea
| | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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16
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Jiao F, Li J, Liu T, Zhu Y, Che W, Bleris L, Jia C. What can we learn when fitting a simple telegraph model to a complex gene expression model? PLoS Comput Biol 2024; 20:e1012118. [PMID: 38743803 PMCID: PMC11125521 DOI: 10.1371/journal.pcbi.1012118] [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: 02/06/2024] [Revised: 05/24/2024] [Accepted: 04/27/2024] [Indexed: 05/16/2024] Open
Abstract
In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.
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Affiliation(s)
- Feng Jiao
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Jing Li
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Ting Liu
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Yifeng Zhu
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Wenhao Che
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Leonidas Bleris
- Bioengineering Department, The University of Texas at Dallas, Richardson, Texas, United States of America
- Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas, United States of America
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas, United States of America
| | - Chen Jia
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing, China
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17
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Ohta N, Christiaen L. Cellular remodeling and JAK inhibition promote zygotic gene expression in the Ciona germline. EMBO Rep 2024; 25:2188-2201. [PMID: 38649664 PMCID: PMC11094015 DOI: 10.1038/s44319-024-00139-0] [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: 01/30/2023] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/25/2024] Open
Abstract
Transcription control is a major determinant of cell fate decisions in somatic tissues. By contrast, early germline fate specification in numerous vertebrate and invertebrate species relies extensively on RNA-level regulation, exerted on asymmetrically inherited maternal supplies, with little-to-no zygotic transcription. However delayed, a maternal-to-zygotic transition is nevertheless poised to complete the deployment of pre-gametic programs in the germline. Here, we focus on early germline specification in the tunicate Ciona to study zygotic genome activation. We first demonstrate that a peculiar cellular remodeling event excludes localized postplasmic Pem-1 mRNA, which encodes the general inhibitor of transcription. Subsequently, zygotic transcription begins in Pem-1-negative primordial germ cells (PGCs), as revealed by histochemical detection of elongating RNA Polymerase II, and nascent Mef2 transcripts. In addition, we uncover a provisional antagonism between JAK and MEK/BMPRI/GSK3 signaling, which controls the onset of zygotic gene expression, following cellular remodeling of PGCs. We propose a 2-step model for the onset of zygotic transcription in the Ciona germline and discuss the significance of germ plasm dislocation and remodeling in the context of developmental fate specification.
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Affiliation(s)
- Naoyuki Ohta
- Michael Sars Centre, University of Bergen, Bergen, Norway.
| | - Lionel Christiaen
- Michael Sars Centre, University of Bergen, Bergen, Norway.
- Center for Developmental Genetics, Department of Biology, New York University, New York, NY, USA.
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18
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Wang K, Hou L, Wang X, Zhai X, Lu Z, Zi Z, Zhai W, He X, Curtis C, Zhou D, Hu Z. PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes. Nat Biotechnol 2024; 42:778-789. [PMID: 37524958 DOI: 10.1038/s41587-023-01887-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 06/28/2023] [Indexed: 08/02/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.
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Affiliation(s)
- Kun Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- School of Mathematical Sciences, Xiamen University, Xiamen, China
| | - Liangzhen Hou
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Xin Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiangwei Zhai
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Zhaolian Lu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhike Zi
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weiwei Zhai
- CAS Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Xionglei He
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Christina Curtis
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen, China.
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
| | - Zheng Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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19
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Zhang Y, Kang Z, Liu M, Wang L, Liu F. Single-cell omics identifies inflammatory signaling as a trans-differentiation trigger in mouse embryos. Dev Cell 2024; 59:961-978.e7. [PMID: 38508181 DOI: 10.1016/j.devcel.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 01/08/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024]
Abstract
Trans-differentiation represents a direct lineage conversion; however, insufficient characterization of this process hinders its potential applications. Here, to explore a potential universal principal for trans-differentiation, we performed single-cell transcriptomic analysis of endothelial-to-hematopoietic transition (EHT), endothelial-to-mesenchymal transition, and epithelial-to-mesenchymal transition in mouse embryos. We applied three scoring indexes of entropies, cell-type signature transcription factor expression, and critical transition signals to show common features underpinning the fate plasticity of transition states. Cross-model comparison identified inflammatory-featured transition states and a common trigger role of interleukin-33 in promoting fate conversions. Multimodal profiling (integrative transcriptomic and chromatin accessibility analysis) demonstrated the inflammatory regulation of hematopoietic specification. Furthermore, multimodal omics and fate-mapping analyses showed that endothelium-specific Spi1, as an inflammatory effector, governs appropriate chromatin accessibility and transcriptional programs to safeguard EHT. Overall, our study employs single-cell omics to identify critical transition states/signals and the common trigger role of inflammatory signaling in developmental-stress-induced fate conversions.
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Affiliation(s)
- Yifan Zhang
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University, Qingdao, China
| | - Zhixin Kang
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Membrane Biology, Institute for Stem Cell and Regeneration, Institute of Zoology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Mengyao Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Lu Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Feng Liu
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University, Qingdao, China; Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Membrane Biology, Institute for Stem Cell and Regeneration, Institute of Zoology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China.
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20
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Pina C. Contributions of transcriptional noise to leukaemia evolution: KAT2A as a case-study. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230052. [PMID: 38432321 PMCID: PMC10909511 DOI: 10.1098/rstb.2023.0052] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 12/04/2023] [Indexed: 03/05/2024] Open
Abstract
Transcriptional noise is proposed to participate in cell fate changes, but contributions to mammalian cell differentiation systems, including cancer, remain associative. Cancer evolution is driven by genetic variability, with modulatory or contributory participation of epigenetic variants. Accumulation of epigenetic variants enhances transcriptional noise, which can facilitate cancer cell fate transitions. Acute myeloid leukaemia (AML) is an aggressive cancer with strong epigenetic dependencies, characterized by blocked differentiation. It constitutes an attractive model to probe links between transcriptional noise and malignant cell fate regulation. Gcn5/KAT2A is a classical epigenetic transcriptional noise regulator. Its loss increases transcriptional noise and modifies cell fates in stem and AML cells. By reviewing the analysis of KAT2A-depleted pre-leukaemia and leukaemia models, I discuss that the net result of transcriptional noise is diversification of cell fates secondary to alternative transcriptional programmes. Cellular diversification can enable or hinder AML progression, respectively, by differentiation of cell types responsive to mutations, or by maladaptation of leukaemia stem cells. KAT2A-dependent noise-responsive genes participate in ribosome biogenesis and KAT2A loss destabilizes translational activity. I discuss putative contributions of perturbed translation to AML biology, and propose KAT2A loss as a model for mechanistic integration of transcriptional and translational control of noise and fate decisions. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.
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Affiliation(s)
- Cristina Pina
- College of Health, Medicine and Life Sciences, Brunel University London, Kingston Lane, Uxbridge, London, UB8 3PH, United Kingdom
- CenGEM – Centre for Genome Engineering and Maintenance, Brunel University London, Kingston Lane, Uxbridge, London, UB8 3PH, United Kingdom
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21
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Uechi L, Vasudevan S, Vilenski D, Branciamore S, Frankhouser D, O'Meally D, Meshinchi S, Marcucci G, Kuo YH, Rockne R, Kravchenko-Balasha N. Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemia. NPJ Syst Biol Appl 2024; 10:32. [PMID: 38527998 DOI: 10.1038/s41540-024-00352-6] [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: 08/07/2023] [Accepted: 02/26/2024] [Indexed: 03/27/2024] Open
Abstract
Acute myeloid leukemia (AML) is prevalent in both adult and pediatric patients. Despite advances in patient categorization, the heterogeneity of AML remains a challenge. Recent studies have explored the use of gene expression data to enhance AML diagnosis and prognosis, however, alternative approaches rooted in physics and chemistry may provide another level of insight into AML transformation. Utilizing publicly available databases, we analyze 884 human and mouse blood and bone marrow samples. We employ a personalized medicine strategy, combining state-transition theory and surprisal analysis, to assess the RNA transcriptome of individual patients. The transcriptome is transformed into physical parameters that represent each sample's steady state and the free energy change (FEC) from that steady state, which is the state with the lowest free energy.We found the transcriptome steady state was invariant across normal and AML samples. FEC, representing active molecular processes, varied significantly between samples and was used to create patient-specific barcodes to characterize the biology of the disease. We discovered that AML samples that were in a transition state had the highest FEC. This disease state may be characterized as the most unstable and hence the most therapeutically targetable since a change in free energy is a thermodynamic requirement for disease progression. We also found that distinct sets of ongoing processes may be at the root of otherwise similar clinical phenotypes, implying that our integrated analysis of transcriptome profiles may facilitate a personalized medicine approach to cure AML and restore a steady state in each patient.
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Affiliation(s)
- Lisa Uechi
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Swetha Vasudevan
- The Institute of Biomedical and Oral Research, Faculty of Dental Medicine, The Hebrew University of Jerusalem, P.O.B. 12272, Ein Kerem, Jerusalem, 91120, Israel
| | - Daniela Vilenski
- The Institute of Biomedical and Oral Research, Faculty of Dental Medicine, The Hebrew University of Jerusalem, P.O.B. 12272, Ein Kerem, Jerusalem, 91120, Israel
| | - Sergio Branciamore
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - David Frankhouser
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Denis O'Meally
- Department of Diabetes and Cancer Discovery Science, Arthur Riggs Diabetes and Metabolism Research Institute, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Soheil Meshinchi
- Clinical Research Division, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, D5-112, Seattle, WA, 98109, USA
| | - Guido Marcucci
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, USA
| | - Ya-Huei Kuo
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, USA
| | - Russell Rockne
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA.
| | - Nataly Kravchenko-Balasha
- The Institute of Biomedical and Oral Research, Faculty of Dental Medicine, The Hebrew University of Jerusalem, P.O.B. 12272, Ein Kerem, Jerusalem, 91120, Israel.
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22
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Paczkó M, Vörös D, Szabó P, Jékely G, Szathmáry E, Szilágyi A. A neural network-based model framework for cell-fate decisions and development. Commun Biol 2024; 7:323. [PMID: 38486083 PMCID: PMC10940658 DOI: 10.1038/s42003-024-05985-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
Gene regulatory networks (GRNs) fulfill the essential function of maintaining the stability of cellular differentiation states by sustaining lineage-specific gene expression, while driving the progression of development. However, accounting for the relative stability of intermediate differentiation stages and their divergent trajectories remains a major challenge for models of developmental biology. Here, we develop an empirical data-based associative GRN model (AGRN) in which regulatory networks store multilineage stage-specific gene expression profiles as associative memory patterns. These networks are capable of responding to multiple instructive signals and, depending on signal timing and identity, can dynamically drive the differentiation of multipotent cells toward different cell state attractors. The AGRN dynamics can thus generate diverse lineage-committed cell populations in a robust yet flexible manner, providing an attractor-based explanation for signal-driven cell fate decisions during differentiation and offering a readily generalizable modelling tool that can be applied to a wide variety of cell specification systems.
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Affiliation(s)
- Mátyás Paczkó
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary
| | - Dániel Vörös
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary
| | - Péter Szabó
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
| | - Gáspár Jékely
- Living Systems Institute, University of Exeter, Stocker Road 4QD, EX4, Exeter, UK
| | - Eörs Szathmáry
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary.
- Center for the Conceptual Foundations of Science, Parmenides Foundation, Hindenburgstr. 15, 82343, Pöcking, Germany.
- Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary.
| | - András Szilágyi
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
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23
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Bárcenas-Walls JR, Ansaloni F, Hervé B, Strandback E, Nyman T, Castelo-Branco G, Bartošovič M. Nano-CUT&Tag for multimodal chromatin profiling at single-cell resolution. Nat Protoc 2024; 19:791-830. [PMID: 38129675 DOI: 10.1038/s41596-023-00932-6] [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: 03/28/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
The ability to comprehensively analyze the chromatin state with single-cell resolution is crucial for understanding gene regulatory principles in heterogenous tissues or during development. Recently, we developed a nanobody-based single-cell CUT&Tag (nano-CT) protocol to simultaneously profile three epigenetic modalities-two histone marks and open chromatin state-from the same single cell. Nano-CT implements a new set of secondary nanobody-Tn5 fusion proteins to direct barcoded tagmentation by Tn5 transposase to genomic targets labeled by primary antibodies raised in different species. Such nanobody-Tn5 fusion proteins are currently not commercially available, and their in-house production and purification can be completed in 3-4 d by following our detailed protocol. The single-cell indexing in nano-CT is performed on a commercially available platform, making it widely accessible to the community. In comparison to other multimodal methods, nano-CT stands out in data complexity, low sample requirements and the flexibility to choose two of the three modalities. In addition, nano-CT works efficiently with fresh brain samples, generating multimodal epigenomic profiles for thousands of brain cells at single-cell resolution. The nano-CT protocol can be completed in just 3 d by users with basic skills in standard molecular biology and bioinformatics, although previous experience with single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is beneficial for more in-depth data analysis. As a multimodal assay, nano-CT holds immense potential to reveal interactions of various chromatin modalities, to explore epigenetic heterogeneity and to increase our understanding of the role and interplay that chromatin dynamics has in cellular development.
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Affiliation(s)
| | - Federico Ansaloni
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Bastien Hervé
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Emilia Strandback
- Protein Science Facility, Department of Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Tomas Nyman
- Protein Science Facility, Department of Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- Ming Wai Lau Centre for Reparative Medicine, Stockholm node, Karolinska Institutet, Stockholm, Sweden
| | - Marek Bartošovič
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.
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24
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Falkowski L, Buddenkotte J, Datsi A. Epigenetics in T-cell driven inflammation and cancer. Semin Cell Dev Biol 2024; 154:250-260. [PMID: 36641367 DOI: 10.1016/j.semcdb.2023.01.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 01/06/2023] [Accepted: 01/08/2023] [Indexed: 01/15/2023]
Abstract
For decades, scientists have been investigating how processes such as gene expression, stem cell plasticity, and cell differentiation can be modulated. The discovery of epigenetics helped unravel these processes and enabled the identification of major underlying mechanisms that, for example, are central for T cell maturation. T cells go through various stages in their development evolving from progenitor cells into double positive CD4/CD8 T cells that finally leave the thymus as naïve T cells. One major mechanism driving T cell maturation is the modulation of gene activity by temporally sequenced transcription of spatially exposed gene loci. DNA methylation, demethylation, and acetylation are key processes that enable a sequenced gene expression required for T cell differentiation. In vivo, differentiated T cells are subjected to enormous pressures originating from the microenvironment. Signals from this environment, particularly from an inflammatory or a tumor microenvironment, can push T cells to differentiate into specific effector and memory T cells, and even prompt T cells to adopt a state of dysfunctional exhaustion, en route of an epigenetically controlled mechanism. Fundamentals of these processes will be discussed in this review highlighting potential therapeutic interventions, in particular those beneficial to revive exhausted T cells.
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Affiliation(s)
- Lea Falkowski
- Institute for Transplantational Diagnostics and Cell Therapeutics, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Joerg Buddenkotte
- Department of Dermatology and Venereology, Hamad Medical Corporation, Doha, Qatar; Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar.
| | - Angeliki Datsi
- Institute for Transplantational Diagnostics and Cell Therapeutics, University Hospital Düsseldorf, Düsseldorf, Germany
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25
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Zhang C, Jiao F. Using steady-state formula to estimate time-dependent parameters of stochastic gene transcription models. Biosystems 2024; 236:105128. [PMID: 38280446 DOI: 10.1016/j.biosystems.2024.105128] [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: 11/01/2023] [Revised: 01/20/2024] [Accepted: 01/21/2024] [Indexed: 01/29/2024]
Abstract
When studying stochastic gene transcription, it is important to understand how system parameters are temporally modulated in response to varying environments. Experimentally, the dynamic distribution data of RNA copy numbers measured at multiple time points are often fitted to stochastic transcription models to estimate time-dependent parameters. However, current methods require determining which parameters are time-dependent, as well as their analytical formulas, before the optimal fit. In this study, we developed a method to estimate time-dependent parameters in a classical two-state model without prior assumptions regarding the system parameters. At each measured time point, the method fitted the dynamic distribution data using a steady-state distribution formula, in which the estimated constant parameters were approximated as time-dependent parameter values at the measured time point. The accuracy of this method can be guaranteed for RNA molecules with relatively high degradation rates and genes with relatively slow responses to induction. We quantify the accuracy of the method and implemented this method on two sets of dynamic distribution data from prokaryotic and eukaryotic cells, and revealed the temporal modulation of transcription burst size in response to environmental changes.
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Affiliation(s)
- Congrun Zhang
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, PR China; College of Mathematics and Information Sciences, Guangzhou University, Guangzhou 51006, China
| | - Feng Jiao
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, PR China; College of Mathematics and Information Sciences, Guangzhou University, Guangzhou 51006, China.
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26
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Pan X, Zhang X. Studying temporal dynamics of single cells: expression, lineage and regulatory networks. Biophys Rev 2024; 16:57-67. [PMID: 38495440 PMCID: PMC10937865 DOI: 10.1007/s12551-023-01090-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/27/2023] [Indexed: 03/19/2024] Open
Abstract
Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-023-01090-5.
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Affiliation(s)
- Xinhai Pan
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
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27
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Huang R, Situ Q, Lei J. Dynamics of cell-type transition mediated by epigenetic modifications. J Theor Biol 2024; 577:111664. [PMID: 37977478 DOI: 10.1016/j.jtbi.2023.111664] [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: 04/15/2023] [Revised: 10/20/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023]
Abstract
Maintaining tissue homeostasis requires appropriate regulation of stem cell differentiation. The Waddington landscape posits that gene circuits in a cell form a potential landscape of different cell types, wherein cells follow attractors of the probability landscape to develop into distinct cell types. However, how adult stem cells achieve a delicate balance between self-renewal and differentiation remains unclear. We propose that random inheritance of epigenetic states plays a pivotal role in stem cell differentiation and present a hybrid model of stem cell differentiation induced by epigenetic modifications. Our comprehensive model integrates gene regulation networks, epigenetic state inheritance, and cell regeneration, encompassing multi-scale dynamics ranging from transcription regulation to cell population. Through model simulations, we demonstrate that random inheritance of epigenetic states during cell divisions can spontaneously induce cell differentiation, dedifferentiation, and transdifferentiation. Furthermore, we investigate the influences of interfering with epigenetic modifications and introducing additional transcription factors on the probabilities of dedifferentiation and transdifferentiation, revealing the underlying mechanism of cell reprogramming. This in silico model provides valuable insights into the intricate mechanism governing stem cell differentiation and cell reprogramming and offers a promising path to enhance the field of regenerative medicine.
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Affiliation(s)
- Rongsheng Huang
- School of Science, Jimei University, Xiamen, Fujian, 361021, China
| | - Qiaojun Situ
- Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing, 100084, China
| | - Jinzhi Lei
- School of Mathematical Sciences, Center for Applied Mathematics, Tiangong University, Tianjin, 300387, China.
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28
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Croydon-Veleslavov IA, Stumpf MPH. Repeated Decision Stumping Distils Simple Rules from Single-Cell Data. J Comput Biol 2024; 31:21-40. [PMID: 38170180 DOI: 10.1089/cmb.2021.0613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Abstract
Single-cell data afford unprecedented insights into molecular processes. But the complexity and size of these data sets have proved challenging and given rise to a large armory of statistical and machine learning approaches. The majority of approaches focuses on either describing features of these data, or making predictions and classifying unlabeled samples. In this study, we introduce repeated decision stumping (ReDX) as a method to distill simple models from single-cell data. We develop decision trees of depth one-hence "stumps"-to identify in an inductive manner, gene products involved in driving cell fate transitions, and in applications to published data we are able to discover the key players involved in these processes in an unbiased manner without prior knowledge. Our algorithm is deliberately targeting the simplest possible candidate hypotheses that can be extracted from complex high-dimensional data. There are three reasons for this: (1) the predictions become straightforwardly testable hypotheses; (2) the identified candidates form the basis for further mechanistic model development, for example, for engineering and synthetic biology interventions; and (3) this approach complements existing descriptive modeling approaches and frameworks. The approach is computationally efficient, has remarkable predictive power, including in simulation studies where the ground truth is known, and yields robust and statistically stable predictors; the same set of candidates is generated by applying the algorithm to different subsamples of experimental data.
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Affiliation(s)
- Ivan A Croydon-Veleslavov
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, United Kingdom
| | - Michael P H Stumpf
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, United Kingdom
- School of BioSciences, University of Melbourne, Parkville, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, Australia
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29
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Shah A. Rethinking cancer initiation: The role of large-scale mutational events. Genes Chromosomes Cancer 2024; 63:e23213. [PMID: 37950638 DOI: 10.1002/gcc.23213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/13/2023] Open
Abstract
Cancer initiation is revisited in light of recent discoveries in cancer pathogenesis. Of note is the detection of mutated cancer genes in benign conditions. More significantly, somatic clones, which harbor mutations in cancer genes, arise in normal tissues from early development through adulthood, but seldom do they transform into cancer. Further, clustered mutational events-kataegis, chromothripsis and chromoplexy-are widespread in cancer, generating point mutations and chromosomal rearrangements in a single cellular catastrophe. These observations are contrary to the prevailing somatic mutation theory, which states that a cancer is caused by the gradual accumulation of mutations over time. A different perspective is proposed within the framework of Waddington's epigenetic landscape wherein tumorigenesis is viewed primarily as a disruption of cell development. Cell types are defined by their specific gene-expression profiles, determined by the gene regulatory network, and can be regarded as attractor states of the network dynamics: they represent specific, self-stabilizing patterns of gene activities across the genome. However, large-scale mutational events reshape the landscape topology, creating abnormal "unphysiological" attractors. This is the crux of the process of initiation. Initiation primes the cell for conversion into a tumor phenotype by oncogenes and tumor suppressor genes, which drive cell proliferation and clonal diversification. This view of tumorigenesis calls for a different approach to therapy.
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Affiliation(s)
- Amil Shah
- Department of Medicine, University of British Columbia, Vancouver, Canada
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30
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Wang P, Wen X, Li H, Lang P, Li S, Lei Y, Shu H, Gao L, Zhao D, Zeng J. Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON. Nat Commun 2023; 14:8459. [PMID: 38123534 PMCID: PMC10733330 DOI: 10.1038/s41467-023-44103-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.
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Affiliation(s)
- Peizhuo Wang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Xiao Wen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Peng Lang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Shuya Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Yipin Lei
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, 710071, Xi'an, Shaanxi Province, China
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China.
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31
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Dijkhuis L, Johns A, Ragusa D, van den Brink SC, Pina C. Haematopoietic development and HSC formation in vitro: promise and limitations of gastruloid models. Emerg Top Life Sci 2023; 7:439-454. [PMID: 38095554 PMCID: PMC10754337 DOI: 10.1042/etls20230091] [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: 08/22/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023]
Abstract
Haematopoietic stem cells (HSCs) are the most extensively studied adult stem cells. Yet, six decades after their first description, reproducible and translatable generation of HSC in vitro remains an unmet challenge. HSC production in vitro is confounded by the multi-stage nature of blood production during development. Specification of HSC is a late event in embryonic blood production and depends on physical and chemical cues which remain incompletely characterised. The precise molecular composition of the HSC themselves is incompletely understood, limiting approaches to track their origin in situ in the appropriate cellular, chemical and mechanical context. Embryonic material at the point of HSC emergence is limiting, highlighting the need for an in vitro model of embryonic haematopoietic development in which current knowledge gaps can be addressed and exploited to enable HSC production. Gastruloids are pluripotent stem cell-derived 3-dimensional (3D) cellular aggregates which recapitulate developmental events in gastrulation and early organogenesis with spatial and temporal precision. Gastruloids self-organise multi-tissue structures upon minimal and controlled external cues, and are amenable to live imaging, screening, scaling and physicochemical manipulation to understand and translate tissue formation. In this review, we consider the haematopoietic potential of gastruloids and review early strategies to enhance blood progenitor and HSC production. We highlight possible strategies to achieve HSC production from gastruloids, and discuss the potential of gastruloid systems in illuminating current knowledge gaps in HSC specification.
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Affiliation(s)
- Liza Dijkhuis
- Department of Hematopoiesis, Sanquin Research, 1066 CX Amsterdam, The Netherlands
| | - Ayona Johns
- College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, U.K
- Centre for Genome Engineering and Maintenance, Brunel University London, Uxbridge UB8 3PH, U.K
| | - Denise Ragusa
- College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, U.K
- Centre for Genome Engineering and Maintenance, Brunel University London, Uxbridge UB8 3PH, U.K
| | | | - Cristina Pina
- College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, U.K
- Centre for Genome Engineering and Maintenance, Brunel University London, Uxbridge UB8 3PH, U.K
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32
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Kyaw W, Chai RC, Khoo WH, Goldstein LD, Croucher PI, Murray JM, Phan TG. ENTRAIN: integrating trajectory inference and gene regulatory networks with spatial data to co-localize the receptor-ligand interactions that specify cell fate. Bioinformatics 2023; 39:btad765. [PMID: 38113422 PMCID: PMC10752580 DOI: 10.1093/bioinformatics/btad765] [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: 07/08/2023] [Revised: 12/07/2023] [Accepted: 12/18/2023] [Indexed: 12/21/2023] Open
Abstract
MOTIVATION Cell fate is commonly studied by profiling the gene expression of single cells to infer developmental trajectories based on expression similarity, RNA velocity, or statistical mechanical properties. However, current approaches do not recover microenvironmental signals from the cellular niche that drive a differentiation trajectory. RESULTS We resolve this with environment-aware trajectory inference (ENTRAIN), a computational method that integrates trajectory inference methods with ligand-receptor pair gene regulatory networks to identify extracellular signals and evaluate their relative contribution towards a differentiation trajectory. The output from ENTRAIN can be superimposed on spatial data to co-localize cells and molecules in space and time to map cell fate potentials to cell-cell interactions. We validate and benchmark our approach on single-cell bone marrow and spatially resolved embryonic neurogenesis datasets to identify known and novel environmental drivers of cellular differentiation. AVAILABILITY AND IMPLEMENTATION ENTRAIN is available as a public package at https://github.com/theimagelab/entrain and can be used on both single-cell and spatially resolved datasets.
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Affiliation(s)
- Wunna Kyaw
- Precision Immunology Program, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
- St Vincent’s Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW Sydney, Darlinghurst, NSW 2010, Australia
| | - Ryan C Chai
- St Vincent’s Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW Sydney, Darlinghurst, NSW 2010, Australia
- Cancer Plasticity and Dormancy Program, Garvan Institute of Medical Research, Darlinghurst, NSW 2010Australia
| | - Weng Hua Khoo
- St Vincent’s Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW Sydney, Darlinghurst, NSW 2010, Australia
- Cancer Plasticity and Dormancy Program, Garvan Institute of Medical Research, Darlinghurst, NSW 2010Australia
| | - Leonard D Goldstein
- St Vincent’s Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW Sydney, Darlinghurst, NSW 2010, Australia
- Data Science Platform, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
| | - Peter I Croucher
- St Vincent’s Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW Sydney, Darlinghurst, NSW 2010, Australia
- Cancer Plasticity and Dormancy Program, Garvan Institute of Medical Research, Darlinghurst, NSW 2010Australia
| | - John M Murray
- School of Mathematics and Statistics, Faculty of Science, UNSW Sydney, Kensington, NSW 2033, Australia
| | - Tri Giang Phan
- Precision Immunology Program, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
- St Vincent’s Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW Sydney, Darlinghurst, NSW 2010, Australia
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33
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Westbrook ER, Lenn T, Chubb JR, Antolović V. Collective signalling drives rapid jumping between cell states. Development 2023; 150:dev201946. [PMID: 37921687 PMCID: PMC10730084 DOI: 10.1242/dev.201946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 10/19/2023] [Indexed: 11/04/2023]
Abstract
Development can proceed in 'fits and starts', with rapid transitions between cell states involving concerted transcriptome-wide changes in gene expression. However, it is not clear how these transitions are regulated in complex cell populations, in which cells receive multiple inputs. We address this issue using Dictyostelium cells undergoing development in their physiological niche. A continuous single cell transcriptomics time series identifies a sharp 'jump' in global gene expression marking functionally different cell states. By simultaneously imaging the physiological dynamics of transcription and signalling, we show the jump coincides with the onset of collective oscillations of cAMP. Optogenetic control of cAMP pulses shows that different jump genes respond to distinct dynamic features of signalling. Late jump gene expression changes are almost completely dependent on cAMP, whereas transcript changes at the onset of the jump require additional input. The coupling of collective signalling with gene expression is a potentially powerful strategy to drive robust cell state transitions in heterogeneous signalling environments. Based on the context of the jump, we also conclude that sharp gene expression transitions may not be sufficient for commitment.
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Affiliation(s)
- Elizabeth R. Westbrook
- UCL Laboratory for Molecular Cell Biology and Department of Cell and Developmental Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Tchern Lenn
- UCL Laboratory for Molecular Cell Biology and Department of Cell and Developmental Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Jonathan R. Chubb
- UCL Laboratory for Molecular Cell Biology and Department of Cell and Developmental Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Vlatka Antolović
- UCL Laboratory for Molecular Cell Biology and Department of Cell and Developmental Biology, University College London, Gower Street, London, WC1E 6BT, UK
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34
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Emadi A, Lipniacki T, Levchenko A, Abdi A. Single-Cell Measurements and Modeling and Computation of Decision-Making Errors in a Molecular Signaling System with Two Output Molecules. BIOLOGY 2023; 12:1461. [PMID: 38132287 PMCID: PMC10740708 DOI: 10.3390/biology12121461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/13/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023]
Abstract
A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, a cell may incorrectly perceive these signals. It may mistakenly behave as if there is a signal, although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system with two outputs, and introduce and develop methods to model and compute key cell decision-making parameters based on the two outputs and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions, such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single-cell measured concentration levels of NFκB and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of the signaling system, one can learn more about complex cellular decision-making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions.
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Affiliation(s)
- Ali Emadi
- Center for Wireless Information Processing, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, 323 King Blvd, Newark, NJ 07102, USA;
| | - Tomasz Lipniacki
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106 Warsaw, Poland;
| | - Andre Levchenko
- Yale Systems Biology Institute, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - Ali Abdi
- Center for Wireless Information Processing, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, 323 King Blvd, Newark, NJ 07102, USA;
- Department of Biological Sciences, New Jersey Institute of Technology, 323 King Blvd, Newark, NJ 07102, USA
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35
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Parker M, Rubien J, McCormick D, Li GW. Molecular Time Capsules Enable Transcriptomic Recording in Living Cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.12.562053. [PMID: 37905077 PMCID: PMC10614764 DOI: 10.1101/2023.10.12.562053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Live-cell transcriptomic recording can help reveal hidden cellular states that precede phenotypic transformation. Here we demonstrate the use of protein-based encapsulation for preserving samples of cytoplasmic RNAs inside living cells. These molecular time capsules (MTCs) can be induced to create time-stamped transcriptome snapshots, preserve RNAs after cellular transitions, and enable retrospective investigations of gene expression programs that drive distinct developmental trajectories. MTCs also open the possibility to uncover transcriptomes in difficult-to-reach conditions.
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Affiliation(s)
- Mirae Parker
- Program of Computational and Systems Biology, Massachusetts Institute of Technology; Cambridge USA
- Department of Biology, Massachusetts Institute of Technology; Cambridge USA
| | - Jack Rubien
- Department of Biology, Massachusetts Institute of Technology; Cambridge USA
| | - Dylan McCormick
- Department of Biology, Massachusetts Institute of Technology; Cambridge USA
- Current address: Whitehead Institute for Biomedical Research; Cambridge, USA
| | - Gene-Wei Li
- Department of Biology, Massachusetts Institute of Technology; Cambridge USA
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36
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Hu Y, Shen F, Yang X, Han T, Long Z, Wen J, Huang J, Shen J, Guo Q. Single-cell sequencing technology applied to epigenetics for the study of tumor heterogeneity. Clin Epigenetics 2023; 15:161. [PMID: 37821906 PMCID: PMC10568863 DOI: 10.1186/s13148-023-01574-x] [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: 07/16/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Previous studies have traditionally attributed the initiation of cancer cells to genetic mutations, considering them as the fundamental drivers of carcinogenesis. However, recent research has shed light on the crucial role of epigenomic alterations in various cell types present within the tumor microenvironment, suggesting their potential contribution to tumor formation and progression. Despite these significant findings, the progress in understanding the epigenetic mechanisms regulating tumor heterogeneity has been impeded over the past few years due to the lack of appropriate technical tools and methodologies. RESULTS The emergence of single-cell sequencing has enhanced our understanding of the epigenetic mechanisms governing tumor heterogeneity by revealing the distinct epigenetic layers of individual cells (chromatin accessibility, DNA/RNA methylation, histone modifications, nucleosome localization) and the diverse omics (transcriptomics, genomics, multi-omics) at the single-cell level. These technologies provide us with new insights into the molecular basis of intratumoral heterogeneity and help uncover key molecular events and driving mechanisms in tumor development. CONCLUSION This paper provides a comprehensive review of the emerging analytical and experimental approaches of single-cell sequencing in various omics, focusing specifically on epigenomics. These approaches have the potential to capture and integrate multiple dimensions of individual cancer cells, thereby revealing tumor heterogeneity and epigenetic features. Additionally, this paper outlines the future trends of these technologies and their current technical limitations.
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Affiliation(s)
- Yuhua Hu
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
- Graduate School, Dalian Medical University, Dalian, 116044, Liaoning, China
| | - Feng Shen
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
- Department of Neurosurgery, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
| | - Xi Yang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Tingting Han
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
- Graduate School, Dalian Medical University, Dalian, 116044, Liaoning, China
| | - Zhuowen Long
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
| | - Jiale Wen
- Graduate School, Dalian Medical University, Dalian, 116044, Liaoning, China
- Department of Cardiology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
| | - Junxing Huang
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China.
| | - Jiangfeng Shen
- Department of Thoracic Surgery, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China.
| | - Qing Guo
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China.
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Madsen RR, Toker A. PI3K signaling through a biochemical systems lens. J Biol Chem 2023; 299:105224. [PMID: 37673340 PMCID: PMC10570132 DOI: 10.1016/j.jbc.2023.105224] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/08/2023] Open
Abstract
Following 3 decades of extensive research into PI3K signaling, it is now evidently clear that the underlying network does not equate to a simple ON/OFF switch. This is best illustrated by the multifaceted nature of the many diseases associated with aberrant PI3K signaling, including common cancers, metabolic disease, and rare developmental disorders. However, we are still far from a complete understanding of the fundamental control principles that govern the numerous phenotypic outputs that are elicited by activation of this well-characterized biochemical signaling network, downstream of an equally diverse set of extrinsic inputs. At its core, this is a question on the role of PI3K signaling in cellular information processing and decision making. Here, we review the determinants of accurate encoding and decoding of growth factor signals and discuss outstanding questions in the PI3K signal relay network. We emphasize the importance of quantitative biochemistry, in close integration with advances in single-cell time-resolved signaling measurements and mathematical modeling.
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Affiliation(s)
- Ralitsa R Madsen
- MRC-Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dundee, Scotland, United Kingdom.
| | - Alex Toker
- Department of Pathology and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
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38
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Opitz CA, Holfelder P, Prentzell MT, Trump S. The complex biology of aryl hydrocarbon receptor activation in cancer and beyond. Biochem Pharmacol 2023; 216:115798. [PMID: 37696456 PMCID: PMC10570930 DOI: 10.1016/j.bcp.2023.115798] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/08/2023] [Accepted: 09/08/2023] [Indexed: 09/13/2023]
Abstract
The aryl hydrocarbon receptor (AHR) signaling pathway is a complex regulatory network that plays a critical role in various biological processes, including cellular metabolism, development, and immune responses. The complexity of AHR signaling arises from multiple factors, including the diverse ligands that activate the receptor, the expression level of AHR itself, and its interaction with the AHR nuclear translocator (ARNT). Additionally, the AHR crosstalks with the AHR repressor (AHRR) or other transcription factors and signaling pathways and it can also mediate non-genomic effects. Finally, posttranslational modifications of the AHR and its interaction partners, epigenetic regulation of AHR and its target genes, as well as AHR-mediated induction of enzymes that degrade AHR-activating ligands may contribute to the context-specificity of AHR activation. Understanding the complexity of AHR signaling is crucial for deciphering its physiological and pathological roles and developing therapeutic strategies targeting this pathway. Ongoing research continues to unravel the intricacies of AHR signaling, shedding light on the regulatory mechanisms controlling its diverse functions.
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Affiliation(s)
- Christiane A Opitz
- German Cancer Research Center (DKFZ), Heidelberg, Division of Metabolic Crosstalk in Cancer and the German Cancer Consortium (DKTK), DKFZ Core Center Heidelberg, 69120 Heidelberg, Germany; Neurology Clinic and National Center for Tumor Diseases, 69120 Heidelberg, Germany.
| | - Pauline Holfelder
- German Cancer Research Center (DKFZ), Heidelberg, Division of Metabolic Crosstalk in Cancer and the German Cancer Consortium (DKTK), DKFZ Core Center Heidelberg, 69120 Heidelberg, Germany; Faculty of Bioscience, Heidelberg University, 69120 Heidelberg, Germany
| | - Mirja Tamara Prentzell
- German Cancer Research Center (DKFZ), Heidelberg, Division of Metabolic Crosstalk in Cancer and the German Cancer Consortium (DKTK), DKFZ Core Center Heidelberg, 69120 Heidelberg, Germany; Faculty of Bioscience, Heidelberg University, 69120 Heidelberg, Germany
| | - Saskia Trump
- Molecular Epidemiology Unit, Berlin Institute of Health at Charité and the German Cancer Consortium (DKTK), Partner Site Berlin, a partnership between DKFZ and Charité -Universitätsmedizin Berlin, 10117 Berlin, Germany
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39
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Zhu J, Chu P, Fu X. Unbalanced response to growth variations reshapes the cell fate decision landscape. Nat Chem Biol 2023; 19:1097-1104. [PMID: 36959461 DOI: 10.1038/s41589-023-01302-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 02/27/2023] [Indexed: 03/25/2023]
Abstract
The global regulation of cell growth rate on gene expression perturbs the performance of gene networks, which would impose complex variations on the cell-fate decision landscape. Here we use a simple synthetic circuit of mutual repression that allows a bistable landscape to examine how such global regulation would affect the stability of phenotypic landscape and the accompanying dynamics of cell-fate determination. We show that the landscape experiences a growth-rate-induced bifurcation between monostability and bistability. Theoretical and experimental analyses reveal that this bifurcating deformation of landscape arises from the unbalanced response of gene expression to growth variations. The path of growth transition across the bifurcation would reshape cell-fate decisions. These results demonstrate the importance of growth regulation on cell-fate determination processes, regardless of specific molecular signaling or regulation.
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Affiliation(s)
- Jingwen Zhu
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Pan Chu
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiongfei Fu
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- University of Chinese Academy of Sciences, Beijing, China.
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40
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Liu A, Mair A, Matos JL, Vollbrecht M, Xu SL, Bergmann DC. Cell Fate Programming by Transcription Factors and Epigenetic Machinery in Stomatal Development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.23.554515. [PMID: 37662219 PMCID: PMC10473704 DOI: 10.1101/2023.08.23.554515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
The development of multi-cellular organisms requires coordinated changes in gene expression that are often mediated by the interaction between transcription factors (TFs) and their corresponding cis-regulatory elements (CREs). During development and differentiation, the accessibility of CREs is dynamically modulated by the epigenome. How the epigenome, CREs and TFs together exert control over cell fate commitment remains to be fully understood. In the Arabidopsis leaf epidermis, meristemoids undergo a series of stereotyped cell divisions, then switch fate to commit to stomatal differentiation. Newly created or reanalyzed scRNA-seq and ChIP-seq data confirm that stomatal development involves distinctive phases of transcriptional regulation and that differentially regulated genes are bound by the stomatal basic-helix-loop-helix (bHLH) TFs. Targets of the bHLHs often reside in repressive chromatin before activation. MNase-seq evidence further suggests that the repressive state can be overcome and remodeled upon activation by specific stomatal bHLHs. We propose that chromatin remodeling is mediated through the recruitment of a set of physical interactors that we identified through proximity labeling - the ATPase-dependent chromatin remodeling SWI/SNF complex and the histone acetyltransferase HAC1. The bHLHs and chromatin remodelers localize to overlapping genomic regions in a hierarchical order. Furthermore, plants with stage-specific knock-down of the SWI/SNF components or HAC1 fail to activate specific bHLH targets and display stomatal development defects. Together these data converge on a model for how stomatal TFs and epigenetic machinery cooperatively regulate transcription and chromatin remodeling during progressive fate specification.
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Affiliation(s)
- Ao Liu
- Howard Hughes Medical Institute, Stanford, CA, USA 94305
| | - Andrea Mair
- Howard Hughes Medical Institute, Stanford, CA, USA 94305
| | - Juliana L Matos
- Department of Biology, Stanford University, Stanford, CA, USA 94305
- Current address: Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, USA 94720
| | - Macy Vollbrecht
- Department of Biology, Stanford University, Stanford, CA, USA 94305
| | - Shou-Ling Xu
- Carnegie Institution for Science, Stanford, CA, USA 94305
- Carnegie Mass Spectrometry Facility, Carnegie Institution for Science, Stanford, CA, USA 94305
| | - Dominique C Bergmann
- Howard Hughes Medical Institute, Stanford, CA, USA 94305
- Department of Biology, Stanford University, Stanford, CA, USA 94305
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41
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Miotto M, Rosito M, Paoluzzi M, de Turris V, Folli V, Leonetti M, Ruocco G, Rosa A, Gosti G. Collective behavior and self-organization in neural rosette morphogenesis. Front Cell Dev Biol 2023; 11:1134091. [PMID: 37635866 PMCID: PMC10448396 DOI: 10.3389/fcell.2023.1134091] [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: 12/29/2022] [Accepted: 07/26/2023] [Indexed: 08/29/2023] Open
Abstract
Neural rosettes develop from the self-organization of differentiating human pluripotent stem cells. This process mimics the emergence of the embryonic central nervous system primordium, i.e., the neural tube, whose formation is under close investigation as errors during such process result in severe diseases like spina bifida and anencephaly. While neural tube formation is recognized as an example of self-organization, we still do not understand the fundamental mechanisms guiding the process. Here, we discuss the different theoretical frameworks that have been proposed to explain self-organization in morphogenesis. We show that an explanation based exclusively on stem cell differentiation cannot describe the emergence of spatial organization, and an explanation based on patterning models cannot explain how different groups of cells can collectively migrate and produce the mechanical transformations required to generate the neural tube. We conclude that neural rosette development is a relevant experimental 2D in-vitro model of morphogenesis because it is a multi-scale self-organization process that involves both cell differentiation and tissue development. Ultimately, to understand rosette formation, we first need to fully understand the complex interplay between growth, migration, cytoarchitecture organization, and cell type evolution.
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Affiliation(s)
- Mattia Miotto
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia, Rome, Italy
- Department of Physics, Sapienza University of Rome, Rome, Italy
| | - Maria Rosito
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia, Rome, Italy
- Department of Physiology and Pharmacology V. Erspamer, Sapienza University of Rome, Rome, Italy
| | - Matteo Paoluzzi
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, Spain
| | - Valeria de Turris
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia, Rome, Italy
| | - Viola Folli
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia, Rome, Italy
- D-TAILS srl, Rome, Italy
| | - Marco Leonetti
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia, Rome, Italy
- D-TAILS srl, Rome, Italy
- Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Giancarlo Ruocco
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia, Rome, Italy
- Department of Physics, Sapienza University of Rome, Rome, Italy
| | - Alessandro Rosa
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia, Rome, Italy
- Department of Biology and Biotechnologies Charles Darwin, Sapienza University of Rome, Rome, Italy
| | - Giorgio Gosti
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia, Rome, Italy
- Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Rome, Italy
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42
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Aslan Kamil M, Fourneaux C, Yilmaz A, Stavros S, Parmentier R, Paldi A, Gonin-Giraud S, deMello AJ, Gandrillon O. An image-guided microfluidic system for single-cell lineage tracking. PLoS One 2023; 18:e0288655. [PMID: 37527253 PMCID: PMC10393162 DOI: 10.1371/journal.pone.0288655] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/30/2023] [Indexed: 08/03/2023] Open
Abstract
Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which was demonstrated to be compatible with downstream single-cell RNA sequencing analysis.
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Affiliation(s)
- Mahmut Aslan Kamil
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland
| | - Camille Fourneaux
- Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard, Lyon, France
| | | | - Stavrakis Stavros
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland
| | - Romuald Parmentier
- Ecole Pratique des Hautes Etudes, St-Antoine Research Center, Inserm U938, PSL Research University, Paris, France
| | - Andras Paldi
- Ecole Pratique des Hautes Etudes, St-Antoine Research Center, Inserm U938, PSL Research University, Paris, France
| | - Sandrine Gonin-Giraud
- Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard, Lyon, France
| | - Andrew J deMello
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland
| | - Olivier Gandrillon
- Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard, Lyon, France
- Inria, France
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43
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Luo R, Yan J, Oh JW, Xi W, Shigaki D, Wong W, Cho HS, Murphy D, Cutler R, Rosen BP, Pulecio J, Yang D, Glenn RA, Chen T, Li QV, Vierbuchen T, Sidoli S, Apostolou E, Huangfu D, Beer MA. Dynamic network-guided CRISPRi screen identifies CTCF-loop-constrained nonlinear enhancer gene regulatory activity during cell state transitions. Nat Genet 2023; 55:1336-1346. [PMID: 37488417 PMCID: PMC11012226 DOI: 10.1038/s41588-023-01450-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 06/20/2023] [Indexed: 07/26/2023]
Abstract
Comprehensive enhancer discovery is challenging because most enhancers, especially those contributing to complex diseases, have weak effects on gene expression. Our gene regulatory network modeling identified that nonlinear enhancer gene regulation during cell state transitions can be leveraged to improve the sensitivity of enhancer discovery. Using human embryonic stem cell definitive endoderm differentiation as a dynamic transition system, we conducted a mid-transition CRISPRi-based enhancer screen. We discovered a comprehensive set of enhancers for each of the core endoderm-specifying transcription factors. Many enhancers had strong effects mid-transition but weak effects post-transition, consistent with the nonlinear temporal responses to enhancer perturbation predicted by the modeling. Integrating three-dimensional genomic information, we were able to develop a CTCF-loop-constrained Interaction Activity model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Our study provides generalizable strategies for sensitive and systematic enhancer discovery in both normal and pathological cell state transitions.
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Affiliation(s)
- Renhe Luo
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Jielin Yan
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Jin Woo Oh
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Wang Xi
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Dustin Shigaki
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Wilfred Wong
- Computational & Systems Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
| | - Hyein S Cho
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Dylan Murphy
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York City, NY, USA
| | - Ronald Cutler
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Bess P Rosen
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
| | - Julian Pulecio
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Dapeng Yang
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Rachel A Glenn
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
| | - Tingxu Chen
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Qing V Li
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Thomas Vierbuchen
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Simone Sidoli
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Effie Apostolou
- Department of Medicine, Weill Cornell Medicine, New York City, NY, USA
| | - Danwei Huangfu
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA.
| | - Michael A Beer
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
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44
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Proverbio D, Skupin A, Gonçalves J. Systematic analysis and optimization of early warning signals for critical transitions using distribution data. iScience 2023; 26:107156. [PMID: 37456849 PMCID: PMC10338236 DOI: 10.1016/j.isci.2023.107156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 04/21/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Abrupt shifts between alternative regimes occur in complex systems, from cell regulation to brain functions to ecosystems. Several model-free early warning signals (EWS) have been proposed to detect impending transitions, but failure or poor performance in some systems have called for better investigation of their generic applicability. Notably, there are still ongoing debates whether such signals can be successfully extracted from data in particular from biological experiments. In this work, we systematically investigate properties and performance of dynamical EWS in different deteriorating conditions, and we propose an optimized combination to trigger warnings as early as possible, eventually verified on experimental data from microbiological populations. Our results explain discrepancies observed in the literature between warning signs extracted from simulated models and from real data, provide guidance for EWS selection based on desired systems and suggest an optimized composite indicator to alert for impending critical transitions using distribution data.
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Affiliation(s)
- Daniele Proverbio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QL, UK
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- National Center for Microscopy and Imaging Research, University of California San Diego, Gilman Drive, La Jolla, CA 9500, USA
- Department of Physics and Material Science, University of Luxembourg, 162a Avenue de La Faiencerie, 1511 Luxembourg, Luxembourg
| | - Jorge Gonçalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
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45
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Gao NP, Gandrillon O, Páldi A, Herbach U, Gunawan R. Single-cell transcriptional uncertainty landscape of cell differentiation. F1000Res 2023; 12:426. [PMID: 37545651 PMCID: PMC10400935 DOI: 10.12688/f1000research.131861.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/18/2023] [Indexed: 08/08/2023] Open
Abstract
Background: Single-cell studies have demonstrated the presence of significant cell-to-cell heterogeneity in gene expression. Whether such heterogeneity is only a bystander or has a functional role in the cell differentiation process is still hotly debated. Methods: In this study, we quantified and followed single-cell transcriptional uncertainty - a measure of gene transcriptional stochasticity in single cells - in 10 cell differentiation systems of varying cell lineage progressions, from single to multi-branching trajectories, using the stochastic two-state gene transcription model. Results: By visualizing the transcriptional uncertainty as a landscape over a two-dimensional representation of the single-cell gene expression data, we observed universal features in the cell differentiation trajectories that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceding the increase in the cell transcriptional uncertainty. Conclusions: Our findings suggest a possible universal mechanism during the cell differentiation process, in which stem cells engage stochastic exploratory dynamics of gene expression at the start of the cell differentiation by increasing gene transcriptional bursts, and disengage such dynamics once cells have decided on a particular terminal cell identity. Notably, the peak of single-cell transcriptional uncertainty signifies the decision-making point in the cell differentiation process.
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Affiliation(s)
- Nan Papili Gao
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Zurich, 8093, Switzerland
| | - Olivier Gandrillon
- Laboratoire de Biologie et Modélisation de la Cellule, École Normale Supérieure de Lyon, CNRS, Université Claude Bernard Lyon 1, F69364, France
- Équipe Dracula, Inria Center Lyon, Villeurbanne, F69100, France
| | - András Páldi
- St-Antoine Research Center, Ecole Pratique des Hautes Etudes PSL, Paris, F-75012, France
| | - Ulysse Herbach
- CNRS, Inria, IECL, Université de Lorraine, Nancy, F-54000, France
| | - Rudiyanto Gunawan
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Zurich, 8093, Switzerland
- Department of Chemical and Biological Engineering, University at Buffalo - SUNY, Buffalo, NY, 14260, USA
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46
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Steimle JD, Kim C, Rowton M, Nadadur RD, Wang Z, Stocker M, Hoffmann AD, Hanson E, Kweon J, Sinha T, Choi K, Black BL, Cunningham JM, Moskowitz IP, Ikegami K. ETV2 primes hematoendothelial gene enhancers prior to hematoendothelial fate commitment. Cell Rep 2023; 42:112665. [PMID: 37330911 PMCID: PMC10592526 DOI: 10.1016/j.celrep.2023.112665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 03/14/2023] [Accepted: 06/02/2023] [Indexed: 06/20/2023] Open
Abstract
Mechanisms underlying distinct specification, commitment, and differentiation phases of cell fate determination remain undefined due to difficulties capturing these processes. Here, we interrogate the activity of ETV2, a transcription factor necessary and sufficient for hematoendothelial differentiation, within isolated fate intermediates. We observe transcriptional upregulation of Etv2 and opening of ETV2-binding sites, indicating new ETV2 binding, in a common cardiac-hematoendothelial progenitor population. Accessible ETV2-binding sites are active at the Etv2 locus but not at other hematoendothelial regulator genes. Hematoendothelial commitment coincides with the activation of a small repertoire of previously accessible ETV2-binding sites at hematoendothelial regulators. Hematoendothelial differentiation accompanies activation of a large repertoire of new ETV2-binding sites and upregulation of hematopoietic and endothelial gene regulatory networks. This work distinguishes specification, commitment, and sublineage differentiation phases of ETV2-dependent transcription and suggests that the shift from ETV2 binding to ETV2-bound enhancer activation, not ETV2 binding to target enhancers, drives hematoendothelial fate commitment.
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Affiliation(s)
- Jeffrey D Steimle
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Chul Kim
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, IL 60637, USA; Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL 60637, USA
| | - Megan Rowton
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Rangarajan D Nadadur
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Zhezhen Wang
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Matthew Stocker
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Andrew D Hoffmann
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Erika Hanson
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Junghun Kweon
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Tanvi Sinha
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Kyunghee Choi
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Brian L Black
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - John M Cunningham
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL 60637, USA
| | - Ivan P Moskowitz
- Departments of Pediatrics, Pathology, and Human Genetics, University of Chicago, Chicago, IL 60637, USA.
| | - Kohta Ikegami
- Division of Molecular and Cardiovascular Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati, Cincinnati, OH 45229, USA.
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47
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Mayshar Y, Raz O, Cheng S, Ben-Yair R, Hadas R, Reines N, Mittnenzweig M, Ben-Kiki O, Lifshitz A, Tanay A, Stelzer Y. Time-aligned hourglass gastrulation models in rabbit and mouse. Cell 2023; 186:2610-2627.e18. [PMID: 37209682 DOI: 10.1016/j.cell.2023.04.037] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/07/2023] [Accepted: 04/26/2023] [Indexed: 05/22/2023]
Abstract
The hourglass model describes the convergence of species within the same phylum to a similar body plan during development; however, the molecular mechanisms underlying this phenomenon in mammals remain poorly described. Here, we compare rabbit and mouse time-resolved differentiation trajectories to revisit this model at single-cell resolution. We modeled gastrulation dynamics using hundreds of embryos sampled between gestation days 6.0 and 8.5 and compared the species using a framework for time-resolved single-cell differentiation-flows analysis. We find convergence toward similar cell-state compositions at E7.5, supported by the quantitatively conserved expression of 76 transcription factors, despite divergence in surrounding trophoblast and hypoblast signaling. However, we observed noticeable changes in specification timing of some lineages and divergence of primordial germ cell programs, which in the rabbit do not activate mesoderm genes. Comparative analysis of temporal differentiation models provides a basis for studying the evolution of gastrulation dynamics across mammals.
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Affiliation(s)
- Yoav Mayshar
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ofir Raz
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Saifeng Cheng
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Raz Ben-Yair
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ron Hadas
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Netta Reines
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Markus Mittnenzweig
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Oren Ben-Kiki
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Aviezer Lifshitz
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Amos Tanay
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
| | - Yonatan Stelzer
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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48
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Gomez-Salinero JM, Rafii S. A Notch between vascular morphogenesis and transcriptional identity. NATURE CARDIOVASCULAR RESEARCH 2023; 2:492-493. [PMID: 39195888 DOI: 10.1038/s44161-023-00282-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Affiliation(s)
- Jesus M Gomez-Salinero
- Hartman Institute for Therapeutic Regenerative Medicine, Division of Regenerative Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
- The Ansary Stem Cell Institute, Division of Regenerative Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
| | - Shahin Rafii
- Hartman Institute for Therapeutic Regenerative Medicine, Division of Regenerative Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
- The Ansary Stem Cell Institute, Division of Regenerative Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
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49
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Jia X, Lin W, Wang W. Regulation of chromatin organization during animal regeneration. CELL REGENERATION (LONDON, ENGLAND) 2023; 12:19. [PMID: 37259007 DOI: 10.1186/s13619-023-00162-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/21/2023] [Indexed: 06/02/2023]
Abstract
Activation of regeneration upon tissue damages requires the activation of many developmental genes responsible for cell proliferation, migration, differentiation, and tissue patterning. Ample evidence revealed that the regulation of chromatin organization functions as a crucial mechanism for establishing and maintaining cellular identity through precise control of gene transcription. The alteration of chromatin organization can lead to changes in chromatin accessibility and/or enhancer-promoter interactions. Like embryogenesis, each stage of tissue regeneration is accompanied by dynamic changes of chromatin organization in regeneration-responsive cells. In the past decade, many studies have been conducted to investigate the contribution of chromatin organization during regeneration in various tissues, organs, and organisms. A collection of chromatin regulators were demonstrated to play critical roles in regeneration. In this review, we will summarize the progress in the understanding of chromatin organization during regeneration in different research organisms and discuss potential common mechanisms responsible for the activation of regeneration response program.
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Affiliation(s)
- Xiaohui Jia
- National Institute of Biological Sciences, Beijing, 102206, China
- China Agricultural University, Beijing, 100083, China
| | - Weifeng Lin
- National Institute of Biological Sciences, Beijing, 102206, China
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing, 100084, China
| | - Wei Wang
- National Institute of Biological Sciences, Beijing, 102206, China.
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing, 100084, China.
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50
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McElroy M, Green K, Voulgarakis NK. Self-Regulated Symmetry Breaking Model for Stem Cell Differentiation. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25050815. [PMID: 37238570 DOI: 10.3390/e25050815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/03/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023]
Abstract
In conventional disorder-order phase transitions, a system shifts from a highly symmetric state, where all states are equally accessible (disorder) to a less symmetric state with a limited number of available states (order). This transition may occur by varying a control parameter that represents the intrinsic noise of the system. It has been suggested that stem cell differentiation can be considered as a sequence of such symmetry-breaking events. Pluripotent stem cells, with their capacity to develop into any specialized cell type, are considered highly symmetric systems. In contrast, differentiated cells have lower symmetry, as they can only carry out a limited number of functions. For this hypothesis to be valid, differentiation should emerge collectively in stem cell populations. Additionally, such populations must have the ability to self-regulate intrinsic noise and navigate through a critical point where spontaneous symmetry breaking (differentiation) occurs. This study presents a mean-field model for stem cell populations that considers the interplay of cell-cell cooperativity, cell-to-cell variability, and finite-size effects. By introducing a feedback mechanism to control intrinsic noise, the model can self-tune through different bifurcation points, facilitating spontaneous symmetry breaking. Standard stability analysis showed that the system can potentially differentiate into several cell types mathematically expressed as stable nodes and limit cycles. The existence of a Hopf bifurcation in our model is discussed in light of stem cell differentiation.
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Affiliation(s)
- Madelynn McElroy
- Department of Mathematics and Statistics, Washington State University, Pullman, WA 99164, USA
- Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA 99164, USA
| | - Kaylie Green
- Department of Mathematics and Statistics, Washington State University, Pullman, WA 99164, USA
- Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA 99164, USA
| | - Nikolaos K Voulgarakis
- Department of Mathematics and Statistics, Washington State University, Pullman, WA 99164, USA
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