1
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Skinner DJ, Lemaire P, Mani M. Physical modeling of embryonic transcriptomes identifies collective modes of gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.26.605398. [PMID: 39131269 PMCID: PMC11312445 DOI: 10.1101/2024.07.26.605398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Starting from one totipotent cell, complex multicellular organisms form through a series of differentiation and morphogenetic events, culminating in a multitude of cell types arranged in a functional and intricate spatial pattern. To do so, cells coordinate with each other, resulting in dynamics which follow a precise developmental trajectory, constraining the space of possible embryo-to-embryo variation. Using recent single-cell sequencing data of early ascidian embryos, we leverage natural variation together with modeling and inference techniques from statistical physics to investigate development at the level of a complete interconnected embryo - an embryonic transcriptome. After developing a robust and biophysically motivated approach to identifying distinct transcriptomic states or cell types, a statistical analysis reveals correlations within embryos and across cell types demonstrating the presence of collective variation. From these intra-embryo correlations, we infer minimal networks of cell-cell interactions, which reveal the collective modes of gene expression. Our work demonstrates how the existence and nature of spatial interactions along with the collective modes of expression that they give rise to can be inferred from single-cell gene expression measurements, opening up a wider range of biological questions that can be addressed using sequencing-based modalities.
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Affiliation(s)
- Dominic J Skinner
- Center for Computational Biology, Flatiron Institute, 162 5th Ave, New York, NY 10010, USA
- NSF-Simons Center for Quantitative Biology, Northwestern University, 2205 Tech Drive, Evanston, IL 60208, USA
| | - Patrick Lemaire
- CRBM, Université de Montpellier, CNRS, 34293 Montpellier, France
| | - Madhav Mani
- NSF-Simons Center for Quantitative Biology, Northwestern University, 2205 Tech Drive, Evanston, IL 60208, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA
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2
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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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3
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Hazra R, Utama R, Naik P, Dobin A, Spector DL. Identification of glioblastoma stem cell-associated lncRNAs using single-cell RNA sequencing datasets. Stem Cell Reports 2023; 18:2056-2070. [PMID: 37922916 PMCID: PMC10679778 DOI: 10.1016/j.stemcr.2023.10.004] [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: 01/02/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Glioblastoma multiforme (GBM) is an aggressive, heterogeneous brain tumor in which glioblastoma stem cells (GSCs) are known culprits of therapy resistance. Long non-coding RNAs (lncRNAs) have been shown to play a critical role in both cancer and normal biology. A few studies have suggested that aberrant expression of lncRNAs is associated with GSCs. However, a comprehensive single-cell analysis of the GSC-associated lncRNA transcriptome has not been carried out. Here, we analyzed recently published single-cell RNA sequencing datasets of adult GBM tumors, GBM organoids, GSC-enriched GBM tumors, and developing human brain samples to identify lncRNAs highly expressed in GSCs. We further revealed that the GSC-specific lncRNAs GIHCG and LINC01563 promote proliferation, migration, and stemness in the GSC population. Together, this study identified a panel of uncharacterized GSC-enriched lncRNAs and set the stage for future in-depth studies to examine their role in GBM pathology and their potential as biomarkers and/or therapeutic targets in GBM.
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Affiliation(s)
- Rasmani Hazra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
| | - Raditya Utama
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Payal Naik
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Alexander Dobin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - David L Spector
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
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4
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Yang Y, Lin YT, Li G, Zhong Y, Xu Q, Cai JJ. Interpretable modeling of time-resolved single-cell gene-protein expression with CrossmodalNet. Brief Bioinform 2023; 24:bbad342. [PMID: 37798250 DOI: 10.1093/bib/bbad342] [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: 05/23/2023] [Revised: 08/15/2023] [Accepted: 09/07/2023] [Indexed: 10/07/2023] Open
Abstract
Cell-surface proteins play a critical role in cell function and are primary targets for therapeutics. CITE-seq is a single-cell technique that enables simultaneous measurement of gene and surface protein expression. It is powerful but costly and technically challenging. Computational methods have been developed to predict surface protein expression using gene expression information such as from single-cell RNA sequencing (scRNA-seq) data. Existing methods however are computationally demanding and lack the interpretability to reveal underlying biological processes. We propose CrossmodalNet, an interpretable machine learning model, to predict surface protein expression from scRNA-seq data. Our model with a customized adaptive loss accurately predicts surface protein abundances. When samples from multiple time points are given, our model encodes temporal information into an easy-to-interpret time embedding to make prediction in a time-point-specific manner, and is able to uncover noise-free causal gene-protein relationships. Using three publicly available time-resolved CITE-seq data sets, we validate the performance of our model by comparing it with benchmarking methods and evaluate its interpretability. Together, we show that our method accurately and interpretably profiles surface protein expression using scRNA-seq data, thereby expanding the capacity of CITE-seq experiments for investigating molecular mechanisms involving surface proteins.
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Affiliation(s)
- Yongjian Yang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Yu-Te Lin
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Guanxun Li
- Department of Statistics, Texas A&M University, College Station, TX, USA
| | - Yan Zhong
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, China
| | - Qian Xu
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - James J Cai
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
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5
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Yang Y, Li G, Zhong Y, Xu Q, Chen BJ, Lin YT, Chapkin R, Cai JJ. Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks. Nucleic Acids Res 2023; 51:6578-6592. [PMID: 37246643 PMCID: PMC10359630 DOI: 10.1093/nar/gkad450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 05/02/2023] [Accepted: 05/11/2023] [Indexed: 05/30/2023] Open
Abstract
In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.
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Affiliation(s)
- Yongjian Yang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Guanxun Li
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - Yan Zhong
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062, China
| | - Qian Xu
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA
| | - Bo-Jia Chen
- Graduate Institute of Microbiology and Public Health, College of Veterinary Medicine, National Chung Hsing University, Taichung 402, Taiwan
| | - Yu-Te Lin
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Robert S Chapkin
- Program in Integrative & Complex Diseases, Department of Nutrition, Texas A&M University, College Station, TX 77843, USA
| | - James J Cai
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA
- Interdisciplinary Program of Genetics, Texas A&M University, College Station, TX 77843, USA
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6
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Komatsu J, Cico A, Poncin R, Le Bohec M, Morf J, Lipin S, Graindorge A, Eckert H, Saffarian A, Cathaly L, Guérin F, Majello S, Ulveling D, Vayaboury A, Fernandez N, Dimitrova D, Bussell X, Fourne Y, Chaumat P, André B, Baldivia E, Godet U, Guinin M, Moretto V, Ismail J, Caille O, Roblot N, Beaupère C, Liboz A, Guillemain G, Blondeau B, Walrafen P, Edelstein S. RevGel-seq: instrument-free single-cell RNA sequencing using a reversible hydrogel for cell-specific barcoding. Sci Rep 2023; 13:4866. [PMID: 36964177 PMCID: PMC10039079 DOI: 10.1038/s41598-023-31915-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 03/20/2023] [Indexed: 03/26/2023] Open
Abstract
Progress in sample preparation for scRNA-seq is reported based on RevGel-seq, a reversible-hydrogel technology optimized for samples of fresh cells. Complexes of one cell paired with one barcoded bead are stabilized by a chemical linker and dispersed in a hydrogel in the liquid state. Upon gelation on ice the complexes are immobilized and physically separated without requiring nanowells or droplets. Cell lysis is triggered by detergent diffusion, and RNA molecules are captured on the adjacent barcoded beads for further processing with reverse transcription and preparation for cDNA sequencing. As a proof of concept, analysis of PBMC using RevGel-seq achieves results similar to microfluidic-based technologies when using the same original sample and the same data analysis software. In addition, a clinically relevant application of RevGel-seq is presented for pancreatic islet cells. Furthermore, characterizations carried out on cardiomyocytes demonstrate that the hydrogel technology readily accommodates very large cells. Standard analyses are in the 10,000-input cell range with the current gelation device, in order to satisfy common requirements for single-cell research. A convenient stopping point after two hours has been established by freezing at the cell lysis step, with full preservation of gene expression profiles. Overall, our results show that RevGel-seq represents an accessible and efficient instrument-free alternative, enabling flexibility in terms of experimental design and timing of sample processing, while providing broad coverage of cell types.
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Affiliation(s)
| | | | | | | | - Jörg Morf
- Scipio Bioscience, Paris, France
- Skyhawk Therapeutics, Basel, Switzerland
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Natacha Roblot
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, 75012, Paris, France
| | - Carine Beaupère
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, 75012, Paris, France
| | - Alexandrine Liboz
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, 75012, Paris, France
| | - Ghislaine Guillemain
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, 75012, Paris, France
| | - Bertrand Blondeau
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, 75012, Paris, France
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7
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Mohammadi-Shemirani P, Sood T, Paré G. From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators. Curr Atheroscler Rep 2023; 25:55-65. [PMID: 36595202 PMCID: PMC9807989 DOI: 10.1007/s11883-022-01078-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/31/2022] [Indexed: 01/04/2023]
Abstract
PURPOSE OF REVIEW 'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed. RECENT FINDINGS Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.
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Affiliation(s)
- Pedrum Mohammadi-Shemirani
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
| | - Tushar Sood
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON Canada
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8
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Chou EL, Chaffin M, Simonson B, Pirruccello JP, Akkad AD, Nekoui M, Cardenas CLL, Bedi KC, Nash C, Juric D, Stone JR, Isselbacher EM, Margulies KB, Klattenhoff C, Ellinor PT, Lindsay ME. Aortic Cellular Diversity and Quantitative Genome-Wide Association Study Trait Prioritization Through Single-Nuclear RNA Sequencing of the Aneurysmal Human Aorta. Arterioscler Thromb Vasc Biol 2022; 42:1355-1374. [PMID: 36172868 PMCID: PMC9613617 DOI: 10.1161/atvbaha.122.317953] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 09/16/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Mural cells in ascending aortic aneurysms undergo phenotypic changes that promote extracellular matrix destruction and structural weakening. To explore this biology, we analyzed the transcriptional features of thoracic aortic tissue. METHODS Single-nuclear RNA sequencing was performed on 13 samples from human donors, 6 with thoracic aortic aneurysm, and 7 without aneurysm. Individual transcriptomes were then clustered based on transcriptional profiles. Clusters were used for between-disease differential gene expression analyses, subcluster analysis, and analyzed for intersection with genetic aortic trait data. RESULTS We sequenced 71 689 nuclei from human thoracic aortas and identified 14 clusters, aligning with 11 cell types, predominantly vascular smooth muscle cells (VSMCs) consistent with aortic histology. With unbiased methodology, we found 7 vascular smooth muscle cell and 6 fibroblast subclusters. Differentially expressed genes analysis revealed a vascular smooth muscle cell group accounting for the majority of differential gene expression. Fibroblast populations in aneurysm exhibit distinct behavior with almost complete disappearance of quiescent fibroblasts. Differentially expressed genes were used to prioritize genes at aortic diameter and distensibility genome-wide association study loci highlighting the genes JUN, LTBP4 (latent transforming growth factor beta-binding protein 1), and IL34 (interleukin 34) in fibroblasts, ENTPD1, PDLIM5 (PDZ and LIM domain 5), ACTN4 (alpha-actinin-4), and GLRX in vascular smooth muscle cells, as well as LRP1 in macrophage populations. CONCLUSIONS Using nuclear RNA sequencing, we describe the cellular diversity of healthy and aneurysmal human ascending aorta. Sporadic aortic aneurysm is characterized by differential gene expression within known cellular classes rather than by the appearance of novel cellular forms. Single-nuclear RNA sequencing of aortic tissue can be used to prioritize genes at aortic trait loci.
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Affiliation(s)
- Elizabeth L. Chou
- Division of Vascular and Endovascular Surgery,
Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
| | - Mark Chaffin
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Precision Cardiology Laboratory, The Broad Institute,
Cambridge, MA, USA 02142
| | - Bridget Simonson
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Precision Cardiology Laboratory, The Broad Institute,
Cambridge, MA, USA 02142
| | - James P. Pirruccello
- Cardiology Division, Massachusetts General Hospital,
Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Precision Cardiology Laboratory, The Broad Institute,
Cambridge, MA, USA 02142
- Demoulas Center for Cardiac Arrhythmias, Massachusetts
General Hospital, Boston, Massachusetts, USA
| | - Amer-Denis Akkad
- Precision Cardiology Laboratory, Bayer US LLC, Cambridge,
MA, USA 02142
| | - Mahan Nekoui
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts
General Hospital, Boston, Massachusetts, USA
| | - Christian Lacks Lino Cardenas
- Cardiology Division, Massachusetts General Hospital,
Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
| | - Kenneth C. Bedi
- Perelman School of Medicine, University of Pennsylvania,
Philadelphia, PA, USA 19104
| | - Craig Nash
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Precision Cardiology Laboratory, The Broad Institute,
Cambridge, MA, USA 02142
| | - Dejan Juric
- Cancer Center, Massachusetts General Hospital, Boston,
Massachusetts, USA
| | - James R. Stone
- Department of Pathology, Massachusetts General
Hospital, Boston, Massachusetts, USA
| | - Eric M. Isselbacher
- Cardiology Division, Massachusetts General Hospital,
Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
- Thoracic Aortic Center, Massachusetts General Hospital,
Boston, Massachusetts, USA
| | - Kenneth B. Margulies
- Perelman School of Medicine, University of Pennsylvania,
Philadelphia, PA, USA 19104
| | - Carla Klattenhoff
- Precision Cardiology Laboratory, Bayer US LLC, Cambridge,
MA, USA 02142
| | - Patrick T. Ellinor
- Cardiology Division, Massachusetts General Hospital,
Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Precision Cardiology Laboratory, The Broad Institute,
Cambridge, MA, USA 02142
- Demoulas Center for Cardiac Arrhythmias, Massachusetts
General Hospital, Boston, Massachusetts, USA
| | - Mark E. Lindsay
- Cardiology Division, Massachusetts General Hospital,
Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Thoracic Aortic Center, Massachusetts General Hospital,
Boston, Massachusetts, USA
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9
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Sozen B, Conkar D, Veenvliet JV. Carnegie in 4D? Stem-cell-based models of human embryo development. Semin Cell Dev Biol 2022; 131:44-57. [PMID: 35701286 DOI: 10.1016/j.semcdb.2022.05.023] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 12/14/2022]
Abstract
How cells build embryos is still a major mystery. Many unresolved questions require the study of the processes that pattern and shape the embryo in live specimens, in toto, across spatial and temporal scales. In mammalian embryogenesis, this remains a major challenge as the embryo develops in utero, precluding easy accessibility. For human embryos, technical, ethical and legal limitations further hamper the in-depth investigation of embryogenesis, especially beyond gastrulation stages. This has resulted in an over-reliance on model organisms, particularly mice, to understand mammalian development. However, recent efforts show critical differences between rodent and primate embryos, including timing, architecture and transcriptional regulation. Thus, a human-centric understanding of embryogenesis is much needed. To empower this, novel in vitro approaches, which coax human pluripotent stem cells to form embryonic organoids that model embryo development, are pivotal. Here, we summarize these emergent technologies that recapitulate aspects of human development "in a dish". We show how these technologies can provide insights into the molecular, cellular and morphogenetic processes that fuel the formation of a fully formed fetus, and discuss the potential of these platforms to revolutionize our understanding of human development in health and disease. Despite their clear promise, we caution against over-interpreting the extent to which these in vitro platforms model the natural embryo. In particular, we discuss how fate, form and function - a tightly coupled trinity in vivo, can be disconnected in vitro. Finally, we propose how careful benchmarking of existing models, in combination with rational protocol design based on an increased understanding of in vivo developmental dynamics and insights from mouse in vitro models of embryo development, will help guide the establishment of better models of human embryo development.
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Affiliation(s)
- Berna Sozen
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT, 06510, USA; Yale Stem Cell Center, Yale University, New Haven, CT 06520, USA.
| | - Deniz Conkar
- Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstrasse 108, 01307 Dresden, Germany
| | - Jesse V Veenvliet
- Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstrasse 108, 01307 Dresden, Germany; Cluster of Excellence Physics of Life, Technische Universität Dresden, 01307 Dresden, Germany.
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10
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Doyle JJ. Cell types as species: Exploring a metaphor. FRONTIERS IN PLANT SCIENCE 2022; 13:868565. [PMID: 36072310 PMCID: PMC9444152 DOI: 10.3389/fpls.2022.868565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/29/2022] [Indexed: 06/05/2023]
Abstract
The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.
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11
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Reprogramming neurons for regeneration: The fountain of youth. Prog Neurobiol 2022; 214:102284. [PMID: 35533809 DOI: 10.1016/j.pneurobio.2022.102284] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/03/2022] [Accepted: 05/02/2022] [Indexed: 01/22/2023]
Abstract
Neurons in the central nervous system (CNS) are terminally differentiated cells that gradually lose their ability to support regeneration during maturation due to changes in transcriptomic and chromatin landscape. Similar transcriptomic changes also occur during development when stem cells differentiate into different types of somatic cells. Importantly, differentiated cells can be reprogrammed back to induced pluripotent stems cells (iPSCs) via global epigenetic remodeling by combined overexpression of pluripotent reprogramming factors, including Oct4, Sox2, Klf4, c-Myc, Nanog, and/or Lin28. Moreover, recent findings showed that many proneural transcription factors were able to convert non-neural somatic cells into neurons bypassing the pluripotent stage via direct reprogramming. Interestingly, many of these factors have recently been identified as key regulators of CNS neural regeneration. Recent studies indicated that these factors could rejuvenate mature CNS neurons back to a younger state through cellular state reprogramming, thus favoring regeneration. Here we will review some recent findings regarding the roles of genetic cellular state reprogramming in regulation of neural regeneration and explore the potential underlying molecular mechanisms. Moreover, by using newly emerging techniques, such as multiomics sequencing with big data analysis and Crispr-based gene editing, we will discuss future research directions focusing on better revealing cellular state reprogramming-induced remodeling of chromatin landscape and potential translational application.
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12
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Viroids and Viroid-like Circular RNAs: Do They Descend from Primordial Replicators? LIFE (BASEL, SWITZERLAND) 2022; 12:life12010103. [PMID: 35054497 PMCID: PMC8781251 DOI: 10.3390/life12010103] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 01/09/2023]
Abstract
Viroids are a unique class of plant pathogens that consist of small circular RNA molecules, between 220 and 450 nucleotides in size. Viroids encode no proteins and are the smallest known infectious agents. Viroids replicate via the rolling circle mechanism, producing multimeric intermediates which are cleaved to unit length either by ribozymes formed from both polarities of the viroid genomic RNA or by coopted host RNAses. Many viroid-like small circular RNAs are satellites of plant RNA viruses. Ribozyviruses, represented by human hepatitis delta virus, are larger viroid-like circular RNAs that additionally encode the viral nucleocapsid protein. It has been proposed that viroids are direct descendants of primordial RNA replicons that were present in the hypothetical RNA world. We argue, however, that much later origin of viroids, possibly, from recently discovered mobile genetic elements known as retrozymes, is a far more parsimonious evolutionary scenario. Nevertheless, viroids and viroid-like circular RNAs are minimal replicators that are likely to be close to the theoretical lower limit of replicator size and arguably comprise the paradigm for replicator emergence. Thus, although viroid-like replicators are unlikely to be direct descendants of primordial RNA replicators, the study of the diversity and evolution of these ultimate genetic parasites can yield insights into the earliest stages of the evolution of life.
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