1
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Foltz JA, Tran J, Wong P, Fan C, Schmidt E, Fisk B, Becker-Hapak M, Russler-Germain DA, Johnson J, Marin ND, Cubitt CC, Pence P, Rueve J, Pureti S, Hwang K, Gao F, Zhou AY, Foster M, Schappe T, Marsala L, Berrien-Elliott MM, Cashen AF, Bednarski JJ, Fertig E, Griffith OL, Griffith M, Wang T, Petti AA, Fehniger TA. Cytokines drive the formation of memory-like NK cell subsets via epigenetic rewiring and transcriptional regulation. Sci Immunol 2024; 9:eadk4893. [PMID: 38941480 DOI: 10.1126/sciimmunol.adk4893] [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: 08/25/2023] [Accepted: 05/31/2024] [Indexed: 06/30/2024]
Abstract
Activation of natural killer (NK) cells with the cytokines interleukin-12 (IL-12), IL-15, and IL-18 induces their differentiation into memory-like (ML) NK cells; however, the underlying epigenetic and transcriptional mechanisms are unclear. By combining ATAC-seq, CITE-seq, and functional analyses, we discovered that IL-12/15/18 activation results in two main human NK fates: reprogramming into enriched memory-like (eML) NK cells or priming into effector conventional NK (effcNK) cells. eML NK cells had distinct transcriptional and epigenetic profiles and enhanced function, whereas effcNK cells resembled cytokine-primed cNK cells. Two transcriptionally discrete subsets of eML NK cells were also identified, eML-1 and eML-2, primarily arising from CD56bright or CD56dim mature NK cell subsets, respectively. Furthermore, these eML subsets were evident weeks after transfer of IL-12/15/18-activated NK cells into patients with cancer. Our findings demonstrate that NK cell activation with IL-12/15/18 results in previously unappreciated diverse cellular fates and identifies new strategies to enhance NK therapies.
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Affiliation(s)
| | - Jennifer Tran
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Pamela Wong
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Changxu Fan
- Washington University School of Medicine, Saint Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Evelyn Schmidt
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Bryan Fisk
- Washington University School of Medicine, Saint Louis, MO, USA
| | | | | | | | - Nancy D Marin
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Celia C Cubitt
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Patrick Pence
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Joseph Rueve
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Sushanth Pureti
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Kimberly Hwang
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Feng Gao
- Washington University School of Medicine, Saint Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Alice Y Zhou
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Mark Foster
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Timothy Schappe
- Washington University School of Medicine, Saint Louis, MO, USA
| | - Lynne Marsala
- Washington University School of Medicine, Saint Louis, MO, USA
| | | | - Amanda F Cashen
- Washington University School of Medicine, Saint Louis, MO, USA
| | | | | | - Obi L Griffith
- Washington University School of Medicine, Saint Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Malachi Griffith
- Washington University School of Medicine, Saint Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Ting Wang
- Washington University School of Medicine, Saint Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Allegra A Petti
- Washington University School of Medicine, Saint Louis, MO, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Todd A Fehniger
- Washington University School of Medicine, Saint Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
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2
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Teschendorff AE. Computational single-cell methods for predicting cancer risk. Biochem Soc Trans 2024; 52:1503-1514. [PMID: 38856037 DOI: 10.1042/bst20231488] [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: 01/29/2024] [Revised: 05/22/2024] [Accepted: 05/24/2024] [Indexed: 06/11/2024]
Abstract
Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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3
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Otto DJ, Jordan C, Dury B, Dien C, Setty M. Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon. Nat Methods 2024:10.1038/s41592-024-02302-w. [PMID: 38890426 DOI: 10.1038/s41592-024-02302-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 05/08/2024] [Indexed: 06/20/2024]
Abstract
Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.
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Affiliation(s)
- Dominik J Otto
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Cailin Jordan
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Brennan Dury
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Christine Dien
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Manu Setty
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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4
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Breeur M, Stepaniants G, Keski-Rahkonen P, Rigollet P, Viallon V. Optimal transport for automatic alignment of untargeted metabolomic data. eLife 2024; 12:RP91597. [PMID: 38896449 PMCID: PMC11186628 DOI: 10.7554/elife.91597] [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] [Indexed: 06/21/2024] Open
Abstract
Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.
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Affiliation(s)
- Marie Breeur
- Nutrition and Metabolism Branch, International Agency for Research on CancerLyonFrance
| | - George Stepaniants
- Massachusetts Institute of Technology, Department of MathematicsBostonUnited States
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on CancerLyonFrance
| | - Philippe Rigollet
- Massachusetts Institute of Technology, Department of MathematicsBostonUnited States
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on CancerLyonFrance
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5
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Weiler P, Lange M, Klein M, Pe'er D, Theis F. CellRank 2: unified fate mapping in multiview single-cell data. Nat Methods 2024:10.1038/s41592-024-02303-9. [PMID: 38871986 DOI: 10.1038/s41592-024-02303-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 05/09/2024] [Indexed: 06/15/2024]
Abstract
Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.
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Affiliation(s)
- Philipp Weiler
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Marius Lange
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Michal Klein
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Machine Learning Research, Apple, Paris, France
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Fabian Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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6
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Lee CY, Clatworthy MR, Withers DR. Decoding changes in tumor-infiltrating leukocytes through dynamic experimental models and single-cell technologies. Immunol Cell Biol 2024. [PMID: 38853634 DOI: 10.1111/imcb.12787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 06/11/2024]
Abstract
The ability to characterize immune cells and explore the molecular interactions that govern their functions has never been greater, fueled in recent years by the revolutionary advance of single-cell analysis platforms. However, precisely how immune cells respond to different stimuli and where differentiation processes and effector functions operate remain incompletely understood. Inferring cellular fate within single-cell transcriptomic analyses is now omnipresent, despite the assumptions typically required in such analyses. Recently developed experimental models support dynamic analyses of the immune response, providing insights into the temporal changes that occur within cells and the tissues in which such transitions occur. Here we will review these approaches and discuss how these can be combined with single-cell technologies to develop a deeper understanding of the immune responses that should support the development of better therapeutic options for patients.
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Affiliation(s)
- Colin Yc Lee
- Cambridge Institute of Therapeutic Immunology and Infection Disease, University of Cambridge, Cambridge, UK
| | - Menna R Clatworthy
- Cambridge Institute of Therapeutic Immunology and Infection Disease, University of Cambridge, Cambridge, UK
| | - David R Withers
- Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
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7
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Huycke TR, Häkkinen TJ, Miyazaki H, Srivastava V, Barruet E, McGinnis CS, Kalantari A, Cornwall-Scoones J, Vaka D, Zhu Q, Jo H, Oria R, Weaver VM, DeGrado WF, Thomson M, Garikipati K, Boffelli D, Klein OD, Gartner ZJ. Patterning and folding of intestinal villi by active mesenchymal dewetting. Cell 2024; 187:3072-3089.e20. [PMID: 38781967 PMCID: PMC11166531 DOI: 10.1016/j.cell.2024.04.039] [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: 06/12/2023] [Revised: 12/30/2023] [Accepted: 04/26/2024] [Indexed: 05/25/2024]
Abstract
Tissue folds are structural motifs critical to organ function. In the intestine, bending of a flat epithelium into a periodic pattern of folds gives rise to villi, finger-like protrusions that enable nutrient absorption. However, the molecular and mechanical processes driving villus morphogenesis remain unclear. Here, we identify an active mechanical mechanism that simultaneously patterns and folds the intestinal epithelium to initiate villus formation. At the cellular level, we find that PDGFRA+ subepithelial mesenchymal cells generate myosin II-dependent forces sufficient to produce patterned curvature in neighboring tissue interfaces. This symmetry-breaking process requires altered cell and extracellular matrix interactions that are enabled by matrix metalloproteinase-mediated tissue fluidization. Computational models, together with in vitro and in vivo experiments, revealed that these cellular features manifest at the tissue level as differences in interfacial tensions that promote mesenchymal aggregation and interface bending through a process analogous to the active dewetting of a thin liquid film.
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Affiliation(s)
- Tyler R Huycke
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Teemu J Häkkinen
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Hikaru Miyazaki
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Vasudha Srivastava
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Emilie Barruet
- Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA
| | - Christopher S McGinnis
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Ali Kalantari
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jake Cornwall-Scoones
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Dedeepya Vaka
- Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA
| | - Qin Zhu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Roger Oria
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA; Comprehensive Cancer Center, Helen Diller Family Cancer Research Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Therapeutic Sciences, Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Valerie M Weaver
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA; Comprehensive Cancer Center, Helen Diller Family Cancer Research Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Therapeutic Sciences, Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - William F DeGrado
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Matt Thomson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Krishna Garikipati
- Departments of Mechanical Engineering, and Mathematics, University of Michigan, Ann Arbor, MI, USA
| | - Dario Boffelli
- Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA
| | - Ophir D Klein
- Program in Craniofacial Biology and Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA; Department of Pediatrics, Cedars-Sinai Guerin Children's, Los Angeles, CA, USA.
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA.
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8
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Haviv D, Kunes RZ, Dougherty T, Burdziak C, Nawy T, Gilbert A, Pe'er D. Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers. ARXIV 2024:arXiv:2404.09411v4. [PMID: 38827453 PMCID: PMC11142316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Optimal transport (OT) and the related Wasserstein metric ( W ) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standard multidimensional scaling (MDS). We present Wasserstein Wormhole, a transformer-based autoencoder that embeds empirical distributions into a latent space wherein Euclidean distances approximate OT distances. Extending MDS theory, we show that our objective function implies a bound on the error incurred when embedding non-Euclidean distances. Empirically, distances between Wormhole embeddings closely match Wasserstein distances, enabling linear time computation of OT distances. Along with an encoder that maps distributions to embeddings, Wasserstein Wormhole includes a decoder that maps embeddings back to distributions, allowing for operations in the embedding space to generalize to OT spaces, such as Wasserstein barycenter estimation and OT interpolation. By lending scalability and interpretability to OT approaches, Wasserstein Wormhole unlocks new avenues for data analysis in the fields of computational geometry and single-cell biology. Software is available at http://wassersteinwormhole.readthedocs.io/en/latest/.
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Affiliation(s)
- Doron Haviv
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine
| | - Russell Zhang Kunes
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center
- Department of Statistics, Columbia University
| | - Thomas Dougherty
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine
| | - Cassandra Burdziak
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center
| | - Tal Nawy
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center
| | - Anna Gilbert
- Department of Mathematics and Statistics, Yale University
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center
- Howard Hughes Medical Institute
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9
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Lee H, Kim SY, Kwon NJ, Jo SJ, Kwon O, Kim JI. Single-Cell and Spatial Transcriptome Analysis of Dermal Fibroblast Development in Perinatal Mouse Skin: Dynamic Lineage Differentiation and Key Driver Genes. J Invest Dermatol 2024; 144:1238-1250.e11. [PMID: 38072389 DOI: 10.1016/j.jid.2023.11.008] [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: 02/20/2023] [Revised: 11/13/2023] [Accepted: 11/21/2023] [Indexed: 01/21/2024]
Abstract
Several single-cell RNA studies of developing mouse skin have elucidated the molecular and cellular processes involved in skin development. However, they have primarily focused on either the fetal or early postnatal period, leaving a gap in our understanding of skin development. In this study, we conducted a comprehensive time-series analysis by combining single-cell RNA-sequencing datasets collected at different stages of development (embryonic days 13.5, 14.5, and 16.5 and postnatal days 0, 2, and 4) and validated our findings through multipanel in situ spatial transcriptomics. Our analysis indicated that embryonic fibroblasts exhibit heterogeneity from a very early stage and that the rapid determination of each lineage occurs within days after birth. The expression of putative key driver genes, including Hey1, Ebf1, Runx3, and Sox11 for the dermal papilla trajectory; Lrrc15 for the dermal sheath trajectory; Zfp536 and Nrn1 for the papillary fibroblast trajectory; and Lrrn4cl and Mfap5 for the fascia fibroblast trajectory, was detected in the corresponding, spatially identified cell types. Finally, cell-to-cell interaction analysis indicated that the dermal papilla lineage is the primary source of the noncanonical Wnt pathway during skin development. Together, our study provides a transcriptomic reference for future research in the field of skin development and regeneration.
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Affiliation(s)
- Hanjae Lee
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea; Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea
| | - So Young Kim
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea; Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | | | - Seong Jin Jo
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea; Laboratory of Cutaneous Aging and Hair Research, Clinical Research Institute, Seoul National University Hospital, Seoul, Korea; Institute of Human-Environment Interface Biology, Seoul National University College of Medicine, Seoul, Korea
| | - Ohsang Kwon
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea; Laboratory of Cutaneous Aging and Hair Research, Clinical Research Institute, Seoul National University Hospital, Seoul, Korea; Institute of Human-Environment Interface Biology, Seoul National University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea.
| | - Jong-Il Kim
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea; Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea; Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Korea; Cancer Research Institute, Seoul National University, Seoul, Korea
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10
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Massri AJ, Berrio A, Afanassiev A, Greenstreet L, Pipho K, Byrne M, Schiebinger G, McClay DR, Wray GA. Single-cell transcriptomics reveals evolutionary reconfiguration of embryonic cell fate specification in the sea urchin Heliocidaris erythrogramma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.30.591752. [PMID: 38746376 PMCID: PMC11092583 DOI: 10.1101/2024.04.30.591752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Altered regulatory interactions during development likely underlie a large fraction of phenotypic diversity within and between species, yet identifying specific evolutionary changes remains challenging. Analysis of single-cell developmental transcriptomes from multiple species provides a powerful framework for unbiased identification of evolutionary changes in developmental mechanisms. Here, we leverage a "natural experiment" in developmental evolution in sea urchins, where a major life history switch recently evolved in the lineage leading to Heliocidaris erythrogramma, precipitating extensive changes in early development. Comparative analyses of scRNA-seq developmental time courses from H. erythrogramma and Lytechinus variegatus (representing the derived and ancestral states respectively) reveals numerous evolutionary changes in embryonic patterning. The earliest cell fate specification events, and the primary signaling center are co-localized in the ancestral dGRN but remarkably, in H. erythrogramma they are spatially and temporally separate. Fate specification and differentiation are delayed in most embryonic cell lineages, although in some cases, these processes are conserved or even accelerated. Comparative analysis of regulator-target gene co-expression is consistent with many specific interactions being preserved but delayed in H. erythrogramma, while some otherwise widely conserved interactions have likely been lost. Finally, specific patterning events are directly correlated with evolutionary changes in larval morphology, suggesting that they are directly tied to the life history shift. Together, these findings demonstrate that comparative scRNA-seq developmental time courses can reveal a diverse set of evolutionary changes in embryonic patterning and provide an efficient way to identify likely candidate regulatory interactions for subsequent experimental validation.
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Affiliation(s)
- Abdull J Massri
- Department of Biology, Duke University, Durham, NC 27701 USA
| | | | - Anton Afanassiev
- Department of Mathematics, University of British Colombia, Vancouver, BC V6T 1Z4 Canada
| | - Laura Greenstreet
- Department of Mathematics, University of British Colombia, Vancouver, BC V6T 1Z4 Canada
| | - Krista Pipho
- Department of Biology, Duke University, Durham, NC 27701 USA
| | - Maria Byrne
- School of Life and Environmental Sciences, Sydney University, Sydney, NSW Australia
| | - Geoffrey Schiebinger
- Department of Mathematics, University of British Colombia, Vancouver, BC V6T 1Z4 Canada
| | - David R McClay
- Department of Biology, Duke University, Durham, NC 27701 USA
| | - Gregory A Wray
- Department of Biology, Duke University, Durham, NC 27701 USA
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11
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Xue G, Zhang X, Li W, Zhang L, Zhang Z, Zhou X, Zhang D, Zhang L, Li Z. A logic-incorporated gene regulatory network deciphers principles in cell fate decisions. eLife 2024; 12:RP88742. [PMID: 38652107 PMCID: PMC11037919 DOI: 10.7554/elife.88742] [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] [Indexed: 04/25/2024] Open
Abstract
Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.
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Affiliation(s)
- Gang Xue
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaoyi Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Wanqi Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Zongxu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaolin Zhou
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Di Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lei Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Beijing International Center for Mathematical Research, Center for Machine Learning Research, Peking UniversityBeijingChina
| | - Zhiyuan Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
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12
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Azagury M, Buganim Y. Unlocking trophectoderm mysteries: In vivo and in vitro perspectives on human and mouse trophoblast fate induction. Dev Cell 2024; 59:941-960. [PMID: 38653193 DOI: 10.1016/j.devcel.2024.03.029] [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: 09/07/2023] [Revised: 11/10/2023] [Accepted: 03/18/2024] [Indexed: 04/25/2024]
Abstract
In recent years, the pursuit of inducing the trophoblast stem cell (TSC) state has gained prominence as a compelling research objective, illuminating the establishment of the trophoblast lineage and unlocking insights into early embryogenesis. In this review, we examine how advancements in diverse technologies, including in vivo time course transcriptomics, cellular reprogramming to TSC state, chemical induction of totipotent stem-cell-like state, and stem-cell-based embryo-like structures, have enriched our insights into the intricate molecular mechanisms and signaling pathways that define the mouse and human trophectoderm/TSC states. We delve into disparities between mouse and human trophectoderm/TSC fate establishment, with a special emphasis on the intriguing role of pluripotency in this context. Additionally, we re-evaluate recent findings concerning the potential of totipotent-stem-like cells and embryo-like structures to fully manifest the trophectoderm/trophoblast lineage's capabilities. Lastly, we briefly discuss the potential applications of induced TSCs in pregnancy-related disease modeling.
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Affiliation(s)
- Meir Azagury
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem 91120, Israel
| | - Yosef Buganim
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem 91120, Israel.
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13
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Qu R, Cheng X, Sefik E, Stanley Iii JS, Landa B, Strino F, Platt S, Garritano J, Odell ID, Coifman R, Flavell RA, Myung P, Kluger Y. Gene trajectory inference for single-cell data by optimal transport metrics. Nat Biotechnol 2024:10.1038/s41587-024-02186-3. [PMID: 38580861 DOI: 10.1038/s41587-024-02186-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/26/2024] [Indexed: 04/07/2024]
Abstract
Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell-cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.
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Affiliation(s)
- Rihao Qu
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Xiuyuan Cheng
- Department of Mathematics, Duke University, Durham, NC, USA
| | - Esen Sefik
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | | | - Boris Landa
- Program in Applied Mathematics, Yale University, New Haven, CT, USA
| | | | - Sarah Platt
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
| | - James Garritano
- Program in Applied Mathematics, Yale University, New Haven, CT, USA
| | - Ian D Odell
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
| | - Ronald Coifman
- Program in Applied Mathematics, Yale University, New Haven, CT, USA
- Department of Mathematics, Yale University, New Haven, CT, USA
- Department of Electrical Engineering, Yale University, New Haven, CT, USA
| | - Richard A Flavell
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
- Howard Hughes Medical Institute, Yale University School of Medicine, New Haven, CT, USA
| | - Peggy Myung
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
| | - Yuval Kluger
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA.
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
- Program in Applied Mathematics, Yale University, New Haven, CT, USA.
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14
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Marmarelis MG, Littman R, Battaglin F, Niedzwiecki D, Venook A, Ambite JL, Galstyan A, Lenz HJ, Ver Steeg G. q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics. Commun Biol 2024; 7:400. [PMID: 38565955 DOI: 10.1038/s42003-024-06104-w] [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: 06/23/2023] [Accepted: 03/25/2024] [Indexed: 04/04/2024] Open
Abstract
Unlocking the full dimensionality of single-cell RNA sequencing data (scRNAseq) is the next frontier to a richer, fuller understanding of cell biology. We introduce q-diffusion, a framework for capturing the coexpression structure of an entire library of genes, improving on state-of-the-art analysis tools. The method is demonstrated via three case studies. In the first, q-diffusion helps gain statistical significance for differential effects on patient outcomes when analyzing the CALGB/SWOG 80405 randomized phase III clinical trial, suggesting precision guidance for the treatment of metastatic colorectal cancer. Secondly, q-diffusion is benchmarked against existing scRNAseq classification methods using an in vitro PBMC dataset, in which the proposed method discriminates IFN-γ stimulation more accurately. The same case study demonstrates improvements in unsupervised cell clustering with the recent Tabula Sapiens human atlas. Finally, a local distributional segmentation approach for spatial scRNAseq, driven by q-diffusion, yields interpretable structures of human cortical tissue.
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Affiliation(s)
- Myrl G Marmarelis
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA.
| | - Russell Littman
- University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Francesca Battaglin
- Keck School of Medicine, University of Southern California, 1975 Zonal Ave., Los Angeles, CA, 90033, USA
| | | | - Alan Venook
- University of California San Francisco, San Francisco, CA, 94143, USA
| | - Jose-Luis Ambite
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
| | - Aram Galstyan
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
| | - Heinz-Josef Lenz
- Keck School of Medicine, University of Southern California, 1975 Zonal Ave., Los Angeles, CA, 90033, USA
| | - Greg Ver Steeg
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
- University of California Riverside, Riverside, CA, 92521, USA
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15
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Hashemi Gheinani A, Kim J, You S, Adam RM. Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment. Nat Rev Urol 2024; 21:214-242. [PMID: 37604982 DOI: 10.1038/s41585-023-00805-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 08/23/2023]
Abstract
The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.
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Affiliation(s)
- Ali Hashemi Gheinani
- Department of Urology, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Urology, Inselspital, Bern, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Jina Kim
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sungyong You
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rosalyn M Adam
- Department of Urology, Boston Children's Hospital, Boston, MA, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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16
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Hong T, Xing J. Data- and theory-driven approaches for understanding paths of epithelial-mesenchymal transition. Genesis 2024; 62:e23591. [PMID: 38553870 PMCID: PMC11017362 DOI: 10.1002/dvg.23591] [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: 01/02/2024] [Revised: 02/16/2024] [Accepted: 03/16/2024] [Indexed: 04/02/2024]
Abstract
Reversible transitions between epithelial and mesenchymal cell states are a crucial form of epithelial plasticity for development and disease progression. Recent experimental data and mechanistic models showed multiple intermediate epithelial-mesenchymal transition (EMT) states as well as trajectories of EMT underpinned by complex gene regulatory networks. In this review, we summarize recent progress in quantifying EMT and characterizing EMT paths with computational methods and quantitative experiments including omics-level measurements. We provide perspectives on how these studies can help relating fundamental cell biology to physiological and pathological outcomes of EMT.
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Affiliation(s)
- Tian Hong
- Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville TN, USA
| | - Jianhua Xing
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- UPMC-Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA
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17
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Samuels TJ, Gui J, Gebert D, Karam Teixeira F. Two distinct waves of transcriptome and translatome changes drive Drosophila germline stem cell differentiation. EMBO J 2024; 43:1591-1617. [PMID: 38480936 PMCID: PMC11021484 DOI: 10.1038/s44318-024-00070-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 03/18/2024] Open
Abstract
The tight control of fate transitions during stem cell differentiation is essential for proper tissue development and maintenance. However, the challenges in studying sparsely distributed adult stem cells in a systematic manner have hindered efforts to identify how the multilayered regulation of gene expression programs orchestrates stem cell differentiation in vivo. Here, we synchronised Drosophila female germline stem cell (GSC) differentiation in vivo to perform in-depth transcriptome and translatome analyses at high temporal resolution. This characterisation revealed widespread and dynamic changes in mRNA level, promoter usage, exon inclusion, and translation efficiency. Transient expression of the master regulator, Bam, drives a first wave of expression changes, primarily modifying the cell cycle program. Surprisingly, as Bam levels recede, differentiating cells return to a remarkably stem cell-like transcription and translation program, with a few crucial changes feeding into a second phase driving terminal differentiation to form the oocyte. Altogether, these findings reveal that rather than a unidirectional accumulation of changes, the in vivo differentiation of stem cells relies on distinctly regulated and developmentally sequential waves.
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Affiliation(s)
- Tamsin J Samuels
- Department of Genetics, University of Cambridge, Downing Street, CB2 3EH, Cambridge, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, CB2 3DY, Cambridge, UK
| | - Jinghua Gui
- Department of Genetics, University of Cambridge, Downing Street, CB2 3EH, Cambridge, UK
| | - Daniel Gebert
- Department of Genetics, University of Cambridge, Downing Street, CB2 3EH, Cambridge, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, CB2 3DY, Cambridge, UK
| | - Felipe Karam Teixeira
- Department of Genetics, University of Cambridge, Downing Street, CB2 3EH, Cambridge, UK.
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, CB2 3DY, Cambridge, UK.
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18
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Senoussi M, Artieres T, Villoutreix P. Partial label learning for automated classification of single-cell transcriptomic profiles. PLoS Comput Biol 2024; 20:e1012006. [PMID: 38578796 PMCID: PMC11023635 DOI: 10.1371/journal.pcbi.1012006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 04/17/2024] [Accepted: 03/18/2024] [Indexed: 04/07/2024] Open
Abstract
Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for individual cells. Because of the fast accumulation of datasets and the high dimensionality of the data, it has become challenging to explore and annotate single-cell transcriptomic profiles by hand. To overcome this challenge, automated classification methods are needed. Classical approaches rely on supervised training datasets. However, due to the difficulty of obtaining data annotated at single-cell resolution, we propose instead to take advantage of partial annotations. The partial label learning framework assumes that we can obtain a set of candidate labels containing the correct one for each data point, a simpler setting than requiring a fully supervised training dataset. We study and extend when needed state-of-the-art multi-class classification methods, such as SVM, kNN, prototype-based, logistic regression and ensemble methods, to the partial label learning framework. Moreover, we study the effect of incorporating the structure of the label set into the methods. We focus particularly on the hierarchical structure of the labels, as commonly observed in developmental processes. We show, on simulated and real datasets, that these extensions enable to learn from partially labeled data, and perform predictions with high accuracy, particularly with a nonlinear prototype-based method. We demonstrate that the performances of our methods trained with partially annotated data reach the same performance as fully supervised data. Finally, we study the level of uncertainty present in the partially annotated data, and derive some prescriptive results on the effect of this uncertainty on the accuracy of the partial label learning methods. Overall our findings show how hierarchical and non-hierarchical partial label learning strategies can help solve the problem of automated classification of single-cell transcriptomic profiles, interestingly these methods rely on a much less stringent type of annotated datasets compared to fully supervised learning methods.
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Affiliation(s)
- Malek Senoussi
- Aix Marseille Univ, Université de Toulon, CNRS, LIS, Turing Centre for Living Systems, Marseille, France
| | - Thierry Artieres
- Aix Marseille Univ, Université de Toulon, CNRS, LIS, Turing Centre for Living Systems, Marseille, France
- Ecole Centrale de Marseille, Marseille, France
| | - Paul Villoutreix
- Aix Marseille Univ, Université de Toulon, CNRS, LIS, Turing Centre for Living Systems, Marseille, France
- Aix-Marseille Université, MMG, Inserm U1251, Turing Centre for Living systems, Marseille, France
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19
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Zhang K, Zhu J, Kong D, Zhang Z. Modeling single cell trajectory using forward-backward stochastic differential equations. PLoS Comput Biol 2024; 20:e1012015. [PMID: 38620017 PMCID: PMC11018287 DOI: 10.1371/journal.pcbi.1012015] [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: 10/25/2023] [Accepted: 03/22/2024] [Indexed: 04/17/2024] Open
Abstract
Recent advances in single-cell sequencing technology have provided opportunities for mathematical modeling of dynamic developmental processes at the single-cell level, such as inferring developmental trajectories. Optimal transport has emerged as a promising theoretical framework for this task by computing pairings between cells from different time points. However, optimal transport methods have limitations in capturing nonlinear trajectories, as they are static and can only infer linear paths between endpoints. In contrast, stochastic differential equations (SDEs) offer a dynamic and flexible approach that can model non-linear trajectories, including the shape of the path. Nevertheless, existing SDE methods often rely on numerical approximations that can lead to inaccurate inferences, deviating from true trajectories. To address this challenge, we propose a novel approach combining forward-backward stochastic differential equations (FBSDE) with a refined approximation procedure. Our FBSDE model integrates the forward and backward movements of two SDEs in time, aiming to capture the underlying dynamics of single-cell developmental trajectories. Through comprehensive benchmarking on multiple scRNA-seq datasets, we demonstrate the superior performance of FBSDE compared to other methods, highlighting its efficacy in accurately inferring developmental trajectories.
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Affiliation(s)
- Kevin Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Junhao Zhu
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Dehan Kong
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Zhaolei Zhang
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
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20
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Ranek JS, Stallaert W, Milner JJ, Redick M, Wolff SC, Beltran AS, Stanley N, Purvis JE. DELVE: feature selection for preserving biological trajectories in single-cell data. Nat Commun 2024; 15:2765. [PMID: 38553455 PMCID: PMC10980758 DOI: 10.1038/s41467-024-46773-z] [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: 05/18/2023] [Accepted: 03/07/2024] [Indexed: 04/02/2024] Open
Abstract
Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .
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Affiliation(s)
- Jolene S Ranek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wayne Stallaert
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - J Justin Milner
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Margaret Redick
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Samuel C Wolff
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adriana S Beltran
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Human Pluripotent Cell Core, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Natalie Stanley
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Jeremy E Purvis
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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21
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Barra J, Taverna F, Bong F, Ahmed I, Karakach TK. Error modelled gene expression analysis (EMOGEA) provides a superior overview of time course RNA-seq measurements and low count gene expression. Brief Bioinform 2024; 25:bbae233. [PMID: 38770716 PMCID: PMC11106635 DOI: 10.1093/bib/bbae233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/03/2024] [Accepted: 04/30/2024] [Indexed: 05/22/2024] Open
Abstract
Temporal RNA-sequencing (RNA-seq) studies of bulk samples provide an opportunity for improved understanding of gene regulation during dynamic phenomena such as development, tumor progression or response to an incremental dose of a pharmacotherapeutic. Moreover, single-cell RNA-seq (scRNA-seq) data implicitly exhibit temporal characteristics because gene expression values recapitulate dynamic processes such as cellular transitions. Unfortunately, temporal RNA-seq data continue to be analyzed by methods that ignore this ordinal structure and yield results that are often difficult to interpret. Here, we present Error Modelled Gene Expression Analysis (EMOGEA), a framework for analyzing RNA-seq data that incorporates measurement uncertainty, while introducing a special formulation for those acquired to monitor dynamic phenomena. This method is specifically suited for RNA-seq studies in which low-count transcripts with small-fold changes lead to significant biological effects. Such transcripts include genes involved in signaling and non-coding RNAs that inherently exhibit low levels of expression. Using simulation studies, we show that this framework down-weights samples that exhibit extreme responses such as batch effects allowing them to be modeled with the rest of the samples and maintain the degrees of freedom originally envisioned for a study. Using temporal experimental data, we demonstrate the framework by extracting a cascade of gene expression waves from a well-designed RNA-seq study of zebrafish embryogenesis and an scRNA-seq study of mouse pre-implantation and provide unique biological insights into the regulation of genes in each wave. For non-ordinal measurements, we show that EMOGEA has a much higher rate of true positive calls and a vanishingly small rate of false negative discoveries compared to common approaches. Finally, we provide two packages in Python and R that are self-contained and easy to use, including test data.
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Affiliation(s)
- Jasmine Barra
- Laboratory of Integrative Multi-Omics Research, Department of Pharmacology, Dalhousie University, 5850 College Street, Halifax, NS, B3H 4R2, Canada
- Beatrice Hunter Cancer Research Institute, 5743 University Avenue, Suite 98, Halifax, NS, B3H 0A2, Canada
- Department of Microbiology & Immunology, Dalhousie University, 5850 College Street, Halifax, NS, B3H 4R2, Canada
| | - Federico Taverna
- Laboratory of Integrative Multi-Omics Research, Department of Pharmacology, Dalhousie University, 5850 College Street, Halifax, NS, B3H 4R2, Canada
- Beatrice Hunter Cancer Research Institute, 5743 University Avenue, Suite 98, Halifax, NS, B3H 0A2, Canada
| | - Fabian Bong
- Laboratory of Integrative Multi-Omics Research, Department of Pharmacology, Dalhousie University, 5850 College Street, Halifax, NS, B3H 4R2, Canada
- Beatrice Hunter Cancer Research Institute, 5743 University Avenue, Suite 98, Halifax, NS, B3H 0A2, Canada
| | - Ibrahim Ahmed
- Laboratory of Integrative Multi-Omics Research, Department of Pharmacology, Dalhousie University, 5850 College Street, Halifax, NS, B3H 4R2, Canada
- Beatrice Hunter Cancer Research Institute, 5743 University Avenue, Suite 98, Halifax, NS, B3H 0A2, Canada
| | - Tobias K Karakach
- Laboratory of Integrative Multi-Omics Research, Department of Pharmacology, Dalhousie University, 5850 College Street, Halifax, NS, B3H 4R2, Canada
- Beatrice Hunter Cancer Research Institute, 5743 University Avenue, Suite 98, Halifax, NS, B3H 0A2, Canada
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22
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Liang S, Dou J, Iqbal R, Chen K. Label-aware distance mitigates temporal and spatial variability for clustering and visualization of single-cell gene expression data. Commun Biol 2024; 7:326. [PMID: 38486077 PMCID: PMC10940680 DOI: 10.1038/s42003-024-05988-y] [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: 07/03/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Label-Aware Distance (LAD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate LAD on simulated data as well as apply it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). LAD provides better cell embedding than state-of-the-art batch correction methods on longitudinal datasets. It can be used in distance-based clustering and visualization methods to combine the power of multiple samples to help make biological findings.
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Affiliation(s)
- Shaoheng Liang
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA.
- Department of Computer Science, Rice University, Houston, TX, USA.
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Jinzhuang Dou
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ramiz Iqbal
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA.
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23
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Halmos P, Liu X, Gold J, Chen F, Ding L, Raphael BJ. DeST-OT: Alignment of Spatiotemporal Transcriptomics Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583575. [PMID: 38496660 PMCID: PMC10942402 DOI: 10.1101/2024.03.05.583575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Spatially resolved transcriptomics (SRT) measures mRNA transcripts at thousands of locations within a tissue slice, revealing spatial variations in gene expression and distribution of cell types. In recent studies, SRT has been applied to tissue slices from multiple timepoints during the development of an organism. Alignment of this spatiotemporal transcriptomics data can provide insights into the gene expression programs governing the growth and differentiation of cells over space and time. We introduce DeST-OT (Developmental SpatioTemporal Optimal Transport), a method to align SRT slices from pairs of developmental timepoints using the framework of optimal transport (OT). DeST-OT uses semi-relaxed optimal transport to precisely model cellular growth, death, and differentiation processes that are not well-modeled by existing alignment methods. We demonstrate the advantage of DeST-OT on simulated slices. We further introduce two metrics to quantify the plausibility of a spatiotemporal alignment: a growth distortion metric which quantifies the discrepancy between the inferred and the true cell type growth rates, and a migration metric which quantifies the distance traveled between ancestor and descendant cells. DeST-OT outperforms existing methods on these metrics in the alignment of spatiotemporal transcriptomics data from the development of axolotl brain.
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Affiliation(s)
- Peter Halmos
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08544
| | - Xinhao Liu
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08544
| | - Julian Gold
- Center for Statistics and Machine Learning, Princeton University, 26 Prospect Ave, Princeton, NJ 08544
| | - Feng Chen
- Departments of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110
| | - Li Ding
- Departments of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108
| | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08544
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24
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Zhang R, Qiu C, Filippova G, Li G, Shendure J, Vert JP, Deng X, Disteche C, Noble WS. Multi-condition and multi-modal temporal profile inference during mouse embryonic development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.03.583179. [PMID: 38496477 PMCID: PMC10942306 DOI: 10.1101/2024.03.03.583179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The emergence of single-cell time-series datasets enables modeling of changes in various types of cellular profiles over time. However, due to the disruptive nature of single-cell measurements, it is impossible to capture the full temporal trajectory of a particular cell. Furthermore, single-cell profiles can be collected at mismatched time points across different conditions (e.g., sex, batch, disease) and data modalities (e.g., scRNA-seq, scATAC-seq), which makes modeling challenging. Here we propose a joint modeling framework, Sunbear, for integrating multi-condition and multi-modal single-cell profiles across time. Sunbear can be used to impute single-cell temporal profile changes, align multi-dataset and multi-modal profiles across time, and extrapolate single-cell profiles in a missing modality. We applied Sunbear to reveal sex-biased transcription during mouse embryonic development and predict dynamic relationships between epigenetic priming and transcription for cells in which multi-modal profiles are unavailable. Sunbear thus enables the projection of single-cell time-series snapshots to multi-modal and multi-condition views of cellular trajectories.
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Affiliation(s)
- Ran Zhang
- Department of Genome Sciences, University of Washington
- eScience Institute, University of Washington
| | | | | | - Gang Li
- Department of Genome Sciences, University of Washington
- eScience Institute, University of Washington
| | - Jay Shendure
- Brotman Baty Institute for Precision Medicine, University of Washington
- Howard Hughes Medical Institute
- Allen Center for Cell Lineage Tracing
| | | | - Xinxian Deng
- Department of Pathology, University of Washington
| | | | - William Stafford Noble
- Department of Genome Sciences, University of Washington
- Paul G. Allen School of Computer Science and Engineering, University of Washington
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25
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Peidli S, Green TD, Shen C, Gross T, Min J, Garda S, Yuan B, Schumacher LJ, Taylor-King JP, Marks DS, Luna A, Blüthgen N, Sander C. scPerturb: harmonized single-cell perturbation data. Nat Methods 2024; 21:531-540. [PMID: 38279009 DOI: 10.1038/s41592-023-02144-y] [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: 01/28/2023] [Accepted: 12/04/2023] [Indexed: 01/28/2024]
Abstract
Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.
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Affiliation(s)
- Stefan Peidli
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Tessa D Green
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Ciyue Shen
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | | | - Joseph Min
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Samuele Garda
- Institute of Biology, Humboldt-Universität, Berlin, Germany
- Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bo Yuan
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Linus J Schumacher
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Augustin Luna
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
- Computational Biology Branch, National Library of Medicine and Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD, USA.
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Chris Sander
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
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26
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Vo HK, Dawes JHP, Kelsh RN. Oscillatory differentiation dynamics fundamentally restricts the resolution of pseudotime reconstruction algorithms. J R Soc Interface 2024; 21:20230537. [PMID: 38503342 PMCID: PMC10950464 DOI: 10.1098/rsif.2023.0537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 02/20/2024] [Indexed: 03/21/2024] Open
Abstract
The challenge to understand differentiation and cell lineages in development has resulted in many bioinformatics software tools, notably those working with gene expression data obtained via single-cell RNA sequencing obtained at snapshots in time. Reconstruction methods for trajectories often proceed by dimension reduction, data clustering and then computation of a tree graph in which edges indicate closely related clusters. Cell lineages can then be deduced by following paths through the tree. In the case of multi-potent cells undergoing differentiation, this trajectory reconstruction involves the reconstruction of multiple distinct lineages corresponding to commitment to each of a set of distinct fates. Recent work suggests that there may be cases in which the cell differentiation process involves trajectories that explore, in a dynamic and oscillatory fashion, propensity to differentiate into a number of possible cell fates before commitment finally occurs. Here, we show theoretically that the presence of such oscillations provides intrinsic constraints on the quality and resolution of the trajectory reconstruction process, even for idealized noise-free data. These constraints point to inherent common limitations of current methodologies and serve both to provide additional challenge in the development of software tools and also may help to understand features observed in recent experiments.
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Affiliation(s)
- Huy K. Vo
- Department of Mathematical Sciences, University of Bath, BA2 7AY Bath, UK
| | | | - Robert N. Kelsh
- Department of Life Sciences, University of Bath, BA2 7AY Bath, UK
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27
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Huyghe A, Trajkova A, Lavial F. Cellular plasticity in reprogramming, rejuvenation and tumorigenesis: a pioneer TF perspective. Trends Cell Biol 2024; 34:255-267. [PMID: 37648593 DOI: 10.1016/j.tcb.2023.07.013] [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: 05/10/2023] [Revised: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023]
Abstract
The multistep process of in vivo reprogramming, mediated by the transcription factors (TFs) Oct4, Sox2, Klf4, and c-Myc (OSKM), holds great promise for the development of rejuvenating and regenerative strategies. However, most of the approaches developed so far are accompanied by a persistent risk of tumorigenicity. Here, we review the groundbreaking effects of in vivo reprogramming with a particular focus on rejuvenation and regeneration. We discuss how the activity of pioneer TFs generates cellular plasticity that may be critical for inducing not only reprogramming and regeneration, but also cancer initiation. Finally, we highlight how a better understanding of the uncoupled control of cellular identity, plasticity, and aging during reprogramming might pave the way to the development of rejuvenating/regenerating strategies in a nontumorigenic manner.
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Affiliation(s)
- Aurélia Huyghe
- Cellular Reprogramming, Stem Cells and Oncogenesis Laboratory, Equipe Labellisée la Ligue Contre le Cancer, Labex Dev2Can - Univeristy of Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France
| | - Aneta Trajkova
- Cellular Reprogramming, Stem Cells and Oncogenesis Laboratory, Equipe Labellisée la Ligue Contre le Cancer, Labex Dev2Can - Univeristy of Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France
| | - Fabrice Lavial
- Cellular Reprogramming, Stem Cells and Oncogenesis Laboratory, Equipe Labellisée la Ligue Contre le Cancer, Labex Dev2Can - Univeristy of Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France.
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28
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Farah EN, Hu RK, Kern C, Zhang Q, Lu TY, Ma Q, Tran S, Zhang B, Carlin D, Monell A, Blair AP, Wang Z, Eschbach J, Li B, Destici E, Ren B, Evans SM, Chen S, Zhu Q, Chi NC. Spatially organized cellular communities form the developing human heart. Nature 2024; 627:854-864. [PMID: 38480880 PMCID: PMC10972757 DOI: 10.1038/s41586-024-07171-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/07/2024] [Indexed: 03/18/2024]
Abstract
The heart, which is the first organ to develop, is highly dependent on its form to function1,2. However, how diverse cardiac cell types spatially coordinate to create the complex morphological structures that are crucial for heart function remains unclear. Here we integrated single-cell RNA-sequencing with high-resolution multiplexed error-robust fluorescence in situ hybridization to resolve the identity of the cardiac cell types that develop the human heart. This approach also provided a spatial mapping of individual cells that enables illumination of their organization into cellular communities that form distinct cardiac structures. We discovered that many of these cardiac cell types further specified into subpopulations exclusive to specific communities, which support their specialization according to the cellular ecosystem and anatomical region. In particular, ventricular cardiomyocyte subpopulations displayed an unexpected complex laminar organization across the ventricular wall and formed, with other cell subpopulations, several cellular communities. Interrogating cell-cell interactions within these communities using in vivo conditional genetic mouse models and in vitro human pluripotent stem cell systems revealed multicellular signalling pathways that orchestrate the spatial organization of cardiac cell subpopulations during ventricular wall morphogenesis. These detailed findings into the cellular social interactions and specialization of cardiac cell types constructing and remodelling the human heart offer new insights into structural heart diseases and the engineering of complex multicellular tissues for human heart repair.
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Affiliation(s)
- Elie N Farah
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Robert K Hu
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Colin Kern
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Qingquan Zhang
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Ting-Yu Lu
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA
| | - Qixuan Ma
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Shaina Tran
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Bo Zhang
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Daniel Carlin
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Alexander Monell
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Andrew P Blair
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Zilu Wang
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Jacqueline Eschbach
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Bin Li
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Eugin Destici
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Sylvia M Evans
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
- Department of Pharmacology, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Shaochen Chen
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of NanoEngineering, University of California San Diego, La Jolla, CA, USA
- Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA
| | - Quan Zhu
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Neil C Chi
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
- Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA.
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29
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Wang G, Zhang D, Qin L, Liu Q, Tang W, Liu M, Xu F, Tang F, Cheng L, Mo H, Yuan X, Wang Z, Huang B. Forskolin-driven conversion of human somatic cells into induced neurons through regulation of the cAMP-CREB1-JNK signaling. Theranostics 2024; 14:1701-1719. [PMID: 38389831 PMCID: PMC10879881 DOI: 10.7150/thno.92700] [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] [Received: 12/01/2023] [Accepted: 01/22/2024] [Indexed: 02/24/2024] Open
Abstract
Human somatic cells can be reprogrammed into neuron cell fate through regulation of a single transcription factor or application of small molecule cocktails. Methods: Here, we report that forskolin efficiently induces the conversion of human somatic cells into induced neurons (FiNs). Results: A large population of neuron-like phenotype cells was observed as early as 24-36 h post-induction. There were >90% TUJ1-, >80% MAP2-, and >80% NEUN-positive neurons at 5 days post-induction. Multiple subtypes of neurons were present among TUJ1-positive cells, including >60% cholinergic, >20% glutamatergic, >10% GABAergic, and >5% dopaminergic neurons. FiNs exhibited typical neural electrophysiological activity in vitro and the ability to survive in vitro and in vivo more than 2 months. Mechanistically, forskolin functions in FiN reprogramming by regulating the cAMP-CREB1-JNK signals, which upregulates cAMP-CREB1 expression and downregulates JNK expression. Conclusion: Overall, our studies identify a safer and efficient single-small-molecule-driven reprogramming approach for induced neuron generation and reveal a novel regulatory mechanism of neuronal cell fate acquisition.
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Affiliation(s)
- Guodong Wang
- Guangxi Key Laboratory of Eye Health, Department of Technical Support, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, 530021, China
- School of Animal Science and Technology, Guangxi University, Nanning, 530005, Guangxi, China
| | - Dandan Zhang
- Guangxi Key Laboratory of Eye Health, Department of Technical Support, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, 530021, China
| | - Liangshan Qin
- Guangxi Key Laboratory of Eye Health, Department of Technical Support, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, 530021, China
- School of Animal Science and Technology, Guangxi University, Nanning, 530005, Guangxi, China
| | - Quanhui Liu
- Guangxi Key Laboratory of Eye Health, Department of Technical Support, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, 530021, China
- School of Animal Science and Technology, Guangxi University, Nanning, 530005, Guangxi, China
| | - Wenkui Tang
- Guangxi Key Laboratory of Eye Health, Department of Technical Support, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, 530021, China
- School of Animal Science and Technology, Guangxi University, Nanning, 530005, Guangxi, China
| | - Mingxing Liu
- Guangxi Key Laboratory of Eye Health, Department of Technical Support, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, 530021, China
- School of Animal Science and Technology, Guangxi University, Nanning, 530005, Guangxi, China
| | - Fan Xu
- Guangxi Key Laboratory of Eye Health, Department of Technical Support, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, 530021, China
| | - Fen Tang
- Guangxi Key Laboratory of Eye Health, Department of Technical Support, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, 530021, China
| | - Leping Cheng
- Key Laboratory of Longevity and Aging-related Diseases of Chinese Ministry of Education, Guangxi-ASEAN Collaborative Innovation Center for Major Disease Prevention and Treatment, and Guangxi Key Laboratory of Regenerative Medicine, Center for Translational Medicine, Guangxi Medical University, Nanning, 530021, China
| | - Huiming Mo
- Key Laboratory of Longevity and Aging-related Diseases of Chinese Ministry of Education, Guangxi-ASEAN Collaborative Innovation Center for Major Disease Prevention and Treatment, and Guangxi Key Laboratory of Regenerative Medicine, Center for Translational Medicine, Guangxi Medical University, Nanning, 530021, China
| | - Xiang Yuan
- Guangxi Key Laboratory of Eye Health, Department of Technical Support, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, 530021, China
| | - Zhiqiang Wang
- Guangxi Key Laboratory of Eye Health, Department of Technical Support, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, 530021, China
| | - Ben Huang
- Guangxi Key Laboratory of Eye Health, Department of Technical Support, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, 530021, China
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30
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Tejwani L, Ravindra NG, Lee C, Cheng Y, Nguyen B, Luttik K, Ni L, Zhang S, Morrison LM, Gionco J, Xiang Y, Yoon J, Ro H, Haidery F, Grijalva RM, Bae E, Kim K, Martuscello RT, Orr HT, Zoghbi HY, McLoughlin HS, Ranum LPW, Shakkottai VG, Faust PL, Wang S, van Dijk D, Lim J. Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1. Neuron 2024; 112:362-383.e15. [PMID: 38016472 PMCID: PMC10922326 DOI: 10.1016/j.neuron.2023.10.039] [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: 12/15/2022] [Revised: 09/10/2023] [Accepted: 10/27/2023] [Indexed: 11/30/2023]
Abstract
Neurodegeneration is a protracted process involving progressive changes in myriad cell types that ultimately results in the death of vulnerable neuronal populations. To dissect how individual cell types within a heterogeneous tissue contribute to the pathogenesis and progression of a neurodegenerative disorder, we performed longitudinal single-nucleus RNA sequencing of mouse and human spinocerebellar ataxia type 1 (SCA1) cerebellar tissue, establishing continuous dynamic trajectories of each cell population. Importantly, we defined the precise transcriptional changes that precede loss of Purkinje cells and, for the first time, identified robust early transcriptional dysregulation in unipolar brush cells and oligodendroglia. Finally, we applied a deep learning method to predict disease state accurately and identified specific features that enable accurate distinction of wild-type and SCA1 cells. Together, this work reveals new roles for diverse cerebellar cell types in SCA1 and provides a generalizable analysis framework for studying neurodegeneration.
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Affiliation(s)
- Leon Tejwani
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA.
| | - Neal G Ravindra
- Cardiovascular Research Center, Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, USA; Department of Computer Science, Yale University, New Haven, CT 06510, USA
| | - Changwoo Lee
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Yubao Cheng
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Billy Nguyen
- University of California, San Francisco School of Medicine, San Francisco, CA 94143, USA
| | - Kimberly Luttik
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Luhan Ni
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Shupei Zhang
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Logan M Morrison
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - John Gionco
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, New York, NY 10032, USA
| | - Yangfei Xiang
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA; Yale Stem Cell Center, Yale School of Medicine, New Haven, CT 06510, USA
| | | | - Hannah Ro
- Yale College, New Haven, CT 06510, USA
| | | | - Rosalie M Grijalva
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | | | - Kristen Kim
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, USA
| | - Regina T Martuscello
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, New York, NY 10032, USA
| | - Harry T Orr
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Huda Y Zoghbi
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hayley S McLoughlin
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109-2200, USA
| | - Laura P W Ranum
- Department of Molecular Genetics and Microbiology, Center for Neurogenetics, College of Medicine, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Vikram G Shakkottai
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Phyllis L Faust
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, New York, NY 10032, USA
| | - Siyuan Wang
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA; Department of Cell Biology, Yale School of Medicine, New Haven, CT 06510, USA.
| | - David van Dijk
- Cardiovascular Research Center, Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, USA; Department of Computer Science, Yale University, New Haven, CT 06510, USA.
| | - Janghoo Lim
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA; Yale Stem Cell Center, Yale School of Medicine, New Haven, CT 06510, USA; Program in Cellular Neuroscience, Neurodegeneration and Repair, Yale School of Medicine, New Haven, CT 06510, USA; Wu Tsai Institute, Yale School of Medicine, New Haven, CT 06510, USA.
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31
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Lin Y, Wu TY, Chen X, Wan S, Chao B, Xin J, Yang JYH, Wong WH, Wang YXR. Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE. Genome Res 2024; 34:119-133. [PMID: 38190633 PMCID: PMC10903952 DOI: 10.1101/gr.277960.123] [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: 04/06/2023] [Accepted: 12/13/2023] [Indexed: 01/10/2024]
Abstract
Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space by using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal data sets, we show scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome data set we generated from differentiating mouse embryonic stem cells over time, we show scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.
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Affiliation(s)
- Yingxin Lin
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR 999077, China
| | - Tung-Yu Wu
- Department of Statistics, Stanford University, Stanford, California 94305-4020, USA
| | - Xi Chen
- Department of Statistics, Stanford University, Stanford, California 94305-4020, USA
| | - Sheng Wan
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
| | - Brian Chao
- Department of Electrical Engineering, Stanford University, Stanford, California 94305-9505, USA
| | - Jingxue Xin
- Department of Statistics, Stanford University, Stanford, California 94305-4020, USA
| | - Jean Y H Yang
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR 999077, China
| | - Wing H Wong
- Department of Statistics, Stanford University, Stanford, California 94305-4020, USA;
- Department of Biomedical Data Science, Stanford University, Stanford, California 94305-5464, USA
- Bio-X Program, Stanford University, Stanford, California 94305, USA
| | - Y X Rachel Wang
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia;
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32
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Wang Z, Zhan Q, Yang S, Mu S, Chen J, Garai S, Orzechowski P, Wagenaar J, Shen L. QOT: Efficient Computation of Sample Level Distance Matrix from Single-Cell Omics Data through Quantized Optimal Transport. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.06.578032. [PMID: 38370767 PMCID: PMC10871252 DOI: 10.1101/2024.02.06.578032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Single-cell technologies have emerged as a transformative technology enabling high-dimensional characterization of cell populations at an unprecedented scale. The data's innate complexity and voluminous nature pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e., generation of sample level distance matrices). Optimal Transport (OT) is a mathematical tool that captures the intrinsic structure of data geometrically and has been applied to many bioinformatics tasks. In this paper, we propose QOT (Quantized Optimal Transport), a new method enables efficient computation of sample level distance matrix from large-scale single-cell omics data through a quantization step. We apply our algorithm to real-world single-cell genomics and pathomics datasets, aiming to extrapolate cell-level insights to inform sample level categorizations. Our empirical study shows that QOT outperforms OT-based algorithms in terms of accuracy and robustness when obtaining a distance matrix at the sample level from high throughput single-cell measures. Moreover, the sample level distance matrix could be used in downstream analysis (i.e. uncover the trajectory of disease progression), highlighting its usage in biomedical informatics and data science.
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Affiliation(s)
- Zexuan Wang
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania
| | - Qipeng Zhan
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - Shizhuo Mu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - Jiong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - Sumita Garai
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - Patryk Orzechowski
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
- AGH University of Science and Technology, Poland
| | - Joost Wagenaar
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
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33
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Atsuta Y, Lee C, Rodrigues AR, Colle C, Tomizawa RR, Lujan EG, Tschopp P, Galan L, Zhu M, Gorham JM, Vannier JP, Seidman CE, Seidman JG, Ros MA, Pourquié O, Tabin CJ. Direct reprogramming of non-limb fibroblasts to cells with properties of limb progenitors. Dev Cell 2024; 59:415-430.e8. [PMID: 38320485 PMCID: PMC10932627 DOI: 10.1016/j.devcel.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 09/25/2022] [Accepted: 12/20/2023] [Indexed: 02/08/2024]
Abstract
The early limb bud consists of mesenchymal limb progenitors derived from the lateral plate mesoderm (LPM). The LPM also gives rise to the mesodermal components of the flank and neck. However, the cells at these other levels cannot produce the variety of cell types found in the limb. Taking advantage of a direct reprogramming approach, we find a set of factors (Prdm16, Zbtb16, and Lin28a) normally expressed in the early limb bud and capable of imparting limb progenitor-like properties to mouse non-limb fibroblasts. The reprogrammed cells show similar gene expression profiles and can differentiate into similar cell types as endogenous limb progenitors. The further addition of Lin41 potentiates the proliferation of the reprogrammed cells. These results suggest that these same four factors may play pivotal roles in the specification of endogenous limb progenitors.
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Affiliation(s)
- Yuji Atsuta
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Biology, Kyushu University, Fukuoka 819-0395, Japan
| | - ChangHee Lee
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.
| | - Alan R Rodrigues
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA
| | - Charlotte Colle
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Reiko R Tomizawa
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Ernesto G Lujan
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA
| | - Patrick Tschopp
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Zoological Institute, University of Basel, 4051 Basel, Switzerland
| | - Laura Galan
- Instituto de Biomedicina y Biotecnologia de Cantabria, CSIC, SODERCAN- Universidad de Cantabria, 39011 Santander, Spain
| | - Meng Zhu
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Joshua M Gorham
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | | | - Christine E Seidman
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Jonathan G Seidman
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Marian A Ros
- Instituto de Biomedicina y Biotecnologia de Cantabria, CSIC, SODERCAN- Universidad de Cantabria, 39011 Santander, Spain
| | - Olivier Pourquié
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Clifford J Tabin
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.
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34
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Piechka A, Sparanese S, Witherspoon L, Hach F, Flannigan R. Molecular mechanisms of cellular dysfunction in testes from men with non-obstructive azoospermia. Nat Rev Urol 2024; 21:67-90. [PMID: 38110528 DOI: 10.1038/s41585-023-00837-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2023] [Indexed: 12/20/2023]
Abstract
Male factor infertility affects 50% of infertile couples worldwide; the most severe form, non-obstructive azoospermia (NOA), affects 10-15% of infertile males. Treatment for individuals with NOA is limited to microsurgical sperm extraction paired with in vitro fertilization intracytoplasmic sperm injection. Unfortunately, spermatozoa are only retrieved in ~50% of patients, resulting in live birth rates of 21-46%. Regenerative therapies could provide a solution; however, understanding the cell-type-specific mechanisms of cellular dysfunction is a fundamental necessity to develop precision medicine strategies that could overcome these abnormalities and promote regeneration of spermatogenesis. A number of mechanisms of cellular dysfunction have been elucidated in NOA testicular cells. These mechanisms include abnormalities in both somatic cells and germ cells in NOA testes, such as somatic cell immaturity, aberrant growth factor signalling, increased inflammation, increased apoptosis and abnormal extracellular matrix regulation. Future cell-type-specific investigations in identifying modulators of cellular transcription and translation will be key to understanding upstream dysregulation, and these studies will require development of in vitro models to functionally interrogate spermatogenic niche dysfunction in both somatic and germ cells.
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Affiliation(s)
- Arina Piechka
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada
| | - Sydney Sparanese
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Luke Witherspoon
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
- Division of Urology, Department of Surgery, University of Ottawa, Ontario, Canada
| | - Faraz Hach
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada
| | - Ryan Flannigan
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada.
- Department of Urology, Weill Cornell Medicine, New York, NY, USA.
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35
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Imaz-Rosshandler I, Rode C, Guibentif C, Harland LTG, Ton MLN, Dhapola P, Keitley D, Argelaguet R, Calero-Nieto FJ, Nichols J, Marioni JC, de Bruijn MFTR, Göttgens B. Tracking early mammalian organogenesis - prediction and validation of differentiation trajectories at whole organism scale. Development 2024; 151:dev201867. [PMID: 37982461 PMCID: PMC10906099 DOI: 10.1242/dev.201867] [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: 04/12/2023] [Accepted: 10/30/2023] [Indexed: 11/21/2023]
Abstract
Early organogenesis represents a key step in animal development, during which pluripotent cells diversify to initiate organ formation. Here, we sampled 300,000 single-cell transcriptomes from mouse embryos between E8.5 and E9.5 in 6-h intervals and combined this new dataset with our previous atlas (E6.5-E8.5) to produce a densely sampled timecourse of >400,000 cells from early gastrulation to organogenesis. Computational lineage reconstruction identified complex waves of blood and endothelial development, including a new programme for somite-derived endothelium. We also dissected the E7.5 primitive streak into four adjacent regions, performed scRNA-seq and predicted cell fates computationally. Finally, we defined developmental state/fate relationships by combining orthotopic grafting, microscopic analysis and scRNA-seq to transcriptionally determine cell fates of grafted primitive streak regions after 24 h of in vitro embryo culture. Experimentally determined fate outcomes were in good agreement with computationally predicted fates, demonstrating how classical grafting experiments can be revisited to establish high-resolution cell state/fate relationships. Such interdisciplinary approaches will benefit future studies in developmental biology and guide the in vitro production of cells for organ regeneration and repair.
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Affiliation(s)
- Ivan Imaz-Rosshandler
- Department of Haematology, University of Cambridge, Cambridge CB2 0RE, UK
- Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
- MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
| | - Christina Rode
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Carolina Guibentif
- Department of Microbiology and Immunology, University of Gothenburg, 405 30 Gothenburg, Sweden
| | - Luke T. G. Harland
- Department of Haematology, University of Cambridge, Cambridge CB2 0RE, UK
- Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Mai-Linh N. Ton
- Department of Haematology, University of Cambridge, Cambridge CB2 0RE, UK
- Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Parashar Dhapola
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, 221 00 Lund, Sweden
| | - Daniel Keitley
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Ricard Argelaguet
- Epigenetics Programme, Babraham Institute, Cambridge CB22 3AT, UK
- Altos Labs Cambridge Institute, Granta Park, Cambridge CB21 6GP, UK
| | - Fernando J. Calero-Nieto
- Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Jennifer Nichols
- Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - John C. Marioni
- Wellcome Sanger Institute, Wellcome Genome Campus, Saffron Walden CB10 1SA, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Saffron Walden CB10 1SA, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK
| | - Marella F. T. R. de Bruijn
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Berthold Göttgens
- Department of Haematology, University of Cambridge, Cambridge CB2 0RE, UK
- Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
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36
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Yan F, Suzuki A, Iwaya C, Pei G, Chen X, Yoshioka H, Yu M, Simon LM, Iwata J, Zhao Z. Single-cell multiomics decodes regulatory programs for mouse secondary palate development. Nat Commun 2024; 15:821. [PMID: 38280850 PMCID: PMC10821874 DOI: 10.1038/s41467-024-45199-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: 02/23/2023] [Accepted: 01/17/2024] [Indexed: 01/29/2024] Open
Abstract
Perturbations in gene regulation during palatogenesis can lead to cleft palate, which is among the most common congenital birth defects. Here, we perform single-cell multiome sequencing and profile chromatin accessibility and gene expression simultaneously within the same cells (n = 36,154) isolated from mouse secondary palate across embryonic days (E) 12.5, E13.5, E14.0, and E14.5. We construct five trajectories representing continuous differentiation of cranial neural crest-derived multipotent cells into distinct lineages. By linking open chromatin signals to gene expression changes, we characterize the underlying lineage-determining transcription factors. In silico perturbation analysis identifies transcription factors SHOX2 and MEOX2 as important regulators of the development of the anterior and posterior palate, respectively. In conclusion, our study charts epigenetic and transcriptional dynamics in palatogenesis, serving as a valuable resource for further cleft palate research.
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Affiliation(s)
- Fangfang Yan
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Akiko Suzuki
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Department of Oral and Craniofacial Sciences, School of Dentistry, University of Missouri - Kansas City, Kansas City, Missouri, 64108, USA
| | - Chihiro Iwaya
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
| | - Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Xian Chen
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Hiroki Yoshioka
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
| | - Meifang Yu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Lukas M Simon
- Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Junichi Iwata
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA.
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA.
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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37
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Sinenko SA, Tomilin AN. Metabolic control of induced pluripotency. Front Cell Dev Biol 2024; 11:1328522. [PMID: 38274274 PMCID: PMC10808704 DOI: 10.3389/fcell.2023.1328522] [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: 10/26/2023] [Accepted: 12/13/2023] [Indexed: 01/27/2024] Open
Abstract
Pluripotent stem cells of the mammalian epiblast and their cultured counterparts-embryonic stem cells (ESCs) and epiblast stem cells (EpiSCs)-have the capacity to differentiate in all cell types of adult organisms. An artificial process of reactivation of the pluripotency program in terminally differentiated cells was established in 2006, which allowed for the generation of induced pluripotent stem cells (iPSCs). This iPSC technology has become an invaluable tool in investigating the molecular mechanisms of human diseases and therapeutic drug development, and it also holds tremendous promise for iPSC applications in regenerative medicine. Since the process of induced reprogramming of differentiated cells to a pluripotent state was discovered, many questions about the molecular mechanisms involved in this process have been clarified. Studies conducted over the past 2 decades have established that metabolic pathways and retrograde mitochondrial signals are involved in the regulation of various aspects of stem cell biology, including differentiation, pluripotency acquisition, and maintenance. During the reprogramming process, cells undergo major transformations, progressing through three distinct stages that are regulated by different signaling pathways, transcription factor networks, and inputs from metabolic pathways. Among the main metabolic features of this process, representing a switch from the dominance of oxidative phosphorylation to aerobic glycolysis and anabolic processes, are many critical stage-specific metabolic signals that control the path of differentiated cells toward a pluripotent state. In this review, we discuss the achievements in the current understanding of the molecular mechanisms of processes controlled by metabolic pathways, and vice versa, during the reprogramming process.
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Affiliation(s)
- Sergey A. Sinenko
- Institute of Cytology, Russian Academy of Sciences, Saint-Petersburg, Russia
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38
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Kobayashi-Kirschvink KJ, Comiter CS, Gaddam S, Joren T, Grody EI, Ounadjela JR, Zhang K, Ge B, Kang JW, Xavier RJ, So PTC, Biancalani T, Shu J, Regev A. Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA. Nat Biotechnol 2024:10.1038/s41587-023-02082-2. [PMID: 38200118 DOI: 10.1038/s41587-023-02082-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 12/01/2023] [Indexed: 01/12/2024]
Abstract
Single-cell RNA sequencing and other profiling assays have helped interrogate cells at unprecedented resolution and scale, but are inherently destructive. Raman microscopy reports on the vibrational energy levels of proteins and metabolites in a label-free and nondestructive manner at subcellular spatial resolution, but it lacks genetic and molecular interpretability. Here we present Raman2RNA (R2R), a method to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and domain translation. We predict single-cell RNA sequencing profiles nondestructively from Raman images using either anchor-based integration with single molecule fluorescence in situ hybridization, or anchor-free generation with adversarial autoencoders. R2R outperformed inference from brightfield images (cosine similarities: R2R >0.85 and brightfield <0.15). In reprogramming of mouse fibroblasts into induced pluripotent stem cells, R2R inferred the expression profiles of various cell states. With live-cell tracking of mouse embryonic stem cell differentiation, R2R traced the early emergence of lineage divergence and differentiation trajectories, overcoming discontinuities in expression space. R2R lays a foundation for future exploration of live genomic dynamics.
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Affiliation(s)
- Koseki J Kobayashi-Kirschvink
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Charles S Comiter
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shreya Gaddam
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Taylor Joren
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Emanuelle I Grody
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Johain R Ounadjela
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ke Zhang
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Baoliang Ge
- Department of Mechanical and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeon Woong Kang
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ramnik J Xavier
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Peter T C So
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mechanical and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tommaso Biancalani
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Genentech, South San Francisco, CA, USA.
| | - Jian Shu
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Genentech, South San Francisco, CA, USA.
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39
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Marzoog BA. Transcription Factors in Brain Regeneration: A Potential Novel Therapeutic Target. Curr Drug Targets 2024; 25:46-61. [PMID: 38444255 DOI: 10.2174/0113894501279977231210170231] [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: 08/30/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 03/07/2024]
Abstract
Transcription factors play a crucial role in providing identity to each cell population. To maintain cell identity, it is essential to balance the expression of activator and inhibitor transcription factors. Cell plasticity and reprogramming offer great potential for future therapeutic applications, as they can regenerate damaged tissue. Specific niche factors can modify gene expression and differentiate or transdifferentiate the target cell to the required fate. Ongoing research is being carried out on the possibilities of transcription factors in regenerating neurons, with neural stem cells (NSCs) being considered the preferred cells for generating new neurons due to their epigenomic and transcriptome memory. NEUROD1/ASCL1, BRN2, MYTL1, and other transcription factors can induce direct reprogramming of somatic cells, such as fibroblasts, into neurons. However, the molecular biology of transcription factors in reprogramming and differentiation still needs to be fully understood.
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Affiliation(s)
- Basheer Abdullah Marzoog
- World-Class Research Center, Digital Biodesign and Personalized Healthcare», I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
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40
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Xie F, Jain S, Butrus S, Shekhar K, Zipursky SL. Vision sculpts a continuum of L2/3 cell types in the visual cortex during the critical period. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.572244. [PMID: 38187533 PMCID: PMC10769288 DOI: 10.1101/2023.12.18.572244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
We previously reported that vision specifies Layer 2/3 (L2/3) glutamatergic cell-type identity in the primary visual cortex (V1). Using unsupervised clustering of single-nucleus RNA-sequencing data, we identified molecularly distinct L2/3 cell types in normal-reared (NR) and dark-reared (DR) mice, but the two sets exhibited poor correspondence. Here, we show that classification of cell types was confounded in DR by vision-dependent gene programs that are orthogonal to gene programs underlying cell-type identity. A focused clustering analysis successfully matches cell types between DR and NR, suggesting that cell identity-defining gene programs persist under vision deprivation but are overshadowed by vision-dependent transcriptomic variation. Using multi-tasking theory we show that L2/3 cell types form a continuum between three cell-archetypes. Visual deprivation markedly shifts this distribution along the continuum. Thus, dark-rearing markedly influences cell states thereby masking cell-type-identities and changes the distribution of L2/3 types along a transcriptomic continuum.
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Affiliation(s)
- Fangming Xie
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- These authors contributed equally
| | - Saumya Jain
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- These authors contributed equally
| | - Salwan Butrus
- Department of Chemical and Biomolecular Engineering, Helen Wills Neuroscience Institute, California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, CA 94720, USA
| | - Karthik Shekhar
- Department of Chemical and Biomolecular Engineering, Helen Wills Neuroscience Institute, California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, CA 94720, USA
- Faculty Scientist, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - S. Lawrence Zipursky
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Lead contact
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Mao Q, Ye Q, Xu Y, Jiang J, Fan Y, Zhuang L, Liu G, Wang T, Zhang Z, Feng T, Kong S, Lu J, Zhang H, Wang H, Lin CP. Murine trophoblast organoids as a model for trophoblast development and CRISPR-Cas9 screening. Dev Cell 2023; 58:2992-3008.e7. [PMID: 38056451 DOI: 10.1016/j.devcel.2023.11.007] [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: 03/06/2023] [Revised: 07/27/2023] [Accepted: 11/10/2023] [Indexed: 12/08/2023]
Abstract
The placenta becomes one of the most diversified organs during placental mammal radiation. The main in vitro model for studying mouse trophoblast development is the 2D differentiation model of trophoblast stem cells, which is highly skewed to certain lineages and thus hampers systematic screens. Here, we established culture conditions for the establishment, maintenance, and differentiation of murine trophoblast organoids. Murine trophoblast organoids under the maintenance condition contain stem cell-like populations, whereas differentiated organoids possess various trophoblasts resembling placental ones in vivo. Ablation of Nubpl or Gcm1 in trophoblast organoids recapitulated their deficiency phenotypes in vivo, suggesting that those organoids are valid in vitro models for trophoblast development. Importantly, we performed an efficient CRISPR-Cas9 screening in mouse trophoblast organoids using a focused sgRNA (single guide RNA) library targeting G protein-coupled receptors. Together, our results establish an organoid model to investigate mouse trophoblast development and a practicable approach to performing forward screening in trophoblast lineages.
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Affiliation(s)
- Qian Mao
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Qinying Ye
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yiwen Xu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jingwei Jiang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yunhao Fan
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Lili Zhuang
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200125, China
| | - Guohui Liu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Tengfei Wang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Zhenwu Zhang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Teng Feng
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Shuangbo Kong
- Fujian Provincial Key Laboratory of Reproductive Health Research, Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Jinhua Lu
- Fujian Provincial Key Laboratory of Reproductive Health Research, Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Hui Zhang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Haopeng Wang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
| | - Chao-Po Lin
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
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Long C, Li H, Liang P, Chao L, Hong Y, Zhang J, Xi Q, Zuo Y. Deciphering the decisive factors driving fate bifurcations in somatic cell reprogramming. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 34:102044. [PMID: 37869261 PMCID: PMC10585637 DOI: 10.1016/j.omtn.2023.102044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/28/2023] [Indexed: 10/24/2023]
Abstract
Single-cell studies have demonstrated that somatic cell reprogramming is a continuous process of cell fates transition. Only partial reprogramming intermediates can overcome the molecular bottlenecks to acquire pluripotency. To decipher the underlying decisive factors driving cell fate, we identified induced pluripotent stem cells or stromal-like cells (iPSCs/SLCs) and iPSCs or trophoblast-like cells (iPSCs/TLCs) fate bifurcations by reconstructing cellular trajectory. The mesenchymal-epithelial transition and the activation of pluripotency networks are the main molecular series in successful reprogramming. Correspondingly, intermediates diverge into SLCs accompanied by the inhibition of cell cycle genes and the activation of extracellular matrix genes, whereas the TLCs fate is characterized by the up-regulation of placenta development genes. Combining putative gene regulatory networks, seven (Taf7, Ezh2, Klf2, etc.) and three key factors (Cdc5l, Klf4, and Nanog) were individually identified as drivers of the successful reprogramming by triggering downstream pluripotent networks during iPSCs/SLCs and iPSCs/TLCs fate bifurcation. Conversely, 11 factors (Cebpb, Sox4, Junb, etc.) and four factors (Gata2, Jund, Ctnnb1, etc.) drive SLCs fate and TLCs fate, respectively. Our study sheds new light on the understanding of decisive factors driving cell fate, which is helpful for improving reprogramming efficiency through manipulating cell fates to avoid alternative fates.
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Affiliation(s)
- Chunshen Long
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Hanshuang Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Lemuge Chao
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yan Hong
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Junping Zhang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Qilemuge Xi
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
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Ilia K, Shakiba N, Bingham T, Jones RD, Kaminski MM, Aravera E, Bruno S, Palacios S, Weiss R, Collins JJ, Del Vecchio D, Schlaeger TM. Synthetic genetic circuits to uncover the OCT4 trajectories of successful reprogramming of human fibroblasts. SCIENCE ADVANCES 2023; 9:eadg8495. [PMID: 38019912 PMCID: PMC10686568 DOI: 10.1126/sciadv.adg8495] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023]
Abstract
Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. We develop a system that accurately reports OCT4 protein levels in live cells and use it to reveal the trajectories of OCT4 in successful reprogramming. Our system comprises a synthetic genetic circuit that leverages noise to generate a wide range of OCT4 trajectories and a microRNA targeting endogenous OCT4 to set total cellular OCT4 protein levels. By fusing OCT4 to a fluorescent protein, we are able to track OCT4 trajectories with clonal resolution via live-cell imaging. We discover that a supraphysiological, stable OCT4 level is required, but not sufficient, for efficient iPSC colony formation. Our synthetic genetic circuit design and high-throughput live-imaging pipeline are generalizable for investigating TF dynamics for other cell fate programming applications.
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Affiliation(s)
- Katherine Ilia
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Nika Shakiba
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z3 Canada
| | - Trevor Bingham
- Stem Cell Program, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard University, Boston, MA 02115, USA
| | - Ross D. Jones
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z3 Canada
| | - Michael M. Kaminski
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz-Association, Berlin 10115, Germany
- Department of Nephrology and Medical Intensive Care, Charité – Universitätsmedizin Berlin, Medizinische Klinik m.S. Nephrologie und Intensivmedizin, Berlin 10117, Germany
- Berlin Institute of Health, Berlin 13125, Germany
| | - Eliezer Aravera
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Simone Bruno
- Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA
| | - Sebastian Palacios
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
| | - James J. Collins
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02215, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
| | - Domitilla Del Vecchio
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA
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44
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Zhang M, Chen Y, Yu D, Zhong W, Zhang J, Ma P. Elucidating dynamic cell lineages and gene networks in time-course single cell differentiation. ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2023; 3:100068. [PMID: 37426065 PMCID: PMC10328540 DOI: 10.1016/j.ailsci.2023.100068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Single cell RNA sequencing (scRNA-seq) technologies provide researchers with an unprecedented opportunity to exploit cell heterogeneity. For example, the sequenced cells belong to various cell lineages, which may have different cell fates in stem and progenitor cells. Those cells may differentiate into various mature cell types in a cell differentiation process. To trace the behavior of cell differentiation, researchers reconstruct cell lineages and predict cell fates by ordering cells chronologically into a trajectory with a pseudo-time. However, in scRNA-seq experiments, there are no cell-to-cell correspondences along with the time to reconstruct the cell lineages, which creates a significant challenge for cell lineage tracing and cell fate prediction. Therefore, methods that can accurately reconstruct the dynamic cell lineages and predict cell fates are highly desirable. In this article, we develop an innovative machine-learning framework called Cell Smoothing Transformation (CellST) to elucidate the dynamic cell fate paths and construct gene networks in cell differentiation processes. Unlike the existing methods that construct one single bulk cell trajectory, CellST builds cell trajectories and tracks behaviors for each individual cell. Additionally, CellST can predict cell fates even for less frequent cell types. Based on the individual cell fate trajectories, CellST can further construct dynamic gene networks to model gene-gene relationships along the cell differentiation process and discover critical genes that potentially regulate cells into various mature cell types.
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Affiliation(s)
| | - Yongkai Chen
- Department of Statistics, University of Georgia, Athens, GA 30602, United Stated
| | - Dingyi Yu
- Department of Industrial Engineering, Center for Statistical Science, Tsinghua University, Beijing, China
| | - Wenxuan Zhong
- Department of Statistics, University of Georgia, Athens, GA 30602, United Stated
| | - Jingyi Zhang
- Department of Industrial Engineering, Center for Statistical Science, Tsinghua University, Beijing, China
| | - Ping Ma
- Department of Statistics, University of Georgia, Athens, GA 30602, United Stated
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45
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Sha Y, Qiu Y, Zhou P, Nie Q. Reconstructing growth and dynamic trajectories from single-cell transcriptomics data. NAT MACH INTELL 2023; 6:25-39. [PMID: 38274364 PMCID: PMC10805654 DOI: 10.1038/s42256-023-00763-w] [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: 02/08/2023] [Accepted: 10/25/2023] [Indexed: 01/27/2024]
Abstract
Time-series single-cell RNA sequencing (scRNA-seq) datasets provide unprecedented opportunities to learn dynamic processes of cellular systems. Due to the destructive nature of sequencing, it remains challenging to link the scRNA-seq snapshots sampled at different time points. Here we present TIGON, a dynamic, unbalanced optimal transport algorithm that reconstructs dynamic trajectories and population growth simultaneously as well as the underlying gene regulatory network from multiple snapshots. To tackle the high-dimensional optimal transport problem, we introduce a deep learning method using a dimensionless formulation based on the Wasserstein-Fisher-Rao (WFR) distance. TIGON is evaluated on simulated data and compared with existing methods for its robustness and accuracy in predicting cell state transition and cell population growth. Using three scRNA-seq datasets, we show the importance of growth in the temporal inference, TIGON's capability in reconstructing gene expression at unmeasured time points and its applications to temporal gene regulatory networks and cell-cell communication inference.
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Affiliation(s)
- Yutong Sha
- Department of Mathematics, University of California, Irvine, Irvine, CA USA
| | - Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, MI USA
| | - Peijie Zhou
- Department of Mathematics, University of California, Irvine, Irvine, CA USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA USA
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA USA
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46
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Wang NB, Lende-Dorn BA, Adewumi HO, Beitz AM, Han P, O'Shea TM, Galloway KE. Proliferation history and transcription factor levels drive direct conversion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.26.568736. [PMID: 38077004 PMCID: PMC10705288 DOI: 10.1101/2023.11.26.568736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
The sparse and stochastic nature of reprogramming has obscured our understanding of how transcription factors drive cells to new identities. To overcome this limit, we developed a compact, portable reprogramming system that increases direct conversion of fibroblasts to motor neurons by two orders of magnitude. We show that subpopulations with different reprogramming potentials are distinguishable by proliferation history. By controlling for proliferation history and titrating each transcription factor, we find that conversion correlates with levels of the pioneer transcription factor Ngn2, whereas conversion shows a biphasic response to Lhx3. Increasing the proliferation rate of adult human fibroblasts generates morphologically mature, induced motor neurons at high rates. Using compact, optimized, polycistronic cassettes, we generate motor neurons that graft with the murine central nervous system, demonstrating the potential for in vivo therapies.
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Affiliation(s)
- Nathan B Wang
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | | | - Honour O Adewumi
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Adam M Beitz
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | - Patrick Han
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | - Timothy M O'Shea
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Kate E Galloway
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
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47
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Rao J, Djeffal Y, Chal J, Marchianò F, Wang CH, Al Tanoury Z, Gapon S, Mayeuf-Louchart A, Glass I, Sefton EM, Habermann B, Kardon G, Watt FM, Tseng YH, Pourquié O. Reconstructing human brown fat developmental trajectory in vitro. Dev Cell 2023; 58:2359-2375.e8. [PMID: 37647896 PMCID: PMC10873093 DOI: 10.1016/j.devcel.2023.08.001] [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: 10/19/2021] [Revised: 08/23/2022] [Accepted: 08/01/2023] [Indexed: 09/01/2023]
Abstract
Brown adipocytes (BAs) represent a specialized cell type that is able to uncouple nutrient catabolism from ATP generation to dissipate energy as heat. In humans, the brown fat tissue is composed of discrete depots found throughout the neck and trunk region. BAs originate from a precursor common to skeletal muscle, but their developmental trajectory remains poorly understood. Here, we used single-cell RNA sequencing to characterize the development of interscapular brown fat in mice. Our analysis identified a transient stage of BA differentiation characterized by the expression of the transcription factor GATA6. We show that recapitulating the sequence of signaling cues identified in mice can lead to efficient differentiation of BAs in vitro from human pluripotent stem cells. These precursors can in turn be efficiently converted into functional BAs that can respond to signals mimicking adrenergic stimuli by increasing their metabolism, resulting in heat production.
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Affiliation(s)
- Jyoti Rao
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - Yannis Djeffal
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - Jerome Chal
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - Fabio Marchianò
- Aix-Marseille University, CNRS, IBDM, The Turing Center for Living Systems, 13009 Marseille, France
| | - Chih-Hao Wang
- Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Ziad Al Tanoury
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - Svetlana Gapon
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | | | - Ian Glass
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Elizabeth M Sefton
- Department of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
| | - Bianca Habermann
- Aix-Marseille University, CNRS, IBDM, The Turing Center for Living Systems, 13009 Marseille, France
| | - Gabrielle Kardon
- Department of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
| | - Fiona M Watt
- King's College London Centre for Stem Cells and Regenerative Medicine, Great Maze Pond, London SE1 9RT, UK
| | - Yu-Hua Tseng
- Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Olivier Pourquié
- Department of Pathology, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA.
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48
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Yampolskaya M, Herriges MJ, Ikonomou L, Kotton DN, Mehta P. scTOP: physics-inspired order parameters for cellular identification and visualization. Development 2023; 150:dev201873. [PMID: 37756586 PMCID: PMC10629677 DOI: 10.1242/dev.201873] [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: 04/11/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
Advances in single-cell RNA sequencing provide an unprecedented window into cellular identity. The abundance of data requires new theoretical and computational frameworks to analyze the dynamics of differentiation and integrate knowledge from cell atlases. We present 'single-cell Type Order Parameters' (scTOP): a statistical, physics-inspired approach for quantifying cell identity given a reference basis of cell types. scTOP can accurately classify cells, visualize developmental trajectories and assess the fidelity of engineered cells. Importantly, scTOP does this without feature selection, statistical fitting or dimensional reduction (e.g. uniform manifold approximation and projection, principle components analysis, etc.). We illustrate the power of scTOP using human and mouse datasets. By reanalyzing mouse lung data, we characterize a transient hybrid alveolar type 1/alveolar type 2 cell population. Visualizations of lineage tracing hematopoiesis data using scTOP confirm that a single clone can give rise to multiple mature cell types. We assess the transcriptional similarity between endogenous and donor-derived cells in the context of murine pulmonary cell transplantation. Our results suggest that physics-inspired order parameters can be an important tool for understanding differentiation and characterizing engineered cells. scTOP is available as an easy-to-use Python package.
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Affiliation(s)
| | - Michael J. Herriges
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA
- The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Laertis Ikonomou
- Department of Oral Biology, University at Buffalo, The State University of New York, Buffalo, NY 14215, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University at Buffalo, The State University of New York, Buffalo, NY 14215, USA
| | - Darrell N. Kotton
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA
- The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Pankaj Mehta
- Department of Physics, Boston University, Boston, MA 02215, USA
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA
- Faculty of Computing and Data Science, Boston University, Boston, MA 02215, USA
- Biological Design Center, Boston University, Boston, MA 02215, USA
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49
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Neural optimal transport predicts perturbation responses at the single-cell level. Nat Methods 2023; 20:1639-1640. [PMID: 37770713 DOI: 10.1038/s41592-023-01968-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
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50
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Caronni N, La Terza F, Vittoria FM, Barbiera G, Mezzanzanica L, Cuzzola V, Barresi S, Pellegatta M, Canevazzi P, Dunsmore G, Leonardi C, Montaldo E, Lusito E, Dugnani E, Citro A, Ng MSF, Schiavo Lena M, Drago D, Andolfo A, Brugiapaglia S, Scagliotti A, Mortellaro A, Corbo V, Liu Z, Mondino A, Dellabona P, Piemonti L, Taveggia C, Doglioni C, Cappello P, Novelli F, Iannacone M, Ng LG, Ginhoux F, Crippa S, Falconi M, Bonini C, Naldini L, Genua M, Ostuni R. IL-1β + macrophages fuel pathogenic inflammation in pancreatic cancer. Nature 2023; 623:415-422. [PMID: 37914939 DOI: 10.1038/s41586-023-06685-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 09/27/2023] [Indexed: 11/03/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with high resistance to therapies1. Inflammatory and immunomodulatory signals co-exist in the pancreatic tumour microenvironment, leading to dysregulated repair and cytotoxic responses. Tumour-associated macrophages (TAMs) have key roles in PDAC2, but their diversity has prevented therapeutic exploitation. Here we combined single-cell and spatial genomics with functional experiments to unravel macrophage functions in pancreatic cancer. We uncovered an inflammatory loop between tumour cells and interleukin-1β (IL-1β)-expressing TAMs, a subset of macrophages elicited by a local synergy between prostaglandin E2 (PGE2) and tumour necrosis factor (TNF). Physical proximity with IL-1β+ TAMs was associated with inflammatory reprogramming and acquisition of pathogenic properties by a subset of PDAC cells. This occurrence was an early event in pancreatic tumorigenesis and led to persistent transcriptional changes associated with disease progression and poor outcomes for patients. Blocking PGE2 or IL-1β activity elicited TAM reprogramming and antagonized tumour cell-intrinsic and -extrinsic inflammation, leading to PDAC control in vivo. Targeting the PGE2-IL-1β axis may enable preventive or therapeutic strategies for reprogramming of immune dynamics in pancreatic cancer.
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Affiliation(s)
- Nicoletta Caronni
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy.
| | - Federica La Terza
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesco M Vittoria
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Giulia Barbiera
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Luca Mezzanzanica
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Vincenzo Cuzzola
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Simona Barresi
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | | | - Garett Dunsmore
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
| | - Carlo Leonardi
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisa Montaldo
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Eleonora Lusito
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Erica Dugnani
- Diabetes Research Institute (DRI), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Antonio Citro
- Diabetes Research Institute (DRI), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Melissa S F Ng
- Singapore Immunology Network (SIgN), A*STAR, Singapore, Singapore
| | | | - Denise Drago
- Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Annapaola Andolfo
- Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Brugiapaglia
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Alessandro Scagliotti
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Alessandra Mortellaro
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Zhaoyuan Liu
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Anna Mondino
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Lorenzo Piemonti
- Vita-Salute San Raffaele University, Milan, Italy
- Diabetes Research Institute (DRI), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Claudio Doglioni
- Vita-Salute San Raffaele University, Milan, Italy
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Cappello
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Francesco Novelli
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Matteo Iannacone
- Vita-Salute San Raffaele University, Milan, Italy
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Lai Guan Ng
- Shanghai Immune Therapy Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Florent Ginhoux
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- Singapore Immunology Network (SIgN), A*STAR, Singapore, Singapore
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Translational Immunology Institute, SingHealth/Duke-NUS Academic Medical Centre, Singapore, Singapore
| | - Stefano Crippa
- Vita-Salute San Raffaele University, Milan, Italy
- Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Falconi
- Vita-Salute San Raffaele University, Milan, Italy
- Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Chiara Bonini
- Vita-Salute San Raffaele University, Milan, Italy
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Luigi Naldini
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Marco Genua
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Renato Ostuni
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
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