1
|
Sciarretta F, Ninni A, Zaccaria F, Chiurchiù V, Bertola A, Karlinsey K, Jia W, Ceci V, Di Biagio C, Xu Z, Gaudioso F, Tortolici F, Tiberi M, Zhang J, Carotti S, Boudina S, Grumati P, Zhou B, Brestoff JR, Ivanov S, Aquilano K, Lettieri-Barbato D. Lipid-associated macrophages reshape BAT cell identity in obesity. Cell Rep 2024; 43:114447. [PMID: 38963761 DOI: 10.1016/j.celrep.2024.114447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 03/04/2024] [Accepted: 06/20/2024] [Indexed: 07/06/2024] Open
Abstract
Obesity and type 2 diabetes cause a loss in brown adipose tissue (BAT) activity, but the molecular mechanisms that drive BAT cell remodeling remain largely unexplored. Using a multilayered approach, we comprehensively mapped a reorganization in BAT cells. We uncovered a subset of macrophages as lipid-associated macrophages (LAMs), which were massively increased in genetic and dietary model of BAT expansion. LAMs participate in this scenario by capturing extracellular vesicles carrying damaged lipids and mitochondria released from metabolically stressed brown adipocytes. CD36 scavenger receptor drove LAM phenotype, and CD36-deficient LAMs were able to increase brown fat genes in adipocytes. LAMs released transforming growth factor β1 (TGF-β1), which promoted the loss of brown adipocyte identity through aldehyde dehydrogenase 1 family member A1 (Aldh1a1) induction. These findings unfold cell dynamic changes in BAT during obesity and identify LAMs as key responders to tissue metabolic stress and drivers of loss of brown adipocyte identity.
Collapse
Affiliation(s)
| | - Andrea Ninni
- Department of Biology, University of Rome Tor Vergata, Rome, Italy; PhD Program in Evolutionary Biology and Ecology, Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Fabio Zaccaria
- Department of Biology, University of Rome Tor Vergata, Rome, Italy; PhD Program in Evolutionary Biology and Ecology, Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Valerio Chiurchiù
- Laboratory of Resolution of Neuroinflammation, IRCCS Santa Lucia Foundation, Rome, Italy; Institute of Translational Pharmacology, National Research Council, Rome, Italy
| | | | - Keaton Karlinsey
- Department of Immunology, School of Medicine, University of Connecticut, Farmington, CT, USA
| | - Wentong Jia
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Veronica Ceci
- Department of Biology, University of Rome Tor Vergata, Rome, Italy; PhD Program in Evolutionary Biology and Ecology, Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | | | - Ziyan Xu
- NOMIS Center for Immunobiology and Microbial Pathogenesis, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Francesco Gaudioso
- IRCCS Santa Lucia Foundation, Rome, Italy; Department of Biology, University of Rome Tor Vergata, Rome, Italy; PhD Program in Evolutionary Biology and Ecology, Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Flavia Tortolici
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Marta Tiberi
- Laboratory of Resolution of Neuroinflammation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Jiabi Zhang
- Department of Nutrition & Integrative Physiology, University of Utah, Salt Lake City, UT, USA
| | - Simone Carotti
- Integrated Research Center (PRAAB), Campus Biomedico University of Rome, Rome, Italy
| | - Sihem Boudina
- Department of Nutrition & Integrative Physiology, University of Utah, Salt Lake City, UT, USA; Molecular Medicine Program (U2M2), University of Utah, Salt Lake City, UT, USA
| | - Paolo Grumati
- Telethon Institute of Genetics and Medicine, Pozzuoli, Italy; Department of Clinical Medicine and Surgery, University Federico II, Naples, Italy
| | - Beiyan Zhou
- Department of Immunology, School of Medicine, University of Connecticut, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Jonathan R Brestoff
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | | | - Katia Aquilano
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Daniele Lettieri-Barbato
- Department of Biology, University of Rome Tor Vergata, Rome, Italy; IRCCS Fondazione Bietti, Rome, Italy.
| |
Collapse
|
2
|
Abebayehu D, Pfaff BN, Bingham GC, Miller AE, Kibet M, Ghatti S, Griffin DR, Barker TH. A Thy-1-negative immunofibroblast population emerges as a key determinant of fibrotic outcomes to biomaterials. SCIENCE ADVANCES 2024; 10:eadf2675. [PMID: 38875340 PMCID: PMC11177936 DOI: 10.1126/sciadv.adf2675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 05/10/2024] [Indexed: 06/16/2024]
Abstract
Fibrosis-associated fibroblasts have been identified across various fibrotic disorders, but not in the context of biomaterials, fibrotic encapsulation, and the foreign body response. In other fibrotic disorders, a fibroblast subpopulation defined by Thy-1 loss is strongly correlated with fibrosis yet we do not know what promotes Thy-1 loss. We have previously shown that Thy-1 is an integrin regulator enabling normal fibroblast mechanosensing, and here, leveraging nonfibrotic microporous annealed particle (MAP) hydrogels versus classical fibrotic bulk hydrogels, we demonstrate that Thy1-/- mice mount a fibrotic response to MAP gels that includes inflammatory signaling. We found that a distinct and cryptic α-smooth muscle actin-positive Thy-1- fibroblast population emerges in response to interleuklin-1β (IL-1β) and tumor necrosis factor-α (TNFα). Furthermore, IL-1β/TNFα-induced Thy-1- fibroblasts consist of two distinct subpopulations that are strongly proinflammatory. These findings illustrate the emergence of a unique proinflammatory, profibrotic fibroblast subpopulation that is central to fibrotic encapsulation of biomaterials.
Collapse
Affiliation(s)
- Daniel Abebayehu
- Department of Biomedical Engineering, Schools of Engineering and Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Robert Berne Cardiovascular Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Blaise N. Pfaff
- Department of Biomedical Engineering, Schools of Engineering and Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Grace C. Bingham
- Department of Biomedical Engineering, Schools of Engineering and Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Andrew E. Miller
- Department of Biomedical Engineering, Schools of Engineering and Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Mathew Kibet
- Department of Biomedical Engineering, Schools of Engineering and Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Surabhi Ghatti
- Department of Biomedical Engineering, Schools of Engineering and Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Donald R. Griffin
- Department of Biomedical Engineering, Schools of Engineering and Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Thomas H. Barker
- Department of Biomedical Engineering, Schools of Engineering and Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Robert Berne Cardiovascular Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Cell Biology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| |
Collapse
|
3
|
Armingol E, Baghdassarian HM, Lewis NE. The diversification of methods for studying cell-cell interactions and communication. Nat Rev Genet 2024; 25:381-400. [PMID: 38238518 PMCID: PMC11139546 DOI: 10.1038/s41576-023-00685-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2023] [Indexed: 05/20/2024]
Abstract
No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.
Collapse
Affiliation(s)
- Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA.
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
| | - Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Nathan E Lewis
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
| |
Collapse
|
4
|
Konecny AJ, Huang Y, Setty M, Prlic M. Signals that control MAIT cell function in healthy and inflamed human tissues. Immunol Rev 2024; 323:138-149. [PMID: 38520075 DOI: 10.1111/imr.13325] [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: 03/25/2024]
Abstract
Mucosal-associated invariant T (MAIT) cells have a semi-invariant T-cell receptor that allows recognition of antigen in the context of the MHC class I-related (MR1) protein. Metabolic intermediates of the riboflavin synthesis pathway have been identified as MR1-restricted antigens with agonist properties. As riboflavin synthesis occurs in many bacterial species, but not human cells, it has been proposed that the main purpose of MAIT cells is antibacterial surveillance and protection. The majority of human MAIT cells secrete interferon-gamma (IFNg) upon activation, while some MAIT cells in tissues can also express IL-17. Given that MAIT cells are present in human barrier tissues colonized by a microbiome, MAIT cells must somehow be able to distinguish colonization from infection to ensure effector functions are only elicited when necessary. Importantly, MAIT cells have additional functional properties, including the potential to contribute to restoring tissue homeostasis by expression of CTLA-4 and secretion of the cytokine IL-22. A recent study provided compelling data indicating that the range of human MAIT cell functional properties is explained by plasticity rather than distinct lineages. This further underscores the necessity to better understand how different signals regulate MAIT cell function. In this review, we highlight what is known in regards to activating and inhibitory signals for MAIT cells with a specific focus on signals relevant to healthy and inflamed tissues. We consider the quantity, quality, and the temporal order of these signals on MAIT cell function and discuss the current limitations of computational tools to extrapolate which signals are received by MAIT cells in human tissues. Using lessons learned from conventional CD8 T cells, we also discuss how TCR signals may integrate with cytokine signals in MAIT cells to elicit distinct functional states.
Collapse
Affiliation(s)
- Andrew J Konecny
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Immunology, University of Washington, Seattle, Washington, USA
| | - Yin Huang
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Herbold Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, USA
| | - Manu Setty
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Herbold Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Martin Prlic
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Immunology, University of Washington, Seattle, Washington, USA
- Department of Global Health, University of Washington, Seattle, Washington, USA
| |
Collapse
|
5
|
Hachemi Y, Perrin S, Ethel M, Julien A, Vettese J, Geisler B, Göritz C, Colnot C. Multimodal analyses of immune cells during bone repair identify macrophages as a therapeutic target in musculoskeletal trauma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.29.591608. [PMID: 38746344 PMCID: PMC11092472 DOI: 10.1101/2024.04.29.591608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Musculoskeletal traumatic injuries (MTI) involve soft tissue lesions adjacent to a bone fracture leading to fibrous nonunion. The impact of MTI on the inflammatory response to fracture and on the immunomodulation of skeletal stem/progenitor cells (SSPCs) remains unknown. Here, we used single cell transcriptomic analyses to describe the immune cell dynamics after bone fracture and identified distinct macrophage subsets with successive pro-inflammatory, pro-repair and anti-inflammatory profiles. Concurrently, SSPCs transition via a pro- and anti-inflammatory fibrogenic phase of differentiation prior to osteochondrogenic differentiation. In a preclinical MTI mouse model, the injury response of immune cells and SSPCs is disrupted leading to a prolonged pro-inflammatory phase and delayed resolution of inflammation. Macrophage depletion improves bone regeneration in MTI demonstrating macrophage involvement in fibrous nonunion. Finally, pharmacological inhibition of macrophages using the CSF1R inhibitor Pexidartinib ameliorates healing. These findings reveal the coordinated immune response of macrophages and skeletal stem/progenitor cells as driver of bone healing and as a primary target for the treatment of trauma-associated fibrosis. Summary Hachemi et al. report the immune cell atlas of bone repair revealing macrophages as pro-fibrotic regulators and a therapeutic target for musculoskeletal regeneration. Genetic depletion or pharmacological inhibition of macrophages improves bone healing in musculoskeletal trauma.
Collapse
|
6
|
Zhou L, Wang X, Peng L, Chen M, Wen H. SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference. J Cell Mol Med 2024; 28:e18372. [PMID: 38747737 PMCID: PMC11095317 DOI: 10.1111/jcmm.18372] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 04/10/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024] Open
Abstract
Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.
Collapse
Affiliation(s)
- Liqian Zhou
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Xiwen Wang
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Lihong Peng
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Min Chen
- School of Computer ScienceHunan Institute of TechnologyHengyangChina
| | - Hong Wen
- School of Computer ScienceHunan University of TechnologyHunanChina
| |
Collapse
|
7
|
Baghdassarian HM, Dimitrov D, Armingol E, Saez-Rodriguez J, Lewis NE. Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. CELL REPORTS METHODS 2024; 4:100758. [PMID: 38631346 PMCID: PMC11046036 DOI: 10.1016/j.crmeth.2024.100758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/22/2023] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.
Collapse
Affiliation(s)
- Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany
| | - Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
| |
Collapse
|
8
|
Foltz L, Avabhrath N, Lanchy JM, Levy T, Possemato A, Ariss M, Peterson B, Grimes M. Craniofacial chondrogenesis in organoids from human stem cell-derived neural crest cells. iScience 2024; 27:109585. [PMID: 38623327 PMCID: PMC11016914 DOI: 10.1016/j.isci.2024.109585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 02/27/2024] [Accepted: 03/25/2024] [Indexed: 04/17/2024] Open
Abstract
Knowledge of cell signaling pathways that drive human neural crest differentiation into craniofacial chondrocytes is incomplete, yet essential for using stem cells to regenerate craniomaxillofacial structures. To accelerate translational progress, we developed a differentiation protocol that generated self-organizing craniofacial cartilage organoids from human embryonic stem cell-derived neural crest stem cells. Histological staining of cartilage organoids revealed tissue architecture and staining typical of elastic cartilage. Protein and post-translational modification (PTM) mass spectrometry and snRNA-seq data showed that chondrocyte organoids expressed robust levels of cartilage extracellular matrix (ECM) components: many collagens, aggrecan, perlecan, proteoglycans, and elastic fibers. We identified two populations of chondroprogenitor cells, mesenchyme cells and nascent chondrocytes, and the growth factors involved in paracrine signaling between them. We show that ECM components secreted by chondrocytes not only create a structurally resilient matrix that defines cartilage, but also play a pivotal autocrine cell signaling role in determining chondrocyte fate.
Collapse
Affiliation(s)
- Lauren Foltz
- Division of Biological Sciences, Center for Biomolecular Structure and Dynamics, Center for Structural and Functional Neuroscience, The University of Montana, Missoula, MT 59812, USA
| | - Nagashree Avabhrath
- Division of Biological Sciences, Center for Biomolecular Structure and Dynamics, Center for Structural and Functional Neuroscience, The University of Montana, Missoula, MT 59812, USA
| | - Jean-Marc Lanchy
- Division of Biological Sciences, Center for Biomolecular Structure and Dynamics, Center for Structural and Functional Neuroscience, The University of Montana, Missoula, MT 59812, USA
| | - Tyler Levy
- Cell Signaling Technology, Danvers, MA 01923, USA
| | | | - Majd Ariss
- Cell Signaling Technology, Danvers, MA 01923, USA
| | | | - Mark Grimes
- Division of Biological Sciences, Center for Biomolecular Structure and Dynamics, Center for Structural and Functional Neuroscience, The University of Montana, Missoula, MT 59812, USA
| |
Collapse
|
9
|
Luecken MD, Gigante S, Burkhardt DB, Cannoodt R, Strobl DC, Markov NS, Zappia L, Palla G, Lewis W, Dimitrov D, Vinyard ME, Magruder DS, Andersson A, Dann E, Qin Q, Otto DJ, Klein M, Botvinnik OB, Deconinck L, Waldrant K, Bloom JM, Pisco AO, Saez-Rodriguez J, Wulsin D, Pinello L, Saeys Y, Theis FJ, Krishnaswamy S. Defining and benchmarking open problems in single-cell analysis. RESEARCH SQUARE 2024:rs.3.rs-4181617. [PMID: 38645152 PMCID: PMC11030530 DOI: 10.21203/rs.3.rs-4181617/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.
Collapse
Affiliation(s)
- Malte D Luecken
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Institute of Lung Health & Immunity, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | | | | | - Robrecht Cannoodt
- Data Intuitive, Lebbeke, Belgium
- Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
| | - Daniel C Strobl
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Germany
| | - Nikolay S Markov
- Division of Pulmonary and Critical Care Medicine, Feinberg School of Medicine, Northwestern University
| | - Luke Zappia
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - Giovanni Palla
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Germany
| | - Wesley Lewis
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Michael E Vinyard
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - D S Magruder
- Department of Computer Science, Yale University, New Haven CT, USA
| | - Alma Andersson
- Genentech Inc
- Royal Institute of Technology (KTH), Gene Technology
- Science for Life Laboratory (SciLifeLab)
| | - Emma Dann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Qian Qin
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Dominik J Otto
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
| | | | - Olga Borisovna Botvinnik
- Data Sciences Platform, Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158
- Bridge Bio Pharma, 3160 Porter Drive, Suite 250, Palo Alto, CA, 94304
| | - Louise Deconinck
- Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
| | | | | | - Angela Oliveira Pisco
- Data Sciences Platform, Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158
- Insitro, South San Francisco
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | | | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
- VIB Center for AI & Computational Biology (VIB.AI), Gent, Belgium
| | - Fabian J Theis
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, UK (associated faculty)
| | - Smita Krishnaswamy
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
- Department of Computer Science, Yale University, New Haven CT, USA
- Department of Genetics, Yale University, New Haven CT, USA
| |
Collapse
|
10
|
Sussman JH, Oldridge DA, Yu W, Chen CH, Zellmer AM, Rong J, Parvaresh-Rizi A, Thadi A, Xu J, Bandyopadhyay S, Sun Y, Wu D, Emerson Hunter C, Brosius S, Ahn KJ, Baxter AE, Koptyra MP, Vanguri RS, McGrory S, Resnick AC, Storm PB, Amankulor NM, Santi M, Viaene AN, Zhang N, Raedt TD, Cole K, Tan K. A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583588. [PMID: 38496580 PMCID: PMC10942465 DOI: 10.1101/2024.03.06.583588] [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
Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities to adult glioma, it is increasingly recognized as a molecularly distinct, yet highly heterogeneous disease. In this study, we longitudinally profiled a molecularly diverse cohort of 16 pHGG patients before and after standard therapy through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of the tumor microenvironment during progression following treatment. We found that the canonical neoplastic cell phenotypes of adult glioblastoma are insufficient to capture the range of tumor cell states in a pediatric cohort and observed differential tumor-myeloid interactions between malignant cell states. We identified key transcriptional regulators of pHGG cell states and did not observe the marked proneural to mesenchymal shift characteristic of adult glioblastoma. We showed that essential neuromodulators and the interferon response are upregulated post-therapy along with an increase in non-neoplastic oligodendrocytes. Through in vitro pharmacological perturbation, we demonstrated novel malignant cell-intrinsic targets. This multiomic atlas of longitudinal pHGG captures the key features of therapy response that support distinction from its adult counterpart and suggests therapeutic strategies which are targeted to pediatric gliomas.
Collapse
Affiliation(s)
- Jonathan H. Sussman
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Derek A. Oldridge
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Wenbao Yu
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
- Department of Pediatrics, University of Pennsylvania Perelman
School of Medicine, Philadelphia, PA
| | - Chia-Hui Chen
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
| | - Abigail M. Zellmer
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Jiazhen Rong
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Statistics and Data Science, University of
Pennsylvania, Philadelphia, PA
| | | | - Anusha Thadi
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
| | - Jason Xu
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Shovik Bandyopadhyay
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Cellular and Molecular Biology Graduate Group, Perelman School of
Medicine, University of Pennsylvania, PA
| | - Yusha Sun
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Neuroscience Graduate Group, Perelman School of Medicine,
University of Pennsylvania, PA
| | - David Wu
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - C. Emerson Hunter
- Medical Scientist Training Program, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Stephanie Brosius
- Graduate Group in Genomics and Computational Biology, Perelman
School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kyung Jin Ahn
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
| | - Amy E. Baxter
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Mateusz P. Koptyra
- Department of Neurosurgery, Children’s Hospital of
Philadelphia, Philadelphia, PA
| | - Rami S. Vanguri
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Stephanie McGrory
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
| | - Adam C. Resnick
- Department of Neurosurgery, Children’s Hospital of
Philadelphia, Philadelphia, PA
| | - Phillip B. Storm
- Department of Neurosurgery, Children’s Hospital of
Philadelphia, Philadelphia, PA
| | - Nduka M. Amankulor
- Department of Neurosurgery, Perelman School of Medicine,
Philadelphia, PA
| | - Mariarita Santi
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Angela N. Viaene
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Nancy Zhang
- Department of Statistics and Data Science, University of
Pennsylvania, Philadelphia, PA
| | - Thomas De Raedt
- Department of Pathology and Laboratory Medicine, Perelman School
of Medicine at the University of Pennsylvania, Philadelphia, PA
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
| | - Kristina Cole
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
- Department of Pediatrics, University of Pennsylvania Perelman
School of Medicine, Philadelphia, PA
| | - Kai Tan
- Center for Childhood Cancer Research, Children’s Hospital
of Philadelphia, Philadelphia, PA
- Department of Pediatrics, University of Pennsylvania Perelman
School of Medicine, Philadelphia, PA
- Center for Single Cell Biology, Children’s Hospital of
Philadelphia, Philadelphia, PA
| |
Collapse
|
11
|
Peng L, Gao P, Xiong W, Li Z, Chen X. Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis. Comput Biol Med 2024; 171:108110. [PMID: 38367445 DOI: 10.1016/j.compbiomed.2024.108110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/24/2024] [Accepted: 02/04/2024] [Indexed: 02/19/2024]
Abstract
Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve intercellular communication analysis. Meantime, due to lack of a "gold standard" dataset, it remains a challenge to evaluate LRI-mediated intercellular communication results. Here, we introduce CellGiQ, a high-confident LRI prediction framework for intercellular communication analysis. Highly confident LRIs are first inferred by LRI feature extraction with BioTriangle, LRI selection using LightGBM, and LRI classification based on ensemble of gradient boosted neural network and interpretable boosting machine. Subsequently, known and identified high-confident LRIs are filtered by combining single-cell RNA sequencing (scRNA-seq) data and further applied to intercellular communication inference through a quartile scoring strategy. To validation the predictions, CellGiQ exploited several evaluation strategies: using AUC and AUPR, it surpassed six competing LRI prediction models on four LRI datasets; through Venn diagrams and molecular docking, its predicted LRIs were validated by five other popular intercellular communication inference methods; based on the overlapping LRIs, it computed high Jaccard index with six other state-of-the-art intercellular communication prediction tools within human HNSCC tissues; by comparing with classical models and literature retrieve, its inferred HNSCC-related intercellular communication results was further validated. The novelty of this study is to identify high-confident LRIs based on machine learning as well as design several LRI validation ways, providing reference for computational LRI prediction. CellGiQ provides an open-source and useful tool to decompose LRI-mediated intercellular communication at single cell resolution. CellGiQ is freely available at https://github.com/plhhnu/CellGiQ.
Collapse
Affiliation(s)
- Lihong Peng
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Pengfei Gao
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Wei Xiong
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Zejun Li
- School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang, 421002, Hunan, China.
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
| |
Collapse
|
12
|
Wilk AJ, Shalek AK, Holmes S, Blish CA. Comparative analysis of cell-cell communication at single-cell resolution. Nat Biotechnol 2024; 42:470-483. [PMID: 37169965 PMCID: PMC10638471 DOI: 10.1038/s41587-023-01782-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 04/05/2023] [Indexed: 05/13/2023]
Abstract
Inference of cell-cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell-cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell-cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche-phenotype relationships in health and disease.
Collapse
Affiliation(s)
- Aaron J Wilk
- Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA.
| | - Alex K Shalek
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Susan Holmes
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Catherine A Blish
- Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| |
Collapse
|
13
|
Pong A, Mah CK, Yeo GW, Lewis NE. Computational cell-cell interaction technologies drive mechanistic and biomarker discovery in the tumor microenvironment. Curr Opin Biotechnol 2024; 85:103048. [PMID: 38142648 PMCID: PMC11168798 DOI: 10.1016/j.copbio.2023.103048] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 11/11/2023] [Accepted: 11/25/2023] [Indexed: 12/26/2023]
Abstract
Complex networks of cell-cell interactions (CCIs) within the tumor microenvironment (TME) play a crucial role in cancer persistence. These communication axes represent prime targets for therapeutic intervention, but our incomplete understanding of the cellular heterogeneity and interacting partners within the TME remains a stubborn barrier to complete drug responses. This review outlines recent advances in the study of CCIs that leverage single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies that can clarify TME dynamics. We anticipate that these strategies will promote discovery of CCIs critical to the tumor-immune interface and will, by extension, expand the repertoire of druggable tumor biomarkers.
Collapse
Affiliation(s)
- Avery Pong
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Clarence K Mah
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Gene W Yeo
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Nathan E Lewis
- Departments of Pediatrics and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
| |
Collapse
|
14
|
Chang JT, Liu LB, Wang PG, An J. Single-cell RNA sequencing to understand host-virus interactions. Virol Sin 2024; 39:1-8. [PMID: 38008383 PMCID: PMC10877424 DOI: 10.1016/j.virs.2023.11.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 11/23/2023] [Indexed: 11/28/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has allowed for the profiling of host and virus transcripts and host-virus interactions at single-cell resolution. This review summarizes the existing scRNA-seq technologies together with their strengths and weaknesses. The applications of scRNA-seq in various virological studies are discussed in depth, which broaden the understanding of the immune atlas, host-virus interactions, and immune repertoire. scRNA-seq can be widely used for virology in the near future to better understand the pathogenic mechanisms and discover more effective therapeutic strategies.
Collapse
Affiliation(s)
- Jia-Tong Chang
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China
| | - Li-Bo Liu
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China
| | - Pei-Gang Wang
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China.
| | - Jing An
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China.
| |
Collapse
|
15
|
Hachey SJ, Hatch CJ, Gaebler D, Mocherla A, Nee K, Kessenbrock K, Hughes CCW. Targeting tumor-stromal interactions in triple-negative breast cancer using a human vascularized micro-tumor model. Breast Cancer Res 2024; 26:5. [PMID: 38183074 PMCID: PMC10768273 DOI: 10.1186/s13058-023-01760-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 12/21/2023] [Indexed: 01/07/2024] Open
Abstract
Triple-negative breast cancer (TNBC) is highly aggressive with limited available treatments. Stromal cells in the tumor microenvironment (TME) are crucial in TNBC progression; however, understanding the molecular basis of stromal cell activation and tumor-stromal crosstalk in TNBC is limited. To investigate therapeutic targets in the TNBC stromal niche, we used an advanced human in vitro microphysiological system called the vascularized micro-tumor (VMT). Using single-cell RNA sequencing, we revealed that normal breast tissue stromal cells activate neoplastic signaling pathways in the TNBC TME. By comparing interactions in VMTs with clinical data, we identified therapeutic targets at the tumor-stromal interface with potential clinical significance. Combining treatments targeting Tie2 signaling with paclitaxel resulted in vessel normalization and increased efficacy of paclitaxel in the TNBC VMT. Dual inhibition of HER3 and Akt also showed efficacy against TNBC. These data demonstrate the potential of inducing a favorable TME as a targeted therapeutic approach in TNBC.
Collapse
Affiliation(s)
- Stephanie J Hachey
- Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, USA.
| | | | - Daniela Gaebler
- Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, USA
| | - Aneela Mocherla
- Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, USA
| | - Kevin Nee
- Biological Chemistry, University of California, Irvine, Irvine, CA, USA
| | - Kai Kessenbrock
- Biological Chemistry, University of California, Irvine, Irvine, CA, USA
| | - Christopher C W Hughes
- Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, USA
- Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| |
Collapse
|
16
|
Dai Q, Epstein MP, Yang J. STACCato: Supervised Tensor Analysis tool for studying Cell-cell Communication using scRNA-seq data across multiple samples and conditions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.15.571918. [PMID: 38168391 PMCID: PMC10760171 DOI: 10.1101/2023.12.15.571918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Research on cell-cell communication (CCC) is crucial for understanding biology and diseases. Many existing CCC inference tools neglect potential confounders, such as batch and demographic variables, when analyzing multi-sample, multi-condition scRNA-seq datasets. To address this significant gap, we introduce STACCato, a Supervised Tensor Analysis tool for studying Cell-cell Communication, that identifies CCC events and estimates the effects of biological conditions (e.g., disease status, tissue types) on such events, while adjusting for potential confounders. Application of STACCato to both simulated data and real scRNA-seq data of lupus and autism studies demonstrate that incorporating sample-level variables into CCC inference consistently provides more accurate estimations of disease effects and cell type activity patterns than existing methods that ignore sample-level variables. A computational tool implementing the STACCato framework is available on GitHub.
Collapse
Affiliation(s)
- Qile Dai
- Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, Georgia 30322, United States of America
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322, United States of America
| | - Michael P. Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322, United States of America
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322, United States of America
| |
Collapse
|
17
|
Tsuyuzaki K, Ishii M, Nikaido I. Sctensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data. BMC Bioinformatics 2023; 24:420. [PMID: 37936079 PMCID: PMC10631077 DOI: 10.1186/s12859-023-05490-y] [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: 07/05/2023] [Accepted: 09/21/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression but the analytical methods are not appropriate to detect many-to-many CCIs. RESULTS In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. CONCLUSIONS Through extensive studies with simulated and empirical datasets, we have shown that scTensor can detect some hypergraphs that cannot be detected using conventional CCI detection methods, especially when they include many-to-many relationships. scTensor is implemented as a freely available R/Bioconductor package.
Collapse
Affiliation(s)
- Koki Tsuyuzaki
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
- Japan Science and Technology Agency, PRESTO, 7 Gobancho, Chiyoda-ku, Tokyo, 102-0076, Japan.
| | - Manabu Ishii
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Itoshi Nikaido
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
- Department of Functional Genome Informatics, Division of Biological Data Science, Medical Research Institute, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan.
| |
Collapse
|
18
|
Gaydosik AM, Stonesifer CJ, Tabib T, Lafyatis R, Geskin LJ, Fuschiotti P. The mycosis fungoides cutaneous microenvironment shapes dysfunctional cell trafficking, antitumor immunity, matrix interactions, and angiogenesis. JCI Insight 2023; 8:e170015. [PMID: 37669110 PMCID: PMC10619438 DOI: 10.1172/jci.insight.170015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 08/31/2023] [Indexed: 09/07/2023] Open
Abstract
Malignant T lymphocyte proliferation in mycosis fungoides (MF) is largely restricted to the skin, implying that malignant cells are dependent on their specific cutaneous tumor microenvironment (TME), including interactions with non-malignant immune and stromal cells, cytokines, and other immunomodulatory factors. To explore these interactions, we performed a comprehensive transcriptome analysis of the TME in advanced-stage MF skin tumors by single-cell RNA sequencing. Our analysis identified cell-type compositions, cellular functions, and cell-to-cell interactions in the MF TME that were distinct from those from healthy skin and benign dermatoses. While patterns of gene expression were common among patient samples, high transcriptional diversity was also observed in immune and stromal cells, with dynamic interactions and crosstalk between these cells and malignant T lymphocytes. This heterogeneity mapped to processes such as cell trafficking, matrix interactions, angiogenesis, immune functions, and metabolism that affect cancer cell growth, migration, and invasion, as well as antitumor immunity. By comprehensively characterizing the transcriptomes of immune and stromal cells within the cutaneous microenvironment of individual MF tumors, we have identified patterns of dysfunction common to all tumors that represent a resource for identifying candidates with therapeutic potential as well as patient-specific heterogeneity that has important implications for personalized disease management.
Collapse
Affiliation(s)
- Alyxzandria M. Gaydosik
- Department of Medicine, Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | | | - Tracy Tabib
- Department of Medicine, Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Robert Lafyatis
- Department of Medicine, Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | | | - Patrizia Fuschiotti
- Department of Medicine, Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| |
Collapse
|
19
|
Peng R, Deng M. Mapping the protein-protein interactome in the tumor immune microenvironment. Antib Ther 2023; 6:311-321. [PMID: 38098892 PMCID: PMC10720949 DOI: 10.1093/abt/tbad026] [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: 06/06/2023] [Revised: 10/01/2023] [Accepted: 11/02/2023] [Indexed: 12/17/2023] Open
Abstract
The cell-to-cell communication primarily occurs through cell-surface and secreted proteins, which form a sophisticated network that coordinates systemic immune function. Uncovering these protein-protein interactions (PPIs) is indispensable for understanding the molecular mechanism and elucidating immune system aberrances under diseases. Traditional biological studies typically focus on a limited number of PPI pairs due to the relative low throughput of commonly used techniques. Encouragingly, classical methods have advanced, and many new systems tailored for large-scale protein-protein screening have been developed and successfully utilized. These high-throughput PPI investigation techniques have already made considerable achievements in mapping the immune cell interactome, enriching PPI databases and analysis tools, and discovering therapeutic targets for cancer and other diseases, which will definitely bring unprecedented insight into this field.
Collapse
Affiliation(s)
- Rui Peng
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing 100191, PR China
- School of Basic Medical Sciences, Health Science Center, Peking University, Beijing 100191, PR China
| | - Mi Deng
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing 100191, PR China
- School of Basic Medical Sciences, Health Science Center, Peking University, Beijing 100191, PR China
- Peking University Cancer Hospital and Institute, Peking University, Beijing 100142, PR China
| |
Collapse
|
20
|
Wang X, Almet AA, Nie Q. The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics. Semin Cancer Biol 2023; 95:42-51. [PMID: 37454878 PMCID: PMC10627116 DOI: 10.1016/j.semcancer.2023.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/02/2023] [Accepted: 07/13/2023] [Indexed: 07/18/2023]
Abstract
Cell-cell interactions instruct cell fate and function. These interactions are hijacked to promote cancer development. Single-cell transcriptomics and spatial transcriptomics have become powerful new tools for researchers to profile the transcriptional landscape of cancer at unparalleled genetic depth. In this review, we discuss the rapidly growing array of computational tools to infer cell-cell interactions from non-spatial single-cell RNA-sequencing and the limited but growing number of methods for spatial transcriptomics data. Downstream analyses of these computational tools and applications to cancer studies are highlighted. We finish by suggesting several directions for further extensions that anticipate the increasing availability of multi-omics cancer data.
Collapse
Affiliation(s)
- Xinyi Wang
- Department of Mathematics, University of California, Irvine, Irvine, CA, United States
| | - Axel A Almet
- Department of Mathematics, University of California, Irvine, Irvine, CA, United States; The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, United States.
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, United States; The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, United States; Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, United States.
| |
Collapse
|
21
|
Luo J, Deng M, Zhang X, Sun X. ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods. Genome Res 2023; 33:1788-1805. [PMID: 37827697 PMCID: PMC10691505 DOI: 10.1101/gr.278001.123] [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: 04/22/2023] [Accepted: 09/21/2023] [Indexed: 10/14/2023]
Abstract
Cell-cell communication (CCC) is critical for determining cell fates and functions in multicellular organisms. With the advent of single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), an increasing number of CCC inference methods have been developed. Nevertheless, a thorough comparison of their performances is yet to be conducted. To fill this gap, we developed a systematic benchmark framework called ESICCC to evaluate 18 ligand-receptor (LR) inference methods and five ligand/receptor-target inference methods using a total of 116 data sets, including 15 ST data sets, 15 sets of cell line perturbation data, two sets of cell type-specific expression/proteomics data, and 84 sets of sampled or unsampled scRNA-seq data. We evaluated and compared the agreement, accuracy, robustness, and usability of these methods. Regarding accuracy evaluation, RNAMagnet, CellChat, and scSeqComm emerge as the three best-performing methods for intercellular ligand-receptor inference based on scRNA-seq data, whereas stMLnet and HoloNet are the best methods for predicting ligand/receptor-target regulation using ST data. To facilitate the practical applications, we provide a decision-tree-style guideline for users to easily choose best tools for their specific research concerns in CCC inference, and develop an ensemble pipeline CCCbank that enables versatile combinations of methods and databases. Moreover, our comparative results also uncover several critical influential factors for CCC inference, such as prior interaction information, ligand-receptor scoring algorithm, intracellular signaling complexity, and spatial relationship, which may be considered in the future studies to advance the development of new methodologies.
Collapse
Affiliation(s)
- Jiaxin Luo
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing, 100871, China
| | - Xuegong Zhang
- Bioinformatics Division of BNRIST and Department of Automation, MOE Key Lab of Bioinformatics, Tsinghua University, Beijing, 100084, China
| | - Xiaoqiang Sun
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China;
| |
Collapse
|
22
|
Cheng C, Chen W, Jin H, Chen X. A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication. Cells 2023; 12:1970. [PMID: 37566049 PMCID: PMC10417635 DOI: 10.3390/cells12151970] [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: 06/13/2023] [Revised: 07/10/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.
Collapse
Affiliation(s)
- Changde Cheng
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| | - Wenan Chen
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Xiang Chen
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| |
Collapse
|
23
|
Scavuzzo MA, Letai KC, Maeno-Hikichi Y, Wulftange WJ, Shah IK, Rameshbabu JS, Tomar A, Shick HE, Shah AK, Xiong Y, Cohn EF, Allan KC, Tesar PJ. Enteric glial hub cells coordinate intestinal motility. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.07.544052. [PMID: 37333182 PMCID: PMC10274798 DOI: 10.1101/2023.06.07.544052] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Enteric glia are the predominant cell type in the enteric nervous system yet their identities and roles in gastrointestinal function are not well classified. Using our optimized single nucleus RNA-sequencing method, we identified distinct molecular classes of enteric glia and defined their morphological and spatial diversity. Our findings revealed a functionally specialized biosensor subtype of enteric glia that we call "hub cells." Deletion of the mechanosensory ion channel PIEZO2 from adult enteric glial hub cells, but not other subtypes of enteric glia, led to defects in intestinal motility and gastric emptying in mice. These results provide insight into the multifaceted functions of different enteric glial cell subtypes in gut health and emphasize that therapies targeting enteric glia could advance the treatment of gastrointestinal diseases.
Collapse
Affiliation(s)
- Marissa A. Scavuzzo
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - Katherine C. Letai
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - Yuka Maeno-Hikichi
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - William J. Wulftange
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - Isha K. Shah
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - Jeyashri S. Rameshbabu
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - Alka Tomar
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - H. Elizabeth Shick
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - Aakash K. Shah
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - Ying Xiong
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - Erin F. Cohn
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - Kevin C. Allan
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| | - Paul J. Tesar
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
| |
Collapse
|
24
|
Baghdassarian H, Dimitrov D, Armingol E, Saez-Rodriguez J, Lewis NE. Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.28.538731. [PMID: 37162916 PMCID: PMC10168343 DOI: 10.1101/2023.04.28.538731] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. While multiple cell-cell communication tools exist, results are specific to the tool of choice, due to the diverse assumptions made across computational frameworks. Moreover, tools are often limited to analyzing single samples or to performing pairwise comparisons. As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for generalizable methods to decipher cell-cell communication in such scenarios. Here, we integrate two tools, LIANA and Tensor-cell2cell, which combined can deploy multiple existing methods and resources, to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this protocol, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step-by-step in both Python and R, and we provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This protocol typically takes ~1.5h to complete from installation to downstream visualizations on a GPU-enabled computer, for a dataset of ~63k cells, 10 cell types, and 12 samples.
Collapse
Affiliation(s)
- Hratch Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, 69120, Heidelberg, Germany
| | - Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, 69120, Heidelberg, Germany
| | - Nathan E. Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| |
Collapse
|
25
|
Leiby KL, Yuan Y, Ng R, Raredon MSB, Adams TS, Baevova P, Greaney AM, Hirschi KK, Campbell SG, Kaminski N, Herzog EL, Niklason LE. Rational engineering of lung alveolar epithelium. NPJ Regen Med 2023; 8:22. [PMID: 37117221 PMCID: PMC10147714 DOI: 10.1038/s41536-023-00295-2] [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: 10/19/2022] [Accepted: 04/06/2023] [Indexed: 04/30/2023] Open
Abstract
Engineered whole lungs may one day expand therapeutic options for patients with end-stage lung disease. However, the feasibility of ex vivo lung regeneration remains limited by the inability to recapitulate mature, functional alveolar epithelium. Here, we modulate multimodal components of the alveolar epithelial type 2 cell (AEC2) niche in decellularized lung scaffolds in order to guide AEC2 behavior for epithelial regeneration. First, endothelial cells coordinate with fibroblasts, in the presence of soluble growth and maturation factors, to promote alveolar scaffold population with surfactant-secreting AEC2s. Subsequent withdrawal of Wnt and FGF agonism synergizes with tidal-magnitude mechanical strain to induce the differentiation of AEC2s to squamous type 1 AECs (AEC1s) in cultured alveoli, in situ. These results outline a rational strategy to engineer an epithelium of AEC2s and AEC1s contained within epithelial-mesenchymal-endothelial alveolar-like units, and highlight the critical interplay amongst cellular, biochemical, and mechanical niche cues within the reconstituting alveolus.
Collapse
Affiliation(s)
- Katherine L Leiby
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale School of Medicine, New Haven, CT, USA
| | - Yifan Yuan
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | - Ronald Ng
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Micha Sam Brickman Raredon
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale School of Medicine, New Haven, CT, USA
| | - Taylor S Adams
- Department of Internal Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Pavlina Baevova
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | - Allison M Greaney
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Karen K Hirschi
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Yale Cardiovascular Research Center, Yale School of Medicine, New Haven, CT, USA
- Department of Cell Biology, University of Virginia, Charlottesville, VA, USA
- Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA
| | - Stuart G Campbell
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Cellular and Molecular Physiology, Yale School of Medicine, New Haven, CT, USA
| | - Naftali Kaminski
- Department of Internal Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Erica L Herzog
- Department of Internal Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Laura E Niklason
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA.
| |
Collapse
|
26
|
Yang Y, Li G, Zhong Y, Xu Q, Lin YT, Roman-Vicharra C, Chapkin RS, Cai JJ. scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs. Cell Syst 2023; 14:302-311.e4. [PMID: 36787742 PMCID: PMC10121998 DOI: 10.1016/j.cels.2023.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/22/2022] [Accepted: 01/20/2023] [Indexed: 02/16/2023]
Abstract
We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.
Collapse
Affiliation(s)
- Yongjian Yang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Guanxun Li
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - Yan Zhong
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062, China
| | - Qian Xu
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA
| | - Yu-Te Lin
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Cristhian Roman-Vicharra
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA
| | - Robert S Chapkin
- Department of Nutrition and the Program in Integrative Nutrition & Complex Diseases, Texas A&M University, College Station, TX 77843, USA.
| | - James J Cai
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA; Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA; Interdisciplinary Program of Genetics, Texas A&M University, College Station, TX 77843, USA.
| |
Collapse
|
27
|
Greaney AM, Raredon MSB, Kochugaeva MP, Niklason LE, Levchenko A. SARS-CoV-2 leverages airway epithelial protective mechanism for viral infection. iScience 2023; 26:106175. [PMID: 36788793 PMCID: PMC9912025 DOI: 10.1016/j.isci.2023.106175] [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: 03/11/2022] [Revised: 01/05/2023] [Accepted: 02/03/2023] [Indexed: 02/12/2023] Open
Abstract
Despite much concerted effort to better understand severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral infection, relatively little is known about the dynamics of early viral entry and infection in the airway. Here we analyzed a single-cell RNA sequencing dataset of early SARS-CoV-2 infection in a humanized in vitro model, to elucidate key mechanisms by which the virus triggers a cell-systems-level response in the bronchial epithelium. We find that SARS-CoV-2 virus preferentially enters the tissue via ciliated cell precursors, giving rise to a population of infected mature ciliated cells, which signal to basal cells, inducing further rapid differentiation. This feedforward loop of infection is mitigated by further cell-cell communication, before interferon signaling begins at three days post-infection. These findings suggest hijacking by the virus of potentially beneficial tissue repair mechanisms, possibly exacerbating the outcome. This work both elucidates the interplay between barrier tissues and viral infections and may suggest alternative therapeutic approaches targeting non-immune response mechanisms.
Collapse
Affiliation(s)
- Allison Marie Greaney
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
- Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT 06511, USA
| | - Micha Sam Brickman Raredon
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
- Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT 06511, USA
- Medical Scientist Training Program, Yale University, New Haven, CT 06511, USA
| | - Maria P. Kochugaeva
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
- Yale Systems Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Laura E. Niklason
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
- Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT 06511, USA
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT 06510, USA
- Humacyte Inc., Durham, NC 27713, USA
| | - Andre Levchenko
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
- Yale Systems Biology Institute, Yale University, West Haven, CT 06516, USA
- Corresponding author
| |
Collapse
|
28
|
Tang Z, Zhang T, Yang B, Su J, Song Q. spaCI: deciphering spatial cellular communications through adaptive graph model. Brief Bioinform 2023; 24:bbac563. [PMID: 36545790 PMCID: PMC9851335 DOI: 10.1093/bib/bbac563] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/26/2022] [Accepted: 11/18/2022] [Indexed: 12/24/2022] Open
Abstract
Cell-cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand-receptor (L-R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L-R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L-R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L-R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L-R interactions including THBS1-ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets.
Collapse
Affiliation(s)
- Ziyang Tang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Tonglin Zhang
- Department of Statistics, Purdue University, Indiana, USA
| | - Baijian Yang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
| | - Qianqian Song
- Center for Cancer Genomics and Precision Oncology, Wake Forest Baptist Comprehensive Cancer Center, Atrium Health Wake Forest Baptist, Winston Salem, NC, USA
- Department of Cancer Biology, Wake Forest School of Medicine, Winston Salem, NC, USA
| |
Collapse
|
29
|
Olaniru OE, Kadolsky U, Kannambath S, Vaikkinen H, Fung K, Dhami P, Persaud SJ. Single-cell transcriptomic and spatial landscapes of the developing human pancreas. Cell Metab 2023; 35:184-199.e5. [PMID: 36513063 DOI: 10.1016/j.cmet.2022.11.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 10/27/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022]
Abstract
Current differentiation protocols have not been successful in reproducibly generating fully functional human beta cells in vitro, partly due to incomplete understanding of human pancreas development. Here, we present detailed transcriptomic analysis of the various cell types of the developing human pancreas, including their spatial gene patterns. We integrated single-cell RNA sequencing with spatial transcriptomics at multiple developmental time points and revealed distinct temporal-spatial gene cascades. Cell trajectory inference identified endocrine progenitor populations and branch-specific genes as the progenitors differentiate toward alpha or beta cells. Spatial differentiation trajectories indicated that Schwann cells are spatially co-located with endocrine progenitors, and cell-cell connectivity analysis predicted that they may interact via L1CAM-EPHB2 signaling. Our integrated approach enabled us to identify heterogeneity and multiple lineage dynamics within the mesenchyme, showing that it contributed to the exocrine acinar cell state. Finally, we have generated an interactive web resource for investigating human pancreas development for the research community.
Collapse
Affiliation(s)
- Oladapo Edward Olaniru
- Department of Diabetes, School of Cardiovascular and Metabolic Medicine & Sciences, King's College London, Guy's Campus, London SE1 1UL, UK.
| | - Ulrich Kadolsky
- Genomics Research Platform and Single Cell Laboratory, Biomedical Research Centre, Guy's and St. Thomas' NHS Trust, London, UK; Genomics WA, University of Western Australia, Harry Perkins Institute of Medical Research and Telethon Kids Institute QEII Campus, Nedlands, Perth, WA 6009, Australia
| | - Shichina Kannambath
- Genomics Research Platform and Single Cell Laboratory, Biomedical Research Centre, Guy's and St. Thomas' NHS Trust, London, UK
| | - Heli Vaikkinen
- Genomics Research Platform and Single Cell Laboratory, Biomedical Research Centre, Guy's and St. Thomas' NHS Trust, London, UK
| | - Kathy Fung
- Genomics Research Platform and Single Cell Laboratory, Biomedical Research Centre, Guy's and St. Thomas' NHS Trust, London, UK
| | - Pawan Dhami
- Genomics Research Platform and Single Cell Laboratory, Biomedical Research Centre, Guy's and St. Thomas' NHS Trust, London, UK
| | - Shanta J Persaud
- Department of Diabetes, School of Cardiovascular and Metabolic Medicine & Sciences, King's College London, Guy's Campus, London SE1 1UL, UK.
| |
Collapse
|
30
|
Raredon MSB, Yang J, Kothapalli N, Lewis W, Kaminski N, Niklason LE, Kluger Y. Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES. Bioinformatics 2023; 39:6865029. [PMID: 36458905 PMCID: PMC9825783 DOI: 10.1093/bioinformatics/btac775] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 10/31/2022] [Accepted: 11/30/2022] [Indexed: 12/03/2022] Open
Abstract
MOTIVATION Recent years have seen the release of several toolsets that reveal cell-cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level. RESULTS NICHES allows embedding of ligand-receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand-receptor signaling mechanisms, is compatible with existing single-cell packages, and allows rapid, flexible analysis of cell-cell signaling at single-cell resolution. AVAILABILITY AND IMPLEMENTATION NICHES is an open-source software implemented in R under academic free license v3.0 and it is available at http://github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | | | - Neeharika Kothapalli
- Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, CT 06511, USA
| | - Wesley Lewis
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Naftali Kaminski
- Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, CT 06511, USA
| | - Laura E Niklason
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT 06511, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | | |
Collapse
|
31
|
Yuan Y. Clinical Translation of Engineered Pulmonary Vascular Models. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1413:273-288. [PMID: 37195536 DOI: 10.1007/978-3-031-26625-6_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Diseases in pulmonary vasculature remain a major cause of morbidity and mortality worldwide. Numerous pre-clinical animal models were developed to understand lung vasculature during diseases and development. However, these systems are typically limited in their ability to represent human pathophysiology for the study of disease and drug mechanisms. In recent years, a growing number of studies have focused on developing in vitro experimental platforms that mimic human tissues/organs. In this chapter, we discuss the key components involved in developing engineered pulmonary vascular modeling systems and provide perspectives on ways to improve the translational potential of existing models.
Collapse
Affiliation(s)
- Yifan Yuan
- Department of Medicine (Pulmonary), Department of Anesthesiology, Yale University, New Haven, CT, USA.
| |
Collapse
|
32
|
Breast cancer prevention by short-term inhibition of TGFβ signaling. Nat Commun 2022; 13:7558. [PMID: 36476730 PMCID: PMC9729304 DOI: 10.1038/s41467-022-35043-5] [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: 02/08/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
Cancer prevention has a profound impact on cancer-associated mortality and morbidity. We previously identified TGFβ signaling as a candidate regulator of mammary epithelial cells associated with breast cancer risk. Here, we show that short-term TGFBR inhibitor (TGFBRi) treatment of peripubertal ACI inbred and Sprague Dawley outbred rats induces lasting changes and prevents estrogen- and carcinogen-induced mammary tumors, respectively. We identify TGFBRi-responsive cell populations by single cell RNA-sequencing, including a unique epithelial subpopulation designated secretory basal cells (SBCs) with progenitor features. We detect SBCs in normal human breast tissues and find them to be associated with breast cancer risk. Interactome analysis identifies SBCs as the most interactive cell population and the main source of insulin-IGF signaling. Accordingly, inhibition of TGFBR and IGF1R decrease proliferation of organoid cultures. Our results reveal a critical role for TGFβ in regulating mammary epithelial cells relevant to breast cancer and serve as a proof-of-principle cancer prevention strategy.
Collapse
|
33
|
Shan N, Lu Y, Guo H, Li D, Jiang J, Yan L, Gao J, Ren Y, Zhao X, Hou L. CITEdb: a manually curated database of cell-cell interactions in human. Bioinformatics 2022; 38:5144-5148. [PMID: 36179089 PMCID: PMC9665858 DOI: 10.1093/bioinformatics/btac654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 08/31/2022] [Accepted: 09/29/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The interactions among various types of cells play critical roles in cell functions and the maintenance of the entire organism. While cell-cell interactions are traditionally revealed from experimental studies, recent developments in single-cell technologies combined with data mining methods have enabled computational prediction of cell-cell interactions, which have broadened our understanding of how cells work together, and have important implications in therapeutic interventions targeting cell-cell interactions for cancers and other diseases. Despite the importance, to our knowledge, there is no database for systematic documentation of high-quality cell-cell interactions at the cell type level, which hinders the development of computational approaches to identify cell-cell interactions. RESULTS We develop a publicly accessible database, CITEdb (Cell-cell InTEraction database, https://citedb.cn/), which not only facilitates interactive exploration of cell-cell interactions in specific physiological contexts (e.g. a disease or an organ) but also provides a benchmark dataset to interpret and evaluate computationally derived cell-cell interactions from different tools. CITEdb contains 728 pairs of cell-cell interactions in human that are manually curated. Each interaction is equipped with structured annotations including the physiological context, the ligand-receptor pairs that mediate the interaction, etc. Our database provides a web interface to search, visualize and download cell-cell interactions. Users can search for cell-cell interactions by selecting the physiological context of interest or specific cell types involved. CITEdb is the first attempt to catalogue cell-cell interactions at the cell type level, which is beneficial to both experimental, computational and clinical studies of cell-cell interactions. AVAILABILITY AND IMPLEMENTATION CITEdb is freely available at https://citedb.cn/ and the R package implementing benchmark is available at https://github.com/shanny01/benchmark. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Nayang Shan
- School of Statistics, Capital University of Economics and Business, Beijing 100070, China
| | - Yao Lu
- Department of Industrial Engineering, Center for Statistical Science, Tsinghua University, Beijing 100084, China
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Hao Guo
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd, Nanjing 210042, China
- Nanjing Simcere Medical Laboratory Science Co., Ltd, Nanjing 210042, China
| | - Dongyu Li
- Department of Industrial Engineering, Center for Statistical Science, Tsinghua University, Beijing 100084, China
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Jitong Jiang
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Linlin Yan
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd, Nanjing 210042, China
- Nanjing Simcere Medical Laboratory Science Co., Ltd, Nanjing 210042, China
| | - Jiudong Gao
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd, Nanjing 210042, China
- Nanjing Simcere Medical Laboratory Science Co., Ltd, Nanjing 210042, China
| | - Yong Ren
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd, Nanjing 210042, China
- Nanjing Simcere Medical Laboratory Science Co., Ltd, Nanjing 210042, China
| | - Xingming Zhao
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (LCNBI) and ZJLab, Wuxi, China
| | - Lin Hou
- Department of Industrial Engineering, Center for Statistical Science, Tsinghua University, Beijing 100084, China
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
| |
Collapse
|
34
|
Liu Z, Sun D, Wang C. Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information. Genome Biol 2022; 23:218. [PMID: 36253792 PMCID: PMC9575221 DOI: 10.1186/s13059-022-02783-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022] Open
Abstract
Background Cell-cell interactions are important for information exchange between different cells, which are the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable the characterization of cell-cell interactions using computational methods. However, it is hard to evaluate these methods since no ground truth is provided. Spatial transcriptomics (ST) data profiles the relative position of different cells. We propose that the spatial distance suggests the interaction tendency of different cell types, thus could be used for evaluating cell-cell interaction tools. Results We benchmark 16 cell-cell interaction methods by integrating scRNA-seq with ST data. We characterize cell-cell interactions into short-range and long-range interactions using spatial distance distributions between ligands and receptors. Based on this classification, we define the distance enrichment score and apply an evaluation workflow to 16 cell-cell interaction tools using 15 simulated and 5 real scRNA-seq and ST datasets. We also compare the consistency of the results from single tools with the commonly identified interactions. Our results suggest that the interactions predicted by different tools are highly dynamic, and the statistical-based methods show overall better performance than network-based methods and ST-based methods. Conclusions Our study presents a comprehensive evaluation of cell-cell interaction tools for scRNA-seq. CellChat, CellPhoneDB, NicheNet, and ICELLNET show overall better performance than other tools in terms of consistency with spatial tendency and software scalability. We recommend using results from at least two methods to ensure the accuracy of identified interactions. We have packaged the benchmark workflow with detailed documentation at GitHub (https://github.com/wanglabtongji/CCI). Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02783-y.
Collapse
Affiliation(s)
- Zhaoyang Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai, 200092, China.,Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai, 200092, China.,Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai, 200092, China. .,Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| |
Collapse
|
35
|
Lu H, Ping J, Zhou G, Zhao Z, Gao W, Jiang Y, Quan C, Lu Y, Zhou G. CommPath: An R package for inference and analysis of pathway-mediated cell-cell communication chain from single-cell transcriptomics. Comput Struct Biotechnol J 2022; 20:5978-5983. [PMID: 36382188 PMCID: PMC9647193 DOI: 10.1016/j.csbj.2022.10.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/14/2022] [Accepted: 10/20/2022] [Indexed: 11/25/2022] Open
Abstract
Single-cell transcriptomics offers opportunities to investigate ligand-receptor (LR) interactions between heterogeneous cell populations within tissues. However, most existing tools for the inference of intercellular communication do not allow prioritization of functional LR associations that provoke certain biological responses in the receiver cells. In addition, current tools do not enable the identification of the impact on the downstream cell types of the receiver cells. We present CommPath, an open-source R package and webserver, to analyze and visualize the LR interactions and pathway-mediated intercellular communication chain with single-cell transcriptomic data. CommPath curates a comprehensive signaling pathway database to interpret the consequences of LR associations and therefore infers functional LR interactions. Furthermore, CommPath determines cell-cell communication chain by considering both the upstream and downstream cells of user-defined cell populations. Applying CommPath to human hepatocellular carcinoma dataset shows its ability to decipher complex LR interaction patterns and the associated intercellular communication chain, as well as their changes in disease versus homeostasis.
Collapse
|
36
|
Xin Y, Lyu P, Jiang J, Zhou F, Wang J, Blackshaw S, Qian J. LRLoop: a method to predict feedback loops in cell-cell communication. Bioinformatics 2022; 38:4117-4126. [PMID: 35788263 PMCID: PMC9438954 DOI: 10.1093/bioinformatics/btac447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/24/2022] [Accepted: 07/03/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Intercellular communication (i.e. cell-cell communication) plays an essential role in multicellular organisms coordinating various biological processes. Previous studies discovered that feedback loops between two cell types are a widespread and vital signaling motif regulating development, regeneration and cancer progression. While many computational methods have been developed to predict cell-cell communication based on gene expression datasets, these methods often predict one-directional ligand-receptor interactions from sender to receiver cells and are not suitable to identify feedback loops. RESULTS Here, we describe ligand-receptor loop (LRLoop), a new method for analyzing cell-cell communication based on bi-directional ligand-receptor interactions, where two pairs of ligand-receptor interactions are identified that are responsive to each other and thereby form a closed feedback loop. We first assessed LRLoop using bulk datasets and found our method significantly reduces the false positive rate seen with existing methods. Furthermore, we developed a new strategy to assess the performance of these methods in single-cell datasets. We used the between-tissue interactions as an indicator of potential false-positive prediction and found that LRLoop produced a lower fraction of between-tissue interactions than traditional methods. Finally, we applied LRLoop to the single-cell datasets obtained from retinal development. We discovered many new bi-directional ligand-receptor interactions among individual cell types that potentially control proliferation, neurogenesis and/or cell fate specification. AVAILABILITY AND IMPLEMENTATION An R package is available at https://github.com/Pinlyu3/LRLoop. The source code can be found at figshare (https://doi.org/10.6084/m9.figshare.20126138.v1). The datasets can be found at figshare (https://doi.org/10.6084/m9.figshare.20126021.v1). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | | | - Junyao Jiang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Fengquan Zhou
- Department of Orthopedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA,Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jie Wang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Seth Blackshaw
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA,Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jiang Qian
- To whom correspondence should be addressed.
| |
Collapse
|
37
|
Context-aware deconvolution of cell-cell communication with Tensor-cell2cell. Nat Commun 2022; 13:3665. [PMID: 35760817 PMCID: PMC9237099 DOI: 10.1038/s41467-022-31369-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 06/14/2022] [Indexed: 12/23/2022] Open
Abstract
Cell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell–cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell–cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which deciphers context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. As such, Tensor-cell2cell robustly improves upon and extends the analytical capabilities of existing tools. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell–cell and ligand-receptor pairs) linked to severities of Coronavirus Disease 2019 and to Autism Spectrum Disorder. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions. Cellular contexts such as disease state, organismal life stage and tissue microenvironment, shape intercellular communication, and ultimately affect an organism’s phenotypes. Here, the authors present Tensor-cell2cell, an unsupervised method for deciphering context-driven intercellular communication.
Collapse
|
38
|
Marchetti L, Porciani D, Mitola S, Giacomelli C. Editorial: Molecular Insights Into Ligand-Receptor Interactions on the Cell Surface. Front Mol Biosci 2022; 9:921677. [PMID: 35647034 PMCID: PMC9140802 DOI: 10.3389/fmolb.2022.921677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 04/28/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
| | - David Porciani
- Department of Molecular Microbiology and Immunology, University of Missouri, Columbia, MO, United States
- Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
| | - Stefania Mitola
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Chiara Giacomelli
- Department of Pharmacy, University of Pisa, Pisa, Italy
- *Correspondence: Chiara Giacomelli,
| |
Collapse
|