1
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Joodaki M, Shaigan M, Parra V, Bülow RD, Kuppe C, Hölscher DL, Cheng M, Nagai JS, Goedertier M, Bouteldja N, Tesar V, Barratt J, Roberts IS, Coppo R, Kramann R, Boor P, Costa IG. Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT). Mol Syst Biol 2024; 20:57-74. [PMID: 38177382 PMCID: PMC10883279 DOI: 10.1038/s44320-023-00003-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/20/2023] [Accepted: 11/24/2023] [Indexed: 01/06/2024] Open
Abstract
Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.
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Affiliation(s)
- Mehdi Joodaki
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
| | - Mina Shaigan
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
| | - Victor Parra
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
| | - Roman D Bülow
- Institute of Pathology, RWTH Aachen University Medical School, Aachen, Germany
| | - Christoph Kuppe
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - David L Hölscher
- Institute of Pathology, RWTH Aachen University Medical School, Aachen, Germany
| | - Mingbo Cheng
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
| | - James S Nagai
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
| | - Michaël Goedertier
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
- Institute of Pathology, RWTH Aachen University Medical School, Aachen, Germany
| | - Nassim Bouteldja
- Institute of Pathology, RWTH Aachen University Medical School, Aachen, Germany
| | - Vladimir Tesar
- Department of Nephrology, 1st Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | - Jonathan Barratt
- John Walls Renal Unit, University Hospital of Leicester National Health Service Trust, Leicester, UK
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Ian Sd Roberts
- Department of Cellular Pathology, Oxford University Hospitals National Health Services Foundation Trust, Oxford, UK
| | - Rosanna Coppo
- Fondazione Ricerca Molinette, Regina Margherita Children's University Hospital, Torino, Italy
| | - Rafael Kramann
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
- Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, Netherlands
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Medical School, Aachen, Germany.
| | - Ivan G Costa
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany.
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2
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Pritchard JE, Pearce JE, Snoeren IAM, Fuchs SNR, Götz K, Peisker F, Wagner S, Benabid A, Lutterbach N, Klöker V, Nagai JS, Hannani MT, Galyga AK, Sistemich E, Banjanin B, Flosdorf N, Bindels E, Olschok K, Biaesch K, Chatain N, Bhagwat N, Dunbar A, Sarkis R, Naveiras O, Berres ML, Koschmieder S, Levine RL, Costa IG, Gleitz HFE, Kramann R, Schneider RK. Non-canonical Hedgehog signaling mediates profibrotic hematopoiesis-stroma crosstalk in myeloproliferative neoplasms. Cell Rep 2024; 43:113608. [PMID: 38117649 PMCID: PMC10828549 DOI: 10.1016/j.celrep.2023.113608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 09/28/2023] [Accepted: 12/06/2023] [Indexed: 12/22/2023] Open
Abstract
The role of hematopoietic Hedgehog signaling in myeloproliferative neoplasms (MPNs) remains incompletely understood despite data suggesting that Hedgehog (Hh) pathway inhibitors have therapeutic activity in patients. We aim to systematically interrogate the role of canonical vs. non-canonical Hh signaling in MPNs. We show that Gli1 protein levels in patient peripheral blood mononuclear cells (PBMCs) mark fibrotic progression and that, in murine MPN models, absence of hematopoietic Gli1, but not Gli2 or Smo, significantly reduces MPN phenotype and fibrosis, indicating that GLI1 in the MPN clone can be activated in a non-canonical fashion. Additionally, we establish that hematopoietic Gli1 has a significant effect on stromal cells, mediated through a druggable MIF-CD74 axis. These data highlight the complex interplay between alterations in the MPN clone and activation of stromal cells and indicate that Gli1 represents a promising therapeutic target in MPNs, particularly that Hh signaling is dispensable for normal hematopoiesis.
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Affiliation(s)
- Jessica E Pritchard
- Institute for Cell and Tumor Biology, RWTH Aachen University Hospital, Aachen, Germany; Department of Developmental Biology, Erasmus University Medical Center, Rotterdam, the Netherlands; Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Juliette E Pearce
- Institute for Cell and Tumor Biology, RWTH Aachen University Hospital, Aachen, Germany
| | - Inge A M Snoeren
- Department of Developmental Biology, Erasmus University Medical Center, Rotterdam, the Netherlands; Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Stijn N R Fuchs
- Department of Developmental Biology, Erasmus University Medical Center, Rotterdam, the Netherlands; Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Katrin Götz
- Institute for Cell and Tumor Biology, RWTH Aachen University Hospital, Aachen, Germany
| | - Fabian Peisker
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University Hospital, Aachen, Germany
| | - Silke Wagner
- Institute for Cell and Tumor Biology, RWTH Aachen University Hospital, Aachen, Germany
| | - Adam Benabid
- Institute for Cell and Tumor Biology, RWTH Aachen University Hospital, Aachen, Germany
| | - Niklas Lutterbach
- Institute for Cell and Tumor Biology, RWTH Aachen University Hospital, Aachen, Germany
| | - Vanessa Klöker
- Institute for Computational Genomics, RWTH Aachen University Hospital, Aachen, Germany
| | - James S Nagai
- Institute for Computational Genomics, RWTH Aachen University Hospital, Aachen, Germany
| | - Monica T Hannani
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University Hospital, Aachen, Germany; Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Anna K Galyga
- Institute for Cell and Tumor Biology, RWTH Aachen University Hospital, Aachen, Germany
| | - Ellen Sistemich
- Institute for Cell and Tumor Biology, RWTH Aachen University Hospital, Aachen, Germany
| | - Bella Banjanin
- Department of Developmental Biology, Erasmus University Medical Center, Rotterdam, the Netherlands; Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Niclas Flosdorf
- Institute for Cell and Tumor Biology, RWTH Aachen University Hospital, Aachen, Germany
| | - Eric Bindels
- Department of Hematology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Kathrin Olschok
- Department of Hematology, Oncology, Hemostaseology, and Stem Cell Transplantation, RWTH Aachen University Hospital, Aachen, Germany; Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD), Aachen, Germany
| | - Katharina Biaesch
- Department of Hematology, Oncology, Hemostaseology, and Stem Cell Transplantation, RWTH Aachen University Hospital, Aachen, Germany; Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD), Aachen, Germany
| | - Nicolas Chatain
- Department of Hematology, Oncology, Hemostaseology, and Stem Cell Transplantation, RWTH Aachen University Hospital, Aachen, Germany; Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD), Aachen, Germany
| | | | - Andrew Dunbar
- Human Oncology and Pathogenesis Program, Leukemia Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rita Sarkis
- Laboratory of Regenerative Hematopoiesis, Department of Biomedical Sciences (DSB), Université de Lausanne (UNIL), Lausanne, Switzerland
| | - Olaia Naveiras
- Laboratory of Regenerative Hematopoiesis, Department of Biomedical Sciences (DSB), Université de Lausanne (UNIL), Lausanne, Switzerland
| | - Marie-Luise Berres
- Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD), Aachen, Germany; Medical Department III, RWTH University Hospital Aachen, Aachen, Germany
| | - Steffen Koschmieder
- Department of Hematology, Oncology, Hemostaseology, and Stem Cell Transplantation, RWTH Aachen University Hospital, Aachen, Germany; Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD), Aachen, Germany
| | - Ross L Levine
- Human Oncology and Pathogenesis Program, Leukemia Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ivan G Costa
- Institute for Computational Genomics, RWTH Aachen University Hospital, Aachen, Germany
| | - Hélène F E Gleitz
- Department of Developmental Biology, Erasmus University Medical Center, Rotterdam, the Netherlands; Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Rafael Kramann
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University Hospital, Aachen, Germany; Department of Internal Medicine, Nephrology and Transplantation, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Nephrology and Clinical Immunology, RWTH Aachen University Hospital, Aachen, Germany
| | - Rebekka K Schneider
- Institute for Cell and Tumor Biology, RWTH Aachen University Hospital, Aachen, Germany; Department of Developmental Biology, Erasmus University Medical Center, Rotterdam, the Netherlands; Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands.
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3
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Aono AH, Nagai JS, Dickel GDSM, Marinho RC, de Oliveira PEAM, Papa JP, Faria FA. Correction: A stomata classification and detection system in microscope images of maize cultivars. PLoS One 2024; 19:e0296551. [PMID: 38165869 PMCID: PMC10760676 DOI: 10.1371/journal.pone.0296551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2024] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pone.0258679.].
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4
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Li Z, Nagai JS, Kuppe C, Kramann R, Costa IG. scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference. Bioinform Adv 2023; 3:vbad003. [PMID: 36698768 PMCID: PMC9853317 DOI: 10.1093/bioadv/vbad003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/08/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023]
Abstract
Summary The increasing availability of single-cell multi-omics data allows to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) that enables an end-to-end analysis of multi-omics data for gene regulatory network inference including modalities integration, trajectory analysis, enhancer-to-promoter association, network analysis and visualization. This enables to study the complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease-driven cellular remodeling. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction. Availability and implementation scMEGA is implemented in R, released under the MIT license and available from https://github.com/CostaLab/scMEGA. Tutorials are available from https://costalab.github.io/scMEGA. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Zhijian Li
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen 52062, Germany
| | - James S Nagai
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen 52062, Germany
| | - Christoph Kuppe
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen 52062, Germany,Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen 52062, Germany
| | - Rafael Kramann
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen 52062, Germany,Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen 52062, Germany,Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam 3042, The Netherlands
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5
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Schreibing F, Hannani MT, Kim H, Nagai JS, Ticconi F, Fewings E, Bleckwehl T, Begemann M, Torow N, Kuppe C, Kurth I, Kranz J, Frank D, Anslinger TM, Ziegler P, Kraus T, Enczmann J, Balz V, Windhofer F, Balfanz P, Kurts C, Marx G, Marx N, Dreher M, Schneider RK, Saez-Rodriguez J, Costa I, Hayat S, Kramann R. Dissecting CD8+ T cell pathology of severe SARS-CoV-2 infection by single-cell immunoprofiling. Front Immunol 2022; 13:1066176. [PMID: 36591270 PMCID: PMC9800604 DOI: 10.3389/fimmu.2022.1066176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction SARS-CoV-2 infection results in varying disease severity, ranging from asymptomatic infection to severe illness. A detailed understanding of the immune response to SARS-CoV-2 is critical to unravel the causative factors underlying differences in disease severity and to develop optimal vaccines against new SARS-CoV-2 variants. Methods We combined single-cell RNA and T cell receptor sequencing with CITE-seq antibodies to characterize the CD8+ T cell response to SARS-CoV-2 infection at high resolution and compared responses between mild and severe COVID-19. Results We observed increased CD8+ T cell exhaustion in severe SARS-CoV-2 infection and identified a population of NK-like, terminally differentiated CD8+ effector T cells characterized by expression of FCGR3A (encoding CD16). Further characterization of NK-like CD8+ T cells revealed heterogeneity among CD16+ NK-like CD8+ T cells and profound differences in cytotoxicity, exhaustion, and NK-like differentiation between mild and severe disease conditions. Discussion We propose a model in which differences in the surrounding inflammatory milieu lead to crucial differences in NK-like differentiation of CD8+ effector T cells, ultimately resulting in the appearance of NK-like CD8+ T cell populations of different functionality and pathogenicity. Our in-depth characterization of the CD8+ T cell-mediated response to SARS-CoV-2 infection provides a basis for further investigation of the importance of NK-like CD8+ T cells in COVID-19 severity.
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Affiliation(s)
- Felix Schreibing
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Renal and Hypertensive Disorders, Rheumatological and Immunological Diseases (Medical Clinic II), Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Monica T. Hannani
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Hyojin Kim
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - James S. Nagai
- Institute for Computational Genomics, Medical Faculty, RWTH Aachen University, Aachen, Germany,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Fabio Ticconi
- Institute for Computational Genomics, Medical Faculty, RWTH Aachen University, Aachen, Germany,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Eleanor Fewings
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Tore Bleckwehl
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Matthias Begemann
- Institute of Human Genetics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Natalia Torow
- Institute of Medical Microbiology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Christoph Kuppe
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Renal and Hypertensive Disorders, Rheumatological and Immunological Diseases (Medical Clinic II), Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Ingo Kurth
- Institute of Human Genetics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Jennifer Kranz
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Urology and Pediatric Urology, RWTH Aachen University, Aachen, Germany,Department of Urology and Kidney Transplantation, Martin Luther University (Saale), Halle, Germany
| | - Dario Frank
- Department of Medicine, St Antonius Hospital, Eschweiler, Germany
| | - Teresa M. Anslinger
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Renal and Hypertensive Disorders, Rheumatological and Immunological Diseases (Medical Clinic II), Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Patrick Ziegler
- Institute for Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Thomas Kraus
- Institute for Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Jürgen Enczmann
- Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Vera Balz
- Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Frank Windhofer
- Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Paul Balfanz
- Department of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Christian Kurts
- Institute of Molecular Medicine and Experimental Immunology, Medical Faculty, University of Bonn, Bonn, Germany
| | - Gernot Marx
- Department of Intensive and Intermediate Care, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Nikolaus Marx
- Department of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Michael Dreher
- Department of Pneumology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Rebekka K. Schneider
- Institute of Cell and Tumor Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Developmental Biology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Ivan Costa
- Institute for Computational Genomics, Medical Faculty, RWTH Aachen University, Aachen, Germany,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Sikander Hayat
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Rafael Kramann
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Renal and Hypertensive Disorders, Rheumatological and Immunological Diseases (Medical Clinic II), Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Internal Medicine, Erasmus Medical Center (MC), Rotterdam, Netherlands,*Correspondence: Rafael Kramann,
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6
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Dimitrov D, Türei D, Garrido-Rodriguez M, Burmedi PL, Nagai JS, Boys C, Ramirez Flores RO, Kim H, Szalai B, Costa IG, Valdeolivas A, Dugourd A, Saez-Rodriguez J. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nat Commun 2022; 13:3224. [PMID: 35680885 PMCID: PMC9184522 DOI: 10.1038/s41467-022-30755-0] [Citation(s) in RCA: 101] [Impact Index Per Article: 50.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 05/17/2022] [Indexed: 12/18/2022] Open
Abstract
The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods’ predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods. Multiple methods to infer cell-cell communication (CCC) from single cell data are currently available. Here, the authors systematically compare 16 CCC inference resources and 7 methods, and develop the LIANA framework as an interface to use and compare all these approaches.
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Affiliation(s)
- Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Dénes Türei
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Martin Garrido-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Paul L Burmedi
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - James S Nagai
- Institute for Computational Genomics, Faculty of Medicine, RWTH Aachen University, Aachen, 52074, Germany.,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Charlotte Boys
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Ricardo O Ramirez Flores
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Hyojin Kim
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Bence Szalai
- Faculty of Medicine, Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Ivan G Costa
- Institute for Computational Genomics, Faculty of Medicine, RWTH Aachen University, Aachen, 52074, Germany.,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Alberto Valdeolivas
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Aurélien Dugourd
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany.
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7
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Jansen J, van den Berge BT, van den Broek M, Maas RJ, Daviran D, Willemsen B, Roverts R, van der Kruit M, Kuppe C, Reimer KC, Di Giovanni G, Mooren F, Nlandu Q, Mudde H, Wetzels R, den Braanker D, Parr N, Nagai JS, Drenic V, Costa IG, Steenbergen E, Nijenhuis T, Dijkman H, Endlich N, van de Kar NCAJ, Schneider RK, Wetzels JFM, Akiva A, van der Vlag J, Kramann R, Schreuder MF, Smeets B. Human pluripotent stem cell-derived kidney organoids for personalized congenital and idiopathic nephrotic syndrome modeling. Development 2022; 149:275031. [PMID: 35417019 PMCID: PMC9148570 DOI: 10.1242/dev.200198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 03/28/2022] [Indexed: 12/21/2022]
Abstract
Nephrotic syndrome (NS) is characterized by severe proteinuria as a consequence of kidney glomerular injury due to podocyte damage. In vitro models mimicking in vivo podocyte characteristics are a prerequisite to resolve NS pathogenesis. The detailed characterization of organoid podocytes resulting from a hybrid culture protocol showed a podocyte population that resembles adult podocytes and was superior compared with 2D counterparts, based on single-cell RNA sequencing, super-resolution imaging and electron microscopy. In this study, these next-generation podocytes in kidney organoids enabled personalized idiopathic nephrotic syndrome modeling, as shown by activated slit diaphragm signaling and podocyte injury following protamine sulfate, puromycin aminonucleoside treatment and exposure to NS plasma containing pathogenic permeability factors. Organoids cultured from cells of a patient with heterozygous NPHS2 mutations showed poor NPHS2 expression and aberrant NPHS1 localization, which was reversible after genetic correction. Repaired organoids displayed increased VEGFA pathway activity and transcription factor activity known to be essential for podocyte physiology, as shown by RNA sequencing. This study shows that organoids are the preferred model of choice to study idiopathic and congenital podocytopathies. Summary: Kidney organoid podocytes generated from human pluripotent stem cells using a hybrid differentiation protocol allow podocyte pathophysiology modeling that leads to congenital as well as idiopathic nephrotic syndrome in patients.
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Affiliation(s)
- Jitske Jansen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands.,Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Amalia Children's Hospital, PO Box 9101, 6500 HB Nijmegen, The Netherlands.,Division of Nephrology and Clinical Immunology, Institute of Experimental Medicine and Systems Biology, Medical Faculty RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Bartholomeus T van den Berge
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands.,Department of Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Martijn van den Broek
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands.,Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Amalia Children's Hospital, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Rutger J Maas
- Department of Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Deniz Daviran
- Department of Biochemistry, Electron Microscopy Center, Radboudumc Technology Center Microscopy, Radboud Institute of Molecular Life Sciences, Radboud University Medical Center, Geert Grooteplein 29, 6525 GA Nijmegen, The Netherlands
| | - Brigith Willemsen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Rona Roverts
- Department of Biochemistry, Electron Microscopy Center, Radboudumc Technology Center Microscopy, Radboud Institute of Molecular Life Sciences, Radboud University Medical Center, Geert Grooteplein 29, 6525 GA Nijmegen, The Netherlands
| | - Marit van der Kruit
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Christoph Kuppe
- Division of Nephrology and Clinical Immunology, Institute of Experimental Medicine and Systems Biology, Medical Faculty RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany.,Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen 52062, Germany
| | - Katharina C Reimer
- Division of Nephrology and Clinical Immunology, Institute of Experimental Medicine and Systems Biology, Medical Faculty RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany.,Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen 52062, Germany.,Institute for Biomedical Technologies, Department of Cell Biology, RWTH Aachen University, Aachen 52062, Germany
| | - Gianluca Di Giovanni
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands.,Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Amalia Children's Hospital, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Fieke Mooren
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Quincy Nlandu
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Helmer Mudde
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Roy Wetzels
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Dirk den Braanker
- Department of Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Naomi Parr
- Department of Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - James S Nagai
- Institute for Computational Genomics, University Hospital RWTH Aachen, Achen 52062, Germany.,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen 52062, Germany
| | | | - Ivan G Costa
- Institute for Computational Genomics, University Hospital RWTH Aachen, Achen 52062, Germany.,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen 52062, Germany
| | - Eric Steenbergen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Tom Nijenhuis
- Department of Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Henry Dijkman
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Nicole Endlich
- NIPOKA, 17489 Greifswald, Germany.,Department of Anatomy and Cell Biology, University Medicine Greifswald, 17489 Greifswald, Germany
| | - Nicole C A J van de Kar
- Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Amalia Children's Hospital, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Rebekka K Schneider
- Institute for Biomedical Technologies, Department of Cell Biology, RWTH Aachen University, Aachen 52062, Germany.,Department of Developmental Biology, Erasmus Medical Center, Rotterdam 3015 GD, The Netherlands.,Oncode Institute, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Jack F M Wetzels
- Department of Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Anat Akiva
- Department of Biochemistry, Electron Microscopy Center, Radboudumc Technology Center Microscopy, Radboud Institute of Molecular Life Sciences, Radboud University Medical Center, Geert Grooteplein 29, 6525 GA Nijmegen, The Netherlands
| | - Johan van der Vlag
- Department of Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Rafael Kramann
- Division of Nephrology and Clinical Immunology, Institute of Experimental Medicine and Systems Biology, Medical Faculty RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany.,Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen 52062, Germany.,Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam 3015 GD, The Netherlands
| | - Michiel F Schreuder
- Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Amalia Children's Hospital, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Bart Smeets
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
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8
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Stalmann USA, Banjanin B, Snoeren IAM, Nagai JS, Leimkühler NB, Li R, Benabid A, Pritchard J, Malyaran H, Neuss S, Bindels E, Costa IG, Schneider RK. Single cell analysis of cultured bone marrow stromal cells reveals high similarity to fibroblasts in situ. Exp Hematol 2022; 110:28-33. [PMID: 35341805 DOI: 10.1016/j.exphem.2022.03.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/23/2022] [Accepted: 03/20/2022] [Indexed: 11/27/2022]
Affiliation(s)
- U S A Stalmann
- Department of Developmental Biology, Erasmus Medical Center, Rotterdam, Netherlands; Oncode Institute, Erasmus Medical Center Cancer Institute, Rotterdam, Netherlands
| | - B Banjanin
- Department of Developmental Biology, Erasmus Medical Center, Rotterdam, Netherlands; Oncode Institute, Erasmus Medical Center Cancer Institute, Rotterdam, Netherlands
| | - I A M Snoeren
- Department of Developmental Biology, Erasmus Medical Center, Rotterdam, Netherlands; Oncode Institute, Erasmus Medical Center Cancer Institute, Rotterdam, Netherlands
| | - J S Nagai
- Institute for Computational Genomics, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - N B Leimkühler
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - R Li
- Institute for Computational Genomics, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - A Benabid
- Department of Cell Biology, Faculty of Medicine, Institute for Biomedical Engineering, (RWTH) Aachen University, Aachen, Germany
| | - J Pritchard
- Department of Developmental Biology, Erasmus Medical Center, Rotterdam, Netherlands; Oncode Institute, Erasmus Medical Center Cancer Institute, Rotterdam, Netherlands; Department of Cell Biology, Faculty of Medicine, Institute for Biomedical Engineering, (RWTH) Aachen University, Aachen, Germany
| | - H Malyaran
- Institute of Pathology, Faculty of Medicine, (RWTH) Aachen University, Aachen, Germany; Helmholtz Institute for Biomedical Engineering, Biointerface Group, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - S Neuss
- Institute of Pathology, Faculty of Medicine, (RWTH) Aachen University, Aachen, Germany; Helmholtz Institute for Biomedical Engineering, Biointerface Group, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - E Bindels
- Department of Hematology, Erasmus Medical Center Cancer Institute, Rotterdam, Netherlands
| | - I G Costa
- Institute for Computational Genomics, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - R K Schneider
- Department of Developmental Biology, Erasmus Medical Center, Rotterdam, Netherlands; Oncode Institute, Erasmus Medical Center Cancer Institute, Rotterdam, Netherlands; Department of Cell Biology, Faculty of Medicine, Institute for Biomedical Engineering, (RWTH) Aachen University, Aachen, Germany.
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9
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Jansen J, Reimer KC, Nagai JS, Varghese FS, Overheul GJ, de Beer M, Roverts R, Daviran D, Fermin LA, Willemsen B, Beukenboom M, Djudjaj S, von Stillfried S, van Eijk LE, Mastik M, Bulthuis M, Dunnen WD, van Goor H, Hillebrands JL, Triana SH, Alexandrov T, Timm MC, van den Berge BT, van den Broek M, Nlandu Q, Heijnert J, Bindels EM, Hoogenboezem RM, Mooren F, Kuppe C, Miesen P, Grünberg K, Ijzermans T, Steenbergen EJ, Czogalla J, Schreuder MF, Sommerdijk N, Akiva A, Boor P, Puelles VG, Floege J, Huber TB, van Rij RP, Costa IG, Schneider RK, Smeets B, Kramann R, Achdout H, Aimon A, Bar-David E, Barr H, Ben-Shmuel A, Bennett J, Boby ML, Borden B, Bowman GR, Brun J, BVNBS S, Calmiano M, Carbery A, Cattermole E, Chernychenko E, Choder JD, Clyde A, Coffland JE, Cohen G, Cole J, Contini A, Cox L, Cvitkovic M, Dias A, Donckers K, Dotson DL, Douangamath A, Duberstein S, Dudgeon T, Dunnett L, Eastman PK, Erez N, Eyermann CJ, Fairhead M, Fate G, Fearon D, Federov O, Ferla M, Fernandes RS, Ferrins L, Foster R, Foster H, Gabizon R, Garcia-Sastre A, Gawriljuk VO, Gehrtz P, Gileadi C, Giroud C, Glass WG, Glen R, Itai glinert, Godoy AS, Gorichko M, Gorrie-Stone T, Griffen EJ, Hart SH, Heer J, Henry M, Hill M, Horrell S, Hurley MF, Israely T, Jajack A, Jnoff E, Jochmans D, John T, De Jonghe S, Kantsadi AL, Kenny PW, Kiappes J, Koekemoer L, Kovar B, Krojer T, Lee AA, Lefker BA, Levy H, London N, Lukacik P, Macdonald HB, Maclean B, Malla TR, Matviiuk T, McCorkindale W, McGovern BL, Melamed S, Michurin O, Mikolajek H, Milne BF, Morris A, Morris GM, Morwitzer MJ, Moustakas D, Nakamura AM, Neto JB, Neyts J, Nguyen L, Noske GD, Oleinikovas V, Oliva G, Overheul GJ, Owen D, Psenak V, Pai R, Pan J, Paran N, Perry B, Pingle M, Pinjari J, Politi B, Powell A, Puni R, Rangel VL, Reddi RN, Reid SP, Resnick E, Ripka EG, Robinson MC, Robinson RP, Rodriguez-Guerra J, Rosales R, Rufa D, Schofield C, Shafeev M, Shaikh A, Shi J, Shurrush K, Sing S, Sittner A, Skyner R, Smalley A, Smilova MD, Solmesky LJ, Spencer J, Strain-Damarell C, Swamy V, Tamir H, Tennant R, Thompson W, Thompson A, Thompson W, Tomasia S, Tumber A, Vakonakis I, van Rij RP, van Geel L, Varghese FS, Vaschetto M, Vitner EB, Voelz V, Volkamer A, von Delft F, von Delft A, Walsh M, Ward W, Weatherall C, Weiss S, White KM, Wild CF, Wittmann M, Wright N, Yahalom-Ronen Y, Zaidmann D, Zidane H, Zitzmann N. SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids. Cell Stem Cell 2021; 29:217-231.e8. [PMID: 35032430 PMCID: PMC8709832 DOI: 10.1016/j.stem.2021.12.010] [Citation(s) in RCA: 121] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 09/03/2021] [Accepted: 12/16/2021] [Indexed: 12/20/2022]
Abstract
Kidney failure is frequently observed during and after COVID-19, but it remains elusive whether this is a direct effect of the virus. Here, we report that SARS-CoV-2 directly infects kidney cells and is associated with increased tubule-interstitial kidney fibrosis in patient autopsy samples. To study direct effects of the virus on the kidney independent of systemic effects of COVID-19, we infected human-induced pluripotent stem-cell-derived kidney organoids with SARS-CoV-2. Single-cell RNA sequencing indicated injury and dedifferentiation of infected cells with activation of profibrotic signaling pathways. Importantly, SARS-CoV-2 infection also led to increased collagen 1 protein expression in organoids. A SARS-CoV-2 protease inhibitor was able to ameliorate the infection of kidney cells by SARS-CoV-2. Our results suggest that SARS-CoV-2 can directly infect kidney cells and induce cell injury with subsequent fibrosis. These data could explain both acute kidney injury in COVID-19 patients and the development of chronic kidney disease in long COVID. COVID-19 patients present tubulo-interstitial kidney fibrosis compared with controls SARS-CoV-2 infection stimulates profibrotic signaling in human kidney organoids SARS-CoV-2 infection can be inhibited by a protease blocker in human kidney organoids
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10
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Aono AH, Nagai JS, Dickel GDSM, Marinho RC, de Oliveira PEAM, Papa JP, Faria FA. A stomata classification and detection system in microscope images of maize cultivars. PLoS One 2021; 16:e0258679. [PMID: 34695146 PMCID: PMC8544852 DOI: 10.1371/journal.pone.0258679] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 10/03/2021] [Indexed: 11/18/2022] Open
Abstract
Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.
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Affiliation(s)
- Alexandre H. Aono
- Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, São José dos Campos, São Paulo, Brazil
| | - James S. Nagai
- Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, São José dos Campos, São Paulo, Brazil
| | | | - Rafaela C. Marinho
- Instituto de Biologia, Universidade Federal de Uberlândia, Uberlândia, Minas Gerais, Brazil
| | | | - João P. Papa
- Department of Computing, São Paulo State University, Bauru, São Paulo, Brazil
| | - Fabio A. Faria
- Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, São José dos Campos, São Paulo, Brazil
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11
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Nagai JS, Leimkühler NB, Schaub MT, Schneider RK, Costa IG. CrossTalkeR: Analysis and Visualisation of Ligand Receptor Networks. Bioinformatics 2021; 37:4263-4265. [PMID: 35032393 PMCID: PMC9502146 DOI: 10.1093/bioinformatics/btab370] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 05/07/2021] [Accepted: 05/11/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Ligand-receptor (LR) network analysis allows the characterization of cellular crosstalk based on single cell RNA-seq data. However, current methods typically provide a list of inferred LR interactions and do not allow the researcher to focus on specific cell types, ligands or receptors. In addition, most of these methods cannot quantify changes in crosstalk between two biological phenotypes. RESULTS CrossTalkeR is a framework for network analysis and visualisation of LR interactions. CrossTalkeR identifies relevant ligands, receptors and cell types contributing to changes in cell communication when contrasting two biological phenotypes, i.e. disease vs. homeostasis. A case study on scRNA-seq of human myeloproliferative neoplasms reinforces the strengths of CrossTalkeR for characterisation of changes in cellular crosstalk in disease. AVAILABILITY CrosstalkeR is an R package available at:Github: https://github.com/CostaLab/CrossTalkeR.Zenodo: https://zenodo.org/record/4740646. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- James S Nagai
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Germany
| | - Nils B Leimkühler
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, Germany.,Department of Hematology, Erasmus Medical Center, the Netherlands
| | | | - Rebekka K Schneider
- Department of Hematology, Erasmus Medical Center, the Netherlands.,Department of Cell Biology, Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, Germany.,Oncode Institute, Erasmus Medical Center, the Netherlands
| | - Ivan G Costa
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Germany
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12
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Aono AH, Nagai JS, Dickel GDSM, Marinho RC, de Oliveira PEAM, Papa JP, Faria FA. A stomata classification and detection system in microscope images of maize cultivars. PLoS One 2021; 16:e0258679. [PMID: 34695146 DOI: 10.1101/538165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 10/03/2021] [Indexed: 05/20/2023] Open
Abstract
Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.
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Affiliation(s)
- Alexandre H Aono
- Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, São José dos Campos, São Paulo, Brazil
| | - James S Nagai
- Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, São José dos Campos, São Paulo, Brazil
| | | | - Rafaela C Marinho
- Instituto de Biologia, Universidade Federal de Uberlândia, Uberlândia, Minas Gerais, Brazil
| | | | - João P Papa
- Department of Computing, São Paulo State University, Bauru, São Paulo, Brazil
| | - Fabio A Faria
- Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, São José dos Campos, São Paulo, Brazil
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