A robust platform for integrative spatial multi-omics analysis to map immune responses to SARS-CoV-2 infection in lung tissues.

COVID-19 infection spatial integration spatial proteomics spatial transcriptomics

Journal

Immunology
ISSN: 1365-2567
Titre abrégé: Immunology
Pays: England
ID NLM: 0374672

Informations de publication

Date de publication:
Nov 2023
Historique:
received: 20 02 2023
accepted: 03 07 2023
pubmed: 22 8 2023
medline: 22 8 2023
entrez: 22 8 2023
Statut: ppublish

Résumé

The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal samples, bronchoalveolar lavage fluid and similar, but a robust platform for deep tissue characterization of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterize and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected samples, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.

Identifiants

pubmed: 37605469
doi: 10.1111/imm.13679
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

401-418

Subventions

Organisme : National Health and Medical Research Council
ID : APP2008542
Organisme : National Health and Medical Research Council
ID : 1135898
Organisme : National Health and Medical Research Council
ID : 1157741
Organisme : National Health and Medical Research Council
ID : 1140406
Organisme : National Health and Medical Research Council
ID : 2001514
Organisme : National Health and Medical Research Council
ID : GNT2008928

Informations de copyright

© 2023 The Authors. Immunology published by John Wiley & Sons Ltd.

Références

Milross L, Majo J, Cooper N, Kaye PM, Bayraktar O, Filby A, et al. Post-mortem lung tissue: the fossil record of the pathophysiology and immunopathology of severe COVID-19. Lancet Respir Med. 2022;10(1):95-106.
Eyre DW, Taylor D, Purver M, Chapman D, Fowler T, Pouwels KB, et al. Effect of Covid-19 vaccination on transmission of alpha and Delta variants. N Engl J Med. 2022;386(8):744-756.
Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27(4):601-615.
McElvaney OJ, McEvoy NL, McElvaney OF, Carroll TP, Murphy MP, Dunlea DM, et al. Characterization of the inflammatory response to severe COVID-19 illness. Am J Respir Crit Care Med. 2020;202(6):812-821.
Biancolella M, Colona VL, Mehrian-Shai R, Watt JL, Luzzatto L, Novelli G, et al. COVID-19 2022 update: transition of the pandemic to the endemic phase. Hum Genomics. 2022;16(1):19.
Wang X, Gang X, Liu X, Liu Y, Zhang S, Zhang Z. Multiomics: unraveling the panoramic landscapes of SARS-CoV-2 infection. Cell Mol Immunol. 2021;18(10):2313-2324.
Knyazev S, Chhugani K, Sarwal V, Ayyala R, Singh H, Karthikeyan S, et al. Unlocking capacities of genomics for the COVID-19 response and future pandemics. Nat Methods. 2022;19(4):374-380.
Lu T, Wang Y, Guo T. Multi-omics in COVID-19: seeing the unseen but overlooked in the clinic. Cell Reports Medicine. 2022;3(3):100580.
Kim D-K, Weller B, Lin C-W, Sheykhkarimli D, Knapp JJ, Dugied G, et al. A proteome-scale map of the SARS-CoV-2-human contactome. Nat Biotechnol. 2022;41(1):140-149.
Stukalov A, Girault V, Grass V, Karayel O, Bergant V, Urban C, et al. Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV. Nature. 2021;594(7862):246-252.
Delorey TM, Ziegler CGK, Heimberg G, Normand R, Yang Y, Segerstolpe Å, et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature. 2021;595(7865):107-113.
Stephenson E, Reynolds G, Botting RA, Calero-Nieto FJ, Morgan MD, Tuong ZK, et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat Med. 2021;27(5):904-916.
Li C-X, Wheelock CE, Magnus Sköld C, Wheelock ÅM. Integration of multi-omics datasets enables molecular classification of COPD. The European Respiratory Journal. 2018;51(5):1701930. https://doi.org/10.1183/13993003.01930-2017
Rendeiro AF, Ravichandran H, Bram Y, Chandar V, Kim J, Meydan C, et al. The spatial landscape of lung pathology during COVID-19 progression. Nature. 2021;593(7860):564-569.
Kulasinghe A, Tan CW, Miggiolaro AFRDS, Monkman J, SadeghiRad H, Bhuva DD, et al. Profiling of lung SARS-CoV-2 and influenza virus infection dissects virus-specific host responses and gene signatures. The European Respiratory Journal. 2022;59(6):2101881. https://doi.org/10.1183/13993003.01881-2021
Lewis SM, Asselin-Labat M-L, Nguyen Q, Berthelet J, Tan X, Wimmer VC, et al. Spatial omics and multiplexed imaging to explore cancer biology. Nat Methods. 2021;18(9):997-1012.
Tran M, Yoon S, Teoh M, Andersen S, Lam PY, Purdue BW, et al. A robust experimental and computational analysis framework at multiple resolutions, modalities and coverages. Front Immunol. 2022;13:911873.
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888-1902.e21. https://doi.org/10.1016/j.cell.2019.05.031
Melms JC, Biermann J, Huang H, Wang Y, Nair A, Tagore S, et al. A molecular single-cell lung atlas of lethal COVID-19. Nature. 2021;595(7865):114-119. https://doi.org/10.1038/s41586-021-03569-1
Laurent P, Yang C, Rendeiro AF, Nilsson-Payant BE, Carrau L, Chandar V, et al. Sensing of SARS-CoV-2 by pDCs and their subsequent production of IFN-I contribute to macrophage-induced cytokine storm during COVID-19. Science Immunology. 2022;7:eadd4906. https://doi.org/10.1126/sciimmunol.add4906
der Sluis V, Marije R, Holm CK, Jakobsen MR. Plasmacytoid dendritic cells during COVID-19: ally or adversary? Cell Rep. 2022;40(4):111148.
Venet M, Ribeiro MS, Décembre E, Bellomo A, Joshi G, Nuovo C, et al. Severe COVID-19 patients have impaired plasmacytoid dendritic cell-mediated control of SARS-CoV-2. Nat Commun. 2023;14(1):1-21.
Reyes L, Sanchez-Garcia MA, Morrison T, Howden AJM, Watts ER, Arienti S, et al. A type I IFN, prothrombotic hyperinflammatory neutrophil signature is distinct for COVID-19 ARDS. Wellcome Open Research. 2021;6:38. https://doi.org/10.12688/wellcomeopenres.16584.2
Montazersaheb S, Khatibi SMH, Hejazi MS, Tarhriz V, Farjami A, Sorbeni FG, et al. COVID-19 infection: an overview on cytokine storm and related interventions. Virol J. 2022;19(1):92.
Amanat F, Krammer F. SARS-CoV-2 vaccines: status report. Immunity. 2020;52(4):583-589.
Thoutam A, Breitzig M, Lockey R, Kolliputi N. Coronavirus: a shift in focus Away from IFN response and towards other inflammatory targets. J Cell Commun Signal. 2020;14:469-470.
Farber JM. A macrophage mRNA selectively induced by gamma-interferon encodes a member of the platelet factor 4 family of cytokines. Proc Natl Acad Sci U S A. 1990;87(14):5238-5242.
Callahan V, Hawks S, Crawford MA, Lehman CW, Morrison HA, Ivester HM, et al. The pro-inflammatory chemokines CXCL9, CXCL10 and CXCL11 are upregulated following SARS-CoV-2 infection in an AKT-dependent manner. Viruses. 2021;13(6):1062-1079. https://doi.org/10.3390/v13061062
Li C-X, Gao J, Zicheng Zhang L, Chen XL, Zhou M, Wheelock ÅM. Multiomics integration-based molecular characterizations of COVID-19. Brief Bioinform. 2022;23(1):1-17. https://doi.org/10.1093/bib/bbab485
Barh D, Tiwari S, Weener ME, Azevedo V, Aristóteles Góes-Neto M, Gromiha M, et al. Multi-omics-based identification of SARS-CoV-2 infection biology and candidate drugs against COVID-19. Comput Biol Med. 2020;126:104051.
Su A, Lee H, Tan X, Suarez CJ, Andor N, Nguyen Q, et al. A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images. NPJ Precision Oncology. 2022;6(1):14.
Hadjadj J, Yatim N, Barnabei L, Corneau A, Boussier J, Smith N, et al. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science. 2020;369:718-724. https://doi.org/10.1126/science.abc6027
Schroeder S, Pott F, Niemeyer D, Veith T, Richter A, Muth D, et al. Interferon antagonism by SARS-CoV-2: a functional study using reverse genetics. The Lancet Microbe. 2021;2(5):e210-e218.
Zhao X, Chen D, Li X, Griffith L, Chang J, An P, et al. Interferon control of human coronavirus infection and viral evasion: mechanistic insights and implications for antiviral drug and vaccine development. J Mol Biol. 2022;434(6):167438.
Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573-3587.
Grant RA, Morales-Nebreda L, Markov NS, Swaminathan S, Querrey M, Guzman ER, et al. Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia. Nature. 2021;590(7847):635-641.
Jafarzadeh A, Chauhan P, Saha B, Jafarzadeh S, Nemati M. Contribution of monocytes and macrophages to the local tissue inflammation and cytokine storm in COVID-19: lessons from SARS and MERS, and potential therapeutic interventions. Life Sci. 2020;257:118102. https://doi.org/10.1016/j.lfs.2020.118102
Black S, Phillips D, Hickey JW, Kennedy-Darling J, Venkataraaman VG, Samusik N, et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat Protoc. 2021;16(8):3802-3835.
Bannon D, Moen E, Schwartz M, Borba E, Kudo T, Greenwald N, et al. DeepCell kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes. Nat Methods. 2021;18(1):43-45.
Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19(1):15.
McCarthy DJ, Campbell KR, Lun ATL, Wills QF. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-Seq data in R. Bioinformatics. 2017;33(8):1179-1186.
Chen Y, Aaron TLL, Smyth GK. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research. 2016;5(June):1438.
Lun ATL, McCarthy DJ, Marioni JC. A step-by-step workflow for low-level analysis of single-cell RNA-Seq data with bioconductor. F1000Research. 2016;5(August):2122.
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411-420.
Zappia L, Oshlack A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. GigaScience. 2018;7:1-9. https://doi.org/10.1093/gigascience/giy083
Shojaei M, Shamshirian A, Monkman J, Grice L, Tran M, Tan CW, et al. IFI27 transcription is an early predictor for COVID-19 outcomes; a multi-cohort observational study. Front Immunol. 2023;13:1060438. https://doi.org/10.3389/fimmu.2022
Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Cambridge (Mass)). 2021;2(3):100141.
Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083-1086.
Beare R, Lowekamp B, Yaniv Z. Image segmentation, registration and characterization in R with SimpleITK. J Stat Softw. 2018;86(August):1-35. https://doi.org/10.18637/jss.v086.i08
Danaher P, Kim Y, Nelson B, Griswold M, Yang Z, Piazza E, et al. Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data. Nat Commun. 2022;13(1):1-13.
Chan Zuckerberg Initiative Single-Cell COVID-19 Consortia, Ballestar E, Farber DL, Glover S, Horwitz B, Meyer K, et al. Single cell profiling of COVID-19 patients: an international data resource from multiple tissues. medRxiv. 2020;1-47. https://doi.org/10.1101/2020.11.20.20227355
Stevens M, Nanou A, Terstappen LWMM, Driemel C, Stoecklein NH, Coumans FAW. StarDist image segmentation improves circulating tumor cell detection. Cancer. 2022;14(12):1-13. https://doi.org/10.3390/cancers14122916

Auteurs

Xiao Tan (X)

Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.

Laura F Grice (LF)

Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia.

Minh Tran (M)

Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.

Onkar Mulay (O)

Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.

James Monkman (J)

Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, Australia.

Tony Blick (T)

Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, Australia.

Tuan Vo (T)

Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.

Ana Clara Almeida (AC)

Pontifícia Universidade Católica do Paraná, PUCPR, Curitiba, Paraná, Brazil.
Laboratório de Patologia Experimental, PPGCS da PUCPR, Curitiba, Brazil.

Jarbas da Silva Motta (J)

Pontifícia Universidade Católica do Paraná, PUCPR, Curitiba, Paraná, Brazil.

Karen Fernandes de Moura (KF)

Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, Australia.

Cleber Machado-Souza (C)

Faculdades Pequeno Príncipe-Instituto de Pesquisa Pelé Pequeno príncipe, Curitiba, Paraná, Brazil.

Paulo Souza-Fonseca-Guimaraes (P)

Pontifícia Universidade Católica do Paraná, PUCPR, Curitiba, Paraná, Brazil.

Cristina Pellegrino Baena (CP)

Pontifícia Universidade Católica do Paraná, PUCPR, Curitiba, Paraná, Brazil.

Lucia de Noronha (L)

Pontifícia Universidade Católica do Paraná, PUCPR, Curitiba, Paraná, Brazil.
Laboratório de Patologia Experimental, PPGCS da PUCPR, Curitiba, Brazil.

Fernanda Simoes Fortes Guimaraes (FSF)

Positive University-School of Medicine, Curitiba, Brazil.

Hung N Luu (HN)

UMPC Hillman Cancer Center & School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Tingsheng Drennon (T)

10x Genomics, Pleasanton, California, USA.

Stephen Williams (S)

10x Genomics, Pleasanton, California, USA.

Jacob Stern (J)

10x Genomics, Pleasanton, California, USA.

Cedric Uytingco (C)

10x Genomics, Pleasanton, California, USA.

Liuliu Pan (L)

NanoString Technologies Inc, Seattle, Washington, USA.

Andy Nam (A)

NanoString Technologies Inc, Seattle, Washington, USA.

Caroline Cooper (C)

Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, Australia.

Kirsty Short (K)

School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia.

Gabrielle T Belz (GT)

Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, Australia.

Fernando Souza-Fonseca-Guimaraes (F)

Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, Australia.

Arutha Kulasinghe (A)

Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, Australia.

Quan Nguyen (Q)

Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
QIMR Berghofer Medical Reseach Institute, Queensland, Australia.

Classifications MeSH