CD200
Journal
Nature immunology
ISSN: 1529-2916
Titre abrégé: Nat Immunol
Pays: United States
ID NLM: 100941354
Informations de publication
Date de publication:
23 Feb 2024
23 Feb 2024
Historique:
received:
15
03
2023
accepted:
30
01
2024
medline:
24
2
2024
pubmed:
24
2
2024
entrez:
24
2
2024
Statut:
aheadofprint
Résumé
Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3
Identifiants
pubmed: 38396288
doi: 10.1038/s41590-024-01774-4
pii: 10.1038/s41590-024-01774-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : RA 2506/4-2
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : RA 2506/7-1
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : CRC1181/C6
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : CRC/TRR 369
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : SO 1735/2-1
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : Clinician Scientist Program NOTICE
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : 853508 - BARRIER BREAK
Organisme : Innovative Medicines Initiative (IMI)
ID : HIPPOCRATES
Organisme : Innovative Medicines Initiative (IMI)
ID : RTCure
Organisme : Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie (Federal Ministry for Education, Science, Research and Technology)
ID : MASCARA
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Davidson, S. et al. Fibroblasts as immune regulators in infection, inflammation and cancer. Nat. Rev. Immunol. 21, 704–717 (2021).
pubmed: 33911232
doi: 10.1038/s41577-021-00540-z
Croft, A. P. et al. Distinct fibroblast subsets drive inflammation and damage in arthritis. Nature 570, 246–251 (2019).
pubmed: 31142839
pmcid: 6690841
doi: 10.1038/s41586-019-1263-7
Wei, K. et al. Notch signalling drives synovial fibroblast identity and arthritis pathology. Nature 582, 259–264 (2020).
pubmed: 32499639
pmcid: 7841716
doi: 10.1038/s41586-020-2222-z
Armaka, M. et al. Single-cell multimodal analysis identifies common regulatory programs in synovial fibroblasts of rheumatoid arthritis patients and modeled TNF-driven arthritis. Genome Med. 14, 78 (2022).
pubmed: 35879783
pmcid: 9316748
doi: 10.1186/s13073-022-01081-3
Yan, M. et al. ETS1 governs pathological tissue-remodeling programs in disease-associated fibroblasts. Nat. Immunol. 23, 1330–1341 (2022).
pubmed: 35999392
doi: 10.1038/s41590-022-01285-0
Floudas, A. et al. Distinct stromal and immune cell interactions shape the pathogenesis of rheumatoid and psoriatic arthritis. Ann. Rheum. Dis. annrheumdis-2021-221761 (2022).
Mizoguchi, F. et al. Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis. Nat. Commun. 9, 789 (2018).
pubmed: 29476097
pmcid: 5824882
doi: 10.1038/s41467-018-02892-y
Ospelt, C. & Gay, S. The role of resident synovial cells in destructive arthritis. Best. Pract. Res. Clin. Rheumatol. 22, 239–252 (2008).
pubmed: 18455682
doi: 10.1016/j.berh.2008.01.004
Komatsu, N. & Takayanagi, H. Mechanisms of joint destruction in rheumatoid arthritis - immune cell-fibroblast-bone interactions. Nat. Rev. Rheumatol. 18, 415–429 (2022).
pubmed: 35705856
doi: 10.1038/s41584-022-00793-5
Friščić, J. et al. The complement system drives local inflammatory tissue priming by metabolic reprogramming of synovial fibroblasts. Immunity 54, 1002–1021 (2021).
pubmed: 33761330
doi: 10.1016/j.immuni.2021.03.003
Schett, G., McInnes, I. B. & Neurath, M. F. Reframing immune-mediated inflammatory diseases through signature cytokine hubs. N. Engl. J. Med. 385, 628–639 (2021).
pubmed: 34379924
doi: 10.1056/NEJMra1909094
McInnes, I. B. & Schett, G. Pathogenetic insights from the treatment of rheumatoid arthritis. Lancet 389, 2328–2337 (2017).
pubmed: 28612747
doi: 10.1016/S0140-6736(17)31472-1
Schmidkonz, C. et al. Fibroblast activation protein inhibitor imaging in nonmalignant diseases: a new perspective for molecular imaging. J. Nucl. Med. 63, 1786–1792 (2022).
pubmed: 36109182
doi: 10.2967/jnumed.122.264205
Kuwert, T., Schmidkonz, C., Prante, O., Schett, G. & Ramming, A. FAPI PET opens a new window to understanding immune-mediated inflammatory diseases. J. Nucl. Med. 63, 1136–1137 (2022).
pubmed: 35393350
doi: 10.2967/jnumed.122.263922
Lindner, T. et al. Development of quinoline-based theranostic ligands for the targeting of fibroblast activation protein. J. Nucl. Med. 59, 1415–1422 (2018).
pubmed: 29626119
doi: 10.2967/jnumed.118.210443
Loktev, A. et al. Development of fibroblast activation protein-targeted radiotracers with improved tumor retention. J. Nucl. Med. 60, 1421–1429 (2019).
pubmed: 30850501
pmcid: 6785792
doi: 10.2967/jnumed.118.224469
Jansen, K. et al. Selective inhibitors of fibroblast activation protein (FAP) with a (4-quinolinoyl)-glycyl-2-cyanopyrrolidine sScaffold. ACS Med. Chem. Lett. 4, 491–496 (2013).
pubmed: 24900696
pmcid: 4027141
doi: 10.1021/ml300410d
Jansen, K. et al. Extended structure-activity relationship and pharmacokinetic investigation of (4-quinolinoyl)glycyl-2-cyanopyrrolidine inhibitors of fibroblast activation protein (FAP). J. Med. Chem. 57, 3053–3074 (2014).
pubmed: 24617858
doi: 10.1021/jm500031w
Dorst, D. N. et al. Targeting of fibroblast activation protein in rheumatoid arthritis patients: imaging and ex vivo photodynamic therapy. Rheumatology 61, 2999–3009 (2022).
pubmed: 34450633
doi: 10.1093/rheumatology/keab664
Ge, L. et al. Preclinical evaluation and pilot clinical study of [
pubmed: 35715613
doi: 10.1007/s00259-022-05836-3
Squair, J. W. et al. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12, 5692 (2021).
pubmed: 34584091
pmcid: 8479118
doi: 10.1038/s41467-021-25960-2
Buechler, M. B. et al. Cross-tissue organization of the fibroblast lineage. Nature 593, 575–579 (2021).
pubmed: 33981032
doi: 10.1038/s41586-021-03549-5
Collins, F. L. et al. Taxonomy of fibroblasts and progenitors in the synovial joint at single-cell resolution. Ann. Rheum. Dis. 82, 428–437 (2022).
pubmed: 36414376
doi: 10.1136/ard-2021-221682
Burkhardt, D. B. et al. Quantifying the effect of experimental perturbations at single-cell resolution. Nat. Biotechnol. 39, 619–629 (2021).
pubmed: 33558698
pmcid: 8122059
doi: 10.1038/s41587-020-00803-5
Reshef, Y. A. et al. Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics. Nat. Biotechnol. 40, 355–363 (2022).
pubmed: 34675423
doi: 10.1038/s41587-021-01066-4
Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D. & Marioni, J. C. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat. Biotechnol. 40, 245–253 (2022).
pubmed: 34594043
doi: 10.1038/s41587-021-01033-z
Lange, M. et al. CellRank for directed single-cell fate mapping. Nat. Methods 19, 159–170 (2022).
pubmed: 35027767
pmcid: 8828480
doi: 10.1038/s41592-021-01346-6
Omata, Y. et al. Group 2 innate lymphoid cells attenuate inflammatory arthritis and protect from bone destruction in mice. Cell Rep. 24, 169–180 (2018).
pubmed: 29972778
doi: 10.1016/j.celrep.2018.06.005
Rauber, S. et al. Resolution of inflammation by interleukin-9-producing type 2 innate lymphoid cells. Nat. Med. 23, 938–944 (2017).
pubmed: 28714991
pmcid: 5575995
doi: 10.1038/nm.4373
Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E. & Swarup, V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods 3, 100498 (2023).
pubmed: 37426759
pmcid: 10326379
doi: 10.1016/j.crmeth.2023.100498
Qiu, X. et al. Mapping transcriptomic vector fields of single cells. Cell 185, 690–711 (2022).
pubmed: 35108499
pmcid: 9332140
doi: 10.1016/j.cell.2021.12.045
Penkava, F. et al. Single-cell sequencing reveals clonal expansions of pro-inflammatory synovial CD8 T cells expressing tissue-homing receptors in psoriatic arthritis. Nat. Commun. 11, 4767 (2020).
pubmed: 32958743
pmcid: 7505844
doi: 10.1038/s41467-020-18513-6
Carlberg, K. et al. Exploring inflammatory signatures in arthritic joint biopsies with spatial transcriptomics. Sci. Rep. 9, 18975 (2019).
pubmed: 31831833
pmcid: 6908624
doi: 10.1038/s41598-019-55441-y
Smith, M. H. et al. Drivers of heterogeneity in synovial fibroblasts in rheumatoid arthritis. Nat. Immunol. 24, 1200–1210 (2023).
pubmed: 37277655
pmcid: 10307631
doi: 10.1038/s41590-023-01527-9
Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39, 1375–1384 (2021).
pubmed: 34083791
pmcid: 8763026
doi: 10.1038/s41587-021-00935-2
Lories, R. J., Luyten, F. P. & de Vlam, K. Progress in spondylarthritis. Mechanisms of new bone formation in spondyloarthritis. Arthritis Res Ther. 11, 221 (2009).
pubmed: 19439035
pmcid: 2688182
doi: 10.1186/ar2642
Andreev, D. et al. Regulatory eosinophils induce the resolution of experimental arthritis and appear in remission state of human rheumatoid arthritis. Ann. Rheum. Dis. 80, 451–468 (2021).
pubmed: 33148700
doi: 10.1136/annrheumdis-2020-218902
Chen, Z., Bozec, A., Ramming, A. & Schett, G. Anti-inflammatory and immune-regulatory cytokines in rheumatoid arthritis. Nat. Rev. Rheumatol. 15, 9–17 (2019).
pubmed: 30341437
doi: 10.1038/s41584-018-0109-2
Filer, A. The fibroblast as a therapeutic target in rheumatoid arthritis. Curr. Opin. Pharmacol. 13, 413–419 (2013).
pubmed: 23562164
doi: 10.1016/j.coph.2013.02.006
Gorczynski, R. M., Chen, Z., Yu, K. & Hu, J. CD200 immunoadhesin suppresses collagen-induced arthritis in mice. Clin. Immunol. 101, 328–334 (2001).
pubmed: 11726225
doi: 10.1006/clim.2001.5117
Schmidkonz, C. et al. Disentangling inflammatory from fibrotic disease activity by fibroblast activation protein imaging. Ann. Rheum. Dis. 79, 1485–1491 (2020).
pubmed: 32719042
doi: 10.1136/annrheumdis-2020-217408
Aletaha, D. et al. 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 62, 2569–2581 (2010).
pubmed: 20872595
doi: 10.1002/art.27584
Taylor, W. et al. Classification criteria for psoriatic arthritis: development of new criteria from a large international study. Arthritis Rheum. 54, 2665–2673 (2006).
pubmed: 16871531
doi: 10.1002/art.21972
Rudwaleit, M. et al. The development of Assessment of SpondyloArthritis International Society classification criteria for axial spondyloarthritis (part II): validation and final selection. Ann. Rheum. Dis. 68, 777–783 (2009).
pubmed: 19297344
doi: 10.1136/ard.2009.108233
Toms, J. et al. Targeting fibroblast activation protein: radiosynthesis and preclinical evaluation of an
pubmed: 32332144
doi: 10.2967/jnumed.120.242958
Keffer, J. et al. Transgenic mice expressing human tumour necrosis factor: a predictive genetic model of arthritis. EMBO J. 10, 4025–4031 (1991).
pubmed: 1721867
pmcid: 453150
doi: 10.1002/j.1460-2075.1991.tb04978.x
Nussbaum, J. C. et al. Type 2 innate lymphoid cells control eosinophil homeostasis. Nature 502, 245–248 (2013).
pubmed: 24037376
pmcid: 3795960
doi: 10.1038/nature12526
Kouskoff, V. et al. Organ-specific disease provoked by systemic autoimmunity. Cell 87, 811–822 (1996).
pubmed: 8945509
doi: 10.1016/S0092-8674(00)81989-3
Liu, F., Song, Y. & Liu, D. Hydrodynamics-based transfection in animals by systemic administration of plasmid DNA. Gene Ther. 6, 1258–1266 (1999).
pubmed: 10455434
doi: 10.1038/sj.gt.3300947
Zhang, G., Budker, V. & Wolff, J. A. High levels of foreign gene expression in hepatocytes after tail vein injections of naked plasmid DNA. Hum. Gene Ther. 10, 1735–1737 (1999).
pubmed: 10428218
doi: 10.1089/10430349950017734
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
pubmed: 22743772
doi: 10.1038/nmeth.2019
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
pubmed: 34062119
pmcid: 8238499
doi: 10.1016/j.cell.2021.04.048
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
pubmed: 25867923
pmcid: 4430369
doi: 10.1038/nbt.3192
van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).
pubmed: 28960196
doi: 10.1038/nmeth.4437
Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol. 23, 27 (2022).
pubmed: 35042561
pmcid: 8764781
doi: 10.1186/s13059-021-02584-9
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
pubmed: 31178118
pmcid: 6687398
doi: 10.1016/j.cell.2019.05.031
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
pubmed: 31740819
pmcid: 6884693
doi: 10.1038/s41592-019-0619-0
Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).
pubmed: 31061482
pmcid: 6551256
doi: 10.1038/s41587-019-0113-3
Gayoso, A. et al. A Python library for probabilistic analysis of single-cell omics data. Nat. Biotechnol. 40, 163–166 (2022).
pubmed: 35132262
doi: 10.1038/s41587-021-01206-w
Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A. & Theis, F. J. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43–49 (2019).
pubmed: 30573817
doi: 10.1038/s41592-018-0254-1
Mikolajewicz, N. et al. Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline. Commun. Biol. 5, 1142 (2022).
pubmed: 36307536
pmcid: 9616830
doi: 10.1038/s42003-022-04093-2
Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
pubmed: 34557778
pmcid: 8454663
Borcherding, N. et al. Mapping the immune environment in clear cell renal carcinoma by single-cell genomics. Commun. Biol. 4, 122 (2021).
pubmed: 33504936
pmcid: 7840906
doi: 10.1038/s42003-020-01625-6
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).
Andreatta, M. & Carmona, S. J. UCell: robust and scalable single-cell gene signature scoring. Comput Struct. Biotechnol. J. 19, 3796–3798 (2021).
pubmed: 34285779
pmcid: 8271111
doi: 10.1016/j.csbj.2021.06.043
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
pubmed: 30089906
pmcid: 6130801
doi: 10.1038/s41586-018-0414-6
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).
pubmed: 32747759
doi: 10.1038/s41587-020-0591-3
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
pubmed: 19910308
doi: 10.1093/bioinformatics/btp616
Amezquita, R. A. et al. Orchestrating single-cell analysis with Bioconductor. Nat. Methods 17, 137–145 (2020).
pubmed: 31792435
doi: 10.1038/s41592-019-0654-x
Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).
pubmed: 33597522
pmcid: 7889871
doi: 10.1038/s41467-021-21246-9
Kolde, R. Pheatmap: pretty heatmaps. R package version 1.0.10. https://cran.r-project.org/web/packages/pheatmap/index.html (2019).
Zanotelli, V. R. & Bodenmiller, B. ImcSegmentationPipeline: a pixel-classification-based multiplexed image segmentation pipeline. https://doi.org/10.5281/zenodo.3841961 (2022).
Ashhurst, T. M. et al. Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre. Cytometry A 101, 237–253 (2022).
pubmed: 33840138
doi: 10.1002/cyto.a.24350