Transferability of radiomic signatures from experimental to human interstitial lung disease

BACKGROUND Interstitial lung disease (ILD) defines a group of parenchymal lung disorders, characterized by fibrosis as their common final pathophysiological stage. To improve diagnosis and treatment of ILD, there is a need for repetitive non-invasive characterization of lung tissue by quantitative parameters. In this study, we investigated whether CT image patterns found in mice with bleomycin induced lung fibrosis can be translated as prognostic factors to human patients diagnosed with ILD. METHODS Bleomycin was used to induce lung fibrosis in mice (n_control = 36, n_experimental = 55). The patient cohort consisted of 98 systemic sclerosis (SSc) patients (n_ILD = 65). Radiomic features (n_histogram = 17, n_texture = 137) were extracted from microCT (mice) and HRCT (patients) images. Predictive performance of the models was evaluated with the area under the receiver-operating characteristic curve (AUC). First, predictive performance of individual features was examined and compared between murine and patient data sets. Second, multivariate models predicting ILD were trained on murine data and tested on patient data. Additionally, the models were reoptimized on patient data to reduce the influence of the domain shift on the performance scores. RESULTS Predictive power of individual features in terms of AUC was highly correlated between mice and patients (r = 0.86). A model based only on mean image intensity in the lung scored AUC = 0.921 ± 0.048 in mice and AUC = 0.774 (CI95% 0.677-0.859) in patients. The best radiomic model based on three radiomic features scored AUC = 0.994 ± 0.013 in mice and validated with AUC = 0.832 (CI95% 0.745-0.907) in patients. However, reoptimization of the model weights in the patient cohort allowed to increase the model's performance to AUC = 0.912 ± 0.058. CONCLUSION Radiomic signatures of experimental ILD derived from microCT scans translated to HRCT of humans with SSc-ILD. We showed that the experimental model of BLM-induced ILD is a promising system to test radiomic models for later application and validation in human cohorts

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. </p

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

Multilineage circuits of regenerative cell states.

Regenerating the lungs' architecture after injury requires rebuilding its fibroelastic extracellular matrix scaffold. Konkimalla et al. establish that regenerative cell states (RCSs) of both epithelial and mesenchymal origin are functionally linked and indispensable for this process. Experimental ablation of RCSs causes organ degeneration, whereas their induction causes organ fibrosis

Transferability of radiomic signatures from experimental to human interstitial lung disease.

BACKGROUND Interstitial lung disease (ILD) defines a group of parenchymal lung disorders, characterized by fibrosis as their common final pathophysiological stage. To improve diagnosis and treatment of ILD, there is a need for repetitive non-invasive characterization of lung tissue by quantitative parameters. In this study, we investigated whether CT image patterns found in mice with bleomycin induced lung fibrosis can be translated as prognostic factors to human patients diagnosed with ILD. METHODS Bleomycin was used to induce lung fibrosis in mice (n_control = 36, n_experimental = 55). The patient cohort consisted of 98 systemic sclerosis (SSc) patients (n_ILD = 65). Radiomic features (n_histogram = 17, n_texture = 137) were extracted from microCT (mice) and HRCT (patients) images. Predictive performance of the models was evaluated with the area under the receiver-operating characteristic curve (AUC). First, predictive performance of individual features was examined and compared between murine and patient data sets. Second, multivariate models predicting ILD were trained on murine data and tested on patient data. Additionally, the models were reoptimized on patient data to reduce the influence of the domain shift on the performance scores. RESULTS Predictive power of individual features in terms of AUC was highly correlated between mice and patients (r = 0.86). A model based only on mean image intensity in the lung scored AUC = 0.921 ± 0.048 in mice and AUC = 0.774 (CI95% 0.677-0.859) in patients. The best radiomic model based on three radiomic features scored AUC = 0.994 ± 0.013 in mice and validated with AUC = 0.832 (CI95% 0.745-0.907) in patients. However, reoptimization of the model weights in the patient cohort allowed to increase the model's performance to AUC = 0.912 ± 0.058. CONCLUSION Radiomic signatures of experimental ILD derived from microCT scans translated to HRCT of humans with SSc-ILD. We showed that the experimental model of BLM-induced ILD is a promising system to test radiomic models for later application and validation in human cohorts

Ex vivo tissue perturbations coupled to single-cell RNA-seq reveal multilineage cell circuit dynamics in human lung fibrogenesis.

Pulmonary fibrosis develops as a consequence of failed regeneration after injury. Analyzing mechanisms of regeneration and fibrogenesis directly in human tissue has been hampered by the lack of organotypic models and analytical techniques. In this work, we coupled ex vivo cytokine and drug perturbations of human precision-cut lung slices (hPCLS) with single-cell RNA sequencing and induced a multilineage circuit of fibrogenic cell states in hPCLS. We showed that these cell states were highly similar to the in vivo cell circuit in a multicohort lung cell atlas from patients with pulmonary fibrosis. Using micro-CT-staged patient tissues, we characterized the appearance and interaction of myofibroblasts, an ectopic endothelial cell state, and basaloid epithelial cells in the thickened alveolar septum of early-stage lung fibrosis. Induction of these states in the hPCLS model provided evidence that the basaloid cell state was derived from alveolar type 2 cells, whereas the ectopic endothelial cell state emerged from capillary cell plasticity. Cell-cell communication routes in patients were largely conserved in hPCLS, and antifibrotic drug treatments showed highly cell type-specific effects. Our work provides an experimental framework for perturbational single-cell genomics directly in human lung tissue that enables analysis of tissue homeostasis, regeneration, and pathology. We further demonstrate that hPCLS offer an avenue for scalable, high-resolution drug testing to accelerate antifibrotic drug development and translation

An integrated cell atlas of the lung in health and disease.

Acknowledgements: This publication is part of the Human Cell Atlas (www.humancellatlas.org/publications/). This work was supported by the Chan Zuckerberg Initiative (CZI; LLC Seed Network grant CZF2019-002438 (Lung Cell Atlas 1.0) to P.B., M.D.L., A.V.M., M.C.N., D.P.S., J.R., P.R.T., K.B.M., F.J.T. and H.B.S.); National Institutes of Health (NIH; R01HL145372) and Department of Defense (W81XWH-19-1-0416) (to J.A.K. and N.E.B.); Fondation pour la Recherche Médicale (DEQ20180339158), Conseil Départemental des Alpes Maritimes (2016-294DGADSH-CV), Inserm Cross-cutting Scientific Program HuDeCA 2018, Agence Nationale de la Recherche SAHARRA (ANR-19-CE14-0027), ANR-19-P3IA-0002-3IA, National Infrastructure France Génomique (ANR-10-INBS-09-03) and PPIA 4D-OMICS (21-ESRE-0052) (to P.B.); H2020-SC1-BHC-2018-2020 Discovair (grant agreement 874656) (to P.B., K.B.M., S.A.T., M.C.N., F.J.T., M.P., H.B.S. and J.L.); NIH 1U54HL145608-01 (to M.D.L., K.Z., X.S., J.S.H. and G.P.); Wellcome (WT211276/Z/18/Z) and Sanger core grant WT206194 (to K.B.M. and S.A.T.); ESPOD fellowship of the European Molecular Biology Laboratory European Bioinformatics Institute and Sanger Institute (to E.M.); R01 HL153312, U19 AI135964, P01 AG049665, R01 HL158139, R01 ES034350 and U54 AG079754 (to A.V.M.); Lung Foundation Netherlands project numbers 5.1.14.020 and 4.1.18.226 (to M.C.N.); NIH grants R01HL146557 and R01HL153375 (to P.R.T.); German Center for Lung Research and Helmholtz Association (to H.B.S.); Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI (ZT-I-PF-5-01) and Bavarian Ministry of Science and the Arts in the framework of the Bavarian Research Association ForInter (Interaction of Human Brain Cells) (to F.J.T.); Doris Duke Charitable Foundation (to J.A.K.); Joachim Herz Foundation (to L.D.); Ministry of Economic Affairs and Climate Policy by means of the Public–Private Partnership (to T.M.K.); 3IA Cote d’Azur PhD program (to A.C.); R01 HL135156, R01 MD010443, R01 HL128439, P01 HL132821, P01 HL107202, R01 HL117004 and Department of Defense grant W81WH-16-2-0018 (to M.A.S.); HL142568 and HL14507 from the NHLBI (to D.S.); P50 AR060780-06A1 (to R.L. and T.T.); Medical Research Council Clinician Scientist Fellowship (MR/W00111X/1) (to M.Z.N.); Jikei University School of Medicine (to M.Y.); University College London Birkbeck Medical Research Council Doctoral Training Programme (to K.B.W.); CZI (to J.W., Y.X. and N.K.); 5U01HL148856 (to J.W. and Y.X.); R01 HL153045 (to Y.X.); R01HL127349, R01HL141852 and U01HL145567 (to N.K.); 2R01HL068702 (to D.P.S. and J.R.); 5R01HL14254903 and 4UH3CA25513503 (to T.J.D.); R21HL156124, R56HL157632 and R21HL161760 (to A.M.T.); NIH U54 AG075931 and 5R01 HL146519 (to O.E.); Swedish Research Council and Cancerfonden (to C.S.); CZI Deep Visual Proteomics (to P.H.); U01HL148861-03 (to G.P.); CZI 2021-237918 (to J.S.H., P.R.T., H.B.S. and F.J.T.); CZIF2022-007488 from the CZI Foundation (F.J.T., S.A.T., M.D.L. and K.B.M.); European Respiratory Society and European Union’s Horizon 2020 Research and Innovation Programme under Marie Sklodowska-Curie grant agreement number 847462 (to J.G.-S. and A.J.O.); and Fondation de l’Institut Universitaire de Cardiologie et de Pneumologie de Québec (to Y.B.). We thank E. Spiegel from the Core Facility Statistical Consulting at the Helmholtz Center Munich Institute of Computational Biology for statistical consulting.Funder: Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0” NIH 1U54HL145608-01 CZIF2022-007488 from the Chan Zuckerberg Initiative Foundation CZIF2022-007488 from the Chan Zuckerberg Initiative FoundationFunder: ESPOD fellowship of EMBL-EBI and Sanger InstituteFunder: 3IA Cote d’Azur PhD programFunder: The Ministry of Economic Affairs and Climate Policy by means of the PPPFunder: Joachim Herz Stiftung (Joachim Herz Foundation); doi: https://doi.org/10.13039/100008662Funder: P50 AR060780-06A1Funder: University College London, Birkbeck MRC Doctoral Training ProgrammeFunder: Jikei University School of Medicine (Jikei University); doi: https://doi.org/10.13039/501100007962Funder: 5R01HL14254903, 4UH3CA25513503Funder: R01HL127349, R01HL141852, U01HL145567 and CZIFunder: MRC Clinician Scientist Fellowship (MR/W00111X/1)Funder: Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0” 2R01HL068702Funder: R01 HL135156, R01 MD010443, R01 HL128439, P01 HL132821, P01 HL107202, R01 HL117004, and DOD Grant W81WH-16-2-0018Funder: HL142568 and HL14507 from the NHLBIFunder: Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0”, 2R01HL068702Funder: Wellcome (WT211276/Z/18/Z) Sanger core grant WT206194 CZIF2022-007488 from the Chan Zuckerberg Initiative FoundationFunder: R21HL156124, R56HL157632, and R21HL161760Funder: CZI, 5U01HL148856Funder: CZI, 5U01HL148856, R01 HL153045Funder: The National Institute of Health R01HL145372Funder: Inserm Cross-cutting Scientific Program HuDeCA 2018, ANR SAHARRA (ANR-19-CE14–0027), ANR-19-P3IA-0002–3IA, the National Infrastructure France Génomique (ANR-10-INBS-09-03), PPIA 4D-OMICS (21-ESRE-0052), and the Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0”.Funder: Sanger core grant WT206194 Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0” CZIF2022-007488 from the Chan Zuckerberg Initiative FoundationFunder: Doris Duke Charitable Foundation (DDCF); doi: https://doi.org/10.13039/100000862Funder: The National Institute of Health R01HL145372 Department of Defense W81XWH-19-1-0416Funder: The National Institute of Health R01HL146557 and R01HL153375 and funds from Chan Zuckerberg Initiative - Human Lung Cell Atlas-pilot awardFunder: 1U54HL145608-01Funder: CZI Deep Visual ProteomicsFunder: 1U54HL145608-01, U01HL148861-03Funder: 1) the Chan Zuckerberg Initiative, LLC Seed Network grant CZF2019-002438 “Lung Cell Atlas 1.0”; 2) R01 HL153312; 3) U19 AI135964; 4) P01 AG049665Funder: Netherlands Lung Foundation project nos. 5.1.14.020 and 4.1.18.226, LLC Seed Network grant CZF2019-002438 “Lung Cell Atlas 1.0”Funder: grant number 2019-002438 from the Chan Zuckerberg Foundation, by the Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI [ZT-I-PF-5-01] and by the Bavarian Ministry of Science and the Arts in the framework of the Bavarian Research Association “ForInter” (Interaction of human brain cells)Funder: 1 U01 HL14555-01, R01 HL123766-04Funder: NIH U54 AG075931, 5R01 HL146519Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1(+) profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.A single-cell atlas of the human lungs, integrating data from 2.4 million cells from 486 individuals and including samples from healthy and diseased lungs, provides a roadmap for the generation of organ-scale cell atlases.Peer reviewe