TY - JOUR

T1 - Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers

AU - Mayr, Christoph H

AU - Simon, Lukas M

AU - Leuschner, Gabriela

AU - Ansari, Meshal

AU - Schniering, Janine

AU - Geyer, Philipp E

AU - Angelidis, Ilias

AU - Strunz, Maximilian

AU - Singh, Pawandeep

AU - Kneidinger, Nikolaus

AU - Reichenberger, Frank

AU - Silbernagel, Edith

AU - Böhm, Stephan

AU - Adler, Heiko

AU - Lindner, Michael

AU - Maurer, Britta

AU - Hilgendorff, Anne

AU - Prasse, Antje

AU - Behr, Jürgen

AU - Mann, Matthias

AU - Eickelberg, Oliver

AU - Theis, Fabian J

AU - Schiller, Herbert B

PY - 2021

Y1 - 2021

N2 - The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single-cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single-cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type-2 epithelial cell health status in lavage fluid and plasma. Using cross-modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes.

AB - The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single-cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single-cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type-2 epithelial cell health status in lavage fluid and plasma. Using cross-modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes.

U2 - 10.15252/emmm.202012871

DO - 10.15252/emmm.202012871

M3 - Journal article

C2 - 33650774

JO - EMBO Molecular Medicine

JF - EMBO Molecular Medicine

SN - 1757-4676

M1 - e12871

ER -