Rationale: Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are debilitating diseases associated with divergent histopathological changes in the lungs. At present, due to cost and technical limitations, profiling cell types is not practical in large epidemiology cohorts (n>1000). Here, we used computational deconvolution to identify cell types in COPD and IPF lungs whose abundances and cell type-specific gene expression are associated with disease diagnosis and severity. Methods: We analyzed lung tissue RNA-seq data from 1026 subjects (COPD, n=465; IPF, n=213; control, n=348) from the Lung Tissue Research Consortium. We performed RNA-seq deconvolution, querying thirty-eight discrete cell-type varieties in the lungs. We tested whether deconvoluted cell-type abundance and cell type-specific gene expression were associated with disease severity. Results: The abundance score of twenty cell types significantly differed between IPF and control lungs. In IPF subjects, eleven and nine cell types were significantly associated with forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO), respectively. Aberrant basaloid cells, a rare cells found in fibrotic lungs, were associated with worse FVC and DLCO in IPF subjects, indicating that this aberrant epithelial population increased with disease severity. Alveolar type 1 and vascular endothelial (VE) capillary A were decreased in COPD lungs compared to controls. An increase in macrophages and classical monocytes was associated with lower DLCO in IPF and COPD subjects. In both diseases, lower non-classical monocytes and VE capillary A cells were associated with increased disease severity. Alveolar type 2 cells and alveolar macrophages had the highest number of genes with cell type-specific differential expression by disease severity in COPD and IPF. In IPF, genes implicated in the pathogenesis of IPF, such as matrix metallopeptidase 7, growth differentiation factor 15, and eph receptor B2, were associated with disease severity in a cell type-specific manner. Conclusion: Utilization of RNA-seq deconvolution enabled us to pinpoint cell types present in the lungs that are associated with the severity of COPD and IPF. This knowledge offers valuable insight into the alterations within tissues in more advanced illness, ultimately providing a better understanding of the underlying pathological processes that drive disease progression.

Dr. Hersh reports grant support from Bayer, Boehringer-Ingelheim, and Vertex, and consulting fees from Chiesi, Sanofi, and Takeda, unrelated to this manuscript. Dr. Silverman reports grant support from Bayer and Northpond Laboratories. Dr. Cho reports grant support from Bayer. Dr. DeMeo reports grant support from Bayer and Alpha-1 Foundation. Dr. Castaldi reports grant support from Bayer, Sanofi and consulting fees from Verona Pharmaceuticals. Dr. Yun reports grant support from Bayer and consulting fees from Bridge Biotherapeutics, and travel reimbursement from the Korean Academy of Tuberculosis and Respiratory Disease unrelated to this manuscript. Dr. Flaherty reports grant funding from Boehringer Ingelheim unrelated to this manuscript. Dr. Martinez reports grant supports from NHLBI, AstraZeneca, Chiesi, Boehringer-Ingelheim, GalaxoSmithCline, Novartis, Polarean, Sanofi/Regeneron, Sunovion, and TEVA Pharmaceuticals. Dr. Martinez reports receiving consulting fee from AstraZeneca, Boehringer-Ingelheim and Bristol Myers Squibb. Dr. Wise reports receiving consulting fees from Boehringer-Ingelheim, AstraZenica, Abb-Vie, and Galderma.

Present work was supported by grants from NHLBI (R01HL166231, P01HL114501, R01HL133135, and X01HL139404), K25 HL136846, K08 HL146972, Alpha-1 Foundation Research Grant, and TOPMed Fellowship. MHC was supported by R01HL162813, R01HL153248, R01HL14.

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The participating centers Institutional Review Boards approved the study, and all subjects provided written informed consent.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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Data are available on the NCBI database of Genotypes and Phenotypes (dbGaP), accession phs001662 (LTRC). LTRC RNA-seq data from TOPMed (https://topmed.nhlbi.nih.gov) are available through dbGaP. The analysis results and code can be obtained by contacting the corresponding author with a reasonable request.