Evidence map›Paper›PMID 38585732›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Computational Deconvolution of Cell Type-Specific Gene Expression in COPD and IPF Lungs Reveals Disease Severity Associations.

Min Hyung Ryu, Jeong H Yun, Kangjin Kim, Michele Gentili, Auyon Ghosh, Frank Sciurba, Lucas Barwick, Andrew Limper, Gerard Criner, Kevin K Brown and 9 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed, 0 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors at 10 institutions in 1 country.

Kangjin Kim
Michele Gentili
Auyon Ghosh
Frank Sciurba
Lucas Barwick
Andrew Limper
Gerard Criner
Kevin K Brown
Robert Wise
Fernando J Martinez
Kevin R Flaherty
Michael H Cho
Peter J Castaldi
Dawn L DeMeo
Edwin K Silverman
Jarrett D Morrow
Brigham and Women's Hospital · USEmmes (United States) · USJohns Hopkins University · USMayo Clinic · USMichigan Medicine · USNational Jewish Health · USNewYork–Presbyterian Hospital · USSUNY Upstate Medical University · USTemple University · USUniversity of Pittsburgh · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

rationaleChronic 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.

methodsWe 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.

resultsThe 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.

conclusionUtilization 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.

Identifiers

PMID38585732
PMCPMC10996764
OpenAlexW4393214784

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.