Abstract
Background Obstructive sleep apnoea (OSA) is a highly prevalent disease and a major cause of systemic inflammation leading to neurocognitive, behavioural, metabolic and cardiovascular dysfunction in children and adults. However, the impact of OSA on the heterogeneity of circulating immune cells remains to be determined.
Methods We applied single-cell transcriptomics analysis (scRNA-seq) to identify OSA-induced changes in transcriptional landscape in peripheral blood mononuclear cell (PBMC) composition, which uncovered severity-dependent differences in several cell lineages. Furthermore, a machine-learning approach was used to combine scRNAs-seq cell-specific markers with those differentially expressed in OSA.
Results scRNA-seq demonstrated OSA-induced heterogeneity in cellular composition and enabled the identification of previously undescribed cell types in PBMCs. We identified a molecular signature consisting of 32 genes, which distinguished OSA patients from various controls with high precision (area under the curve 0.96) and accuracy (93% positive predictive value and 95% negative predictive value) in an independent PBMC bulk RNA expression dataset.
Conclusion OSA deregulates systemic immune function and displays a molecular signature that can be assessed in standard cellular RNA without the need for pre-analytical cell separation, thereby making the assay amenable to application in a molecular diagnostic setting.
Abstract
This study used scRNA-seq technology to investigate for the first time the fluctuation of cellular populations in PBMCs of OSA children and combine this information to generate a molecular biomarker signature for OSA diagnosis https://bit.ly/3ftGUTq
Footnotes
Data availability: De-identified data for this study, including scRNA-seq profiles, will be available immediately following publication with no end date to anyone who wishes to access the data. The dataset is available at the NCBI Gene Expression Omnibus (GEO) repository (accession number GSE214865).
This article has an editorial commentary: https://doi.org/10.1183/13993003.02316-2022
Author contributions: R. Cortese participated in the conceptual framework of the project, performed experiments, analysed data, and drafted components of the manuscript. K.H. Cataldo performed experiments. T.S. Adams and J. Hummel conducted data analysis. N. Kaminski and L. Kheirandish-Gozal provided expert guidance, participated in data analysis and interpretation of results, and served as blinded observers. D. Gozal conceptualised the project, provided critical input in all phases of the experiments, analysed data, drafted the ulterior versions of the manuscript, and is responsible for the financial support of the project and the manuscript content. All authors have reviewed and approved the final version of the manuscript.
Conflict of interests: All authors have nothing to disclose.
Support statement: This work was partially supported by a Leda J. Sears Charitable Trust Grant and a BioNexus KC 21-1 Patton Trust Research Grant to R. Cortese. D. Gozal is supported by NIH grant AG061824 and by Tier 2 and TRIUMPH grants from the University of Missouri. Funding information for this article has been deposited with the Crossref Funder Registry.
- Received July 25, 2022.
- Accepted September 22, 2022.
- Copyright ©The authors 2023. For reproduction rights and permissions contact permissions{at}ersnet.org
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