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Am J Physiol Lung Cell Mol Physiol. 2019 Jul 3. doi: 10.1152/ajplung.00475.2018. [Epub ahead of print]

Integration of transcriptomic and proteomic data identifies biological functions in cell populations from human infant lung.

Author information

1
The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, United States.
2
Pacific Northwest National Laboratory, United States.
3
Surgery, Children's Hospital Los Angeles, United States.
4
Children's Hospital Los Angeles.
5
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, United States.
6
Pediatrics, Cincinnati Children's Hospital.
7
Department of Pediatrics, University of Rochester, United States.
8
Pediatrics, University of Rochester Medical Center, United States.
9
Develpomental Biology and Regenerative Medicine Program, Saban Research Institute, Children's Hospital Los Angeles, United States.
10
Pediatrics, University of Rochester, United States.
11
Cincinnati Childrens' Hospital.

Abstract

Systems biology uses computational approaches to integrate diverse data types to understand cell and organ behavior. Data derived from complementary technologies, for example transcriptomic and proteomic analyses, are providing new insights into development and disease. We compared mRNA and protein profiles from purified endothelial, epithelial, immune, and mesenchymal cells from normal human infant lung tissue. Signatures for each cell type were identified and compared at both mRNA and protein levels. Cell specific biological processes and pathways were predicted by analysis of concordant and discordant RNA-protein pairs. Cell clustering and gene set enrichment comparisons identified shared versus unique processes associated with transcriptomic and/or proteomic data. Clear cell-cell correlations between mRNA and protein data were obtained from each cell type. Approximately 40% of RNA-protein pairs were coherently expressed. While the correlation between RNA and their protein products was relatively low (Spearman rank coefficient rs ~0.4), cell specific signature genes involved in functional processes characteristic of each cell type, were more highly correlated with their protein products. Consistency of cell specific RNA-protein signatures indicated an essential framework for the function of each cell type. Visualization and reutilization of the protein and RNA profiles are supported by a new web application, "LungProteomics" which is freely accessible to the public.

KEYWORDS:

Human lung; Integrated omics; Proteome; Sorted cells; Transcriptome

PMID:
31268347
DOI:
10.1152/ajplung.00475.2018

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