4.7 Article

Integrated View of Baseline Protein Expression in Human Tissues

期刊

JOURNAL OF PROTEOME RESEARCH
卷 22, 期 3, 页码 729-742

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.2c00406

关键词

mass spectrometry; quantitative proteomics; public data re-use; human proteome

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The availability of proteomics datasets, especially in the PRIDE database, has significantly increased in recent years, providing an opportunity for combined analyses of datasets to obtain organism-wide protein abundance data. In this study, we reanalyzed 24 public proteomics datasets to assess baseline protein abundance in 31 organs of healthy individuals. We compared protein abundances between organs, studied protein distribution, and performed gene ontology and pathway-enrichment analyses. The results are integrated into the Expression Atlas resource to enhance accessibility for life scientists.
The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

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