Integrated View of Baseline Protein Expression in Human Tissues

Abstract

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.

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

Figure 1

Overview of the study design and reanalysis pipeline. QA: Quality assessment. Reprinted (Adapted or Reprinted in part) with permission from . Copyright 2022 EMBL-EBI.

Figure 2

(A) Number of canonical proteins identified across different organs. The number within the parenthesis indicates the number of samples. (B) Range of normalized iBAQ protein abundances across different organs. The number within the parenthesis indicates the number of samples. In panels (A) and (B), the term heart is used in a broader sense to mean the cardiovascular system. (C) Canonical proteins identified across different datasets. The number within the parenthesis indicates the number of unique tissues in the dataset. (D) Range of normalized iBAQ protein abundances across different datasets. The number within the parenthesis indicates the number of unique tissues in the dataset. (E) Comparison of total spectral data with the number of canonical proteins identified in each dataset and organ. (F) Distribution of canonical proteins identified across organs.

Figure 3

Heatmap of pairwise Pearson correlation coefficients across all samples. The color on the heatmap represents the correlation coefficient, which was calculated using the bin-transformed iBAQ values. The samples are hierarchically clustered on columns and rows using Euclidean distances. The clusters composed of the brain and cardiovascular system (heart) samples are highlighted with black borders. The abbreviations used in the organs’ header are B: brain, C: colon, H: heart, L: liver, O: ovary, and P: pancreas.

Figure 4

(A) PCA of brain samples colored by the tissue types. (B) PCA of brain samples colored by their respective dataset identifiers. (C) PCA of cardiovascular system (heart) samples colored by the tissue types. (D) PCA of cardiovascular system (heart) samples colored by their respective dataset identifiers. The numbers in parenthesis indicate the number of datasets for each tissue. Binned values of canonical proteins quantified in at least 50% of the samples were used to perform the PCA.

Figure 5

(A) Analysis of organ-specific canonical proteins. The analysis comprises the number of canonical proteins found in 31 organs, classified in three groups: “organ-enriched”, “group-enriched”, and “group mixed”. (B) Pathway analysis of the over-represented canonical proteins, showing the statistically significant representative pathways (p-value <0.05) in 31 organs. In panels (A) and (B), the term heart is used in a broader sense to mean the cardiovascular system.