High throughput tear proteomics with data independent acquisition enables biomarker discovery in allergic conditions

Abstract

The search for pathological biomarkers in biological fluids that can provide valuable insight into an individual's health status, is a relevant area of research for multiple pathologies. Currently, the use of proteomics for the identification of differences in protein expression profiles between samples from healthy subjects and patients, has emerged as a powerful strategy to improve the current diagnosis of various pathologies or propose novel therapeutic approaches. Among the biological fluids from which new pathological biomarkers can be identified, tear secretion is highly attractive, since it can be collected non-invasively and could better concentrate proteins that sensitively reflect allergic responses, owing to their exposure to environmental factors and its connection to the respiratory system. Despite its potential, tear fluid remains underexplored, offering significant research opportunities. In this study, we collected human tear samples using the Shirmer Test from healthy and allergic individuals. Our optimized workflow, combining sample preparation and high-throughput proteomics using the data-independent acquisition (DIA) strategy, identified 2542 proteins and enabled the successful differentiation of the two groups. We identified 99 differentially expressed proteins. Our results show the feasibility of protein analysis in human tear samples, highlighting tears as a highly sensitive fluid for detecting health conditions. Data are available via ProteomeXchange with identifier PXD067099.

Keywords: Biomarkers; DiaPASEF; High-throughput proteomics; Tear secretion.

Fig. 1

Experimental design to study the proteomic profile of the human tear’s samples using Nc-MS/MS timsTOF PRO 2.

Fig. 2

Proteomic quality analysis. (A) Principal components analysis. The lilac and light blue colors show the allergic and healthy subjects, respectively. (B) Hierarchy heatmap representing the clustering of proteins from tears, showing the 99 differentially expressed proteins clustered into healthy controls and allergic subjects, each group consisting of 4 subjects. The heatmap plot was generated using Pearson correlation and Euclidean distance between proteins. (C) Volcano plot showing the distribution of proteins from healthy controls and allergic subjects tears. The x-axis represents the log₂ of fold change (FC) and the y-axis the log₁₀ p value provided by EdgeR. The red dots represent 47 up-regulated proteins, and the light blue dots represent 52 down-regulated proteins (Pvalue < 0.05, and FC 0.585 ≥ x ≥ -0.585).

Fig. 3

Enrichment analyses of pathways in tears proteome from allergic subjects. The bubble chart shows enriched proteins in several categories from 99 DEPs in allergic subjects using -log (p-value-adjust) with IPA analysis. The negative Z-score represents down-regulated pathways, and the positive Z-score represents up-regulated pathways. The up-regulated proteins were involved in 10 pathways categorized mainly in Traslocation processes. The downregulated pathways with the most abundant proteins were related to immune responses. Count: represents the protein abundance in each pathway.