Investigative Ophthalmology & Visual Science Cover Image for Volume 65, Issue 7
June 2024
Volume 65, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2024
Tear fluid proteomic analysis with improved LC-MS/MS protocol
Author Affiliations & Notes
  • James Xiao
    InnSight Technology, Inc, Pennsylvania, United States
  • Yunxiang Fu
    InnSight Technology, Inc, Pennsylvania, United States
    Allegheny-Singer Research Institute, Pittsburgh, Pennsylvania, United States
  • Kyla Frenia
    InnSight Technology, Inc, Pennsylvania, United States
  • Jaclyn Sikora
    InnSight Technology, Inc, Pennsylvania, United States
  • Kathleen Garwood
    Saint Joseph's University, Philadelphia, Pennsylvania, United States
  • Rick Mental
    InnSight Technology, Inc, Pennsylvania, United States
  • Leanne Labriola
    InnSight Technology, Inc, Pennsylvania, United States
  • Footnotes
    Commercial Relationships   James Xiao None; Yunxiang Fu Allegheny-Singer Research Institute, Code E (Employment), InnSight Technology, Inc, Code E (Employment); Kyla Frenia None; Jaclyn Sikora None; Kathleen Garwood None; Rick Mental None; Leanne Labriola InnSight Technology, Inc, Code O (Owner), InnSight Technology, Inc, Code P (Patent), InnSight Technology, Inc, Code S (non-remunerative)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2024, Vol.65, 6557. doi:
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      James Xiao, Yunxiang Fu, Kyla Frenia, Jaclyn Sikora, Kathleen Garwood, Rick Mental, Leanne Labriola; Tear fluid proteomic analysis with improved LC-MS/MS protocol. Invest. Ophthalmol. Vis. Sci. 2024;65(7):6557.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : The low sample volume and dynamic composition of tear fluid pose challenges as a biological sample for analytical testing. In response to this challenge, we investigated an enhanced approach for bottom-up shotgun proteomics for tear fluid biomarker discovery, leveraging liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify a higher number of proteins with greater consistency in tear fluid collected with capillary tubes.

Methods : Tear fluid samples were collected from 16 healthy subjects with no history of eye disease in accordance with the guidelines and regulations established by the Institutional Review Board (IRB). Tear fluid was collected from each subject using 0.5 μL glass capillary tubes. Samples were stored at -80 °C until use. Tear fluid collections were pooled to create a single uniform sample, facilitating the precision and reproducibility of both qualitative and quantitative analyses of peptides and proteins across analytical workflows. Evosep LC-Bruker timsTOF Pro2 mass spectrometer using varied starting sample amounts, LC gradients, and trypsin-to-protein ratios. Raw proteomic data was processed using the filter criteria: (i) Correlation Coefficent = 0.8; (ii) IonCoverage = 0.4; (iii) XCorr ≥1.5 (for +2 ion), 2.0 (for +3 ion), and 3.0 (for +4 ion), and (iv) two missed cleavages allowed. The number of proteins between runs and the proteins identified were compared. The coefficient of variation (CV) for each protein was calculated and the average CV for each workflow was compared. The top-performing workflow was then replicated using individual patient samples.

Results : Across all pooled sample runs, 600-800 unique protein IDs were identified. Of the proteins identified in the pooled sample, 400-500 were identified across all runs. The top-performing method was able to identify an average of 500-600 unique proteins per run with a variance of ~50 proteins. For this method, the average coefficient of variation (CV) between replicates was <30%.

Conclusions : Glass capillary tubes to minimize the risk of cellular contamination. Tear fluid samples collected with glass capillary tubes can yield results comparable to those obtained with Schirmer strips when analyzed with LC-MS/MS. Moving forward, our focus is on further improving this approach by incorporating Parallel Reaction Monitoring (PRM)-based targeted mass spectrometry technique to achieve a more precise quantification of potential biomarkers.

This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.

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