Metabolomics of Vitreous Humourfrom Retinoblastoma Patients

Significance of the Topic

Retinoblastoma is the most prevalent pediatric intraocular malignancy. Investigating the metabolome of the vitreous humour—the fluid closest to retinal tissue—can reveal exo-metabolites that mirror tumor metabolism. Such insights may improve understanding of disease mechanisms and support the discovery of metabolic biomarkers for diagnosis, prognosis, and tailored therapies.

Study Objectives and Overview

This study aimed to profile metabolite alterations in the vitreous humour of retinoblastoma patients compared to non-ocular pediatric controls. By combining liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) and gas chromatography–QTOF-MS (GC-QTOF-MS), the authors sought to identify differential metabolites, link them to disrupted biochemical pathways, and integrate findings with gene expression data for a multi-omic perspective.

Methodology

Sample Preparation and Extraction:

• Vitreous humour (25 µL) from nine patients and two controls extracted with methanol:ethanol (1:1, v/v).
• Dry-down and reconstitution for LC-MS and derivatization for GC-MS.

Data Acquisition and Processing:

• LC-QTOF-MS in positive and negative electrospray ionization using C18 and HILIC columns.
• GC-QTOF-MS with electron ionization on a DB-5ms column using the Fiehn RTL method.
• Feature extraction and alignment with MassHunter Qualitative Analysis and Profinder.
• Database matching against METLIN and Fiehn RTL libraries; lipid identification via SimLipid.
• Statistical analysis, volcano plotting, and hierarchical clustering in GeneSpring 13.1-MPP.

Instrumentation Used

• Agilent 6550 QTOF for LC-MS/MS (ZORBAX RRHD SB-Aq and Poroshell 120 HILIC columns).
• Agilent 7200 QTOF and 5975C MS for GC-MS (DB-5ms column).
• MassHunter Qualitative Analysis and Profinder software.
• Agilent Unknown Analysis Software with Fiehn RTL library.
• GeneSpring 13.1-MPP for multi-omic integration and pathway analysis.

Key Results and Discussion

Metabolite Coverage:

• Over 1,000 molecular features detected; 350 significant differential metabolites (p≤0.05, fold-change ≥ 2).

Lipid Alterations:

• Phosphatidylcholines and ether-linked phosphatidylethanolamines upregulated by up to 5-fold.
• Elevated ceramides, sphingomyelins, sphinganines, free fatty acids, and carnitines.
• Findings point to altered peroxisomal lipid biosynthesis in retinoblastoma.

Statistical Clustering:

• High-risk patient group samples exhibited strong positive inter-sample correlation, distinct from controls and low-risk cases.

Pathway Insights:

• Enriched pathways include sphingolipid metabolism, ABC transporters, glycolysis/gluconeogenesis, amino acid metabolism (alanine, aspartate, glutamate), and xenobiotic metabolism.
• Multi-omic visualization revealed concordant changes in glycerophospholipid and sphingolipid pathways with transcriptomic data.

Benefits and Practical Applications

• Non-invasive metabolic profiling of vitreous humour offers direct access to tumor-derived metabolites.
• Combined LC- and GC-QTOF strategies ensure broad coverage from polar metabolites to complex lipids.
• Integration with gene expression enhances mechanistic understanding and biomarker discovery.

Future Trends and Potential Applications

• Expansion of multi-omic pipelines incorporating proteomics and single-cell metabolomics for finer resolution.
• Application of less invasive biofluids (e.g., tear fluid) for longitudinal monitoring and early detection.
• Validation of candidate biomarkers in larger cohorts to support clinical translation.

Conclusion

This multi-platform metabolomics study of vitreous humour in retinoblastoma patients revealed significant dysregulation of lipid and energy metabolism pathways. The integration of metabolomic and genomic data underscores key alterations in peroxisomal and sphingolipid biochemistry. These findings pave the way for metabolic biomarker development and enhanced understanding of retinoblastoma pathophysiology.