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Identification and validation of a tear fluid-derived protein biomarker signature in patients with amyotrophic lateral sclerosis.
Acta Neuropathol. Commun. 13:187 (2025)
The diagnosis of Amyotrophic Lateral Sclerosis (ALS) remains challenging, particularly in early stages, where characteristic symptoms may be subtle and nonspecific. The development of disease-specific and clinically validated biomarkers is crucial to optimize diagnosis. Here, we explored tear fluid (TF) as a promising ALS biomarker source, given its accessibility, anatomical proximity to the brainstem as an important site of neurodegeneration, and proven discriminative power in other neurodegenerative diseases. Using a discovery approach, we profiled protein abundance in TF of ALS patients (n = 49) and controls (n = 54) via data-independent acquisition mass spectrometry. Biostatistical analysis and machine learning identified differential protein abundance and pathways in ALS, leading to a protein signature. These proteins were validated by Western blot in an independent cohort (ALS n = 51; controls n = 52), and their discriminatory performance was assessed in-silico employing machine learning. 876 proteins were consistently detected in TF, with 106 differentially abundant in ALS. A six-protein signature, including CRYM, PFKL, CAPZA2, ALDH16A1, SERPINC1, and HP, exhibited discriminatory potential. We replicated significant differences of SERPINC1 and HP levels between ALS and controls across the cohorts, and their combination yielded the best in-silico performance. Overall, this investigation of TF proteomics in ALS and controls revealed dysregulated proteins and pathways, highlighting inflammation as a key disease feature, strengthening the potential of TF as a source for biomarker discovery.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Amyotrophic Lateral Sclerosis ; Biomarker ; Diagnosis ; Neurodegeneration ; Proteomics ; Tear Fluid; Data-independent Acquisition; Disease Progression; Proteomic Analysis; Expression; Diagnosis; Insights; Platform; Models; Genes
e-ISSN
2051-5960
Quellenangaben
Volume: 13,
Issue: 1,
Article Number: 187
Publisher
Bmc
Publishing Place
Campus, 4 Crinan St, London N1 9xw, England
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
Grants
Technische Universitt Mnchen (1025)