PuSH - Publication Server of Helmholtz Zentrum München: Identification and validation of a tear fluid-derived protein biomarker signature in patients with amyotrophic lateral sclerosis.

Information

Clues to quality of journals

Open Access Policy of Helmholtz Association 2016

CC Licencences

Publication Metrics

Navigation

Home

Deutsch

EVA: Electronic Publishing

New EVA Application

Research

Advanced Search

Browse by ...

... HMGU-Authors/Consortia

... Organizational Structure

... Journal

... Publication Type

... Research Data

... Arbeitsgruppen

... Publication Year

Publication overview

Statistics

Statistics (last 5 years)

OA Publications

Publish

Submit Publication

Import publication from...

...EVA

Report missing publication

Highlights

Support & Contact

Contact persons

Help

Data protection

Helmholtz Open Science

JANE Journal Estimator

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Scholl, L.S.* ; Demleitner, A.F.* ; Riedel, J. ; Adachi, S.* ; Neuenroth, L.* ; Meijs, C. ; Tzeplaeff, L.* ; Caldi Gomes, L.* ; Galhoz, A. ; Cordts, I.* ; Lenz, C.* ; Menden, M.P. ; Lingor, P.*

Identification and validation of a tear fluid-derived protein biomarker signature in patients with amyotrophic lateral sclerosis.

Acta Neuropathol. Commun. 13:187 (2025)

Publ. Version/Full Text

Research data

DOI

PMC

	Open Access Gold

Abstract
Metrics
Extra information

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.

Impact Factor

Scopus SNIP

Altmetric

0.000