A mass spectrometry-based strategy allows signature metabolite identification in tear fluid from people with diabetic cataracts

Metabolic biomarker discovery in trace body fluids remains a significant challenge, toward molecular diagnosis and pathology studies in many diseases. Especially for eye-related diseases, such an approach based on non-invasive tear fluids remains an unsatisfied urgent need in ophthalmology. Here we construct a metabolic biomarker panel from 10 nL of tear fluids in seconds using nanoparticle-enhanced laser desorption/ionization -mass spectrometry (MS), which achieves an area under the curve of 0.923 for discriminating diabetic cataracts from alone age-related cataracts. Importantly, we integrate liquid chromatography -MS into the above analysis process to construct an integrated strategy, allowing reliable metabolite annotation by nanoliter sample volume without compromising high throughput. Further, using matched aqueous humors, we identify 1,5-anhydroglucitol as a biomarker of diabetic cataracts, revealing its protective effect against high glucose-induced lens oxidative stress and opacification, as a demonstration of the metabolic reprogramming. Our approach can be universally applied to uncover biomarkers using trace body fluid, promising next-generation metabolic reprogramming identification.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2025-11-21
DOI
https://doi.org/10.1038/s41467-025-65082-7
Primary Topic
Connexins and lens biology
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A mass spectrometry-based strategy allows signature metabolite identification in tear fluid from people with diabetic cataracts

Weikang Shu, Ziheng Qi, Jiaxin Hou, Jingjing Wan et al.
Nature Communications
Connexins and lens biology
article

A mass spectrometry-based strategy allows signature metabolite identification in tear fluid from people with diabetic cataracts

Weikang Shu, Ziheng Qi, Jiaxin Hou, Jingjing Wan, Wen-Ying Zhuang, Miao Wang, Tao Guo, Shunxiang Li, Yanhui Wang, Xianqun Fan, Yinbing Zhao, Xiaonan Chen, Yingying Lin, Yating Wang, Yun Su, Chenxi Yan
article en

Abstract

Metabolic biomarker discovery in trace body fluids remains a significant challenge, toward molecular diagnosis and pathology studies in many diseases. Especially for eye-related diseases, such an approach based on non-invasive tear fluids remains an unsatisfied urgent need in ophthalmology. Here we construct a metabolic biomarker panel from 10 nL of tear fluids in seconds using nanoparticle-enhanced laser desorption/ionization -mass spectrometry (MS), which achieves an area under the curve of 0.923 for discriminating diabetic cataracts from alone age-related cataracts. Importantly, we integrate liquid chromatography -MS into the above analysis process to construct an integrated strategy, allowing reliable metabolite annotation by nanoliter sample volume without compromising high throughput. Further, using matched aqueous humors, we identify 1,5-anhydroglucitol as a biomarker of diabetic cataracts, revealing its protective effect against high glucose-induced lens oxidative stress and opacification, as a demonstration of the metabolic reprogramming. Our approach can be universally applied to uncover biomarkers using trace body fluid, promising next-generation metabolic reprogramming identification.

Nature CommunicationsVol. 16(1)
Shanghai University (CN), Shanghai Jiao Tong University (CN), Shanghai Ninth People's Hospital (CN), Shanghai Institute of Hematology (CN), Shanghai Clinical Research Center (CN), East China Normal University (CN)
National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, Shanghai Jiao Tong University, School of Medicine, Shanghai Jiao Tong University, National Key Research and Development Program of China
Openalex Percentile: Top 68%
Connexins and lens biology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.