Lanthanide Metal–Organic Framework Flowers for Proteome Profiling and Biomarker Identification in Ultratrace Biofluid Samples

Identifying effective biomarkers has long been a persistent need for early diagnosis and targeted therapy of disease. While mass spectrometry-based label-free proteomics with trace cell has been demonstrated, deep proteomics with ultratrace human biofluid remains challenging due to low protein concentration, extremely limited patient sample volume, and substantial protein contact losses during preprocessing. Herein, we proposed and validated lanthanide metal-organic framework flowers (MOF-flowers), as effective materials, to trap and enrich protein in biofluid jointly through cation-π interaction and O-Ln coordination. We further developed a <b>M</b>OF-flower <b>a</b>ssisted <b>s</b>implified and <b>s</b>ingle-pot <b>S</b>ample <b>P</b>reparation (Mass-SP) workflow that incorporates protein capture, digest, and peptide elute into one single PCR tube to maximally avoid adsorptive sample loss. We adopted Mass-SP to decipher aqueous humor (AH) proteome signatures from cataract and retinal vein occlusion (RVO) patients and quantified ∼3900 proteins in merely 1 μL of AH. Combined with machine learning, we further identified PFKL as a prioritization biomarker for RVO disease with the areas under the curves of 0.95 ± 0.04. Mass-SP presents a strategy to identify de novo biomarkers and explore potential therapeutic targets with extremely limited clinical human body fluid resources.

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Institutions

Publication Details

Journal
ACS Nano
Published
2025-01-22
DOI
https://doi.org/10.1021/acsnano.4c12280
Citations
9
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
6.81

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article

Lanthanide Metal–Organic Framework Flowers for Proteome Profiling and Biomarker Identification in Ultratrace Biofluid Samples

Guangxia Shen, Zhixiao Xu, Shuang Zhang, Xianting Ding et al.
9 citations
ACS Nano
Advanced Proteomics Techniques and Applications
6.81
article

Lanthanide Metal–Organic Framework Flowers for Proteome Profiling and Biomarker Identification in Ultratrace Biofluid Samples

Guangxia Shen, Zhixiao Xu, Shuang Zhang, Xianting Ding, Lai Jiang, Aiting Wang, Youming Chen
article en
9 citations

Abstract

Identifying effective biomarkers has long been a persistent need for early diagnosis and targeted therapy of disease. While mass spectrometry-based label-free proteomics with trace cell has been demonstrated, deep proteomics with ultratrace human biofluid remains challenging due to low protein concentration, extremely limited patient sample volume, and substantial protein contact losses during preprocessing. Herein, we proposed and validated lanthanide metal-organic framework flowers (MOF-flowers), as effective materials, to trap and enrich protein in biofluid jointly through cation-π interaction and O-Ln coordination. We further developed a <b>M</b>OF-flower <b>a</b>ssisted <b>s</b>implified and <b>s</b>ingle-pot <b>S</b>ample <b>P</b>reparation (Mass-SP) workflow that incorporates protein capture, digest, and peptide elute into one single PCR tube to maximally avoid adsorptive sample loss. We adopted Mass-SP to decipher aqueous humor (AH) proteome signatures from cataract and retinal vein occlusion (RVO) patients and quantified ∼3900 proteins in merely 1 μL of AH. Combined with machine learning, we further identified PFKL as a prioritization biomarker for RVO disease with the areas under the curves of 0.95 ± 0.04. Mass-SP presents a strategy to identify de novo biomarkers and explore potential therapeutic targets with extremely limited clinical human body fluid resources.

ACS NanoVol. 19(4)
Shanghai Jiao Tong University (CN), XinHua Hospital (CN), State Key Laboratory of Oncogene and Related Genes (CN)
Shanghai Municipal Health Commission, National Natural Science Foundation of China, Shanghai Municipal Education Commission, Shanghai Jiao Tong University, Shanghai City Youth Science and Technology Star Project, National Key Research and Development Program of China
Zero hunger
Openalex Percentile: Top 3%
Advanced Proteomics Techniques and Applications
6.81
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