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Updated: Oct 21, 2026

Laser-induced Breakdown Spectroscopy: A New Approach for Nanoparticle's Mapping and Quantification in Organ Tissue
Published on: June 18, 2014
Quadruple Boronic Acid-Functionalized Lanthanide MOFs Enable Deep and Unbiased Spatial Proteomics from Ultra-Trace
Yuankang Zhu1,2,3,4, Jiahui Wu1,2, Qi Luo1,2
1Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai200030, China.
Abstract:
Spatial proteomics of laser capture microdissection (LCM) specimens is fundamentally constrained by protein loss during processing of ultra-trace samples, limiting proteomic depth and biological insight. Here, we report the integration of a quadruple metal-organic framework (qMOF)-enhanced surfactant-assisted one-pot sample preparation (MSOP) workflow for deep proteomic profiling of micro-scale tissues. The qMOF composite, featuring high-density boronic acid functionalities, acts as a potent protein capture vehicle, drastically minimizing surface adsorption losses. Applied to LCM-isolated mouse myocardial sections as small as 100 × 100 μm (∼20 cells), the MSOP workflow approximately doubled the number of confidently identified proteins, compared to the conventional surfactant-assisted one-pot sample preparation (SOP) method (mean of 2238 vs. 1116), while exhibiting lower quantification variance and preserving unbiased coverage of the proteome's physicochemical landscape. We further demonstrate the workflow's utility by resolving spatially distinct proteomic signatures in perivascular regions of aged cardiac tissue, identifying molecular hallmarks of aging characterized by impaired cytoskeletal stability and dysregulated mitochondrial functions. Protein-protein interaction network analysis identified Rack1 as a potential key node in cardiac aging, which was further validated by immunohistochemistry. This MSOP-assisted platform provides a robust, cost-effective, and highly sensitive chemical strategy to significantly enhance the depth of discovery-driven spatial proteomics.
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