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Brain Landscape In Situ Crosslinking Mass Spectrometry (BLIS-XL-MS) Enables Global Analysis of Protein Structural Remodeling in the Brain

Pathological protein conformational remodeling is a key molecular feature of brain disorders. However, conventional approaches to understand the changes in potein conformation are targeted and difficult to scale proteome-wide analysis. Emerging mass spectrometry-based structural proteomic methods broaden coverage but often involve complex workflows and trade-offs between labeling efficiency and…

Pathological protein conformational changes are a crucial characteristic of brain disorders. Traditional methods for understanding protein conformational alterations are targeted and challenging to scale for comprehensive proteome-wide examination. Recent mass spectrometry-based structural proteomic techniques have expanded coverage, yet they frequently entail intricate workflows and trade-offs between labeling efficiency and maintaining the in vivo molecular state.

Consequently, quantitative analysis of proteome-wide structural remodeling has remained constrained.

We introduce Brain Landscape In Situ Crosslinking Mass Spectrometry (BLIS-XL-MS), a novel experimental and computational framework aimed at analyzing protein and protein-complex remodeling within brain tissues. BLIS-XL-MS ensures the molecular state remains stable before secondary crosslinking, enhances reagent accessibility through sectioning and permeabilization, and seamlessly integrates quantitative crosslink analysis with structural and protein-interaction-network interpretation.

When applied to brains from GluA1A636T knock-in mice, which model a neurodevelopmental disorder, BLIS-XL-MS unveiled a coordinated remodeling of protein systems involved in AMPA receptor-trafficking, mitochondrial function, and cell-death pathways. We demonstrate that BLIS-XL-MS offers a scalable framework for mapping disease-associated structural remodeling throughout the entire brain proteome.

Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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