Dataset Information

Proteoform Imaging by Combining MALDI MSI and High-sensitivity Top-down Proteomics

ABSTRACT: Spatially resolved characterization of proteoforms has great potential to significantly advance our understanding of physiological and disease mechanisms. However, it faces challenges regarding throughput and coverage. Herein, we developed a robust method for high-throughput proteoform imaging (HTPi) by combining matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) and region-specific top-down proteomic analysis.

ORGANISM(S): Mus Musculus

SUBMITTER: Lihua Zhang

PROVIDER: PXD058552 | iProX | Tue Dec 03 00:00:00 GMT 2024

REPOSITORIES: iProX

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Publications

High-Throughput Proteoform Imaging for Revealing Spatial-Resolved Changes in Brain Tissues Associated with Alzheimer's Disease.

Sun Yue Y Liu Dan D Liang Yu Y Yang Xue X Liu Xinxin X Zhao Baofeng B Liang Zhen Z Zhang Yukui Y Zhang Lihua L

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20250312 17

Spatially resolved characterization of proteoforms has substantial potential to significantly advance the understanding of physiological and disease mechanisms. However, challenges remain regarding throughput and coverage. A robust method is developed for high-throughput proteoform imaging (HTPi) by combining matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) and region-specific top-down proteomic analysis. MALDI MSI enables the imaging of proteoforms on tissue sec ...[more]

PMID: 40071752

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Project description:Highly specialized cells are fundamental for proper functioning of complex organs. Variations in cell-type specific gene expression and protein composition have been linked to a variety of diseases. Although single cell technologies have emerged as valuable tools to address this cellular heterogeneity, a majority of these workflows lack sufficient in situ resolution for functional classification of cells and are associated with extremely long analysis time, especially when it comes to in situ proteomics. In addition, lack of understanding of single cell dynamics within their native environment limits our ability to explore the altered physiology in disease development. This limitation is particularly relevant in the mammalian brain, where different cell types perform unique functions and exhibit varying sensitivities to insults. The hippocampus, a brain region crucial for learning and memory, is of particular interest due to its obvious involvement in various neurological disorders. Here, we present a combination of experimental and data integration approaches for investigation of cellular heterogeneity and functional disposition within the mouse brain hippocampus using MALDI Imaging mass spectrometry (MALDI-IMS) and shotgun proteomics (LC-MS/MS) coupled with laser-capture microdissection (LCM) along with spatial transcriptomics. Within the dentate gyrus granule cells we identified two proteomically distinct cellular subpopulations that are characterized by a substantial number of discriminative proteins. These cellular clusters contribute to the overall functionality of the dentate gyrus by regulating redox homeostasis, mitochondrial organization, RNA processing, and microtubule organization. Importantly, most of the identified proteins matched their transcripts, verifying the in situ protein identification and supporting their functional analyses. By combining high-throughput spatial proteomics with transcriptomics, our approach enables reliable near-single-cell scale identification of proteins and profiling of inter-cellular heterogeneity within similar cell-types in tissues. This methodology has the potential to be applied to different biological conditions and tissues, providing a deeper understanding of cellular subpopulations in situ.

Dataset Information

Proteoform Imaging by Combining MALDI MSI and High-sensitivity Top-down Proteomics

Publications

High-Throughput Proteoform Imaging for Revealing Spatial-Resolved Changes in Brain Tissues Associated with Alzheimer's Disease.

Similar Datasets

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