Mass Spectrometry-Based Spatial Proteomics: Methods, Challenges, and Applications
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Precisely select regions of interest (e.g., tumor invasive margins, specific brain regions).
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Suitable for analyzing microregion functional heterogeneity.
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Limitation: low throughput and demanding sample preparation.
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Conduct point-by-point mass spectrometry scanning on tissue sections.
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High spatial resolution (10–50 µm).
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Best for visualizing protein distribution, though protein identification depth is limited.
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Punch multiple tissue microregions and label each with TMT.
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Enables multi-sample comparison and higher throughput.
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Spatial resolution is moderate, suitable for analyzing tissue-level spatial differences.
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For precise analysis of microregion differences, such as tumor margins or neuronal clusters, LCM + mass spectrometry is preferred.
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For in situ visualization of protein distribution across whole tissue sections, MALDI-IMS is suitable.
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For multi-sample high-throughput comparison at moderate spatial resolution, TMT punch labeling offers a balance between resolution and throughput.
Spatial proteomics, an emerging technology, integrates the quantitative capabilities of mass spectrometry-based proteomics with the preservation of tissue spatial information, redefining our understanding of tissue heterogeneity, cell–microenvironment interactions, and the spatial progression of diseases. Unlike traditional linear extract-and-analyze workflows, spatial proteomics aims for a three-dimensional understanding of in situ expression, local distribution, and spatial correlation. This approach is particularly valuable for elucidating highly spatially dependent biological phenomena, including tumor immune evasion, functional partitioning of brain regions, and organ development. Conducting mass spectrometry-based spatial proteomics research efficiently and rigorously requires coordinated technologies and integrated data across multiple steps, including sample preparation, microregion isolation, mass spectrometry analysis, and spatial visualization.
Value and Challenges of Spatial Proteomics
1. Importance of Spatial Proteomics
(1) Revealing tissue spatial heterogeneity: distinct regions exhibit substantial differences in cell types, functional states, and signaling pathways.
(2) Understanding spatial mechanisms of disease: for example, tumor boundary regions and core regions show marked differences in immune evasion and metabolic status.
(3) Discovery of spatial targets: provides data to support tissue-specific drug development and biomarker validation.
(4) Supporting spatial multi-omics integration: can be combined with spatial transcriptomics and metabolomics to construct integrated spatial multi-omics networks.
2. Technical Challenges
(1) Extremely low protein content in microregions, necessitating high-sensitivity instruments and efficient extraction methods.
(2) Accurate spatial localization depends on precise image registration, often requiring tissue staining or auxiliary markers.
(3) Low throughput and technical complexity in microregion isolation.
(4) Data integration is challenging, requiring close collaboration with bioinformatics experts.
Workflow of Mass Spectrometry-Based Spatial Proteomics
1. Tissue Sample Preparation
FFPE or frozen tissue sections are recommended, with a thickness of 5–10 µm. H&E staining or immunostaining may be applied to facilitate spatial region identification. Sections should be mounted on membrane-coated slides (e.g., gold or PEN membranes) to enable laser microdissection.
2. Spatial Region Selection and Microregion Isolation
Mainstream mass spectrometry strategies for spatial proteomics include:
(1) Laser Capture Microdissection (LCM) + Mass Spectrometry
(2) Imaging Mass Spectrometry (MALDI-IMS)
(3) Microregion Punch + TMT Labeling + LC-MS/MS
3. Protein Extraction and Digestion
For microregion samples, SP3 bead-based or S-Trap small-volume lysis systems improve recovery and reduce sample loss. Key considerations include:
(1) Optimizing paraffin removal and antigen retrieval procedures.
(2) Controlling digestion time to prevent over- or under-digestion.
(3) Performing desalting and concentration before MS analysis to improve compatibility.
4. Mass Spectrometry Analysis
(1) Recommended platforms: Orbitrap Exploris, timsTOF, QE HF-X, and other high-resolution mass spectrometers.
(2) Data acquisition: DIA (data-independent acquisition) for comprehensive quantification, DDA for building protein libraries.
(3) Separation: nanoLC systems enhance sensitivity and resolution for trace proteins.
5. Data Analysis and Spatial Visualization
(1) Quantify proteins using software such as MaxQuant or Spectronaut.
(2) Map protein abundance values to original tissue images.
(3) Generate spatial heatmaps, identify differential regions, and perform GO/KEGG enrichment analyses.
(4) Optional: spatial clustering, subtype annotation, and integration with spatial transcriptomics.
Choosing the Technical Pathway for Spatial Proteomics
Selection of the technical pathway should balance spatial resolution, data depth, and operational complexity according to research objectives, sample types, and resources:
Spatial proteomics represents a significant advance in the post-omics era. With rapid progress in mass spectrometry, researchers can now determine not only the quantity of proteins but also their spatial location, interactions, and regulatory context. For investigators studying the cellular microenvironment, tumor heterogeneity, or tissue functional partitioning, spatial proteomics constitutes both a technological upgrade and an evolution in research methodology. MtoZ Biolabs continues to provide high-sensitivity mass spectrometry platforms and professional data analysis support to advance spatial proteomics projects, enabling a more accurate and three-dimensional understanding of protein expression in biological systems.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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