Spatial Metabolomics Mass Spectrometry Imaging Service by MALDI-MSI and DESI-MSI
CD Genomics provides an end-to-end Spatial Metabolomics Service built on two complementary mass spectrometry imaging (MSI) platforms — MALDI-MSI (Matrix-Assisted Laser Desorption/Ionization) and DESI-MSI (Desorption Electrospray Ionization). These techniques map the spatial distribution of metabolites, lipids, drugs, and other small molecules directly in tissue sections, preserving the anatomical context that is lost in homogenate-based LC-MS metabolomics. By overlaying molecular information onto tissue histology, spatial metabolomics reveals where specific metabolic changes occur — in tumor cores vs. invasive margins, in drug-exposed vs. unaffected regions, or across histologically distinct tissue compartments.
- Dual-platform: MALDI-MSI for high-resolution metabolite/lipid mapping; DESI-MSI for rapid, ambient lipid and drug imaging
- Broad analyte coverage: endogenous metabolites, lipids, drugs and their metabolites, neurotransmitters, and other small molecules (m/z 50–2000)
- Compatible with fresh-frozen tissue sections; MALDI-MSI also supports FFPE for retrospective lipid analysis
- Integrated histology: H&E or IF staining on the same or serial section for direct molecular–morphological correlation
Technology Overview: Mass Spectrometry Imaging (MSI)
Mass spectrometry imaging (MSI) — the core technology underpinning spatial metabolomics — combines mass spectrometric detection with spatial visualization to map hundreds to thousands of molecules directly on tissue sections. Unlike conventional LC-MS metabolomics, which requires tissue homogenization, MSI is label-free and non-targeted: a single acquisition simultaneously detects diverse molecular classes (metabolites, lipids, drugs, peptides) while preserving their exact spatial coordinates. Each pixel in the resulting ion image carries not only an intensity value but also a full mass spectrum that can be mined for structural information — including MS/MS fragmentation patterns for compound identification.
Our spatial metabolomics service leverages two complementary MSI platforms — MALDI-MSI and DESI-MSI — covering different trade-offs in spatial resolution, sample preparation complexity, and analyte coverage. The sections below detail each platform independently, followed by a practical selection guide for study design.
How the two platforms complement each other. MALDI-MSI excels at high-resolution, broad-coverage metabolite and lipid mapping, while DESI-MSI provides a rapid, minimal-prep route for lipid profiling and drug distribution studies. The image on the left summarizes the key differences in ionization mechanism, sample preparation, and spatial resolution. See the individual platform sections below for technical depth, and the selection guide at the end for decision support.
MALDI-MSI: High-Resolution Spatial Metabolomics
MALDI-MSI works by embedding the tissue surface in a thin layer of organic matrix (e.g., DHB, CHCA, 2-MBT) that co-crystallizes with endogenous analytes. When the pulsed laser strikes a pixel, matrix molecules absorb the energy, undergo rapid heating and sublimation, and carry co-crystallized analytes into the gas phase — a process that simultaneously desorbs and ionizes the sample.
The resulting mass spectra are acquired in a raster pattern, producing ion intensity maps for hundreds to thousands of m/z features simultaneously. Because the laser spot size can be tuned (typically 5–100 μm), MALDI-MSI supports spatial resolutions fine enough to resolve metabolic differences between adjacent tissue microstructures — for example, distinguishing tumor epithelium from surrounding stroma based on lipid profiles alone.
MALDI-MSI is particularly suited to projects requiring:
- High spatial resolution (down to 5–10 μm) for microregional metabolite profiling
- Broad analyte coverage including lipids, small metabolites, peptides, and certain drug compounds
- Dual-polarity acquisition (positive and negative ion modes) for complementary molecular coverage
- MS/MS-based metabolite identification directly on tissue
The matrix application step (sublimation or spray-coating) is a critical QC checkpoint. Inconsistent matrix deposition causes spatial variation in ion suppression, which can be misinterpreted as biological difference. Our protocol includes matrix homogeneity assessment before acquisition.
DESI-MSI: Ambient, Minimal-Prep Spatial Imaging
DESI-MSI operates at atmospheric pressure without matrix coating. A pneumatically assisted electrospray directs charged solvent microdroplets onto the tissue surface, where they form a thin liquid film that extracts and desorbs analytes. Secondary droplets carrying dissolved molecules are ejected from the surface and drawn into the mass spectrometer — an ambient ionization process that requires no vacuum, no matrix, and minimal sample handling.
This mechanism is inherently gentler than laser-based desorption, making DESI-MSI well-suited for detecting labile metabolites and for rapid screening across large tissue areas.
DESI-MSI is typically selected when:
- Minimal sample preparation is preferred (no matrix, no vacuum)
- The analytical focus is lipids, drug compounds, or abundant small metabolites
- Large tissue sections or whole-organ imaging is required (practical spatial resolution typically 50–200 μm)
- A non-destructive or minimally destructive workflow is needed, allowing subsequent histology or other analyses on the same section
How to Choose Between MALDI-MSI and DESI-MSI
The choice depends primarily on the spatial resolution required and the analyte classes of interest. The table below summarizes the key trade-offs; our team advises on final platform selection during study design based on your tissue type, target analytes, and biological question.
| Consideration | MALDI-MSI | DESI-MSI |
|---|---|---|
| Spatial resolution | 5–100 μm | 50–200 μm |
| Analyte range | Lipids, metabolites, peptides, drugs | Lipids, abundant metabolites, drugs |
| Sample preparation | Matrix coating required | Minimal; direct analysis |
| MS/MS on tissue | Supported | More limited for low-abundance species |
| Large-area imaging | Feasible but longer acquisition | Faster per unit area |
| Post-MSI histology | Tissue consumed | Same section can be re-stained |
For projects where both high spatial resolution and broad analyte coverage are needed, MALDI-MSI is recommended. For rapid lipid profiling or drug distribution mapping across large tissue sections, DESI-MSI is the more practical choice.
If your project would benefit from both approaches — for example, high-resolution MALDI-MSI on a key ROI combined with broad DESI-MSI screening of the whole section — we can design a dual-platform workflow. Contact our team to discuss the best strategy for your samples and research goals.
Spatial Metabolomics MSI Workflow
CD Genomics manages the complete spatial metabolomics workflow from tissue receipt to interpreted results.
- Study design and platform selection
Biological question, tissue type, target analyte classes, and spatial resolution requirements are discussed. The appropriate platform (MALDI-MSI vs. DESI-MSI), ionization polarity, mass range, and spatial resolution are selected. ROI strategy is defined when specific tissue regions are of primary interest.
- Tissue sectioning and preparation
Fresh-frozen tissue blocks are cryosectioned at 8–12 μm thickness onto conductive slides (ITO-coated for MALDI-MSI; standard glass for DESI-MSI). Serial sections are prepared for H&E or IF staining to guide ROI selection and correlate molecular images with histology. For MALDI-MSI, matrix is applied via optimized sublimation or spray-coating protocol. Matrix homogeneity is verified by optical inspection and test-line acquisition.
- MSI data acquisition
The tissue section is raster-scanned at the selected spatial resolution. For MALDI-MSI, spectra are acquired in reflectron mode with mass resolution sufficient to resolve isobaric lipid species. For DESI-MSI, solvent composition and gas flow parameters are optimized for the target analyte class. Both positive and negative ion modes are acquired when project goals require complementary coverage.
- Data preprocessing and metabolite annotation
Raw spectra are converted to ion intensity maps. Preprocessing includes baseline subtraction, peak picking, spectral alignment across pixels, and normalization (TIC, RMS, or internal standard-based). Metabolite and lipid annotations are performed by matching accurate mass to public databases (HMDB, METLIN, LipidMaps) and confirmed by on-tissue MS/MS where sensitivity permits. All annotations include mass error (ppm) and identification confidence level.
- Spatial analysis and biological interpretation
Segmented ion maps are overlaid onto histological images for direct molecular–morphological correlation. Regions of interest are defined based on histology or unsupervised spatial segmentation. Statistical comparisons between ROIs, conditions, or tissue compartments are performed. Results are interpreted in the biological context of the project and delivered with publication-ready figures.
Spatial Metabolomics MSI Sample Requirements
| Sample Type | Requirement | Storage & Shipping | Notes |
|---|---|---|---|
| Fresh-frozen tissue block | ≥ 3 × 3 mm cross-section; ≥ 2 mm thickness; recommended one-dimensional size ≤ 1 cm (larger samples must be confirmed in advance) | −80°C; ship on dry ice (~4 kg dry ice/day; tissue fully buried in dry ice inside thick-walled foam box) | Preferred sample type. Avoid OCT embedding when possible — OCT polymers (PEG) produce intense MS background. If OCT must be used, remove as much as possible before sectioning. CMC or FSC22 are acceptable alternatives. Keep a backup sample. |
| Fresh-frozen tissue section | 8–12 μm thickness; mounted on ITO-coated slides (MALDI-MSI) or standard glass slides (DESI-MSI) | −80°C; ship in slide box on dry ice | Provide 3–4 serial sections per sample (1 for H&E/IF, 2 for MSI, 1 backup). |
| FFPE tissue section (MALDI-MSI only) | 4–5 μm thickness; mounted on ITO-coated slides | Room temperature | Limited to lipid analysis. Formalin cross-linking and paraffin embedding deplete most small polar metabolites. Tissues must not have undergone H&E staining or fluorescent labeling before MSI. |
Avoid repeated freeze–thaw cycles at all stages. When shipping by liquid nitrogen, do not fully seal sample tubes or boxes — leave a small opening to prevent pressure buildup. Sample quality directly determines data quality; minimize the time from collection to freezing, and contact us before shipment to coordinate timing.
Bioinformatics & Spatial Data Analysis
Spatial metabolomics data analysis goes beyond generating ion images — it extracts statistically grounded, biologically interpretable spatial information from tens of gigabytes of MSI data per sample.
Standard analysis
- Spectral preprocessing: baseline correction, normalization (TIC, RMS), peak alignment, and pixel-level QC filtering
- Ion image generation: spatial distribution maps for all detected m/z features
- Multivariate analysis: PCA, PLS-DA, and hierarchical clustering to identify major sources of metabolic variance across tissue regions
- ROI-based statistical comparison: defined regions (tumor vs. normal, treated vs. untreated) compared by univariate (t-test, Wilcoxon) and multivariate methods
- Metabolite/lipid annotation: accurate mass matching to HMDB, METLIN, LipidMaps; on-tissue MS/MS confirmation for high-priority features
- Pathway enrichment: KEGG and Reactome pathway mapping of spatially differential metabolites
- Spatial segmentation: unsupervised clustering of pixels by spectral similarity to define chemically distinct tissue zones
- Data visualization: ion heatmaps overlaid on histology, volcano plots, PCA scores plots, clustered heatmaps, and spatial segmentation maps
Advanced analysis (optional)
- Spatial co-localization and correlation networks: identifying metabolites that co-vary spatially and constructing metabolite–metabolite correlation networks
- Drug and metabolite co-mapping: correlating drug distribution with endogenous metabolic response in the same tissue section
- Multi-omics spatial integration: overlaying MALDI/DESI metabolomics data with spatial transcriptomics or proteomics data from the same or serial sections
- Quantitative MSI (qMSI): calibration-curve-based absolute quantification of target analytes using isotopically labeled internal standards applied to tissue
- 3D reconstruction: serial section MSI for volumetric metabolite mapping across tissue depth
Deliverables
Every spatial metabolomics project includes structured, ready-to-use deliverables.
- Processed MSI data
- Ion intensity matrices with spatial coordinates, peak lists with annotations and identification confidence levels, and raw spectral files in open formats (imzML, mzML)
- QC report
- Matrix homogeneity assessment, spectral quality metrics, mass accuracy distribution, and pixel-level QC filtering summary
- Spatial metabolomics report
- Ion distribution maps for all annotated and differentially abundant features, ROI-based statistical comparisons, multivariate analysis results, metabolite annotation table with database matches and mass errors, and pathway enrichment results
- Publication-ready figures
- High-resolution spatial ion maps overlaid on histology (300+ dpi TIFF/PNG), PCA/PLS-DA plots, volcano plots, clustered heatmaps, spatial segmentation maps, and correlation network diagrams
- Reproducible analysis documentation
- Fully documented data processing scripts with software versions and parameter settings
- Experimental methods documentation
- Detailed protocols in English covering all workflow steps — tissue preparation, matrix application (MALDI-MSI), acquisition parameters, and data processing — suitable for inclusion in manuscript methods sections
- Statistical analysis tables
- Quantitative results as Excel spreadsheets with metabolite annotation tables, differential abundance statistics, and pathway enrichment results
- Raw MSI data
- Original spectral files (imzML) and optical tissue images, preserving full data integrity for independent re-analysis
Spatial Metabolomics Applications
Our spatial metabolomics mass spectrometry imaging service supports a wide range of biological research questions.
Tumor microenvironment and biomarker discovery
Spatial metabolomics reveals metabolic heterogeneity within tumors — distinguishing metabolic profiles of the tumor core, invasive margin, and surrounding stroma. Lipid and metabolite signatures specific to immune-infiltrated vs. immune-excluded regions, hypoxic zones, or necrotic areas can be mapped at pixel-level resolution. See also: Spatial Omics Solutions for Tumor Microenvironment.
Drug distribution and spatial pharmacology
MSI maps the penetration, accumulation, and local metabolism of drug compounds in target and off-target tissues without requiring radiolabeling. Drug and metabolite co-mapping in the same tissue section reveals whether a drug reaches its intended compartment and how local tissue metabolism responds to exposure. This is particularly valuable for oncology drug candidates where heterogeneous tumor perfusion creates variable drug exposure.
Neuroscience and lipidomics
The brain's regional biochemical specialization — neurotransmitter gradients, myelin lipid composition, region-specific metabolic activity — is inherently spatial. MALDI-MSI at 10–20 μm resolution resolves lipid and metabolite distributions across cortical layers, individual brain nuclei, and white-matter tracts, supporting studies of neurodegeneration, neuroinflammation, and neuropharmacology. See also: Spatial Omics Solutions for Neuroscience.
Metabolic disease and liver research
Hepatic metabolic zonation — the spatial segregation of metabolic functions along the portal–central axis — cannot be captured by homogenate-based metabolomics. Spatial MSI directly visualizes zonal lipid accumulation, glycogen distribution, and bile acid profiles in NAFLD/NASH, drug-induced liver injury, and metabolic syndrome models. See also: Spatial Omics Solutions for Liver Disease.
Plant and natural product metabolomics
Spatial metabolomics maps the distribution of secondary metabolites, defense compounds, and bioactive natural products across plant tissues — roots, stems, leaves, seeds, and fruits. Specific applications include medicinal compound localization in herbal tissues, chemical-ecology studies of pest–plant and microbe–plant interactions at tissue interfaces, and crop trait characterization linked to spatial metabolic phenotypes.
Developmental biology
Metabolite distributions shift dynamically during embryogenesis, organ maturation, and seed development. Spatial MSI captures these gradients without requiring transgenic reporters, revealing how metabolic programs map onto anatomical structures at successive developmental stages.
Cardiovascular and inflammatory disease
Atherosclerotic plaques, myocardial infarction zones, and inflamed tissues show spatially structured metabolic changes. MSI maps lipid accumulation, inflammatory mediators, and drug distribution within lesions at resolutions that resolve different plaque compartments. See also: Spatial Omics Solutions for Cardiovascular Disease.
Case Study: MALDI-MSI Reveals Metabolic Reprogramming in Prostate Cancer Under Matrine Treatment
Source: Xu J, Qin L, Liang X, et al., Frontiers in Pharmacology, 2025
Background
Prostate cancer progression is accompanied by extensive metabolic reprogramming across multiple pathways — lipid metabolism, amino acid utilization, and nucleotide biosynthesis. Matrine, a natural alkaloid from Sophora flavescens, has demonstrated anti-tumor activity, but its metabolic mechanism of action at the tissue level remains incompletely characterized. Understanding where and how matrine alters the tumor metabolic landscape requires spatially resolved analysis that bulk tissue homogenization cannot provide.
Methods
PC-3 human prostate cancer xenografts were established in BALB/c nude mice, with three experimental groups: normal control (NC), untreated prostate cancer (PCa), and matrine-treated prostate cancer (PCa+MAT). Fresh-frozen tumor tissues were cryosectioned and analyzed by MALDI-MSI in both positive and negative ion modes (m/z 100–500, 75 μm spatial resolution) using a Bruker Autoflex Speed MALDI-TOF/TOF. On-tissue MS/MS confirmed metabolite identities against the HMDB database (mass error <10 ppm). Multivariate statistical analysis (PCA, PLS-DA) identified differentially abundant metabolites (VIP >1.0) across groups, and spatial ion maps were correlated with H&E and Ki-67 immunohistochemistry.
Results
Nineteen differentially abundant metabolites were identified spanning lipid signaling, amino acid metabolism, nucleotide biosynthesis, and oxidative stress pathways. Linoleic acid and oleic acid were significantly depleted in tumors, indicating accelerated fatty acid utilization — a pattern partially reversed by matrine treatment. Glutamine, adenine, adenosine, and multiple nucleotide intermediates were elevated in tumors and reduced after matrine treatment, suggesting suppressed nucleotide biosynthesis. The oxidative stress marker 8-hydroxyguanine was elevated in tumors, reflecting genomic damage. Spatial ion maps revealed that metabolic alterations were not uniform across the tumor section — the tumor core and peripheral zones showed distinct metabolic profiles, visible only through spatial MSI.
Conclusion
This study demonstrates the capacity of MALDI-MSI to resolve spatially heterogeneous metabolic responses to drug treatment — identifying not just which metabolites change, but where in the tissue the changes occur. The workflow — from xenograft tissue processing through dual-polarity MALDI-MSI acquisition, on-tissue MS/MS validation, multivariate analysis, and histological correlation — reflects the end-to-end spatial metabolomics service CD Genomics provides.
Adapted from Xu et al., Frontiers in Pharmacology, 2025, doi:10.3389/fphar.2025.1627864, Figure 6A–B.
Frequently Asked Questions (FAQ)
References
- Xu J, Qin L, Liang X, et al. "Multimode MALDI-MSI deciphers matrine-induced metabolic reprogramming in prostate cancer xenografts: spatial mapping of low-molecular-weight compound alterations." Frontiers in Pharmacology, vol. 16, 2025, 1627864.
- Alexandrov T. "Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence." Annual Review of Biomedical Data Science, vol. 3, 2020, pp. 183–201.
- Pang Z, Chong J, Zhou G, et al. "MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation." Nucleic Acids Research, vol. 52, no. W1, 2024, pp. W398–W406.
- Shariatgorji M, Nilsson A, Fridjonsdottir E, et al. "Comprehensive mapping of neurotransmitter networks by MALDI-MS imaging." Nature Methods, vol. 16, 2019, pp. 1021–1028.
- Wishart DS, Guo A, Oler E, et al. "HMDB 5.0: the Human Metabolome Database for 2022." Nucleic Acids Research, vol. 50, no. D1, 2022, pp. D622–D631.
