Spatial Metabolomics Services - Creative Proteomics Metabolomics

What is Spatial Metabolomics (MSI)

Spatial metabolomics uses mass spectrometry imaging (MSI—e.g., MALDI, DESI, nano-DESI) to map where metabolites, lipids, and xenobiotics reside directly on tissue sections at micrometer-scale pixel sizes. Instead of averaging signals from homogenized extracts, MSI preserves tissue architecture and produces ion images—two-dimensional molecular maps—co-registered to brightfield/H&E for region-of-interest (ROI) analysis. Typical study parameters include:

Pixel size (spatial resolution): ~10–200 µm (selected per objective and matrix).
m/z range: ~50–1,500 for small molecules and lipids.
Ionization: positive and negative modes; matrix/solvent chemistry tuned per analyte class.
Data outputs: imzML + vendor raw files, ROI statistics tables, pathway summaries, and publication-ready images.

Compatible sample types include fresh-frozen tissues, selected FFPE (lipid-focused and defined metabolite classes), organoids/spheroids, microbial biofilms, and plant/food matrices (method dependent).

Why Choose Spatial Metabolomics for Your Research

Localize rather than average

Detect microenvironments, boundaries, and gradients that bulk LC-MS misses; analyze by ROI with robust normalization (TIC, internal calibrants, or ROI-specific strategies).

Function-proximal readouts

Metabolites/lipids reflect pathway activity and microenvironmental constraints (energy/redox, membrane remodeling), supporting mechanism-of-action studies and research-use biomarker exploration.

On-tissue visibility of small molecules

Map compounds and metabolites in situ, evaluate unexpected accumulation, and assess off-region signals with dual polarity and adduct-aware annotation.

Spatial statistics & co-localization

Compute spatial autocorrelation (e.g., Moran's I), co-localization matrices, neighborhood enrichment, and differential abundance across predefined ROIs.

Seamless image alignment

Co-register ion images to histology for ROI fidelity; export layered figures for reports and review.

Multi-omics readiness

Feature tables and mappings are ready for integration with spatial transcriptomics/proteomics and image-based assays.

Which Spatial Metabolomics Platform Fits Your Research?

Not all MSI platforms are equivalent. The table below compares the three methods we offer across the dimensions that matter for experimental design: spatial resolution, analyte coverage, sample prep requirements, and workflow fit.

Dimension	MALDI-MSI	DESI-MSI	Nano-DESI
Spatial resolution	10-50 um (down to 5 um on timsTOF)	50-200 um	10-50 um (capillary-controlled)
Analyte coverage	Lipids, primary metabolites, peptides; broad coverage with matrix selection	Lipids, drug compounds, small metabolites; ambient ionization, minimal fragmentation	Polar metabolites, neurotransmitters, TCA intermediates; high sensitivity for low-abundance targets
Sample prep	Matrix application required (DHB, CHCA, DAN); tissue section mounted on ITO slide	Minimal to none -- ambient ionization directly from tissue surface	Liquid extraction via capillary bridge; minimal prep, well-suited for FFPE and delicate tissues
Best for	High-res lipidomics, boundary mapping, large-area tissue screening	Rapid lipid profiling, FFPE-compatible workflows, drug distribution studies	Targeted polar metabolites, neurochemical mapping, low-abundance compound imaging
Throughput	High -- up to 50 pixels/sec at 50 um; automated acquisition	Moderate -- solvent flow rate limits speed	Lower -- capillary bridge limits acquisition speed; best for ROI-focused studies

Not sure which platform fits your tissue type and research question? Contact us during study design -- we will recommend the optimal method or a multi-platform approach based on your sample and target analytes.

Why Now Is the Right Time to Use Spatial Metabolomics

Mature imaging performance: Micrometer-scale pixel sizes with high mass accuracy (ppm-level on high-resolution analyzers), dual-polarity acquisition, and lock-mass/internal-calibrant workflows deliver reproducibility suitable for decision-grade research.
Expanded chemical coverage: On-tissue derivatization (carbonyls, amines, carboxylates) and optimized matrices/solvents improve detection of difficult metabolite classes.
Standardized, auditable pipelines: imzML-centered workflows support peak picking, deisotoping/adduct deconvolution, recalibration, normalization, annotation, and pathway enrichment with transparent QC.
Broader sample compatibility & throughput: Robust methods for fresh-frozen, selected FFPE (lipid-forward and defined metabolite classes), organoids, and complex matrices enable consistent sectioning, prep, and imaging at study scale.
Actionable deliverables: Clear ROI effect sizes and pathway panels shorten time from raw images to interpretable insights; assets export cleanly to common analytics environments.

What You Can Accomplish with This Service?

Map microenvironments with micrometer precision to reveal gradients and borders that guide hypothesis refinement.
Differentiate molecular phenotypes between ROIs (e.g., core vs. periphery, interface zones, treatment vs. control) with statistics suitable for reports and submissions.
Prioritize program targets by linking localized metabolite patterns to pathways and phenotypes.
Reduce project risk by flagging off-region accumulation of small molecules and lipid remodeling early.
Elevate documentation with reproducible ion images, ROI statistics, and exportable analysis artifacts.

Typical Questions We Answer

Which metabolites or lipids are enriched at defined ROIs?

Do energy or redox metabolite gradients exist across a boundary?

How does a compound or genetic perturbation remodel the local metabolome?

Which pathways are spatially co-activated or mutually exclusive?

Service Scope and Key Differentiators in Our Spatial Metabolomics Analysis

Multi-platform MSI: MALDI-MSI for broad coverage and high throughput; DESI-MSI for minimal prep; nano-DESI for enhanced sensitivity on select targets.
Dual-polarity acquisition to maximize coverage of lipids, primary metabolites, xenobiotics, and secondary metabolites.
On-tissue chemistry options (derivatization for carbonyls, amines, and carboxylates; matrix selection tailored to analyte classes).
Targeted + untargeted pipelines with library and formula-based annotation, isotopolog detection, and adduct deconvolution.
Quantitative region statistics with multiple normalization strategies (TIC, internal calibrants, ROI-specific).
Regimented QC: section-to-section alignment, lock-mass correction, batch monitors, and drift control with calibrant arrays.
Rich deliverables: imzML and vendor files, ion images, ROI maps, annotated feature tables, pathway overlays, and analysis notebooks.

Technology Platforms and Analytical Parameters for Spatial Metabolomics

Mass spectrometry imaging platforms

MALDI-MSI (Orbitrap/FT-ICR/TOF): broad metabolite & lipid coverage, fast scanning.
DESI-MSI / nano-DESI: ambient ionization; ideal for delicate or limited-prep samples.

Typical operating ranges (customizable by study)

Spatial resolution (pixel size): ~10–200 µm (study-dependent).
Section thickness: ~5–20 µm for frozen tissue; FFPE thickness per method.
Mass range (m/z): ~50–1,500 (expandable with method).
Mass accuracy: down to low-ppm on high-resolution platforms with internal/lock mass.
Ionization modes: positive and negative; adduct engineering where relevant.
Matrices (MALDI examples): CHCA, DHB, 9-AA, DAN, and analyte-specific formulations.
Normalization: TIC, RMS, internal standards, or histology-guided strategies.
File formats: imzML + native vendor formats; processed outputs in CSV/Parquet; figures in PNG/TIFF/SVG.

Bruker solariX XR FT-ICR

Thermo Scientific Q Exactive™ Orbitrap with MALDI source

Thermo Scientific Orbitrap Exploris 240/480 with Prosolia DESI 2D

Guidance on Choosing the Right Spatial Metabolomics Method for Your Study

Choose MALDI-MSI when you need:

Broad lipidomics + metabolite coverage and rapid section throughput.

Fine pixel sizes for boundary mapping and micro-region analysis.

Choose DESI/nano-DESI when you need:

Minimal sample prep or FFPE-oriented lipid mapping.

Improved sensitivity for specific polar targets (method-dependent).

Consider on-tissue derivatization when you need:

Enhanced visibility of carbonyls (e.g., oxo-metabolites), amines (neurochemicals, amino acids), or carboxylates (TCA intermediates).

Why Choose Our Spatial Metabolomics Services

Multi-Platform MSI Under One Roof
MALDI-MSI, DESI-MSI, and nano-DESI are all available in-house -- no subcontracting, no fragmented data packages. We select the right platform (or combination) for your tissue type and research question, not the one we happen to own.
Quantitative, Not Just Pretty Pictures
Every ion map is backed by ROI-specific quantification with TIC normalization, internal calibrant correction, and batch-level QC. You receive concentration heatmaps and comparative statistics -- not just uncalibrated intensity images.
Histology-Co-Registered for Biological Context
H&E or IF images are aligned to ion maps at the pixel level, identifying which tissue compartments (cortex vs. medulla, tumor vs. stroma, lesion vs. adjacent normal) correspond to each metabolic signature.
Open-Format Data Delivery
All data delivered in imzML (open standard) plus vendor-native formats, enabling downstream analysis in SCiLS Lab, Cardinal, METASPACE, or your preferred bioinformatics pipeline -- no proprietary lock-in.

Step-by-Step Workflow of Our Spatial Metabolomics Analysis Service

Project design

We refine biological hypotheses, ROIs, and endpoints. We select platforms, spatial resolution, and quant/qual balance. A method sheet is finalized with acceptance criteria and QC checkpoints.

Sample receipt & accessioning

Chain-of-custody logging; inspection for integrity and orientation; cryo or FFPE-appropriate storage.

Sectioning & on-tissue preparation

Cryosection or microtomy to specified thickness; slide mounting; desiccation; matrix deposition (sublimation or spray) or ambient ionization setup. Optional on-tissue derivatization for enhanced coverage of difficult metabolite classes.

Imaging MS acquisition

Tiled scanning at defined pixel size; dual polarity as planned; internal calibrant application or lock-mass for accuracy. Batch controls positioned on each slide set.

Core processing & QC

Peak picking, deisotoping/adduct grouping, mass recalibration, intensity normalization, and image co-registration with brightfield images. QC reports summarize resolution, mass error, drift, pixel completeness, and ROI coverage.

Annotation & statistics

Untargeted: database-aided annotation (exact mass ± ppm, isotope, adduct rules), spectral matching where available, and formula scoring.
Targeted: custom inclusion lists and on-tissue or serial LC-MS validation of markers where appropriate.
ROI statistics, differential abundance (non-parametric tests where assumptions are unmet), spatial autocorrelation (e.g., Moran's I), co-localization metrics, and pathway enrichment using curated metabolite-to-pathway maps.

Reporting & delivery

Interactive ion images, ROI overlays, ranked feature tables with confidence flags, pathway summaries, and a methods dossier enabling reproducibility. Optional consultative review to interpret spatial biology in the context of your study.

Sample Types We Support

Sample Type	Description / Notes
Fresh-frozen tissues	Mammalian, plant, microbial consortia, xenografts; embedded in OCT or equivalent
FFPE sections	Lipid-focused MSI and select metabolite panels; optimized deparaffinization and antigen retrieval as appropriate
Organoids & spheroids	Includes 3D cultures; prepared for sectioning and MSI imaging
Microbial biofilms	Suitable for spatial metabolomics of microbial communities
Tissue microarrays (TMAs)	Multiplexed ROI analysis possible
Materials science samples	Method permitting; matrix-dependent optimization
Food matrices	Method permitting; compatibility evaluated during project design

For detailed preparation and shipping guidelines, please contact our team before sending samples.

Quality Assurance You Can Audit

We implement rigorous, transparent QC protocols to ensure data accuracy, reproducibility, and traceability:

Lock-mass & Internal Calibrants — Applied to every batch and slide set for consistent mass accuracy.
Replicate Regions & Check Slides — Precision assessment through repeated ROI analysis.
Background Controls — Blank regions and matrix-only slides to monitor noise and adduct formation.
Carryover Checks — Inter-tile and inter-slide controls to detect contamination.
Acceptance Criteria — Defined thresholds for mass accuracy, spatial fidelity, pixel completeness, and S/N ratio.
Change-Controlled Methods — Any procedural deviations are fully documented in the final report.

Data Analysis, Bioinformatics, and Reporting Deliverables

Ion images for prioritized features (publication-ready).
ROI-level tables (mean, median, variance, effect sizes, FDR-controlled p-values).
Quantitation Approaches
- Relative quantitation with normalization and batch controls
- Absolute/semi-absolute quantitation using stable-isotope standards (feasibility dependent)
- Drift/batch correction via calibrant arrays and pooled slides
Spatial stats (co-localization matrices, gradient analysis, neighborhood enrichment).
Pathway panels summarizing localized pathway activity trends.
Audit-ready methods: instrument settings, calibration details, matrix recipes, derivatization protocols, and QC metrics.
All raw & processed data (imzML, vendor raw, peak lists, metadata JSON) for downstream re-analysis.

Ion Image

Spatial distribution of a selected metabolite within a tissue section, visualized as an ion image with intensity represented from low (blue) to high (red).

Three-Image Ion Image

Comparison of three ion images showing spatial distribution patterns for metabolites at m/z 782.5, m/z 520.8, and m/z 183.1, each with independent color scale bars.

Tissue section heatmap showing metabolite co-localization in blue, green, and magenta, alongside a network diagram with colored nodes representing metabolites and lines indicating spatial correlations.

Spatial Co-localization & Network Visualization

Spatial co-localization map of multiple metabolites overlaid on a tissue section (left) and corresponding network diagram (right) illustrating metabolite spatial relationships.

Bar chart of metabolic pathways sorted by adjusted p-value, paired with a network diagram highlighting enriched pathways as magenta nodes among blue pathway nodes.

Pathway Enrichment Summary

Pathway enrichment results visualized as a horizontal bar chart ranked by adjusted p-value, with enriched pathways highlighted in a network diagram.

Customizable Specifications for Your Spatial Metabolomics Study

Dimension	Option Set (examples)
Pixel size	~10, 25, 50, 100, 200 µm (trade-off vs. sensitivity & coverage)
Polarity	Positive, Negative, Sequential Dual
Mass analyzer	Orbitrap, FT-ICR, TOF (study-dependent)
m/z range	~50–1,500 (custom windows possible)
Matrices	CHCA, DHB, 9-AA, DAN; custom blends
Derivatization	Carbonyl-, amine-, or carboxyl-targeted chemistries
Normalization	TIC, internal standard, ROI-specific
Deliverables	imzML + raw, ion images, ROI stats, pathway summaries, methods dossier

Getting Started: What We Need from You

Study objective and hypotheses; target metabolites/classes if known.
Sample list, matrix, storage conditions, and any prior staining or treatments.
Preferred pixel size, ROI definitions, and required statistics or comparisons.
Any constraints on sample consumption or serial-section usage.

We'll propose a fit-for-purpose design with clear acceptance criteria, an instrument/chemistry plan, and data/figure deliverables aligned to your milestones.

What is the typical sample size required for analysis?

We can work with small tissue sections, typically in the range of a few square millimeters. The optimal size depends on desired spatial resolution and the number of regions of interest (ROIs). Larger sections may allow for more extensive mapping and comparative studies.

How should I prepare and ship my samples?

We provide detailed preparation and shipping guidelines upon project initiation. This includes recommendations on embedding media, section thickness, temperature control, and documentation to ensure sample integrity upon arrival.

Can spatial metabolomics be performed on archived samples?

Yes, selected FFPE (formalin-fixed, paraffin-embedded) samples can be analyzed, particularly for lipid-focused studies or predefined metabolite classes. Preparation and deparaffinization steps must be optimized for each sample type.

What types of molecules can be detected?

Depending on the ionization method, we can detect a broad range of small molecules, including primary metabolites, lipids, xenobiotics, and certain secondary metabolites. Specialized on-tissue derivatization can extend coverage to otherwise low-abundance or challenging analyte classes.

How is spatial resolution selected for a project?

Pixel size is determined by balancing spatial detail with sensitivity and coverage. Higher resolution (e.g., 10 µm) provides finer detail for boundary mapping, while lower resolution allows for faster acquisition and broader area coverage.

Can you integrate my histology data with the metabolomics results?

Yes. Brightfield or H&E-stained images can be co-registered with ion maps for precise ROI alignment, enabling histology-guided metabolomics analysis.

Is method development required for every project?

While we maintain validated protocols for many sample types, unique projects—especially involving unusual matrices or novel analytes—may require customized method development to ensure optimal performance.

What formats are the results delivered in?

We provide both raw and processed datasets (e.g., imzML, vendor native files, feature tables) along with publication-ready figures and statistical summaries. This ensures compatibility with most downstream analysis platforms.

Can the results be integrated with other omics data?

Yes. Output is structured to facilitate integration with transcriptomics, proteomics, and other spatially resolved datasets, supporting comprehensive multi-omics analysis.