Spatial Targeted Lipidomics Service

What is Spatial Targeted Lipidomics?

Spatial Targeted Lipidomics is a sophisticated analytical technique that merges the spatial fidelity of high-resolution microscopy with the molecular specificity of mass spectrometry. Unlike untargeted imaging, which scans a sample indiscriminately for thousands of unknown features, this targeted approach functions as a molecular microscope tuned to precise frequencies. The process begins when a laser (MALDI) or charged solvent spray (DESI) impacts the tissue surface at exact coordinate pixels, desorbing lipid molecules directly into the gas phase. Instead of capturing a broad spectrum of noise, the mass spectrometer is programmed to filter for specific mass-to-charge (m/z) ratios corresponding to a pre-defined panel of interest, such as specific phospholipids, sphingolipids, or drug metabolites.

This targeted filtration allows for a depth of analysis unattainable by standard imaging methods. By narrowing the analytical window, we dramatically increase the signal-to-noise ratio, enabling the detection of low-abundance signaling lipids that are often lost in the background of full-scan modes. Furthermore, this method often employs tandem mass spectrometry (MS/MS) to fragment ions on the fly, verifying lipid headgroups and fatty acid chains to ensure that the signal observed is the exact molecule intended. The final result is a reconstruction of these molecular intensities into a detailed heatmap overlaid on the tissue image, providing a direct correlation between metabolic changes and histological features like tumor margins, necrotic cores, or distinct brain regions.

The Workflow

We offer a streamlined, end-to-end solution from tissue processing to bioinformatics.

Panel Coverage – Custom Spatial Targeted Lipidomics

The table above presents some representative services in spatially targeted lipidomics. We can provide customized panels based on the specific needs of your research project, offering target molecule lists tailored to different tissues, species, and diseases. Please contact us for technical support before you start.

Applications

Spatial Targeted Lipidomics is transforming research in diverse fields:

1. Oncology & Cancer Metabolism

Map lipid alterations in the tumor microenvironment vs. healthy tissue.

Visualize the penetration of lipid-based drug carriers (e.g., liposomes) into solid tumors.

2. Neuroscience

Map ganglioside and sulfatide distributions in brain structures (e.g., hippocampus, cortex).

Investigate demyelination or lipid peroxidation in neurodegenerative models (Alzheimer's, Parkinson's).

3. Pharmacology & ADME

Drug Distribution: Track the spatial distribution of lipophilic drugs.

Toxicity: Monitor lipid dysregulation in liver or kidney toxicity studies.

4. Dermatology

Analyze skin barrier lipids (ceramides, fatty acids) in varying strata of the epidermis.

Why choose?

• High-Definition Resolution: We offer resolutions ranging from 5 to 50 μm, with 10 μm approaching the single-cell level. You can select the resolution that best meets the specific needs of your project, ensuring optimal balance between detail and coverage.
• Semi-Quantitative Precision: We offer Quantitative MSI (qMSI) options using sprayed isotopically labeled internal standards to normalize data and allow for reliable comparison between samples.
• Customizable Panels: We build custom panels tailored to your research, covering Phospholipids, Sphingolipids, Sterols, and fatty acids.

FAQs

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Sample Preparation Guidelines

Proper sample preparation is critical for the success of Mass Spectrometry Imaging.

• Sample Type: Ship fresh-frozen tissue directly to preserve native lipid distribution. FFPE samples are less suitable for MSI because formalin fixation causes chemical crosslinking that alters biomolecules and reduces ionization efficiency. In addition, the dehydration and solvent steps during paraffin embedding can remove many small molecules (such as lipids and metabolites) and may disrupt their native spatial distribution.）
• Pre-Cut Tissue Sections (Optional):
  • If embedding is needed prior to sectioning, we recommended embedding with CMC (carboxymethyl cellulose) or gelatin.
  • Sections should be placed on ITO-coated conductive slides for shipping.
  • Prepare 4–6 sections per sample as backups to ensure sufficient material for analysis.
• Expert Consultation: Tissue type, size, and study goals can affect optimal preparation. Consult with our experts before preparing your samples to determine the best approach for your project.

Spatial targeted Lipidomics Case Study

Title: Functional mass spectrometry imaging maps phospholipase-A2 enzyme activity during osteoarthritis progression

Journal: Theranostics

Published: 2023

• Background
• Methods
• Results
• Conclusion
• References

Osteoarthritis represents a complex degenerative joint disorder where enzymatic activities play pivotal roles in disease progression, yet conventional analytical approaches have struggled to spatially resolve functional enzyme dynamics within heterogeneous articular tissues. The disconnect between protein abundance measurements and actual enzymatic function has particularly hindered understanding of phospholipase A2-mediated inflammatory pathways, despite their suspected importance in cartilage degradation. This methodological gap has limited researchers' ability to correlate specific enzymatic activities with histopathological features across different tissue zones, preventing precise mapping of metabolic changes during disease advancement.

Functional MALDI imaging mass spectrometry was used to spatially map enzyme activity. Tissue sections were incubated with an isotope-labeled substrate to capture enzymatic conversion in situ, followed by high-resolution imaging and quantitative processing to generate activity distribution maps.

The spatial overlap of enzymatic substrates and their corresponding products provides a functional readout of localized enzyme activity. The researchers applied MALDI mass spectrometry imaging to osteochondral sections to map regional variations in arachidonic acid-enriched phospholipids—specifically PI 38:4, PE 38:4, and PS 38:4—with molecular identities verified through nano-ESI tandem mass spectrometry. In non-osteoarthritic tissues, PI 38:4 predominantly accumulated in subchondral bone and marrow regions, showing minimal presence in cartilage zones. Osteoarthritic samples exhibited dramatically elevated PI 38:4 signals across all tissue compartments, with the most pronounced increases occurring in non-calcified cartilage and bone marrow (P < 0.0001) (Figure 2B-C). This spatial redistribution and quantitative amplification of AA-containing phospholipids directly correlates with disease state, highlighting region-specific lipid remodeling during osteoarthritis progression.

Figure 2. Multiple AA-containing phospholipids change in the osteochondral unit during OA progression.

The investigation successfully established that phospholipase A2 activity, particularly from the PLA2G2A isoform, demonstrates significant spatial elevation in both superficial and deep cartilage layers of osteoarthritic tissues compared to healthy counterparts, with these findings validated through both clinical samples and cytokine-induced experimental models. This spatially resolved enzymatic mapping revealed previously unrecognized patterns of lipid mediator production directly tied to tissue degradation zones, suggesting that targeted inhibition of specific PLA2 isoforms might offer therapeutic promise while the functional mass spectrometry imaging approach developed provides a transformative framework for studying enzyme dynamics in complex tissue environments beyond mere protein abundance measurements.

References

1. Fan, Xiwei et al. "Functional mass spectrometry imaging maps phospholipase-A2 enzyme activity during osteoarthritis progression." Theranostics vol. 13,13 4636-4649. 21 Aug. 2023, doi:10.7150/thno.86623
2. Hamilton, Brett R et al. "Mapping Enzyme Activity on Tissue by Functional Mass Spectrometry Imaging." Angewandte Chemie (International ed. in English) vol. 59,10 (2020): 3855-3858. doi:10.1002/anie.201911390