Spatial Untargeted Lipidomics Service

What Is Spatial Untargeted Lipidomics?

Spatial Untargeted Lipidomics combines non-targeted lipidomics with in situ mass spectrometry imaging (MSI) to profile a wide range of lipid species while preserving their natural spatial distribution within tissues. Unlike traditional bulk lipidomics, which averages signals across homogenized samples, this approach captures region-specific lipid signatures at the organ, tissue, or cellular microdomain level. With no prior assumptions about which lipids to detect, it is ideal for exploring new mechanisms, discovering biomarkers, and understanding complex lipid metabolism.

Lipids are one of the most diverse and essential biomolecules in biology, including phospholipids, sphingolipids, glycerolipids, sterols, and more. They are key players in cell membrane integrity, energy storage, signaling pathways, and intercellular communication, influencing nearly every aspect of cellular function. By mapping lipids in their native tissue context, spatial untargeted lipidomics provides unique insights into disease mechanisms, drug responses, and metabolic regulation, making it a powerful tool for modern biomedical research.

Workflow

Instrument Platforms

Creative Proteomics integrates advanced MALDI-MSI platforms with a standardized spatial lipidomics workflow to deliver reliable, high-resolution, and discovery-driven lipid profiling directly within tissue sections.

Data Analysis & Bioinformatics

Our end-to-end bioinformatics workflow converts raw spatial lipidomics data into publication-ready insights:

• Spatial Feature Processing: Automated quality control, spectral alignment, and normalization to ensure data integrity across tissue regions.
• Confident Lipid Annotation: Multi-tier validation against curated lipid databases with adduct/isomer resolution support.
• Spatial Analytics: Region-specific quantification, multivariate statistics (PCA, clustering), spatial co-localization, and heterogeneity mapping.
• Biological Context Integration: Pathway enrichment, spatial correlation with histopathology (H&E overlay), and customizable visualization (heatmaps, 3D renderings).

All deliverables include interactive reports and structured datasets. Analysis strategies are collaboratively refined with your team to align precisely with research objectives—no black-box pipelines.

Applications

• Disease VS normal tissue comparison
• Tumor heterogeneity and microenvironment studies
• Neurodegenerative and metabolic disease research
• Drug- or therapy-induced lipid remodeling
• Discovery of spatially restricted lipid biomarkers

Why Choose Us

• Unbiased, hypothesis-free discovery

Comprehensive non-targeted lipid profiling without predefined targets, ideal for biomarker discovery and mechanism exploration.

• True spatial resolution

In situ analysis preserves native tissue architecture and reveals region-specific lipid heterogeneity.

• Advanced MSI technology

State-of-the-art MALDI-MSI platforms optimized for sensitive, broad-coverage lipid detection.

• Broad lipid class coverage

Simultaneous detection of phospholipids, sphingolipids, glycerolipids, and sterol lipids.

• Robust spatial data analysis

ROI-based comparisons, multivariate statistics, and high-quality spatial visualization.

• Seamless multi-omics integration

Fully compatible with histopathology and other spatial omics workflows for integrative insights.

FAQs

Learn about other Q&A.

Sample Submission Notes

• Sample type: We strongly recommend fresh frozen tissue samples, as they preserve native lipid profiles and spatial architecture with minimal degradation or artifacts.
• Recommended section thickness: 5–12 μm
• Storage and shipping: Dry ice shipment required
• Avoid: Formalin fixation, paraffin embedding, repeated freeze–thaw cycles

Contact our technical team before preparing or shipping samples. We provide study-specific protocols tailored to your tissue type, research goals, and platform requirements, ensuring your samples are optimized for high-quality spatial lipidomics.

Spatial Untargeted Lipidomics Case Study

Title: MALDI Mass Spectrometry Imaging Highlights Specific Metabolome and Lipidome Profiles in Salivary Gland Tumor Tissues

Journal: Metabolites

Published: 2022

• Background
• Methods
• Results
• Conclusion
• References

Salivary gland tumors present significant diagnostic challenges due to their histological heterogeneity and rarity (<5% of head and neck neoplasms). Current diagnostic approaches, particularly fine-needle aspiration biopsy, often lack specificity for accurate tumor classification. While cancer metabolism research has revealed characteristic metabolic reprogramming in tumors, conventional analytical techniques fail to preserve spatial molecular distribution information critical for understanding tumor heterogeneity. Mass spectrometry imaging (MSI) bridges this gap by enabling label-free, spatially resolved molecular mapping directly from tissue sections, offering a promising approach for improving salivary gland tumor diagnosis and characterization.

Spatial untargeted lipidomics was performed using MALDI imaging mass spectrometry. Tissue sections underwent standardized matrix application and dual-polarity (positive/negative mode) high-resolution imaging. Detected lipid features were annotated against LIPIDMAPS and HMDB databases, with spatial distribution maps and regional quantification delivered for all confidently identified lipids.

Figure 3 showed the molecular distribution of lipid species between tumor and healthy regions in parotid gland tissue sections from the patients. The color scale bar represents the percentage of maximum intensity and is adjusted for each ion image to show clear distribution. The data were normalized to the total ion count (TIC) of each individual spectrum. The results showed that glycerophospholipids (such as phosphatidylcholines (PCs) and phosphatidylethanolamines (PEs)) are significantly increased in tumor tissue of salivary gland cancer patients; sphingomyelins (SMs) and triglycerides (TGs) are significantly decreased in the tumor region; and lysophosphatidylcholine (LPC) 16:0 levels are reduced in the tumor, consistent with the metabolic characteristics of various cancers.

Figure 3. Molecular distribution of lipid species between the tumor and healthy regions of patient-derived parotid sections.

This research concludes that MALDI mass spectrometry imaging provides a powerful, label-free approach for characterizing the spatial metabolic landscape of salivary gland tumors, revealing distinctive lipidomic reprogramming with elevated glycerophospholipids and depleted sphingolipids in malignant regions. The technique's ability to maintain spatial context while identifying molecular signatures offers significant diagnostic potential, achieving 95% accuracy in tumor classification without requiring tissue homogenization. While validation in larger cohorts is necessary, this methodology represents a promising complement to conventional histopathology that could enhance diagnostic precision for challenging salivary gland lesions and potentially inform targeted therapeutic strategies based on tumor-specific metabolic vulnerabilities.

References

1. Sommella, Eduardo et al. "MALDI Mass Spectrometry Imaging Highlights Specific Metabolome and Lipidome Profiles in Salivary Gland Tumor Tissues." Metabolites vol. 12,6 530. 8 Jun. 2022, doi:10.3390/metabo12060530
2. Sládková, Katerina et al. "Laser desorption ionization of red phosphorus clusters and their use for mass calibration in time-of-flight mass spectrometry." Rapid communications in mass spectrometry : RCM vol. 23,19 (2009): 3114-8. doi:10.1002/rcm.4230
3. Denti, Vanna et al. "Reproducible Lipid Alterations in Patient-Derived Breast Cancer Xenograft FFPE Tissue Identified with MALDI MSI for Pre-Clinical and Clinical Application." Metabolites vol. 11,9 577. 26 Aug. 2021, doi:10.3390/metabo11090577
4. Ogretmen, Besim. "Sphingolipid metabolism in cancer signalling and therapy." Nature reviews. Cancer vol. 18,1 (2018): 33-50. doi:10.1038/nrc.2017.96