Is Your Spatial Metabolomics Sample Ready?

Spatial Metabolomics is an advanced analytical technique that combines mass spectrometry imaging (MSI) with metabolomics to study the spatial distribution and dynamic changes of metabolites within biological samples. Unlike traditional metabolomics, which lacks spatial resolution, spatial metabolomics directly captures the spatial distribution of metabolites in biological samples, providing a more comprehensive understanding of metabolic processes in tissues and cells.

Advantages of Spatial Metabolomics?

Spatial metabolomics offers several unique advantages that make it an indispensable tool in modern life sciences research. By leveraging high-resolution imaging technology, it reveals metabolic heterogeneity across distinct cell or tissue regions, providing critical insights into functional complexity. A key strength lies in its non-destructive in situ detection, enabling metabolite analysis directly within intact biological tissues, thereby preserving spatial information—a feature invaluable for studying rare or fragile samples. Beyond static spatial mapping, this technology integrates a temporal dimension, tracking dynamic metabolic changes during processes like disease progression or environmental adaptation, bridging the gap between spatial and functional dynamics. Furthermore, its multi-omics integration capability allows seamless combination with genomics, transcriptomics, and proteomics data, constructing multi-layered biological networks to unravel regulatory mechanisms. These strengths fuel cross-disciplinary applications: in medicine, it accelerates biomarker discovery and drug development; in agriculture, it enhances crop resilience strategies; and in ecology, it deciphers organism-environment metabolic interactions. Together, these advantages position spatial metabolomics as a cornerstone of precision medicine, sustainable agriculture, and ecological research, driving innovation across scientific frontiers.

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How to Prepare Your Sample for Spatial Metabolomics

Best Sample Types for Spatial Metabolomics

Spatial metabolomics is suitable for samples that can be prepared into thin sections without compromising the types, concentrations, or spatial distribution of metabolites due to non-experimental factors. Fresh tissues are the most suitable sample type, as they best preserve the original state of metabolites, ensuring the accuracy of detection results. Examples include tumor tissues from clinical surgeries, animal organs (e.g., liver, brain), and plant leaves. To prevent metabolite degradation or loss during preparation, samples are typically flash-frozen in liquid nitrogen or embedded in a suitable medium before being stored for spatial metabolomics analysis.

For embedding, CMC (carboxymethyl cellulose) or FSC22 blue embedding medium is recommended. Other embedding methods, such as OCT or paraffin embedding, result in lower metabolite detection rates, while gelatin is not ideal for section adhesion and is generally not recommended. To prepare the embedding solution, a 2%-4% CMC solution is commonly used. At room temperature, CMC forms a thick, transparent, and flowable solution.

Sample Embedding Procedure:

1) Add 1 mL of the embedding medium (avoiding bubbles) to the embedding mold, spreading it evenly.

2) Place the tissue sample into the embedding medium, adjusting its position carefully (the orientation of the tissue in the mold affects the subsequent sectioning plane).

3) Cover the tissue completely with additional embedding medium.

4) Freeze the mold on dry ice or in a -80°C freezer until the embedding medium turns completely white, indicating solidification.

5) Store the embedded samples at -80°C and transport them on dry ice.

Cryosectioning Process

Prioritize maintaining tissue integrity, and select the largest cross-sectional area for sectioning to ensure comprehensive spatial analysis.

1) Sample Preparation: Remove the tissue from the -80°C freezer and allow it to equilibrate at -20°C for 30 minutes.

2) Mounting the Sample: Fix the tissue onto the sample holder using a small amount of CMC solution to ensure stability.

3) Sectioning: Adjust the sample orientation and set the section thickness on the cryostat. Rotate the handwheel to cut sections, placing them onto pre-cooled ITO (indium tin oxide) glass slides.

4) Section Transfer: Gently press the back of the slide against your hand to slightly thaw the tissue section, ensuring it adheres firmly to the slide. Evaporate any remaining moisture by rubbing the back of the slide.

5) Storage: Store the slides containing tissue sections in slide boxes at -80°C until further analysis.

Spatial Metabolomics Sample Sectioning

Data Acquisition in Spatial Metabolomics

1) Matrix Coating: Use a TM-Sprayer to evenly coat the tissue sections on ITO slides with DHB (2,5-dihydroxybenzoic acid) matrix solution.

2) Mass Spectrometry Imaging: Place the coated slides on the MALDI target plate. Using DataImaging (Bruker) software, define the tissue region of interest and set the imaging resolution to 50 µm (i.e., each pixel represents a 50 µm x 50 µm area). The imaging range is typically set between 50-1300 Da.

3) Laser Scanning: The laser beam scans the tissue region, ionizing the metabolites in the presence of the matrix. The released ions are then detected by the mass spectrometer, generating raw data containing mass-to-charge ratio (m/z) and intensity information for each pixel.

4) Data Processing: Import the raw data into SCiLS Lab software for smoothing and root mean square (RMS) normalization. This process converts the data into spatial heatmaps, visualizing the relative intensity of different m/z values across the tissue.

Spatial Metabolomics Data Acquisition Process

Read more

• Spatial Metabolomics: Transforming Biomedical and Agricultural Research
• Spatial Metabolomics Explained: How It Works and Its Role in Cancer Research
• MALDI, DESI, or SIMS? How to Choose the Best MSI Techniques for Spatial Metabolomics
• How to Prepare Samples for Spatial Metabolomics: The Essential Guide You Need
• Unlocking Precision in Spatial Metabolomics: Essential Detection Parameters for Cutting-Edge Research