Valentina Torres


Health Science, '21


torrespellens.v@husky.neu.edu


Website


Metabolite Quantification from Cancer Tissues using Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging


Mentor: Nathalie Agar, Ph.D. (Brigham and Women’s Hospital)

Mass spectrometry imaging (MSI) is a promising analytical tool due to its high sensitivity and specificity in addition to providing the spatial distribution of a wide range of biomolecules such as metabolites, lipids, peptides, and xenobiotics. The most common MSI technique is matrix-assisted laser desorption/ionization (MALDI) which has been an emerging method in the last decade as a powerful technique in clinically-related applications such as: mapping the degree of drug penetration through the blood-brain-barrier, evaluation of tumor margins, tumor heterogeneity, and drug quantitation. Metabolite distributions and abundance levels can aid in the evaluation of tumor type and could improve rapid screening methods during tumor resection surgeries. For example, elevated levels of AMP and adenosine are observed with the expression of CD73 on the surface of tumor cells which could potentially be used as a marker for tumor localization. This proposed research plan at the Agar Surgical Molecular Imaging Lab is to establish a large-scale metabolite quantification library (including ~600 common metabolite standards) to establish a screening method for potential biomarkers in brain tumor tissues. This approach will use tissue microarray molds with varying concentrations of cancer-related metabolites and undergo the same MALDI preparations and analysis as the biological tissues (Figure 1). The intensity response from the standards will allow to establish a calibration curve for quantification from the tissues. The vision is to be able to quantify cancer metabolism by focusing on specific metabolites associated with cancer in a single tissue section. The success of this project could have result in clinical applications to potentially quantify the underlying processes of cancer metabolism, resulting in a deeper and more holistic understanding. This new knowledge could open the doors to new ways of assessing and improving patient outcomes.


Figure 1. Workflow of metabolite quantification from tumor specimens by MALDI-MSI. Source:

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