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Updated: Feb 15, 2026
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Sample Preparation for Metabolic Profiling using MALDI Mass Spectrometry Imaging
Published on: December 22, 2020
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MALDI imaging: beyond classic diagnosis.
Laura Denise Manzanares-Meza1, Claudia Ivonne Gutiérrez-Román2, Oscar Medina-Contreras3
1Laboratorio de Inmunología y Proteómica, Hospital Infantil de México Federico Gómez, Mexico City, Mexico; Departmento de Biomedicina Molecular, Centro de Investigación y de Estudios Avanzados, Mexico City, Mexico.
Boletin Medico Del Hospital Infantil De Mexico
|February 1, 2018
Summary
Matrix-assisted laser desorption ionization imaging mass spectrometry offers a novel approach for analyzing complex biological tissues. This technique maps molecules within tissue sections, aiding cancer diagnosis, prognosis, and therapy selection.
Area of Science:
- Biochemistry
- Analytical Chemistry
- Molecular Imaging
Background:
- Mass spectrometry has been extensively developed for imaging applications over decades.
- Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) is an emerging technology for analyzing biological samples.
Purpose of the Study:
- To highlight MALDI-IMS as an effective tool for molecular studies of complex biological samples.
- To demonstrate the utility of MALDI-IMS in correlating molecular data with histological and clinical features.
- To showcase its potential in discovering cancer biomarkers and guiding therapeutic decisions.
Main Methods:
- Utilizing matrix-assisted laser desorption ionization (MALDI) for imaging mass spectrometry.
- Analyzing intact tissue sections to preserve histological features.
- Generating spatial distribution maps of multiple analytes.
Main Results:
- MALDI-IMS allows for the assessment of spatial molecular arrangements without target-specific reagents.
- Distribution maps of analytes can be correlated with intact histological features.
- The technique facilitates the discovery of diagnostic and prognostic markers for various cancer types.
Conclusions:
- MALDI-IMS is a powerful tool for molecular pathology and biomarker discovery.
- This technology enables a deeper understanding of cancer biology and personalized treatment strategies.
- Preservation of tissue architecture alongside molecular data is a key advantage.
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