Related Experiment Video
Updated: Apr 18, 2026
Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples
Published on: June 29, 2022
A MALDI-Mass Spectrometry Imaging method applicable to different formalin-fixed paraffin-embedded human tissues
Gabriele De Sio1, Andrew James Smith, Manuel Galli
1Department of Health Sciences, Clinical Proteomics Unit, University Milan-Bicocca, Milan, Italy.
Recent advancements in Matrix Assisted Laser Desorption/Ionisation (MALDI) Mass Spectrometry Imaging (MSI) technology have enabled the analysis of formalin-fixed paraffin-embedded (FFPE) tissue samples, unlocking a wealth of new proteomic information and facilitating the possibility of performing studies with higher statistical power as well as multi-centric collaborations within the field of proteomics research. However, current methods used to analyse these specimens are often time-consuming and they need to be modified when applied to human tissues of different origin. Here we present a reproducible and time-effective method that could address these aforementioned issues and widen the applicability of this technology to a number of challenging tissue types. Additionally, tissue molecular images show high spatial resolution and a strong correlation with the morphological features, enabling the identification of tissue morphology using statistically derived visualisation, without any prior knowledge.
Recent advancements in Matrix Assisted Laser Desorption/Ionisation (MALDI) Mass Spectrometry Imaging (MSI) technology have enabled the analysis of formalin-fixed paraffin-embedded (FFPE) tissue samples, unlocking a wealth of new proteomic information and facilitating the possibility of performing studies with higher statistical power as well as multi-centric collaborations within the field of proteomics research. However, current methods used to analyse these specimens are often time-consuming and they need to be modified when applied to human tissues of different origin. Here we present a reproducible and time-effective method that could address these aforementioned issues and widen the applicability of this technology to a number of challenging tissue types. Additionally, tissue molecular images show high spatial resolution and a strong correlation with the morphological features, enabling the identification of tissue morphology using statistically derived visualisation, without any prior knowledge.
