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| Funder | European Commission |
|---|---|
| Recipient Organization | Universitat Rovira I Virgili |
| Country | Spain |
| Start Date | Jun 01, 2022 |
| End Date | May 31, 2024 |
| Duration | 730 days |
| Number of Grantees | 2 |
| Roles | Coordinator; Associated Partner |
| Data Source | European Commission |
| Grant ID | 101067953 |
Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry imaging (MSI) is increasingly used for clinical application.
Based on untargeted, spatial chemical information, tissue samples can be analysed with superior density of information compared to conventional histology techniques.
However, MALDI suffers from several significant limitations like strong ion suppression effects, inherently low ion yields limited to a few classes of polar to mid-polar analytes, and impeded detection of low-weight compounds. This crucially hampers a comprehensive analysis of complex biological samples.
Strategies for post ionisation (PI) – most prominently MALDI-2 – significantly increase the ion yields for numerous mid-polar and polar compounds.
Surface-assisted LDI (SALDI) uses photoactive surfaces, which feature specific ablation and ionisation that can majorly extend the range of detectable compounds and can be prepared in a superiorly homogeneous manner for SALDI-MSI.The proposed study aims to develop a novel means for extensive metabolome and lipidome analysis by SALDI-PI-MSI to overcome limitations of MALDI.
Therefore, a new PI strategy coined Single-Photon Induced Chemical Ionisation (SPICI) will be combined with functionalised Au layers for comprehensive tissue characterisation of an extended polarity range with high mass resolution.
The versatile software package rMSI will provides the computational means for the analysis of the complex measured data sets.
We will apply our approach to human samples of bladder cancer tumour and achieve detailed monitoring of physiological processes in the cancerous tissue in unpreceded analytical depth. Newly identified biomarkers will help the urgent need for prognostic and diagnostic means for cancer.
This comprehensive approach can be a massive generator of hypotheses for disease characterisation and foster the implementation of MSI into further fields of medical research and help to mature MSI in a multidisciplinary fashion.
Universitat Rovira I Virgili; Aliri France Sas
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