Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology
Lead Research Organisation:
National Physical Laboratory
Department Name: Analytical Science Division
Abstract
The aim of this research project is to provide a step change in the measurement and understanding of ionisation biases in mass spectrometry imaging (MSI). MSI is an important emerging technology which enables the mapping of thousands of molecules, including metabolites and drugs, detected as ions in the mass spectrometry instrument. MSI, as a suite of modalities, can be employed to analyse almost any molecule in almost any sample type and so has the potential to revolutionise how we evaluate living systems. Diseases such as cancer involve the disruption of the bodies' natural processes including cellular metabolism. MSI, in mapping thousands of metabolites in every image pixel, can therefore provide powerful insights into how different cancers grow and evolve, helping identify new targets for treatment. Despite this promise, MSI suffers from unknown biases in detected ion signal. These can lead to misleading observations with potentially costly implications.
Ionisation biases, or matrix effects, encompass a range of phenomena which can lead to unknown relationships between the number of detected ions and the original number of associated molecules in the sample. These biases may also be non-linear across concentration ranges present within biological samples. Therefore, if the MSI practitioner cannot be certain that, for example, a 5 fold increase in detected ion intensity reflects a 5 fold increase in the original sample metabolite concentration, it is clear that a significant hurdle is present. Furthermore, this phenomenon will be present to varying unknow extents for every ion in every pixel of a dataset. This corresponds to significantly upwards of 1,000,000 ion measurements per image, each with different (unknown) ionisation bias. Therefore, drastically limiting opportunities for quantitation and providing erroneous impression of endogenous metabolite concentrations.
Typical approaches for characterising ionisation biases or quantifying endogenous metabolite concentration in MSI will only study a few molecules at most. Currently there are no existing methods allowing generalized study and correction of ionisation biases in MSI. Additionally, no standard samples have been developed to allow assessment of these phenomena and so there is a lack of understanding of these biases across between MSI modalities.
This project aims to produce a robust foundation for the study and correction of ionisation biases in MSI. A rigorous empirical approach will be pursued through the development of standard samples suitable for studying ionisation bias behaviours. A suite of molecules will be selected for: their relevance to critical pathologies e.g. cancer metabolism; physico-chemical properties; relevance to the mass spectrometry imaging field.
These samples will used to characterize the biases in detection across multiple mass spectrometry imaging modalities including MALDI and DESI MSI. Models describing these ionisation behaviours will be produced and computational approaches for evaluation and transformation of these models will be developed. Multivariate and machine learning approaches will be employed to evaluate the contribution and association of mass spectral and physico-chemical properties of the systems in question.
Ionisation biases, or matrix effects, encompass a range of phenomena which can lead to unknown relationships between the number of detected ions and the original number of associated molecules in the sample. These biases may also be non-linear across concentration ranges present within biological samples. Therefore, if the MSI practitioner cannot be certain that, for example, a 5 fold increase in detected ion intensity reflects a 5 fold increase in the original sample metabolite concentration, it is clear that a significant hurdle is present. Furthermore, this phenomenon will be present to varying unknow extents for every ion in every pixel of a dataset. This corresponds to significantly upwards of 1,000,000 ion measurements per image, each with different (unknown) ionisation bias. Therefore, drastically limiting opportunities for quantitation and providing erroneous impression of endogenous metabolite concentrations.
Typical approaches for characterising ionisation biases or quantifying endogenous metabolite concentration in MSI will only study a few molecules at most. Currently there are no existing methods allowing generalized study and correction of ionisation biases in MSI. Additionally, no standard samples have been developed to allow assessment of these phenomena and so there is a lack of understanding of these biases across between MSI modalities.
This project aims to produce a robust foundation for the study and correction of ionisation biases in MSI. A rigorous empirical approach will be pursued through the development of standard samples suitable for studying ionisation bias behaviours. A suite of molecules will be selected for: their relevance to critical pathologies e.g. cancer metabolism; physico-chemical properties; relevance to the mass spectrometry imaging field.
These samples will used to characterize the biases in detection across multiple mass spectrometry imaging modalities including MALDI and DESI MSI. Models describing these ionisation behaviours will be produced and computational approaches for evaluation and transformation of these models will be developed. Multivariate and machine learning approaches will be employed to evaluate the contribution and association of mass spectral and physico-chemical properties of the systems in question.
