Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology

计量学解决多模态质谱成像中的离子抑制问题及其在肿瘤学中的应用

• 批准号: MR/X03657X/1
• 负责人: Rory Steven
• 金额: $ 146.75万
• 依托单位: National Physical Laboratory
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX03657X%2F1
• 关键词: Metrology; address; ion; suppression; multimodal

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.

本研究项目的目的是在质谱成像（MSI）电离偏差的测量和理解提供一个步骤的变化。MSI是一项重要的新兴技术，它可以绘制数以千计的分子，包括代谢物和药物，在质谱仪中作为离子检测。MSI作为一套模式，可以用于分析几乎任何类型样品中的几乎任何分子，因此有可能彻底改变我们评估生命系统的方式。癌症等疾病涉及人体自然过程的破坏，包括细胞代谢。MSI在每个图像像素中绘制数千种代谢物，因此可以为不同癌症的生长和进化提供强有力的见解，帮助确定新的治疗靶点。尽管有这样的前景，但微si在检测到的离子信号中存在未知的偏差。这些可能导致误导性的观察结果，并可能带来代价高昂的后果。电离偏倚或基质效应包括一系列现象，这些现象可能导致检测到的离子数量与样品中相关分子的原始数量之间的未知关系。这些偏差在生物样品的浓度范围内也可能是非线性的。因此，如果MSI从业者不能确定，例如，检测到的离子强度增加5倍反映了原始样品代谢物浓度增加5倍，那么很明显存在一个重大障碍。此外，对于数据集的每个像素中的每个离子，这种现象将以不同的未知程度存在。这对应于每张图像超过1,000,000个离子测量，每个都有不同的（未知的）电离偏置。因此，大大限制了定量的机会，并提供了内源性代谢物浓度的错误印象。表征电离偏倚或定量内源性代谢物浓度的典型方法在MSI中最多只能研究几个分子。目前，还没有现有的方法可以对MSI中的电离偏差进行广泛的研究和校正。此外，没有开发标准样本来评估这些现象，因此缺乏对MSI模式之间这些偏差的理解。本项目旨在为MSI中电离偏差的研究和校正奠定坚实的基础。通过开发适合研究电离偏置行为的标准样品，将采用严格的经验方法。一组分子将被选择：它们与关键病理的相关性，如癌症代谢；物理化学性质;与质谱成像领域相关。这些样品将用于表征多种质谱成像模式（包括MALDI和DESI MSI）的检测偏差。将产生描述这些电离行为的模型，并开发用于评估和转换这些模型的计算方法。将采用多元和机器学习方法来评估所讨论系统的质谱和物理化学性质的贡献和关联。