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.

该研究项目的目的是在质谱成像 (MSI) 中电离偏差的测量和理解方面做出重大改变。 MSI 是一项重要的新兴技术，能够绘制数千种分子的图谱，包括代谢物和药物，并在质谱仪器中检测为离子。 MSI 作为一套模式，可用于分析几乎任何样本类型中的几乎任何分子，因此有可能彻底改变我们评估生命系统的方式。癌症等疾病涉及身体自然过程（包括细胞代谢）的破坏。因此，MSI 通过绘制每个图像像素中的数千种代谢物，可以提供有关不同癌症如何生长和进化的强大见解，帮助确定新的治疗靶点。尽管有这样的承诺，MSI 在检测到的离子信号中仍然存在未知的偏差。这些可能会导致误导性的观察，并带来潜在的代价高昂的影响。电离偏差或基质效应涵盖了一系列现象，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内存在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了 5 倍，则显然存在重大障碍。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出现。这对应于每幅图像明显超过 1,000,000 个离子测量，每个测量都有不同的（未知）电离偏差。因此，极大地限制了定量的机会并提供了内源代谢物浓度的错误印象。用于表征电离偏差或量化 MSI 中内源代谢物浓度的典型方法最多仅研究几个分子。目前尚无允许对 MSI 中的电离偏差进行广义研究和校正的现有方法。此外，尚未开发出标准样本来评估这些现象，因此对 MSI 模式之间的这些偏差缺乏了解。该项目旨在为 MSI 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压行为的标准样品，将采用严格的经验方法。将选择一组分子： 它们与关键病理学的相关性，例如，癌症代谢；物理化学特性；与质谱成像领域的相关性。这些样本将用于表征多种质谱成像模式（包括 MALDI 和 DESI MSI）检测中的偏差。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。将采用多变量和机器学习方法来评估相关系统的质谱和物理化学特性的贡献和关联。