Towards comprehensive myelin assessment: combining multi-contrast quantitative MRI with spatially resolved lipodomics in the human brain

本项目的目的是建立基质辅助激光解吸/电离质谱成像（MALDI-MSI）作为一种工具，用于验证和开发基于定量MRI （qMRI）的髓磷脂标志物。髓磷脂是一种富含脂质的物质，对健康的大脑功能至关重要。髓磷脂在体内的研究依赖于qMRI。各种qMRI参数对髓鞘形成敏感，但特异性只能通过生物物理或数据驱动的建模方法来实现。这些模型的开发和验证都依赖于可靠的组织学“基本事实”髓磷脂图谱。目前，经典组织学或免疫组织化学染色强度经常被用作组织髓磷脂含量的近似值。在初步研究中，我们观察到不同的组织学方法在皮层中产生的髓鞘形成模式不同。这种差异可能与不同脑组织类型髓磷脂成分的差异有关。考虑到这种成分在qMRI的背景下是至关重要的，因为已知不同的脂质种类在其MR性质上是不同的。到目前为止，将脂质组学与qMRI联系起来的研究仅限于对幽灵进行的研究。在这里，我们想开发一种方法，可以捕获髓磷脂的生化组成在人类脑组织切片：MALDI-MSI。在先前收集的试点数据的基础上，我们将开发一个优化的工作流程，用于获取和分析MALDI-MSI数据。因此，我们将解决与MALDI-MSI脂质定量相关的一些方法学挑战：识别最重要峰下的脂质，通过应用内部标准校准脂质图，基于其脂质组成的脑组织聚类，以及脂质浓度图与qMRI图的共同注册。我们将解决以下研究问题：a)用于髓磷脂定量的最佳maldi - msi衍生指标是什么？b)组织脂质组成对不同qMR参数有何影响？d)能否从qMRI参数组合推断组织脂质组成？由于缺乏准确量化体内髓鞘形成的工具，脱髓鞘神经系统疾病的诊断、患者分层和治疗受到限制。目前可用的测量方法特异性较低，因此不容易在个体间或时间间进行比较。这限制了它们在临床研究中的可解释性，并阻碍了它们在个性化医疗中的适用性。qMRI数据和人脑组织切片的脂质组学的结合是满足对组织髓鞘形成特异性的可靠MR标记的需求的重要一步。本项目收集的数据将作为未来建立生物物理和数据驱动模型的独特参考材料。

The aim of this project is to establish matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) as a tool for the validation and development of quantitative MRI (qMRI) – based markers of myelin. Myelin is a lipid-rich substance that is crucial for healthy brain functioning. The study of myelin in vivo relies on qMRI. Various qMRI parameters are sensitive to myelination, but specificity can only be achieved by biophysical or data-driven modeling approaches. Both the development and validation of such models rely on reliable ‘ground-truth’ myelin maps from histology. Currently, staining intensity from classical histology or immunohistochemistry is frequently used as an approximation for tissue myelin content. In preliminary research, we have observed that different histological methods differ in the patterns of myelination they yield in the cortex. This difference is likely related to variability in myelin composition across different brain tissue types. Considering this composition is crucial in the context of qMRI, as different lipid species are known to differ in their MR properties. So far, relating lipidomics to qMRI has been limited to studies conducted with phantoms.Here, we want to exploit a method that can capture the biochemical composition of myelin in sections of human brain tissue: MALDI-MSI. Building upon previously collected pilot data, we will develop an optimized workflow for the acquisition and analysis of MALDI-MSI data. Hereby, we will tackle a number of methodological challenges related to lipid quantification with MALDI-MSI: identification of the lipids underlying most important peaks, calibration of the lipid maps through application of internal standards, clustering of the brain tissue based on its lipid composition, and the co-registration of lipid concentration maps with qMRI maps. We will address the following research questions: a) What is the best MALDI-MSI-derived metric for myelin quantification? b) How does the tissue lipid composition impact on different qMR parameters? d) Can tissue lipid composition be inferred from a combination of qMRI parameters? Diagnosis, patient stratification and treatment of demyelinating neurological diseases is constrained by a lack of tools to accurately quantify myelination in vivo. Currently available measures suffer from low specificity and can therefore not easily be compared across individuals or over time. This limits their interpretability in clinical studies and hinders their applicability to personalised medicine. The combination of qMRI data and lipidomics in human brain tissue sections is an important step towards meeting the need for reliable MR markers that are specific to tissue myelination. The data collected in this project will act as unique reference material for future efforts in establishing biophysical and data-driven models.