Multimodal mass spectrometry imaging of mouse and human liver

我们建议开发一种具有新型解吸源的多模式质谱成像管道， 数据集成，这将使同时映射生物分子丰度的3维生物 组织在高空间分辨率（微米至亚微米）和高速（>10毫秒/像素），在近原生 环境这将提供以前无法获得的关于细胞和组织组织的信息， 体内平衡和疾病在组织生理学水平上是如何交叉的。一个主要的挑战是执行多- 使用质谱成像的组学的缺点是（i）缺乏通用的电离方法，（ii）有限的样品 用于保持化学梯度的制备方案，（iii）低灵敏度，和（iv）有限的整合工具 大量的数据。我们的实验室正在开发系统的MS成像，以实现高灵敏度和高分辨率。 不同组织的分辨率分析。我们发现水基气体团簇离子束（H2O-GCIB） 在高能量下操作产生多个生物分子的电离增强（例如，代谢物、脂质和 肽/蛋白质片段），在1 µm横向分辨率下具有高灵敏度，无需标记或复杂的 样品制备。再加上独特的二次离子质谱（西姆斯）仪器， 通过低温样品处理，我们已经直接在细胞和组织中以接近天然状态（即， 冷冻水化），特征分辨率为1-10 µm。低浓度生物分子（例如心磷脂和 以前不可能在单个细胞中定位的代谢物）现在可以通过三维成像看到。 本地化而且，每像素信号足够多，我们可以使用自动数据分析来表征 1 µm2内的生物活性功能位点和单细胞中的感兴趣区域。我们进一步开发了数据 集成方法来组合来自相邻切片的联合收割机成像数据以创建多模型成像数据集。 我们建议开发一种用于生物分子的MS成像分析的管道，并阐明分子 使用多模态成像的组织异质性。为了支持多模态分析管道，我们将开发 综合数据分析平台。多组学的整合仍然具有挑战性，特别是空间定位 在单细胞水平上的多个生物分子。细胞内容物的直接可视化提供了以下信息 生物分子组成、相互作用和功能。这种生物分子网络是特定的 细胞在生理状态下的行为。尽管如此，全面掌握这些相互作用在细胞 级别尚未超出隔离方法。我们的努力将导致一个综合的多模式成像 平台，以召集每个图像形式的最佳特征，获取生物分子的完整图像， 网络的空间分辨率为1 µm。通过这种直接的可视化，我们将讨论新陈代谢如何与 源于代谢相关蛋白复合物和相分离的功能性生物标志物 在亚细胞水平上的无膜细胞器，以及这如何驱动不同的细胞死亡方式，包括 不同的细胞死亡模式。

We propose to develop a multimodal mass spectrometry imaging pipeline with novel desorption sources and data integration that will enable simultaneously mapping of biomolecule abundance in 3-dimensions in biological tissues at high spatial resolution (micron to submicron) and high speed (>10 ms/pixel) in a near-native environment. This would provide previously inaccessible information on cellular and tissue organization, and how homeostasis and disease intersect at the level of tissue physiology. A major challenge for performing multi- omics using mass spectrometry imaging has been the (i) lack of universal ionization methods, (ii) limited sample preparation protocols for preserving chemical gradients, (iii) low sensitivity, and (iv) limited tools for integration of large quantities of data. Our laboratories are developing systematic MS imaging for high sensitivity and high resolution analysis of diverse tissues. We discovered that water-based gas cluster ion beams (H2O-GCIB) operating at high energy yield ionization enhancements of multiple biomolecules (e.g., metabolites, lipids, and peptides/protein fragments) with high sensitivity at 1 µm lateral resolution and without labeling or complicated sample preparation. Coupled with unique Secondary Ion Mass Spectrometry (SIMS) instrumentation and cryogenic sample handling, we have imaged biomolecules directly in cells and tissues in a near-native state (i.e., frozen-hydration) with feature resolution of 1-10 µm. Low concentration biomolecules (e.g. cardiolipin and metabolites) that were impossible to localize in single cells previously are now visible with 3-dimensional localization. Moreover, the sufficient signal per pixel, we can use automated data analysis to characterize biologically active functional sites within 1 µm2 and areas of interest in single cells. We further developed data integration methods to combine imaging data from adjacent sections to create a multi-model imaging data sets. We propose to develop a pipeline for MS imaging analysis of biomolecules, and to elucidate molecular heterogeneity in tissues using multimodal imaging. To support the multi-modal analysis pipeline, we will develop an integrated data analysis platform. Integration of multiomics remains challenging, particularly spatially localize multiple biomolecules at single cell level. The direct visualization of cellular contents provides information on biomolecular composition, interactions and functions. This network of biomolecules is the driving force of specific behavior of cells in physiological states. Despite this, a comprehensive grasp of these interactions at cellular level has not moved beyond segregated methods. Our efforts will result in an integrated multimodal imaging platform to summon the best characteristics of each image form, acquiring a complete picture the biomolecular network at spatial resolution of 1 µm. With this direct visualization, we will address how metabolism links with functional biomarkers that stem from metabolism-associated protein complexes and phase-separated membrane-less organelles at the subcellular level, and how this drive different cell death modalities, including different modes of cell death.