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学成像管道 数据集成将能够同时绘制生物分子丰度的三维生物学图 高空间分辨率(微米到亚微米)和高速(&gt；10毫秒/像素)的组织 环境。这将提供以前无法获取的关于细胞和组织组织的信息，以及 动态平衡和疾病是如何在组织生理学水平上相交的。执行多任务的主要挑战是 使用质谱学成像的组学一直存在(I)缺乏通用的电离方法，(Ii)样本有限 保存化学梯度的制备方案；(3)低灵敏度；(4)有限的积分工具 海量数据。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性能 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) 在高能量产额下工作，增强多种生物分子(例如代谢物、脂类和 多肽/蛋白质片段)具有高灵敏度，横向分辨率为1微米，无需标记或复杂 样品制备。加上独特的二次离子质谱仪(SIMS)仪器和 在低温样品处理方面，我们已经在接近自然状态的细胞和组织中直接成像生物分子(即， 冷冻水化)，具有1-10微米的特征分辨率。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)现在可以用三维 本地化。而且，每个像素足够的信号，我们可以使用自动化的数据分析来表征 1微米范围内的生物活性功能部位和单个细胞的感兴趣区域。我们进一步开发了数据 综合方法结合来自相邻断面的成像数据来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成像分析的流水线，并阐明分子 使用多模式成像技术研究组织的异质性。为了支持多模式分析管道，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，特别是在空间上的局部化 单细胞水平的多个生物分子。蜂窝内容的直接可视化提供了关于 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，要全面掌握这些相互作用，请参阅Cell. 级别并未超出隔离方法的范围。我们的努力将导致一个集成的多模式成像 平台集合了每一种图像形式的最佳特征，获取了生物分子的完整画面 网络的空间分辨率为1微米。通过这种直接的可视化，我们将解决新陈代谢如何与 源于代谢相关蛋白复合体和相分离的功能生物标记物 亚细胞水平的无膜细胞器，以及这如何驱动不同的细胞死亡模式，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.