Multimodal mass spectrometry imaging of mouse and human liver

小鼠和人类肝脏的多模态质谱成像

• 批准号: 10118811
• 负责人: Brent R Stockwell
• 金额: $ 30万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-10 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10118811
• 关键词: 3-Dimensional; Active Sites; Address; Age; Algorithms; Apoptosis; Area; Atlases; Biochemical; Biological; Biological Markers; Biopsy; Blood; Brain; Cardiolipins; Cell Death; Cells; Characteristics; Chemicals; Chemistry; Computer Vision Systems; Coupled; Cytometry; Data; Data Analyses; Data Set; Development; Disease; Electrospray Ionization; Environment; Eosine Yellowish; Freezing; Gases; Genetic Transcription; Health; Heart; Heterogeneity; Homeostasis; Human; Hydration status; Image; Immunohistochemistry; Individual; Ions; Kidney; Knowledge; Label; Laboratories; Lateral; Link; Lipids; Liver; Liver Fibrosis; Liver diseases; Machine Learning; Mass Spectrum Analysis; Membrane; Messenger RNA; Metabolic Marker; Metabolism; Methods; Modality; Modeling; Modification; Molecular; Morphology; Multimodal Imaging; Mus; Optics; Organelles; Peptides; Pharmaceutical Preparations; Phase; Phenotype; Physiological; Physiology; Preparation; Primary carcinoma of the liver cells; Protein Fragment; Protocols documentation; Resolution; Sampling; Signal Transduction; Site; Source; Spatial Distribution; Spectrometry, Mass, Electrospray Ionization; Spectrometry, Mass, Secondary Ion; Speed; Technology; Time; Tissue imaging; Tissues; Transcript; Visualization; Water; analysis pipeline; base; cell behavior; cell type; cryogenics; data analysis pipeline; data integration; data mining; data visualization; driving force; experimental study; grasp; high resolution imaging; human tissue; image reconstruction; imaging platform; improved; insight; instrumentation; interest; ionization; ionization technique; molecular imaging; multimodality; multiple omics; novel; preservation; protein complex; reconstruction; single-cell RNA sequencing; stem; submicron; tool; tumorigenesis

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.

我们建议开发一种具有新型解吸源的多模态质谱成像管道 数据集成，将能够同时绘制生物分子三维生物分子丰度图 组织以高空间分辨率（微米到亚微米）和高速（> 10 毫秒/像素）在近乎天然的 环境。这将提供以前无法获得的有关细胞和组织组织的信息，并且 稳态和疾病如何在组织生理学水平上交叉。执行多任务的一个主要挑战 使用质谱成像的组学一直存在以下问题：(i) 缺乏通用的电离方法，(ii) 样本有限 用于保留化学梯度的制备方案，(iii) 低灵敏度，以及 (iv) 有限的整合工具 大量数据。我们的实验室正在开发系统性 MS 成像，以实现高灵敏度和高 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） 以高能量产率运行，增强多种生物分子（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，并且无需标记或复杂 样品制备。结合独特的二次离子质谱 (SIMS) 仪器和 通过低温样品处理，我们直接对接近天然状态的细胞和组织中的生物分子进行成像（即， 冷冻水合），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 3 维观察到 本土化。而且，每个像素的信号足够，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 集成方法将相邻部分的成像数据组合起来创建多模型成像数据集。 我们建议开发一个用于生物分子 MS 成像分析的管道，并阐明分子 使用多模态成像观察组织的异质性。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 单细胞水平的多个生物分子。细胞内容的直接可视化提供了以下信息 生物分子的组成、相互作用和功能。这种生物分子网络是特定的驱动力 细胞在生理状态下的行为。尽管如此，对细胞中这些相互作用的全面掌握 水平尚未超越隔离方法。我们的努力将带来集成的多模态成像 平台召唤每种图像形式的最佳特征，获取生物分子的完整图像 网络空间分辨率为 1 µm。通过这种直接的可视化，我们将解决新陈代谢如何与 源自代谢相关蛋白复合物和相分离的功能生物标志物 亚细胞水平的无膜细胞器，以及它如何驱动不同的细胞死亡方式，包括 不同的细胞死亡模式。