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: 10687346
• 负责人: Brent R Stockwell
• 金额: $ 60万
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10687346
• 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; 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; mass spectrometric imaging; 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.

我们建议使用新的解吸源开发多模式质谱成像管道和 数据集成将同时在生物学中同时映射生物分子抽象 高空间分辨率的组织（微米至亚微分辨率）和高速（> 10 ms/像素） 环境。这将提供先前有关细胞和组织组织的无法访问的信息，以及 稳态和疾病如何在组织生理水平上相交。执行多的主要挑战 使用质谱成像的OMICS是（i）缺乏通用电离方法，（ii）有限的样本 保存化学梯度，（iii）低灵敏度和（iv）有限的集成工具的准备协议 大量数据。我们的实验室正在开发系统的MS成像，以提高灵敏度和高度 多样性组织的分辨率分析。我们发现水基气体簇离子束（H2O-GCIB） 在高能量产量的高产量电离增强（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，而无标记或复杂 样品制备。结合独特的二次离子质谱法（SIMS）仪器和 低温样品处理，我们直接在细胞和组织中成像生物分子（即 冷冻水合）的特征分辨率为1-10 µm。低浓度生物分子（例如心磷脂和 代谢物）现在不可能在以前的单个细胞中进行定位 本土化。此外，每个像素的足够信号，我们可以使用自动数据分析来表征 在1 µm2之内的生物活性功能部位以及单个细胞中感兴趣的区域。我们进一步开发了数据 集成方法结合来​​自相邻部分的成像数据以创建多模型成像数据集。 我们建议开发一条用于生物分子的MS成像分析的管道，并阐明分子 使用多模式成像在组织中的异质性。为了支持多模式分析管道，我们将开发 一个集成的数据分析平台。多组学的整合仍然受到挑战，尤其是空间本地化 单细胞水平的多种生物分子。蜂窝内容的直接可视化提供了有关 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驱动力 细胞在物理状态下的行为。尽管如此，这些相互作用在细胞上的全面抓地力 级别尚未超越隔离方法。我们的努力将导致集成的多模式成像 召唤每种图像形式的最佳特征的平台，以仿生的形式获得完整的图像 空间分辨率为1 µm的网络。通过这种直接可视化，我们将解决新陈代谢如何与 功能性生物标志物源于代谢相关的蛋白质复合物和相位分离的生物标志物 亚细胞水平的无膜细胞器，以及这种驱动不同的细胞死亡方式，包括 不同的细胞死亡模式。