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: 10261546
• 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/10261546
• 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.

我们建议开发一个多模态质谱成像管道与新的解吸源和