Development of a high throughput system for molecular imaging of different cell types in mouse brain tissues

开发用于小鼠脑组织中不同细胞类型的分子成像的高通量系统

• 批准号: 10369883
• 负责人: GAURAV CHOPRA
• 金额: $ 151.14万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10369883
• 关键词: 3-Dimensional; Address; Anatomy; Atlases; Biological; Brain; Brain imaging; Cells; Censuses; Chemicals; Classification; Coupling; Data; Data Analyses; Data Sources; Data Storage and Retrieval; Development; Disease; Electrospray Ionization; Goals; Graph; Health; Hour; Human; Human BioMolecular Atlas Program; Image; Imaging Device; Imaging technology; Immunofluorescence Immunologic; Immunofluorescence Microscopy; Label; Link; Lipids; Maps; Mass Spectrum Analysis; Measurement; Methods; Microfluidics; Mining; Molecular; Molecular Profiling; Mus; Neuroglia; Neurons; Peptides; Proteins; Proteomics; Research; Resolution; Robotics; Sampling; Scanning; Sensitivity and Specificity; Signal Transduction; Spectrometry, Mass, Electrospray Ionization; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Speed; Structure; System; Techniques; Tissue imaging; Tissues; base; brain cell; brain tissue; cell type; data acquisition; data format; data integration; data mining; data sharing; deep learning; experimental study; high resolution imaging; high throughput technology; image registration; imaging approach; imaging modality; imaging platform; imaging system; improved; innovation; interest; ion mobility; ionization; ionization technique; large scale data; learning strategy; lipidomics; mass spectrometer; member; metabolomics; molecular imaging; nano; novel; tool; 3-Dimensional; Address; Anatomy; Atlases; Biological; Brain; Brain imaging; Cells; Censuses; Chemicals; Classification; Coupling; Data; Data Analyses; Data Sources; Data Storage and Retrieval; Development; Disease; Electrospray Ionization; Goals; Graph; Health; Hour; Human; Human BioMolecular Atlas Program; Image; Imaging Device; Imaging technology; Immunofluorescence Immunologic; Immunofluorescence Microscopy; Label; Link; Lipids; Maps; Mass Spectrum Analysis; Measurement; Methods; Microfluidics; Mining; Molecular; Molecular Profiling; Mus; Neuroglia; Neurons; Peptides; Proteins; Proteomics; Research; Resolution; Robotics; Sampling; Scanning; Sensitivity and Specificity; Signal Transduction; Spectrometry, Mass, Electrospray Ionization; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Speed; Structure; System; Techniques; Tissue imaging; Tissues; base; brain cell; brain tissue; cell type; data acquisition; data format; data integration; data mining; data sharing; deep learning; experimental study; high resolution imaging; high throughput technology; image registration; imaging approach; imaging modality; imaging platform; imaging system; improved; innovation; interest; ion mobility; ionization; ionization technique; large scale data; learning strategy; lipidomics; mass spectrometer; member; metabolomics; molecular imaging; nano; novel; tool

Development of a high throughput system for molecular imaging of different cell types in mouse brain tissues Mass spectrometry imaging (MSI) is a powerful tool for developing detailed molecular maps of biological tissues with high specificity and sensitivity. This label-free technique enables simultaneous imaging of multiple classes of molecules including lipids, metabolites, and proteins thereby advancing the understanding of tissue organization and function. In this project, we will advance brain cell census research by developing an innovative platform for the acquisition of a comprehensive spatially-resolved cell-specific atlas of lipids, metabolites, and proteins in mouse brain tissue. The platform will combine MSI with immunofluorescence imaging to place the detailed molecular maps into the spatial cellular context within the brain tissue, which will facilitate image registration to the common coordinate system. Furthermore, we will develop deep learning and data mining approaches to enable automated assignment of molecular signatures to different cell types and efficient data sharing and comparison with other techniques. This research will address the existing bottlenecks in the experimental throughout and molecular coverage of the current MSI technologies along with the limitations of data analysis tools. Furthermore, our approach will compensate for signal suppression during ionization also called ‘matrix effects’, which is particularly severe in imaging of brain tissue. Such matrix effects common to all the MSI modalities including commercial MALDI MSI instruments interfere with the accurate measurement of the spatial localization of molecules. To overcome these challenges, we will advance the capabilities of nanospray desorption electrospray ionization (nano-DESI) - an ambient ionization technique, which efficiently compensates for matrix effects and thereby enables accurate and sensitive imaging of chemical gradients for hundreds of metabolites and lipids in biological tissue sections. Nano-DESI MSI does not require sample pretreatment, has a sub-femtomole sensitivity, and high spatial resolution. The new nano-DESI MSI system will provide a 5-fold increase in the experimental throughput and improve the spatial resolution of protein imaging in whole tissue sections from ~80 µm to ~10 µm. Coupling nano-DESI MSI with a high- resolution ion mobility mass spectrometer will substantially enhance molecular coverage. Meanwhile, co- registration of MSI with immunofluorescence data will be used to generate comprehensive 3D molecular maps of the mouse brain tissue. Collectively, these efforts will establish a robust imaging platform, which will transform our ability to generate detailed molecular maps of different cell types in brain tissues.

开发高吞吐量系统，用于小鼠大脑中不同细胞类型的分子成像 组织 质谱成像（MSI）是开发生物学详细分子图的强大工具 具有高特异性和灵敏度的组织。这种无标签技术可以同时进行成像 多种类别的分子，包括脂质，代谢产物和蛋白质，从而推进 了解组织组织和功能。在这个项目中，我们将提高脑细胞人口普查 通过开发一个创新平台来获取综合空间分辨的创新平台 小鼠脑组织中脂质，代谢产物和蛋白质的细胞特异性地图集。该平台将结合MSI 通过免疫荧光成像将详细的分子图放入空间细胞环境 在脑组织中，这将促进对公共坐标系的图像注册。 此外，我们将开发深度学习和数据挖掘方法，以实现自动化任务 分子特征与不同细胞类型的分子特征以及有效的数据共享和与其他细胞的比较 技术。这项研究将解决整个实验中现有的瓶颈和 当前MSI技术的分子覆盖范围以及数据分析工具的局限性。 此外，我们的方法将补偿电离期间的信号抑制也称为'矩阵 效果”，这在脑组织的成像中尤为严重。所有MSI常见的矩阵效应 包括商业MALDI MSI仪器在内的方式进行了精确测量 分子的空间定位。为了克服这些挑战，我们将提高 纳米喷雾解动电喷雾电离（Nano -desi） - 一种环境电离技术，该技术该技术 有效地补偿基质效应，从而实现了化学物质的准确和敏感成像 生物组织切片中数百种代谢产物和脂质的梯度。 Nano-Desi MSI不需要 样品预处理，具有亚tomole敏感性和高空间分辨率。新的Nano-Desi MSI 系统将为实验吞吐量增加5倍，并改善 整个组织切片中的蛋白质成像从〜80 µm至〜10 µm。将纳米 - 戴西MSI与高级耦合 分辨率离子迁移率质谱仪将大大增强分子覆盖率。同时，共同 使用免疫荧光数据的MSI注册将用于生成全面的3D分子 小鼠脑组织的地图。总的来说，这些努力将建立一个强大的成像平台，该平台将 改变了我们在脑组织中生成不同细胞类型的详细分子图的能力。