Molecular and cellular imaging of bone biopsies using AI augmented deep UV Raman microscopy

使用 AI 增强深紫外拉曼显微镜对骨活检进行分子和细胞成像

• 批准号: 10657760
• 负责人: Mikhail Y. Berezin
• 金额: $ 19.92万
• 依托单位: TEXAS ENGINEERING EXPERIMENT STATION
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10657760
• 关键词: Acceleration; Address; Algorithms; Animal Model; Binding Proteins; Biological; Biological Assay; Biopsy; Bone Tissue; Bone neoplasms; Breast Cancer cell line; Cancer Biology; Cancer Detection; Cancer Diagnostics; Chemicals; Clinical; Computer software; Data; Data Analyses; Detection; Development; Dimensions; Early Diagnosis; Electronics; Ensure; Frequencies; Goals; Image; Image Analysis; Individual; Knowledge; Label; Legal patent; Lesion; Libraries; Machine Learning; Malignant Neoplasms; Metastatic Neoplasm to the Bone; Methods; Microscope; Microscopic; Microscopy; Molecular; Molecular Structure; Optics; Pathology; Pathway interactions; Performance; Pharmaceutical Preparations; Phase; Process; Protein Conformation; Raman Spectrum Analysis; Research; Research Project Grants; Research Proposals; Resolution; Risk Assessment; Screening for cancer; Sensitivity and Specificity; Signal Transduction; Specificity; Speed; Techniques; Technology; Time; Tissue Sample; Tissues; Variant; accurate diagnostics; bone; bone imaging; cancer imaging; cancer initiation; cancer risk; cellular imaging; clinical diagnostics; commercialization; cost; data acquisition; deep field survey; deep learning; deep learning algorithm; detection sensitivity; diagnostic tool; drug discovery; follow-up; imaging modality; imaging platform; imaging system; improved; instrument; machine learning algorithm; molecular imaging; novel; novel diagnostics; novel strategies; optical imaging; practical application; pre-clinical; prevent; prognostic value; programs; protein structure; prototype; research study; response; screening; spectroscopic imaging; success; tool; tumor; tumor progression

An exploratory research project will develop deep-UV Raman microscopic hyperspectral imaging for molecular and/or cellular analysis of biological tissues with a goal of the early detection, improved screening, and clinical diagnostics of cancer. Raman microscopy is often used in cancer biology to identify occurring chemical changes; however, the sensitivity and specificity of detection remain to be a challenge. This gap of fundamental knowledge on how to improve the information context of such images will be addressed by utilizing deep UV excitation, which, through resonance excitation of specific molecules will enhance specificity of molecular detection and improve the sensitivity by enhancing the signal against the background. To further improve the image-based analysis and screening, a novel hyperspectral image analysis platform will be developed. The proposed research program fills the technology gaps by developing an instrument, capable of performing Raman imaging at least 100 times faster, acquire new information through assessing low-frequency Raman modes, while reducing the cost and the footprint to accelerate the wide-spread availability of the instrument. The new imaging system augmented with novel hyperspectral imaging algorithms to handle multidimensional imaging data will be applied to advance a challenging biopsy of bone tumors, one of the most devastating consequences of many cancers with the goal to achieve 95% specificity. In Aim 1, a novel, patent-pending, wide-field deep UV hyperspectral Raman imaging platform will be optimized for cancer tissue samples. A working prototype will be built, and its performance will be experimentally characterized. In Aim 2, a data analysis platform with machine and deep learning algorithms for pathology of bone tissue will be developed. Advanced imaging algorithms that take into account many small changes in addition to a traditional analysis of Raman spectra will be used. Machine learning and deep learning techniques will be developed to automatically determine abnormalities beyond current yes/no tumor paradigm. In Aim 3, the developed platform will be validated as a novel analysis strategy. Research will focus on distinguishing tumors in the animal model of metastatic bone cancer and developing a set of optical markers to enable rapid identification of tumors. The proposed strategy offers a novel enabling technology to elucidate basic mechanisms underlying cancer initiation and progression and will facilitate early cancer detection, screening, and/or cancer risk assessment, by differentiating, evaluating and/or observing cancer stages and progression. The overall approach targets the wide spread of the technology, its relatively low-cost and seamless transition to clinical setting. The R33 phase will improve the sensitivity of detection and identify the pathways toward commercialization. The research study will also provide a roadmap to develop a new advanced approach for studying a variety of bone-related tumors and identify novel preclinical and clinical assays.

一个探索性的研究项目将开发深紫外拉曼显微高光谱成像的分子 和/或生物组织的细胞分析，目的是早期检测、改进筛选和临床诊断。 癌症的诊断拉曼显微镜常用于癌症生物学，以识别发生的化学变化; 然而，检测的灵敏度和特异性仍然是一个挑战。这种基础知识的差距 关于如何改善这种图像的信息背景将通过利用深UV激发来解决， 其通过特定分子的共振激发将增强分子检测的特异性， 通过增强背景信号来提高灵敏度。为了进一步改善基于图像的 通过分析和筛选，将开发一个新的高光谱图像分析平台。拟议研究 该计划通过开发一种仪器填补了技术空白，该仪器至少能够进行拉曼成像 速度提高100倍，通过评估低频拉曼模式获取新信息，同时减少 成本和占地面积，以加快仪器的广泛可用性。新的成像系统 将应用新的高光谱成像算法来处理多维成像数据 骨肿瘤是许多癌症最具破坏性的后果之一， 目标是达到95%的特异性。在目标1中，一种新型的、正在申请专利的、宽视场深紫外高光谱 拉曼成像平台将针对癌症组织样本进行优化。将建立一个工作原型，其 性能将通过实验表征。在Aim 2中，使用机器和深度的数据分析平台 将开发用于骨组织病理学的学习算法。先进的成像算法， 考虑到除了传统的拉曼光谱分析之外，还将使用许多小的变化。机器学习 深度学习技术将被开发，以自动确定当前是/否之外的异常情况。 肿瘤范例在目标3中，开发的平台将被验证为一种新的分析策略。研究将 重点是在转移性骨癌动物模型中区分肿瘤，并开发一套光学 标记物，以便能够快速识别肿瘤。拟议的战略提供了一种新的使能技术， 阐明癌症发生和发展的基本机制，并将促进早期癌症检测， 通过区分、评估和/或观察癌症阶段进行筛查和/或癌症风险评估，以及 进展总体方法的目标是广泛传播该技术，其相对低成本和无缝 过渡到临床环境。R33阶段将提高检测的灵敏度，并确定途径 走向商业化。该研究还将为开发新的先进方法提供路线图 用于研究各种骨相关肿瘤，并确定新的临床前和临床试验。