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.

一个探索性研究项目将开发分子的深紫外拉曼显微高光谱成像 和/或生物组织的细胞分析，目的是早期检测、改进筛查和临床 癌症的诊断。拉曼显微镜经常用于癌症生物学来识别发生的化学变化； 然而，检测的敏感性和特异性仍然是一个挑战。这种基础知识的差距 将通过利用深紫外激发来解决如何改善此类图像的信息背景， 通过特定分子的共振激发，增强分子检测的特异性， 通过增强背景信号来提高灵敏度。进一步完善基于图像的 分析和筛选，将开发一种新型的高光谱图像分析平台。拟议的研究 该计划通过开发至少能够执行拉曼成像的仪器来填补技术空白 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，以加速仪器的广泛可用性。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 推进骨肿瘤的挑战性活检，骨肿瘤是许多癌症最具破坏性的后果之一 目标是实现 95% 的特异性。在目标 1 中，一种新颖的、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。将构建一个工作原型，其 性能将通过实验来表征。在Aim 2中，一个具有机器和深度的数据分析平台 将开发骨组织病理学的学习算法。先进的成像算法，考虑到 除了使用拉曼光谱的传统分析之外，还考虑了许多小的变化。机器学习 将开发深度学习技术来自动确定超出当前是/否范围的异常情况 肿瘤范式。在目标 3 中，开发的平台将被验证为一种新颖的分析策略。研究将 专注于区分转移性骨癌动物模型中的肿瘤并开发一套光学 标记物能够快速识别肿瘤。所提出的策略提供了一种新颖的使能技术 阐明癌症发生和进展的基本机制，并将促进早期癌症检测， 通过区分、评估和/或观察癌症阶段和/或癌症风险评估 进展。总体方法的目标是技术的广泛传播，其相对低成本和无缝 过渡到临床环境。 R33 相将提高检测的灵敏度并识别途径 走向商业化。该研究还将提供开发新的先进方法的路线图 用于研究各种骨相关肿瘤并确定新的临床前和临床检测方法。