Dynamic µOCT for cellular tissue phenotyping

用于细胞组织表型分析的动态 µOCT

• 批准号: 10653989
• 负责人: Oliver Jonas
• 金额: $ 60.69万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10653989
• 关键词: 3-Dimensional; ANXA5 gene; Algorithms; Animal Model; Antineoplastic Agents; Apoptosis; Apoptotic; Architecture; Area; Basic Science; Biological; Biological Sciences; Cancerous; Cell Death; Cell Line; Cell Maturation; Cell Proliferation; Cell physiology; Cells; Clinical; Clinical Sciences; Cytoskeleton; Data; Data Set; Devices; Diagnostic; Dimensions; Disease; Epithelium; Fluorescein-5-isothiocyanate; Fluorescence; Fluorescence Microscopy; Frequencies; Grant; Growth; Human; Image; Imaging technology; Immunohistochemistry; Implant; Individual; Label; Lateral; Learning; Light; Measures; Melanoma Cell; Metabolic; Metabolism; Methods; Microscopic; Microscopy; Modification; Molecular; Morphologic artifacts; Morphology; Motion; Movement; Mus; Nature; Necrosis; Optical Coherence Tomography; Optics; Organism; Organoids; Pathology; Patients; Pattern; Pharmaceutical Preparations; Phase; Phase-Contrast Microscopy; Phenotype; Pilot Projects; Process; Proliferating; Propidium Diiodide; Proxy; Research; Resolution; Sampling; Scanning; Series; Signal Transduction; Skin; Source; Speed; Stains; Techniques; Technology; Testing; Time; Tissue Model; Tissue imaging; Tissues; Training; Upper digestive tract structure; Visualization; analysis pipeline; animal tissue; cell motility; clinical diagnosis; clinically relevant; contrast imaging; data mining; diagnostic accuracy; diagnostic algorithm; disease diagnosis; drug efficacy; experimental study; histological slides; human tissue; imaging system; improved; in vivo; inhibitor; machine learning algorithm; meter; microdevice; mouse model; new technology; next generation; pharmacologic; response; spatiotemporal; technology validation; temporal measurement; three dimensional cell culture; tool; treatment response; tumor; ultra high resolution; validation studies

Phenotyping cells and tissue is a critical function that spans basic science to clinical diagnosis. Yet, established methods for phenotyping cells in tissue are static, are evaluated when the tissue is dead, and typically involve destruction of the sample. This paradigm misses an entire dimension represented by cellular function and activity, information that is potentially of great significance in understanding cell/tissue state. Recently, a new field has emerged that uses coherence-gated imaging to quantify living tissue motion as a proxy of cellular function and activity. Coherence-based motility imaging is relatively new - much remains to be learned about the nature of its dynamic signal. In addition, many of the coherence-gated technologies described to date lack the resolution to investigate individual cells. The ones that are capable of seeing cells do not provide cross-sectional images and thus miss important architectural patterns associated with tissue maturation. We have developed a form of coherence-gated imaging called 1-µm optical coherence tomography (µOCT). µOCT has a resolution of 1 µm axial by 2 µm lateral, enabling cross-sectional visualization of tissue at the cellular level. Recently, we have discovered that by sequentially acquiring multiple µOCT images and computing the pixel-per-pixel power spectrum, we observe a dramatic increase in image contrast and new information emerging from the µOCT datasets. Preliminary studies with this new technology, termed dynamic µOCT (DµOCT), suggest that it can be used to visualize epithelial maturation, cell death/apoptosis, and cellular activity. In this grant, we will mature this technology by conducting key validation studies in a variety of clinically relevant human tissues, animal models, and spheroids to understand the dynamic signal and determine its accuracy for diagnosing pathology, activity, and response to therapy (apoptosis/necrosis) (Aim 1). We also will advance DµOCT further by increasing spatial and temporal resolution, creating new data mining analysis pipelines, and developing and validating technology and probes that enable DµOCT to be implemented in vivo (Aim 2). By expanding our understanding and implementation of this exciting technology, we hope to provide a powerful new tool that will have significant and wide-reaching impact in the biological and clinical sciences.

对细胞和组织进行表型分析是一项关键功能，它涵盖了基础科学和临床诊断。然而， 用于对组织中的细胞进行表型分析的方法是静态的，当组织死亡时进行评估，并且通常包括 销毁样品。这种模式忽略了细胞功能所代表的整个维度， 活性，在理解细胞/组织状态中潜在地具有重要意义的信息。最近，一个新的 已经出现了使用相干门控成像来量化活组织运动作为细胞运动的代理的领域。 功能和活动。基于相干性的运动成像是相对较新的-关于运动成像的许多方面仍有待了解。 其动态信号的性质。此外，迄今为止描述的许多相干选通技术缺乏对相干性的限制。 研究单个细胞的分辨率。那些能够看到细胞的细胞不能提供横截面 图像，从而错过与组织成熟相关的重要结构模式。 我们开发了一种称为1-µm光学相干断层扫描（µOCT）的相干门控成像。 µOCT的轴向分辨率为1 µm，横向分辨率为2 µm，可实现细胞内组织的横截面可视化。 水平最近，我们发现，通过顺序获取多个µOCT图像并计算 每像素功率谱，我们观察到一个显着增加图像对比度和新的信息出现 从µOCT数据集。这项新技术的初步研究被称为动态µOCT（DµOCT），表明 它可用于观察上皮成熟、细胞死亡/凋亡和细胞活性。在这份协议中，我们 将通过在各种临床相关的人体组织中进行关键验证研究， 动物模型和球体来理解动态信号并确定其诊断的准确性 病理学、活性和对治疗的反应（凋亡/坏死）（目的1）。我们还将进一步推进DµOCT 通过提高空间和时间分辨率，创建新的数据挖掘分析管道，以及开发和 验证能够在体内实施DµOCT的技术和探针（目标2）。通过扩大我们 了解和实施这项令人兴奋的技术，我们希望提供一个强大的新工具， 在生物和临床科学中具有重大和广泛的影响。