权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Dynamic µOCT for cellular tissue phenotyping

用于细胞组织表型分析的动态 µOCT

基本信息

批准号：
10439661
负责人：
Oliver Jonas
金额：
$ 59.68万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
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/10439661
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 Data Data Set Devices Diagnostic Dimensions Disease Epithelial Fluorescein-5-isothiocyanate Fluorescence Fluorescence Microscopy Frequencies Gold Grant Growth Human Image Imaging technology Immunohistochemistry Individual Label Lateral Light Measures Melanoma Cell Metabolic Metabolism Methods Microscopic Modification Molecular Morphologic artifacts Morphology Motion Movement Mus Nature Necrosis Optical Coherence Tomography Optics Organism Organoids Pathology Patients Pattern Pharmaceutical Preparations Pharmacology Phase Phase-Contrast Microscopy Phenotype Pilot Projects Process 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 base 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 microdevice mouse model new technology next generation response spatiotemporal temporal measurement three dimensional cell culture tool treatment response tumor validation studies

项目摘要

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.

细胞和组织的表型是跨越基础科学到临床诊断的关键功能。然而,建立