Whole-Organism, Real-time Decision-enabled 3D Tissue Imaging and Recovery for Molecular Analysis

用于分子分析的全生物体、实时决策 3D 组织成像和恢复

• 批准号: 10546698
• 负责人: SUSANN M BRADY-KALNAY
• 金额: $ 22.42万
• 依托单位: BIOINVISION, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-05 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10546698
• 关键词: 3-Dimensional; Academia; Address; Adopted; Anatomy; Animal Organ; Area; Australia; Automation; Biological Markers; Biological Process; Biomedical Research; Brain; Breast cancer metastasis; Cancer Biology; Cells; Clinical; Collection; Color; Communities; Computer software; Consumption; Contracts; Data; Detection; Disease; Disseminated Malignant Neoplasm; Drug Delivery Systems; Economics; Environment; Europe; Face; Feasibility Studies; Fee-for-Service Plans; Feedback; Felis catus; Fluorescence; Freezing; Funding; Gene Expression; Gene Expression Profiling; Genes; Glean; Grant; Hand; Histologic; Histology; Human; Human Resources; Image; Imaging technology; Immune; Immunohistochemistry; In Situ; Industry; Institution; Intervention; Label; Lead; Liver; Lung; Maintenance; Malignant Neoplasms; Mammary Neoplasms; Manuals; Metastatic breast cancer; Middle East; Modeling; Molecular; Molecular Analysis; Mus; Neoplasm Metastasis; North America; Organ; Organism; Paraffin Embedding; Phase; Preparation; Process; Recovery; Regenerative Medicine; Reporter; Research; Research Personnel; Resolution; Route; Sampling; Scientist; Sensitivity and Specificity; Service delivery model; Signal Transduction; Speed; Supervision; Technology; Temperature; Testing; Thinness; Time; Tissue imaging; Tissues; Tracer; Training; Validation; Vision; Whole Organism; animal tissue; base; bone; cancer cell; cell type; cost; cost effective; deep learning; experimental study; fluorophore; high resolution imaging; histological slides; histological stains; image guided; imaging agent; imaging software; improved; instrument; interest; microscopic imaging; molecular marker; novel; operation; pandemic disease; pre-clinical; preclinical imaging; repository; research facility; software as a service; software development; stem cells; targeted imaging; tumor microenvironment; ultra high resolution; user-friendly

Summary. Tissue capture and recovery for molecular analyses such as immunostaining and gene expression analysis is important for gleaning information from tissue blocks relating to disease. The difficulty with existing solutions is that if tissue recovery is envisioned, sectioning and recovering thin tissue sections from target tissues or organs from an entire organism is not possible. Existing solutions provide manual tissue recovery, requiring the operator to be present to determine whether or not to collect a section from a tissue of interest. To address this challenge, BioInVision will bring to market a 3D tissue imaging solution for preclinical applications that em- ploys automatic, deep-learning, on-the-fly target tissue recognition from a whole organism and semi-automated tissue recovery. BioInVision pioneered the CryoVizTM instrument and has successfully commercialized fee-for- service CryoVizTM imaging for over 10 years. Our existing fee-for-service framework is well-adopted and em- braced by 75+ customers and institutions all over the world. High-resolution, broad-fluorescence-support (visible and NIR fluorophores), high-sensitivity block-face CryoVizTM imaging of preclinical frozen tissue blocks creates anatomical brightfield and molecular marker fluorescence 3D microscopic image volumes. AI-based software for deep-learning that will notify the operator in real-time upon encountering tissues of interest in color anatomy, or upon encountering molecular fluorescence such as eGFP cancer cells, enabling further interrogation of these tissues either through ultra-high-resolution tissue imaging (2µm-scale), or semi-automatic tissue capture for his- tological analyses and immunostaining (what we term “image-guided histology”). Semi-automatic tissue capture through speed and temperature control will enable recovery of tissue for molecular analyses. A demonstration project is outlined that involves characterizing whole mice with fluorescent-reporter metastatic cancer cells and fluorophore-tethered cancer targeted imaging agent. Here, we will study distribution of breast cancer metastases throughout the whole mouse and co-localization with immune cells or disease biomarkers. Our solution will also make possible drug delivery studies with fluorescent tracers. It will enable tracking of multiple fluorescently la- beled markers of cell types helping one better understand the tumor micro-environment in cancer biology. It will reduce manual labor and personnel costs and lead to better throughput to enable image-guided histology. This novel solution will cater to a wide variety of application areas including cancer biology, drug delivery, imaging agents and gene expression.

摘要：用于分子分析（例如免疫染色和基因表达）的组织捕获和回收。 分析对于从与疾病相关的组织块中收集信息非常重要。 解决方案是，如果设想组织恢复，则从目标组织中切下并恢复薄组织切片 现有的解决方案不可能提供手动组织恢复，这需要。 操作员在场以确定是否从感兴趣的组织中收集切片。 针对这一挑战，BioInVision 将向市场推出一种用于临床前应用的 3D 组织成像解决方案，该解决方案 采用来自整个生物体的自动、深度学习、动态目标组织识别和半自动化 BioInVision 率先推出了 CryoVizTM 仪器，并已成功将收费商业化。 我们现有的按服务付费框架已被广泛采用和实施。 由全球 75 多个客户和机构提供支持。 和 NIR 荧光团），临床前冷冻组织块的高灵敏度块面 CryoVizTM 成像创建 基于 AI 的解剖明场和分子标记荧光 3D 显微图像体积软件。 深度学习将在遇到彩色解剖感兴趣的组织时实时通知操作员，或者 当遇到 eGFP 癌细胞等分子荧光时，可以进一步询问这些细胞 通过超高分辨率组织成像（2μm 级）或半自动组织捕获来获取组织 组织学分析和免疫染色（我们称之为“图像引导组织学”）。 通过速度和温度控制将能够恢复组织以进行分子分析。 概述了该项目，其中涉及用荧光报告转移性癌细胞来表征整个小鼠，以及 在这里，我们将研究乳腺癌转移的分布。 我们的解决方案也将遍及整个小鼠并与免疫细胞或疾病生物标志物共定位。 使荧光示踪剂的药物递送研究成为可能，它将能够跟踪多种荧光标记。 细胞类型的标记有助于人们更好地了解癌症生物学中的肿瘤微环境。 减少体力劳动和人员成本，并提高通量以实现图像引导组织学。 新颖的解决方案将满足广泛的应用领域，包括癌症生物学、药物输送、成像 剂和基因表达。