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

总结。用于免疫染色和基因表达等分子分析的组织捕获和回收 对于从与疾病相关的组织块中收集信息来说，分析是重要的。现有的困难 解决方案是，如果设想组织恢复，则从目标组织中分割和恢复薄组织切片 或者来自整个有机体的器官是不可能的。现有的解决方案提供手动组织恢复，需要 操作员在场，以确定是否从感兴趣的组织中收集切片。致信地址 此次挑战，BioInVision将向市场推出适用于临床前应用的3D组织成像解决方案，该解决方案可以- 采用自动的、深度学习的、即时的、从整个生物体和半自动的目标组织识别 组织恢复。BioInVision开创了CryoVizTM仪器的先河，并成功地将付费 服务CryoVizTM成像超过10年。我们现行的按服务收费架构已被广泛采用，而且- 在全球75家以上客户和机构的支持下。高分辨率、宽荧光支持(可见 和近红外荧光团)，临床前冷冻组织块的高灵敏度块面CryoVizTM成像创建 解剖亮场和分子标记荧光三维显微图像体积。基于AI的软件，用于 深度学习，当遇到颜色解剖感兴趣的组织时，将实时通知操作员，或 在遇到诸如EGFP癌细胞的分子荧光时，使得能够进一步询问这些 通过超高分辨率组织成像(2微米尺度)或用于HIS的半自动组织捕获- 组织学分析和免疫染色(我们称之为“图像引导组织学”)。半自动组织捕捉 通过速度和温度控制，将能够回收组织进行分子分析。一次演示 该项目概述了用荧光报告转移性癌细胞和 荧光团结合型癌症靶向显像剂。在这里，我们将研究乳腺癌转移的分布。 在整个小鼠体内，并与免疫细胞或疾病生物标志物共同定位。我们的解决方案还将 利用荧光示踪剂进行药物传递研究。它将实现对多个荧光标记的跟踪- 佩带的细胞类型标记帮助人们更好地了解癌症生物学中的肿瘤微环境。会的 减少人工和人力成本，并提高吞吐量，以实现图像引导组织学。这 新的解决方案将迎合广泛的应用领域，包括癌症生物学、药物输送、成像 毒剂和基因表达。

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.