Dynamic µOCT for cellular tissue phenotyping

细胞和组织的表型鉴定是从基础科学到临床诊断的重要功能。然而，已经确立了 组织中细胞表型的方法是静态的，在组织死亡时进行评估，通常涉及 样品的销毁。这一范例遗漏了细胞功能所代表的整个维度 活性，对了解细胞/组织状态具有潜在重要意义的信息。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代理的领域 功能和活动。基于相干的运动成像是相对较新的-关于 其动态信号的性质。此外，到目前为止描述的许多相干选通技术都缺乏 研究单个细胞的分辨率。那些能够看到细胞的没有提供横截面 图像，因此错过了与组织成熟相关的重要建筑模式。 我们开发了一种称为1-微米光学相干层析成像(µOCT)的相干选通成像。 µOCT的轴向分辨率为1微米，横向分辨率为2微米，可实现细胞内组织的横截面可视化 水平。最近，我们发现，通过顺序采集多个µOCT图像并计算 每个像素的功率谱，我们观察到图像对比度的急剧增加和新信息的出现 来自µOCT数据集。对这项名为Dynamic？OCT(D？OCT)的新技术的初步研究表明 它可以用来观察上皮成熟、细胞死亡/凋亡和细胞活动。在这笔赠款中，我们 将通过在各种临床相关的人体组织中进行关键的验证研究来成熟这项技术， 动物模型和椭球体，以了解动态信号并确定其诊断的准确性 病理、活动和对治疗的反应(细胞凋亡/坏死)(目标1)。我们还将进一步推进D？OCT 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 验证使D？OCT能够在体内实施的技术和探针(目标2)。通过扩展我们的 理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，将 对生物科学和临床科学产生重大而广泛的影响。

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.