Hyperplexed Quantum Dots for Multidimensional Cell Classification in Intact Tissue

摘要 这项生物工程研究资助(BRG)计划的目标是为单细胞开发荧光标记 通过对完整的三维组织成像进行分类。我们专注于半导体量子 点(量子点)，表现出明亮的荧光和独特的光学和电子性能的纳米晶体。我们 我们设计的新类型的量子点将允许对30个或更多的量子点进行定量的多光谱分析 不同的分子，以便高光谱光片显微镜可以用来分析蛋白质组和 在一个单一的染色步骤之后，在完整的组织中全面映射20种或更多不同的细胞类型。这 技术解决了三维组织光学显微镜的一个突出瓶颈，为此 只有3个不同的分子标记可以很容易地区分，限制了精确分类细胞类型的能力 并共同定位不同类型的细胞。这项提议提出的时候，新的光片显微镜 最近被广泛用于亚细胞分辨率的光学透明组织的全厚度成像 因此，多路复用的快速发展可能会产生快速的影响。作为一个例子，这个项目的调查人员 开发的工作流程用于光学透明、免疫标记和成像来自瘦身和肥胖的完整脂肪组织 啮齿动物模型，除了基于荧光免疫染色全面识别细胞的软件外，以及 自动进行微环境分割的深度学习算法。有了这些进步，我们能够 在脂肪组织中发现新的免疫微环境，据信可促进合并症 肥胖的风险，如2型糖尿病和心脏病。然而，关于这些问题的性质的关键假设 在我们能够根据细胞的分子对细胞进行离散分类之前，微环境是不容易解决的 在其上下文微环境中的表达模式。在这个提案中，我们的技术目标是 开发用于在完整组织中进行高含量多路传输的荧光团。我们的生物学目标是利用这些工具 了解在肥胖状态下调节脂肪组织的免疫细胞微环境。我们的特定 目标是(1)设计新类别的基于量子点的标记的光物理，(2)将这些标记共轭到 抗体片段并验证其作为分子探针的靶向性；(3)定量评价细胞 三维脂肪组织的标记和分类的准确性，以及(4)应用探针板进行定量 瘦身和肥胖状态下细胞水平的脂肪免疫微环境。这是一个协作性的 工程师和在量子点和分子探测器方面有专长的科学家之间的提议(安德鲁史密斯) 高级光学显微镜(Paul Selvin)、生物医学图像计算(Mark Anastasio)、细胞免疫学 (Erik Nelson)和肥胖动物模型(Kelly Swanson)。

ABSTRACT The goal of this Bioengineering Research Grant (BRG) proposal is to develop fluorescent labels for single-cell classification through imaging of intact three-dimensional tissue. We are focusing on semiconductor quantum dots (QDs), nanocrystals that exhibit bright fluorescence and unique optical and electronic properties. We designed new classes of QDs which we propose will allow quantitative, multispectral analysis of 30 or more distinct molecules so that hyperspectral light sheet microscopy can be used to proteomically profile and comprehensively map 20 or more distinct cell types throughout an intact tissue after a single staining step. This technology addresses an outstanding bottleneck in optical microscopy of three-dimensional tissue, for which only 3 distinct molecular markers can be easily distinguished, limiting the capacity to precisely classify cell types and to co-localize different cell types. This proposal comes at a time when new light sheet microscopes have recently become widely available for full-thickness imaging of optically cleared tissues at sub-cellular resolution such that rapid advances in multiplexing could yield rapid impacts. As an example, the investigators of this project developed workflows to optically clear, immunolabel, and image intact adipose tissues from lean and obese rodent models, in addition to software to comprehensively identify cells based on fluorescent immunostains, and deep learning algorithms to automate microenvironment segmentation. With these advances, we were able to discover new classes of immune microenvironments in adipose tissue that are believed to promote comorbidities of obesity, such as type 2 diabetes and heart disease. However key hypotheses regarding the nature of these microenvironments cannot be readily addressed until we can discretely categorize cells based on their molecular expression patterns within their contextual microenvironments. In this proposal, our technological goal is to develop fluorophores for high-content multiplexing in intact tissues. Our biological goal is to use these tools to understand immune cell microenvironments that regulate adipose tissue in the state of obesity. Our Specific Aims are to (1) engineer the photophysics of new classes of QD-based labels, (2) conjugate these labels to antibody fragments and validate their target specificity as molecular probes, (3) quantitatively evaluate cell labeling and classification accuracy in three-dimensional adipose tissue, and (4) apply probe panels to quantify adipose immune microenvironments at the cellular level in the lean and obese states. This is a collaborative proposal between engineers and scientists with expertise in quantum dots and molecular probes (Andrew Smith), advanced optical microscopy (Paul Selvin), biomedical image computing (Mark Anastasio), cellular immunology (Erik Nelson), and animal models of obesity (Kelly Swanson).