Hyperplexed Quantum Dots for Multidimensional Cell Classification in Intact Tissue

用于完整组织中多维细胞分类的超复合量子点

• 批准号: 10450143
• 负责人: Andrew Michael Smith
• 金额: $ 55.84万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10450143
• 关键词: 3-Dimensional; Address; Adipose tissue; Animal Model; Applications Grants; Binding; Biological; Biological Sciences; Biomedical Engineering; Biophysics; Cell membrane; Cells; Cellular Immunology; Characteristics; Chemicals; Classification; Code; Color; Computer software; Cytometry; Dimensions; Disease; Dyes; Engineering; Environment; Exhibits; Flow Cytometry; Fluorescence; Fluorescent Probes; Goals; Health; Heart Diseases; Image; Immune; Immunoglobulin Fragments; Immunophenotyping; In Situ; Individual; Label; Light; Location; Maps; Measurement; Measures; Methods; Microscope; Microscopy; Molecular; Molecular Probes; Nature; Non-Insulin-Dependent Diabetes Mellitus; Nucleic Acids; Obesity; Optics; Outcome; Pattern; Property; Proteins; Proteomics; Quantum Dots; Research Personnel; Research Project Grants; Resolution; Rodent Model; Scientist; Semiconductors; Signal Transduction; Specificity; Stains; Technology; Testing; Thick; Thinness; Time; Tissue imaging; Tissues; Validation; Work; base; biomedical imaging; cell dimension; cell type; comorbidity; deep learning algorithm; design; fluorescence imaging; fluorophore; high dimensionality; human tissue; insight; molecular marker; nanocrystal; optical imaging; tool; tumor-immune system interactions

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).

抽象的 这项生物工程研究补助金（BRG）提案的目标是开发单细胞的荧光标签 通过成像完整的三维组织进行分类。我们专注于半导体量子 点（QD），表现出明亮荧光以及独特的光学和电子特性的纳米晶体。我们 设计了新的QD类别，我们提出的将允许30或更多的定量，多光谱分析 不同的分子，因此高光谱的光片显微镜可以用于蛋白质组谱，并且 单个染色步骤后，全面绘制整个完整组织中的20个或更多不同的细胞类型。这 技术解决了三维组织的光学显微镜的出色瓶颈，为此 只能很容易区分3个不同的分子标记物，从而限制了精确分类细胞类型的能力 并共同定位不同的细胞类型。该提案是在新的轻度显微镜有的时候提出的 最近，在亚细胞分辨率下广泛用于对光学清除组织的全厚度成像 因此，多路复用的快速进步可能会产生快速影响。例如，该项目的调查人员 开发的工作流程可在光学上清除，免疫标记和图像完整的脂肪组织。 啮齿动物模型，除了软件外，还可以根据荧光免疫抑制剂进行全面识别细胞，并 深度学习算法以自动化微环境细分。通过这些进步，我们能够 在脂肪组织中发现新的免疫微环境，这些环境被认为可以促进合并症 肥胖症，例如2型糖尿病和心脏病。但是关于这些的性质的关键假设 微环境无法轻易解决，直到我们可以根据其分子对细胞进行离散分类分类 其上下文微环境中的表达模式。在此提案中，我们的技术目标是 在完整的组织中开发用于高素质多路复用的荧光团。我们的生物学目标是使用这些工具 了解调节肥胖状态脂肪组织的免疫细胞微环境。我们的具体 目的是（1）设计新类的基于QD的标签的光体物理，（2）将这些标签缀合到 抗体片段并作为分子探针验证其靶特异性，（3）定量评估细胞 三维脂肪组织中的标记和分类精度，（4）应用探针面板来量化 瘦和肥胖状态的细胞水平的脂肪免疫微环境。这是一个协作 工程师与具有量子点和分子探针专业知识的科学家之间的提案（安德鲁·史密斯）， 晚期光学显微镜（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（凯利·斯旺森）。