High-throughput Phenotyping of iPSC-derived Airway Epithelium by Multiscale Machine Learning Microscopy

通过多尺度机器学习显微镜对 iPSC 衍生的气道上皮进行高通量表型分析

• 批准号: 10659397
• 负责人: Hakho Lee
• 金额: $ 78.12万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659397
• 关键词: 3-Dimensional; Adopted; Algorithms; Biological Process; Cell physiology; Cells; Cellular Structures; Cellular biology; Code; Color; Complex; Cystic Fibrosis; Cystic Fibrosis Transmembrane Conductance Regulator; Data; Data Analyses; Data Set; Detection; Development; Diagnosis; Disease; Disease model; Epithelium; Goals; Heterogeneity; Homeostasis; Human; Image; Image Analysis; Individual; Knowledge; Learning; Light; Lighting; Lung; Machine Learning; Maps; Methods; Microfluidic Microchips; Microscope; Microscopy; Modeling; Molecular; Monitor; Morbidity - disease rate; Mucociliary Clearance; Optics; Phenotype; Play; Population; Positioning Attribute; Process; Protocols documentation; Research; Resolution; Respiratory Disease; Role; Scanning; Series; Source; Structure; Supervision; System; Techniques; Technology; Testing; Time; Tissues; Transcend; airway epithelium; analytical tool; cell type; cellular imaging; cost; deep learning; design; digital imaging; disease phenotype; feature selection; high resolution imaging; imaging platform; imaging system; in vitro Model; induced pluripotent stem cell; innovation; insight; large datasets; live cell imaging; live cell microscopy; mortality; movie; multidimensional data; novel; operation; reconstruction; respiratory; single cell analysis; single-cell RNA sequencing; spatiotemporal; supervised learning; temporal measurement; therapeutically effective; three dimensional cell culture; tomography; tool; trait

PROJECT SUMMARY/ABSTRACT Challenges. The airway epithelium consists of various cell types – understanding cellular and functional heterogeneity will have a significant impact on diagnosing and treating diseases. However, few analytical tools are available to investigate spatiotemporal phenotypes of these cells on a global population scale. Conventional high-throughput microscopy (HTM), although powerful for dissecting heterogeneous biological processes, is significantly limited in multiscale imaging and analytics. Most HTM systems are constructed by combining high-magnification microscopes with scanning stages; this configuration would entail high complexity in the system design and operation, high cost, and slow image acquisition rates. Follow-on data analyses, based on traditional ensemble averaging approaches, often lead to the loss of detailed mechanistic information. Innovations. We will advance a “smart” imaging platform, M3 (Multiscale Machine-learning Microscopy) for large-scale, live-cell analyses. M3 will integrate cutting-edge breakthroughs: Fourier ptychographic microscopy (FPM) and deep learning (DL). FPM is based on a spatially coded-illumination technique, collecting low-resolution image sequences while changing the position of a point-light source. These images are then numerically combined to restore the whole Fourier space, allowing FPM to achieve both wide field-of-view and high spatial resolution simultaneously. DL is potent in discovering intricate, hidden structures in high-dimensional data sets with limited human supervision. We will integrate DL with time-series modeling to learn disease-related cellular traits. Goals. We will implement the M3 platform and adopt it to analyze cellular phenotypes during airway epithelium development. Aim 1. We will construct the M3 imaging system based on the FPM technology. This system will feature i) a new numerical algorithm to reconstruct 3D volumetric images and ii) multi-color imaging capacity for molecular detection. Aim 2. We will advance a DL framework for M3 image analyses. This framework will be designed to recognize different cell types and learn their spatiotemporal features to unravel multiscale cellular heterogeneity. Aim 3. We will apply M3 to phenotype cells in the airway epithelium. We will use an in-vitro model that uses induced pluripotent stem cells (iPSCs) to derive lung epithelium. M3 will monitor cellular differentiation during epithelium development and examine the correlation between cellular phenotypes and functionals. Impact. The M3 will bring unprecedented analytical power to characterize diverse cells within the airway epithelium, allowing us to discover many hidden phenotypes in cellular and tissue levels. Such knowledge would have implications for early disease detection as well as designing effective therapeutics.

项目摘要/摘要 挑战。气道上皮由各种细胞类型组成 - 了解细胞和功能 异质性将对诊断和治疗疾病产生重大影响。但是，很少有分析工具 可以在全球人口量表上研究这些细胞的时空表型。 常规的高通量显微镜（HTM），尽管强大用于剖析异质生物学 多尺度成像和分析中的过程受到显着限制。大多数HTM系统是由 将高磁化显微镜与扫描阶段相结合；这种配置需要高 系统设计和操作的复杂性，高成本以及图像采集速度缓慢。后续数据 基于传统合奏平均方法的分析通常会导致详细机械的丧失 信息。创新。我们将推进一个“智能”成像平台M3（多尺度机器学习） 显微镜）用于大规模的活细胞分析。 M3将整合尖头突破：傅立叶 Ptychographic显微镜（FPM）和深度学习（DL）。 FPM基于空间编码的弹片 技术，收集低分辨率图像序列，同时更改点光源的位置。这些 然后将图像组合在一起以恢复整个傅立叶空间，从而使FPM达到两个 视野和高空间分辨率。 DL在发现复杂的隐藏结构方面有潜力 在高维数据集中，人类监督有限。我们将将DL与时间序列建模集成到 学习与疾病相关的细胞性状。目标。我们将实施M3平台并采用它来分析蜂窝 气道上皮发育过程中的表型。目标1。我们将基于 FPM技术。该系统将包含i）重建3D体积图像的新数值算法 ii）分子检测的多色成像能力。目标2。我们将推进M3的DL框架 图像分析。该框架将旨在识别不同的单元格类型并学习他们的 时空特征阐明多尺度细胞异质性。目标3。我们将将M3应用于表型 气道上皮中的细胞。我们将使用使用诱导的多能干细胞（IPSC）的体外模型 推导肺上皮。 M3将监测上皮发育过程中的细胞分化并检查 细胞表型与功能之间的相关性。影响。 M3将带来前所未有的分析 表征在气道上皮中的潜水细胞的能力，使我们发现许多隐藏 细胞和组织水平的表型。这种知识对早期疾病检测有影响 以及设计有效的疗法。