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.

项目概要/摘要 挑战。气道上皮由多种细胞类型组成——了解细胞和功能。 异质性将对疾病的诊断和治疗产生重大影响，但分析工具却很少。 可用于在全球群体规模上研究这些细胞的时空表型。 传统的高通量显微镜 (HTM)，尽管对于解剖异质生物体来说功能强大 过程，在多尺度成像和分析方面受到很大限制，大多数 HTM 系统都是由 将高倍显微镜与扫描台相结合；这种配置需要高倍率 系统设计和操作复杂，成本高，后续数据采集速度慢。 基于传统集成平均方法的分析常常导致详细机制的丢失 我们将推进“智能”成像平台 M3（多尺度机器学习）。 用于大规模活细胞分析的显微镜）M3 将整合尖端突破：傅立叶。 叠层显微镜（FPM）和深度学习（FPM）基于空间编码照明。 技术，在改变点光源位置的同时收集低分辨率图像序列。 然后对图像进行数值组合以恢复整个傅立叶空间，从而使 FPM 能够实现宽幅 同时实现视场和高空间分辨率的深度学习能够有效地发现复杂的隐藏结构。 在人类监督有限的高维数据集中，我们将把深度学习与时间序列建模结合起来。 了解与疾病相关的细胞特征。我们将实施 M3 平台并采用它来分析细胞。 目标1.我们将构建基于M3成像系统。 该系统将采用 i) 一种新的数值算法来重建 3D 体积图像。 ii) 用于分子检测的多色成像能力。我们将推进 M3 的深度学习框架。 该框架旨在识别不同的细胞类型并了解它们的特征。 目标 3.我们将 M3 应用于表型。 我们将使用诱导多能干细胞 (iPSC) 的体外模型来研究气道上皮细胞。 衍生肺上皮细胞将监测上皮发育过程中的细胞分化并检查 M3 将带来前所未有的分析影响。 具有表征气道上皮内不同细胞的能力，使我们能够发现许多隐藏的 细胞和组织水平的表型对于早期疾病检测具有重要意义。 以及设计有效的治疗方法。