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

项目总结/摘要 挑战气道上皮由各种细胞类型组成-了解细胞和功能 异质性将对疾病的诊断和治疗产生重大影响。然而，很少有分析工具 可以在全球人口规模上研究这些细胞的时空表型。 传统的高通量显微镜（HTM），虽然强大的解剖异质生物 在多尺度成像和分析方面受到很大限制。大多数HTM系统是由 结合高放大率显微镜与扫描阶段;这种配置将需要高 系统设计和操作的复杂性、高成本和低图像获取速率。随访数据 分析，基于传统的合奏平均方法，往往导致损失的详细机制， 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）用于大规模活细胞分析。M3将整合前沿突破：傅立叶 重叠关联显微镜（FPM）和深度学习（DL）。FPM基于空间编码照明 技术，收集低分辨率的图像序列，同时改变点光源的位置。这些 然后，将图像进行数字组合以恢复整个傅立叶空间，从而使FPM能够实现宽 高分辨率和高空间分辨率。DL在发现复杂的隐藏结构方面很有潜力 在有限的人类监督下的高维数据集中。我们将DL与时间序列建模相结合， 学习疾病相关的细胞特征。目标.我们将实现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.我们将根据以下内容构建M3成像系统： FPM技术。该系统的特点是i）一种新的数值算法来重建3D体积图像 和ii）用于分子检测的多色成像能力。目标2.我们将推进M3的DL框架 图像分析该框架将被设计为识别不同的细胞类型，并了解它们的 时空特征来解开多尺度细胞异质性。目标3.我们将M3应用于表型 气道上皮细胞。我们将使用一种体外模型，该模型使用诱导多能干细胞（iPSC）， 衍生肺上皮。M3将监测上皮发育期间的细胞分化，并检查 细胞表型和功能之间的相关性。冲击M3将带来前所未有的分析 描述气道上皮内不同细胞的能力，使我们能够发现许多隐藏的 在细胞和组织水平的表型。这些知识将对早期疾病检测产生影响 以及设计有效的治疗方法。

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.