3-D Imaging Flow Cytometry

3-D 成像流式细胞仪

• 批准号: 10023268
• 负责人: Yongjin Sung
• 金额: $ 15.58万
• 依托单位: UNIVERSITY OF WISCONSIN MILWAUKEE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-24 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10023268
• 关键词: 3-Dimensional; Address; Adopted; Algorithms; Biological; Blood; Blood specimen; Cells; Classification; Code; Computer software; Cultured Cells; Data; Diagnosis; Dimensions; Flow Cytometry; Functional Imaging; Geometry; Hematological Disease; Holography; Image; Laboratory Organism; Leukocytes; Lifting; Lighting; Masks; Methods; Microscope; Microscopy; Optical Tomography; Optics; Organelles; Patients; Phase; Process; Pupil; Records; Resolution; Sampling; Scanning; Specimen; Speed; Stains; Structure; System; Techniques; Technology; Testing; Three-Dimensional Image; Three-Dimensional Imaging; Time; Work; absorption; base; cellular imaging; commercialization; computerized data processing; convolutional neural network; deep learning; design; diffraction of light; digital; feature extraction; fluorescence imaging; imaging system; innovation; lens; novel; optical imaging; reconstruction; software development; targeted imaging; theories; three dimensional structure; tomography

PROJECT SUMMARY This project aims to develop and test two innovative platforms and related software for 3-D imaging flow cytometry of fluorescent or absorbing (stained) samples. These systems will allow 3-D structural and functional imaging of many single cells at a subcellular resolution and at a scale that used to be available only in flow cytometry or recently in 2-D imaging. Thereby, the proposed methods have the potential to fundamentally change the ways cultured cells, patient-derived samples, and small experimental organisms are studied. Automated classification based on the 3-D features will enable the diagnosis of hematologic disorders at single-cell precision. Existing 3-D microscopy methods can provide the same information at higher resolution; however, by relying on a scanning mechanism they cannot be applied to suspending cells, especially in a flow configuration, which is essential for high-speed interrogation. Snapshot 3-D microscopy techniques have been developed to address this challenge, but they have insufficient spatial resolution for single-cell imaging and suffer from long data processing time. We overcome these limitations by combining two novel snapshot techniques developed by the PI with the most rigorous optical imaging theories and cutting-edge component technologies. We will use an array of lenslets, which simultaneously records many projection images corresponding with different viewing angles. The use of pupil phase masks, designed using wavefront coding and a theory of 3-D high-numerical-aperture optical imaging, will increase the resolution of each projection image to the theoretical limit given by the objective-lens numerical aperture. The target resolution is 0.5 µm, which is comparable to existing 2-D imaging flow cytometry systems. The target imaging throughputs based on current component technologies are 120 volumes/sec for fluorescence imaging and 700 volumes/sec for absorption imaging, which are higher than 100 volumes/sec of cutting-edge 3-D optical microscopy for stationary specimens. The vast amount of data acquired by these 3-D imaging systems imposes a serious challenge to data processing. The developed systems record true projection images, which obviate iterative deconvolution process, thereby allowing much faster tomographic reconstruction than in existing snapshot techniques. Using general-purpose graphics processing units and optical diffraction tomography, which includes the diffraction of light by subcellular organelles, our tomographic reconstruction algorithm will be faster yet more accurate than existing approaches. Further, we will explore the feasibility of applying a deep convolutional neural network to the images acquired by the developed systems for accurate single-cell classification based on 3-D features.

项目概要 该项目旨在开发和测试两个用于 3D 成像流程的创新平台和相关软件 荧光或吸收（染色）样品的细胞计数。这些系统将允许 3D 结构和功能 以亚细胞分辨率和以前只能在流式细胞术中实现的规模对许多单细胞进行成像 细胞计数术或最近的二维成像。因此，所提出的方法有可能从根本上 改变培养细胞、患者样本和小型实验生物体的研究方式。 基于 3D 特征的自动分类将使血液疾病的诊断成为可能 单细胞精度。 现有的 3D 显微镜方法可以以更高分辨率提供相同的信息；然而，通过依靠 在扫描机制上，它们不能应用于悬浮细胞，特别是在流动配置中，这 对于高速审讯至关重要。快照 3D 显微镜技术已发展到 解决了这一挑战，但它们的空间分辨率不足以进行单细胞成像，并且遭受长时间的困扰 数据处理时间。我们通过结合开发的两种新颖的快照技术克服了这些限制 由PI拥有最严谨的光学成像理论和最尖端的元件技术。 我们将使用一个小透镜阵列，它同时记录许多与 不同的视角。使用光瞳相位掩模，利用波前编码和 3-D 理论设计 高数值孔径光学成像，将每个投影图像的分辨率提高到理论值 由物镜数值孔径给出的限制。目标分辨率为 0.5 µm，与 现有的二维成像流式细胞术系统。基于当前组件的目标成像吞吐量 荧光成像技术为 120 体积/秒，吸收成像为 700 体积/秒， 对于固定样本而言，尖端 3D 光学显微镜的速度高于 100 体积/秒。 这些3D成像系统采集的海量数据对数据提出了严峻的挑战 加工。开发的系统记录真实的投影图像，从而避免了迭代反卷积 过程，从而允许比现有快照技术更快的断层扫描重建。使用 通用图形处理单元和光学衍射断层扫描，其中包括衍射 通过亚细胞器的光，我们的断层扫描重建算法将比 现有的方法。此外，我们将探讨应用深度卷积神经网络的可行性 所开发的系统获取的图像可基于 3D 特征进行精确的单细胞分类。