3D-FACS: 3D image-based fluorescence activated cell sorting

3D-FACS：基于 3D 图像的荧光激活细胞分选

• 批准号: 9910011
• 负责人: Sung Hwan Cho
• 金额: $ 74.57万
• 依托单位: NANOCELLECT BIOMEDICAL, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9910011
• 关键词: 3-Dimensional; Adopted; Area; Biological; Biological Assay; Biology; Biotechnology; Brain; Cell Separation; Cells; Characteristics; Communities; Computer Systems; Computer software; DNA Damage; Data; Data Set; Detection; Development; Devices; Disease; Electronics; Fluorescence-Activated Cell Sorting; Gene Expression; Genomics; Goals; Image; Individual; Ionizing radiation; Ions; Light; Lighting; Link; Medical; Microfluidic Microchips; Microfluidics; Microscopy; Mission; Nuclear; Optics; Organelles; Output; Performance; Phase; Phenotype; Process; Property; Protein translocation; Real-Time Systems; Research; Research Design; Research Personnel; Resolution; Sampling; Scanning; Scheme; Signal Transduction; Sorting - Cell Movement; Speed; Spottings; Structure; Support System; Supporting Cell; System; Technology; Three-Dimensional Image; Three-Dimensional Imaging; Time; Travel; Tube; Validation; analog; base; cell transformation; cell type; cellular imaging; computerized data processing; data acquisition; design; drug development; experimental study; fluorescence activated cell sorter device; image guided; image processing; image reconstruction; imaging system; improved; innovation; instrument; interest; invention; meetings; microscopic imaging; next generation sequencing; novel; optical imaging; photomultiplier; programs; protein transport; prototype; real-time images; remote control; tomography; tool; transcription factor; transcriptomics; tumor

SUMMARY The primary goal of the proposed research is to demonstrate a high throughput flow cytometer system that can sort cells based on high-content 3D cell image features. For each single cell flowing in a microfluidic channel, the system will produce cell tomography from spatially resolved fluorescent and scattering signals at a rate of 1000 cells/s. Each multi-parameter 3D cell image will be reconstructed, hundreds of image features will be extracted, and cells with their spatial features meeting the user-defined criteria will be sorted (3D image-guided cell sorting). Essentially the proposed system combines the merits of high throughput cell analysis and sorting capabilities of a fluorescence-activated cell sorter (FACS) with a high-content 3D imaging microscope to offer researchers and clinicians unprecedented features and capabilities to analyze, classify, and isolate cells at single cell resolution. The invention of this tool is anticipated to transform cell phenotype studies, greatly accelerate cell type discoveries, and enhance studies of highly heterogeneous biological samples such as tumors and brain. To realize such ambitious goal, we will take several innovative approaches. To produce high-quality 3D cell images for individual cells travelling fast in a flow channel, we invent a camera-less imaging system using a design that combines scanning structured light excitation and the scheme of confocal detection, which transforms 3D spatial information into temporal signals at the output of high-speed photomultiplier tubes (PMTs). For cell sorting mechanism, we adopt a microfluidic chip/cartridge design with an on-chip piezoelectric actuator to sort cells without causing flow jitters that can disrupt imaging of cells passing the optical imaging area. To achieve real-time image processing and image feature extraction, as well as handling the transport and storage of the large amount of 3D cell image data, we propose an electronic system consisting of a field programmable gate array (FPGA) module and graphics processing unit (GPU), having the FPGA process PMT signals, cell detection, segmentation and image reconstruction, and sorting decision control while having the GPU extract hundreds of 3D image related features and define sorting criteria (i.e. 3D image- guided gating) in parallel. To evaluate the performance of the system, we will perform experiments to sort cells based on the properties of protein translocation and trafficking, spot counting, organelle tracking, and features that help understand the disease biology and drug development. The proposed instrument will offer biomedical community a powerful tool to advance phenotype studies and cell type discoveries, and to link gene expression studies to cell phenotypic characteristics at single cell resolution and high throughput. The impact of the research will be significant and profound.

概括 拟议的研究的主要目标是证明一个高通量流式细胞仪系统，可以 根据高含量3D细胞图像特征对单元进行分类。对于在微流体通道中流动的每个单元， 该系统将以空间分辨的荧光和散射信号的速度产生细胞层析成像 1000个单元/s。每个多参数3D单元格图像将被重建，数百个图像功能将是 提取，将符合用户定义标准的空间特征的单元格进行分类（3D图像引导 细胞分类）。本质上，提出的系统结合了高吞吐量细胞分析和分类的优点 具有高含量3D成像显微镜的荧光激活细胞分选仪（FACS）的功能 研究人员和临床医生空前的分析，分类和分离细胞的功能 在单细胞分辨率下。预计该工具的发明将改变细胞表型研究，极大地 加速细胞类型的发现，并增强对高度异质生物样品的研究，例如 肿瘤和大脑。 为了实现这种雄心勃勃的目标，我们将采取几种创新的方法。产生高质量的3D单元 我们在流通通道中快速行驶的单个单元的图像，我们使用A发明无摄像机成像系统 结合扫描结构化光激发和共聚焦检测方案的设计， 将3D空间信息转换为高速光电管（PMTS）输出的时间信号。 对于细胞分类机制，我们采用带有片上压电的微流体芯片/墨盒设计 执行器以对单元进行排序而不会引起流动抖动，从而破坏了通过光学成像的细胞成像 区域。实现实时图像处理和图像特征提取，并处理运输和 存储大量的3D单元图像数据，我们提出了一个由字段组成的电子系统 可编程门阵列（FPGA）模块和图形处理单元（GPU），具有FPGA进程 信号，单元检测，分割和图像重建以及对决策控制的分类 具有GPU提取数百个3D图像相关特征并定义排序标准（即3D图像 - 引导门）并联。为了评估系统的性能，我们将执行实验对单元进行分类 基于蛋白质易位和运输的特性，点计数，细胞器跟踪和特征 这有助于了解疾病生物学和药物开发。 拟议的仪器将为生物医学社区提供一个强大的工具，以推进表型研究和 细胞类型的发现，并将基因表达研究与单细胞的细胞表型特征联系起来 分辨率和高通量。研究的影响将是巨大而深刻的。