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An Affordable and Versatile Two-Dimensional Cell Isolation and Tracking Platform Based on Image Machine Learning and Maskless Photolithography Single Cell Encapsulation

基于图像机器学习和无掩模光刻单细胞封装的经济实惠且多功能的二维细胞分离和跟踪平台

基本信息

批准号：
10684026
负责人：
YEVGENY BERDICHEVSKY
金额：
$ 23.72万
依托单位：
LEHIGH UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2025-06-30
项目状态：
未结题

项目摘要

An Affordable and Versatile Two-Dimensional Cell Isolation and Tracking Platform Based on Image Machine Learning and Maskless Photolithography Single Cell Encapsulation Current commercial cell sorters typically use sheath flow to align cells into a single profile and sort cells based on fluorescence signal or images. The single profile alignment limits the throughput and requires complex hardware and expensive equipment for high-speed sorting. The usage of high-speed sheath flow also generates high stress on cells, which makes it not suitable for fragile or sensitive cells such as stem cells for downstream application. Some sticky cells such as monocytes or too many dead cells in the sample can interrupt or even clog the flow. Such cell sorter also usually requires a significant amount of starting cell number. Considering the yield, purity, and fluid dead volume, it is challenging to sort out cells of rare population such as subset of stem cells or circulating tumor cells in blood sample. There are strong needs from small labs for an affordable and versatile cell sorting platform applicable to a variety of cell types. The objectives of the proposed work are to: 1. Develop a high-speed machine learning-based cell classification module. The module will enable real-time detection of target cells inside a wide microfluidic channel based on brightfield or fluorescent images. 2. Develop a stop flow lithography-based 2D cell sorting platform in combination with acoustic field cell array patterning that will generate encoded encapsulations of target cells of different sizes. 3. Integrate the machine learning detection and maskless lithography with the size-based filtering/sorting of the cell into an affordable cell sorter. The setup can be mounted onto existing microscope and high-resolution camera, along with a web-lab flow controller and a UV projector, makes a versatile and affordable cell sorter. The proposed method can sort multiple cell types based on high content image information and machine learning. This eliminates the dependency on specific antibody types which is the basis of fluorescence-activated cell sorting (FACS) or magnetics-activated cell sorting (MACS). The proposed method can use simple microfluidic devices for sorting different types of target cells in high purity with minimum requirement on starting cell number, thus is applicable to rare subset of a large sample or rare cells. Maskless lithography based on digital micromirror device (DMD) is used to stamp encoded ID to track individual cells which is convenient for downstream analysis. The 2D wide platform can avoid high shear flow-induced cell damage or property change in the cell sorting channel, thus is suitable for gentle cells such as stem cells. The wide channel can also avoid the potential cell clogging problem in a regular cell sorter. By updating the machine learning algorithm and sharing datasets and pre-trained models, as well as the availability of cameras and projectors of better resolution, the proposed project leads to an affordable, expandable, powerful, and universal cell sorting platform.

一种实惠、通用的基于图像机的二维细胞分离与跟踪平台学习和无掩模光刻单细胞封装目前的商业细胞分选器通常使用鞘流将细胞排列成单个轮廓，并基于在荧光信号或图像上。单一的纵断面线形限制了吞吐能力，要求复杂硬件和昂贵的设备，用于高速分拣。高速鞘流的使用也产生了对细胞压力大，不适合干细胞等脆弱或敏感细胞下游使用申请。样本中的一些粘性细胞，如单核细胞或过多的死亡细胞，会中断甚至堵住人流。这种细胞分类器通常还需要相当数量的起始细胞编号。考虑到产量，纯度和液体死亡体积，它是具有挑战性的稀有群体的细胞，如干细胞的子集血液样本中的细胞或循环中的肿瘤细胞。小型实验室对经济实惠的多功能细胞分选平台，适用于多种细胞类型。拟议工作的目标是：1. 开发了一个高速的基于机器学习的细胞分类模块。该模块将启用实时基于Brightfield或荧光图像检测宽微流控通道内的目标细胞。2.发展一种结合声场细胞阵列构图的基于停流光刻的2D细胞分选平台，将生成不同大小的目标单元格的编码封装。3.集成机器学习检测以及无掩膜光刻，其具有基于大小的细胞过滤/分选到负担得起的细胞分选器中。设置可以安装在现有的显微镜和高分辨率摄像头上，以及网络实验室流量控制器和一种紫外线投影仪，使之成为一种多功能和负担得起的细胞分选器。该方法可以基于高含量图像信息和机器学习对多种细胞类型进行分类。这消除了对特定抗体类型的依赖，这是荧光激活细胞的基础分选(FACS)或磁激活细胞分选(MACS)。所提出的方法可以使用简单的微流控技术对起始细胞数要求最低的高纯度分选不同类型的靶细胞的装置，因此适用于大样本或稀有细胞的稀有子集。基于数字微镜的无掩模光刻设备(DMD)被用来标记编码的ID以跟踪单个细胞，这便于后续分析。 2D宽平台可以避免高剪切流导致的细胞损伤或细胞分选中的性质变化通道，因此适合温和的细胞，如干细胞。宽沟道还可以避免潜在的电池常规细胞分选机中的堵塞问题。通过更新机器学习算法和共享数据集预先培训的模型以及提供更高分辨率的照相机和投影仪，拟议的项目带来了一个负担得起、可扩展、功能强大和通用的细胞分类平台。