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.

一种经济实惠、多功能的基于图像机的二维细胞分离与跟踪平台学习与无掩模光刻单细胞封装目前的商业细胞分选仪通常使用鞘流将细胞排列成单一轮廓并基于细胞分选荧光信号或图像。单一轮廓对齐限制了吞吐量并且需要复杂的高速分拣的硬件和昂贵的设备。使用高速鞘流还产生对细胞的高压力，这使得它不适合下游干细胞等脆弱或敏感细胞应用。样品中的一些粘性细胞（例如单核细胞）或过多的死细胞可能会中断甚至堵塞水流。这种细胞分选仪通常还需要大量的起始细胞数。考虑到产量、纯度和液体死体积，分选稀有细胞群（例如干细胞亚群）的细胞具有挑战性细胞或血液样本中的循环肿瘤细胞。小型实验室强烈需要负担得起的、适用于多种细胞类型的多功能细胞分选平台。拟议工作的目标是： 1. 开发基于机器学习的高速细胞分类模块。该模块将启用实时基于明场或荧光图像检测宽微流体通道内的靶细胞。 2. 开发基于停流光刻的二维细胞分选平台与声场细胞阵列图案相结合，将生成不同大小的目标单元的编码封装。 3. 集成机器学习检测以及无掩模光刻，根据尺寸对细胞进行过滤/分选，形成经济实惠的细胞分选机。设置可以安装到现有的显微镜和高分辨率相机上，以及网络实验室流量控制器和紫外线投影仪是一款多功能且经济实惠的细胞分选仪。该方法可以基于高内容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体用于高纯度分选不同类型目标细胞的设备，对起始细胞数量的要求最低，因此适用于大样本的稀有子集或稀有细胞。基于数字微镜的无掩模光刻设备（DMD）用于标记编码ID以跟踪单个细胞，方便下游分析。 2D宽平台可以避免细胞分选中高剪切流引起的细胞损伤或性质改变通道，因此适合干细胞等温和细胞。宽通道还可以避免潜在的细胞常规细胞分选仪中的堵塞问题。通过更新机器学习算法和共享数据集预训练的模型，以及具有更高分辨率的相机和投影仪的可用性，拟议的项目打造一个经济实惠、可扩展、功能强大且通用的细胞分选平台。