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

• 批准号: 10432980
• 负责人: Yaling Liu
• 金额: $ 19.7万
• 依托单位: LEHIGH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10432980
• 关键词: Acoustics; Antibodies; Biological Markers; Biomedical Research; Blood specimen; Cancer cell line; Cell Count; Cell Differentiation process; Cell Separation; Cell Size; Cells; Cellular Morphology; Charge; Classification; Clinical Medicine; Code; Complex; Computer software; Core Facility; Data; Data Set; Dependence; Detection; Devices; Dimensions; Drops; Encapsulated; Equipment; Fluorescence; Fluorescence Microscopy; Fluorescence-Activated Cell Sorting; Hematopoietic stem cells; Hydrogels; Image; In Situ; Individual; Intelligence; Internet; Interruption; Label; Laboratories; Learning; Learning Module; Light; Liquid substance; Machine Learning; Magnetism; Methods; Microfluidic Microchips; Microfluidics; Microscope; Modeling; Neoplasm Circulating Cells; Optics; Pattern; Performance; Phenotype; Population; Price; Procedures; Property; Recovery; Research; Resolution; Sample Size; Sampling; Shapes; Signal Transduction; Sorting - Cell Movement; Speed; Stress; System; Systems Analysis; Technology; Time; Training; Update; Work; base; biological research; biomaterial compatibility; capsule; cell injury; cell type; cellular imaging; cost; cost estimate; design; digital; electric field; fluorescence imaging; fluorescence microscope; in situ imaging; lithography; machine learning algorithm; machine learning model; monocyte; open source; operation; shear stress; stem cells; tool; two-dimensional; voltage

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机通常使用鞘流将细胞排列成单个轮廓，并基于鞘流分选细胞。 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 硬件和昂贵的设备进行高速分拣。高速鞘流的使用还产生 对细胞的高压力，这使得它不适合脆弱或敏感的细胞，如下游的干细胞， 应用程序.一些粘性细胞如单核细胞或样本中过多的死细胞会中断甚至 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 产量、纯度和流体死体积，分选稀有群体的细胞是具有挑战性的，例如干细胞亚群， 细胞或循环肿瘤细胞。小型实验室强烈需要一个负担得起的， 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。 开发基于高速机器学习的细胞分类模块。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.发展 基于停流光刻的2D细胞分选平台结合声场细胞阵列图案化， 将生成不同大小的目标细胞的编码序列。3.集成机器学习检测 以及无掩模光刻，其具有将细胞基于尺寸过滤/分选到可负担的细胞分选器中。设置 可以安装到现有的显微镜和高分辨率相机上，沿着网络实验室流量控制器， 紫外线投影仪，使一个多功能和负担得起的细胞分选。 所提出的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。 这消除了对特异性抗体类型的依赖，而特异性抗体类型是荧光激活细胞免疫的基础。 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 用于以高纯度分选不同类型的靶细胞的装置， 因此适用于大样本的稀有子集或稀有细胞。基于数字光刻技术的无掩模光刻 DMD装置用于标记编码ID以跟踪单个细胞，这便于下游分析。 二维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变化 通道，因此适用于温和的细胞，如干细胞。宽的通道也可以避免潜在的细胞 在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， 预先训练的模型，以及更好的分辨率的相机和投影仪的可用性，拟议的项目 这是一个经济实惠、可扩展、功能强大且通用的细胞分选平台。