High throughput single cell linear displacement adhesion assay

摘要 细胞粘附是任何活细胞的基本过程。它对细胞分化、分裂、迁移 和专业化。细胞粘附功能障碍是各种病理表型的标志，包括 慢性肾脏疾病、癌症和许多其他疾病。为了发现新的疾病治疗方法， 为了提高我们对单细胞性质基本理解，强烈需要允许快速 和精确的单细胞粘附测量。不幸的是，目前的技术是缺乏的，因为它们 仅测量整个细胞群中的粘附（例如，微流体，旋转盘）而不是单个细胞， 过于复杂或昂贵（例如，原子力显微镜、光学镊子）的广泛采用，需要 复杂的功能化表面（牵引力显微镜），或仅探测靶向受体（细胞 粘附力显微术），这妨碍了测量总细胞粘附潜力。因此，有一个伟大的 需要一个自动化的，高通量和成本效益的平台，能够同时测量单个 细胞粘附对于大的细胞群体。在这里，我们建议开发一种新的方法论途径 利用简单的一次性微流体盒（MiC），振荡驱动的细胞移位，以及 每个细胞通过机器学习算法跟踪粘附力。我们的初步研究提供了强大的 支持该方法的可行性。这个为期一年的第一阶段项目将产生一个功能齐全的 通过开发和集成其关键组件，其中包括可编程的 细胞移位致动器（BioShake）、一次性MiC、细胞粘附分析算法和软件（SA1）。在 此外，整个工作流程和原理证明实验将使用多个细胞系进行， 各种粘合性能（SA2）。我们预计，充分开发的成熟产品将提供高 影响工具，以促进对细胞粘附在健康和疾病中的关键作用的机械生物学研究， 包括这些病理状况，如癌症，从而促进细胞内的进一步基础研究， 生物学和转化研究。

ABSTRACT Cell adhesion is an essential process for any living cell. It is critical for cell differentiation, division, migration and specialization. Dysfunction of cell adhesion is a hallmark of various pathological phenotypes, including chronic kidney diseases, cancer, and many others. In the effort to discover new disease treatments and improve our basic understanding of single cell properties there is a strong demand for methods permitting rapid and accurate single cell adhesion measurements. Unfortunately, current technologies are lacking since they only measure adhesion in entire cell populations (e.g., microfluidics, spin disks) rather than individual cells, are too complex or costly (e.g., atomic force microscopy, optical tweezers) for widespread adoption, require sophisticated functionalized surfaces (traction force microscopy), or probe only targeted receptors (cell adhesive force microscopy) that precludes measuring total cell adhesion potential. Therefore, there is a great need for an automated, high-throughput and cost-efficient platform capable of simultaneously measuring single cell adhesion for a large population of cells. Here, we propose to develop a novel methodological approach utilizing simple disposable microfluidics cassettes (MiCs), oscillation driven cell shifts, and a conversion of each individual cell track to adhesion force via machine learning algorithms. Our initial studies provide strong support for the feasibility of the approach. This one-year Phase I project will result in a fully functional instrument through the development and integration of its critical components, which include a programmable cell shift actuator (BioShake), disposable MiCs, cell adhesion analysis algorithms, and software (SA1). In addition, the entire workflow and proof of principle experiments will be performed using multiple cell lines with various adhesive properties (SA2). We anticipate that the fully developed mature product will provide a high impact tool to promote mechanobiology studies on the key role of cell adhesion in health and disease, including such pathological conditions, such as cancer, thus facilitating further fundamental studies in cell biology and translational research.