Tools4Cells: Machine-learning aided morphodynamics characterization of stem cell differentiation using label-free microscopies

一个胚胎可以发育成由不同类型的细胞组成的有机体，而且不同类型的细胞在一定条件下可以相互转化，这是大自然的一大奇迹。对不同细胞类型之间转换的机制理解和操纵成为令人兴奋的研究前沿。 这一研究领域有可能有助于我们对发展的理解以及组织和器官再生的创新。该项目的目标是开发用于分析细胞电影的计算工具，以了解单个细胞如何从一种类型转变为另一种类型。这类似于追踪人们从一个城市开车到另一个城市的路线，因此人们可以确定主要路线并尝试将交通引导到特定路线。 这项工作的更广泛影响包括研究的内在价值，因为该平台可以证明对许多其他项目有用。 研究机会将提供给本科生和研究生沿着博士后研究人员。 推广工作将包括利用YouTube向公众推广科学。不同表型的细胞具有不同的形态和基因表达模式。当受到特定的刺激和微环境时，它们可以在不同的表型之间转换，重新编程成为诱导多能干细胞。然而，对这种细胞表型转变（CPT）过程的机械理解受到快照数据不能提供时间信息的挑战。活细胞成像提供了一种替代方法，用于连续监测单个细胞随时间的转变。该项目旨在开发一个普遍适用的平台和相关的计算机软件包，（1）允许典型的细胞生物学实验室应用该平台并研究细胞过程，而无需强大的机器学习，图像分析或动力系统理论背景;（2）扩展到各种成像平台和模态，尤其是基于定量相位成像的无标记成像，其与机器学习方法一起得到普及。除了传统的生物图像信息学方法，这里的新奇是CPT动力学的动力系统理论/系统生物学的框架内的机制研究。此外，受图像数据库ImageNet在人工智能发展的最新进展中发挥的关键作用的启发，研究人员将提供从该项目中生成的注释细胞图像，作为社区开发和测试图像分析方法的基准。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

It is a great wonder of Nature that an embryo can develop into an organism composed with different types of cells, and different types of cells can interconvert under certain conditions. Mechanistic understanding and manipulation of conversion between different cell types emerge as an exciting frontier of research. This area of research has the potential to contribute to our understanding of development as well as to innovations in tissue and organ regeneration. The goal of this project is to develop computational tools for analyzing movies of cells to learn how individual cells change from one type to another one. It is analogous to tracing the routes people take for driving from one city to another one, so one can identify the dominant routes and try to direct traffic to specific route. The Broader Impacts of the work include the intrinsic merit of the research as the platform could prove useful to a number of other projects. Research opportunities will be made available to undergraduate and graduate students along with post-doctoral researchers. Outreach effort will involve the use of YouTube to promote science to the general public.Cells of different phenotypes are characterized by distinct morphology and gene expression patterns. When subject to specific stimuli and microenvironments they can transit between distinct phenotypes, reprogramming to become induced pluripotent stem cells. Mechanistic understanding of such cell phenotypic transition (CPT) processes, however, suffers from the challenge that snapshot data cannot provide temporal information. Live-cell imaging provides an alternative approach for monitoring transitions of individual cells continuously over time. The project aims to develop a generally applicable platform and associated computer package that (1) allows typical cell biology labs to apply the platform and study cellular processes without requirements of a strong background in machine-learning, image analyses, or dynamical systems theories; (2) extends to various imaging platforms and modalities, especially quantitative phase imaging-based label-free imaging that has gained popularity together with machine-learning approaches. Beyond the traditional bioimage informatics approaches, the novelty here is for mechanistic studies of CPT dynamics in the framework of dynamical systems theory/systems biology. In addition, inspired by the critical role the image database ImageNet has played on the recent advance of artificial intelligence development, the researchers will provide annotated cell images generated from this project as benchmarks for the community to develop and test image analysis approaches. The developed framework will be generally applicable to study a large number of cell phenotype conversion processes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.