Deep Learning (DL) is becoming an increasingly popular technology being employed in life sciences research due to its ability to perform complex and time-consuming tasks with significantly greater speed, accuracy, and reproducibility than human researchers – allowing them to dedicate their time to more complex tasks. One potential application of DL is to analyze cell images taken by microscopes. Quantitative analysis of cell microscopy images remain a challenge – with manual cell characterization requiring excessive amounts of time and effort. DL can address these issues, by quickly extracting such data and enabling rigorous, empirical analysis of images. Here, DL is used to quantitively analyze images of Mesenchymal Stem Cells (MSCs) differentiating into Osteoblasts (OBs), tracking morphological changes throughout this transition. The changes in morphology throughout the differentiation protocol provide evidence for a distinct path of morphological transformations that the cells undergo in their transition, with changes in perimeter being observable before changes in eceentricity. Subsequent differentiation experiments can be quantitatively compared with our dataset to concretely evaluate how different conditions affect differentiation and this paper can also be used as a guide for researchers on how to utilize DL workflows in their own labs.

深度学习(DL) 正在成为生命科学研究中越来越受欢迎的技术，因为它能够以比人类研究人员更高的速度、准确性和可重复性执行复杂且耗时的任务，从而使他们能够将时间投入到更多的工作中复杂的任务。深度学习的一项潜在应用是分析显微镜拍摄的细胞图像。细胞显微镜图像的定量分析仍然是一个挑战——手动细胞表征需要大量的时间和精力。深度学习可以通过快速提取此类数据并对图像进行严格的实证分析来解决这些问题。在这里，DL 用于定量分析分化为成骨细胞 (OB) 的间充质干细胞 (MSC) 的图像，跟踪整个转变过程中的形态变化。整个分化方案中形态的变化为细胞在转变过程中经历的形态转变的独特路径提供了证据，在偏心度变化之前可以观察到周长的变化。随后的分化实验可以与我们的数据集进行定量比较，以具体评估不同条件如何影响分化，本文也可以作为研究人员如何在自己的实验室中利用深度学习工作流程的指南。