背景:造血干细胞(HSC)和多能祖细胞(MPPs)在维持终生造血中起关键作用。干细胞和其他祖细胞之间的区别,以及对其职能的评估,长期以来一直是干细胞研究的中心焦点。近年来,深度学习已经成为细胞图像分析和分类/预测的强大工具。
方法:在本研究中,我们探索了采用深度学习技术来区分小鼠HSC和MPPs的可行性,仅基于它们的形态,通过光学显微镜(DIC)图像观察。
结果:经过使用大量图像数据集的严格培训和验证,我们成功开发了一个三类分类器,称为LSM模型,能够可靠地区分长期HSC,短期HSC,和MPPs。LSM模型提取了不同细胞类型特有的内在形态特征,无论用于细胞鉴定和分离的方法如何,例如表面标记或细胞内GFP标记。此外,采用相同的深度学习框架,我们创建了一个有效区分老年HSC和年轻HSC的二类分类器.这一发现是特别重要的,因为两种细胞类型共享相同的表面标记,但具有不同的功能。这个分类器有可能提供一种新颖的,快速,以及评估HSC功能状态的有效方法,从而避免了耗时的移植实验。
结论:我们的研究代表了深度学习在稳态条件下分化HSC和MPPs的开创性应用。这个新颖而强大的基于深度学习的平台将为未来新一代干细胞识别和分离系统的开发提供基础。它还可能为干细胞自我更新的分子机制提供新的见解。
BACKGROUND: Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction.
METHODS: In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images.
RESULTS: After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs, short-term HSCs, and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular
GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments.
CONCLUSIONS: Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into the molecular mechanisms underlying stem cell self-renewal.