基于图像辨别间充质干细胞分化的早期阶段。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
ACS Applied Electronic Materials Pub Date : 2024-08-01 Epub Date: 2024-06-05 DOI:10.1091/mbc.E24-02-0095
Justin Hoffman, Shiyuan Zheng, Huaiying Zhang, Robert F Murphy, Kris Noel Dahl
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引用次数: 0

摘要

间充质干细胞(MSCs)是一种可自我更新的多能细胞,可用于细胞和组织治疗。间充质干细胞的细胞数量可在体外扩增,但过早分化会导致细胞数量减少,影响治疗效果。目前的技术无法将 "干样 "细胞群与分化后间叶干细胞群的早期阶段(12 小时)区分开来。在这里,我们利用免疫荧光成像核结构和肌动蛋白结构,并使用基于深度学习的计算机视觉技术来区分间充质干细胞分化的早期阶段(6-12 小时)。由细胞核和肌动蛋白图像训练而成的卷积神经网络(CNN)模型在报告间充质干细胞分化方面具有很高的准确性;仅核图像就能识别分化的早期阶段。同时,我们还发现染色质流动性和异染色质水平或定位在间充质干细胞早期分化过程中发生了变化。这项研究量化了间充质干细胞早期分化过程中细胞结构的变化,并描述了一种基于图像的新型诊断工具,可广泛应用于间充质干细胞的培养、扩增和利用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image-based discrimination of the early stages of mesenchymal stem cell differentiation.

Mesenchymal stem cells (MSCs) are self-renewing, multipotent cells, which can be used in cellular and tissue therapeutics. MSCs cell number can be expanded in vitro, but premature differentiation results in reduced cell number and compromised therapeutic efficacies. Current techniques fail to discriminate the "stem-like" population from early stages (12 h) of differentiated MSC population. Here, we imaged nuclear structure and actin architecture using immunofluorescence and used deep learning-based computer vision technology to discriminate the early stages (6-12 h) of MSC differentiation. Convolutional neural network models trained by nucleus and actin images have high accuracy in reporting MSC differentiation; nuclear images alone can identify early stages of differentiation. Concurrently, we show that chromatin fluidity and heterochromatin levels or localization change during early MSC differentiation. This study quantifies changes in cell architecture during early MSC differentiation and describes a novel image-based diagnostic tool that could be widely used in MSC culture, expansion and utilization.

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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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