Image-based discrimination of the early stages of mesenchymal stem cell differentiation.

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
{"title":"Image-based discrimination of the early stages of mesenchymal stem cell differentiation.","authors":"Justin Hoffman, Shiyuan Zheng, Huaiying Zhang, Robert F Murphy, Kris Noel Dahl","doi":"10.1091/mbc.E24-02-0095","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11321037/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1091/mbc.E24-02-0095","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/5 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

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.

基于图像辨别间充质干细胞分化的早期阶段。
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。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信