使用高通量显微镜和基于深度学习的细胞识别算法Cellpose跟踪纳米颗粒被细胞摄取

IF 4.1 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Boxuan Yang, Ceri J Richards, Timea B Gandek, Isa de Boer, Itxaso Aguirre-Zuazo, Else Niemeijer, Christoffer Åberg
{"title":"使用高通量显微镜和基于深度学习的细胞识别算法Cellpose跟踪纳米颗粒被细胞摄取","authors":"Boxuan Yang, Ceri J Richards, Timea B Gandek, Isa de Boer, Itxaso Aguirre-Zuazo, Else Niemeijer, Christoffer Åberg","doi":"10.3389/fnano.2023.1181362","DOIUrl":null,"url":null,"abstract":"How many nanoparticles are taken up by human cells is a key question for many applications, both within medicine and safety. While many methods have been developed and applied to this question, microscopy-based methods present some unique advantages. However, the laborious nature of microscopy, in particular the consequent image analysis, remains a bottleneck. Automated image analysis has been pursued to remedy this situation, but offers its own challenges. Here we tested the recently developed deep-learning based cell identification algorithm Cellpose on fluorescence microscopy images of HeLa cells. We found that the algorithm performed very well, and hence developed a workflow that allowed us to acquire, and analyse, thousands of cells in a relatively modest amount of time, without sacrificing cell identification accuracy. We subsequently tested the workflow on images of cells exposed to fluorescently-labelled polystyrene nanoparticles. This dataset was then used to study the relationship between cell size and nanoparticle uptake, a subject where high-throughput microscopy is of particular utility.","PeriodicalId":34432,"journal":{"name":"Frontiers in Nanotechnology","volume":" ","pages":""},"PeriodicalIF":4.1000,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Following nanoparticle uptake by cells using high-throughput microscopy and the deep-learning based cell identification algorithm Cellpose\",\"authors\":\"Boxuan Yang, Ceri J Richards, Timea B Gandek, Isa de Boer, Itxaso Aguirre-Zuazo, Else Niemeijer, Christoffer Åberg\",\"doi\":\"10.3389/fnano.2023.1181362\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"How many nanoparticles are taken up by human cells is a key question for many applications, both within medicine and safety. While many methods have been developed and applied to this question, microscopy-based methods present some unique advantages. However, the laborious nature of microscopy, in particular the consequent image analysis, remains a bottleneck. Automated image analysis has been pursued to remedy this situation, but offers its own challenges. Here we tested the recently developed deep-learning based cell identification algorithm Cellpose on fluorescence microscopy images of HeLa cells. We found that the algorithm performed very well, and hence developed a workflow that allowed us to acquire, and analyse, thousands of cells in a relatively modest amount of time, without sacrificing cell identification accuracy. We subsequently tested the workflow on images of cells exposed to fluorescently-labelled polystyrene nanoparticles. This dataset was then used to study the relationship between cell size and nanoparticle uptake, a subject where high-throughput microscopy is of particular utility.\",\"PeriodicalId\":34432,\"journal\":{\"name\":\"Frontiers in Nanotechnology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2023-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Nanotechnology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fnano.2023.1181362\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Nanotechnology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fnano.2023.1181362","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

人类细胞吸收了多少纳米颗粒是医学和安全领域许多应用的关键问题。虽然已经开发并应用了许多方法来解决这个问题,但基于显微镜的方法具有一些独特的优势。然而,显微镜的费力性质,特别是随之而来的图像分析,仍然是一个瓶颈。自动图像分析一直在寻求解决这种情况,但也带来了自身的挑战。在这里,我们在HeLa细胞的荧光显微镜图像上测试了最近开发的基于深度学习的细胞识别算法Cellpose。我们发现该算法表现非常好,因此开发了一个工作流程,使我们能够在相对适中的时间内获取和分析数千个细胞,而不会牺牲细胞识别的准确性。随后,我们在暴露于荧光标记的聚苯乙烯纳米颗粒的细胞图像上测试了工作流程。然后,该数据集被用于研究细胞大小和纳米颗粒摄取之间的关系,高通量显微镜在这一主题上特别有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Following nanoparticle uptake by cells using high-throughput microscopy and the deep-learning based cell identification algorithm Cellpose
How many nanoparticles are taken up by human cells is a key question for many applications, both within medicine and safety. While many methods have been developed and applied to this question, microscopy-based methods present some unique advantages. However, the laborious nature of microscopy, in particular the consequent image analysis, remains a bottleneck. Automated image analysis has been pursued to remedy this situation, but offers its own challenges. Here we tested the recently developed deep-learning based cell identification algorithm Cellpose on fluorescence microscopy images of HeLa cells. We found that the algorithm performed very well, and hence developed a workflow that allowed us to acquire, and analyse, thousands of cells in a relatively modest amount of time, without sacrificing cell identification accuracy. We subsequently tested the workflow on images of cells exposed to fluorescently-labelled polystyrene nanoparticles. This dataset was then used to study the relationship between cell size and nanoparticle uptake, a subject where high-throughput microscopy is of particular utility.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Frontiers in Nanotechnology
Frontiers in Nanotechnology Engineering-Electrical and Electronic Engineering
CiteScore
7.10
自引率
0.00%
发文量
96
审稿时长
13 weeks
×
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