Label-Free Prediction of Fluorescently Labeled Fibrin Networks.

IF 8.1 Q1 ENGINEERING, BIOMEDICAL
Biomaterials research Pub Date : 2025-05-28 eCollection Date: 2025-01-01 DOI:10.34133/bmr.0211
Sarah Eldeen, Andres Felipe Guerrero Ramirez, Bora Keresteci, Peter D Chang, Elliot L Botvinick
{"title":"Label-Free Prediction of Fluorescently Labeled Fibrin Networks.","authors":"Sarah Eldeen, Andres Felipe Guerrero Ramirez, Bora Keresteci, Peter D Chang, Elliot L Botvinick","doi":"10.34133/bmr.0211","DOIUrl":null,"url":null,"abstract":"<p><p>While fluorescent labeling has been the standard for visualizing fibers within fibrillar scaffold models of the extracellular matrix (ECM), the use of fluorescent dyes can compromise cell viability and photobleach prematurely. The intricate fibrillar composition of ECM is crucial for its viscoelastic properties, which regulate intracellular signaling and provide structural support for cells. Naturally derived biomaterials such as fibrin and collagen replicate these fibrillar structures, but longitudinal confocal imaging of fibers using fluorescent dyes may impact cell function and photobleach the sample long before termination of the experiment. An alternative technique is reflection confocal microscopy (RCM) that provides high-resolution images of fibers. However, RCM is sensitive to fiber orientation relative to the optical axis, and consequently, many fibers are not detected. We aim to recover these fibers. Here, we propose a deep learning tool for predicting fluorescently labeled optical sections from unlabeled image stacks. Specifically, our model is conditioned to reproduce fluorescent labeling using RCM images at 3 laser wavelengths and a single laser transmission image. The model is implemented using a fully convolutional image-to-image mapping architecture with a hybrid loss function that includes both low-dimensional statistical and high-dimensional structural components. Upon convergence, the proposed method accurately recovers 3-dimensional fibrous architecture without substantial differences in fiber length or fiber count. However, the predicted fibers were slightly wider than original fluorescent labels (0.213 ± 0.009 μm). The model can be implemented on any commercial laser scanning microscope, providing wide use in the study of ECM biology.</p>","PeriodicalId":93902,"journal":{"name":"Biomaterials research","volume":"29 ","pages":"0211"},"PeriodicalIF":8.1000,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12117218/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomaterials research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34133/bmr.0211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

While fluorescent labeling has been the standard for visualizing fibers within fibrillar scaffold models of the extracellular matrix (ECM), the use of fluorescent dyes can compromise cell viability and photobleach prematurely. The intricate fibrillar composition of ECM is crucial for its viscoelastic properties, which regulate intracellular signaling and provide structural support for cells. Naturally derived biomaterials such as fibrin and collagen replicate these fibrillar structures, but longitudinal confocal imaging of fibers using fluorescent dyes may impact cell function and photobleach the sample long before termination of the experiment. An alternative technique is reflection confocal microscopy (RCM) that provides high-resolution images of fibers. However, RCM is sensitive to fiber orientation relative to the optical axis, and consequently, many fibers are not detected. We aim to recover these fibers. Here, we propose a deep learning tool for predicting fluorescently labeled optical sections from unlabeled image stacks. Specifically, our model is conditioned to reproduce fluorescent labeling using RCM images at 3 laser wavelengths and a single laser transmission image. The model is implemented using a fully convolutional image-to-image mapping architecture with a hybrid loss function that includes both low-dimensional statistical and high-dimensional structural components. Upon convergence, the proposed method accurately recovers 3-dimensional fibrous architecture without substantial differences in fiber length or fiber count. However, the predicted fibers were slightly wider than original fluorescent labels (0.213 ± 0.009 μm). The model can be implemented on any commercial laser scanning microscope, providing wide use in the study of ECM biology.

荧光标记纤维蛋白网络的无标记预测。
虽然荧光标记已成为细胞外基质(ECM)纤维支架模型中纤维可视化的标准,但荧光染料的使用可能会损害细胞活力并过早地进行光漂白。ECM复杂的纤维组成对其粘弹性特性至关重要,粘弹性特性调节细胞内信号传导并为细胞提供结构支持。天然来源的生物材料,如纤维蛋白和胶原蛋白复制了这些纤维结构,但使用荧光染料对纤维进行纵向共聚焦成像可能会影响细胞功能,并在实验结束前很久使样品发生光漂白。另一种技术是反射共聚焦显微镜(RCM),它可以提供高分辨率的纤维图像。然而,RCM对光纤相对于光轴的方向很敏感,因此,许多光纤没有被检测到。我们的目标是回收这些纤维。在这里,我们提出了一种深度学习工具,用于从未标记的图像堆栈中预测荧光标记的光学切片。具体来说,我们的模型可以使用3个激光波长的RCM图像和单个激光透射图像来重现荧光标记。该模型使用全卷积图像到图像映射架构实现,该架构具有混合损失函数,包括低维统计和高维结构组件。在收敛后,该方法可以准确地恢复三维纤维结构,而不存在纤维长度或纤维计数的实质性差异。然而,预测的纤维比原始荧光标签略宽(0.213±0.009 μm)。该模型可在任何商用激光扫描显微镜上实现,为ECM生物学的研究提供了广泛的应用。
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
自引率
0.00%
发文量
0
×
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