Faisal Quadri , Mano Govindaraj , Soja Soman , Niti M. Dhutia , Sanjairaj Vijayavenkataraman
{"title":"发掘隐藏的宝藏:利用深度学习绘制人类间充质干细胞向成骨细胞分化过程中的形态变化图","authors":"Faisal Quadri , Mano Govindaraj , Soja Soman , Niti M. Dhutia , Sanjairaj Vijayavenkataraman","doi":"10.1016/j.micron.2023.103581","DOIUrl":null,"url":null,"abstract":"<div><p>Deep Learning<span> (DL) is becoming an increasingly popular technology being employed in life sciences research due to its ability to perform complex and time-consuming tasks with significantly greater speed, accuracy, and reproducibility than human researchers – allowing them to dedicate their time to more complex tasks. One potential application of DL is to analyze cell images taken by microscopes. Quantitative analysis of cell microscopy images remain a challenge – with manual cell characterization requiring excessive amounts of time and effort. DL can address these issues, by quickly extracting such data and enabling rigorous, empirical analysis of images. Here, DL is used to quantitively analyze images of Mesenchymal Stem Cells (MSCs) differentiating into Osteoblasts (OBs), tracking morphological changes throughout this transition. The changes in morphology throughout the differentiation protocol provide evidence for a distinct path of morphological transformations that the cells undergo in their transition, with changes in perimeter being observable before changes in eceentricity. Subsequent differentiation experiments can be quantitatively compared with our dataset to concretely evaluate how different conditions affect differentiation and this paper can also be used as a guide for researchers on how to utilize DL workflows in their own labs.</span></p></div>","PeriodicalId":18501,"journal":{"name":"Micron","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Uncovering hidden treasures: Mapping morphological changes in the differentiation of human mesenchymal stem cells to osteoblasts using deep learning\",\"authors\":\"Faisal Quadri , Mano Govindaraj , Soja Soman , Niti M. Dhutia , Sanjairaj Vijayavenkataraman\",\"doi\":\"10.1016/j.micron.2023.103581\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Deep Learning<span> (DL) is becoming an increasingly popular technology being employed in life sciences research due to its ability to perform complex and time-consuming tasks with significantly greater speed, accuracy, and reproducibility than human researchers – allowing them to dedicate their time to more complex tasks. One potential application of DL is to analyze cell images taken by microscopes. Quantitative analysis of cell microscopy images remain a challenge – with manual cell characterization requiring excessive amounts of time and effort. DL can address these issues, by quickly extracting such data and enabling rigorous, empirical analysis of images. Here, DL is used to quantitively analyze images of Mesenchymal Stem Cells (MSCs) differentiating into Osteoblasts (OBs), tracking morphological changes throughout this transition. The changes in morphology throughout the differentiation protocol provide evidence for a distinct path of morphological transformations that the cells undergo in their transition, with changes in perimeter being observable before changes in eceentricity. Subsequent differentiation experiments can be quantitatively compared with our dataset to concretely evaluate how different conditions affect differentiation and this paper can also be used as a guide for researchers on how to utilize DL workflows in their own labs.</span></p></div>\",\"PeriodicalId\":18501,\"journal\":{\"name\":\"Micron\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-01-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Micron\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0968432823001798\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MICROSCOPY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Micron","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968432823001798","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MICROSCOPY","Score":null,"Total":0}
Uncovering hidden treasures: Mapping morphological changes in the differentiation of human mesenchymal stem cells to osteoblasts using deep learning
Deep Learning (DL) is becoming an increasingly popular technology being employed in life sciences research due to its ability to perform complex and time-consuming tasks with significantly greater speed, accuracy, and reproducibility than human researchers – allowing them to dedicate their time to more complex tasks. One potential application of DL is to analyze cell images taken by microscopes. Quantitative analysis of cell microscopy images remain a challenge – with manual cell characterization requiring excessive amounts of time and effort. DL can address these issues, by quickly extracting such data and enabling rigorous, empirical analysis of images. Here, DL is used to quantitively analyze images of Mesenchymal Stem Cells (MSCs) differentiating into Osteoblasts (OBs), tracking morphological changes throughout this transition. The changes in morphology throughout the differentiation protocol provide evidence for a distinct path of morphological transformations that the cells undergo in their transition, with changes in perimeter being observable before changes in eceentricity. Subsequent differentiation experiments can be quantitatively compared with our dataset to concretely evaluate how different conditions affect differentiation and this paper can also be used as a guide for researchers on how to utilize DL workflows in their own labs.
期刊介绍:
Micron is an interdisciplinary forum for all work that involves new applications of microscopy or where advanced microscopy plays a central role. The journal will publish on the design, methods, application, practice or theory of microscopy and microanalysis, including reports on optical, electron-beam, X-ray microtomography, and scanning-probe systems. It also aims at the regular publication of review papers, short communications, as well as thematic issues on contemporary developments in microscopy and microanalysis. The journal embraces original research in which microscopy has contributed significantly to knowledge in biology, life science, nanoscience and nanotechnology, materials science and engineering.