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Characterizing liver sinusoidal endothelial cell fenestrae on soft substrates upon AFM imaging and deep learning

文献类型：期刊论文

作者Li PW(李佩文)3,4,5; Zhou J(周瑾)3,4; Li W(李旺)2,3,4; Wu H(吴欢)1,3,4; Hu JR(胡锦荣)3,4; Ding QH(丁奇寒)2,3,4; Lv SQ(吕守芹)2,3,4; Pan J1; Zhang CY5; Li N(李宁)2,3,4
刊名BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS
出版日期2020-12-01
卷号1864期号:12页码:9
ISSN号0304-4165
关键词Liver sinusoidal endothelial cells Fenestrae Atomic force microscopy Imaging recognition Fully convolutional networks Substrate stiffness
DOI10.1016/j.bbagen.2020.129702
通讯作者Zhang, Chunyu(zzccyy1977@bit.edu.cn) ; Li, Ning(lining_1@imech.ac.cn) ; Long, Mian(mlong@imech.ac.cn)
英文摘要Background: Liver sinusoidal endothelial cells (LSECs) display unique fenestrated morphology. Alterations in the size and number of fenestrae play a crucial role in the progression of various liver diseases. While their features have been visualized using atomic force microscopy (AFM), the in situ imaging methods and off-line analyses are further required for fenestra quantification. Methods: Primary mouse LSECs were cultured on a collagen-I-coated culture dish, or a polydimethylsiloxane (PDMS) or polyacrylamide (PA) hydrogel substrate. An AFM contact mode was applied to visualize fenestrae on individual fixed LSECs. Collected images were analyzed using an in-house developed image recognition program based on fully convolutional networks (FCN). Results: Key scanning parameters were first optimized for visualizing the fenestrae on LSECs on culture dish, which was also applicable for the LSECs cultured on various hydrogels. The intermediate-magnification morphology images of LSECs were used for developing the FCN-based, fenestra recognition program. This program enabled us to recognize the vast majority of fenestrae from AFM images after twice trainings at a typical accuracy of 81.6% on soft substrate and also quantify the statistics of porosity, number of fenestrae and distribution of fenestra diameter. Conclusions: Combining AFM imaging with FCN training is able to quantify the morphological distributions of LSEC fenestrae on various substrates. Significance: AFM images acquired and analyzed here provided the global information of surface ultramicroscopic structures over an entire cell, which is fundamental in understanding their regulatory mechanisms and pathophysiological relevance in fenestra-like evolution of individual cells on stiffness-varied substrates.
分类号二类
WOS关键词STIFFNESS ; DYNAMICS
资助项目National Natural Science Foundation of China[91642203] ; National Natural Science Foundation of China[31627804] ; National Natural Science Foundation of China[31661143044] ; National Natural Science Foundation of China[31870930] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC018] ; Chinese Academy of Sciences[XDB22040101] ; National Key Research and Development Program of China[2017YFC0108500]
WOS研究方向Biochemistry & Molecular Biology ; Biophysics
语种英语
WOS记录号WOS:000573901700002
资助机构National Natural Science Foundation of China ; Chinese Academy of Sciences ; National Key Research and Development Program of China
其他责任者Zhang, Chunyu ; Li, Ning ; Long, Mian
源URL[http://dspace.imech.ac.cn/handle/311007/85309]  
专题力学研究所_国家微重力实验室
作者单位1.Chongqing Univ, Minist Educ, Key Lab Biorheol Sci & Technol, Chongqing 400044, Peoples R China
2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China;
3.Chinese Acad Sci, Beijing Key Lab Engn Construct & Mechanobiol, Inst Mech, Beijing 100190, Peoples R China;
4.Chinese Acad Sci, Ctr Biomech & Bioengn, Inst Mech, Key Lab Micrograv,Natl Micrograv Lab, Beijing 100190, Peoples R China;
5.Beijing Inst Technol, Sch Life Sci, Beijing 10081, Peoples R China;
推荐引用方式
GB/T 7714		 Li PW,Zhou J,Li W,et al. Characterizing liver sinusoidal endothelial cell fenestrae on soft substrates upon AFM imaging and deep learning[J]. BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS,2020,1864(12):9.
APA 李佩文.,周瑾.,李旺.,吴欢.,胡锦荣.,...&Li W.(2020).Characterizing liver sinusoidal endothelial cell fenestrae on soft substrates upon AFM imaging and deep learning.BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS,1864(12),9.
MLA 李佩文,et al."Characterizing liver sinusoidal endothelial cell fenestrae on soft substrates upon AFM imaging and deep learning".BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS 1864.12(2020):9.

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来源：力学研究所

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