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.

中文翻译：

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通过AFM成像和深度学习表征软质基质上的肝窦内皮细胞窗孔。

背景

肝窦窦内皮细胞（LSEC）显示独特的窗状形态。窗ene的大小和数量的变化在各种肝脏疾病的进展中起着至关重要的作用。尽管已使用原子力显微镜（AFM）可视化了它们的特征，但对于窗孔定量，还需要原位成像方法和离线分析。

方法

在胶原蛋白包被的培养皿或聚二甲基硅氧烷（PDMS）或聚丙烯酰胺（PA）水凝胶基质上培养原代小鼠LSEC。应用AFM接触模式以可视化单个固定LSEC上的窗饰。使用基于完全卷积网络（FCN）的内部开发的图像识别程序分析收集的图像。

结果

首先对关键扫描参数进行了优化，以可视化培养皿上LSEC上的窗孔，这也适用于在各种水凝胶上培养的LSEC。LSEC的中间放大形态图像用于开发基于FCN的fenestra识别程序。该程序使我们能够在两次训练后在AFM图像上识别出绝大多数的窗孔，其在软质基材上的典型精度为81.6％，并且还可以量化孔隙率，窗孔数和窗孔直径分布的统计数据。

结论

将AFM成像与FCN训练相结合能够量化LSEC窗孔在各种底物上的形态分布。

意义

此处获取和分析的AFM图像提供了整个细胞表面超微结构的全局信息，这对于理解其在刚度可变的基质上单个细胞的窗状样进化中的调控机制和病理生理相关性至关重要。