PDF(Pubmed)
Abstract：
Astrocytes are glycolytically active cells in the central nervous system playing a crucial role in various brain processes from homeostasis to neurotransmission. Astrocytes possess a complex branched morphology, frequently examined by fluorescent microscopy. However, staining and fixation may impact the properties of astrocytes, thereby affecting the accuracy of the experimental data of astrocytes dynamics and morphology. On the other hand, phase contrast microscopy can be used to study astrocytes morphology without affecting them, but the post-processing of the resulting low-contrast images is challenging. The main result of this work is a novel approach for recognition and morphological analysis of unstained astrocytes based on machine-learning recognition of microscopic images. We conducted a series of experiments involving the cultivation of isolated astrocytes from the rat brain cortex followed by microscopy. Using the proposed approach, we tracked the temporal evolution of the average total length of branches, branching, and area per astrocyte in our experiments. We believe that the proposed approach and the obtained experimental data will be of interest and benefit to the scientific communities in cell biology, biophysics, and machine learning.
摘要：
星形胶质细胞是中枢神经系统中的糖酵解活性细胞，在从稳态到神经传递的各种脑过程中发挥关键作用。星形胶质细胞具有复杂的分支形态，经常用荧光显微镜检查。然而,染色和固定可能会影响星形胶质细胞的特性，从而影响星形胶质细胞动力学和形态学实验数据的准确性。另一方面,相差显微镜可用于研究星形胶质细胞的形态而不影响它们，但产生的低对比度图像的后处理是具有挑战性的。这项工作的主要结果是一种基于显微图像的机器学习识别的未染色星形胶质细胞的识别和形态分析的新方法。我们进行了一系列实验，涉及从大鼠大脑皮层中培养分离的星形胶质细胞，然后进行显微镜检查。使用所提出的方法，我们追踪了分支平均总长度的时间演变，分支，在我们的实验中每个星形胶质细胞的面积。我们相信，提出的方法和获得的实验数据将对细胞生物学的科学界感兴趣和有益，生物物理学,和机器学习。
