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Detection of silent cells, synchronization and modulatory activity in developing cellular networks.

机译:在发展中的蜂窝网络中检测静默小区,同步和调制活动。

摘要

Developing networks in the immature nervous system and in cellular cultures are characterized by waves of synchronous activity in restricted clusters of cells. Synchronized activity in immature networks is proposed to regulate many different developmental processes, from neuron growth and cell migration, to the refinement of synapses, topographic maps, and the mature composition of ion channels. These emergent activity patterns are not present in all cells simultaneously within the network and more immature "silent" cells, potentially correlated with the presence of silent synapses, are prominent in different networks during early developmental periods. Many current network analyses for detection of synchronous cellular activity utilize activity-based pixel correlations to identify cellular-based regions of interest (ROIs) and coincident cell activity. However, using activity-based correlations, these methods first underestimate or ignore the inactive silent cells within the developing network and second, are difficult to apply within cell-dense regions commonly found in developing brain networks. In addition, previous methods may ignore ROIs within a network that shows transient activity patterns comprising both inactive and active periods. We developed analysis software to semi-automatically detect cells within developing neuronal networks that were imaged using calcium-sensitive reporter dyes. Using an iterative threshold, modulation of activity was tracked within individual cells across the network. The distribution pattern of both inactive and active, including synchronous cells, could be determined based on distance measures to neighboring cells and according to different anatomical layers.
机译:在不成熟的神经系统和细胞培养物中发展的网络的特征是在受限细胞簇中同步活动的波。提出了不成熟网络中的同步活动来调节许多不同的发育过程,从神经元生长和细胞迁移到突触的细化,地形图和离子通道的成熟组成。这些新兴的活动模式并非同时存在于网络中的所有细胞中,在发育的早期,不同网络中的未成熟“沉默”细胞(可能与沉默突触的存在相关)也很突出。用于检测同步细胞活动的许多当前网络分析利用基于活动的像素相关性来识别基于细胞的关注区域(ROI)和一致的细胞活动。然而,使用基于活动的相关性,这些方法首先低估或忽略了正在发育的网络内的非活动沉默细胞,其次,很难在发展中的大脑网络中常见的细胞密集区域内应用。此外,先前的方法可能会忽略网络中的ROI,这些ROI显示出包括非活动时间段和活动时间段的瞬态活动模式。我们开发了分析软件,可半自动检测正在发育的神经元网络中的细胞,这些细胞使用钙敏感的报告染料成像。使用迭代阈值,可以跟踪网络中各个小区内的活动调制。可以基于到相邻单元格的距离度量并根据不同的解剖层来确定非活动和活动(包括同步单元)的分布模式。

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