BetaBuddy: An end-to-end computer vision pipeline for the automated analysis of insulin secreting β-cells.
BetaBuddy: An end-to-end computer vision pipeline for the automated analysis of insulin secreting β-cells.
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BetaBuddy：用于自动分析胰岛素分泌β细胞的端到端计算机视觉管道。
DOI：
10.1101/2023.04.06.535890
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Luber,JacobM
中科院分区：
文献类型：
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作者：
Alsup,AnneM;Fowlds,Kelli;Cho,Michael;Luber,JacobM
胰腺β细胞的胰岛素分泌在维持血糖水平的微妙平衡中是不可或缺的。钙是已知的一个关键调节器，并触发胰岛素的释放。这种亚细胞过程可以通过活细胞成像和随后的细胞分割、配准、跟踪和每个细胞中钙水平的分析来监测和跟踪。目前的分析方法通常需要手动概述β细胞，涉及多个软件包，并需要多个研究人员-所有这些都倾向于引入偏倚。因此，利用深度学习算法，我们创建了一个自动分割和跟踪数千个细胞的管道，这大大减少了收集和分析大量亚细胞图像所需的时间，并提高了准确性。在时间序列图像堆栈上跟踪细胞还允许研究人员分离特定的钙尖峰模式并在空间上识别感兴趣的钙尖峰模式，从而创建一个高效且用户友好的分析工具。使用我们的自动化流水线，重新分析了先前用于评价电场刺激后β细胞中钙尖峰活性变化的数据集。发现先前手动分割低估了尖峰活动的变化。此外，机器学习管道提供了一种强大而快速的计算方法来检查，例如，钙信号如何通过β细胞簇中的细胞内相互作用进行调节。
Insulin secretion from pancreatic β-cells is integral in maintaining the delicate equilibrium of blood glucose levels. Calcium is known to be a key regulator and triggers the release of insulin. This sub-cellular process can be monitored and tracked through live-cell imaging and subsequent cell segmentation, registration, tracking, and analysis of the calcium level in each cell. Current methods of analysis typically require the manual outlining of β-cells, involve multiple software packages, and necessitate multiple researchers - all of which tend to introduce biases. Utilizing deep learning algorithms, we have therefore created a pipeline to automatically segment and track thousands of cells, which greatly reduces the time required to gather and analyze a large number of sub-cellular images and improve accuracy. Tracking cells over a time-series image stack also allows researchers to isolate specific calcium spiking patterns and spatially identify those of interest, creating an efficient and user-friendly analysis tool. Using our automated pipeline, a previous dataset used to evaluate changes in calcium spiking activity in β-cells post-electric field stimulation was reanalyzed. Changes in spiking activity were found to be underestimated previously with manual segmentation. Moreover, the machine learning pipeline provides a powerful and rapid computational approach to examine, for example, how calcium signaling is regulated by intracellular interactions in a cluster of β-cells.