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Spheroid Trapping and Calcium Spike Estimation Techniques toward Automation of 3D Culture.
SLAS Technology: Translating Life Sciences Innovation ( IF 2.5 ) Pub Date : 2020-07-16 , DOI: 10.1177/2472630320938319
Kenneth Ndyabawe 1 , Mark Haidekker 2 , Amish Asthana 1 , William S Kisaalita 1
Affiliation  

We present a spheroid trapping device, compatible with traditional tissue culture plates, to confine microtissues in a small area and allow suspension cultures to be treated like adherent cultures with minimal loss of spheroids due to aspiration. We also illustrate an automated morphology-independent procedure for cell recognition, segmentation, and a calcium spike detection technique for high-throughput analysis in 3D cultured tissue. Our cell recognition technique uses a maximum intensity projection of spatial-temporal data to create a binary mask, which delineates individual cell boundaries and extracts mean fluorescent data for each cell through a series of intensity thresholding and cluster labeling operations. The temporal data are subject to sorting for imaging artifacts, baseline correction, smoothing, and spike detection algorithms. We validated this procedure through analysis of calcium data from 2D and 3D SHSY-5Y cell cultures. Using this approach, we rapidly created regions of interest (ROIs) and extracted fluorescent intensity data from hundreds of cells in the field of view with superior data fidelity over hand-drawn ROIs even in dense (3D tissue) cell populations. We sorted data from cells with imaging artifacts (such as photo bleaching and dye saturation), classified nonfiring and firing cells, estimated the number of spikes in each cell, and documented the results, facilitating large-scale calcium imaging analysis in both 2D and 3D cultures. Since our recognition and segmentation technique is independent of morphology, our protocol provides a versatile platform for the analysis of large confocal calcium imaging data from neuronal cells, glial cells, and other cell types.



中文翻译:

用于 3D 培养自动化的球体捕获和钙尖峰估计技术。

我们提出了一种与传统组织培养板兼容的球体捕获装置,可将微组织限制在一个小区域内,并允许将悬浮培养物像贴壁培养物一样处理,从而最大限度地减少因抽吸造成的球体损失。我们还说明了用于细胞识别、分割和钙尖峰检测技术的自动形态独立程序,用于在 3D 培养组织中进行高通量分析。我们的细胞识别技术使用时空数据的最大强度投影来创建二进制掩码,它描绘了单个细胞边界并通过一系列强度阈值和集群标记操作提取每个细胞的平均荧光数据。时间数据要经过排序以进行成像伪影、基线校正、平滑和尖峰检测算法。我们通过分析来自 2D 和 3D SHSY-5Y 细胞培养物的钙数据验证了这一过程。使用这种方法,我们快速创建了感兴趣区域 (ROI),并从视野中的数百个细胞中提取了荧光强度数据,即使在密集(3D 组织)细胞群中,其数据保真度也优于手绘 ROI。我们从具有成像伪影(如光漂白和染料饱和)的细胞中分类数据,分类非发射和发射细胞,估计每个细胞中的尖峰数量,并记录结果,促进 2D 和 3D 中的大规模钙成像分析文化。由于我们的识别和分割技术独立于形态学,我们的协议提供了一个多功能平台,用于分析来自神经元细胞、神经胶质细胞、

更新日期:2020-07-16
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