Live cell calcium (Ca2+) imaging is one of the important tools to record cellular activity during in vitro and in vivo preclinical studies. Specially, high-resolution microscopy can provide valuable dynamic information at the single cell level. One of the major challenges in the implementation of such imaging schemes is to extract quantitative information in the presence of significant heterogeneity in Ca2+ responses attained due to variation in structural arrangement and drug distribution. To fill this gap, we propose time-lapse imaging using spinning disk confocal microscopy and machine learning-enabled framework for automated grouping of Ca2+ spiking patterns. Time series analysis is performed to correlate the drug induced cellular responses to self-assembly pattern present in multicellular systems. The framework is designed to reduce the large-scale dynamic responses using uniform manifold approximation and projection (UMAP). In particular, we propose the suitability of hierarchical DBSCAN (HDBSCAN) in view of reduced number of hyperparameters. We find UMAP-assisted HDBSCAN outperforms existing approaches in terms of clustering accuracy in segregation of Ca2+ spiking patterns. One of the novelties includes the application of non-linear dimension reduction in segregation of the Ca2+ transients with statistical similarity. The proposed pipeline for automation was also proved to be a reproducible and fast method with minimal user input. The algorithm was used to quantify the effect of cellular arrangement and stimulus level on collective Ca2+ responses induced by GPCR targeting drug. The analysis revealed a significant increase in subpopulation containing sustained oscillation corresponding to higher packing density. In contrast to traditional measurement of rise time and decay ratio from Ca2+ transients, the proposed pipeline was used to classify the complex patterns with longer duration and cluster-wise model fitting. The two-step process has a potential implication in deciphering biophysical mechanisms underlying the Ca2+ oscillations in context of structural arrangement between cells.

中文翻译：

---

细胞结构排列和集体钙瞬变的映射：使用共聚焦显微镜和基于 UMAP 辅助 HDBSCAN 的方法结合活细胞成像的集成框架。

活细胞钙 (Ca2+) 成像是体外和体内临床前研究中记录细胞活动的重要工具之一。特别是，高分辨率显微镜可以在单细胞水平上提供有价值的动态信息。实施此类成像方案的主要挑战之一是在由于结构排列和药物分布的变化而获得的 Ca2+ 响应存在显着异质性的情况下提取定量信息。为了填补这一空白，我们建议使用旋转圆盘共聚焦显微镜和支持机器学习的框架进行延时成像，以自动分组 Ca2+ 尖峰模式。进行时间序列分析以将药物诱导的细胞反应与多细胞系统中存在的自组装模式相关联。该框架旨在使用统一流形近似和投影 (UMAP) 来减少大规模动态响应。特别是，鉴于超参数数量减少，我们提出了分层 DBSCAN (HDBSCAN) 的适用性。我们发现 UMAP 辅助的 HDBSCAN 在 Ca2+ 尖峰模式分离的聚类准确性方面优于现有方法。其中一项创新包括非线性降维在具有统计相似性的 Ca2+ 瞬态分离中的应用。拟议的自动化管道也被证明是一种可重现且快速的方法，用户输入最少。该算法用于量化细胞排列和刺激水平对 GPCR 靶向药物诱导的集体 Ca2+ 反应的影响。分析表明，包含与较高堆积密度相对应的持续振荡的亚群显着增加。与传统的 Ca2+ 瞬态上升时间和衰减率测量相比，所提出的管道用于对持续时间较长的复杂模式进行分类，并进行集群模型拟合。两步过程对破译细胞间结构排列背景下 Ca2+ 振荡的生物物理机制具有潜在意义。