Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, India.
Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India.
Integr Biol (Camb). 2022 Dec 30;14(8-12):184-203. doi: 10.1093/intbio/zyac017.
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.
活细胞钙离子（Ca2+）成像技术是记录体外和体内临床前研究中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。在实施这种成像方案时，主要挑战之一是在由于结构排列和药物分布的变化而导致的 Ca2+反应存在显著异质性的情况下提取定量信息。为了填补这一空白，我们提出了使用旋转盘共聚焦显微镜进行延时成像和基于机器学习的自动 Ca2+ 尖峰模式分组框架。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。该框架旨在使用一致流形逼近和投影（UMAP）减少大规模动态响应。特别是，我们提出了在减少超参数数量方面的层次 DBSCAN（HDBSCAN）的适用性。我们发现，在 Ca2+ 尖峰模式的聚类准确性方面，UMAP 辅助的 HDBSCAN 优于现有方法。新颖性之一包括在 Ca2+ 瞬变的分离中应用非线性降维，具有统计相似性。还证明了自动化的流水线是一种可重复且快速的方法，用户输入最小。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，与更高的堆积密度相对应的持续振荡的亚群数量显著增加。与从 Ca2+ 瞬变中传统测量上升时间和衰减比不同，所提出的流水线用于对具有更长持续时间和聚类内模型拟合的复杂模式进行分类。两步过程对于理解细胞之间结构排列的 Ca2+ 振荡的生物物理机制具有潜在意义。
