Torres-García Wandaliz, Ashili Shashanka, Kelbauskas Laimonas, Johnson Roger H, Zhang Weiwen, Runger George C, Meldrum Deirdre R
Arizona State University, Tempe, AZ 85287-5906, USA.
Mol Biosyst. 2012 Mar;8(3):804-17. doi: 10.1039/c2mb05429a. Epub 2012 Jan 5.
Phenotypic characterization of individual cells provides crucial insights into intercellular heterogeneity and enables access to information that is unavailable from ensemble averaged, bulk cell analyses. Single-cell studies have attracted significant interest in recent years and spurred the development of a variety of commercially available and research-grade technologies. To quantify cell-to-cell variability of cell populations, we have developed an experimental platform for real-time measurements of oxygen consumption (OC) kinetics at the single-cell level. Unique challenges inherent to these single-cell measurements arise, and no existing data analysis methodology is available to address them. Here we present a data processing and analysis method that addresses challenges encountered with this unique type of data in order to extract biologically relevant information. We applied the method to analyze OC profiles obtained with single cells of two different cell lines derived from metaplastic and dysplastic human Barrett's esophageal epithelium. In terms of method development, three main challenges were considered for this heterogeneous dynamic system: (i) high levels of noise, (ii) the lack of a priori knowledge of single-cell dynamics, and (iii) the role of intercellular variability within and across cell types. Several strategies and solutions to address each of these three challenges are presented. The features such as slopes, intercepts, breakpoint or change-point were extracted for every OC profile and compared across individual cells and cell types. The results demonstrated that the extracted features facilitated exposition of subtle differences between individual cells and their responses to cell-cell interactions. With minor modifications, this method can be used to process and analyze data from other acquisition and experimental modalities at the single-cell level, providing a valuable statistical framework for single-cell analysis.
单个细胞的表型特征为深入了解细胞间异质性提供了关键见解，并能获取从整体平均的大量细胞分析中无法获得的信息。近年来，单细胞研究引起了广泛关注，并推动了各种商业可用和研究级技术的发展。为了量化细胞群体中细胞间的变异性，我们开发了一个实验平台，用于在单细胞水平实时测量氧消耗（OC）动力学。这些单细胞测量存在一些固有的独特挑战，并且没有现有的数据分析方法可以解决这些挑战。在此，我们提出一种数据处理和分析方法，以应对这种独特类型数据所遇到的挑战，从而提取生物学相关信息。我们应用该方法分析了从化生和发育异常的人巴雷特食管上皮衍生的两种不同细胞系的单细胞获得的OC谱。在方法开发方面，针对这个异质动态系统考虑了三个主要挑战：（i）高噪声水平，（ii）缺乏单细胞动力学的先验知识，以及（iii）细胞类型内和细胞类型间细胞间变异性的作用。本文提出了应对这三个挑战的几种策略和解决方案。为每个OC谱提取诸如斜率、截距、断点或变化点等特征，并在单个细胞和细胞类型之间进行比较。结果表明，提取的特征有助于揭示单个细胞之间的细微差异及其对细胞间相互作用的反应。只需进行少量修改，该方法就可用于处理和分析单细胞水平上来自其他采集和实验模式的数据，为单细胞分析提供了一个有价值的统计框架。
