Blanchoud Simon, Nicolas Damien, Zoller Benjamin, Tidin Onur, Naef Félix
The Institute of Bioengineering (IBI), School of Life Sciences, Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland.
The Institute of Bioengineering (IBI), School of Life Sciences, Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland.
Methods. 2015 Sep 1;85:3-11. doi: 10.1016/j.ymeth.2015.04.023. Epub 2015 Apr 28.
Fluorescence and bioluminescence time-lapse imaging allows to investigate a vast range of cellular processes at single-cell or even subcellular resolution. In particular, time-lapse imaging can provide uniquely detailed information on the fine kinetics of transcription, as well as on biological oscillations such as the circadian and cell cycles. However, we face a paucity of automated methods to quantify time-lapse imaging data with single-cell precision, notably throughout multiple cell cycles. We developed CAST (Cell Automated Segmentation and Tracking platform) to automatically and robustly detect the position and size of cells or nuclei, quantify the corresponding light signals, while taking into account both cell divisions (lineage tracking) and migration events. We present here how CAST analyzes bioluminescence data from a short-lived transcriptional luciferase reporter. However, our flexible and modular implementation makes it easily adaptable to a wide variety of time-lapse recordings. We exemplify how CAST efficiently quantifies single-cell gene expression over multiple cell cycles using mouse NIH3T3 culture cells with a luminescence expression driven by the Bmal1 promoter, a central gene of the circadian oscillator. We further illustrate how such data can be used to quantify transcriptional bursting in conditions of lengthened circadian period, revealing thereby remarkably similar bursting signature compared to the endogenous circadian condition despite marked period lengthening. In summary, we establish CAST as novel tool for the efficient segmentation, signal quantification, and tracking of time-lapse images from mammalian cell culture.
荧光和生物发光延时成像能够在单细胞甚至亚细胞分辨率下研究广泛的细胞过程。特别是，延时成像可以提供关于转录精细动力学以及诸如昼夜节律和细胞周期等生物振荡的独特详细信息。然而，我们面临着缺乏能够以单细胞精度对延时成像数据进行量化的自动化方法，尤其是在多个细胞周期中。我们开发了CAST（细胞自动分割与跟踪平台），以自动且稳健地检测细胞或细胞核的位置和大小，量化相应的光信号，同时考虑细胞分裂（谱系追踪）和迁移事件。我们在此展示CAST如何分析来自短命转录荧光素酶报告基因的生物发光数据。然而，我们灵活且模块化的实现方式使其易于适应各种延时记录。我们举例说明CAST如何使用由昼夜节律振荡器的核心基因Bmal1启动子驱动发光表达的小鼠NIH3T3培养细胞，在多个细胞周期中高效量化单细胞基因表达。我们进一步说明如何利用这些数据在昼夜节律周期延长的条件下量化转录爆发，从而揭示尽管周期明显延长，但与内源性昼夜节律条件相比，转录爆发特征却非常相似。总之，我们将CAST确立为用于高效分割、信号量化以及跟踪来自哺乳动物细胞培养的延时图像的新型工具。
