CAST: An automated segmentation and tracking tool for the analysis of transcriptional kinetics from single-cell time-lapse recordings

• Published in: Methods (San Diego, Calif.) Calif.), 2015-09, Vol.85, p.3-11
• Main Authors: Blanchoud, Simon, Nicolas, Damien, Zoller, Benjamin, Tidin, Onur, Naef, Félix
• Format: Article
• Language: English
• Subjects: Animals; Automation, Laboratory - methods; Image analysis; Image Processing, Computer-Assisted - methods; Kinetics; Mice; NIH 3T3 Cells; Quantitative transcription; Single-cell analysis; Single-Cell Analysis - methods; Time-Lapse Imaging - methods; Time-lapse microscopy; Transcription, Genetic - physiology
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2015.04.023

[Display omitted] •Automated segmentation and tracking platform for single-cell imaging.•Single-cell gene expression traces over multiple cell cycles.•Quantification of transcriptional bursting using bioluminescence imaging.•Comparison between Bmal1 promoter-driven expression in normal and lengthened circadian cycles. 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.