A high-throughput imaging assay for studying gene expression dynamics in live single cells

Authors

• PR Gudla
• G Zaki
• Y Wan
• D Stavreva
• G Pegoraro
• D Larson
• G Hager

Abstract

RNA transcription and splicing are important cellular processes that control gene expression in both prokaryotes and eukaryotes. Concomitant advances in fluorescence microscopy and fluorescent probes have allowed researchers to observe transcription events at single molecule level in single live cells at high spatial resolution and at different orders of temporal scales [1]. The data analysis pipelines used to extract this rich spatial and temporal information at single cell level generally require continuous and hands-on experimenter interaction with the workflow, thus limiting these approaches to a few tens of cells per sample, and to few samples per experiment. We will present a semi-automated high throughput analysis pipeline for studying transcription events (dynamics) in live eukaryotes cells. The proposed data analysis pipeline is based on the open-source workflow software KNIME [2]. Briefly, the analysis pipeline comprises of the following main modules: (i) fully automated, long term 4D time-lapse image acquisition of fluorescently-tagged mRNA molecules using a high-throughput spinning disk confocal microscope; (ii) automatic sub-pixel registration (affine/rigid body) of cells in time-lapse images; (iii) automatic detection and tracking [3] of fluorescently-tagged transcription sites in each cell; (iv) extraction of temporal intensity trajectories for each transcription site; and (5) kinetic modeling of the transcription bursts in the temporal intensity trajectories. When compared to existing methods, the two main advantages of this automated pipeline are: (i) scalability – the pipeline can be run unassisted, and without modifications, on high-performance computing clusters; and (ii) reusability – most of the modules can be used for quantitative analysis of single cell and/or single molecule fluorescent imaging. Altogether, we expect that this approach will increase the throughput of gene expression dynamics experiments by a factor of at least hundred (100), thus opening the possibility of using this assay to screen hundreds of cells per experimental condition in a few hundred samples per experiment. Acknowledgements: The authors would like to acknowledge the support received from High Performance Computing, Center for Information Technology, National Institute of Health and Center for Biomedical Informatics and Information Technology, National Cancer Institute for computational infrastructure. References: 1) D. Larson et. al., “Real-Time Observation of Transcription Initiation and Elongation on an Endogenous Yeast Gene”, Science, 332(6028):475–478, 2011. 2) M. R. Berthold et. al., “KNIME: The Konstanz Information Miner”, Studies in Classification, Data Analysis, and Knowledge Organization, Springer, 2007. 3) K. Jaqaman et. al., “Robust single particle tracking in live cell time-lapse sequences”, Nature Methods, 5(8): 695–702, 2008.

Scientific Focus Area: Computational Biology

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