Abstract
Transcription at individual genes in single cells is often pulsatile and stochastic. A key question emerges regarding how this behaviour contributes to tissue phenotype, but it has been a challenge to quantitatively analyse this in living cells over time, as opposed to studying snap-shots of gene expression state. We have used imaging of reporter gene expression to track transcription in living pituitary tissue. We integrated live-cell imaging data with statistical modelling for quantitative real-time estimation of the timing of switching between transcriptional states across a whole tissue. Multiple levels of transcription rate were identified, indicating that gene expression is not a simple binary 'on-off' process. Immature tissue displayed shorter durations of high-expressing states than the adult. In adult pituitary tissue, direct cell contacts involving gap junctions allowed local spatial coordination of prolactin gene expression. Our findings identify how heterogeneous transcriptional dynamics of single cells may contribute to overall tissue behaviour.
Highlights
Gene expression in single living cells is often pulsatile and heterogeneous between cells (Sanchez and Golding, 2013; Coulon et al, 2013)
We show that shorter durations of activity occur at high transcription rates in immature pituitary glands compared to the adult pituitary; we were unable to detect differences in the distribution of transcription rates in different pituitary states
Patterns of prolactin gene transcription activity in adult pituitary tissue To quantify PRL gene transcription dynamics within living pituitary tissue, we used transgenic rats that contain a destabilised EGFP (d2EGFP) reporter gene expressed under the control of the human PRL (hPRL) gene locus (Semprini et al, 2009)
Summary
Gene expression in single living cells is often pulsatile and heterogeneous between cells (Sanchez and Golding, 2013; Coulon et al, 2013). Transcriptional bursting, defined by periods of RNA synthesis followed by usually longer silent periods, occurs at many genes with characteristic gene-specific timing (Suter et al, 2011). These dynamics have been proposed to be influenced by intrinsic factors that appear stochastic and extrinsic factors that reflect the state of the cell. The analysis of gene expression in single living cells using real-time direct RNA imaging systems confirms these pulsatile kinetics (Chubb et al, 2006; Larson et al, 2013; Martin et al, 2013). Direct RNA analysis is technically challenging and relatively low throughput, even over short time
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