Spatially coordinated dynamic gene transcription in living pituitary tissue

Featherstone, Karen; Hey, Kirsty; Momiji, Hiroshi; McNamara, A.; Patist, Amanda L.; Woodburn, Joanna; Spiller, David G.; Christian, Helen C.; McNeilly, Alan S.; Mullins, John J.; Finkenstädt, Bärbel; Rand, D.A.J.; White, Michael R. H.; Davis, Julian

doi:10.7554/elife.08494

Cited by 56 publications

(83 citation statements)

References 60 publications

(93 reference statements)

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“…In order to model the process of transcription and extract the kinetic parameters of promoter switching, we augmented classic HMMs to account for memory (details about implementation of the method are given in Appendix 3). Similar approaches were recently introduced to study transcriptional dynamics in cell culture and tissue samples (Suter et al, 2011; Molina et al, 2013; Zechner et al, 2014; Zoller et al, 2015; Hey et al, 2015; Bronstein et al, 2015; Corrigan et al, 2016; Featherstone et al, 2016). We used simulated data to establish that mHMM reliably extracts the kinetic parameters of transcriptional bursting from live-imaging data (Appendix 4), providing an ideal tool for dissecting the contributions from individual bursting parameters to observed patterns of transcriptional activity across space and time.…”

Section: Resultsmentioning

confidence: 99%

Multimodal transcriptional control of pattern formation in embryonic development

Lammers

Galstyan

Reimer

et al. 2018

Preprint

110

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Predicting developmental outcomes from regulatory DNA sequence and transcription 17 factor patterns remains an open challenge in physical biology. Using stripe 2 of the even-skipped 18 gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key 19 step along the developmental decision-making cascade: the generation of cytoplasmic mRNA 20 patterns via the control of transcription in individual cells. Using live imaging and computational 21 approaches, we found that the transcriptional burst frequency is modulated across the stripe to 22 control the mRNA production rate. However, we discovered that bursting alone cannot 23 quantitatively recapitulate the formation of the stripe, and that control of the window of time over 24 which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical 25 modeling revealed that these regulatory strategies-bursting and the time window-obey different 26 kinds of regulatory logic, suggesting that the stripe is shaped by the interplay of two distinct 27 underlying molecular processes. 28 29 35the regulatory logic of the enhancer elements that dictate the behavior of these networks, the 36 precise prediction of how gene expression patterns and developmental outcomes are driven by 37 transcription factor concentrations remains a central challenge in the field (Vincent et al., 2016). 38 Predicting developmental outcomes demands a quantitative understanding of the flow of 39 information along the central dogma: how input transcription factors dictate the output rate of 40 1 of 69 Manuscript submitted to bioRxiv mRNA production, how this rate of mRNA production dictates cytoplasmic patterns of mRNA, 41 and how these mRNA patterns lead to protein patterns that feed back into the gene regulatory 42 network. While the connection between transcription factor concentration and output mRNA 43 production rate has been the subject of active research over the last three decades (Lawrence 44 et al.connection between this output rate and 47 In order to uncover the quantitative contribution of these three regulatory strategies to pattern 62 formation, and to determine whether other regulatory strategies are at play, it is necessary to 63 measure the rate of RNA polymerase loading in individual nuclei, in real time, in a living embryo. 64 2 of 69 Manuscript submitted to bioRxiv However, to date, most studies have relied on fixed-tissue techniques such as mRNA FISH and 65 immunofluorescence in order to obtain snapshots of the cytoplasmic distributions of mRNA and 66 protein as development progresses (Jaeger et al., 2004; Fakhouri et al., 2010; Parker et al., 2011; 67 Estrada et al., 2016; Crocker et al., 2016; Verd et al., 2017; Park et al., 2018). Such techniques 68 are virtually silent regarding the regulation of single-cell gene expression over time, and are thus 69 ill-suited to the study of how spatiotemporal variations in transcriptional dynamics give rise to 70 patterns of cytoplasmic mRNA. 71...

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Section: Resultsmentioning

confidence: 99%

Multimodal transcriptional control of pattern formation in embryonic development

Lammers

Galstyan

Reimer

et al. 2018

Preprint

110

View full text Add to dashboard Cite

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“…In the case of the pituitary gland, the length and intensity of the prolactin transcriptional burst become shorter and less intense over age and the frequency is slightly unchanged. Interestingly, in the adult gland, cells that are proximal to each other have a better coordination in expression than distal ones, suggesting a role of the spatial organization and inter-cellular communication in dampening the noise (49). …”

Section: Perspectives Gained From Single-cell Analysismentioning

confidence: 99%

Single-Cell and Single-Molecule Analysis of Gene Expression Regulation

et al. 2016

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Recent advancements in single-cell and single-molecule imaging technologies have resolved biological processes in time and space that are fundamental to understanding the regulation of gene expression. Observations of single-molecule events in their cellular context have revealed highly dynamic aspects of transcriptional and post-transcriptional control in eukaryotic cells. This approach can relate transcription with mRNA abundance and lifetimes. Another key aspect of single-cell analysis is the cell-to-cell variability among populations of cells. Definition of heterogeneity has revealed stochastic processes, determined characteristics of under-represented cell types or transitional states, and integrated cellular behaviors in the context of multicellular organisms. In this review, we discuss novel aspects of gene expression of eukaryotic cells and multicellular organisms revealed by the latest advances in single-cell and single-molecule imaging technology.

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“…In fluorescence microscopy, converting measurements of fluorescence into numbers of molecules is a long-standing challenge. This deficit limits both the ability to combine fluorescence measurements from different experiments into one data set and the application of quantitative analyses of time-series that assumes the numbers of molecules are known [1,2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Estimating numbers of fluorescent molecules in single cells by analysing fluctuations in photobleaching

Bakker

Swain

2018

Preprint

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The impact of fluorescence microscopy has been limited by the difficulties of expressing measurements of fluorescent proteins in numbers of molecules. Absolute numbers enable the integration of results from different laboratories, empower mathematical modelling, and are the bedrock for a quantitative, predictive biology. Here we develop a general algorithm to infer numbers of molecules from fluctuations in the photobleaching of proteins tagged with Green Fluorescent Protein. To untangle measurement noise from stochastic fluctuations, we use the linear noise approximation and Kalman filtering within a framework of Bayesian inference. Not only do our results agree with biochemical measurements for multiple proteins in budding yeast, but we also provide a statistically verified model of measurement noise for fluorescence microscopes. The experiments we require are straightforward and use only a wide-field fluorescence microscope. As such, our approach has the potential to become standard for those practising quantitative fluorescence microscopy.

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Spatially coordinated dynamic gene transcription in living pituitary tissue

Cited by 56 publications

References 60 publications

Multimodal transcriptional control of pattern formation in embryonic development

Multimodal transcriptional control of pattern formation in embryonic development

Single-Cell and Single-Molecule Analysis of Gene Expression Regulation

Estimating numbers of fluorescent molecules in single cells by analysing fluctuations in photobleaching

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