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3 Publications

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    06/21/18 | Imaging dynamic and selective low-complexity domain interactions that control gene transcription.
    Chong S, Dugast-Darzacq C, Liu Z, Dong P, Dailey GM, Cattoglio C, Heckert A, Banala S, Lavis L, Darzacq X, Tjian R
    Science (New York, N.Y.). 2018 Jun 21;361(6400):eaar2555. doi: 10.1126/science.aar2555

    Many eukaryotic transcription factors (TFs) contain intrinsically disordered low-complexity domains (LCDs), but how they drive transactivation remains unclear. Here, live-cell single-molecule imaging reveals that TF-LCDs form local high-concentration interaction hubs at synthetic and endogenous genomic loci. TF-LCD hubs stabilize DNA binding, recruit RNA polymerase II (Pol II), and activate transcription. LCD-LCD interactions within hubs are highly dynamic, display selectivity with binding partners, and are differentially sensitive to disruption by hexanediols. Under physiological conditions, rapid and reversible LCD-LCD interactions occur between TFs and the Pol II machinery without detectable phase separation. Our findings reveal fundamental mechanisms underpinning transcriptional control and suggest a framework for developing single-molecule imaging screens for novel drugs targeting gene regulatory interactions implicated in disease.

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    03/18/18 | Model-free quantification and visualization of colocalization in fluorescence images.
    Taylor AB, Ioannou MS, Aaron J, Chew T
    Cytometry. Part A : the journal of the International Society for Analytical Cytology. 2018 Mar 13:. doi: 10.1002/cyto.a.23356

    The spatial association between fluorescently tagged biomolecules in situ provides valuable insight into their biological relationship. Within the limits of diffraction, such association can be measured using either Pearson's Correlation Coefficient (PCC) or Spearman's Rank Coefficient (SRC), which are designed to measure linear and monotonic correlations, respectively. However, the relationship between real biological signals is often more complex than these measures assume, rendering their results difficult to interpret. Here, we have adapted methods from the field of information theory to measure the association between two probes' concentrations based on their statistical dependence. Our approach is mathematically more general than PCC or SRC, making no assumptions about the type of relationship between the probes. We show that when applied to biological images, our measures provide more intuitive results that are also more robust to outliers and the presence of multiple relationships than PCC or SRC. We also devise a display technique to highlight regions in the input images where the probes' association is higher versus lower. We expect that our methods will allow biologists to more accurately and robustly quantify and visualize the association between two probes in a pair of fluorescence images. © 2018 International Society for Advancement of Cytometry.

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    01/29/18 | Visualizing transcription factor dynamics in living cells.
    Liu Z, Tjian R
    The Journal of Cell Biology. 2018 Jan 29;217(4):1181-91. doi: 10.1083/jcb.201710038

    The assembly of sequence-specific enhancer-binding transcription factors (TFs) at cis-regulatory elements in the genome has long been regarded as the fundamental mechanism driving cell type-specific gene expression. However, despite extensive biochemical, genetic, and genomic studies in the past three decades, our understanding of molecular mechanisms underlying enhancer-mediated gene regulation remains incomplete. Recent advances in imaging technologies now enable direct visualization of TF-driven regulatory events and transcriptional activities at the single-cell, single-molecule level. The ability to observe the remarkably dynamic behavior of individual TFs in live cells at high spatiotemporal resolution has begun to provide novel mechanistic insights and promises new advances in deciphering causal-functional relationships of TF targeting, genome organization, and gene activation. In this review, we review current transcription imaging techniques and summarize converging results from various lines of research that may instigate a revision of models to describe key features of eukaryotic gene regulation.

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