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WIREs Dev Biol
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New methods to image transcription in living fly embryos: the insights so far, and the prospects

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The regulation of transcription is a fundamental process underlying the determination of cell identity and its maintenance during development. In the last decades, most of the transcription factors, which have to be expressed at the right place and at the right time for the proper development of the fly embryo, have been identified. However, mostly because of the lack of methods to visualize transcription as the embryo develops, their coordinated spatiotemporal dynamics remains largely unexplored. Efforts have been made to decipher the transcription process with single molecule resolution at the single cell level. Recently, the fluorescent labeling of nascent RNA in developing fly embryos allowed the direct visualization of ongoing transcription at single loci within each nucleus. Together with powerful imaging and quantitative data analysis, these new methods provide unprecedented insights into the temporal dynamics of the transcription process and its intrinsic noise. Focusing on the Drosophila embryo, we discuss how the detection of single RNA molecules enhanced our comprehension of the transcription process and we outline the potential next steps made possible by these new imaging tools. In combination with genetics and theoretical analysis, these new imaging methods will aid the search for the mechanisms responsible for the robustness of development. WIREs Dev Biol 2016, 5:296–310. doi: 10.1002/wdev.221 This article is categorized under: Gene Expression and Transcriptional Hierarchies > Regulatory Mechanisms Gene Expression and Transcriptional Hierarchies > Quantitative Methods and Models
Noise in gene expression from single cells to Drosophila embryos, as detected in fixed tissues. (a) Hypothetical situation in which an isogenic population of cells within a homogenous environment exhibits cell‐to‐cell variability in the gene expression pattern at a given time. Time‐invariant heterogeneity and time‐fluctuating heterogeneity are two very distinct scenarios which could explain this variability. However, as at a given time the distributions of mRNA/cell are very similar for both scenarios, and thus the snapshot analysis on fixed tissues cannot provide a direct insight into the underlying transcriptional dynamics. (b) Transcriptional activation of the hunchback (hb) gene by the Bicoid transcription factor gradient. Transcription of hb is highly synchronous (a majority of bi‐allelic expression) in the anterior half of the early Drosophila embryo whereas transcription in not detected in the posterior half. While the transition zone is narrow, most of the active nuclei within this zone express only one allele and the border separating expressing from nonexpressing nuclei in the transition zone is not rectilinear. (c) The transcription from the Sex combs reduced (Scr) gene within the parasegment 2 (PS2) at nc14 embryo using a single mRNA resolution. A high variability in the distribution of nascent versus mature mRNA is observed among those cells. The absence of correlation between the amount of nascent transcript and the total amount of mRNA per cell argues for transcriptional bursting of Scr. The Fano factor is an expression of this cell‐to‐cell variability, and values greater than 1 indicate bursting. (c) Transcription dynamics of the two Decapentaplegic target genes, tail‐up and panier at nc14. The two genes are first heterogeneously expressed among nuclei and reach a more synchronous expression at the end of nc14. The time interval it takes for tail‐up to be expressed in 50% of the nuclei is shorter than for panier. The length of the transition period from heterogeneous to synchronous expression is anti‐correlated to the amount of RNA PolII pausing at the promoter.
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Bursting or nonbursting gene activity, the two types of transcription dynamics of a promoter. A continuum between the two extreme situations portrayed here can be found for different genes. (a) The amount of nascent RNA produced at the promoter fluctuates around a given positive value. The RNA PolII initiates transcription at an average constant rate. The promoter is active and nonbursting. (b) The activity of the promoter (measured as the amount of nascent RNA produced at a given time) alternates between periods of strong production (bursts) and periods of inactivity. The promoter is bursting. The characteristics of a bursting promoter include the frequency of the bursts, the intensity of the bursts, and their duration.
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Detecting ongoing transcription in young Drosophila embryos. (a) The young fruit fly embryo is a unique cell of ellipsoidal shape. At egg laying, the nucleus of the fertilized egg undergoes 13 rapid divisions over a two hour period. These divisions first occur in the center of the embryo. At nuclear cycle 6 (nc 6), nuclei start their migration from the center to the periphery of the cell and spread in a single layer at the surface of the cell to give rise after about one hour of development (nc 8–nc 9) to the syncytial blastoderm. Once at the periphery, nuclei continue to divide for 5 more rapid divisions before reaching the long nc14 during which the cellularization process occurs. The first hints of zygotic transcription are detected at nc8. (b) On the right, nuclei (blue, nuclear envelope labeled with WGA‐AlexaFluor‐633) are visualized at the surface of the whole embryo at nc11 and on the left, a close up of expressing nuclei (taken from the dashed square on the right). Expression of a given gene of interest (here hunchback) can be detected by RNA FISH with fluorescently labeled anti‐sense RNA probes. Expression is revealed by two type of staining: speckle‐like dots (arrow heads) corresponding to single mRNA and bright intense foci (arrow) corresponding to the accumulation of several nascent pre‐mRNAs at their site of synthesis, as schematically diagrammed for the two hunchback loci.
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The fluctuations of the promoter cycle and of the fluorescent signal are better correlated when the SL tagging is inserted in 3′ of the transcribed sequence. The black curves in panels a, b, and c represent the autocorrelation function of the promoter activity simulated as a multi‐off state model with seven different conditions (cartoon) corresponding to different kON rate and kOFF rates. The fluorescent signal was calculated for three different constructs and each of the seven conditions of the promoter cycle model. The autocorrelation functions corresponding to the fluorescent signal (readout, color) were compared to the autocorrelation of the promoter state (input, black). The SL sequence for fluorescent tagging of the RNA was inserted in the 3′ end of a 5 kb‐long transcribed sequence (a, blue), in the 5′ end of a 5 kb‐long transcribed sequence (b, red) or in the 5′ end of a 10 kb‐long transcribed sequence (c, pink). When the SL tagging sequence is inserted in the 3′ end of the transcribed sequence, the autocorrelation of the promoter cycle and the calculated fluorescent signal are almost superimposed. The similarity is weaker when the SL tagging sequence is inserted in the 5′ end and the difference increases with the length of the transcribed sequence. (d) Comparison of the decay time of the promoter activity autocorrelation and the decay time of the fluorescent readout autocorrelation, for the situations described in a (blue), b (red), and c (pink). The dashed gray line indicates perfect agreement indicative of an exact readout. The autocorrelation functions where calculated on 25 different computed traces 10 s long (2.8 hours). The shadows in all panels are standard deviations.
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The autocorrelation function. The autocorrelation function determines the degree of correlation existing in any fluctuating signal (mean m and variance σ). The signal S(t) is multiplied for any time ti with its value at ti + τ, where τ is a delay time. The autocorrelation function at a given time delay is defined as the average of the overlap between the values of the signal taken at two time points at a fixed delay. This function is used to compare the fluctuating signal at two different times in order to quantify temporal correlations. The autocorrelation function describes the persistence of the signal (for example the promoter state). This persistence is characterized by the relaxation time of the autocorrelation function, which is the time at which the autocorrelation function of the signal decays by 1/e, and which represents the average time scale on which the signal has a similar value to its initial value.
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A lower sensitivity to noise background with 5’ insertions but a better correlation between promoter activity and signal readout with 3’ insertions. Simulations were performed assuming an irreversible promoter cycle (with three inactive states) model for transcription activation and deactivation at different frequencies of ON/OFF transitions (high (A), intermediate (B), and low (C)). For each case, we show: the change in the state of promoter activity with the distributions of ON (state 1) and OFF (state 0) waiting times as collected at the promoter (top panel), the simulation of the fluorescent signal detected when the SL tagging sequence is inserted in a 3’ (middle panel) or 5’ (bottom panel) position of the transcribed sequence, assuming a SL length of ~1.3 kb, a transcribed length of 5.4 kb, and an occupancy of ~0.15 kb for one PolII transcribing with a velocity of ~25 bp/s. It is assumed that the concentration of PolIIs is not a limiting factor and that they can bind at every moment when the gene is in the ON state. Multiple PolII molecules can constitutively transcribe the gene during one ON event. The horizontal dashed lines in the middle and bottom panels indicate the background levels frequently encountered. The results do not qualitatively depend on the number of inactive states used, and the reversible two state telegraph model gives qualitatively the same results.
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The position of the MS2 SLs within the transcribed sequence impact on the signal intensity and its persistence. On the left: snapshots of the gene at a given time during the ON state of the promoter (burst). On the right: fluorescent intensity at the locus as a function of time during the burst of transcription. (a) The insertion of the SL tagging sequence at a 5′ position in the gene allows accumulation of labeled transcripts along the whole length of the transcribed locus. This produces a strong fluorescent signal whose initiation time closely coincides with the initiation time of the burst of transcription. However, since this strong fluorescent signal will take longer to decay once the promoter is turned OFF, the overall persistence of the signal will be longer than the ON‐time period of the promoter (right panel). (b) The insertion of the SL tagging sequence inside an intron leads to a longer delay between the onset of the transcription period and signal detection, which depends on the position of the intron relative to the transcription initiation site. Moreover, in this case, the persistence of the signal will also depend on the splicing process. Since splicing takes place during the transcription process or after release, the signal persistence will not properly reflect the real activity period of the promoter (right panel). (c) When the SL tagging sequence is inserted at a 3′ position in the transcribed locus, the delay between the onset of the promoter activity period and the detection of the signal will be even longer than in previous cases. The intensity of the signal is also much lower and therefore more sensitive to the signal‐to‐noise ratio than in the previous cases. However, in this case, because the buffering time is shorter, the persistence of the signal is closer to the activity period of the promoter (right panel). See Movie S1, Supporting Information, for a movie of the transcription process.
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Available and potential methods to fluorescently‐tag nascent RNA in living embryos. (a) Detection of RNA in fixed embryos by RNA FISH only offers a snapshot view. However, it allows studying expression of endogenous loci without genome modification. In addition, it is mostly background free and can provide single molecule resolution. The sun icon indicates advantages of the method; the cloud indicates disadvantages. (b) Available live‐imaging methods provide access to the temporal dynamics of the transcription process. However, they require genome editing and the insertion of exogenous RNA reporter sequences, which can potentially bias the behavior of the promoter or the processing of the RNA. In addition, so far, these methods are limited by an inherent background of fluorescent signals for quantitative analysis. This limitation can potentially be overcome by new approaches such as the IMAGEtag, which combine the tagging of the RNA with FRET.
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