Regulation of Transcriptional Bursting and Spatial Patterning in Early Drosophila Embryo Development

Reviewer #1 (Public review):

Summary:

In this article, the authors develop a method to re-analyze published data measuring the transcription dynamics of developmental genes within Drosophila embryos. Using a simple framework, they identify periods of transcriptional activity from traces of MS2 signal and analyze several parameters of these traces. In the five data sets they analyzed, the authors find that each transcriptional "burst" has a largely invariant duration, both across spatial positions in the embryo and across different enhancers and genes, while the time between transcriptional bursts varies more. However, they find that the best predictor of the mean transcription levels at different spatial positions in the embryo is the "activity time" -- the total time from the first to the last transcriptional burst in the observed cell cycle.

Strengths:

(1) The algorithm for analyzing the MS2 transcriptional traces is clearly described and appropriate for the data.

(2) The analysis of the four transcriptional parameters -- the transcriptional burst duration, the time between bursts, the activity time, and the polymerase loading rate is clearly done and logically explained, allowing the reader to observe the different distributions of these values and the relationship between each of these parameters and the overall expression output in each cell. The authors make a convincing case that the activity time is the best predictor of a cell's expression output.

(3) The figures are clearly presented and easy to follow.

Weaknesses:

(1) The strength of the relationship between the different transcriptional parameters and the mean expression output is displayed visually in Figures 5 and 7, but is not formally quantified. Given that the tau_off times seem more correlated to mean activity for some enhancers (e.g., rho) than others (e.g., sna SE), the quantification might be useful.

(2) There are some mechanistic details that are not discussed in depth. For example, the authors observe that the accumulation and degradation of the MS2 signal have similar slopes. However, given that the accumulation represents the transcription of MS2 loops, while the degradation represents diffusion of nascent transcripts away from the site of transcription, there is no mechanistic expectation for this. The degradation of signal seems likely to be a property of the mRNA itself, which shouldn't vary between cells or enhancer reporters, but the accumulation rate may be cell- or enhancer-specific. Similarly, the activity time depends both on the time of transcription onset and the time of transcription cessation. These two processes may be controlled by different transcription factor properties or levels and may be interesting to disentangle.

(3) There are previous analyses of the eve stripe dynamics, which the authors cite, but do not compare the results of their work to the previous work in depth.

Reviewer #2 (Public review):

Summary:

In this work, Nieto et al. investigate how spatial gene expression patterns in the early Drosophila embryo are regulated at the level of transcriptional bursting. Using live-cell MS2 imaging data of four reporter constructs and the endogenous eve gene, the authors extract temporal dynamics of nascent transcription at single-cell resolution. They implement a novel, simplified algorithm to infer promoter ON/OFF states based on fluorescence slope dynamics and use this to quantify burst duration (Ton), inter-burst duration (Toff), and total activity time across space.

The key finding is that while Ton and Toff remain relatively constant across space, the activity time-the window between first and last burst-is spatially modulated and best explains mean expression differences across the embryo. This uncovers a general strategy where early embryonic patterning genes modulate the duration of their transcriptionally permissive states, rather than the frequency or strength of bursting itself. The manuscript also shows that different enhancers of the same gene (e.g., sna proximal vs. shadow) can differentially modulate Toff and activity time, providing mechanistic insight into enhancer function.

Strengths:

The manuscript introduces activity time as a major, previously underappreciated determinant of spatial gene expression, distinct from Ton and Toff, providing an intuitive mechanistic link between temporal bursting and spatial patterning.

The authors develop a tractable inference algorithm based on linear accumulation/decay rates of MS2 fluorescence, allowing efficient burst state segmentation across thousands of trajectories.

Analysis across multiple biological replicates and different genes/enhancers lends confidence to the reproducibility and generalizability of the findings.

By analyzing both synthetic reporter constructs and an endogenous gene (eve), the work provides a coherent view of how enhancer architecture and spatial regulation are intertwined with transcriptional kinetics.

The supplementary information extends the biological findings with a gene expression noise model that accounts for non-exponential dwell times and illustrates how low-variability Ton buffers stochasticity in transcript levels.

Weaknesses:

The manuscript does not clearly delineate how this analysis extends beyond the prior landmark study (citation #40: Fukaya et al., 2016). While the current manuscript offers new modeling and statistics, more explicit clarification of what is novel in terms of biological conclusions and methodological advancement would help position the work.

While the methods are explained in detail in the Supplementary Information, the manuscript would benefit from including a diagrammatic model and explicitly clarifying whether the model is descriptive or predictive in scope.

The interpretation that fluorescence decay reflects RNA degradation could be confounded by polymerase runoff or transcript diffusion from the transcription site. These potential limitations are not thoroughly discussed.

The so-called loading rate is used as an empirical parameter in fitting fluorescence traces, but is not convincingly linked to distinct biological processes. The manuscript would benefit from a more precise definition or reframing of this term.

Impact and Utility:

The study provides a general and scalable framework for dissecting transcriptional kinetics in developing embryos, with implications for understanding enhancer logic and developmental robustness. The algorithm is suitable for adaptation to other live-imaging datasets and could be useful across systems where temporal transcriptional variability is being quantified. By highlighting activity time as a key regulatory axis, the work shifts attention to transcriptionally permissive windows as a primary developmental control layer.

This work will be of interest to: developmental biologists investigating spatial gene expression, researchers studying transcriptional regulation and noise, quantitative biologists developing models for transcriptional dynamics, and imaging and computational biologists working with live single-cell data.

Reviewer #3 (Public review):

Summary:

In this paper, the authors developed a simple algorithm to analyse live imaging transcription data (MS2) and infer various kinetic parameters. They then applied it to analyse data from previous publications on Drosophila that measured the dynamics of reporter genes driven by various enhancers alone (sna, Kr, rho), or in an endogenous context (eve).

The authors find that the main correlate with mean gene expression levels is the activity time, that is, the time during which the gene is bursting. They also find a correlation with the variation of the off time.

Strengths:

(1) The findings are very clearly presented.

(2) The simplicity of the algorithm is nice, and the comparative analysis among the various enhancers can be helpful for the field.

Weaknesses:

(1) The algorithm is not benchmarked against previously used algorithms in the field to infer ON and OFF times, for example, those based on Hidden Markov models. A comparison would help strengthen the support for this algorithm (if it really works well) or show at which point one must be careful when interpreting this data.

(2) More broadly, the novelty of the findings and how those fit within the knowledge of the field is not super clear. A better account of previous findings that have already quantified ON, OFF times and so on, and how the current findings fit within those, would help better appreciate the significance of the work.

Author response:

Reviewer #1 (Public review):

(1) The strength of the relationship between the different transcriptional parameters and the mean expression output is displayed visually in Figures 5 and 7, but is not formally quantified. Given that the tau_off times seem more correlated to mean activity for some enhancers (e.g., rho) than others (e.g., sna SE), the quantification might be useful.

We re-plot Figure 5 and Figure 7 to present the correlation between the studied burst parameters. As the reviewer suggested, after quantifying the correlation we can better study the correlation between the cells averaged tau-off and the cell-averaged fluorescence signal in some of the selected enhancers. As a result of these findings we decide to change our message and instead of claiming that the burst statistics are homogeneous over the embryo domain, to claim that these statistics have weak but significant correlations with the cell-averaged mean gene fluorescence.

(2) There are some mechanistic details that are not discussed in depth. For example, the authors observe that the accumulation and degradation of the MS2 signal have similar slopes. However, given that the accumulation represents the transcription of MS2 loops, while the degradation represents diffusion of nascent transcripts away from the site of transcription, there is no mechanistic expectation for this. The degradation of signal seems likely to be a property of the mRNA itself, which shouldn't vary between cells or enhancer reporters, but the accumulation rate may be cell- or enhancer-specific. Similarly, the activity time depends both on the time of transcription onset and the time of transcription cessation. These two processes may be controlled by different transcription factor properties or levels and may be interesting to disentangle.

The accumulation slope represents the rate of nascent transcript production, which depends on transcription initiation frequency and RNA polymerase elongation rate. While transcription initiation rates can vary between enhancers, our results show that the loading rates are relatively comparable across different enhancer sequences (Figure 5D). Instead, the primary difference observed was in activity time and burst frequency, consistent with previous findings that enhancers predominantly modulate burst frequency (Fukaya et al., 2016). The degradation slope represents the diffusion of completed transcripts away from the transcription site, which should be an intrinsic property of the mRNA molecule and therefore independent of the regulatory sequences driving transcription.

(3) There are previous analyses of the eve stripe dynamics, which the authors cite, but do not compare the results of their work to the previous work in depth.

The goal of this manuscript is to compare transcriptional bursting properties across different enhancers, rather than to provide an in-depth analysis of eve stripe dynamics specifically. We analyzed four transgenic constructs with different enhancers alongside an endogenous eve construct, focusing on comparative bursting parameters rather than detailed eve expression patterns. Additionally, the previously published eve stripe dynamics data came from BAC constructs, whereas our data comes from the endogenous eve locus. This methodological difference makes direct comparison of stripe dynamics less straightforward and less relevant to our central research question about enhancer-driven bursting variability.

Reviewer #2 (Public review):

(1) The manuscript does not clearly delineate how this analysis extends beyond the prior landmark study (citation #40: Fukaya et al., 2016). While the current manuscript offers new modeling and statistics, more explicit clarification of what is novel in terms of biological conclusions and methodological advancement would help position the work.

The prior study (Fukaya et al., 2016) characterized transcriptional bursting qualitatively, focusing on average burst properties per nucleus without systematic mathematical modeling or statistical analysis of burst-to-burst variability. While they demonstrated that enhancer strength correlates with burst frequency, no quantitative framework was developed to dissect the molecular mechanisms underlying these differences or to connect burst dynamics to spatial gene expression patterns.

(1) We developed an explicit mathematical model with rigorous inference algorithms to quantify transcriptional states from fluorescence trajectories; (2) We performed comprehensive statistical analysis of burst timing distributions, revealing that inter-burst intervals follow exponential distributions while burst durations are hypo-exponentially distributed; (3) Most importantly, we discovered that burst kinetics (τON, τOFF) remain remarkably consistent across different genes and spatial locations, while spatial expression gradients arise primarily through modulation of activity time - the temporal window during which bursting occurs. This mechanistic insight reveals that enhancers regulate spatial patterning not by changing intrinsic burst properties, but by controlling the duration of transcriptionally permissive periods.

(2) While the methods are explained in detail in the Supplementary Information, the manuscript would benefit from including a diagrammatic model and explicitly clarifying whether the model is descriptive or predictive in scope.

We plan to prepare the diagrammatic model in the formal response.

(3) The interpretation that fluorescence decay reflects RNA degradation could be confounded by polymerase runoff or transcript diffusion from the transcription site. These potential limitations are not thoroughly discussed. (Write few lines in the discussion)

This concern, related to the interpretation of the predictive model will be addressed in a future work. The decay in the fluorescence signal can be biologically related to the transcription termination, polymerase detachment, and diffusion. A key limitation of the approach is that the model is phenomenological and does not these capture processes that can be addressed with a more mechanistic model.

(4) The so-called loading rate is used as an empirical parameter in fitting fluorescence traces, but is not convincingly linked to distinct biological processes. The manuscript would benefit from a more precise definition or reframing of this term.

We modify the language of our definition of loading rate as follows: “Loading rate is defined as the rate of increase of fluorescence signal following promoter activation. This quantity is a proxy measurement for the rate of RNA Polymerase II transcription initiation.” The full transcription process has multiple mechanisms including chromatin dynamics, 3D enhancer-promoter interactions, transcription factor binding, mRNA polymerase pausing, and interactions between developmental promoter motifs and associated proteins. We did not have access to specific measurements of these mechanisms and therefore cannot provide a solid biological meaning of the model behind the inference algorithm. However, the fact that we have reproducible results in biological replicas can support the robustness of our method at predicting the promoter state in the studied datasets. In the formal response we will compare the performance of our method with other available ones.

Reviewer #3 (Public review):

(1)The algorithm is not benchmarked against previously used algorithms in the field to infer ON and OFF times, for example, those based on Hidden Markov models. A comparison would help strengthen the support for this algorithm (if it really works well) or show at which point one must be careful when interpreting this data.

We are implementing a benchmarking protocol to compare our results with the proposed and already published models. We expect to present this comparison in the formal response.

(2) More broadly, the novelty of the findings and how those fit within the knowledge of the field is not super clear. A better account of previous findings that have already quantified ON, OFF times and so on, and how the current findings fit within those, would help better appreciate the significance of the work.

To have a better clarity of the new findings we modified the title from “Regulation of Transcriptional Bursting and Spatial Patterning in Early Drosophila Embryo Development” to “Temporal Duration of Gene Activity is the main Regulator of Spatial Expression Patterns in Early Drosophila Embryos”.

In short, (1) We developed an explicit mathematical model with rigorous inference algorithms to quantify transcriptional states from fluorescence trajectories; (2) We performed comprehensive statistical analysis of burst timing distributions, revealing that inter-burst intervals follow exponential distributions while burst durations are hypo-exponentially distributed; (3) Most importantly, we discovered that burst kinetics (τON, τOFF) remain remarkably consistent across different genes and spatial locations, while spatial expression gradients arise primarily through modulation of activity time - the temporal window during which bursting occurs. This mechanistic insight reveals that enhancers regulate spatial patterning not by changing intrinsic burst properties, but by controlling the duration of transcriptionally permissive periods.