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Multimodal transcriptional control of patternformation in embryonic developmentNicholas C. Lammersa,1 , Vahe Galstyanb,c,1, Armando Reimera, Sean A. Medind, Chris H. Wigginse,f,g,h,2,and Hernan G. Garciaa,d,i,j,2
aBiophysics Graduate Group, University of California, Berkeley, CA 94720; bBiochemistry and Molecular Biophysics Option, California Institute ofTechnology, Pasadena, CA 91126; cDepartment of Physics, Columbia University, New York, NY 10027; dDepartment of Physics, University of California,Berkeley, CA 94720; eDepartment of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027; fData Science Institute, ColumbiaUniversity, New York, NY 10027; gDepartment of Systems Biology, Columbia University, New York, NY 10027; hDepartment of Statistics, Columbia University,New York, NY 10027; iDepartment of Molecular and Cell Biology, University of California, Berkeley, CA 94720; and jInstitute for Quantitative Biosciences-QB3, University of California, Berkeley, CA 94720
Edited by Michael Levine, Princeton University, Princeton, NJ, and approved November 26, 2019 (received for review July 19, 2019)
Predicting how interactions between transcription factors andregulatory DNA sequence dictate rates of transcription and, ulti-mately, drive developmental outcomes remains an open challengein physical biology. Using stripe 2 of the even-skipped genein Drosophila embryos as a case study, we dissect the regula-tory forces underpinning a key step along the developmentaldecision-making cascade: the generation of cytoplasmic mRNApatterns via the control of transcription in individual cells. Usinglive imaging and computational approaches, we found that thetranscriptional burst frequency is modulated across the stripe tocontrol the mRNA production rate. However, we discovered thatbursting alone cannot quantitatively recapitulate the formationof the stripe and that control of the window of time over whicheach nucleus transcribes even-skipped plays a critical role in stripeformation. Theoretical modeling revealed that these regulatorystrategies (bursting and the time window) respond in differentways to input transcription factor concentrations, suggesting thatthe stripe is shaped by the interplay of 2 distinct underlyingmolecular processes.
transcriptional bursting | gene regulation | development |hidden Markov models
During embryonic development, tightly choreographed pat-terns of gene expression—shallow gradients, sharp steps,
narrow stripes—specify cell fates. The correct positioning, sharp-ness, and amplitude of these patterns of cytoplasmic mRNA andprotein ensure the reliable determination of animal body plans(1). Yet, despite decades of work mapping the gene regulatorynetworks that drive development, and despite extensive efforts todissect the regulatory logic of the enhancer elements that dictatethe behavior of these networks, a predictive understanding ofhow gene expression patterns and developmental outcomes aredriven by transcription factor concentrations remains a centralchallenge in the field (2).
Predicting developmental outcomes demands a quantitativeunderstanding of the flow of information along the centraldogma: how input transcription factors dictate the output rateof mRNA production, how this rate of mRNA production dic-tates cytoplasmic patterns of mRNA, and how these mRNApatterns lead to protein patterns that feed back into the generegulatory network. While the connection between transcrip-tion factor concentration and output mRNA production rate hasbeen the subject of active research over the last 3 decades (2–11), the connection between this output rate and the resultingcytoplasmic patterns of mRNA has remained largely unexplored.For example, a graded stripe of cytoplasmic mRNA within anembryo could arise as a result of radically different transcrip-tional dynamics at the single-nucleus level (Fig. 1A). Specifically,if individual nuclei along this stripe modulate their average RNApolymerase (RNAP) loading rate, then graded control of themean rate of transcription results: Nuclei in the middle of the
stripe transcribe at a higher average rate than nuclei on the stripeboundaries (Fig. 1B). We identify this graded transcriptionalcontrol strategy with the analog control of gene expression.Alternatively, transcription factors could exert control over thelength of time a nucleus is transcriptionally active (Fig. 1C). Inthis binary control scheme—akin to an on/off switch that dic-tates whether a nucleus is transcriptionally active or quiescent—individual nuclei transcribe at the same average rate regardlessof their position along the stripe, but for different lengths of time.Finally, some nuclei might not engage in transcription at all dur-ing the formation of the pattern (Fig. 1D). Here, a larger fractionof nuclei engage in mRNA production in the stripe center thanin the boundaries. Any of these scenarios, or some combina-tion thereof, can explain the formation of a cytoplasmic mRNApattern.
To quantify the contribution of each regulatory strategy to pat-tern formation and thereby move toward a deeper understandingof the molecular processes at play, it is necessary to measure
Significance
Predicting how the gene expression patterns that specify ani-mal body plans arise from interactions between transcriptionfactor proteins and regulatory DNA remains a major challengein physical biology. We utilize live imaging and theoreticalapproaches to examine how transcriptional control at thesingle-cell level gives rise to a sharp stripe of cytoplasmicmRNA in the fruit fly embryo. While the modulation of tran-scriptional bursting has been implicated as the primary leverfor controlling gene expression, we find that this alone can-not quantitatively recapitulate pattern formation. Instead, wefind that the pattern arises through the joint action of 2regulatory strategies—control of bursting and control of thetotal duration of transcription—that originate from distinctunderlying molecular mechanisms.
Author contributions: C.H.W. and H.G.G. designed research; N.C.L., V.G., and H.G.G. per-formed research; N.C.L., V.G., A.R., S.A.M., and C.H.W. contributed new reagents/analytictools; N.C.L., V.G., S.A.M., and H.G.G. analyzed data; and N.C.L., V.G., and H.G.G. wrotethe paper.y
The authors declare no competing interest.y
This article is a PNAS Direct Submission.y
This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).y
Data deposition: All data sources used in this work can be found in the project’sGitHub repository at https://github.com/GarciaLab/cpHMM/tree/master/multimodalcontrol paper sandbox/dat.y1 N.C.L. and V.G. contributed equally to this work.y2 To whom correspondence may be addressed. Email: [email protected] or [email protected]
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1912500117/-/DCSupplemental.y
First published December 27, 2019.
836–847 | PNAS | January 14, 2020 | vol. 117 | no. 2 www.pnas.org/cgi/doi/10.1073/pnas.1912500117
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Fig. 1. Multiple modes of pattern formation by single-cell transcriptionalactivity. (A–D) Cytoplasmic mRNA patterns (A) could arise from transcriptionfactors exerting control over the mean transcription rate (B), the transcrip-tional time window dictating when a nucleus is transcriptionally active orquiescent (C), or the fraction of active nuclei (D) or some combinationthereof.
the rate of RNAP loading in individual nuclei, in real time,in a living embryo. However, to date, most studies have reliedon fixed-tissue techniques such as mRNA fluorescence in situhybridization (FISH) and immunofluorescence to obtain snap-shots of the cytoplasmic distributions of mRNA and protein asdevelopment progresses (9, 12–15). Such techniques are virtu-ally silent regarding the regulation of single-cell gene expressionover time and are thus ill-suited to the study of how spatiotem-poral variations in transcriptional dynamics give rise to patternsof cytoplasmic mRNA.
In this work, we investigated how single-cell transcriptionalactivity leads to the formation of stripe 2 of the widely studiedeven-skipped (eve) gene in the developing fruit fly embryo (16,17). Previous work has established that the stripe is formedthrough the interplay of transcriptional activators and repressors(16). In addition, recent studies have indicated that the eve stripemRNA profiles are graded and highly reproducible (18–21), sug-gesting that the detailed cytoplasmic distribution of mRNA thatmakes these stripes is key to the transmission of spatial infor-mation along the gene regulatory network that drives Drosophiladevelopment and reinforcing the need to develop models ofgene regulation capable of connecting quantitative variations ininput transcription factor patterns to graded output rates of tran-scription. To do this, we combined live imaging with theoreticalmodeling to study transcription at the single-cell level in realtime, seeking a quantitative connection between the spatiotem-poral variations in input transcription factor concentrations, thecontrol of eve transcription, and the formation of cytoplasmicpatterns of mRNA.
We found that all 3 regulatory strategies outlined in Fig. 1quantitatively contribute to the formation of eve stripe 2. First, asmaller fraction of nuclei become active and engage in transcrip-tion in the periphery of the stripe than in the center, althoughthis regulation of the fraction of active nuclei makes only aminor contribution to stripe formation. Second, consistent withprevious studies, we found that the rate of mRNA productionis significantly elevated in the center of the stripe (18). Strik-
ingly, however, we discovered that this analog control of thetranscription rate is insufficient to quantitatively recapitulate thecytoplasmic mRNA stripe pattern. In addition to the control ofthe rate of mRNA production among nuclei, we also observed apronounced regulation of the window of time during which eveloci were engaged in transcription across the stripe, with thosein the stripe center expressing for approximately 3 times longerthan those in the flanks. While it is widely appreciated that genesare transcriptionally competent for limited windows of time dur-ing development, we found that—in the case of eve stripe 2—thisbinary transcriptionally engaged/disengaged logic is not merely anecessary precondition for pattern formation—it is the primarydriver thereof. Thus, we conclude that the regulation of eve stripe2 is multimodal in nature, with contributions from 3 distinct regu-latory strategies (Fig. 1 B–D). Nonetheless, stripe formation canbe quantitatively explained almost entirely through the interplaybetween 2 distinct control strategies: binary control of the dura-tion of transcriptional engagement (Fig. 1C) and control of themean rate of transcription (Fig. 1B).
Building upon this result, we developed computationalapproaches to uncover the mechanistic underpinning of eachregulatory strategy. We employed a compound-state hiddenMarkov model (cpHMM) to uncover variations in transcriptionalbursting dynamics in individual nuclei across space and time(22–24). We uncovered that, consistent with previous results,transcription factors control the rate of transcription by alter-ing the frequency of transcriptional bursts (25, 26). In addition,we utilized logistic regressions to correlate eve stripe 2 transcrip-tional dynamics with changes in input transcription factor con-centrations. This analysis revealed that the transcriptional timewindow adheres to different regulatory logic than transcriptionalbursting: While repressor levels alone were sufficient to explainthe early silencing of nuclei in the anterior and posterior stripeflanks, the control of bursting among transcriptionally engagednuclei depends upon the input concentrations of both activa-tors and repressors. Thus, our findings point to the presence of2 distinct regulatory mechanisms that control transcription andgene expression patterns in early development, showcasing thepotential for theoretical modeling and biological numeracy toyield additional biological insights when coupled with precise andquantitative experimental observation.
ResultsPredicting Cytoplasmic mRNA Distributions from TranscriptionalActivity. To predict how the transcriptional activity of individualnuclei dictates the formation of cytoplasmic patterns of mRNA,we began with a simple model that considers the balance betweenthe rate of mRNA synthesis and degradation
dmRNAdt
(x , t) = pactive(x )︸ ︷︷ ︸fraction of
active nuclei
R(x , t)︸ ︷︷ ︸synthesis
− γmRNA(x , t)︸ ︷︷ ︸degradation
, [1]
where mRNA(x , t) indicates the mRNA concentration at posi-tion x along the embryo at time t , R(x , t) corresponds to themRNA synthesis rate averaged over multiple nuclei within thesame position x , pactive(x ) is the fraction of active nuclei (cor-responding to the regulatory strategy shown in Fig. 1D), and γis the degradation rate (see SI Appendix, section A for details ofthis derivation).
To examine the quantitative consequences of the 3 poten-tial regulatory strategies (Fig. 1 B–D), we adopted widespreadassumptions in the modeling of transcriptional regulation. First,we assumed that the degradation rate γ is a constant andnot under any kind of spatiotemporal control. Comparisonsbetween model predictions and empirically measured levels ofcytoplasmic mRNA suggest that this assumption is reasonable
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(SI Appendix, section B). Second, we posited that at each posi-tion throughout the embryo the synthesis rate R(x , t) does notvary significantly in time such that it can be approximated byits time average R(x ) = 〈R(x , t)〉. This assumption is revisedlater in the text to account for the time-dependent regulation ofthe mean rate of transcription. Finally, we assumed that nucleialong the axis of the embryo start transcribing at time ton(x ) andstop transcribing and enter a state of transcriptional quiescenceat time toff(x ). Under these assumptions, Eq. 1 can be solvedanalytically, resulting in
mRNA(x , t) =R(x )
γ︸ ︷︷ ︸mean transcription rate
× [2]
(e−γ(t−min{toff (x),t})− e−γ(t−ton(x))
)︸ ︷︷ ︸
transcriptional time window
× pactive(x )︸ ︷︷ ︸fraction active
.
Eq. 2 makes precise predictions about how each regulatory strat-egy contributes to the formation of the cytoplasmic mRNApattern. Thus, measuring how each quantity is regulated acrossthe stripe allows us to predict their relative contributions topattern formation.
Binary Control of the Transcriptional Time Window Is the PrimaryDriver of Stripe Formation. To test the simple model of patternformation put forward in Eq. 2, we quantified transcription ofstripe 2 of eve in the fruit fly. We imaged the transcription of aneve stripe 2 reporter, using the MS2 system (18, 27, 28). Tran-scripts of a reporter gene driven by the eve stripe 2 enhancer andthe eve promoter contain repeats of a DNA sequence that, whentranscribed, form stem loops (29). These stem loops are recog-nized by maternally provided MS2 coat protein fused to GFP(Fig. 2A). As a result, sites of nascent transcript formation appearas fluorescent puncta within individual nuclei (Fig. 2B and MovieS1). As described in SI Appendix, Fig. S2, the intensity of thesefluorescent puncta is proportional to the number of RNAPmolecules actively transcribing the gene. These resulting fluo-rescence values could then be calibrated using single-moleculeFISH to estimate the number of RNAP molecules actively tran-scribing the gene (Materials and Methods and ref. 27). By aligningmultiple embryos (SI Appendix, Fig. S1), we obtained the averagenumber of actively transcribing RNAP molecules as a function oftime and position throughout the embryo (Fig. 2C).
Using the MS2 system, we quantified each potential regulatorystrategy and determined its predicted contribution to patternformation according to our model in Eq. 2. We first used theaverage fluorescence intensities of our MS2 traces to estimatethe time-averaged rate of RNAP loading, R(x ) as described in SIAppendix, section B. We found that this rate is modulated alongthe axis of the embryo (Fig. 3 A and B; Movie S2; SI Appendix,Fig. S3; and Materials and Methods): Whereas in the center of thestripe RNAP molecules are loaded at a rate of ∼16 moleculesper minute, this loading rate decreases to about 8 molecules perminute at the boundaries.
We next used our MS2 data to examine spatial trends inthe transcriptional time window. Our data revealed that thetranscriptional time window is modulated along the stripe (SIAppendix, Fig. S4A). Whereas the time at which each nucleusbecomes transcriptionally active, ton(x ), was constant across thestripe, with all nuclei becoming active 8± 4 min after the previ-ous anaphase (SI Appendix, Fig. S4B), the time at which nucleistop transcribing and become quiescent, toff(x ), showed a strongmodulation along the embryo’s axis (SI Appendix, Fig. S4C). Asa result, the time window over which each transcriptional locusis engaged in transcription, ∆t = toff − ton, is sharply modulated
A
B
C
Fig. 2. Measuring transcriptional dynamics of eve stripe 2 formation usingthe MS2 system. (A) MS2 stem loops introduced in an eve stripe 2 reportergene are bound by MS2 coat protein fused to GFP. (B) Sites of nascenttranscript formation appear as green fluorescent puncta whose intensityreports on the number of actively transcribing RNAP molecules. Nuclei arevisualized through a fusion of RFP to Histone. (C) Mean number of RNAPmolecules actively transcribing the gene as a function of space and time(data averaged over 11 embryos).
along the stripe (Fig. 3 C and D and Movie S3), with nucleiin the stripe center transcribing for >30 min and nuclei on theboundaries transcribing only for∼10 min. We note that, to derivethese results, it was necessary to account for potential effects ofthe detection limit in our experiments of ∼4 RNAP moleculesper locus on estimates of the timing of the appearance and
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Fig. 3. Regulatory strategies for pattern formation in eve stripe 2. (A–F) Time-averaged rate of mRNA production (A and B), transcriptional time window(C and D), and fraction of active nuclei as a function of position along the embryo (E and F). (G) Amplitude of the cytoplasmic mRNA distribution comparedto the contributions to stripe formation of the analog control of the mean transcription rate, the binary control of the transcriptional time window, and thecontrol of the fraction of active nuclei. The combined contribution from the analog and binary strategies is also shown. See SI Appendix, Fig. S5 for detailsof how depicted profiles were derived from raw data. A, C, and E show representative snapshots of an individual embryo 40 min into nuclear cycle 14; B, D,and F show average over 11 embryos; and error bars indicate bootstrap estimate of the SEM.
disappearance of fluorescent puncta. This procedure is outlinedin detail in SI Appendix, section C, as well as in SI Appendix, Figs.S12 and S13.
Finally, our analysis also revealed the magnitude of the mod-ulation of the fraction of active nuclei along the stripe. Mostnuclei along the stripe were engaged in transcription. In thestripe center, nearly 100% of nuclei transcribed at some pointduring the nuclei cycle. This number reduced to about 80% atthe boundaries (Fig. 3 E and F and Movie S4).
The analysis in Fig. 3 A–F reveals that each of the 3 regula-tory strategies identified in Fig. 1 is at play in the embryo andthat they all have the potential to contribute to pattern forma-tion. However, these measurements alone cannot inform us on
how much each of these strategies contributes to the cytoplasmicmRNA pattern. To quantify the degree to which each regulatorystrategy contributes to the formation of eve stripe 2, we employedthe model described in Eq. 2.
Fig. 3G indicates the quantitative contribution of each reg-ulatory strategy (each term on the right-hand side of Eq. 2)to the formation of this cytoplasmic pattern. The cytoplasmicpattern of accumulated mRNA, corresponding to the left-handside of Eq. 2, was obtained by integrating from our live-imagingdata (see SI Appendix, section B for details). Regulation of thefraction of active nuclei along the embryo (Fig. 3G, yellow) con-tributes negligibly to this mRNA pattern. In contrast, both theanalog regulation of the mean rate (Fig. 3G, green) and the
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binary control of the transcriptional time window (Fig. 3G, blue)make significant contributions to the overall pattern, with binarycontrol playing the dominant role. We thus concluded that thejoint effect of these 2 strategies (Fig. 3G, brown) is sufficient toquantitatively recapitulate the stripe of cytoplasmic mRNA fromsingle-cell transcriptional activity.
Mean Transcription Rate Is Dictated by Bursting through Modulationof the Rate of Promoter Turn on. Are the binary and analog con-trol strategies driven by distinct molecular mechanisms, or arethey different manifestations of the same underlying process? Touncover the molecular mechanism behind the analog control ofthe mean rate of transcription, we analyzed the transcriptionalactivity of individual nuclei. Previous work demonstrated that therate of gene expression at individual loci within the eve stripe 2pattern is highly stochastic (18). Indeed, as shown in Fig. 4A, ourdata revealed punctuated peaks and troughs in the number ofactive RNAP molecules. These features have been related to therate of RNAP initiation at the eve promoter by assuming thattranscriptional activity is “burst-like,” with the promoter rapidlyloading multiple RNAP molecules onto the gene at a constantrate during discrete “bursts” of activity interspersed with peri-ods of inactivity (18). This and other evidence from live imaging(18, 25, 30), as well as data from fixed-tissue approaches (26,31–33), support a minimal 2-state model of promoter switch-ing (Fig. 4B): Promoters switch stochastically between ON andOFF states with rates kon and koff . In this model, promoters inthe ON state engage in the loading of RNAP (and, correspond-ingly, mRNA production) at rate r . Thus we find that, to describeeve stripe 2 transcriptional dynamics, we need to account forboth the short, transient ON periods dictated by transcriptionalbursts and a longer transcriptional time window that describesthe period over which loci engage in this transcriptional bursting.
In the bursting model, the mean rate of transcription is givenby the product of the fraction of time spent in the ON state withthe transcription rate in this active state (34–37)
R(x )︸ ︷︷ ︸mean
transcription rate
= r(x )︸︷︷︸RNAP loading
rate
× kon(x )
kon(x ) + koff(x )︸ ︷︷ ︸fraction of time
in ON state
, [3]
where all parameters are allowed to vary as a function of positionalong the embryo, x (see SI Appendix, section A for details of thisderivation). Thus, within this framework, the observed modula-tion of the mean rate of transcription across the stripe (Fig. 3G,green) implies that one or more of these bursting parametersare subject to spatially controlled regulation. However, the meanrate trend alone is not sufficient to identify which of the 3 burst-ing parameters (kon, koff , and r) is being regulated by the inputtranscription factors to control the average transcription rate.While each bursting parameter does not necessarily map directlyto a single molecular step in the transcriptional cycle, identifyingwhich parameter(s) is subject to regulation can help narrow theset of possible molecular mechanisms. For instance, variation inr could indicate that transcription factors play an active role inthe recruitment of RNAP to the promoter or in the release ofRNAP from promoter-proximal pausing (38).
Typically, the in vivo molecular mechanism of transcriptionfactor action is inferred from measurements of transcriptionalnoise obtained through snapshots of dead and fixed embryos orcells using theoretical models (26, 31–33, 39–47). In contrast,MS2-based live imaging can directly inform on the dynamics oftranscriptional bursting in real time. The MS2 approach, how-ever, reports on the total number of actively transcribing RNAPmolecules and not on the instantaneous rate of RNAP loading atthe promoter, which is the relevant quantity for estimating kon,
B
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Fig. 4. Transcriptional bursting in eve stripe 2. (A) Single-nucleus measure-ments reveal that nuclei transcribe in bursts. (B) Two-state model of burstingof a single promoter. (C) The same hidden rate of RNAP loading (Bot-tom) can correspond to different observable numbers of RNAP moleculeson the gene (Top), such that standard hidden Markov model approachescannot be used to infer the hidden promoter state. (D) Fluorescent punctaare composed of 2 distinct transcriptional loci within a diffraction-limitedspot, each corresponding to a sister chromatid. (E) Three-state model ofpromoter switching within a fluorescent punctum that accounts for thecombined action of both sister chromatids. (F) Effective 2-state model oftranscriptional bursting. (In A, error bars are obtained from estimation ofbackground fluorescent fluctuations; Materials and Methods and ref. 27.)
koff , and r . To date, approaches for extracting bursting parame-ters from such data in multicellular organisms have mainly reliedon the manual analysis of single-nucleus transcriptional dynam-ics (18, 25) or autocorrelation-based methods that infer meanbursting parameters across ensembles of traces (30, 48, 49). Acomputational method for inferring the rates of RNAP loading(Fig. 4 C, Bottom) from the total number of actively transcribingRNAP molecules in single cells (Fig. 4 C, Top) is thus needed toobtain the bursting parameters.
Hidden Markov models (HMMs) are widely used to uncoverthe dynamics of a system as it transitions through states that arenot directly accessible to the observer (50). However, our observ-able (the MS2 signal) does not correspond to the hidden variableof interest (the promoter state) in a one-to-one fashion (com-pare Fig. 4 C, Top and Bottom). Instead, the observable MS2
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signal reflects the net effect of promoter switching over a periodequal to the time that an RNAP molecule takes to transcribethe whole gene. Thus, instantaneous fluorescence does not justdepend on the current promoter state; it exhibits a dependenceon how active the promoter has been over a preceding window oftime, which effectively constitutes a memory for recent promoterstates (24, 37, 51, 52). Classic HMM approaches cannot accountfor this kind of system memory.
To model the process of transcription and extract the kineticparameters of promoter switching, we augmented classic HMMsto account for memory (details about implementation of themethod are given in SI Appendix, section D). Similar approacheswere recently introduced to study transcriptional dynamics incell culture and tissue samples (22–24, 53–57). We used sim-ulated data to establish that cpHMM reliably extracts thekinetic parameters of transcriptional bursting from live-imagingdata (SI Appendix, section E), providing an ideal tool for dis-secting the contributions from individual bursting parametersto observed patterns of transcriptional activity across spaceand time.
Before applying our model to real-time transcriptional data,we had to account for the rapid replication of the Drosophilamelanogaster genome at the beginning of each nuclear cycle (58),which leads to the presence of 2 distinct eve loci within eachfluorescent spot (Fig. 4D and Movie S5). The first evidenceof resolved chromatids appears as early as 8 min into nuclearcycle 14 (SI Appendix, Fig. S24)—coincident with the averageonset time of transcription (SI Appendix, Fig. S4B). Moreover,our analysis indicates that replication of the relevant portion ofthe genome likely occurs in all eve-expressing nuclei by no laterthan 10 min following mitosis (SI Appendix, Fig. S24). Thus, weconclude that the vast majority of our data feature 2 distincteve loci within each diffraction-limited transcription spot. More-over, while the distance between sister loci varies over time (e.g.,Fig. 4D), they nonetheless stay in relatively close proximity toensure their proper segregation from each other at the next mito-sis (59) such that the fluorescent intensity signals extracted fromour data reflect the integral over both loci (SI Appendix, Fig. S2).As a result, if we assume that each locus can be well representedby a 2-state model (OFF/ON) of transcriptional bursting, thenan effective 3-state model (OFF/OFF + OFF/ON + OFF/ON +ON/ON) is needed to capture eve dynamics (Fig. 4E). Thus, weelected to employ such a 3-state model in our analysis. Due toconflicting evidence from previous studies (26, 32, 60), we madeno prior assumptions about the nature or degree of cooperativitybetween sister chromatids either in transitions between activitystates or in the rates of initiation in each state (see SI Appendix,section E for details). While these assumptions increased thecomplexity of our model, we believed that a conservativeapproach that left the model free to infer the presence or absenceof sister interactions was warranted, given our ignorance regard-ing the nature and strength of interactions between adjacentgene loci. For ease of exposition, we present our main resultsin the context of an effective 2-state model, in which, as detailedin SI Appendix, section A, the system is considered to be in theON state as long as either chromatid is bursting (Fig. 4F). Notethat none of our conclusions below are affected by this choice ofan effective model as shown in SI Appendix, section G, where wepresent full results for the 3-state model.
A typical experimental trace for a nucleus in the core of thestripe is shown in Fig. 5A, along with its best fit, which corre-sponds to the cpHMM-inferred promoter trajectory in Fig. 5B.Our ability to infer the instantaneous promoter state in indi-vidual nuclei throughout development is further illustrated inFig. 5C and Movie S6. These data revealed that, as devel-opment progresses and the stripe sharpens, the eve promotercontinuously fluctuates between the ON and OFF states on atime scale of ∼1 to 2 min.
ON
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Fig. 5. Inferring bursting dynamics using a memory-adjusted hiddenMarkov model. (A and B) Representative experimental trace along with itsbest fit (A) and its most likely corresponding promoter state trajectory (B).(C) Instantaneous visualization of promoter state in individual cells through-out development through the false coloring of nuclei by promoter state(colors as in B). (D) The rate of initiation for each transcriptional state is notsignificantly modulated along the embryo. (E) Our cpHMM reveals that thetransition rate between the OFF and ON states (equivalent to the burst fre-quency) is up-regulated in the stripe center. (In A, error bars are obtainedfrom estimation of background fluorescent fluctuations, as described inMaterials and Methods and ref. 27; in D and E, error bars indicate the mag-nitude of the difference between the first and third quartiles of cpHMMinference results for bootstrap samples of experimental data taken across11 embryos; see Materials and Methods for details.)
To infer time-averaged bursting parameter values, we groupedtraces by position along the anterior–posterior axis. The rateof RNAP loading, r , remained constant throughout the stripe(Fig. 5D), suggesting that none of the transcription factors reg-ulating eve stripe 2 act directly on the rapid series of molecularsteps involved in the initiation of transcription by RNAP. Sim-ilarly, we noted no significant spatial modulation of the rate ofswitching out of the ON state, koff (Fig. 5E). In contrast, the rateof switching into the ON state (also known as burst frequency),kon, was strongly up-regulated in the stripe center (Fig. 5E).These observations suggested that, to control the mean rate oftranscription, transcription factors act primarily on the rate ofpromoter turning on, consistent with previous results both inembryos (25, 30, 33) and in single cells (41, 43, 44, 46). Thisregulatory modality increases the fraction of time that loci nearthe stripe center spend in the ON state (SI Appendix, Fig. S7and ref. 26).
Binary Control of the Transcriptional Time Window Is Independentof Transcriptional Bursting. Having determined that the analogcontrol of the mean transcriptional rate is realized by the mod-ulation of the burst frequency, kon, we next sought to uncoverthe molecular mechanism by which the binary regulation of thetranscriptional time window is implemented. In one possiblescenario, the onset of transcriptional quiescence at the end ofthe transcriptional time window would reflect a fundamentalchange to the molecular character of the transcriptional locussuch that the bursting framework no longer applies. For instance,
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repressing transcription factors could alter the local chromatinlandscape by repositioning promoter or enhancer nucleosomes(61), changes that could block the binding of activators at thestripe 2 enhancer or of general transcription factors at thepromoter and thus abolish further activator-mediated bursting(Fig. 6 A, i). Alternatively, if the rates of promoter switching varyin time, then the time window could be explained without invok-ing an extra silenced state that is mechanistically distinct from theprocesses driving transcriptional bursting. Specifically, transcrip-tional quiescence could be achieved by progressively reducingthe frequency (kon), intensity (r), and/or duration (1/koff) oftranscriptional bursts. For example, it is possible that increasingrepressor levels in the stripe flanks could disrupt the capac-ity for activators to initiate transcription bursts via short-rangequenching interactions (62), a mechanism that would manifestas a decrease in kon over time.
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To determine whether quiescence can be explained within thebursting framework, we divided the stripe into the 5 regionsshown in Fig. 6B. For each region, we sought to determinewhether the bursting dynamics varied over time in a mannerthat could explain the dynamics of entry into quiescence of indi-vidual nuclei (Fig. 6C). To probe for this time dependence intranscriptional bursting, we extended our cpHMM method toobtain promoter-bursting parameters over discrete periods oftime by performing inference on our live-imaging data usinga sliding window (see SI Appendix, section D for details). Ourinference revealed that the rate of promoter turn on, kon, var-ied significantly in time (Fig. 6D). Specifically, kon decreased inboth the anterior and posterior stripe boundaries (Fig. 6D, blackand red curves) as development progressed and the fraction ofactive nuclei decreased (Fig. 6D, gray shaded region), while lociin the stripe center (Fig. 6D, green and yellow curves) exhibiteda significant increase in kon. Further, while relatively constant atmost positions along the stripe, both the rate of RNAP loadingwhen in the ON state, r , and the rate of promoter turn off, koff ,decreased slightly (Fig. 6 E and F).
These findings confirmed our time-averaged inference results(Fig. 5 D and E) indicating that kon was the primary kineticpathway through which transcription factors influence eve stripe2 transcription dynamics. Moreover, the coincidence of thedecrease in kon in flank nuclei with the onset of transcriptionalquiescence (gray shaded region in Fig. 6D) seemed to suggestthat, at least in part, quiescence in the stripe flanks could bedriven by the temporal modulation of bursting parameters (Fig. 6A, ii). However, other trends in our data were not consistent withthe view that a decrease in kon drives transcriptional quiescence.
Although 70% and 50% of nuclei in the regions directly ante-rior and posterior of the stripe center were quiescent by 40 mininto the nuclear cycle (blue and yellow curves in Fig. 6C), wedetected no corresponding decrease in kon. In fact, kon actu-ally increased in some inner regions of the stripe (Fig. 6D)—atrend that would increase overall transcriptional activity andwould therefore go against the establishment of transcriptionalquiescence.
The divergent outcomes observed in the central stripe regions,with the rate of transcriptional bursting remaining constant orincreasing at eve loci within the engaged population of nucleieven as loci in neighboring nuclei turn off for good, runs counterto the hypothesis that quiescence is driven by the temporal mod-ulation of the promoter switching parameters. It is conceivablethat temporal changes in bursting parameters associated withthe onset of quiescence occur too rapidly to be captured by ourmodel. However, as discussed in SI Appendix, section I, thesechanges would need to occur on the same time scale as burst-ing itself (1 to 3 min). Given that both the other temporal trendsdetected by our inference (Fig. 6) and the shifts in the input tran-scription factors themselves (SI Appendix, section H) unfold onsignificantly slower time scales (5 to 15 min), we concluded thatwhile possible, a scenario where bursting dynamics are changingtoo quickly to detect is unlikely.
The contradictory trends observed in the stripe center andflanks indicated that entry into transcriptional quiescence mightinvolve processes not captured within the bursting model (Fig. 6A, i), thus suggesting that binary control of the transcriptionaltime window and the transcriptional bursting driving the analogcontrol of the mean transcription rate may arise from distinctmolecular processes.
Input–Output Analysis Reveals Distinct Regulatory Logic for Burst-ing and the Transcriptional Time Window. eve stripe 2 is mainlyestablished by the combined action of 2 activators, Bicoid andHunchback, and 2 repressors, Giant and Kruppel (16, 17, 63). Iftranscriptional bursting and the transcriptional time window arecontrolled by distinct molecular processes, then distinct forms
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Fig. 7. Probing the regulatory logic of bursting and the transcriptional time window. (A and B) Fraction of nuclei in the transcriptionally quiescent state(A) and fraction of nuclei in the bursting ON state (B) as a function of time and position along the embryo. (C) Snapshots of input transcription factorlevels and predicted eve mRNA levels of our “average” embryo at 10, 25, and 40 min into nuclear cycle 14. (D) Predicted fraction of quiescent nuclei forprogressively more complex regression models. The simplest model with the highest likelihood is highlighted in purple. The color-coded circles indicatewhich of the 4 transcription factors—Kruppel (Kr), Giant (Gt), Hunchback (Hb), and Bicoid (Bcd)—were included in each version of the model. (E) Modellikelihood indicating that Kruppel and Giant levels are sufficient to recapitulate the fraction of quiescent nuclei in D. (F) Predicted fraction of nuclei in theON state. The simplest and most likely model is highlighted in purple. (G) Model scores reveal that Giant, Kruppel, and Hunchback recapitulate the burstingbehavior in F.
of regulatory logic may be at play. For example, the Bicoidand Hunchback activators could control transcriptional bursting,while the Giant and Kruppel repressors could dictate the entryinto the quiescent state. To reveal the molecular logic control-ling each regulatory strategy, we sought to correlate the fractionof nuclei that have entered the quiescent state (Fig. 7A) and thefraction of nuclei in the bursting ON state (Fig. 7B) with the cor-responding spatiotemporal patterns in the input concentrationsof these 4 transcription factors.
We measured Bicoid concentration profiles using a well-established Bicoid-GFP fusion (64) and obtained spatiotempo-ral concentration profiles for Kruppel, Giant, and Hunchbackfrom published immunofluorescence data (65, 66). We combinedthese data with our live-imaging data of eve stripe 2 transcrip-tional activity to generate an “average embryo” in which theconcentration of all relevant inputs and the output transcrip-tional activity at each point in time and space were known(Fig. 7C and Movie S7). Building upon previous work (67), weutilized logistic regressions to probe the regulatory role played byeach of these 4 factors in the spatiotemporal control of transcrip-tional bursting and the transcriptional time window. The logisticregression is a widely used method of inferring predictive modelsin processes with binary outcomes. For example, to query theregulatory logic behind the control of the transcriptional timewindow, the model probes the impact of each transcription factoron the relative likelihood of a locus entering the quiescent stateversus the likelihood of remaining transcriptionally engaged suchthat
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where the coefficients βn indicate the magnitude and nature(activating or repressing) of the transcription factor’s regulatoryfunction. In estimating these coefficients, we used prior knowl-edge about the function of each transcription factor, requiringBicoid and Hunchback to play activating roles and Kruppel andGiant to play repressing roles (5, 16). We used an analogousmodel to investigate the regulatory logic controlling transcrip-tional bursting by inferring the factors that determine the relativelikelihood that nuclei are in the bursting ON versus the OFFstate, PON /POFF .
Our analysis of the fraction of nuclei in the quiescentstate revealed that no single transcription factor can explainquiescence dynamics (Fig. 7 D and E). However, a sim-ple model in which increasing levels of the repressors Giantand Kruppel drive the onset of transcriptional quiescence inthe anterior and posterior stripe flanks, respectively, recapitu-lated experimentally observed trends. The further addition ofHunchback and/or Bicoid had no impact on the model’s pre-dictive power, suggesting that activator concentrations have noinfluence over the molecular processes responsible for silencing.Relaxing constraints on the functional role of each transcrip-tion factor—for instance, allowing the presumed activators tofunction as repressors—also provided no significant improve-ment over models presented here as shown in SI Appendix,section H.
We next turned our attention to the relationship betweentranscription factor levels and the fraction of nuclei in the ONstate (Fig. 7B). Unlike the transcriptional time window, repressorlevels alone could not recapitulate the observed bursting profile;Hunchback levels were also necessary to fully capture the spa-tiotemporal bursting dynamics (Fig. 7 E and G). Specifically, welinked a rise in Hunchback concentration to an observed rise in
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the fraction of nuclei in the ON state in the stripe center between30 and 35 min into the nuclear cycle (Fig. 7 B and F).
Our input–output analysis thus revealed that bursting andthe transcriptional time window exhibit significantly differentforms of regulator logic: Whereas repressor levels alone aresufficient to explain the transcriptional time window, the jointaction of activators and repressors appears necessary to explainthe observed patterns of transcriptional bursting. These resultsare consistent with the hypothesis that regulation of burst-ing and of the transcriptional time window occur via distinctmolecular processes, therefore supporting a model in which thelong-lived trancriptionally silent state observed in flank nucleiconstitutes a distinct molecular state outside of the burstingmodel.
DiscussionIn Drosophila development, information encoded in a handfulof maternally deposited protein gradients propagates throughincreasingly complex layers of interacting genes, culminatingin the specification of the adult body plan. The prediction ofthis cascade of developmental outcomes requires a quantitativeunderstanding of the mechanisms that facilitate the flow of infor-mation along the central dogma. Here, we utilized live imaging inconjunction with theoretical modeling to shed light on a criticallink in this cascade: how the regulation of transcriptional activityat the single-nucleus level gives rise to a spatiotemporal patternof cytoplasmic mRNA.
A priori, there are several distinct regulatory strategies at thesingle-cell level capable of generating spatially differentiated pat-terns of cytoplasmic mRNA (Fig. 1), each with distinct implica-tions for the nature of the underlying molecular processes at play.Several recent studies have revealed that the average rate of tran-scription is mainly modulated across the embryo by tuning thefrequency of transcriptional bursting (18, 25, 26, 30, 33, 68). Yetit has remained unclear whether this modulation of the rate oftranscription (and thereby mRNA production) is the dominantmodality by which input concentrations of transcription factorsdrive the formation of patterns of gene expression or whether,instead, it is simply the most readily apparent mechanism amongmultiple distinct control strategies.
In this work, we derived a simple theoretical model that pre-dicts how the interplay between regulatory strategies at thesingle-cell level dictates the formation of a cytoplasmic geneexpression pattern (2). We applied this model to single-cell live-imaging measurements of an MS2 reporter driven by the evestripe 2 enhancer, an approach that allowed us to dissect theregulatory logic of a well-characterized regulatory element freefrom the confounding influences of other enhancers located inthe endogenous eve locus. We demonstrated—quantitatively—that the modulation of the mean rate of transcription is aloneinsufficient to account for the formation of a sharp stripe of geneexpression (Fig. 3G, green). We discovered that the window oftime over which promoters engage in transcription is sharply con-trolled along the axis of the embryo (Fig. 3 C and D) and that thejoint action of the analog control of the rate of transcription andthe binary control of the duration of transcription is necessaryand sufficient to quantitatively recapitulate most of the full stripeprofile (Fig. 3G, brown). While this work focused on dissectingthe regulatory logic of the eve stripe 2 enhancer in the contextof a minimal construct, it is important to note that our conclu-sions are not limited to this reporter construct and also apply tothe endogenous regulation of eve. As shown in SI Appendix, Fig.S11, an analogous analysis performed on the expression dynam-ics of a reporter BAC containing the full endogenous eve locus(69) indicated that stripe formation in this endogenous contextis also dominated by the interplay between the regulation ofthe mean rate of transcription and that of the transcriptionaltime window.
Here, we contribute to a growing body of work that illustratesthe utility of using simple quantitative models to extract insightsinto the workings of complex biological phenomena (33, 67, 70).Our discovery of the key role of the differential duration of thetranscriptional time window in pattern formation was made pos-sible only by biological numeracy, that is, by going beyond thequalitative description of pattern formation and demanding aquantitative agreement between our theoretical predictions andthe experimental data (71). While it is widely appreciated thatgenes are expressed for discrete windows of time over the courseof development (27, 28, 72), we have demonstrated that—in thecase of this eve stripe 2 reporter—this binary transcriptionallyengaged/quiescent logic is actively regulated by transcription fac-tors to drive pattern formation. Thus the differential durationof transcriptional activity comprises a necessary element of anyquantitative description of pattern formation.
Our work contributes to an increasingly diverse and excitingdiscourse in quantitative developmental biology regarding theimportance of the temporal component of transcriptional reg-ulation in specifying developmental outcomes. For example, onerecent study has demonstrated that the limited readout timeimposed by short nuclear cycles in early Drosophila developmentplaces strict constraints on the kinds of regulatory architec-ture that could be responsible for driving observed patterns ofhunchback gene expression (73). Other recent work has indi-cated that the pioneer factor Zelda plays a key role in regulatingboth the timing and probability of transcriptional activation fol-lowing mitosis (74, 75). Our work complements these previousobservations by exploring yet another aspect of the interplaybetween timing and transcriptional regulation. We have shownthat, in the case of eve stripe 2, transcription factors regulatethe onset of transcriptional quiescence, toff , across the stripe,thus demonstrating that the embryo actively leverages the dif-ferential duration of transcriptional engagement as a strategyto generate patterns of gene expression. Together, these recentfindings suggest that, if the field is to make progress toward apredictive picture of pattern formation in development, it willbe necessary to go beyond the widespread steady-state, staticpicture of pattern formation in development put forward by pre-vious single-cell transcriptional activity studies that focused onthe study of snapshots of fixed embryos (26, 31–33) and embracea dynamical description that acknowledges that development is aprocess that occurs outside of steady state (69).
To determine whether this binary control of the transcriptionaltime window and the analog control of the mean transcriptionrate share a common molecular mechanism, we utilized a vari-ety of theoretical and computational tools in conjunction withour live-imaging data. Specifically, to uncover how the meanrate of transcription is regulated across the stripe, we devel-oped a cpHMM that is capable of inferring the instantaneousactivity state of individual gene loci from MS2 traces. We usedthis cpHMM to infer average promoter-switching parametersacross the stripe (Fig. 5). In agreement with previous measure-ments of various gene expression patterns (25, 26, 30, 33), ourresults revealed that the burst frequency (kon) is the main burst-ing parameter regulated by the input transcription factors acrosseve stripe 2. This increase in kon in the stripe center functionsto increase the fraction of time that nuclei spend in the activetranscriptional state.
Importantly, our cpHMM algorithm is not limited to the evestripe 2 system and should prove useful to infer the underlyingregulatory kinetics of any gene that is tagged using approachessuch as the MS2 or PP7 systems in any organism (25, 48). Forexample, the method could be used to infer the state of theribosome as mRNA is being translated into protein in novelsingle-molecule in vivo translation assays (76–79). Thus, we envi-sion that our method will be useful for the broader biophysicalanalysis of in vivo cellular processes at the single-molecule level.
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Having identified kon as the primary kinetic mode by whichtranscription factors modulate the mean rate of expressionacross eve stripe 2, we next sought to probe the relationshipbetween bursting and the transcriptional time window (Fig. 6A).We adapted our cpHMM to go beyond time-independent mod-els of promoter switching to infer the regulation of these ratesacross both space and time. We observed striking temporaltrends indicating that the burst frequency responds dynamicallyto time-varying transcription factor inputs. However, we noted asignificant disconnect between temporal trends in the burst fre-quency and the onset of transcriptional quiescence. In particular,kon either increased or remained constant near the stripe cen-ter even as a significant fraction of eve nuclei transitioned intoquiescence (Fig. 6 C and D). We reasoned that the onset oftranscriptional quiescence is likely not the result of a progres-sive reduction in burst frequency, amplitude, or duration andis instead driven by molecular processes that are distinct fromthose that regulate transcriptional bursting, such as a repressor-induced shift in nucleosome position that prevents activatingtranscription factors from binding to the stripe 2 enhancer.
To test this hypothesis, we utilized a logistic regression frame-work and time-resolved data for the primary regulators of evestripe 2 to query the regulatory logic exhibited by the timewindow and bursting, respectively (SI Appendix, section H). Inthis context, the logistic regressions served as a robust statis-tical tool for drawing inferences from existing data that werenot obvious (or verifiable) by simple visual inspection. Consis-tent with our time-resolved cpHMM results, the 2 regulatorystrategies responded to transcription factor concentrations in dif-ferent ways. On the one hand, increasing levels of Giant andKruppel were sufficient to explain the onset of transcriptionalquiescence in the stripe flanks (Fig. 7 A and D). This observationpoints to a model in which repressor levels act unilaterally—without respect to coincident levels of activator proteins—to shutoff transcription at loci in an (at least effectively) irreversiblefashion. Conversely, the joint action of Giant, Kruppel, andHunchback was necessary to recapitulate the observed patternof transcriptional bursting (Fig. 7 B and F).
This difference in the regulatory logic observed for the 2strategies dissected in this work suggests that control of thetranscriptional time window and the modulation of the aver-age transcription rate arise from 2 distinct, orthogonal molec-ular mechanisms. It is also notable that our model finds thatHunchback activation is necessary to fully explain the observedpattern of transcriptional bursting in eve stripe 2. A recent studyhas suggested that Hunchback actually functions as a repressor ofeve stripe 2 and that indirect activation occurs via counter repres-sion of Hunchback by the maternal factor Caudal (80). Whilewe cannot rule out the possibility that Hunchback acts indirectly,the strong link between rising Hunchback levels and the increasein eve 2 activity in the stripe center is most consistent withHunchback playing a traditional activating role. Additional workwill be necessary to determine whether this correlation betweenrising Hunchback levels and increased stripe activity can be rec-onciled with the counter-repression hypothesis proposed in ref.80. Finally, we note that the striking absence of a direct func-tional role for Bicoid in the regulation of either phenomenonsuggests that, while Bicoid is almost certainly necessary for theexpression of eve stripe 2 (16), it does not play a direct role indictating the magnitude or duration of eve stripe 2 transcription.In this interpretation, Bicoid functions like a general transcrip-tion factor, facilitating the transcription of eve 2 without directlyconferring spatiotemporal information.
In addition to gleaning valuable insights into the mecha-nisms driving the regulation of transcription of the eve stripe2 enhancer, our logistic regression framework makes quantita-tive and falsifiable predictions about the regulation of this stripefor combinations of input transcription factor concentrations
that the embryo does not encounter in the wild-type setting.For instance, our finding that repressors alone drive the onsetof transcriptional quiescence predicts that this onset should beunaltered in mutated eve stripe 2 enhancers where some orall Hunchback binding sites have been disrupted. In this sce-nario, transcriptional activity, initially arising due to permissivelevels of Bicoid, would shut off in precisely the same manner asobserved for the full enhancer (compare Fig. 7 D, Upper Rightto Fig. 7 D, Lower Left). In the absence of Hunchback activa-tion, the model also predicts reduced levels of transcriptionalbursting, particularly late in nuclear cycle 14 (compare Fig. 7 F,Upper Right to Fig. 7 F, Lower Left). Similarly, our model couldbe used to predict the expected stripe profile in mutant embryos,where the expression of one or more gap genes has been alteredor abolished. We note, however, that the interconnected natureof the gap gene network (9) means that it would be necessaryto reimage all 3 gap genes that regulate eve stripe 2 to gener-ate data such as shown in Fig. 7C, since any change to one willaffect the expression patterns of all. Thus, additional binding-site mutation studies similar to the one described above likelyrepresent the most direct path to testing our model’s predic-tions. Taken together, we anticipate that the approaches outlinedin this work will serve as a tool both for extracting additionalinsights from experimental data and for motivating additionalexperiments aimed at answering meaningful questions about themechanistic underpinnings of gene regulation.
We also observe that certain aspects of the system remainbeyond the scope of our model. Most notably, while loci engagedin transcriptional bursting appear to continuously sense changesin transcription factor concentrations, it remains an open ques-tion whether loci continue to read out transcription factor con-centrations following the onset of transcriptional quiescence.While the transition appears irreversible in our data, it is possi-ble that quiescence is, in fact, reversible but simply not observedbecause repressor levels increase over time in our region ofinterest. The temporally resolved manipulation of repressor con-centration through, for example, optogenetics (81) could make itpossible to deplete repressors from the nucleus after transcrip-tional quiescence to determine whether this quiescent state isreversible.
To further test these and other hypotheses, it will be criticalto move beyond spatiotemporal averages for transcription factorinputs (Fig. 7C) and, instead, use live single-nucleus measure-ments to directly correlate input transcription factor concentra-tion dynamics with the corresponding transcriptional activity atthe single-cell level (82). Experimentally, we recently demon-strated the simultaneous measurement of inputs and outputs insingle nuclei of a living fly embryo using genetically encodedLlamaTags (83). We believe that using this technique, in con-junction with the theoretical methods presented here, to querythe effects of targeted disruptions to transcription factor bind-ing domains on regulatory enhancers will constitute a powerfulassay for querying transcription factor function at the molecularlevel. Thus, there are clear experimental and theoretical pathsto uncovering the detailed quantitative mechanisms behind themolecular control of transcriptional bursting and quiescence indevelopment. Such a quantitative description is a necessary steptoward a predictive understanding of developmental decisionmaking that makes it possible to calculate developmental out-comes from knowledge of the nature of the transcription factorinteractions within gene regulatory networks.
Materials and MethodsReporter Construct. This work employed the same eve stripe 2 reporter con-struct developed by ref. 18. This construct contains the even-skipped (eve)stripe 2 enhancer and promoter region (spanning −1.7 kbp to +50 bp)upstream of the yellow reporter gene. Twenty-four repeats of the MS2 stemloop sequence were incorporated into the 5′ end of the reporter gene.
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Sample Preparation and Data Collection. Sample preparation followed pro-cedures described in ref. 18. In short, female virgins of yw; His-RFP; MCP-GFP(MCP, MS2 coat protein) were crossed to males bearing the reporter gene.Embryos were collected and mounted in halocarbon oil 27 between asemipermeable membrane (Lumox film; Starstedt) and a coverslip. Datacollection was performed using a Leica SP8 laser scanning confocal micro-scope. Average laser power on the specimen (measured at the output ofa 10× objective) was 35 µW. Image resolution was 256 × 512 pixels, witha pixel size of 212 nm and a pixel dwell time of 1.2 µs. The signal fromeach frame was accumulated over 3 repetitions. At each time point, a stackof 21 images separated by 500 nm was collected. Image stacks were col-lected at a time resolution of 21 s. The MCP-GFP and Histone-RFP wereexcited with laser wavelengths of 488 and 556 nm, respectively, using aWhite Light Laser. Fluorescence was detected with 2 separate Hybrid Detec-tors (HyD) using the 498- to 546-nm and 566- to 669-nm spectral windows.Specimens were imaged for a minimum of 40 min into nuclear cleavagecycle 14.
Image Analysis. Image analysis of live embryo movies was performed basedon the protocol in ref. 27 with modifications to the identification oftranscriptional spots, which were segmented using the Trainable Weka Seg-mentation plugin for FIJI using the FastRandomForest algorithm (84). Incomparison with a previous algorithm based on Difference of Gaussians(18, 27, 32), this alternative spot segmentation approach was found to
be superior for the detection of dim transcription spots—a feature crit-ical to establishing the precise timing of the cessation of activity attranscriptional loci.
cpHMM Inference Code. All scripts relating to the cpHMM inference method-ology developed in this work are available at the GarciaLab/cpHMM GitHubrepository (85). See SI Appendix, Extended Materials and Methods, as wellas SI Appendix, section D for additional details.
ACKNOWLEDGMENTS. We thank Thomas Gregor and Lev Barinov for dis-cussion about an initial implementation of the cpHMM approach; FlorianJug for help with the spot segmentation using machine learning; andElizabeth Eck, Maryam Kazemzadeh-Atoufi, and Jonathan Liu for thehunchback P2 data used in the absolute MS2 calibration. We are also grate-ful to Jack Bateman, Jane Kondev, Rob Phillips, Allyson Sgro, and DonaldRio for comments and discussion on the manuscript. H.G.G. was supportedby the Burroughs Wellcome Fund Career Award at the Scientific Interface,the Sloan Research Foundation, the Human Frontiers Science Program, theSearle Scholars Program, the Shurl & Kay Curci Foundation, the HellmanFoundation, the National Institutes of Health (NIH) Director’s New InnovatorAward (DP2 OD024541-01), and a National Science Foundation (NSF) Fac-ulty Early Career Development Program (CAREER) award (1652236). N.C.L.was supported by NIH Genomics and Computational Biology Training Grant5T32HG000047-18. C.H.W. was supported by the NIH/National Cancer Insti-tute (U54 CA193313), The City University of New York (CUNY) (RFCUNY40D14-A), and the NSF (IIS-1344668).
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