Multimodal transcriptional control of formation in

Nicholas 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 of Technology, Pasadena, CA 91126; cDepartment of , Columbia University, New York, NY 10027; dDepartment of Physics, University of California, Berkeley, CA 94720; eDepartment of Applied Physics and Applied , Columbia University, New York, NY 10027; fData Science Institute, Columbia University, New York, NY 10027; gDepartment of Systems , Columbia University, New York, NY 10027; hDepartment of , 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 and stripe transcribe at a higher average rate than nuclei on the stripe regulatory DNA sequence dictate rates of transcription and, ulti- boundaries (Fig. 1B). We identify this graded transcriptional mately, drive developmental outcomes remains an open challenge control strategy with the analog control of gene expression. in physical biology. Using stripe 2 of the even-skipped gene Alternatively, transcription factors could exert control over the in Drosophila as a case study, we dissect the regula- length of time a nucleus is transcriptionally active (Fig. 1C). In tory forces underpinning a key step along the developmental this binary control scheme—akin to an on/off switch that dic- decision-making cascade: the generation of cytoplasmic mRNA tates whether a nucleus is transcriptionally active or quiescent— via the control of transcription in individual cells. Using individual nuclei transcribe at the same average rate regardless live imaging and computational approaches, we found that the of their position along the stripe, but for different lengths of time. transcriptional burst frequency is modulated across the stripe to Finally, some nuclei might not engage in transcription at all dur- control the mRNA production rate. However, we discovered that ing the formation of the pattern (Fig. 1D). Here, a larger fraction bursting alone cannot quantitatively recapitulate the formation of nuclei engage in mRNA production in the stripe center than of the stripe and that control of the window of time over which in the boundaries. Any of these scenarios, or some combina- each nucleus transcribes even-skipped plays a critical role in stripe tion thereof, can explain the formation of a cytoplasmic mRNA formation. Theoretical modeling revealed that these regulatory pattern. strategies (bursting and the time window) respond in different To quantify the contribution of each regulatory strategy to pat- ways to input concentrations, suggesting that tern formation and thereby move toward a deeper understanding the stripe is shaped by the interplay of 2 distinct underlying of the molecular processes at play, it is necessary to measure molecular processes. Significance transcriptional bursting | gene regulation | development | hidden Markov models Predicting how the gene expression patterns that specify ani- mal body plans arise from interactions between transcription uring embryonic development, tightly choreographed pat- factor proteins and regulatory DNA remains a major challenge Dterns of gene expression—shallow gradients, sharp steps, in physical biology. We utilize live imaging and theoretical narrow stripes—specify cell fates. The correct positioning, sharp- approaches to examine how transcriptional control at the ness, and amplitude of these patterns of cytoplasmic mRNA and single-cell level gives rise to a sharp stripe of cytoplasmic protein ensure the reliable determination of animal body plans mRNA in the fruit fly . While the modulation of tran- (1). Yet, despite decades of work mapping the gene regulatory scriptional bursting has been implicated as the primary lever networks that drive development, and despite extensive efforts to for controlling gene expression, we find that this alone can- dissect the regulatory logic of the enhancer elements that dictate not quantitatively recapitulate pattern formation. Instead, we the behavior of these networks, a predictive understanding of find that the pattern arises through the joint action of 2 how gene expression patterns and developmental outcomes are regulatory strategies—control of bursting and control of the driven by transcription factor concentrations remains a central total duration of transcription—that originate from distinct challenge in the field (2). underlying molecular mechanisms. Predicting developmental outcomes demands a quantitative understanding of the flow of information along the central Author contributions: C.H.W. and H.G.G. designed research; N.C.L., V.G., and H.G.G. per- dogma: how input transcription factors dictate the output rate formed research; N.C.L., V.G., A.R., S.A.M., and C.H.W. contributed new reagents/analytic tools; N.C.L., V.G., S.A.M., and H.G.G. analyzed data; and N.C.L., V.G., and H.G.G. wrote of mRNA production, how this rate of mRNA production dic- the paper.y tates cytoplasmic patterns of mRNA, and how these mRNA The authors declare no competing interest.y patterns lead to protein patterns that feed back into the . While the connection between transcrip- This article is a PNAS Direct Submission.y This open access article is distributed under Creative Commons Attribution-NonCommercial- tion factor concentration and output mRNA production rate has NoDerivatives License 4.0 (CC BY-NC-ND).y been the subject of active research over the last 3 decades (2– Data deposition: All data sources used in this work can be found in the project’s 11), the connection between this output rate and the resulting GitHub repository at https://github.com/GarciaLab/cpHMM/tree/master/multimodal cytoplasmic patterns of mRNA has remained largely unexplored. control paper sandbox/dat.y For example, a graded stripe of cytoplasmic mRNA within an 1 N.C.L. and V.G. contributed equally to this work.y embryo could arise as a result of radically different transcrip- 2 To whom correspondence may be addressed. Email: [email protected] or chris. tional dynamics at the single-nucleus level (Fig. 1A). Specifically, [email protected] if individual nuclei along this stripe modulate their average RNA This article contains supporting information online at https://www.pnas.org/lookup/suppl/ polymerase (RNAP) loading rate, then graded control of the doi:10.1073/pnas.1912500117/-/DCSupplemental.y mean rate of transcription results: Nuclei in the middle of the First published December 27, 2019.

836–847 | PNAS | January 14, 2020 | vol. 117 | no. 2 www.pnas.org/cgi/doi/10.1073/pnas.1912500117 Downloaded by guest on September 25, 2021 Downloaded by guest on September 25, 2021 ssgicnl lvtdi h etro h tie(8.Strik- (18). stripe the of production center mRNA the a of in rate only elevated the makes significantly that nuclei found with is consistent we active although Second, studies, of center, formation. previous stripe the fraction to in the contribution than minor stripe of the regulation of this periphery transcrip- in the engage in and active tion become nuclei of fraction of smaller formation the to contribute quantitatively mRNA. of patterns the concentrations, real of factor in transcription control spatiotem- input the level in between single-cell variations connection poral the quantitative a at theoretical seeking transcription with time, imaging study live to combined we modeling this, tran- of do of rates To output models scription. graded in to develop variations patterns factor quantitative transcription to connecting input of need capable infor- the regulation spatial gene reinforcing of drives and transmission that network the development regulatory to gene the key along is mation that mRNA stripes of these distribution makes cytoplasmic detailed sug- the (18–21), that reproducible formed highly gesting and the graded is that are indicated profiles stripe have mRNA studies the recent repressors addition, and that In activators (16). transcriptional established of interplay studied has the widely through work the of Previous 2 17). stripe of formation even-skipped the to leads activity patterns spatiotem- to how rise of mRNA. give cytoplasmic study dynamics of the transcriptional to in ill-suited variations thus poral expression are gene and virtu- single-cell time of are over regulation techniques as the regarding protein Such silent and 12–15). ally mRNA (9, of progresses distributions development snap- situ cytoplasmic obtain the in time, to of relied fluorescence shots immunofluorescence have real mRNA and studies as in (FISH) most such nuclei, hybridization date, techniques to individual fixed-tissue However, in on embryo. loading living a RNAP in of rate the or active transcriptionally is thereof. nucleus transcrip- a (C the when quiescent (B), dictating rate window transcription time mean tional the over transcription from control arise exerting could factors (A) patterns mRNA Cytoplasmic (A–D) activity. 1. Fig. amr tal. et Lammers D C B A efudta l euaoysrtge ulndi i.1 Fig. in outlined strategies regulatory 3 all that found We transcriptional single-cell how investigated we work, this In RNAP nuclei

utpemdso atr omto ysnl-eltranscriptional single-cell by formation pattern of modes Multiple loading rate rsm combination some or (D) nuclei active of fraction the or ), eve active eei h eeoigfutfl mro(16, embryo fly fruit developing the in gene (eve) rncito,adtefraino cytoplasmic of formation the and transcription, time quiescent active nuclei fraction of control ofthe time window of thetranscriptional binary control transcription rate of themean analog control eve cytoplasmic tie2 is,a First, 2. stripe mRNA Drosophila eve stripe h aeo RAsnhssaddegradation and synthesis between mRNA balance of the rate considers that mRNA, the model of simple a patterns with cytoplasmic began of we formation Transcriptional the dictates from nuclei Distributions mRNA Activity. Cytoplasmic Predicting Results to observation. and numeracy experimental precise with quantitative coupled biological when insights and biological the additional modeling yield showcasing theoretical development, for early and potential in of transcription patterns presence control expression the that activa- gene to mechanisms both point regulatory findings of distinct our 2 concentrations Thus, repressors. input and the engaged tors stripe transcriptionally upon posterior among depends and bursting anterior nuclei of the control in the explain nuclei flanks, to of sufficient silencing were early alone the levels repressor transcriptional While than time logic bursting: regulatory transcriptional different con- the to adheres factor that window revealed transcription analysis input This in centrations. changes with addition, dynamics In correlate tional 26). alter- to regressions (25, by logistic bursts utilized transcription transcriptional we results, of of frequency previous rate time the the with ing and control consistent space factors that, across transcription uncovered nuclei We individual hidden (22–24). in compound-state dynamics transcriptional in a bursting variations each uncover employed to of (cpHMM) model underpinning We Markov mechanistic strategy. the regulatory uncover to approaches the of 1B). control (Fig. and transcription of 1C) dura- rate (Fig. the mean engagement of transcriptional control of binary strategies: tion control interplay distinct the 2 through between entirely almost explained quantitatively be regu- 1 distinct (Fig. 3 strategies from contributions latory with nature, in of multimodal regulation is the 2 primary that conclude the we is Thus, thereof. formation—it driver pattern for a precondition merely not necessary is logic engaged/disengaged of case transcriptionally the binary that—in dur- found time we of genes development, windows that ing limited appreciated for widely longer competent is it transcriptionally times While are 3 those flanks. approximately the with in for those stripe, expressing than the center across stripe which the transcription during in in time engaged of a were window observed loci also the we of of nuclei, regulation control among pronounced production the mRNA the to of addition of rate In the control pattern. stripe the analog mRNA recapitulate this quantitatively cytoplasmic to that insufficient is discovered rate transcription we however, ingly, tion mRNA(x where yolsi RAsgetta hsasmto sreasonable is assumption this Comparisons of that levels control. suggest measured spatiotemporal mRNA empirically and cytoplasmic of predictions rate kind model degradation between any the under that not First, assumed regulation. transcriptional we of modeling the 1 in (Fig. assumptions strategies regulatory tial and 1D), derivation). Fig. this (see in rate shown degradation the strategy is regulatory the the to within responding nuclei multiple position over same averaged rate synthesis mRNA d uliguo hsrsl,w eeoe computational developed we result, this upon Building oeaieteqatttv osqecso h poten- 3 the of consequences quantitative the examine To mRNA x dt ln h mroa time at embryo the along opeithwtetasrpinlatvt findividual of activity transcriptional the how predict To (x , t x = ) , , t p ) active PNAS cienuclei active niae h RAcnetaina posi- at concentration mRNA the indicates rcinof fraction | p active .Nntees tiefraincan formation stripe Nonetheless, B–D). (x {z | ) (x aur 4 2020 14, January stefato fatv uli(cor- nuclei active of fraction the is eto A section Appendix, SI } ) synthesis | R ,w dpe widespread adopted we B–D), t (x , {z R , t (x } ) , − | t γ eve ) | γ o.117 vol. orsod othe to corresponds degradation sacntn and constant a is mRNA(x tie2transcrip- 2 stripe eve {z tie2—this stripe o eal of details for | o 2 no. eve , t } ) , stripe | [1] eve 837 γ

DEVELOPMENTAL BIOPHYSICS AND BIOLOGY COMPUTATIONAL BIOLOGY (SI Appendix, section B). Second, we posited that at each posi- tion throughout the embryo the synthesis rate R(x, t) does not A vary significantly in time such that it can be approximated by its time average R(x) = hR(x, t)i. This assumption is revised later in the text to account for the time-dependent regulation of the mean rate of transcription. Finally, we assumed that nuclei along the axis of the embryo start transcribing at time ton(x) and stop transcribing and enter a state of transcriptional quiescence at time toff (x). Under these assumptions, Eq. 1 can be solved analytically, resulting in

R(x) mRNA(x, t) = × [2] γ | {z } mean transcription rate   −γ(t−min{toff (x),t}) −γ(t−ton(x)) e − e × pactive(x) . | {z } | {z } B transcriptional time window fraction active

Eq. 2 makes precise predictions about how each regulatory strat- egy contributes to the formation of the cytoplasmic mRNA pattern. Thus, measuring how each quantity is regulated across the stripe allows us to predict their relative contributions to pattern formation.

Binary Control of the Transcriptional Time Window Is the Primary Driver of Stripe Formation. To test the simple model of pattern formation put forward in Eq. 2, we quantified transcription of stripe 2 of eve in the fruit fly. We imaged the transcription of an eve stripe 2 reporter, using the MS2 system (18, 27, 28). Tran- scripts of a reporter gene driven by the eve stripe 2 enhancer and the eve promoter contain repeats of a DNA sequence that, when transcribed, 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 appear C as fluorescent puncta within individual nuclei (Fig. 2B and Movie S1). As described in SI Appendix, Fig. S2, the intensity of these fluorescent puncta is proportional to the number of RNAP molecules actively transcribing the gene. These resulting fluo- rescence values could then be calibrated using single-molecule FISH to estimate the number of RNAP molecules actively tran- scribing the gene (Materials and Methods and ref. 27). By aligning multiple embryos (SI Appendix, Fig. S1), we obtained the average number of actively transcribing RNAP molecules as a function of time and position throughout the embryo (Fig. 2C). Using the MS2 system, we quantified each potential regulatory strategy and determined its predicted contribution to pattern formation according to our model in Eq. 2. We first used the average fluorescence intensities of our MS2 traces to estimate the time-averaged rate of RNAP loading, R(x) as described in SI Appendix, section B. We found that this rate is modulated along the axis of the embryo (Fig. 3 A and B; Movie S2; SI Appendix, Fig. S3; and Materials and Methods): Whereas in the center of the Fig. 2. Measuring transcriptional dynamics of eve stripe 2 formation using the MS2 system. ( ) MS2 stem loops introduced in an stripe 2 reporter stripe RNAP molecules are loaded at a rate of ∼16 molecules A eve gene are bound by MS2 coat protein fused to GFP. (B) Sites of nascent per minute, this loading rate decreases to about 8 molecules per transcript formation appear as green fluorescent puncta whose intensity minute at the boundaries. reports on the number of actively transcribing RNAP molecules. Nuclei are We next used our MS2 data to examine spatial trends in visualized through a fusion of RFP to Histone. (C) Mean number of RNAP the transcriptional time window. Our data revealed that the molecules actively transcribing the gene as a function of space and time transcriptional time window is modulated along the stripe (SI (data averaged over 11 embryos). Appendix, Fig. S4A). Whereas the time at which each nucleus becomes transcriptionally active, ton(x), was constant across the stripe, with all nuclei becoming active 8 ± 4 min after the previ- along the stripe (Fig. 3 C and D and Movie S3), with nuclei ous anaphase (SI Appendix, Fig. S4B), the time at which nuclei in the stripe center transcribing for >30 min and nuclei on the stop transcribing and become quiescent, toff (x), showed a strong boundaries transcribing only for ∼10 min. We note that, to derive modulation along the embryo’s axis (SI Appendix, Fig. S4C). As these results, it was necessary to account for potential effects of a result, the time window over which each transcriptional locus the detection limit in our experiments of ∼4 RNAP molecules is engaged in transcription, ∆t = toff − ton, is sharply modulated per locus on estimates of the timing of the appearance and

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DEVELOPMENTAL BIOPHYSICS AND BIOLOGY COMPUTATIONAL BIOLOGY binary control of the transcriptional time window (Fig. 3G, blue) A C make significant contributions to the overall pattern, with binary control playing the dominant role. We thus concluded that the 40 joint effect of these 2 strategies (Fig. 3G, brown) is sufficient to quantitatively recapitulate the stripe of cytoplasmic mRNA from

single-cell transcriptional activity. 20 molecules molecules

number of RNAP same promoter number of RNAP 0 Mean Transcription Rate Is Dictated by Bursting through Modulation 10 20 30 40 state, different RNAP counts of the Rate of Promoter Turn on. Are the binary and analog con- time (min) trol strategies driven by distinct molecular mechanisms, or are B nascent they different manifestations of the same underlying process? To mRNA uncover the molecular mechanism behind the analog control of k on r the mean rate of transcription, we analyzed the transcriptional RNAP

activity of individual nuclei. Previous work demonstrated that the promoterkoff RNA loading rate rate of gene expression at individual loci within the eve stripe 2 polymerase pattern is highly stochastic (18). Indeed, as shown in Fig. 4A, our time D data revealed punctuated peaks and troughs in the number of 0s 20s 40s 60s 80s 100s active RNAP molecules. These features have been related to the rate of RNAP initiation at the eve promoter by assuming that transcriptional activity is “burst-like,” with the promoter rapidly loading multiple RNAP molecules onto the gene at a constant 5 μm rate 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, sister 31–33), support a minimal 2-state model of promoter switch- chromatids ing (Fig. 4B): Promoters switch stochastically between ON and EF OFF states with rates kon and koff . In this model, promoters in three-state model effective two-state model the ON state engage in the loading of RNAP (and, correspond- ingly, mRNA production) at rate r. Thus we find that, to describe eve stripe 2 transcriptional dynamics, we need to account for both the short, transient ON periods dictated by transcriptional r bursts and a longer transcriptional time window that describes ON the period over which loci engage in this transcriptional bursting. In the bursting model, the mean rate of transcription is given or by the product of the fraction of time spent in the ON state with kon koff the transcription rate in this active state (34–37)

k (x) R(x) = r(x) × on , [3] OFF | {z } |{z} kon(x) + koff (x) mean RNAP loading | {z } Fig. 4. Transcriptional bursting in eve stripe 2. (A) Single-nucleus measure- transcription rate rate fraction of time in ON state ments reveal that nuclei transcribe in bursts. (B) Two-state model of bursting of a single promoter. (C) The same hidden rate of RNAP loading (Bot- where all parameters are allowed to vary as a function of position tom) can correspond to different observable numbers of RNAP molecules along the embryo, x (see SI Appendix, section A for details of this on the gene (Top), such that standard hidden Markov model approaches cannot be used to infer the hidden promoter state. (D) Fluorescent puncta derivation). Thus, within this framework, the observed modula- are composed of 2 distinct transcriptional loci within a diffraction-limited tion of the mean rate of transcription across the stripe (Fig. 3G, spot, each corresponding to a sister chromatid. (E) Three-state model of green) implies that one or more of these bursting parameters promoter switching within a fluorescent punctum that accounts for the are subject to spatially controlled regulation. However, the mean combined action of both sister chromatids. (F) Effective 2-state model of rate trend alone is not sufficient to identify which of the 3 burst- transcriptional bursting. (In A, error bars are obtained from estimation of ing parameters (kon, koff , and r) is being regulated by the input background fluorescent fluctuations; Materials and Methods and ref. 27.) transcription factors to control the average transcription rate. While each bursting parameter does not necessarily map directly to a single molecular step in the transcriptional cycle, identifying koff , and r. To date, approaches for extracting bursting parame- which parameter(s) is subject to regulation can help narrow the ters from such data in multicellular organisms have mainly relied set of possible molecular mechanisms. For instance, variation in on the manual analysis of single-nucleus transcriptional dynam- r could indicate that transcription factors play an active role in ics (18, 25) or autocorrelation-based methods that infer mean the recruitment of RNAP to the promoter or in the release of bursting parameters across ensembles of traces (30, 48, 49). A RNAP from promoter-proximal pausing (38). computational method for inferring the rates of RNAP loading Typically, the in vivo molecular mechanism of transcription (Fig. 4 C, Bottom) from the total number of actively transcribing factor action is inferred from measurements of transcriptional RNAP molecules in single cells (Fig. 4 C, Top) is thus needed to noise obtained through snapshots of dead and fixed embryos or obtain the bursting parameters. cells using theoretical models (26, 31–33, 39–47). In contrast, Hidden Markov models (HMMs) are widely used to uncover MS2-based live imaging can directly inform on the dynamics of the dynamics of a system as it transitions through states that are transcriptional bursting in real time. The MS2 approach, how- not directly accessible to the observer (50). However, our observ- ever, reports on the total number of actively transcribing RNAP able (the MS2 signal) does not correspond to the hidden variable molecules and not on the instantaneous rate of RNAP loading at of interest (the promoter state) in a one-to-one fashion (com- the promoter, which is the relevant quantity for estimating kon, pare Fig. 4 C, Top and Bottom). Instead, the observable MS2

840 | www.pnas.org/cgi/doi/10.1073/pnas.1912500117 Lammers et al. 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RNAP loading rate (1/min) OFF number of 10 20 30 ON

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DEVELOPMENTAL BIOPHYSICS AND BIOLOGY COMPUTATIONAL BIOLOGY repressing transcription factors could alter the local chromatin To determine whether quiescence can be explained within the landscape by repositioning promoter or enhancer nucleosomes bursting framework, we divided the stripe into the 5 regions (61), changes that could block the binding of activators at the shown in Fig. 6B. For each region, we sought to determine stripe 2 enhancer or of general transcription factors at the whether the bursting dynamics varied over time in a manner promoter and thus abolish further activator-mediated bursting that could explain the dynamics of entry into quiescence of indi- (Fig. 6 A, i). Alternatively, if the rates of promoter switching vary vidual nuclei (Fig. 6C). To probe for this time dependence in in time, then the time window could be explained without invok- transcriptional bursting, we extended our cpHMM method to ing an extra silenced state that is mechanistically distinct from the obtain promoter-bursting parameters over discrete periods of processes driving transcriptional bursting. Specifically, transcrip- time by performing inference on our live-imaging data using tional quiescence could be achieved by progressively reducing a sliding window (see SI Appendix, section D for details). Our the frequency (kon), intensity (r), and/or duration (1/koff ) of inference revealed that the rate of promoter turn on, kon, var- transcriptional bursts. For example, it is possible that increasing ied significantly in time (Fig. 6D). Specifically, kon decreased in repressor levels in the stripe flanks could disrupt the capac- both the anterior and posterior stripe boundaries (Fig. 6D, black ity for activators to initiate transcription bursts via short-range and red curves) as development progressed and the fraction of quenching interactions (62), a mechanism that would manifest active nuclei decreased (Fig. 6D, gray shaded region), while loci as a decrease in kon over time. in the stripe center (Fig. 6D, green and yellow curves) exhibited a significant increase in kon. Further, while relatively constant at most positions along the stripe, both the rate of RNAP loading

1 when in the ON state, r, and the rate of promoter turn off, koff , A (i) transition to a silent state C decreased slightly (Fig. 6 E and F). 0.8 silenced These findings confirmed our time-averaged inference results k silence 0.6 D E k ON (Fig. 5 and ) indicating that on was the primary kinetic pathway through which transcription factors influence eve stripe 0.4 2 transcription dynamics. Moreover, the coincidence of the 0.2 decrease in kon in flank nuclei with the onset of transcriptional

fraction of quiescent nuclei 0 quiescence (gray shaded region in Fig. 6D) seemed to suggest 10 20 30 40 time (min) that, at least in part, quiescence in the stripe flanks could be

RNAP ? molecules D 2 driven by the temporal modulation of bursting parameters (Fig. 6 A, ii). However, other trends in our data were not consistent with r 1.6 ) the view that a decrease in kon drives transcriptional quiescence. k -1 ON 1.2 Although 70% and 50% of nuclei in the regions directly ante- rate (min rior and posterior of the stripe center were quiescent by 40 min on

k 0.8 kOFF into the nuclear cycle (blue and yellow curves in Fig. 6C), we time 0.4 detected no corresponding decrease in kon. In fact, kon actu- (ii) temporal control of bursting kinetics OFF ON ally increased in some inner regions of the stripe (Fig. 6D)—a 0 10 20 30 40 trend that would increase overall transcriptional activity and time (min) B E would therefore go against the establishment of transcriptional 30 quiescence. 25 The divergent outcomes observed in the central stripe regions, 20 with the rate of transcriptional bursting remaining constant or 15 increasing at eve loci within the engaged population of nuclei 10 even as loci in neighboring nuclei turn off for good, runs counter

5 ON to the hypothesis that quiescence is driven by the temporal mod-

rate of RNAP loading (1/min) ulation of the promoter switching parameters. It is conceivable 0 -6 -4 -2046 2 10 20 30 40 that temporal changes in bursting parameters associated with distance from stripe center time (min) (% embryo length) the onset of quiescence occur too rapidly to be captured by our F 1.5 model. However, as discussed in SI Appendix, section I, these 1.2 changes would need to occur on the same time scale as burst- )

-1 ing itself (1 to 3 min). Given that both the other temporal trends 0.9 detected by our inference (Fig. 6) and the shifts in the input tran- (min

off 0.6 SI Appendix k scription factors themselves ( , section H) unfold on

0.3 significantly slower time scales (5 to 15 min), we concluded that ON OFF while possible, a scenario where bursting dynamics are changing 0 10 20 30 40 too quickly to detect is unlikely. time (min) The contradictory trends observed in the stripe center and Fig. 6. Investigating the molecular character of transcriptional quiescence. flanks indicated that entry into transcriptional quiescence might (A) Two hypotheses explaining promoter quiescence onset: (A, i) a tran- involve processes not captured within the bursting model (Fig. 6 sition into an alternative, long-lived transcriptionally silent state and (A, A, i), thus suggesting that binary control of the transcriptional ii) the modulation of one or more bursting parameters over time. (B–F) time window and the transcriptional bursting driving the analog Division of the stripe into 5 regions (B) for our analysis of the frac- control of the mean transcription rate may arise from distinct tion of quiescent nuclei (C), the transition rate from OFF to ON (D), the molecular processes. rate of RNAP loading when the promoter is in the ON state (E), and the transition rate from ON to OFF as a function of time and position Input–Output Analysis Reveals Distinct Regulatory Logic for Burst- along the stripe (F). Gray shaded region indicates the onset of tran- ing and the Transcriptional Time Window. eve stripe 2 is mainly scriptional quiescence. (In C, error bars indicate bootstrap estimate of the SEM; in D–F, error bars indicate the magnitude of the difference established by the combined action of 2 activators, Bicoid and between the first and third quartiles of cpHMM inference results for boot- Hunchback, and 2 repressors, Giant and Kruppel¨ (16, 17, 63). If strapped samples of experimental data; see Materials and Methods for transcriptional bursting and the transcriptional time window are details.) controlled by distinct molecular processes, then distinct forms

842 | www.pnas.org/cgi/doi/10.1073/pnas.1912500117 Lammers et al. Downloaded by guest on September 25, 2021 Downloaded by guest on September 25, 2021 nterltv ieiodo ou neigteqisetstate quiescent such engaged the that transcriptionally entering remaining of locus likelihood a the of versus time likelihood factor relative transcriptional transcription the each the the of on impact query of the probes to control model the example, the window, For behind outcomes. logic binary regulatory models with predictive inferring processes of in method logistic used The widely window. a is time regression transcrip- transcriptional of the control and spatiotemporal bursting the tional in factors by 4 played these role of regulatory each the probe to regressions logistic utilized indicate circles color-coded The purple. C ( in embryo. highlighted the is along likelihood position highest and Kr the that time factors—Kr with indicating of transcription model likelihood 4 function simplest the a The of as models. which (B) regression state complex ON more bursting progressively the predicted in and nuclei levels of fraction and (A) 7. Fig. amr tal. et Lammers known were space and time in transcrip- point output the each the 7C which (Fig. and at in inputs activity embryo” relevant “average tional all an of generate concentration of to data activity live-imaging tional our with data combined We these 66). (65, data Kr immunofluorescence published for from spatiotempo- profiles obtained concentration and ral (64) fusion Bicoid-GFP established factors. concentrations transcription cor- 4 input the these the with of 7B) in (Fig. the patterns state and spatiotemporal ON 7A) bursting responding (Fig. the state in quiescent nuclei of the fraction fraction entered the control- have correlate logic that to nuclei sought molecular of we the strategy, reveal regulatory Bicoid To each the ling state. quiescent example, the Kr For into and play. Giant the bursting, at transcriptional while control be could activators may Hunchback and logic regulatory of Kr Giant, that reveal scores Model (G) purple. in highlighted is model likely in most behavior and simplest The state. ON log emaue iodcnetainpolsuigawell- a using profiles concentration Bicoid measured We  rbn h euaoylgco usigadtetasrpinltm idw (A window. time transcriptional the and bursting of logic regulatory the Probing P P quiescent and engaged F. .Bidn pnpeiu ok(7,we (67), work previous upon Building S7). Movie  eve = β rdce rcino uecn ulifor nuclei quiescent of fraction Predicted (D) 14. cycle nuclear into min 40 and 25, 10, at embryo “average” our of levels mRNA 0 + pe ersoscuddcaeteentry the dictate could repressors uppel ¨ pe n in eesaesfcett eaiuaetefato fqisetnce in nuclei quiescent of fraction the recapitulate to sufficient are levels Giant and uppel ¨ A -3 E D relative log-likelihood (10 ) time (min) 120 160 β 20 20 10 40 30 40 30 40 80

0 time (min) itnefo tiecne %eby egh distancefromstripecenter(%embryolength) distance fromstripecenter(%embryolength) 1 40 35 30 25 20 15 10 distance fromstripecenter(%embryolength) Gt Kr Kr [Bcd]+ 1234 number oftranscriptionfactors 505 -5 pe,Gat n Hunchback and Giant, uppel, ¨ pe K) in G) ucbc H) n iod(c)wr nlddi ahvrino h oe.(E model. the of version each in included (Bcd)—were Bicoid and (Hb), Hunchback (Gt), Giant (Kr), uppel ¨ β Hb 2 [Hb]+ Gt Gt Kr Kr 505 505 -5 05 -5 eve tie2transcrip- 2 stripe β 3 Bcd Hb

[Gt]+ 0 0.2 0.4 0.6 0.8 1 G nuclei quiescent of fraction

-3 F B relative log-likelihood (10 ) time (min) 40 30 20 40 30 20 10 10 15 0 5 time (min) distance fromstripecenter(%embryolength) 40 35 30 25 20 15 10 Gt Kr Kr β 1234 4 [Kr], number oftranscriptionfactors 505 -5 [4] Hb Gt Gt Kr Kr 505 -5 ikdars nHnhakcnetaint nosre iein rise observed an to concentration Hunchback spa- in rise the a capture linked 7 fully (Fig. to dynamics necessary bursting tiotemporal also profile; were bursting observed levels the ON Hunchback recapitulate the not in could repressor alone window, nuclei time levels transcriptional of the fraction Unlike 7B). the (Fig. state and levels factor transcription etoe oespeetdhr ssonin shown as here improve- presented H significant section models to no over activators provided transcrip- ment presumed repressors—also each the as of allowing function role instance, functional factor—for the tion on silencing. pre- constraints for no responsible model’s Relaxing processes have molecular the concentrations the on activator over influence that impact of suggesting no addition power, had dictive further Bicoid The recapitu- and/or trends. respectively, Hunchback Giant flanks, observed stripe repressors experimentally posterior lated the and of anterior levels the increasing explain Kr which can and in 7 factor model (Fig. transcription ple single dynamics no quiescence that OFF revealed the state versus ON bursting the in are state, analogous nuclei relative transcrip- the an that determine controlling that used likelihood factors logic the We inferring regulatory by 16). bursting the tional (5, investigate roles to repressing Kr model and play requiring roles to activating factor, play Giant transcription to each Hunchback knowl- of and prior Bicoid function used the we about coefficients, edge these regulatory estimating factor’s In transcription function. the of repressing) or (activating coefficients the where enx undoratnint h eainhpbetween relationship the to attention our turned next We u nlsso h rcino ulii h quiescent the in nuclei of fraction the of analysis Our P Bcd pe rv h ne ftasrpinlqisec in quiescence transcriptional of onset the drive uppel ¨ ON Hb 0.1 0.3 0.5 0.7

and . fraction of nuclei in ON state ON in nuclei of fraction /P OFF rcino ulii h rncitoal uecn state quiescent transcriptionally the in nuclei of Fraction B) C 0.2 0.4 0.6 0.8 0.2 normalized 0.4 signal 0.6 0.8 normalized signal 0.2 normalized 0.4 signal 0.6 0.8 0 1 0 1 0 1 2 4 6 80 60 40 20 . PNAS position (%embryolength) pe,adHnhakrcptlt h bursting the recapitulate Hunchback and uppel, ¨ β | n aur 4 2020 14, January niaetemgiueadnature and magnitude the indicate npht fipttasrpinfactor transcription input of Snapshots ) (F D. D rdce rcino ulii the in nuclei of fraction Predicted ) Bcd Hb 40 min 25 min and 10 min eve Gt Kr E and .Hwvr sim- a However, E). | o.117 vol. .Seicly we Specifically, G). IAppendix, SI | o 2 no. pe and uppel ¨ Model ) | 843

DEVELOPMENTAL BIOPHYSICS AND BIOLOGY COMPUTATIONAL BIOLOGY the fraction of nuclei in the ON state in the stripe center between Here, we contribute to a growing body of work that illustrates 30 and 35 min into the nuclear cycle (Fig. 7 B and F). the utility of using simple quantitative models to extract insights Our input–output analysis thus revealed that bursting and into the workings of complex biological phenomena (33, 67, 70). the transcriptional time window exhibit significantly different Our discovery of the key role of the differential duration of the forms of regulator logic: Whereas repressor levels alone are transcriptional time window in pattern formation was made pos- sufficient to explain the transcriptional time window, the joint sible only by biological numeracy, that is, by going beyond the action of activators and repressors appears necessary to explain qualitative description of pattern formation and demanding a the observed patterns of transcriptional bursting. These results quantitative agreement between our theoretical predictions and are consistent with the hypothesis that regulation of burst- the experimental data (71). While it is widely appreciated that ing and of the transcriptional time window occur via distinct genes are expressed for discrete windows of time over the course molecular processes, therefore supporting a model in which the of development (27, 28, 72), we have demonstrated that—in the long-lived trancriptionally silent state observed in flank nuclei case of this eve stripe 2 reporter—this binary transcriptionally constitutes a distinct molecular state outside of the bursting engaged/quiescent logic is actively regulated by transcription fac- model. tors to drive pattern formation. Thus the differential duration of transcriptional activity comprises a necessary element of any Discussion quantitative description of pattern formation. In Drosophila development, information encoded in a handful Our work contributes to an increasingly diverse and exciting of maternally deposited protein gradients propagates through discourse in quantitative regarding the increasingly complex layers of interacting genes, culminating importance of the temporal component of transcriptional reg- in the specification of the adult . The prediction of ulation in specifying developmental outcomes. For example, one this cascade of developmental outcomes requires a quantitative recent study has demonstrated that the limited readout time understanding of the mechanisms that facilitate the flow of infor- imposed by short nuclear cycles in early Drosophila development mation along the central dogma. Here, we utilized live imaging in places strict constraints on the kinds of regulatory architec- conjunction with theoretical modeling to shed light on a critical ture that could be responsible for driving observed patterns of link in this cascade: how the regulation of transcriptional activity hunchback gene expression (73). Other recent work has indi- at the single-nucleus level gives rise to a cated that the pioneer factor Zelda plays a key role in regulating of cytoplasmic mRNA. both the timing and probability of transcriptional activation fol- A priori, there are several distinct regulatory strategies at the lowing mitosis (74, 75). Our work complements these previous single-cell level capable of generating spatially differentiated pat- observations by exploring yet another aspect of the interplay terns of cytoplasmic mRNA (Fig. 1), each with distinct implica- between timing and transcriptional regulation. We have shown tions for the nature of the underlying molecular processes at play. that, in the case of eve stripe 2, transcription factors regulate Several recent studies have revealed that the average rate of tran- the onset of transcriptional quiescence, toff , across the stripe, scription is mainly modulated across the embryo by tuning the thus demonstrating that the embryo actively leverages the dif- frequency of transcriptional bursting (18, 25, 26, 30, 33, 68). Yet ferential duration of transcriptional engagement as a strategy it has remained unclear whether this modulation of the rate of to generate patterns of gene expression. Together, these recent transcription (and thereby mRNA production) is the dominant findings suggest that, if the field is to make progress toward a modality by which input concentrations of transcription factors predictive picture of pattern formation in development, it will drive the formation of patterns of gene expression or whether, be necessary to go beyond the widespread steady-state, static instead, it is simply the most readily apparent mechanism among picture of pattern formation in development put forward by pre- multiple distinct control strategies. vious single-cell transcriptional activity studies that focused on In this work, we derived a simple theoretical model that pre- the study of snapshots of fixed embryos (26, 31–33) and embrace dicts how the interplay between regulatory strategies at the a dynamical description that acknowledges that development is a single-cell level dictates the formation of a cytoplasmic gene process that occurs outside of steady state (69). expression pattern (2). We applied this model to single-cell live- To determine whether this binary control of the transcriptional imaging measurements of an MS2 reporter driven by the eve time window and the analog control of the mean transcription stripe 2 enhancer, an approach that allowed us to dissect the rate share a common molecular mechanism, we utilized a vari- regulatory logic of a well-characterized regulatory element free ety of theoretical and computational tools in conjunction with from the confounding influences of other enhancers located in our live-imaging data. Specifically, to uncover how the mean the endogenous eve locus. We demonstrated—quantitatively— rate of transcription is regulated across the stripe, we devel- that the modulation of the mean rate of transcription is alone oped a cpHMM that is capable of inferring the instantaneous insufficient to account for the formation of a sharp stripe of gene activity state of individual gene loci from MS2 traces. We used expression (Fig. 3G, green). We discovered that the window of this cpHMM to infer average promoter-switching parameters time over which promoters engage in transcription is sharply con- across the stripe (Fig. 5). In agreement with previous measure- trolled along the axis of the embryo (Fig. 3 C and D) and that the ments of various gene expression patterns (25, 26, 30, 33), our joint action of the analog control of the rate of transcription and results revealed that the burst frequency (kon) is the main burst- the binary control of the duration of transcription is necessary ing parameter regulated by the input transcription factors across and sufficient to quantitatively recapitulate most of the full stripe eve stripe 2. This increase in kon in the stripe center functions profile (Fig. 3G, brown). While this work focused on dissecting to increase the fraction of time that nuclei spend in the active the regulatory logic of the eve stripe 2 enhancer in the context transcriptional state. of a minimal construct, it is important to note that our conclu- Importantly, our cpHMM algorithm is not limited to the eve sions are not limited to this reporter construct and also apply to stripe 2 system and should prove useful to infer the underlying the endogenous regulation of eve. As shown in SI Appendix, Fig. regulatory kinetics of any gene that is tagged using approaches S11, an analogous analysis performed on the expression dynam- such as the MS2 or PP7 systems in any organism (25, 48). For ics of a reporter BAC containing the full endogenous eve locus example, the method could be used to infer the state of the (69) indicated that stripe formation in this endogenous context ribosome as mRNA is being translated into protein in novel is also dominated by the interplay between the regulation of single-molecule in vivo translation assays (76–79). Thus, we envi- the mean rate of transcription and that of the transcriptional sion that our method will be useful for the broader biophysical time window. analysis of in vivo cellular processes at the single-molecule level.

844 | www.pnas.org/cgi/doi/10.1073/pnas.1912500117 Lammers et al. Downloaded by guest on September 25, 2021 Downloaded by guest on September 25, 2021 h togln ewe iigHnhaklvl n h increase the and levels While Hunchback in indirectly, rising (80). acts between Caudal Hunchback link that strong factor possibility the the maternal out rule the cannot by we Hunchback of sion of repressor a as eve that functions actually finds Hunchback that observed suggested model the has in our explain bursting transcriptional fully that of to pattern notable necessary molec- also is orthogonal aver- activation is distinct, the Hunchback the It 2 of of from mechanisms. control modulation arise ular rate that the transcription and suggests age window work time this transcriptional in dissected strategies pattern Kr observed 7 Giant, (Fig. the bursting irreversible recapitulate of transcriptional of effectively) to action necessary least joint was (at Hunchback the an unilaterally— in Conversely, act loci fashion. at levels shut transcription repressor proteins—to activator off which of levels in coincident to respect model without and a Giant to 7 (Fig. of points flanks levels stripe the increasing in quiescence hand, one regulatory the Kr 2 On the ways. dif- in ferent results, concentrations Consis- factor cpHMM transcription were inspection. to time-resolved responded that visual strategies our data simple statis- with by existing robust verifiable) tent from a (or time inferences as obvious the drawing not served for by In regressions tool H). exhibited logistic tical section logic the Appendix, context, regulatory (SI this respectively the of bursting, regulators query and primary window to the 2 for data stripe time-resolved and work activating enhancer. 2 prevents stripe the that to position binding from nucleosome factors repressor- from transcription in a distinct as shift are such and bursting, that induced transcriptional processes duration regulate molecular or that progres- by those amplitude, a driven frequency, of instead burst result is in the reduction not likely sive is quiescence transcriptional of 6 fraction (Fig. significant quiescence a as even ter particular, In fre- quiescence. k transcriptional burst of the onset the in and trends quency temporal a temporal noted between we striking disconnect However, significant inputs. dynamically observed factor responds transcription We frequency time-varying rates burst to time. these the that of and indicating regulation trends space the infer both to mod- across switching time-independent promoter beyond go of to els 6A). cpHMM expression (Fig. our window adapted of time We transcriptional the rate and bursting mean between the modulate across factors transcription amr tal. et concentrations Lammers factor transcription input stripe of this of combinations regulation quantita- the for makes about the predictions framework falsifiable of regression and tive logistic transcription our of enhancer, regulation 2 the driving nisms information. transcrip- spatiotemporal of general conferring transcription a the facilitating like factor, functions tion Bicoid interpretation, this of In duration the or magnitude for the phenomenon necessary dictating either certainly func- almost of direct of is regulation a expression Bicoid the of while in absence that, Bicoid striking suggests for the that role note tional ref. in we proposed Finally, rec- hypothesis be 80. counter-repression can the activity with stripe between increased onciled correlation and this levels whether Hunchback determine rising to necessary work Additional be role. will activating traditional a playing Hunchback on hsdfeec ntergltr oi bevdfrte2 the for observed logic regulatory the in difference This frame- regression logistic a utilized we hypothesis, this test To identified Having nadto ogenn aubeisgt notemecha- the into insights valuable gleaning to addition In pe eesfcett xli h ne ftranscriptional of onset the explain to sufficient were uppel ¨ eve tie2adta nietatvto cusvacutrrepres- counter via occurs activation indirect that and 2 stripe ihricesdo eandcntn ertesrp cen- stripe the near constant remained or increased either eve ciiyi h tiecne sms ossetwith consistent most is center stripe the in activity 2 tie2 enx ogtt rb h relationship the probe to sought next we 2, stripe eve tie2(6,i osntpa ietrl in role direct a play not does it (16), 2 stripe C k on and stepiaykntcmd ywhich by mode kinetic primary the as .W esndta h ne of onset the that reasoned We D). B and eve eve A eve and F ulitastoe into transitioned nuclei tie2 eetstudy recent A 2. stripe ). tie2transcription. 2 stripe eve .Ti observation This D). ihu directly without 2 pe,and uppel, ¨ eve stripe eve opsqec eeicroae note5 the into incorporated were (spanning sequence loop region promoter the of the and upstream contains enhancer construct 2 This 18. stripe ref. by developed struct Construct. Reporter Methods and Materials factor transcription the networks. out- regulatory of gene developmental nature within the calculate interactions of to knowledge decision possible from developmental it comes of makes that understanding making step predictive necessary a a is in toward description quiescence quantitative and a Such bursting the development. transcriptional behind paths of mechanisms control theoretical quantitative molecular and detailed experimental the clear uncovering to are molecular the there at Thus, powerful function a level. factor constitute transcription bind- querying will factor for enhancers assay transcription regulatory to on query domains disruptions to con- ing targeted here, in of encoded presented technique, effects methods genetically this the theoretical using using the that embryo with believe junction fly We living (83). a in LlamaTags outputs of demon- and nuclei inputs recently of single we measurement Experimentally, simultaneous the (82). at strated level activity transcriptional single-cell corresponding the the concentra- measure- factor with single-nucleus transcription dynamics input live tion correlate use directly instead, to and, ments 7C) factor (Fig. transcription for inputs averages spatiotemporal beyond move to is state quiescent this whether transcrip- after determine reversible. nucleus to the quiescence from tional of repressors it deplete region make to could our (81) possible optogenetics in example, con- for repressor through, time of centration manipulation over resolved temporally increase observed The not interest. levels simply possi- but is repressor reversible it fact, because in data, is, quiescence. our quiescence in that transcriptional irreversible ble appears of con- transition onset factor the transcription While the out following read ques- to centrations open continue an loci remains whether it tion concentrations, changes factor sense transcription continuously in engaged to loci appear while bursting notably, transcriptional Most in model. our of scope the beyond additional regulation. gene additional extracting the of about underpinnings motivating for questions mechanistic meaningful both for answering tool and at aimed a data experiments as experimental predic- serve from model’s will insights our work outlined testing this approaches likely the in to that above anticipate path we described together, direct Taken one tions. binding- most will the additional one the to to Thus, similar represent change all. studies any of mutation since patterns 7C, site Fig. expression necessary in regulate the be shown that would affect as genes it such gap that data 3 means ate all (9) reimage network nature gene to interconnected gap the altered the that been has however, of genes note, gap We more abolished. or embryos, or one mutant in of profile expression stripe the expected where the predict to 7 used Fig. be transcriptional (compare of 14 cycle Right levels nuclear Upper reduced in late predicts particularly also bursting, model the 7 tion, Fig. as 7 manner (compare same Fig. enhancer the to full precisely the in sce- for off this permissive shut observed to would In Bicoid, due disrupted. of arising levels been initially activity, have transcriptional sites nario, binding be Hunchback should onset all mutated onset setting. the this in drive wild-type that alone unaltered predicts the repressors quiescence in that transcriptional encounter finding of our not instance, does For embryo the that ofrhrts hs n te yohss twl ecritical be will it hypotheses, other and these test further To remain system the of aspects certain that observe also We D, oFg 7 Fig. to .I h bec fHnhakactiva- Hunchback of absence the In Left). Lower yellow hswr mlydtesame the employed work This PNAS eotrgn.Tet-orrpaso h S stem MS2 the of repeats Twenty-four gene. reporter F , eve .Smlry u oe could model our Similarly, Left). Lower | aur 4 2020 14, January tie2ehnesweesm or some where enhancers 2 stripe 0 n fterpre gene. reporter the of end | eve eve o.117 vol. . b o+0bp) +50 to kbp −1.7 tie2rpre con- reporter 2 stripe tie2t gener- to 2 stripe even-skipped D, | pe Right Upper o 2 no. | (eve) 845 F ,

DEVELOPMENTAL BIOPHYSICS AND BIOLOGY COMPUTATIONAL BIOLOGY Sample Preparation and Data Collection. Sample preparation followed pro- be superior for the detection of dim transcription spots—a feature crit- cedures described in ref. 18. In short, female virgins of yw; His-RFP; MCP-GFP ical to establishing the precise timing of the cessation of activity at (MCP, MS2 coat protein) were crossed to males bearing the reporter gene. transcriptional loci. Embryos were collected and mounted in halocarbon oil 27 between a semipermeable membrane (Lumox film; Starstedt) and a coverslip. Data cpHMM Inference Code. All scripts relating to the cpHMM inference method- collection was performed using a Leica SP8 scanning confocal micro- ology developed in this work are available at the GarciaLab/cpHMM GitHub scope. Average laser power on the specimen (measured at the output of repository (85). See SI Appendix, Extended Materials and Methods, as well a 10× objective) was 35 µW. Image resolution was 256 × 512 pixels, with as SI Appendix, section D for additional details. a pixel size of 212 nm and a pixel dwell time of 1.2 µs. The signal from each frame was accumulated over 3 repetitions. At each time point, a stack ACKNOWLEDGMENTS. We thank Thomas Gregor and Lev Barinov for dis- of 21 images separated by 500 nm was collected. Image stacks were col- cussion about an initial implementation of the cpHMM approach; Florian lected at a time resolution of 21 s. The MCP-GFP and Histone-RFP were Jug for help with the spot using ; and Elizabeth Eck, Maryam Kazemzadeh-Atoufi, and Jonathan Liu for the excited with laser wavelengths of 488 and 556 nm, respectively, using a hunchback P2 data used in the absolute MS2 calibration. We are also grate- White Light Laser. Fluorescence was detected with 2 separate Hybrid Detec- ful to Jack Bateman, Jane Kondev, Rob Phillips, Allyson Sgro, and Donald tors (HyD) using the 498- to 546-nm and 566- to 669-nm spectral windows. Rio for comments and discussion on the manuscript. H.G.G. was supported Specimens were imaged for a minimum of 40 min into nuclear cleavage by the Burroughs Wellcome Fund Career Award at the Scientific Interface, cycle 14. the Sloan Research Foundation, the Human Frontiers Science Program, the Searle Scholars Program, the Shurl & Kay Curci Foundation, the Hellman Image Analysis. Image analysis of live embryo movies was performed based Foundation, the National Institutes of Health (NIH) Director’s New Innovator Award (DP2 OD024541-01), and a National Science Foundation (NSF) Fac- on the protocol in ref. 27 with modifications to the identification of ulty Early Career Development Program (CAREER) award (1652236). N.C.L. transcriptional spots, which were segmented using the Trainable Weka Seg- was supported by NIH Genomics and Computational Biology Training Grant mentation plugin for FIJI using the FastRandomForest algorithm (84). In 5T32HG000047-18. C.H.W. was supported by the NIH/National Cancer Insti- comparison with a previous algorithm based on Difference of Gaussians tute (U54 CA193313), The City University of New York (CUNY) (RFCUNY (18, 27, 32), this alternative spot segmentation approach was found to 40D14-A), and the NSF (IIS-1344668).

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