Many of these applications utilize two or more exogenous promoters, imposing a currently unquantifiable metabolic burden on the living system. Here, we engineered a collection of inducible promoters (regulated by LacI-based transcription factors) that maximize the free-state of endogenous RNA polymerase (RNAP). We leveraged this collection of inducible promotors to construct simple two-channel logical controls that enabled us to measure metabolic burden – as it relates to RNAP resource partitioning. The two-channel genetic circuits utilized sets of signal-coupled transcription factors that regulate cognate inducible promoters in a coordinated logical fashion. With this fundamental genetic architecture, we evaluated the performance of each inducible promoter as discrete operations, and as coupled systems to evaluate and quantify the effects of resource partitioning. Obtaining the ability to systematically and accurately measure the apparent RNA-polymerase resource budget will enable researchers to design more robust genetic circuits, with significantly higher fidelity. Moreover, this study presents a workflow that can be used to better understand how living systems adapt RNAP resources,  Synthetic biologists strive to design and instantiate useful genetic circuitry and regulatory functions within living biological hosts. These synthetic regulatory functions alter a biological host's ability to respond or adapt to a variety of input  stimuli. Example systems include genetically programmable clocks , toggle Escherichia coli as a biological host. Homeostasis requires that this resource budget must be distributed across E. coli's ∼4000 endogenous genes  , ribosomus genes  and balaus genes . The comus genes ,5,26. Hous genes ,30. To rus genes . To furt T7 RNAP . While td-1-thiogalactopyranoside (IPTG). This setup readily facilitates the comparative measurement of expression output for a given reporter protein as a function of burden introduced by a competing reporter. When employing our coupled reporter constructs we observed linear depression of inducible (pLac) RFP expression, even when paired with the weakest synthetic promoter sequence (pUV5) on GFP. Next, we examine how promoter strength and repressor response changes as a function of inducer concentration titration and burden. Finally, we evaluated the data collected in this study in aggregate to illustrate the impact of resource partitioning on dual promoter systems – relative to putative synthetic biology performance boundaries.With this objective in mind, we sought to construct a methodology that would enable quantification and interpretation of expression burden to be implemented by molecular and synthetic biologists alike. This system functions by measuring differential expression of super-folder green fluorescent protein (GFP)  and mCheet al. provided the base scaffold from which all reporter plasmids were constructed (−1), donated from pZS*22-sfGFP. The promoter of the GFP gene was deleted by quick change mutagenesis (−1) and subjected to miniprep  to yield an intermediate plasmid incapable of GFP expression. This intermediate plasmid served as the vector for insertion of the RFP gene. The gene for RFP was isolated from the pET28b plasmid, kindly provided by the Kane laboratory, by PCR amplification with deep vent (NEB) and gel extraction (Qiagen). The rrnB T1 terminator  or RFP  expression. Sequencing (Eurofins Genomics) confirmed the presence of both GFP·rrnB T1 and RFP·rrnB T1 constructs in the –pGFP/−pRFP reporter plasmid.Study of promoter competition for endogenous transcriptional resources required the construction of a reporter plasmid expressing the two reporter genes, super folder green fluorescent protein (GFP)  and the structed . All repagenesis , by PCR agenesis . A transrminator  flankingrminator  using derminator  using deex = 485 nm, λem = 510 nm, gain = 400) or RFP  reporter protein. Plasmids belonging to transformants displaying their expected phenotype were extracted by miniprep (Omega) from overnight cultures (37°C with shaking 300 rpm). The insert region of these plasmids were sequenced (Eurofins Genomics) to confirm the presence of the intended promoter cassette and fluorescent protein. This process of CPEC, phenotype characterization, and sequencing was repeated using the –pGFP/pLac RFP plasmid to insert the series of GFP adapted promoter cassettes producing reporter plasmids with pUV5, pSym, pNull, pLac, and pTrc controlling expression of GFP simultaneously with pLac controlling expression of RFP. The reporter plasmids: pLac GFP/pNull RFP, pNull GFP/pTTA RFP and pTrc GFP/pTTA RFP were also produced.Promoter cassettes to be inserted upstream and adjacent to the start codons of the GFP·rrnB T1 and RFP·rrnB T1 constructs were constructed by polymerase cycling assembly (PCA) ,42 using−1) and the p15A origin of replication (estimated 20–30 copies per cell). Five LacI genotypes were employed in this study, including the functional wild-type repressor (+LacI), a copy of the LacI gene with a stop codon mutation in place of the first residue of the repressor incapable of gene translation (−LacI), as well as three DNA binding domain variants: LacIY17I/Q18A/R22N (+IAN), LacIY17T/Q18A/R22N (+TAN) and LacIY17V/Q18A/R22N (+VAN)  . PlasmidN (+VAN)  of the +−1 of kanamycin and 35 μg·ml−1 of chloramphenicol. Three colony forming units from each co-transformation were selected to inoculate three 1 ml cultures of LB and grown overnight (37°C with shaking 500 rpm) maintaining selection with antibiotics . The density of each culture was recorded by spectrophotometry (λabs = 600 nm) and diluted to 0.01 absorbance units using sterile deionized H2O. 12 ul volumes of diluted cultures were passaged into 96 deep well microplates containing 1.2 ml volumes of M9 minimal media supplemented with cas amino acids, antibiotics to maintain selection , and the absence or presence of 10 μM IPTG. Microplates were grown for a total of 26 h (37°C with shaking 500 rpm), withdrawing 100 ul of culture for assaying starting at the 12th hour, in 2 h intervals. Cultures were assayed to determine their density (λabs = 600 nm), GFP expression , RFP expression  and FRET . IPTG titration curves were conducted by repeating the assay workflow, in the presence of various IPTG concentrations , arresting the growth step at the 20-h time-point to the recorded data.All reporter plasmids were co-expressed with one of two pLacI plasmids donating either a functional copy of the lac repressor (+LacI) or a non-translatable gene (–LacI). Co-expression required co-transformation in 3.32 cells by electroporation followed by selection on LB agar spiked with 100 μg·mlvia IPTG the architecture illustrated in Figure To measure the comparative expression profiles from two competing promoters, we developed a collection of two-channel reporter systems Figure . In thisIn a given inducible system Figure , E, G–I,via a qualitative solid media screen, Figure via a green-channel, relative to a red-channel adapted with a fixed inducible pLac element. The qualitative phenotypes presented in Figure via the inducible-promoter pLac, Figure via visual inspection within the observed colony plaques.In a preliminary assessment of promoter coupling, we evaluated five dual-promoter systems via pUV5, pSym, pNull, pLac or pTrc, with a fixed inducible pLac promoter for RFP  and passage into fresh minimal media  for growth, and the sampling of 100 μl culture volumes between 12 and 26 h in 2 h increments. Culture samples were then assayed to determine density (λabs = 600 nm), GFP expression , and RFP expression . The assay data for each reporter system and experimental condition—i.e. with and without LacI and cognate inducer—was extracted and analyzed to determine changes to the expression of GFP and RFP. Specifically, plots of absorbance (ABS) versus time, GFP versus ABS, RFP versus ABS, and Förster resonance energy transfer (FRET) versus ABS (control), were compiled to identify and fit the linear region for each expression metric and extract their value  resulted improved resolution of the assignment of promoter strength. In agreement with the qualitative colony plaque assessment  between RFP output relative to reported GFP promoter strength – though inverted  in our qualitative solid media screen Figure . Promotet Figure , F pUV5 t Figure . The absd Figure . This red Figure  – reconcd Figure , C.via the red-channel. Having previously determined that all assayed reporter systems collected beyond the 14-hour time-point present linear changes in fluorescent output with respect to changes in culture density, the experiment was setup to monitor includible expression changes at the 20-h time-point under variable concentrations of the cognate inducer IPTG. All dose-response curves were fitted to a sigmoid curve describing the coupled system's relative leakiness (E0), dynamic range (D), effective concentration of inducer at 50% output (EC50), and transition slope (k)  .50 of the red-channel isotherm to a lower effective IPTG concentration (i.e. tighter apparent LacI binding) as result of reduced competitive binding due to a reduction in free RNAP. This interpretation was further supported in the context of the control experiment in which the green-channel was completely muted, which provided the maximum degree of competition and most right shifted EC50  and the symmetric variant (Osym | pSym) associate with the wild-type DNA binding domain sequence of LacI Y17/Q18/R22. Mutation of the DNA binding domain to Y17T/Q18A/R22N (TAN) results in a LacI variant that is incapable of repressing transcription regulated with the O1 or symmetric Osym operator elements. Instead, the LacI-TAN variant associates with the Otta (pTTA) operator sequence. Moreover, construction of the pTTA promoter provides an additional avenue with which we can investigate the influence of promoter burden on the apparent behaviour of a given repressor variant  promoter-operator element under the conditions tested. In turn, we constructed a mixed promoter system in which a constitutive pNull green-channel was paired with an inducible pTTA red-channel. Next we paired the pNull | pTTA duel reporter system with LacI variants TAN, IAN or VAN  DNA interaction for the TAN, IAN and VAN variants . These dN Figure . Consistvia the k-means machine learning algorithm  RFP expression in the absence and presence of competing GFP promoter cassettes Figures –9. To falgorithm  as appli0− defines the difference in output signal in the absence and presence of inducer for the reporter genes for GFP or RFP co-expressed with and without LacI, minus promoter elements on the green and red channels, (ii) H0+ defines the difference in output for discrete single channels under constitutive control, in the absence and presence of inducer with and without LacI, and (iii) HA defines the difference between H0− and H0+ under the same conditions. The H0− and H0+ null controls were expected to provide a lower limit on the signal difference when distinguishing between the output of two populations which should remain unchanged, while the HA alternative control provides an upper limit on the expected change in signal difference between the output of two populations in the absence and presence of a promoter , relative to the boundaries established via H0− and H0+ null controls . Monitoring the green-channel 83% of the relative compared states  fell within the performance boundary. Approximately 60% of the lower-boundary outliers utilized pSym on the green-channel (versus pLac on the red-channel). Interestingly, approximately 30% of the lower-boundary outliers involved pNull. In the majority of cases, the lower-boundary outliers fell within the first-third of the time-course, likely the result of sampling the system prior to accomplishing steady-state. Only one value fell beyond the upper-boundary (pLac RFP | pLac GFP) and was represented in approximately the first-third of the steady-state time-course. When monitoring the red-channel over the same time course for the same dual reporter systems, the influence of the promoter strength on the opposing channel became apparent. Namely when comparing constitutive promoter values, all of the pTrc systems fell below the lower-boundary, whereas all of the pUV5 systems fell within the putative boundary – and pNull values fell between the two for the most part. However, there was no apparent correlation with the time-course. Interestingly, nearly 60% of the systems with dual-regulated promoters (i.e. pLac and pSym) fell outside of the putative boundary. In addition, when comparing dual-promoter systems in which the promoter element is the same (i.e. pLac GFP | pLac RFP) one can glean the differences that occur due to variation in output and accumulation. While the green-channel fell within the performance boundary nearly 88% of the time, the red-channel only presented within the performance boundary 63% of the time. Given that both channels are essentially genetically identical and mRNA has been normalized by the addition of an insulator (riboJ), the observed differences can be accounted for by the differences in protein output. Nevertheless, the vast majority of these data (∼98%) are clearly distinguishable from the lower-limit boundaries set by the H0− and H0+ null controls – regardless of channel. Accordingly, this global framework has the potential to better predict the behavior of complex genetic circuits that will include the effects of resource partitioning and related phenomenon.In our first experiment, we evaluated single-channel systems with inducible-promoters  can significantly influence the operational scope of programmed gene expression. Our study provides clear evidence for competition of transcription resource allocation between pairs of bacterial promoter constructs. While this competition does not prevent the correct assignment of the repressed and induced status of regulated gene constructs, the phenomenon does prevent the attainment of consistent expression levels in the presence of variable competing promoter elements. A solution to this problem has been proposed by Segall-Shapiro llocator . Their tet al. provides a promising solution for small multiple gene output genetic programs, our results suggest that increasingly complicated resource allocator systems will remain susceptible to resource competition because the components of the allocator rely on the recruitment of endogenous RNAP. Ultimately, we anticipate that there is a theoretical limit on coupled expression output  of a single cell  which Daber and Lewis use to back-calculate repressor kinetics. Thus, in light of other seminal studies, the incorporation of more accurate metrics for the utilization of various resource budgets will enable the development of better predictive tools for design automation in synthetic biology and metabolic engineering (Finally, the modeling of repressor kinetics and behaviours was also demonstrated to be sensitive to promoter burden, altering the apparent dynamic range and point of saturation of our tested inducer titration curves. Daber and Lewis published a series of impressive papers detailing the assessment of repressor kinetics uilibria . Their aineering .gkaa734_Supplemental_FileClick here for additional data file.


The concept of early termination as an important means of transcriptional control has long been established. Even so, its role in metazoan gene expression is underappreciated. Recent technological advances provide novel insights into premature transcription termination (PTT). This process is frequent, widespread, and can occur close to the transcription start site (TSS), or within the gene body. Stable prematurely terminated transcripts contribute to the transcriptome as instances of alternative polyadenylation (APA). Independently of transcript stability and function, premature termination opposes the formation of full-length transcripts, thereby negatively regulating gene expression, especially of transcriptional regulators. Premature termination can be beneficial or harmful, depending on its context. As a result, multiple factors have evolved to control this process. PTT is widespread in metazoans. It can occur close to the TSS or further downstream in the gene body.PTT generates transcripts that, depending on the circumstances, are either rapidly degraded, or are stabilised by polyadenylation, thus contributing to transcriptome diversification.Stable premature transcripts can have independent functions as noncoding (nc)RNA or mRNA encoding proteins with different properties compared with those generated by the full-length transcript.PTT can negatively regulate expression of the full-length transcript and especially controls genes encoding transcriptional regulators.Factors triggering PTT include not only canonical RNA 3′ processing and termination factors, but also other players. Many metazoan factors oppose PTT, thus limiting its damaging potential. PTT (see RNA polymerase II (Pol II) transcription of a viral gene in mammalian cells TSS and 3′-untranslated region . Consequently, these two terms are often used ambiguously or even confused in the literature. Multiple recent reviews provide a general background to RNA 3′ processing and transcription termination APABox 1PTT has been long known to be a key regulatory event in bacteria, referred to as attenuation. Classically, attenuation was shown to control the expression of enzymes involved in amino acid biosynthesis, such as the tryptophan and histidine operons Saccharomyces cerevisiae, mediated by the Nrd1–Nab3–Sen1 (NNS) complex. The first example of attenuation by NNS was demonstrated for the NRD1 gene, which is autoregulated by PTT in response to Nrd1 activity DEF1 is attenuated by Sen1 and CPA factors, without Nrd1 and Nab3 involvement S. cerevisiae might not be limited to the NNS pathway.PTT is also a well-recognised regulatory mechanism in Arabidopsis thaliana. FLC is a transcription factor that acts as a master regulator of flowering. It is carefully titrated: small changes in FLC transcript levels significantly affect flowering. The accumulation of FLC mRNA is prevented by FCA and FPA, two RNA-binding proteins associated with RNA 3′-processing factors. FCA and FPA autoregulate their own levels by premature polyadenylation and termination, independently of each other COOLAIRCOOLAIR is an antisense transcript to FLC, and functions in early cold-induced silencing of FLC transcription There are no Nrd1/Nab3 homologues known in plants. However, PTT has an elaborate role in the control of flowering time in Alt-text: Box 1It is well established that early termination can serve as an important mechanism for transcriptional control. PTT see , or ‘attPTT of a protein-coding gene can be divided into termination events occurring close to the TSS or within the gene body . We predFor many genes, their TSS is characterised by high accumulation of Pol II, as measured by chromatin immunoprecipitation (ChIP). This depends on the action of negative elongation factor (NELF) and DRB sensitivity-inducing factor (DSIF), typically occurring 30–50 base pairs downstream of the TSS Drosophila, with the aid of a novel single-molecule footprinting method lacO array 2O2 addition to U2OS cells ChIP followed by next-generation sequencing (ChIP-seq), and revealed an almost complete loss of Pol II from promoter-proximal pause sites within 2–5 min. This loss was rapidly reversible and unaffected by transcriptional inhibitors. Therefore, Pol II removal from pause sites appears not to require elongation. Instead, a high rate of assembly and eviction of pre-initiated Pol II complexes at TSS is predicted. Although the above-mentioned studies used different methodologies, they all describe high turnover rates of Pol II at Drosophila or human promoters in various cell types Pol II binding to the genome was recently measured at a single-molecule resolution in While promoter-proximal PTT has been largely overlooked, Pol II pausing in this location is well established in metazoans, and is tightly regulated by negative and positive elongation factors, such as P-TEFb polyadenylation site (PAS), mainly located in introns. PAS-mediated cleavage within internal exons appears to be a small fraction compared with introns, and leads to transcripts without a stop codon, which typically get rapidly degraded through the nonstop decay pathway U1 small nuclear ribonucleoprotein complex (U1 snRNP) or the cap-binding complex (CBC) are depleted intronic polyadenylation (IPA/IpA) or alternative last exon (ALE). We use the term ‘IPA’ here. IPA was first described for specific genes in 1980 noncoding RNA (ncRNA). By contrast, more 3′-proximal IPA events tend to produce truncated proteins Once Pol II overcomes the TSS-associated elongation checkpoint, subsequent PTT likely requires a co-transcriptional cleavage reaction at a cryptic promoter upstream transcripts (PROMPT)/upstream antisense RNA, which also often yield exosome-sensitive RNA PTT may lead to three outcomes: rapid transcript degradation; formation of a more stable ncRNA; or production of a protein-coding, polyadenylated mRNA isoform . The moropen reading frame (ORF) and, therefore, might produce a micropeptide (<100 amino acids). Other ncRNA could serve specific functions. For example, PTT of ASCC3 leads to the formation of a stable ncRNA, which is critical for the recovery of transcription following ultraviolet (UV) damage trans.Stable prematurely terminated RNAs are mainly polyadenylated and contribute to the IPA transcriptome. They can be further subdivided into ncRNA and protein-coding mRNA isoforms . Some ncIn the third scenario, protein-coding IPA isoforms  contribuCSTF3 gene , originally in Drosophila and then in humans CSTF3 is induced by high levels of CSTF3 protein, thus forming a negative feedback loop controlling its own activity, which is important for cell cycle control Regardless of whether the prematurely terminated transcript is stable or unstable, and independently of its ability to produce a functional ncRNA or protein, a potential outcome of early termination is the repression of the corresponding full-length mRNA . Such nepoly(A) signals. Therefore, they are inherently susceptible to PTT, although this is suppressed by U1. Interestingly, PTT is strongly activated upon UV treatment of cells, and correlates with decreased U1 snRNA levels Supporting this possibility, the abundant U1 snRNP has been shown to function not only in splicing, but also by blocking widespread PTT in thousands of vertebrate genes Finally, it was recently shown that a subset of protein-coding genes is downregulated by PTT under physiological conditions, both in cultured human cells and during zebrafish embryogenesis. This is triggered by an RNA 3′-processing and termination factor called PCF11 decapping factors in vivo crosslinking and immunoprecipitation (CLIP) revealed that all analysed CPA factors  were significantly detected on both strands in the vicinity of the TSS mammalian native elongating transcript sequencing (mNET-seq) signal specifically at the TSS in both sense and antisense direction Since PCF11 acts at both gene ends and throughout the gene body, it appears likely that other canonical CPA and termination factors are involved in PTT . NotablyApart from PCF11, the involvement of the termination machinery in premature cleavage within the gene body is less well defined. However, IPA globally correlates with the preferential use of proximal PASs in 3′-UTRs Besides the factors that are known to mediate 3′-processing and transcription termination at gene ends, others have emerged that stimulate PTT . In buddAnother candidate factor for PTT is the Integrator complex. This multiprotein complex interacts with Pol II and was initially identified as a RNA 3′-processing complex for snRNA In terms of the genetic elements that promote PTT, cryptic poly(A) signals within introns are likely candidates. Interestingly, intragenic enhancers also appear to stimulate PTT, because transcription at these enhancers interferes with, and attenuates, host gene transcription during productive elongation Drosophila for the piwi-interacting RNA (piRNA) transcription regulator Cutoff. Cutoff prevents cleavage of nascent RNA at PAS by interfering with recruitment of the CPA complex and also protects processed transcripts from degradation 6A) modification of mRNA and PTT has been reported Aldh1a3 prevented its recognition and consequent PTT, promoting cell self-renewal Since PTT is disruptive to full-length transcription, many mechanisms and factors must exist to suppress this process . A majorModifying Pol II activity may be another way to oppose PTT. When Pol II misincorporates a base or runs into a ‘roadblock’ such as a nucleosome, it arrests and backtracks. Pol II elongation is rescued by TFIIS stimulating the RNA endonuclease activity of Pol II, which produces a new 3′ end in the active site. It was recently shown that TFIIS stimulation of Pol II cleavage activity antagonises both premature TSS-proximal and gene end termination Since premature cleavage and polyadenylation are mutually exclusive with splicing of the intron in which the PAS is located, competition between splicing and PTT can be anticipated. Indeed, knockdown of various splicing factors as well as inhibition of 5′ splice site recognition by antisense nucleotide consistently results in IPA Saccharomyces cerevisiae is absent not only in plants and animals, but also in the less evolutionarily distant Schizosaccharomycespombe. Even so, the mechanisms modulating PTT appear conserved. In budding yeast, NNS cooperates with PCF11 Box 2DMPK gene as associated with myotonic dystrophy type 1 was demonstrated to revert the pathological phenotype of patient-derived induced pluripotent stem cells Aberrant PTT is associated with several pathological processes: inactivation of tumour suppressor genes in chronic lymphocytic leukaemia Alt-text: Box 2Here, we have outlined recent evidence that demonstrates the widespread occurrence of PTT in protein-coding genes, both TSS proximal and intragenic. PTT limits and opposes full-length transcription. Consequently, it may contribute to pathological processes, such as host gene downregulation during viral infection What is the relative contribution of Pol II pausing and transcription termination to Pol II accumulation at promoters and to the control of transcription elongation?What is the molecular mechanism of PTT and which pathways are involved? Is PTT always preceded by RNA cleavage?What is the role of canonical RNA 3′-processing and termination factors in PTT? Are early and normal termination processes at gene ends co-regulated?How are the prematurely terminated RNAs processed and what determines their stability?Which biological processes depend on early termination? Which PTT events are beneficial and which are harmful? How widespread is transcriptional regulation by PTT in physiological conditions?Saccharomyces cerevisiae, many prematurely terminated transcripts use different TSS to the full-length transcript. Does this also occur in metazoans?In Which factors control PTT directly and which influence it indirectly? What further factors trigger or prevent PTT?


Drosophila wing imaginal disc in order to measure nascent and mature mRNA of genes downstream of the Wg and Dpp morphogen gradients. We compared our experimental results with predictions from stochastic models of transcription, which indicated that the transcription levels of these genes appear to share a common method of control via burst frequency modulation. Our data help further elucidate the link between developmental gene regulatory mechanisms and transcriptional bursting.Morphogen signaling contributes to the patterned spatiotemporal expression of genes during development. One mode of regulation of signaling-responsive genes is at the level of transcription. Single-cell quantitative studies of transcription have revealed that transcription occurs intermittently, in bursts. Although the effects of many gene regulatory mechanisms on transcriptional bursting have been studied, it remains unclear how morphogen gradients affect this dynamic property of downstream genes. Here we have adapted single molecule fluorescence in situ hybridization (smFISH) for use in the  Paracrine signaling is a highly conserved means for cells within a tissue to communicate with one another to regulate diverse activities including proliferation, differentiation, apoptosis, and movement. Many of these activities are mediated by changes in gene transcription that are brought about by reception of the signals. Paracrine factors acting as morphogens are a particularly important class of gene regulators. Morphogens form spatially-extended gradients from the source of their synthesis, and elicit different transcription outputs from target genes, depending on local concentration of the morphogen . Many paHowever, transcription is a dynamic process, and thus, molecular models of regulation via PIC assembly or chromatin structure, do not adequately capture what kinetic steps in transcription initiation are being regulated. Recently developed methods have uncovered greater complexity in the transcription initiation process than previously imagined. Genes that are constitutively expressed rarely show uniform and continuous mRNA synthesis. Rather, mRNA synthesis occurs in bursts that are interrupted by periods of dormant output. This phenomenon is known as transcriptional bursting .Drosophila transcription factors Bicoid and Dorsal have been studied in great detail with respect to their effects on transcription burst frequency in the embryo  and BMP protein Decapentaplegic (Dpp) regulate transcription dynamics of genes in the ctor TCF . In canoctor TCF .To explore the effects of Dpp and Wg signaling on transcription dynamics, we have adapted single molecule fluorescent in situ hybridization (smFISH) for use in imaginal disc tissues. We use smFISH to quantify nascent and mature mRNAs for several genes expressed in highly diverse spatial patterns within the wing disc. Taken together, our data suggest that all of the genes investigated are regulated by modulation of their transcription burst frequency by Dpp and Wg even though their mean expression patterns are distinct from one another.In this study, we have explored how the Wg and Dpp morphogens regulate transcription dynamics in the wing disc. Each morphogen is synthesized in a narrow stripe of cells within the disc. Wg is produced in cells at the boundary between Dorsal and Ventral (DV) compartments of the wing pouch, while Dpp is produced in cells at the boundary between Anterior and Posterior (AP) compartments . These fC. elegans, and the Drosophila embryo  gene in the wing disc. Sens is required for cells to adopt a sensory organ fate, and the gene is expressed in two stripes of cells adjacent to and on either side of the DV boundary in the wing pouch  were not identified by both probe-sets , optomoter-blind (omb), daughters-against-dpp (dad), and brinker (brk) are expressed in symmetric domains within the anterior and posterior compartments of the wing pouch  gene. The sd gene is expressed uniformly throughout the wing pouch . Even for these very bright transcription spots, only one transcription site per nucleus was observed –1  and frequency (rate at which bursts occur). The average burst size is defined as off-1)–1 . We systTo directly relate the results of model simulations to experimental data, we performed the following treatment of simulation data. First, we transformed output of each simulation to mimic the experimentally detected fluorescence at a single gene allele. Fluorescence intensity depends on how many probe-binding sites are present in nascent RNAs on a gene allele at a given time . This vaWe then asked what combination of burst size and frequency could theoretically account for the observed frequency of finding cells with a transcription site (this ranged from 20% to 50% of cells). A phase diagram revealed that a broad range of burst size and frequency could explain our experimental observations . Therefosd, which is uniformly expressed in the wing pouch. This frequency was constant across the disc and paralleled the sd mRNA number per cell   . Mad, incription . This ged by Dpp . Thus, oSchnurri . Curious factors .omb, brk, and salm are regulated, it is illuminating that regulation of transcription burst frequency occurs for all of them. In the two-state view of promoter kinetics, the on-rate, on,k then is the most likely rate constant being regulated since it specifically affects burst frequency alone . A list of all stocks and transgenics used in this study is in the Key Resources Table. The sample sizes were not computed when the study was designed. Sample sizes were determined such that >6,000 cells were measured for each genotype. All Drosophila were raised at room temperature and grown on standard molasses- cornmeal food. The sfGFP-sens transgenic line was used as described in dad-GFP and brk-GFP transgenes obtained from Bloomington Drosophila Stock Center . For all transgenic experiments, smFISH was performed on homozygous individuals. Experiments were performed on endogenous omb and salm in 1118w individuals. There was no exclusion of any data or subjects.For all experiments, smFISH Probe Design and Preparation smFISH oligonucleotide probes were designed using Stellaris Probe Designer (Biosearch Technologies). Probes sets contain between 45 and 48 non-overlapping 20-nucleotide oligos. A full list of all probe sets is provided in o C. After hybridization, discs were washed once for 5 min at 62o C in wash buffer . Discs were then incubated with 2.5 μg/mL 4ʹ,6-diamidino-2-phenylindole (DAPI) (Invitrogen) in PBS + 0.1% Tween-20 for 5 min at room temperature. Discs were washed with PBS + 0.1% Tween-20 and transferred to Vectashield (Vector Labs) for mounting. Discs were mounted in 15 μl of Vectashield on glass microscope slides using an 18 × 18 mm No. one coverslip (Zeiss). For eye imaginal discs, discs were dissected from late 3rd instar larva in cold PBS with brain and mouth hooks attached, then smFISH was performed as described. Immediately prior to mounting, brain and mouth hooks were removed from eye discs and discarded.Wing discs were dissected from wandering 3rd instar larva in cold phosphate buffered saline (PBS) and immediately fixed in 0.1% (w/v) paraformaldehyde/PBS for 15 min at room temperature. Discs were then fixed for 30 min in methanol at room temperature. Discs were transferred to hybridization buffer , 1% v/v vanadyl ribonucleoside (NEB S14025), 0.2 mg/mL BSA, 0.1% v/v Tween-20). Oligo probes were added to a 1.5 µM final concentration in the hybridization buffer, and hybridization was performed for 1 hr at 62Wing discs were dissected in room temperature Graces’ Insect Medium (Sigma 69771) supplemented with 1X Pen-Strep (Gibco 15140–122) and 5 mM Bis-Tris (Sigma B4429). Half of the total dissected discs were transferred to 24-well tissue culture dishes containing this prepared medium + 5 µg/mL Actinomycin D, and half were transferred to untreated controls containing culture medium + 1:1000 (v/v) DMSO. Discs were incubated with gentle shaking for 30 min at room temperature, protected from light, before being washed with fresh culture media, and 1X PBS. SmFISH was then performed as described.3D image stacks were collected on a Leica SP8 scanning confocal microscope, using a pinhole size of 1 Airy unit and a 63X oil immersion (NA 1.4) objective. Approximately, 35 optical sections were collected per sample, with each section 700 nm thick. Sections were spaced 345 nm apart. DAPI, ATTO 565, and ATTO 633 were excited by 405, 555, and 630 nm lasers, respectively. ATTO dye fluorescence was collected using a HyD detector on photon counting mode and a scanning speed of 200 Hz, with 16X line accumulation. DAPI fluorescence was collected using a PMT detector using 8X line averaging. Pixel intensity values are 12-bit, and x-y pixel sizes are 76 nm. We modeled each z-section like a plane of width 345 nm for analysis, but in reality the edges of the z point spread function (PSF) overlap between sections. Since the PSF resembles a Gaussian distribution, most of the light is coming from the center of that distribution. Therefore, overlap is needed between sections to ensure equivalent sampling of the entire specimen.https://github.com/bakkerra/smfish_pipeline (https://github.com/elifesciences-publications/smfish_pipeline). The pipeline consists of several modules.Raw smFISH images were processed using a custom Matlab pipeline with no prior preprocessing. Our pipeline is available at RNA segmentation is performed by applying a cutoff intensity value to a stack of optical sections, and transforming all pixels above the cutoff to white and pixels below the cutoff to black. Diffraction-limited fluorescent spots captured with a 63X objective at 633 nm wavelength are estimated to be approximately 600 nm in diameter . This coIt is important to select a cutoff where true RNA fluorescent spots are identified as 2D objects in a section, and background is not. Therefore, a broad range of cutoff values is systematically applied to an image stack, and 2D object number is summed for each cutoff value. The distribution of 2D object number exhibits a plateau across a range of cutoff values , and thrThe position of the plateau varies from image stack to image stack. Therefore, for each image stack, a range of cutoffs is tested, and a cutoff is selected within the plateau to perform segmentation. As a result, each image stack has a unique threshold, allowing robust segmentation of spots despite variation in raw fluorescence between image stacks. In practice, replicate samples from the same experiment captured in the same imaging session did not require thresholds for segmentation more than 15 pixel intensity units apart. If image stacks did not show an identifiable plateau, the signal-to-background of that sample was determined to be insufficient and it was not used for analysis. The smFISH protocol and imaging is robust enough that in our hands, this occurs in less than 10% of image stacks collected. Once a threshold is selected, the following properties of each 2D object are recorded: x-y centroid position, z-section, and a list of the connected pixels.As each z-section is 340 nm in depth, it is assumed that genuine diffraction-limited RNA spots will appear in 2 or three consecutive z-sections, depending on the spot’s position along the z-axis. Therefore, a 2D object must satisfy two criteria in order to be counted as an RNA spot: 1) A 2D object must be linked to one or more 2D objects in at least one neighboring z-section. Linkage is defined when the centroids of all objects are within a diffraction-limited radius of 4 pixels from one another in the x-y plane; 2) A 2D object must be larger (contain more pixels) than linked objects in neighboring z-sections. This criterion prevents RNA fluorescence spots from being counted in multiple z-sections. A candidate that satisfies these criteria is recorded as an mRNA spot, and only the largest 2D object is recorded.The pipeline allows the images to be overlaid with markers indicating recorded spots so that each image stack can be manually inspected for any significant errors or inconsistencies. The most common problem detected at this stage results from images taken of discs that were ‘drifting’ or moving significantly, which can cause a large number of identified spots to be filtered out during processing for not meeting criterion 1. Excessive bleaching across the stack can also cause clear inconsistencies. In this study, such problems were rare enough that any sample experiencing these problems was considered to have failed quality control and was simply not included for further analysis.Intensity measurements are recorded from a circle of pixels of radius four about the centroid of each recorded RNA spot. By keeping the area of each intensity measurement fixed, we uncouple user-generated variation in selection of segmentation thresholds from spot intensity measurements. A 2D circle was used instead of a 3D sphere to extract intensity measurements because the spots only appear in 2 or 3 z-sections. This makes their 3D geometry variable from spot to spot, and they cannot be consistently described using a sphere or ellipse.In our images, transcription sites tend to contain pixels that are many times brighter than mature RNA spots. As a result, the brightest transcription sites are frequently misidentified during segmentation of mature RNA spots because the second criterion for spot identification only records the largest object within the diffraction limit in z. For transcription sites, this is not always the brightest plane. Therefore, we segment transcription sites independent of mature RNAs using a higher cutoff. The objective in cutoff selection for transcription sites is to select one that includes objects with a total fluorescence intensity of twice the average mature RNA, and excludes mature RNA spots. We define the ‘average’ intensity for a spot containing a single mRNA to be the median of the distribution of all identified mature RNA spot objects. We empirically determined that merely doubling the cutoff for segmentation does not achieve this, because mature mRNAs may contain a few pixels above the cutoff, enough to still be identified as objects and included in analysis. Therefore, we use a threshold cutoff by multiplying the median mature RNA intensity by a factor of 2.5 .To test the accuracy of this segmentation procedure, we manually inspected three particularly RNA-dense regions in independent images where automated segmentation found a total of 103 transcription sites and 4066 mature RNAs. We determined that only 7 of 4066 mature RNAs were misidentified as transcription sites, and found no examples of transcription sites that had been missed by automated segmentation. After identification, object intensity measurements are recorded from a circle of pixels of radius 4 (the diffraction limit) about the centroid of each identified transcription site . The aveThe intensity measurement of each identified transcription site in an image stack is divided by the median intensity of identified mature RNAs in that sample . This seDAPI fluorescence images are output as labeled 16-bit images, where each nuclear object corresponds to a ‘level’ in the 16-bit image. These images are input to a nuclei segmentation pipeline, which flattens the images to white nuclei objects and black background. Nuclei images are segmented in 2D using the NucleAIzer platform maskRCNN Network, trained as described in The segmented black and white images are then processed using a custom Matlab script in order to join overlapping 2D objects into 3D. Each nucleus object in each z-slice is assigned an identity index. For each object in the first z-slice, the object with the highest number of overlapping pixels in the next z-slice is identified, and this object’s identity index is altered to be identical to its overlap partner. This proceeds through the entire z-stack of images, creating objects that resemble ‘pancake stacks’ of linked 2D objects in 3D . The 3D-A Voronoi tessellation is built from a grid of points in either 2D or 3D. In our case, each point is the centroid of a segmented 3D nucleus. The Voronoi cells delineate regions consisting of all voxels that are closer to that centroid than to any other centroid . The bouThe 3D Voronoi tessellation used a polytope-bounded Voronoi diagram available for Matlab, which uses the DeLaunay triangulation to calculate the Voronoi diagram . The res3D Voronoi tessellation is a way to democratically assign mature transcripts to cells based on vicinity to the nucleus. Clearly we get the transcript assignment incorrect at a local level. However, being the most democratic approach, the trends of mRNAs/cell assigned to 100's of cell across the wing disc are trustworthy. The same logic has been used by others assigning mRNA transcripts to early embryonic nuclei when cell boundaries are unseen .To assign spot objects to cells, a 3D convex hull of the each Voronoi cell is constructed from the vertices data for that cell. An entire set of image points, either the mRNA or transcription spot centroids, are tested to determine whether they fall inside or outside of each hull . This isEach disc is imaged with the DV boundary located at the y-coordinate midline of the image. Therefore the x-coordinate of the image corresponds to position along the disc's AP axis, and the y-coordinate corresponds to position along the DV axis. In order to analyze data across developmental axes, each image is divided into spatial bins of 64 pixels each, approximately equal to the diameter of one cell nucleus. RNA spots are assigned to a bin according to the position of their associated nuclear centroid.We analyzed image stacks from three independent discs for each experiment. Each image stack contains approximately 1,700 identified nuclei. Therefore, the total sample size is approximately 5,000 cells per experiment. Similar trends in RNA and transcription spots feature are observed in each disc individually, and hence, the analysis is not distorted by artifacts in pooling and cell segmentation .While each disc is imaged roughly in the same region, there is not an unambiguous landmark that precisely registers different disc images with one another. To pool data across space as accurately as possible, we register discs to each other based on their mRNA spot distributions over space. For each image data set, the number of RNAs per spatial bin is summed, and the distributions across bins are compared. Bins are then manually registered such that the distribution profiles of the three datasets line up with one another . The oveMedian nascent RNAs per cell is calculated from normalized intensity measurements for each transcription spot within a spatial bin of pooled data. All nascent RNA spots are included. As the distribution of RNA per cell is not normally distributed and has a long tail, we ascertained that the median was a more robust descriptor of the ‘center’ of the distribution than mean. Because the number of transcription sites varies over space, sample sizes vary for calculating median nascent RNAs per cell. For bins where fewer than 5% of cells contain a transcription site, median nascent RNAs per cell was not calculated, as the sample size was determined to be too small (<15). Fraction of cells with a transcription site is calculated by dividing the number of cells in a pooled spatial bin with at least one transcription site assigned to them by the total number of cells in that spatial bin. Fano factor is calculated for each spatial bin by dividing the variance in the mRNA per cell distribution by the mean mRNA per cell for all cells assigned to that pooled spatial bin.Median mature mRNAs per cell is calculated from total number of mature mRNA spots for each cell within a spatial bin of pooled data. As the distribution of mRNAs per cell is not normally distributed and has a long tail, we ascertained that the median was a more robust descriptor of the ‘center’ of the distribution than mean. Linear models are produced by unweighted least squares linear regression. LOESS fits are performed using the loess fitter in R, with an optimized span to minimize residuals. Confidence intervals are calculated by bootstrap resampling analysis using the bias-corrected and accelerated method. We resample data within each bin of pooled data and calculate the statistic of interest 10,000 times. The mean value of the statistic and a 95% confidence interval are calculated from these resampled values.t are defined as the number of RNA molecules present, as the number of transcriptionally active gene copies, and simulation time, respectively. ini, kdeg, kon,K and offk are rate constants defining the rates of transcription initiation, RNA degradation, promoter state switching from off to on, and promoter state switching from on to off, respectively.We model the various steps of gene expression, based on central dogma, as linear first order reactions. To simulate the stochastic nature of reactions, we implement the model as a Markov process using Gillespie’s Stochastic Simulation Algorithm . Simple As the Markov process gets more complex, the master equation can become too complicated to solve. Gillespie’s Algorithm is a statistically exact method that generates a probability distribution identical to the solution of the corresponding master equation given that a large number of simulations are realized. A brief description of how the Gillespie simulation produces each probability distribution is as follows:We initialize all simulations to start with no mRNA molecules and promoter state is set to OFF.i in the simulation, a total rate totr is calculated by summing all ir reaction rate constants in the model, given the current promoter state and the total number of mRNA molecules present.For each event totr. This τ is the time interval between the current event and the next event.A time-step τ is generated from an exponential probability distribution with mean 1/i is selected from the list of reaction steps in the model available at that time . The probability a reaction step is selected is equal to i/rtotr. An event is selected at random given these probabilities. For each event, the following actions are taken:Each event tot,Promoter switches to ON: Promoter is now in ON state, transcription initiation is now included in rtot.Promoter switches to OFF: Promoter is now in OFF state, transcription initiation is no longer included in rTranscription Initiation: Number of mature mRNA molecules is increased by 1.RNA degradation: Number of mature mRNA molecules is decreased by 1.t + τ where t is the total time elapsed in the simulation.Simulation time is updated as Each simulation is run for 10,000 iterative events to approximate steady-state conditions, at the end of which the number of mRNA molecules present in the simulation is recorded. Independent simulations are then randomly paired to mimic the two alleles within a cell, and the sum of mRNA numbers is recorded as the mRNA output per cell. A minimum of 1000 simulation pairs are generated for each set of rate parameter values.degk is fixed at 0.04/min for all simulations, as this rate had been experimentally determined for sens mRNA  equal to the time it is estimated that RNA polymerase takes to elongate from the binding site for the 5’-most oligo probe to the 3' end of the RNA. To calculate τelong for each gene, we divide the number of nucleotides from 5' probe-binding site to 3' end by the transcription elongation rate. This rate is assumed to be 1100 nucleotides/min, as experimentally determined  and Utpal Banerjee as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "The Wg and Dpp morphogens regulate gene expression by modulating the frequency of transcriptional bursts" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife”. Please let us know if you would like to pursue this option.As the editors have judged that your manuscript is of interest, but as described below but some minor additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is “in revision at Summary:Drosophila wing imaginal discs. They used Wg and Dpp morphogens to examine kinetic steps during transcriptional initiation that they regulate. They first established single-molecule FISH (smFISH) for some of Wg and Dpp target genes and showed that it has a low false negative and false positive rate. They then performed segmentation and image analysis to assign each smFISH signal to a specific cell, and this allowed them to quantify the number of mRNA molecules per cell. They also showed that the intensity of the smFISH signal can be used to determine whether a given mRNA molecule is nascent or mature and used it to quantify the nascent and mature mRNAs for these genes in each cell. These results suggested that transcription downstream of Wg and Dpp occurs in bursts. To ascertain this notion and to determine which aspect of bursting might be regulated by these morphogens, the authors performed mathematical modeling. Comparison of various simulations with the actual smFISH data allowed the authors to conclude that Wg and Dpp morphogens share a common method for the transcriptional control of their downstream genes, despite the different expression patterns of these targets. The data suggested that these morphogens regulate their targets by modulating the burst frequency not the burst size. Hence, this study connects a specific aspect of transcriptional bursting (i.e. burst frequency) to developmental gene regulatory mechanisms downstream of evolutionarily conserved morphogens.The authors have explored mechanism by which two key morphogens regulate the transcription of their downstream targets in Essential revisions:Although linking morphogens to transcriptional burst frequency is exciting, the authors have not made any attempts to find how morphogens regulate burst frequency but not burst size.eLife. However a better understanding of how the data was analyzed needs to be provided.The idea championed in this work – that graded expression of targets of morphogen signaling proteins is a product of transcription "burst frequency modulation" – is intriguing and important, and is consistent with recent findings in other contexts. The results and analysis are impressive, and the reviewers advocate for its publication in Given that the conclusions the authors draw are likely the predicted outcomes, it is especially important to show a control hybridization experiment that analyzes a gene that does not vary in expression across the disc – one that is regulated by Gal4 perhaps?In the first paragraph of the results section, "These factors diffuse from their sources forming concentration gradients across the disc, and regulate gene expression in a concentration-dependent manner." The claim that the morphogens diffuse across the disc is inconsistent with recent reports of other mechanisms (filopodia), and as the mechanism of dispersion is irrelevant, this statement is unnecessary.Figure 1—figure supplement 1A,B: The sens control (B) is uniformly black in the pdf that was provided, but presumably has background spots? Please show the control hybridization of the GFP probe to discs lacking the GFP-sens transgene.Figure 1—figure supplement 1C: The description for calculating the 3D fluorescent objects is unclear to me. Was the PSF determined, how many sections were imaged, what was the distribution of diffraction-limited spots/of control spots in y? The cut-off that parses signal and background seems arbitrary – is it justified by the distribution detected in the sens and no GFP-sens transgene controls? Please show an intensity graph for these control spots. What are the background spots for intron probes?Figure 1—figure supplement 2E: I do not understand how this image was generated or what it's meaning might be.Figure 1—figure supplement 2F: The disc epithelial cells are highly elongated and their morphologies are nothing like the cells depicted in this image. If these theoretical volumes have no actual basis, what is their value? Is there no way (membrane dyes?) to stain cell membranes in the preps? Please at least provide a better description of what "Voronoi tessellation" calculates.omb is near the X telomere – is the omb spot polarized with respect to the apicobasal axis? Would it be interesting/informative to compare transcription spots in haploids and diploids? If we predict that the number of nuclei with >1 transcription spot will not be observed, what is the prediction for frequency, for intensity of the single spots?Detecting only one fluorescent spot per nucleus is interesting, especially as it is not what has been observed for the embryo. Embryo nuclei are polarized so that transcription spots are basal for a gene near the telomere. I am not equipped to evaluate the modeling, so do not appreciate its importance if the number and intensity of transcription spots correlates with the known/observed spatial distributions of RNA and protein in the disc. Again, the methods used to parse the data are not clearly justified and so seem ad hoc. Essential revisions:Although linking morphogens to transcriptional burst frequency is exciting, the authors have not made any attempts to find how morphogens regulate burst frequency but not burst size.Drosophila embryos using lattice light-sheet microscopy – or using the ParS/B system developed by Gregor. Comparable genetic tools will need to be built and tested for the wing disc, conditions for culturing wing discs ex vivo that retain normal development will need to be worked out, and an analytical pipeline will need to be built before experimentation in those directions can begin. Thus, such attempts are well beyond the scope of this present work.Attempts to understand the molecular mechanism of burst frequency modulation by Wg and Dpp will require a major investment in new technologies that are currently available only in the early fly embryo . There, bursts are visualized in live embryos by MS2-tagged nascent RNA. Visualizing transcription factor binding is still in its infancy, notably Darzacq achieving single molecule imaging in living The idea championed in this work – that graded expression of targets of morphogen signaling proteins is a product of transcription "burst frequency modulation" – is intriguing and important, and is consistent with recent findings in other contexts. The results and analysis are impressive, and the reviewers advocate for its publication in eLife. However a better understanding of how the data was analyzed needs to be provided.We thank the reviewers for pointing out our lack of clarity in describing how the data was analyzed. We have addressed this issue and improved the manuscript’s clarity. In particular we have moved some of the description of how we analyzed data from the Materials and methods section to the main text and figure legends to better aid the reader about key issues. We retain in the Materials and methods, a full and comprehensive description of the data analysis.Given that the conclusions the authors draw are likely the predicted outcomes, it is especially important to show a control hybridization experiment that analyzes a gene that does not vary in expression across the disc – one that is regulated by Gal4 perhaps?scalloped (sd) gene, whose expression is uniform across the wing pouch . This data is now shown in Figure 2—figure supplement 1, and discussed in the text. The data clearly shows that the average sd mRNA/cell is constant across the disc, the likelihood of detecting a transcription site is also constant across the disc, and the nascent RNA number is constant across the disc. It demonstrates that the results with the other genes are not artifacts of variable smFISH or errors in the analytical pipeline.We have performed smFISH on the In the first paragraph of the results section, "These factors diffuse from their sources forming concentration gradients across the disc, and regulate gene expression in a concentration- dependent manner." The claim that the morphogens diffuse across the disc is inconsistent with recent reports of other mechanisms (filopodia), and as the mechanism of dispersion is irrelevant, this statement is unnecessary.We have modified the statement and deleted claims of diffusion.Figure 1—figure supplement 1A,B: The sens control (B) is uniformly black in the pdf that was provided, but presumably has background spots? Please show the control hybridization of the GFP probe to discs lacking the GFP-sens transgene.Panel B did show control hybridization of GFP probe to a disc lacking the sens-GFP gene. We have made that more explicit. Shown in Figure 1—figure supplement 1A,B were raw images taken using the same conditions in which diffraction-limited spots were readily apparent by eye for GFP mRNAs in 1A. We have artificially increased the intensity of all pixels in the images four-fold, so that the reviewer and readers can see the background. They are now added along with the original images to Figure 1—figure supplement 1A,B.Figure 1—figure supplement 1C: The description for calculating the 3D fluorescent objects is unclear to me. Was the PSF determined, how many sections were imaged, what was the distribution of diffraction-limited spots/of control spots in y? The cut-off that parses signal and background seems arbitrary – is it justified by the distribution detected in the sens and no GFP-sens transgene controls? Please show an intensity graph for these control spots. What are the background spots for intron probes?We apologize for the lack of details in the figure legend and main text. We have rewritten the section text and figure legend to make it clearer. We had already described in detail the method to calculate the objects in the Materials and methods but this has also been rewritten to improve clarity. The cutoff is chosen based on a characteristic plateau from a parameter sweep of cutoff values performed on each image stack . This method of cutoff choice is based on two QC criteria: (1) it IDs spots with <5% error compared to a manually curated ground truth; (2) it identified 200-fold fewer spots in GFP null control discs when compared to GFP-sens discs. There are so few background control spots (~40) that an intensity graph is not helpful. Background for the intron probe is comparable to the exonic probes.Figure 1—figure supplement 2E: I do not understand how this image was generated or what it's meaning might be.This is 5 contiguous z-sections of segmented nuclei colored and viewed laterally using the ImageJ 3D viewer plugin. It is not intended for any analysis, only to illustrate the three-dimensional “stack of pancakes” nature of the nuclear objects. We have provided more clarity and information in the figure legend.Figure 1—figure supplement 2F: The disc epithelial cells are highly elongated and their morphologies are nothing like the cells depicted in this image. If these theoretical volumes have no actual basis, what is their value? Is there no way (membrane dyes?) to stain cell membranes in the preps? Please at least provide a better description of what "Voronoi tessellation" calculates.We had previously tried to use membrane dyes to visualize cell boundaries but these failed to give adequate results. Exhaustive attempts using many experimental means of visualizing membranes were failures.any system has achieved.A Voronoi tessellation takes a grid of some finite number of points in either 2D or 3D. In our case, each point is the centroid of an imaged 3D nucleus. For each centroid, there is a corresponding region consisting of all other points of the plane that are closer to that centroid than to any other centroid. These regions are called Voronoi cells. The boundaries between Voronoi cells represent points that are equidistant between two centroids. These are taken to represent virtual cell boundaries. It is important to be clear that the Voronoi cells do not accurately describe the pseudostratified epithelial nature of wing disc cells. However, note that 3D segmentation of pseudostratified epithelial cells is still something no one working in Tessellation is just a way to democratically assign mature transcripts to cells based on vicinity to the nucleus. Clearly we get the transcript assignment incorrect at a local level. However, being the most democratic approach, the trends of mRNAs/cell assigned to 100s of cell across the wing disc are trustworthy. The same logic has been used by Gregor and others assigning mRNA transcripts to early embryonic nuclei when cell boundaries are unseen . We have now added a clearer description of the tessellation and its meaning in the text and figure legend.Detecting only one fluorescent spot per nucleus is interesting, especially as it is not what has been observed for the embryo. Embryo nuclei are polarized so that transcription spots are basal for a gene near the telomere. omb is near the X telomere – is the omb spot polarized with respect to the apicobasal axis?omb transcription site centroid and its corresponding nuclear centroid shows a distribution symmetrically clustered around a mean of zero. This suggests that there is no apical-basal polarity in location of omb transcription sites relative to the nucleus can be detected in this data. We ran this analysis for all of the genes, and the distributions look identical to omb.Calculating the z-plane difference between each Would it be interesting/informative to compare transcription spots in haploids and diploids? If we predict that the number of nuclei with >1 transcription spot will not be observed, what is the prediction for frequency, for intensity of the single spots?sfGFP-sens transgene. We used probes specific for sfGFP RNA.Although there are no haploid cells in imaginal disc tissue, we did compare transcription sites with one copy versus two copies of the sens gene. When only one copy of the transgene is present, the likelihood of detected transcription sites is decreased across the disc   see . The medalf) see . BootstrDrosophila tissue, argues that we still need to assume that transcription from a second allele would not appear as a second transcription site.However, for genes probed far upstream of the transcription termination site, it is very likely that we are seeing transcription from both alleles at once, given that a detectable nascent RNA would stay at the transcription site for a long time (~50 minutes). Even for these very bright transcription spots, only one transcription site per nucleus is observed . This observation, and the literature suggesting that allelic pairing occurs extensively in We have added this commentary to the revised manuscript text.Permutation test methods: Data from two experimental groups was pooled and repeatedly randomly shuffled into two new groups. The median of each random group was calculated and the difference between these two groups recorded. After 10,000 reshufflings, 30% of the randomly assigned groups had a difference in median which was equal to or greater than that observed in the actual data. This indicates that the two groups are not significantly different.I am not equipped to evaluate the modeling, so do not appreciate its importance if the number and intensity of transcription spots correlates with the known/observed spatial distributions of RNA and protein in the disc. Again, the methods used to parse the data are not clearly justified and so seem ad hoc.As previously stated, we have now made the methods of data analysis more clear and have taken pains to explain how they relate to modeling. Summarizing, we are able to make 3 measurements at single-cell resolution from our smFISH measurements: (1) the number of mature transcripts in the cytoplasm of cells, (2) the presence/absence of nascent sites of transcription in cell nuclei, and (3) the intensity of nascent RNAs. These 3 empirical observables are downstream of the kinetics of the promoter, which is what we wish to have insight into. The model forms a bridge from promoter kinetics to these three observables. The simulations of the model make manifest that the mapping from promoter kinetics to our observables is sufficiently complex, making intuitive rationalizing of the trends challenging.


The core-promoter, a stretch of DNA surrounding the transcription start site (TSS), is a major integration-point for regulatory-signals controlling gene-transcription. Cellular differentiation is marked by divergence in transcriptional repertoire and cell-cycling behaviour between cells of different fates. The role promoter-associated gene-regulatory-networks play in development-associated transitions in cell-cycle-dynamics is poorly understood. This study demonstrates in a vertebrate embryo, how core-promoter variations define transcriptional output in cells transitioning from a proliferative to cell-lineage specifying phenotype. Assessment of cell proliferation across zebrafish embryo segmentation, using the FUCCI transgenic cell-cycle-phase marker, revealed a spatial and lineage-specific separation in cell-cycling behaviour. To investigate the role differential promoter usage plays in this process, cap-analysis-of-gene-expression (CAGE) was performed on cells segregated by cycling dynamics. This analysis revealed a dramatic increase in tissue-specific gene expression, concurrent with slowed cycling behaviour. We revealed a distinct sharpening in TSS utilization in genes upregulated in slowly cycling, differentiating tissues, associated with enhanced utilization of the TATA-box, in addition to Sp1 binding-sites. In contrast, genes upregulated in rapidly cycling cells carry broad distribution of TSS utilization, coupled with enrichment for the CCAAT-box. These promoter features appear to correspond to cell-cycle-dynamic rather than tissue/cell-lineage origin. Moreover, we observed genes with cell-cycle-dynamic-associated transitioning in TSS distribution and differential utilization of alternative promoters. These results demonstrate the regulatory role of core-promoters in cell-cycle-dependent transcription regulation, during embryo-development. Understanding transcription regulation in development represents a challenge due to the dynamic nature of multiple cellular lineages continuously evolving into an increasingly complex organism. Increasing evidence suggest that cell cycle control affects transcription regulation in development , while tOver the last decade, the core promoter, a stretch of DNA surrounding the transcription start site (TSS) of all genes, has emerged as a key site of transcriptional regulation, integrating signals received from multiple cis-regulatory elements ,6. The aEmbryonic development is marked by several dramatic transitions in the regulatory make up of cells, to permit changes and limitations in their potency, leading to the formation of an organized hierarchical body map. These transitions are often associated with changes in cell cycle dynamics, alongside shifts in transcriptional repertoire . This prin vitro studies of human and murine embryonic stem cells, showing that a key indicator of cell differentiation from pluripotency, are transitions in cell cycle dynamics from rapid cycling to a slower cycling identity, characterized by an elongated G1 phase (The Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) is a system that allows the visualization of cell cycle progression in living cells, through the fusion of fluorescent markers onto cell cycle phase restricted factors (often Cdt1 and Geminin)  as detaiG1 phase . In addiG1 phase  and liveG1 phase , have shG1 phase . These fin vivo model of development, the role the core promoter plays in defining transcriptional output in cells undergoing differentiation coupled changes in cell cycle dynamics, through both promoter-level regulatory and behavioural changes. The zebrafish FUCCI transgenic system differentially marks cells in G1 and S/G2/M phases of the cell cycle and can therefore be used to separate rapidly and slowly cycling cells in vivo, by virtue of the cell cycle stage they primarily inhabit. This system was used to ask how cell cycle dynamics affect promoter usage during differentiation stages. We segregated cells as they go through differentiation coupled changes in cell cycle dynamics in segmentation stage embryos, which are characterized by marked spatial separation of differentiating cells with distinct cell cycle dynamics )rw0410h] . Zebrafish embryos were obtained by sibling crosses from adult FUCCI [(Tg(EF1α:mKO2-zCdt1(1/190))rw0410h]  fish hourw0410h] ,26.Streptomyces griseus (Sigma). For Lightsheet imaging (Zeiss), embryos were screened for fluorescence using a fluorescent microscope (Zeiss axio zoom V16), and mounted in a column of 1% low melting point agarose (Sigma). Zebrafish embryos were imaged using dual side illumination, with maximum intensity projection (MIP) processed images generated using the Zen Black imaging software (Zeiss).Zebrafish embryos were decorionated using 1 mg/ml Protease from For assessment of the relative proportions of red and green fluorescent cell populations, 10 embryos from each embryonic stage were imaged on a fluorescent microscope (Zeiss axio zoom V16) and pixel count for each fluorescent marker extracted using the ImageJ software package .Zebrafish were decorionated and selected as previously described at the 14-somite stage. Roughly 200 embryos were dissociated into a single cell suspension using enzyme-free cell dissociation buffer, PBS based (Gibco). Dissociated cells were pelleted and resuspended in Hanks balanced salt solution without calcium chloride or magnesium sulphate (Sigma), for sorting. Cells were fluorescence-associated cell sorted (FACS) into populations displaying red and green fluorescence. Validation of correct sorting was performed by propidium iodide DNA content analysis following manufacturer's conditions (Invitrogen). RNA was extracted from isolated cells using the miRNeasy kit (Qiagen). RNA quality was analysed by capillary electrophoresis . All samples had an RNA integrity number (RIN) >9.NanoCAGE libraries were generated following a protocol described in . Pooled 6 reads (The zebrafish genome assembly (Zv9) was downloaded from UCSC Genome Browser . Nano-CA6 reads  and defiCTSS that were supported by at least 0.5 tpm in one of the samples were clustered based on a maximum allowed distance of 20 bp between two neighbouring CTSS. These transcriptional clusters (TCs) were then trimmed on the edges to obtain more robust boundaries of TCs, by obtaining the positions of the 10th and 90th percentiles of expression per TC. Only TCs with higher than 5 tpm expression were considered. Finally, TCs across all samples were aggregated if within 100 bp of each other to form consensus clusters (CC) for downstream analyses.The CCs were annotated to the nearest reported TSS from Ensembl (danRer7) using the R/Bioconductor package ChIPseeker . We seleP-value of <0.05. These results were cross referenced to the CC information of the merged samples. In cases of more than one CC mapping to the region, the CC with the highest expression was chosen to represent the region.The raw read counts were extracted for the CCs across triplicates described earlier and collapsed into total count per CC. DESeq2 R/Bioconductor package was used to define differential expression and the threshold of differential expression was set at adjusted CCs were annotated with entrez gene IDs and analysed with GOstats R/Bioconductor package for overrepresentation of GO terms for biological processes. The up and down regulated genes were tested separately against all genes expressed amongst the samples.n = 10 000). Tau and entropy scores for tissue-specificity were determined by RNA-seq expression of eight adult zebrafish tissues from DanioCode series 391: brain, gill, heart, intestine, kidney, liver, muscle and spleen. Analysis of the relative representation of tissue-specific terms, was performed by cross-referencing differentially expressed genes with mRNA in situ expression data extracted from the ZFIN database, for wild-type zygote to 20 days post fertilization larval stages (https://zfin.org/downloads/wildtype-expression_fish.txt). This data was used to generate an anatomical specificity score that for each gene defines the pattern of spatial expression across development . Data was then used to compare restrictedness of spatial localization of gene expression for two groups of genes, stratified across developmental time.The top 600 ranked human genes were downloaded from the Cyclebase database version 3.0. For each category of differential expression, the human orthologs (one to one & one to many) of zebrafish genes were determined using ‘mar2017.archive.ensembl.org’ archive of hg19. The peak time phenotype was determined by Cyclebase. Enrichment of cell cycle genes was determined with a permutation test  for TATA-box, CCAAT-box, Sp1 binding site, INR, and YY1 binding site motifs were obtained from converting frequency matrices from JASPAR . Each CWidth of CCs  were defined as the distance between the positions of the 10th and 90th percentiles of expression per CC. Sharp promoters were characterized by a width <10 bp, peaked broad promoters as ≥10 bp with one CTSS expressing more than 60% of expression of entire CC, and broad promoters as the remaining set.n = 79). These genes were segregated for the relative behaviours of alternative and canonical promoters as follows. Canonical down , Alternative down , Canonical up , Alternative up . Situations falling outside of these criteria were discarded (n = 9).The genomic location of 1612 twinned canonical and alternative promoters identified in , were exin vivo, by virtue of the cell cycle stage they primarily inhabit. Sugiyama et al.  into cells in G1 (red) and S/G2/M (green) , with coNext, we asked about the state of the mRNA transcriptome in fast and slow dividing cells of the embryo, with particular focus on the mRNA 5′ end, in order to identify features of promoter utilization. To achieve this goal, we chose a small cell number optimized protocol for detection of mRNA 5′ ends (nanoCAGE ), which si:ch211-113a14.29 (an orthologue of human histone 2B) and mmp30  , from t) Figure .A high degree of overlap was additionally observed for cluster position between nanoCAGE and previously published data, with 80% the same genes represented in all samples Figure . Identifn = 190 [up regulated in G1], n = 138 [up regulated in S/G2/M])  Figure . This reFluorescence microscopy analysis of cell cycle dynamics in embryo development, revealed significant lineage-specific segregation of cells on the basis of their cell cycle dynamics Figure . NotablyP < 0.0001), with a peak of expression between G2 and M phase of the cell cycle  population  population over the other populations (demonstrated by a higher average Tau and lower average entropy score) Figure . AnalysiIn order to further dissect the contribution of different cell types and developing lineages to the slowly versus rapidly cycling (G1 and S/G2/M) populations, differentially expressed gene sets were cross-referenced with the ZFIN database of gene with known tissue and spatial specific expression at the 14–19 somite stage . The majP-value = 4.22 × 10−12). In the G1 population there was also a strong enrichment for a canonical TATA-box , enrichment for Sp1 binding sites , and a depletion for a YY1 binding site motif . Strikingly, CCAAT-box and TATA-box relative strength of enrichment were inverse for S/G2/M and G1 enriched genes, relative to those with unchanged expression  and a divergence in transcriptional output. In order to determine whether differential gene expression, associated with cell cycle dynamics, is also associated with changes in core promoter regulatory element distribution, the frequency of known, regulatory motifs 120 bp upstream and 50 bp downstream of the dominant TSSs of each promoter was determined. Both groups of differentially expressed genes showed marked changes in motif utilization compared to genes with unchanged expression Figure . In the n Figure . Motif en Figure . Examplen Figure .P < 0.001–0.01) in the promoters of genes upregulated in S/G2/M cells  in gene promoters upregulated in G1, however in 90% [21/23] (TATAA) to 100% [21/21] (ATAAA) of these cases the sequence formed part of a canonical TATA-box rather than a W-box. Overall this suggests a change in the degree of utilization of canonical vs. non-canonical TATA regulatory elements in genes differentially expressed between G1 and S/G2/M populations.Besides altered utilization of specific regulatory motifs, previous analyses of development linked changes in promoter utilization, such as during zygotic genome activation , have fos Figure . Two penCanonical TATA-box utilization is strongly associated with sharp promoters, where a single or condensed cluster of TSSs are used in the promoter  and is an = 38 [S/G2/M], 74 [G1], 1577 [unchanged]; non-tissue specific: n = 100 [S/G2/M], 116 [G1], 6849 [unchanged]. For the tissue independent gene set, promoters assigned to tissues represented in both populations , or with no tissue specificity, were selected . Analysis of motif frequency in the promoter region, 120 bp upstream and 50 bp downstream of the dominant site of transcription, revealed a consistent, significant enrichment for CCAAT-box utilization in the S/G2/M population of each gene set, albeit smaller in the tissue specific population . This analysis also revealed a highly significant enrichment for TATA-box utilization in the G1 population of each gene set , albeit larger in the tissue specific population, alongside an enrichment in Sp1 binding site utilization in the tissue independent gene sets  for example, a gene associated with cell proliferation in human  enrichment for CCAAT-box occurrence in the former population  of cells undergoing differentiation coupled changes in cell cycle dynamics. In order to determine whether, like differentially expressed promoters, these transitioning promoters are marked by different regulatory machinery to each other, promoter proximal regulatory motif analysis, was performed, as before Figure . This ann Figure , furthern Figure . This sun = 1612). Genes with significant expression  were selected for further analysis (n = 231). In order to identify genes with differential utilization of alternative promoters, alternative promoter TPM values were normalized to canonical  and compared between populations ; Alternative down: S/G2/M alternative promoter expression is depleted, but canonical expression is unchanged (n = 22); Canonical up: S/G2/M alternative promoter expression is unchanged, but canonical expression is enhanced (n = 10); Alternative up: S/G2/M alternative promoter expression is enhanced, but canonical expression is unchanged. Situations falling outside of these criteria were discarded (n = 9). Full selection criteria are detailed in the materials and methods. In order to determine whether, like differentially expressed and shape changing promoters, these alternative promoter utilization events are marked by differential regulatory machinery to each other, promoter proximal regulatory motif analysis was performed as before on the promoter, where the change in expression is occurring  and significant CCAAT utilization enrichment where the alternative promoter is enriched in S/G2/M (P < 0.01). An example of the latter is shown in Figure inositol polyphosphate-5-phosphatase B (inpp5b) is unchanged between population, however expression from an alternative, CCAAT-box containing, promoter is enhanced in S/G2/M. Publicly available RNA-seq data from . We then = 10 for each stage).In order to confirm the selected stages have suitably distinct cell cycle dynamic behaviour, FUCCI transgenic embryos from the selected stages were imaged and the ratio of green (rapid cycling) to red (slow cycling) fluorescence pixel density was measured Figure  and B. Tn = 93) and those with expression gained over time  , as the embryo transitions from a predominance of rapid to slowly cycling cells, in agreement with previous analyses in this study , again in agreement with this study's data on motif association with cell cycle dynamics. Interestingly Sp1 motif utilization, previously shown to be associated with slowly cycling cells, was enriched in both gene sets relative to genes with unchanged temporal expression, suggesting some divergence in the gene sets selected by temporal versus cell cycle dynamics. Assessment of promoter interquantile widths in these gene sets revealed a subtle, but measurable broadening of TSS distribution in genes lost versus gained over time (average IQW = 27.5 versus 23)  Figure . Analysiy Figure . Convers) Figure . It is nP < 0.00001) between genes enriched in slowly cycling cells and those with expression gained over the segmentation period of embryo development . Differential expression analysis on these populations revealed a striking separation in the identity of these cells. Beside differences in cell cycle stage, slowly cycling (G1) cells showed extensive specification in terms of gene expression to different tissues, associated with increased representation of tissue-specific regulatory motifs in their promoters. Rapidly cycling (S/G2/M) cells, on the other hand, were far less specified, with overwhelming representation of ubiquitously expressed genes, associated with DNA and chromatin processing required for rapid proliferation. This divergence in cellular identity was found to be concurrent with changes in promoter behaviour. Differentially expressed genes were sharper in the G1 population and associated with an increase in TATA-box utilization, alongside Sp1 binding sites. In contrast, the broader promoter usage in S/G2/M upregulated genes was accompanied by a far greater utilization of CCAAT-box general transcription factor binding sites and a lesser dependence on TATA. TATA-like and W-box motif frequency was found to be enriched in rapidly cycling cells however. These changes in promoter behaviour were found to be predominantly associated with differential cell cycle behaviour, rather than tissue lineage identity. In addition, global analysis of changes in promoter associated TSS distribution (shape change) and usage of alternative promoters, revealed these again to be associated with divergent utilization of TATA and CCAAT-box TF binding sites. Additionally, this pattern of promoter behaviour was found to be replicated in genes with temporal changes in expression over the segmentation period of zebrafish embryo development, a stage at which there is extensive transitioning in cell cycle dynamics.Collectively this data suggests a strong divergence in the utilization of general transcription factor binding sites, TATA and CCAAT, as well as Sp1 binding sites, between cells undergoing differentiation coupled changes in cell cycle dynamics. This divergence impacts on the transcriptome and promoterome of these cells, resulting in differential gene expression as well as differential canonical and alternative promoter utilization.Promoter proximal regulatory elements have previously been associated with marking genes with cell cycle periodic expression. The promoter-associated cell cycle-dependent element (CDE) and the cell cycle genes homology region (CHR) have both been found to regulate genes with maximum expression in G2-M phase, through cell cycle stage dependent binding of transcription factors (reviewed in ). CCAAT-This study shows that cells going through the striking changes in cell cycle dynamics, occurring embryo wide, during the segmentation period, have differential tissue-specification. This may suggest a marked transition in multiple tissue progenitors during this process. Rapidly cycling cells, show weak tissue-specificity despite their clear segregation to distinct tissue domains such as the eye cup, notochord and brain tissues. Additionally, they show a transcriptional profile dominated by ubiquitously expressed DNA processing genes and cell cycle regulators . This poSingle cell RNA-seq analysis of early stage embryo development (from high stage to 6-somite) does reveal an early specification point, differentiating notochord and brain tissues (both rapidly cycling) from somites and cardiac tissues (both slowly cycling) . This daInvestigations into differentiation associated changes in gene regulatory programs, from terminally differentiated gamete, to pluripotent stem cell  and lineD. melanogaster and mammalian systems are often associated with the presence of a promoter proximal, spatially constrained TATA-boxes, alongside sharp transcription initiation, differentiating this class of promoter (type I) from non-TATA dependent housekeeping (type II) or developmentally regulated (type III) gene associated promoters  . Additioin vivo model.Sp1 has also been associated with regulating tissue-specification, in particular through interaction with cell cycle regulated factors . Sp1 hasin vivo. It displays how this process is coupled with transitions in cell cycle dynamics to a slower cycling rate where the G1-phase predominates, alongside transitions in promoter behaviour. This is marked by a sharpening of promoter utilization, from rapidly to slowly cycling cells, mediated by increased utilization of TATA-box and Sp1 regulatory units, at the expense of the CCAAT-box. This transition in the promoterome is reflected in the transcriptome where the predominance of DNA and chromatin processing factors in rapidly cycling unspecified cells is replaced by increased expression of tissue-lineage specifying genes.This investigation demonstrates for the first time the promoter level regulatory changes occurring in cells undergoing the transition from proliferation to tissue-specification, at the segmentation stage of embryo development, Sequencing files for this project can be found at the ArrayExpress accession: E-MTAB-8795.gkaa563_Supplemental_FilesClick here for additional data file.