1. Computational and Systems Biology
  2. Developmental Biology

Noise modulation in retinoic acid signaling sharpens segmental boundaries of gene expression in the embryonic zebrafish hindbrain

  1. Julian Sosnik
  2. Likun Zheng
  3. Christopher V Rackauckas
  4. Michelle Digman
  5. Enrico Gratton
  6. Qing Nie
  7. Thomas F Schilling  Is a corresponding author
  1. University of California, Irvine, United States
  2. Wentworth Institute of Technology, United States
https://doi.org/10.7554/eLife.14034
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Cite this article
  1. Julian Sosnik
  2. Likun Zheng
  3. Christopher V Rackauckas
  4. Michelle Digman
  5. Enrico Gratton
  6. Qing Nie
  7. Thomas F Schilling
(2016)
Noise modulation in retinoic acid signaling sharpens segmental boundaries of gene expression in the embryonic zebrafish hindbrain
eLife 5:e14034.
https://doi.org/10.7554/eLife.14034
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Abstract

Morphogen gradients induce sharply defined domains of gene expression in a concentration-dependent manner, yet how cells interpret these signals in the face of spatial and temporal noise remains unclear. Using fluorescence lifetime imaging microscopy (FLIM) and phasor analysis to measure endogenous retinoic acid (RA) directly in vivo, we have investigated the amplitude of noise in RA signaling, and how modulation of this noise affects patterning of hindbrain segments (rhombomeres) in the zebrafish embryo. We demonstrate that RA forms a noisy gradient during critical stages of hindbrain patterning and that cells use distinct intracellular binding proteins to attenuate noise in RA levels. Increasing noise disrupts sharpening of rhombomere boundaries and proper patterning of the hindbrain. These findings reveal novel cellular mechanisms of noise regulation, which are likely to play important roles in other aspects of physiology and disease.

https://doi.org/10.7554/eLife.14034.001

eLife digest

Animal cells need to be able to communicate with each other so that they can work together in tissues and organs. To do so, cells release signaling molecules that can move around within a tissue and be detected by receptors on other cells.

We tend to assume that the signaling molecules are evenly distributed across a tissue and affect all the receiving cells in the same way. However, random variations (noise) that affect how many of these molecules are produced, how they move through the space between cells and how they bind to receptors makes the reality much more complex. Cells responding to the signal somehow can ignore this noise and establish sharp boundaries between different cell types so that neighboring cells have distinct roles in the tissue. Few studies have attempted to measure such noise or address how cells manage to respond to noisy signals in a consistent manner.

Retinoic acid is a signaling molecule that plays an important role in the development of the brain in animal embryos. It forms a gradient along the body of the embryo from the head end to the tail end, but it has proved difficult to measure this gradient directly. Sosnik et al. exploited the fact that this molecule is weakly fluorescent and used microscopy to directly detect it in zebrafish embryos. The experiments show that retinoic acid forms a gradient in the embryos, with high levels at the tail end and lower levels at the head end.

Sosnik et al. also found that there is a large amount of noise in the retinoic acid gradient. Two cells in the same position can have very different retinoic acid levels, and the levels in a particular cell can vary from one minute to the next. The experiments also show that proteins that interact with retinoic acid help to reduce noise within a cell. This noise reduction is important for sharpening the boundaries between different brain regions in the embryo to allow the brain to develop normally. A future challenge will be to see if similar retinoic acid gradients and noise control occur in other tissues, and if the noise has any positive role to play in development.

https://doi.org/10.7554/eLife.14034.002

Introduction

Cells responding to signals need to be able to distinguish these signals from random fluctuations (i.e., noise) and presumably have evolved mechanisms to do so. Noise is inherent in biological systems, but until recently we have lacked the tools to study such complexity in vivo (Gregor et al., 2007; Holloway et al., 2011). Noise in signaling pathways arises from many sources, including stochastic variation in transcription, protein synthesis, and cellular environment (Elowitz et al., 2002). Morphogens are long-range signals thought to induce different cell behaviors in a concentration-dependent manner, but how such graded signals can be established in the face of noise and how they specify sharp boundaries of target gene expression remain unclear.

Retinoic acid (RA) is thought to act as a morphogen in the embryonic vertebrate hindbrain to pattern cells into a series of segments, called rhombomeres, which give rise to different domains of the adult brainstem (White and Schilling, 2008; Niederreither and Dollé, 2008). A small hydrophobic signaling molecule derived from dietary precursor Vitamin A, RA is produced in mesoderm near the head/trunk boundary and forms an anteriorly declining gradient across the hindbrain progenitor domain, in part through the activities of RA-degrading enzymes, Cyp26s (Sirbu et al., 2005; Hernandez et al., 2007; White et al., 2007). In particular, self-enhanced degradation through induction of Cyp26a1 by RA itself was shown to be critical for gradient formation (White et al., 2007). Excess RA during human embryogenesis can cause anterior-posterior (A-P) patterning defects, and RA has been implicated in the development and maintenance of numerous cell types as well as in cancers (leukemia), stem cells (pancreas) and regenerating organs (cardiomyocytes) (Duester, 2008; Tang and Gudas, 2011; Rhinn and Dolle, 2012). Due to its hydrophobic nature, RA requires proteins to bind and transport it through extracellular and intracellular space, which enhances robustness (Cai et al., 2012) but also introduces various sources of noise (Schilling et al., 2012).

Our computational models suggest that noise in RA signaling can also play a positive role in hindbrain segmentation through noise-induced switches in gene expression at rhombomere boundaries (Zhang et al., 2012), but until recently methods were lacking to test this hypothesis. Here we present a novel methodology utilizing fluorescence lifetime imaging microscopy (FLIM) and a phasor analysis (phasor-FLIM) to study the abundance of endogenous RA in vivo in zebrafish embryos. Using this new tool to visualize endogenous RA in living cells we quantify variability in RA levels and provide some of the first evidence that cells actively control the magnitude of noise in a signaling molecule in a multicellular system in vivo.

Results and discussion

Fluorescence lifetime imaging (FLIM) measures endogenous RA directly in vivo

We took advantage of the fact that RA is a fluorescent molecule to quantify its endogenous abundance in vivo in the developing zebrafish hindbrain. Due to the low abundance of RA in cells and its wide spectra of absorbance and emission, traditional fluorescence microscopic techniques fail to detect RA specifically. We opted instead to visualize RA by its unique fluorescence lifetime, rather than its fluorescence intensity. Focusing on the presumptive neural ectoderm of mid-gastrula stage embryos (8–8.5 hr post fertilization) (Kimmel et al., 1995) we used FLIM to measure the relative abundances of RA as a function of cell position along the A-P axis. This revealed that intracellular free RA forms an anteriorly-declining gradient (Figure 1A,B), similar to that previously reported with FRET reporters for RA (Shimozono et al., 2013) and suggested by the pattern of RARE-lacZ expression in late-gastrula mouse embryos (Sirbu et al., 2005). Relative abundances were calculated using phasor-FLIM (Digman et al., 2008), where within the phasor space, each individual fluorescent species is represented in a characteristic and invariable position. Mixtures of molecules generate a FLIM signature that lies along a line connecting the positions of the individual component species and the position in that line is weighted according to the relative abundances (Figure 1—figure supplement 1A). Because we know the absolute position of pure RA (in the lower right corner of phasor space) (Figure 1—figure supplement 1)(Stringari et al., 2011), we can use the Cartesian distance within this space as a measure of the relative abundance of RA expressed as 1-dRA (Figure 1—figure supplement 1B). We observed that 1-dRA increased progressively as measurements were taken further posteriorly within the hindbrain field, suggesting that our FLIM approach is sensitive enough to detect endogenous RA gradients. An ordinary least squares regression analysis of gradient shape could not distinguish between exponential, linear, and quadratic fits, but confirmed the presence of a gradient (Figure 1—figure supplement 2). Unfortunately FLIM is also sensitive to the fluorescence emitted by transgenic markers of rhombomeres or other landmarks in embryos, making it difficult to determine precise segmental locations within the hindbrain field.

Figure 1 with 2 supplements see all
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Measuring RA gradients in zebrafish embryos with Phasor-FLIM.

(A) Example of a zebrafish embryo at mid-gastrula stage (8.5 hr post-fertilization) with the imaging area (black square – encompassing positions 230–330 in B-D) in the neural ectoderm (NE) centered ~200 μm from the advancing blastoderm margin (white line) (A: anterior, P: posterior, Y: yolk). Scale bar = 150 μm. (B-D) Plots of the relative abundance of RA (as the difference 1-dRA) versus position in μm along the A-P axis (anterior to the left) in WT (B), DEAB-treated (C), and DEAB-treated embryos co-treated with 0.7 nM exogenous RA (D). Solid curves in (B-D) represent best fit.

https://doi.org/10.7554/eLife.14034.003

To confirm that with phasor-FLIM we could detect RA specifically and its relative levels, we treated embryos with 10 μM DEAB to prevent the enzymatic conversion of retinal to RA, which eliminated the gradient at mid-gastrula stage (Figure 1C), and incubating these in 0.7 nM RA re-established the gradient as expected (Figure 1D) (White et al., 2007). We generated artificial gradients of RA by injecting embryos with RA saturated mineral oil and found that the relative abundance of RA decreased as a function of the distance from the source in two orthogonal axes (Figure 1—figure supplement 1C). Phasor analysis of the third harmonic of the laser pulse frequency (240 MHz instead of the standard 80 MHz) revealed similar gradients. Analyzing a different harmonic de-couples dRA from any other component of the mixture – i.e. the locations of each component in the phasor plot will vary independently of changes in dRA - and thus provides an independent means of confirming the specificity of the RA phasor-FLIM signature (Figure 1—figure supplement 1C).

We next asked if we could detect RA gradients at later stages, when rhombomere boundaries are being established, by performing FLIM measurements in the transgenic line MÜ4127 (Egr2b:mCherry), which labels rhombomeres 3 and 5 (r3, r5), in regions devoid of transgene fluorescence to avoid interference (Distel et al., 2009). We found a similar graded increase in dRA on the phasor plot in r4 and r2 relative to r6 at 24 hpf (Figure 1—figure supplement 1D–F) (i.e. 1- increases posteriorly), suggesting a graded reduction in RA content anteriorly. Injection of embryos with morpholino oligonucleotides (MOs) targeting Aldh1a2, to inhibit RA synthesis greatly reduced the separation between FLIM signatures in r2, r4 and r6, which was partially rescued by transplantation of wildtype (WT) paraxial mesoderm to restore the local RA source (Figure 1—figure supplement 1D–F) (White et al., 2007). These results show that the RA gradient persists during gastrulation and establishment of rhombomeres.

FLIM measurements reveal high noise levels in RA

Because phasor-FLIM measures endogenous RA and is not biased by the Kd of a reporter, in contrast to the FRET method previously published (Shimozono et al., 2013), it is more direct and more reliably reflects real-time RA dynamics. Thus we next applied this technique to measure stochastic fluctuations (noise) in RA levels across the embryonic hindbrain. Our models predict that the magnitude of such noise is large (Lander, 2013), as we have argued that these fluctuations help cells switch between stable states of gene expression and thereby sharpen gene expression boundaries, i.e. noise-induced switching (Schilling et al., 2012; Zhang et al., 2012), but direct evidence of such noise is lacking. To assess 'spatial noise' we analyzed five consecutive parallel rows of cells in which each cell within a row lies at the same A-P position within the hindbrain field. This revealed variability as high as 45% of the entire magnitude of the gradient among cells within a row (Figure 2A), consistent with the levels of noise predicted by our stochastic mathematical models (White et al., 2007; Zhang et al., 2012) (Figure 2B; Supplementary file 1). To assess 'temporal noise' we analyzed the same cells repeatedly at 12-second intervals, and also found that their RA levels were very noisy (Figure 2C,D).

Figure 2 with 1 supplement see all
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RA gradients are noisy in space and time.

(A, B) Spatial noise. Plots show relative abundance of RA in five parallel rows of cells (each color corresponds to a different row) along the A-P axis of the neural ectoderm within a single embryo. (A) Experimental – each point represents the integrated signal of 40 consecutive FLIM measurements (2.7 min) (solid line represents best fit). (B) Computational – line represents the mean of 500 model simulations. (C, D) Temporal noise. Graphs show variability in relative abundance of RA in five single cells (each color corresponds to a different cell) at equivalent A-P positions over time. (C) Experimental – FLIM measurements were taken every 12 s. (D) Computational – colors correspond to individual cells for each stochastic realization. See also Supplementary file 1.

https://doi.org/10.7554/eLife.14034.006

In order to rule out the possibility that the noise in our measurements was introduced by systematic artifacts or the measurement itself, we compared the variance in FLIM measurements of pure solutions of fluorescein, rhodamine and RA with the noise measured in cells of 9 independent embryos and found that noise in embryos is two orders of magnitude greater (Figure 2—figure supplement 1). We also calculated the maximum theoretical uncertainty due to photon shot noise and verified that the noise we measured is significantly larger (Colyer et al., 2008). Thus the fluctuations in RA levels that we observed in embryos are clearly biological in origin.

Noise in RA levels could be largely irrelevant for downstream gene expression if its frequency is faster than cellular responses, and is therefore averaged out. To address this possibility we performed an autocorrelation analysis of our temporal noise measurements using a moving window on each cell to search for significant lags. This revealed significant correlations (lags 13 and 14) corresponding to a predominant frequency on the order of 2.7 min. This is significantly slower than the half-life of the RA-Crabp2a complex, which is approximately 1.7 min (Dong et al., 1999). Because Crabp2 helps deliver RA to its nuclear receptor, and considering the scale of noise in transcriptional activation, noise at this time scale in RA signaling could propagate downstream. Thus it seems likely that cells possess mechanisms to limit this noise propagation.

Cellular RA binding proteins actively modulate noise

If cells actively control noise in RA signaling, they likely do it through intracellular RA-binding proteins, Crabps, or RA-degrading enzymes, Cyp26s, that can rapidly alter freely available RA (Kleywegt et al., 1994) and both of which have been shown to play critical roles in RA signaling (Sirbu et al., 2005; Hernandez et al., 2007; White et al., 2007; Cai et al., 2012). To test these candidates we reduced the amount (microinjected MOs) or overexpressed (microinjected mRNA) Crabp2a and Cyp26a1 in zebrafish embryos and measured noise in RA at mid-gastrula stages. Strikingly, MO depletion of Crabp2a increased temporal noise in RA without altering the mean RA level at a given A-P position, while overexpression of Crabp2a decreased variability in RA, again without altering the mean levels of RA (Figure 3). In contrast depletion or overexpression of Cyp26a1 increased or decreased mean RA levels, respectively, without altering noise. These results agree with simulations using our stochastic mathematical model in which we altered the levels of Crabp2a or Cyp26a1 (Figure 3—figure supplement 1). These results reveal a novel, active role for Crabps in modulating noise in RA.

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Crabp2a but not Cyp26a1 attenuates level of noise in RA.

Analysis of the temporal distribution of RA’s relative abundance in wildtype (WT), Crabp2a morpholino (MO)-injected, Crabp2a mRNA-injected (gain-of-function - GOF) and Cyp26a1 MO and mRNA-injected zebrafish embryos. Each column shows the signal obtained for a single representative cell and each point corresponds to a single time point. Lines represent the mean and standard deviation. Embryos with reduced or increased levels of Crabp2a show increased and decreased variability in RA, respectively, while altering Cyp26a1 changes the mean concentrations but not the variance.

https://doi.org/10.7554/eLife.14034.008

Functional roles for noise in RA target gene expression

To determine how altering RA levels influences noise in gene expression within the hindbrain we disrupted Crabp2a or Cyp26a1 and assayed expression of krox20 in r3 and r5. In situ Hybridization Chain Reaction (HCR) (Choi et al., 2014) allowed us to quantify krox20 expression levels by manually segmenting confocal images and measuring total fluorescence of each cell (Figure 4A) (Video 1). All HCR analyses were performed on raw 3D data, with Z-projections performed post-analysis. Either raising or lowering Crabp2a levels increased variance in krox20 expression from cell to cell (Figure 4B,C) when normalized for heterogeneity in gene expression from embryo to embryo, as confirmed by single embryo qPCR (Figure 4—figure supplement 1A). In addition, it decreased the sharpness of boundaries of krox20 expression in r3 and r5, using a sharpness index calculated as the ratio between the length of the theoretical sharp boundary and the actual measured length of the boundary (Figure 4D,E) (Figure 4—figure supplement 1B) (Materials and methods). These results suggest that, in contrast to its effects on RA levels where Crabp2a appears to attenuate noise, an optimal range of Crabp2a is required to induce sharp boundaries of gene expression in rhombomeres and too much Crabp2a is also detrimental to the system (White et al., 2007; Zhang et al., 2012; Cai et al., 2012). Similarly, either raising or lowering Cyp26a1 levels increased variance in krox20 expression (Figure 4). Thus, while Crabp2a may play a unique role in reducing noise in RA levels it appears to function together with Cyp26a1 and potentially other RA signaling components in allowing robust expression of downstream targets.

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Both Crabp2a and Cyp26a1 attenuate noise in krox20 expression and facilitate rhombomere boundary sharpening.

(A) Representative Z projections of r3 and r5 (dorsal views, anterior to the left) analyzed by hybridization chain reaction (HCR) for krox20 (r3, rhombomere 3; r5, rhombomere 5; A, anterior; P, posterior). We performed all HCR analyses on raw 3D data and later generated Z-projections and enhanced contrast to simplify presentation. Colors correspond to total krox20 RNA in each cell as measured by total fluorescence intensity bracketed for maximum and minimum for the 5 conditions and represented in a linear scale. (B) Mean-centered analysis of krox20 expression of a subset of cells for r3 and r5 from 3 randomly selected embryos for each condition. (C) Sharpness indices of the r3/r4 boundary (blue) and r4/r5 boundary (red) for embryos from each of the treatment conditions. Bars correspond to s.d. (D) Analysis of the variance in boundary sharpness from the quantification in (C). All perturbations yielded significant differences from wild-type controls, as noted in the Statistical Analysis. Therefore no asterisks were included to indicate columns representing statistical significance.

https://doi.org/10.7554/eLife.14034.010
Video 1
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3D rendering of HCR dataset.

3D rendering shows the specific HCR signal on rhombomeres 3 and 5 (red) with very low non-specific signal in surrounding tissue, which appears evenly distributed. DAPI signal (blue) demarcates nuclei of cells that are either Krox20 positive (red) or negative (no signal).

https://doi.org/10.7554/eLife.14034.012

Most studies of morphogen gradients and transcriptional noise have focused on the bicoid-hunchback transcriptional network in the Drosophila embryo, prior to cellularization and onset of zygotic transcription (He et al., 2012). The findings in that system indicate that due to the slow diffusion rate of the Bicoid protein, hunchback expression is mostly influenced by its own intrinsic noise and transcriptional noise in the bicoid gene does not propagate (Gregor et al., 2007; Okabe-Oho et al., 2009; Holloway et al., 2011). In contrast, we show that noise in a secreted signal in the multicellular context of the vertebrate hindbrain influences noise in expression of its transcriptional targets. Our FLIM measurements demonstrate noisy concentration gradients of RA along the A-P axis and reveal a novel role for Crabp2a in noise-attenuation distinct from that of Cyp26a1. Crabp2a could control noise in RA levels rapidly by binding RA and facilitating its entry into cells or buffering its availability within the cytoplasm (Maden et al., 1989; Boylan and Gudas, 1992) and our previous studies have demonstrated its critical roles in signal robustness (Cai et al., 2012). In contrast, both Crabp2a and Cyp26a1 inhibit noise in downstream targets of RA. Previous studies have shown that transcriptional inhibitors act as noise filters within narrow levels of expression, since outside of this range, transcriptional noise in their target genes increases (Dublanche et al., 2006). Such a biphasic response resembles our results with Crabp2a and Cyp26a1. Retinoic acid receptors (RARs) often act as transcriptional repressors until they bind RA. Thus Crabp2a and Cyp26a1 may modulate noise in RA targets by altering this balance between activation and repression. As such, both must be present within a narrow optimal range (Dublanche et al., 2006; White and Schilling, 2008). These mechanisms are likely to be similar in other signaling systems and critical for embryonic development and adult physiology, as well as defective in human diseases.

Materials and methods

Reagents

Unless otherwise noted, all of the reagents were obtained from Sigma-Aldrich (St. Louis, MO). All-Trans Retinoic Acid and 4-Diethylaminobenzaldehyde were dissolved at 10 mM and 100 mM, respectively, in anhydrous DMSO to create stocks and kept at -20°C in the dark until used. Morpholino Oligonucleotides (MOs) against aldh1a2, Crabp2a and Cyp26a1 were obtained from Gene Tools (Philomath, OR) and used as previously described (White et al., 2007; Cai et al., 2012). HCR reagents were obtained from Molecular Tools (Pasadena, CA). Restriction enzymes and SuperScriptII reverse transcriptase kit was obtained from NEB (Ipswich, MA). LightCycler 480 SYBR Green I Master mix was obtained from Roche (Indianapolis. IN). mMESSAGE mMACHINE kit, DAPI, Trizol reagent and fluorescein reference standard were obtained from Life Technologies (Eugene, OR).

Animals

All animal work was performed under the guidelines of UCI’s IACUC. Embryos were obtained by natural crosses, raised in embryo medium (EM), and staged according to Kimmel et al. 1995. The AB strain was used for WT experiments. MU4127 transgenics (Tg[shhb:KalTA4,UAS-E1b:mCherry]) to visualize rhombomeres 3 and 5 were kindly provided by Dr. Köster (HelmholtzZentrum, München).

Constructs

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For synthesis of mRNA three constructs were generated as templates. pCS2+GFP-CAAX was generated by isothermal assembly of a pCS2+ backbone digested with EcoRI and an amplimer of GFP-CAAX generated by PCR with the primers (forward) 5’-ggatcccatcgattcgTGGACCATGGTGAGCAAG-3’ and (reverse) 5’-gctcgagaggccttgTCAGGAGAGCACACACTTG-3’. Two C terminal Myc-tagged constructs were generated by traditional restriction-ligation procedure using a pCS2+MT as the backbone. Crabp2a was inserted between the BamHI and the ClaI sites of the proximal MCS and Cyp26a1 was inserted between the BamHI and ClaI sites of the proximal MCS. mRNA was synthesized by digestion of the constructs with NotI-HF and in vitro transcription with mMESSAGE mMACHINE SP6 transcription kit.

Phasor-FLIM

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Phasor-FLIM refers to a combination of fluorescence lifetime imaging microscopy (FLIM) and a methodology to analyze FLIM data. Rather than using the traditional intensity of fluorescence to analyze microscopic samples, FLIM measures the lifetime fluorescence decay of the fluorophore. Fluorophores possess a characteristic lifetime of fluorescence that represents the time that takes an excited electron to relax back to its basal state emitting a photon. This technique eliminates most sources of noise present in intensity-based fluorescence microscopy techniques. This is due to the fact that most sources of noise, like thermal flickering or dark current have no lifetimes (Colyer et al., 2008). However photon shot noise remains a source of uncertainty, but this is inversely proportional to the square-root of the fluorescence signal intensity. FLIM also requires a high numerical aperture objective (40X/NA 1.2), which intrinsically has a short working distance, making it impossible to perform the measurements at lower magnification.

Representing FLIM data in a phasor plot instead of a time-delay histogram allows analysis of the entire image, rather than pixel by pixel. In addition, because each molecular species is represented in a defined area of the phasor space, it allows analysis of samples with multiple fluorescent species (Digman et al., 2008). Individual fluorescent molecules have a constant lifetime, independent of concentration. Because the phasor space operates linearly, analysis of relative concentrations of fluorophores in samples with complex mixes can be performed. Samples with complex mixtures of fluorescent molecules generate a FLIM signature in the phasor plot that corresponds to the linear combination of the positions of the individual fluorescent species in a weighted manner. By calculating the Cartesian distance in the phasor space one can calculate the relative contributions of the different constituents (Digman et al., 2008; Stringari et al., 2011).

An additional advantage of this method over the use of reporters is the direct measurement of the endogenous fluorophore of interest in vivo and without the potential artifacts introduced by genetic manipulations/transgenic reporters. Genetically encoded FRET reporters published previously for RA, bind RA proportionally to their association/dissociation constants (ka/kd) and either emit or stop emitting a signal. This binding biases the data (both spatially and temporally) according to the binding constant of the reporter.

FLIM imaging

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Embryos were dechorionated and mounted dorsally on #1.5 coverslips with 1% low-melt agarose in EM without methylene blue. Acquisition and analysis was performed as previously described (Stringari et al., 2011). Briefly, the embryos were imaged for 2.7 min (for spatial analysis) or in single frames (4 sec –for temporal analysis) on a Zeiss 710 confocal microscope with a Ti:Sapphire laser (Spectra-Physics, Newport Beach, CA) as a two photon excitation source and an ISS A320 FastFLIM box coupled to two H7422P-40 photo-multiplying tubes (Hamamatsu, Japan). Data acquisition and analysis were performed using SimFCS software (LFD, Irvine, CA). Images were acquired with a 40X 1.2 NA water immersion objective. The excitation frequency used was 760 nm and in order to enrich the signal for RA, the emission was filtered through a 495LP dichroic mirror. Solutions of Rhodamine in water and Fluorescein in 100 mM KOH (pH 9.0) were used as references.

Embryo manipulations

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MOs were injected at the one-cell stage and cell transplantations were performed as previously described (White et al., 2007; Cai et al., 2012).

For the generation of ectopic retinoic acid (RA) sources, mineral oil was infused with all-trans RA to saturation. Embryos were dechorionated and temporarily mounted in 1% low-melt soft agar in EM over coverslips. Drops of the RA saturated oil or oil alone were then injected in 6–12 embryos using a mouth pipette and a capillary needle. The embryos were then released and left to heal for two hours when they were mounted for FLIM imaging. This experiment was repeated 3 (three) times.

mRNA was injected into one-cell embryos with glass micropipettes and a Narishige IM 300 microinjector with 50 pg of GFP-CAAX, 50 pg of Crabp2a-Myc or 100 pg of Cyp26a1-Myc. Expression verification was performed by microscopic observation for GFP or by Western blot with anti-Myc antibody (clone 9E10) for Crabp2a-Myc and Cyp26a1-Myc.

Hybridization chain reaction (HCR)

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One-cell stage embryos were injected with mRNA coding for GFP-CAAX to assist in later segmentation. The embryos were then divided into five experimental groups and injected with Crabp2a morpholinos (MOs), Crabp2a-Myc mRNA, Cyp26a1 MOs, Cyp26a1-Myc mRNA or 500 pl of water (WT). Embryos were incubated at 28C in EM until 11 hr postfertilization. Embryos were then treated as previously described (Choi et al., 2014). Briefly, embryos were dechorionated and fixed with fresh 4% PFA at 4°C for 16 hr, washed in PBS and dehydrated with methanol for 1 hr followed by graded rehydration. Embryos were then pre-hybridized at 45°C for 30 min in hybridization buffer. Embryos were hybridized in hybridization buffer containing 1 pmol of each of 5 (five) different DNA probes designed against Krox20 containing the B1 double initiator arms and Hoxb1a containing the B2 double initiator arms (Table 1) at 45°C for 16 hr. Probe specificity was verified by blast search and controlled by adding the hairpins but no initiator probe, which showed no non-specific signal, as well as single probe experiments. Excess probe was removed and embryos were gradually buffer exchanged to 5xSSCT and washed in 5xSSCT for 3.75 hr at 45°C. Samples were then pre-amplified in amplification buffer for 30 min at room temperature (RT) after which they were left to amplify in amplification buffer containing B1H1 and B1H2 snap-cooled hairpins conjugated to Alexa 594 and B2H1 and B2H2 snap-cooled hairpins conjugated to Alexa 647 at room temperature for 16 hr. Finally the embryos were washed in 5xSSCT and counterstained with DAPI before mounting in soft agar on number 1.5 thickness coverslips for confocal imaging. Samples were imaged with a Leica SP8 scanning confocal microscope acquiring z-stacks covering the entire hindbrain as 12 bit 512 X 512 images and analyzed using ImageJ software. Experiments were performed with 12 embryos per condition and repeated 4 (four) times. Microscope settings were kept constant throughout. Mean intensity values obtained from HCR experiments were of 190000 for the WT embryos, with an average cell size of 85 pixels, making the average signal 50% of the maximum.

Table 1

Sequences of the probes used for HCR corresponding to the specific genes as indicated and flanked by the corresponding adaptor sequences (B1 or B2). P1, P2, etc. corresponds to the different probes used for each gene.

https://doi.org/10.7554/eLife.14034.013
ProbeSequence
Krox20_B1-P15'-GAGGAGGGCAGCAAACGGGAAGAGTCTTCCTTTACGATATTAGAAGTGGCTGGGGGAGACTGAGGATGCAGGTGACGAGGATGCTGAGGATATATAGCATTCTTTCTTGAGGAGGGCAGCAAACGGGAAGAG-3'
Krox20_B1-P25'-GAGGAGGGCAGCAAACGGGAAGAGTCTTCCTTTACGATATTGTGGAAAGGAACGCAGACGGGTCTTGATAGACCTCTCCGCATCCAGAGTAATATAGCATTCTTTCTTGAGGAGGGCAGCAAACGGGAAGAG-3'
Krox20_B1-P35'-GAGGAGGGCAGCAAACGGGAAGAGTCTTCCTTTACGATATTAGGTTGGAAAAAGCCGGCGTAGTCCGGGATTATAGGGAACAACCCAGAGTATATAGCATTCTTTCTTGAGGAGGGCAGCAAACGGGAAGAG-3'
Krox20_B1-P45'-GAGGAGGGCAGCAAACGGGAAGAGTCTTCCTTTACGATATTGTTAGAGGAGGCGGTAATTTGAAAGAGTCCAGCGGGCAGGAGAACGGTTTATATAGCATTCTTTCTTGAGGAGGGCAGCAAACGGGAAGAG-3'
Hoxb1a_B2-P15'-CCTCGTAAATCCTCATCAATCATCCAGTAAACCGCCAAAAAAGTGTGGAAAGGGCCCGGGAACGCCTGGTCCAAGTGGTGGTATCCAGCCTAAAAAAGCTCAGTCCATCCTCGTAAATCCTCATCAATCATC-3'
Hoxb1a_B2-P25'-CCTCGTAAATCCTCATCAATCATCCAGTAAACCGCCAAAAACAGTTCCACCATAGGTAAGGCCCATGCCAGTTTGATTTTGGTGCTGGTGAAAAAAAGCTCAGTCCATCCTCGTAAATCCTCATCAATCATC-3'
Hoxb1a_B2-P35'-CCTCGTAAATCCTCATCAATCATCCAGTAAACCGCCAAAAATGTTGAGCATAGTCCGAGTTGGCGCAGGCCTGTGTCCCATAACTTGTTGTAAAAAAGCTCAGTCCATCCTCGTAAATCCTCATCAATCATC-3'
Hoxb1a_B2-P45'-CCTCGTAAATCCTCATCAATCATCCAGTAAACCGCCAAAAAAGTACGCACCGGCCATAGAGCCATAGTGTGGACTGGCATTTGATGTTGAAAAAAAAGCTCAGTCCATCCTCGTAAATCCTCATCAATCATC-3'
Hoxb1a_B2-P55'-CCTCGTAAATCCTCATCAATCATCCAGTAAACCGCCAAAAAGAGTGATCAGATTGATCCTCGAGGTCTTTAGACGAAGTGGAGGAAGCAGGAAAAAAGCTCAGTCCATCCTCGTAAATCCTCATCAATCATC-3'

Sharpness index

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We defined a sharpness index as the ratio between the length of a perfectly sharp boundary and the actual measured length of the boundary according to the following equation:

S=AsharpAreal=∑n=1N(dnsharp×z)∑n=1N(dnxy×z)=(∑n=1Ndnsharp)×z(∑n=1Ndnxy)×z=∑n=1Ndnsharp∑n=1Ndnxy

Where S is the sharpness index, Asharp is the area of the theoretical sharp boundary, Areal is the real measured area of the boundary, n is each individual slice in the z-stack, N is the total number of slices of the z-stack, dsharp is the minimum distance between the rhombomere’s lateral edges (Figure 4—figure supplement 1B) in XY (the theoretical sharp boundary), dxy is the measured distance in XY of the boundary and Z is the thickness of each slice.

qPCR

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Eight embryos injected for HCR were separated after dechorionation and total RNA extracted with 150 μl of Trizol reagent. After chloroform addition and separation of the aqueous phase, the samples were concentrated using the DNA-Free RNA Kit (Zymo Research, Irvine, CA). Poly A-RNA was transcribed using SuperScriptII. SYBR Green qPCR reactions were performed with primers 5’-ATCTATTCGGTGGACGAGC-3’ and 5’-TAATCAGGCCATCTCCTGC-3’ for Krox20 and 5’-CAAGGGATGGAAGATTGAGC-3’ and 5’-AACCATACCAGGCTTGAGGA-3’ for EF1α. Primer sets were tested and confirmed to have an amplification efficiency of 2. In order to study the individual variability in gene expression on each embryo, a homogeneous standard was generated with RNA pooled from 100 embryos at the same stage (3 somites-11 hpf-) of development and reactions of this 'standard' were run in parallel. The △△Ct method was used to analyze the samples. All samples were run in triplicates and the experiment was repeated 4 (four) times.

Statistical analysis

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Unless otherwise noted, statistical analysis was performed using Prism 5 (GraphPad software, La Jolla, CA). In experiments where the graded distribution was analyzed (Figure 1, 2A; Figure 1—figure supplement 1C), a comparison of the best-fit lines was performed.

In assays where the variance was analyzed (Figure 2, 3, 4 and associated figure supplements) a one-way ANOVA of the coefficients of variation was performed. In order to establish significance in the changes in variation, a Levene’s test was performed using MATLAB (MathWorks, Natick, MA). To establish the significance of the changes in mean values, a Newman-Keuls test was used.

For the rescue experiment (Figure 1—figure supplement 1D–F) a two-way ANOVA analysis was performed and significance was established after the Bonferroni post test correction.

To establish the predominant frequency of the noise (Figure 2C) an analysis of the datasets for different cells was performed using a moving window and searching for lags for which a correlation function would provide significant p-values using MATLAB (MathWorks, Natick, MA). Lags 13 and 14 gave p-values between 0.08 and 0.01. Average of these lags corresponds to a frequency of about 2.7 min.

Boundary sharpness was calculated as the ratio between the length of the theoretical sharp boundary and the actual measured length of the boundary (Figure 4—figure supplement 1B). All perturbations yielded significant differences from wild-type controls. Thus no asterisks were included to indicate columns representing statistical significance.

References

    1. Boylan JF
    2. Gudas LJ
    (1992)
    The level of CRABP-I expression influences the amounts and types of all-trans-retinoic acid metabolites in F9 teratocarcinoma stem cells
    The Journal of Biological Chemistry 267:21486–21491.
    1. Distel M
    2. Wullimann MF
    3. Koster RW
    (2009) Optimized Gal4 genetics for permanent gene expression mapping in zebrafish
    Proceedings of the National Academy of Sciences of the United States of America 106:13365–13370.
    https://doi.org/10.1073/pnas.0903060106

Decision letter

  1. Robb Krumlauf
    Reviewing Editor; Stowers Institute for Medical Research, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

[Editors’ note: a previous version of this study was rejected after peer review, but the authors submitted for reconsideration. The first decision letter after peer review is shown below.]

Thank you for submitting your work entitled "Noise modulation in retinoic acid signaling sharpens segmental boundaries of gene expression in the zebrafish hindbrain" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Janet Rossant as the Senior Editor.

The reviewers have discussed the reviews with one another and the Reviewing and Senior Editor have drafted this decision.

This is an interesting manuscript that uses FLIM microscopy for the direct visualization of retinoid concentration gradients in the zebrafish hindbrain. These finding provide insight into the dynamic spatial heterogeneity of RA and its implications for hindbrain patterning. Despite the interesting set of experiments, the paper had several weaknesses in presentation and analysis of data, which we anticipate will require extensive reanalysis, and the generation of additional data. Since it is eLife policy to only invite revisions in cases where revisions can be reasonably completed within two months, we cannot invite revision of the current manuscript. However, we are in principle interested in the study, and we would consider a substantially revised version that addresses our concerns, with the proviso that it will be treated as a new submission.

The key revisions that came from the discussions among the reviewers are:

1) Indicate if their RA measurements show significant shifts in the position of the anterior boundary of RA as time proceeds (as was seen with RARE-lacZ in mouse where RA activity is first at the r2/r3 boundary, then at r3/r4, then at r4/r5) or do they see a more noisy decline in RA as time proceeds since they are detecting RA itself rather than RA activity. (This may require more imaging of the gradients).

2) Describe the shape of the RA relative abundance in the zebrafish hindbrain as a function of A-P position.

3) Show single cell resolution at the presumptive rhombomere boundaries. They also need quantify the HCR data in a linear range and at the level of single cells. (Perform more HCR in situ experiments).

4) How do the authors determine S without the hoxb1 expression or another marker that defines the adjacent rhombomere segment? The sharpness index (S) in their Zhang Mol Sys Biol 2012 paper defined as 'the mean location of the boundary between hoxb1 and krox20 expression domains as the intersection of their distributions at 50% of the normalized value. A decrease in S over time indicates noise attenuation and a sharper boundary. (Perform more imaging and in situ experiments).

Reviewer #1:

This manuscript provides important new information on the mechanism of retinoic acid (RA) signaling during hindbrain development. Determination of RA concentration gradients has been a difficult task, hampering efforts to understand RA function. Here, the authors present a novel methodology utilizing fluorescence lifetime imaging microscopy to determine the abundance of endogenous RA along the A-P axis of the zebrafish hindbrain. They show that inherent noise exists in the concentration of RA in individual cells of the hindbrain, and that CRABP2 (an RA-binding protein) attenuates the noise to allow proper expression of downstream targets. Overall, these studies have significantly increased our understanding of RA gradient action during hindbrain formation.

Specific Points to Address:

1) Paragraph one, subheading “Fluorescence Lifetime Imaging (FLIM) measures endogenous RA directly in vivo” – the authors could add that their observation of a declining RA concentration from posterior to anterior hindbrain is consistent with the pattern of RARE-lacZ expression along the hindbrain of late-gastrulation stage mouse embryos (Sirbu, Development 2005).

2) Paragraph two, subheading “Fluorescence Lifetime Imaging (FLIM) measures endogenous RA directly in vivo” – how do the authors results on differential RA concentration along the hindbrain A-P axis over time compare with results from mouse RARE-lacZ suggesting that RA activity experiences shifting hindbrain boundaries from anterior to posterior as development proceeds (Sirbu Development 2005)?

3) Paragraph two, subheading “Functional roles for noise in RA target gene expression” – it would help if the authors further discuss whether their analysis of Cyp26a1 (an RA-degrading enzyme) supports a role for Cyp26 in generating rhombomere boundaries like suggested in other studies (such as Hernandez, Development 2007).

Reviewer #2:in vivo visualization of retinoic acid (RA) by light microscopy has proved challenging due to the non-peptidic structure of RA, its low abundance in cells, and wide spectra of absorbance and emission. This has made it difficult to address the specific question of whether an RA gradient exists during segmentation of the vertebrate hindbrain into distinct rhombomeres, and if so how noisy RA levels in the hindbrain are modulated by cells in order to refine gene expression at rhombomere segment boundaries.

In this paper, the authors present an excellent application of fluorescence lifetime microscopy (FLIM) and phasor analysis to more accurately measure endogenous RA directly in the living zebrafish embryo, examine noise in RA signaling, and investigate how modulation of this noise (by intracellular RA-binding proteins (Crabps) or RA-degrading enzymes (Cyp26s)) affects patterning of hindbrain segments using fluorescence in-situ hybridization chain reaction technology (HCR) to more clearly define rhombomere-specific gene expression (Krox20). The authors clearly show their Phasor-FLIM strategy is accurate in vivo.

However, there are several significant concerns regarding the data presented that limit my enthusiasm for their primary conclusions and publication.

1) The authors argue this is the first method to directly measure RA concentration levels in the living embryo and only briefly mention a previous FRET-reporter based approach which makes this claim (Shimozono et al., Nature, 2013) and was used to measure RA concentration in the zebrafish hindbrain. This can easily be fixed with more description in the Introduction and a revision of the wording. However, what is troubling here is that the authors appear to move back and forth between two distinctly different tissue architectures (neural ectoderm (2D) vs hindbrain (3D) in their measurements of RA concentration and noise levels. That is, we don't know the shape of the RA relative abundance in the zebrafish hindbrain as a function of A-P position. Also, the result that Crabps modulates noise in RA is again based on measurements made in the neural ectoderm and not the hindbrain.

2) HCR is a more sensitive method to detect mRNA expression that the authors use to examine sharpening/fuzziness of Krox20 expression in response to changes in RA levels. This is precisely the type of experiment to perform in response to their computational modeling simulations of noise-induced switching and boundary sharpening. Unfortunately, the authors completely under-utilize this method and this brings their data and conclusion into question. That is, the HCR images (z-projections) are over-exposed and should be of high enough quality to present an accurate 3D spatial representation of the subregions near presumptive rhombomere boundaries (instead of reducing to a 2D analysis and segmentation that does not include the cell membrane outlines as a 3D volume). Further, this conclusion really begs for single cell resolution at the presumptive rhombomere boundaries which is not clear. Overall, this is a poor presentation compared with the more elegant Phasor-Flim analysis.

Reviewer #3:

The manuscript by Sosnik et al. uses fluorescence lifetime imaging microscopy (FLIM) and phasor analysis to measure endogenous retinoic acid (RA) levels within zebrafish embryos. The authors studied the amplitude of noise in RA signaling, and how modulation of this noise affects rhombomere patterning. The authors argue that RA forms a noisy gradient during critical stages of hindbrain patterning and that cells use distinct intracellular binding proteins to attenuate noise in RA levels. Increasing noise disrupts sharpening of rhombomere boundaries and proper patterning of the hindbrain. The work is very interesting and innovative. The general concerns are that the experiments are difficult to understand and it appears important controls are lacking. We would support the paper being accepted for publication if these issues are addressed.

In the subsection “Functional roles for noise in RA target gene expression”, the authors state: “To determine how altering RA levels influences noise in gene expression within the hindbrain we disrupted Crabp2a or Cyp26a1 and assayed expression of krox20 in r3 and r5.”

Each MO treatment seems to have had an effect on krox20 expression. Considering that MO could have broad effects on RNA expression levels, where any MOs unrelated to the RA pathway injected and analyzed.

It is not apparent that HCR is a reliable indicator of gene expression levels. This is a novel USE of HCR, so proper controls should be considered. What other RNAs besides krox20 were measured? Were other RNAs that should not be affected and presumed to be affected by altering RA levels within the hindbrain also examined? These could also be measured within the other rhombomeres.

Is it possible to use the Tg(shhb:KalTA4,UAS-E1b:mCherry) transgenic zebrafish to compare cherry protein vs. RNA expression levels in the rhombomeres as a quick control?

The HCR images shown in Figure 4A appear saturated, which would prevent accurate quantitation of krox20 RNA levels.

[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]

Thank you for resubmitting your work entitled "Noise modulation in retinoic acid signaling sharpens segmental boundaries of gene expression in the zebrafish hindbrain" for further consideration at eLife. Your revised article has been favorably evaluated by Janet Rossant (Senior editor), a Reviewing editor, and three reviewers. The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

The reviewers have read and discussed your manuscript and in principle feel it is worthy of publication if you can made a few minor changes and address one major area of concern that needs to be clarified.

Major Concern:

The major concern deals centers about the HCR analyses. It is the opinion of the reviewers that the authors’ claim of using HCR for the relative quantification of target mRNAs is novel and important and thus requires solid data to prove that their approach is indeed quantitative. As presented the HCR experiments are difficult to understand and important controls are lacking. The authors should include 1 control data point. That is, no initiator probe + hairpins to show that there is no non-specific signal.

The authors responded to the reviewers’ previous comments with the following statement:

“HCR is an established technique that linearly amplifies the signal of an RNA target (Dirks and Pierce, 2004; molecularinstruments.org; Choi et al., 2010; 2014). As mentioned above, we also analyzed the RNA of hoxb1a in r3, r4 and r5 (only r3 and r5 data are presented here – Author response image 2) at early stages when its expression is low and uniform across the hindbrain field. The MO and RNA injections had no significant effects on hoxb1a expression as assayed by HCR.”

There is disagreement with this response. Choi et al. 2014 (ACS Nano 8, 4284) is the most relevant paper since it describes the use of DNA probes to detect RNA targets and describes the method followed by the authors. However, the Choi paper does not demonstrate HCR's capability of doing relative (or absolute) quantification of target mRNAs. Choi et al. compared signal intensities and signal-to-background between one- and two-initiator probes using multiplex approaches. Thus, the authors’ claim of using HCR for the relative quantification of target mRNAs is novel and important and thus requires a higher burden of proof that it is indeed quantitative.

The basic experimental framework put forth by the authors is based upon the idea of imaging the HCR signals from the tissue/embryonic region of interest (hindbrain r3-5) and examining whether HCR provides a quantitative readout of relative mRNA abundance within single cells for a statistically significant number of image sets. Determining relative intensities using HCR can be complicated by several factors that contribute to background signals (ACS Nano 8, 4284). Auto-fluorescence (AF) in the embryos may prevent an experimenter from observing the true intensities directly in the experiments. Non-specific amplification (NSA) of hairpins and non-specific detection (NSD) of the targets can also corrupt the true signal measurement. All of these controls were presumably run by the authors and would be valuable additions to the paper.

https://doi.org/10.7554/eLife.14034.017

Author response

[Editors’ note: the author responses to the first round of peer review follow.]

Thank you for considering our manuscript for review entitled “Noise modulation in retinoic acid signaling sharpens segmental boundaries of gene expression in the zebrafish hindbrain”. We have addressed all of the comments, both experimentally and in textual revisions. We appreciate the importance of the more detailed spatial and temporal analyses requested both by reviewers and editors (Editor’s comments #1) and we have addressed these experimentally and/or computationally where possible. However, as we explain below there are no morphological landmarks of individual rhombomeres at the stages when most of our measurements were performed, and fluorescent transgenes that mark rhombomeres at late gastrula stages (when segments are sharpening) interfere with the RA emission and therefore with the FLIM measurements. The FLIM technique is based on spontaneous autofluorescence of RA and any fluorescent marker will strongly perturb the basic FLIM- phasor fluctuation analysis. We have clarified these limitations of the technique in the text, and we have managed to perform a more detailed analysis of gradient shape with additional FLIM and computational analyses, as requested (Editors comment #2). We also now include data to address concerns regarding quantification of the HCR data in 3D and at the level of single cells (Editors comment #3), as well as the lack of controls for these experiments (Reviewer 3, major comment #1). Overall, the reviewers agree that these are novel findings that improve our understanding about the function of morphogen gradients and an understudied dimension of cell signaling. Following are our specific responses to the points raised by editors and reviewers to our previously submitted manuscript.

The key revisions that came from the discussions among the reviewers are:

1) Indicate if their RA measurements show significant shifts in the position of the anterior boundary of RA as time proceeds (as was seen with RARE-lacZ in mouse where RA activity is first at the r2/r3 boundary, then at r3/r4, then at r4/r5) or do they see a more noisy decline in RA as time proceeds since they are detecting RA itself rather than RA activity. (This may require more imaging of the gradients).

As we discussed in the overall response, we would love to do these more detailed spatial and temporal analyses. However, fluorescent transgenes that mark rhombomeres at these early stages interfere with FLIM measurements of spontaneous RA autofluorescence. We tried to address the issue of posterior shifts in the gradient by collecting a series of successive FLIM measurements within individual live embryos, leaving them embedded in agar on the microscope to retain the field of view, but this failed because of the excessive laser exposure. These technical limitations were not clearly discussed in the original manuscript and are now clarified in the text (paragraph one, subheading “Fluorescence Lifetime Imaging (FLIM) measures endogenous RA directly in vivo”).

2) Describe the shape of the RA relative abundance in the zebrafish hindbrain as a function of A-P position.

This is an excellent point. We evaluated gradient shape in more detail with additional FLIM measurements and a least squares regression analyses (new Figure 1—figure supplement 2). Exponential, linear and quadratic curve fits were produced for the entire field, but because each of these fits explains similar amounts of the variance it is hard to differentiate between the curve types. We also tried to see if the shape of the gradient differs between anterior and posterior, by splitting the FLIM dataset at the 310 um point along the A-P axis and computed the fits on the two portions. The results were almost identical to the full fit and are shown in Figure 1—figure supplement 2, panels B and C. While it is difficult to distinguish between exponential, linear, and quadratic functions, the measured RA clearly fits a shape with characteristics shared by all three, strongly confirming the presence of a spatial gradient of RA. This is now discussed in the text (paragraph one, subheading “Fluorescence Lifetime Imaging (FLIM) measures endogenous RA directly in vivo”)

3) Show single cell resolution at the presumptive rhombomere boundaries. They also need quantify the HCR data in a linear range and at the level of single cells. (Perform more HCR in situ experiments).

All HCR analyses were performed on raw 3D data and we can provide movies showing the 3D volumes at the editors/reviewers’ discretion. Z-projections and contrast enhancement were performed post-analysis to simplify presentation. This has been clarified in the revised manuscript (paragraph one, subheading “Functional roles for noise in RA target gene expression”; see also response to reviewer #2, item 2). At the onset of krox20 expression, levels are very low. Raw projections are presented here in Author response image 1, which we can add as a supplement or replace Figure 4A (top panel) at the editors/reviewers’ discretion. This has also been further clarified in the figure legend.

4) How do the authors determine S without the hoxb1 expression or another marker that defines the adjacent rhombomere segment? The sharpness index (S) in their Zhang Mol Sys Biol 2012 paper defined as 'the mean location of the boundary between hoxb1 and krox20 expression domains as the intersection of their distributions at 50% of the normalized value. A decrease in S over time indicates noise attenuation and a sharper boundary. (Perform more imaging and in situ experiments).

In Zhang et al. 2012, (Figure 4F) we compared the sharpness index (S) calculated based on models incorporating both krox20 and hoxb1a with the sharpness measured experimentally using krox20 expression alone and found them to be in good agreement. Therefore, in this manuscript we measure the boundary between krox20 positive and negative cells. This is now better explained in the text (see response to Reviewer #2, item 3).

Reviewer #1:

Specific Points to Address:

1) Paragraph one, subheading “Fluorescence Lifetime Imaging (FLIM) measures endogenous RA directly in vivo” – the authors could add that their observation of a declining RA concentration from posterior to anterior hindbrain is consistent with the pattern of RARE-lacZ expression along the hindbrain of late-gastrulation stage mouse embryos (Sirbu, Development 2005).

We have added the Sirbu et al. (2005) reference (paragraph two, Introduction).

2) Paragraph two, subheading “Fluorescence Lifetime Imaging (FLIM) measures endogenous RA directly in vivo” – how do the authors results on differential RA concentration along the hindbrain A-P axis over time compare with results from mouse RARE-lacZ suggesting that RA activity experiences shifting hindbrain boundaries from anterior to posterior as development proceeds (Sirbu Development 2005)?

We address this in the Response to Editor’s comments #1.

3) Paragraph two, subheading “Functional roles for noise in RA target gene expression” – it would help if the authors further discuss whether their analysis of Cyp26a1 (an RA-degrading enzyme) supports a role for Cyp26 in generating rhombomere boundaries like suggested in other studies (such as Hernandez, Development 2007).

Additional discussion of Cyp26s has been added, as well as the Hernandez et al. (2007) reference (Introduction).

Reviewer #2:

1) The authors argue this is the first method to directly measure RA concentration levels in the living embryo and only briefly mention a previous FRET-reporter based approach which makes this claim (Shimozono et al., Nature, 2013) and was used to measure RA concentration in the zebrafish hindbrain. This can easily be fixed with more description in the Introduction and a revision of the wording.

While the FRET-reporters for RA (GEPRAs) directly bind RA, the measurements made in that paper are of the fluorescent FRET reporters and therefore inherently indirect. Our methodology directly measures the endogenous autofluorescence of RA, making it the first truly direct method. The text has been modified (subheading “FLIM measurements reveal high noise levels in RA”).

However, what is troubling here is that the authors appear to move back and forth between two distinctly different tissue architectures (neural ectoderm (2D) vs hindbrain (3D) in their measurements of RA concentration and noise levels. That is, we don't know the shape of the RA relative abundance in the zebrafish hindbrain as a function of A-P position. Also, the result that Crabps modulates noise in RA is again based on measurements made in the neural ectoderm and not the hindbrain.

The “neural ectoderm” in this case refers to the region of the ectoderm of the gastrula stage embryo that will form the hindbrain, based on previous fate maps generated at mid-gastrula stages (8-8.5 hpf; Woo and Fraser (1995) – Development 121, 2595). We (and others) have shown previously that these are the critical stages for RA in hindbrain segmentation (White et al., 2007; Hernandez et al., 2007). FLIM measurements are 2D and performed on individual confocal slices. FLIM requires a high numerical aperture objective (40X/NA 1.2) – that intrinsically has a short working distance – making it impossible to perform the measurements at lower magnification. Fortunately at these stages the neural ectoderm is extremely thin (2-3 cells thick). An explanation of the limitations of the technique has been added to the Phasor-FLIM supplementary text (paragraph one, subheading “Phasor-FLIM”).

2) HCR is a more sensitive method to detect mRNA expression that the authors use to examine sharpening/fuzziness of Krox20 expression in response to changes in RA levels. This is precisely the type of experiment to perform in response to their computational modeling simulations of noise-induced switching and boundary sharpening. Unfortunately, the authors completely under-utilize this method and this brings their data and conclusion into question. That is, the HCR images (z-projections) are over-exposed and should be of high enough quality to present an accurate 3D spatial representation of the subregions near presumptive rhombomere boundaries (instead of reducing to a 2D analysis and segmentation that does not include the cell membrane outlines as a 3D volume). Further, this conclusion really begs for single cell resolution at the presumptive rhombomere boundaries which is not clear. Overall, this is a poor presentation compared with the more elegant Phasor-Flim analysis.

We performed all HCR analyses on raw 3D data. Movies showing the 3D volume of representative datasets can be included at the editors/reviewers’ discretion. We generated Z-projections and enhanced contrast post-analysis to simplify presentation. At the onset of krox20 expression, levels are very low and the background is high. Raw projections are presented here in Author response image 1, which we can add as a supplement or replace Figure 4A (top panel) at the editors/reviewers’ discretion. This has also been further clarified in the figure legend.

Author response image 1
Download asset Open asset
Raw (not contrast enhanced) z-projections of the 3D stacks utilized to quantify the HCR data (Krox20 gene expression and boundary sharpness).
https://doi.org/10.7554/eLife.14034.015

Reviewer #3:

In the subsection “Functional roles for noise in RA target gene expression”, the authors state: “To determine how altering RA levels influences noise in gene expression within the hindbrain we disrupted Crabp2a or Cyp26a1 and assayed expression of krox20 in r3 and r5.”

Each MO treatment seems to have had an effect on krox20 expression. Considering that MO could have broad effects on RNA expression levels, where any MOs unrelated to the RA pathway injected and analyzed.

Due to the high cost of MO and HCR reagents, we did not perform experiments with MOs unrelated to the RA pathway. However, the effects of the Crabpa2 and Cyp26a1 had distinct effects on RA noise levels that were opposite to the overexpression obtained by RNA injections. Furthermore, we analyzed the effects of these MOs and RNAs on another RA target gene (hoxb1a) by HCR and found that they caused no significant changes (Author response image 2). These results can be included as a part of Figure 4 —figure supplement if the editors/reviewers prefer.

Author response image 2
Download asset Open asset
Mean-centered analysis of hoxb1a expression of a subset of cells for r3 and r5 from 3 randomly selected embryos for each indicated treatment.

The results show no significant differences between the treatments and the WT controls

https://doi.org/10.7554/eLife.14034.016

It is not apparent that HCR is a reliable indicator of gene expression levels. This is a novel USE of HCR, so proper controls should be considered. What other RNAs besides krox20 were measured? Were other RNAs that should not be affected and presumed to be affected by altering RA levels within the hindbrain also examined? These could also be measured within the other rhombomeres.

HCR is an established technique that linearly amplifies the signal of an RNA target (Dirks and Pierce, 2004; molecularinstruments.org; Choi et al., 2010; 2014). As mentioned above, we also analyzed the RNA of hoxb1a in r3, r4 and r5 (only r3 and r5 data are presented here – Author response image 2) at early stages when its expression is low and uniform across the hindbrain field. The MO and RNA injections had no significant effects on hoxb1a expression as assayed by HCR.

Is it possible to use the Tg(shhb:KalTA4,UAS-E1b:mCherry) transgenic zebrafish to compare cherry protein vs. RNA expression levels in the rhombomeres as a quick control?

As mentioned above, we include hoxb1a as the control to address this concern.

The HCR images shown in Figure 4A appear saturated, which would prevent accurate quantitation of krox20 RNA levels.

We agree and address this as described in the response to Reviewer 2, comment #2 and in Author response image 1.

[Editors' note: the author responses to the re-review follow.]

Major Concern:

The major concern deals centers about the HCR analyses. It is the opinion of the reviewers that the authors’ claim of using HCR for the relative quantification of target mRNAs is novel and important and thus requires solid data to prove that their approach is indeed quantitative. As presented the HCR experiments are difficult to understand and important controls are lacking. The authors should include 1 control data point. That is, no initiator probe + hairpins to show that there is no non-specific signal.

We appreciate the concern raised by the reviewers and we have done this control, which produces no non-specific signal (at precisely the same settings on the Leica confocal microscope that was used for all of our images), which we now mention on subheading “Hybridization Chain Reaction (HCR)”. We have also expanded the description of HCR in the Materials and methods and include a 3D rendering (Video 1). Further evidence for specificity in the hairpins includes: 1) the signal outside of rhombomeres 3 and 5 (for Krox20 probes) is very low and evenly distributed along the sample (see Video 1), 2) single initiator controls give comparable background, and 3) Hoxb1a initiators show a distinct pattern.

The authors responded to the reviewers’ previous comments with the following statement:

“HCR is an established technique that linearly amplifies the signal of an RNA target (Dirks and Pierce, 2004; molecularinstruments.org; Choi et al., 2010; 2014). As mentioned above, we also analyzed the RNA of hoxb1a in r3, r4 and r5 (only r3 and r5 data are presented here – Author response image 2) at early stages when its expression is low and uniform across the hindbrain field. The MO and RNA injections had no significant effects on hoxb1a expression as assayed by HCR.” There is disagreement with this response. Choi et al. 2014 (ACS Nano 8, 4284) is the most relevant paper since it describes the use of DNA probes to detect RNA targets and describes the method followed by the authors. However, the Choi paper does not demonstrate HCR's capability of doing relative (or absolute) quantification of target mRNAs. Choi et al. compared signal intensities and signal-to-background between one- and two-initiator probes using multiplex approaches. Thus, the authors’ claim of using HCR for the relative quantification of target mRNAs is novel and important and thus requires a higher burden of proof that it is indeed quantitative. The basic experimental framework put forth by the authors is based upon the idea of imaging the HCR signals from the tissue/embryonic region of interest (hindbrain r3-5) and examining whether HCR provides a quantitative readout of relative mRNA abundance within single cells for a statistically significant number of image sets. Determining relative intensities using HCR can be complicated by several factors that contribute to background signals (ACS Nano 8, 4284). Auto-fluorescence (AF) in the embryos may prevent an experimenter from observing the true intensities directly in the experiments. Non-specific amplification (NSA) of hairpins and non-specific detection (NSD) of the targets can also corrupt the true signal measurement. All of these controls were presumably run by the authors and would be valuable additions to the paper.

These issues have now been addressed in the expanded HCR description in Materials and methods.

https://doi.org/10.7554/eLife.14034.018

Article and author information

Author details

  1. Julian Sosnik

    1. Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    2. Center for Complex Biological Systems, University of California, Irvine, Irvine, United States
    3. Department of Interdisciplinary Engineering, Wentworth Institute of Technology, Boston, United States
    Contribution
    JS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.

  2. Likun Zheng

    1. Center for Complex Biological Systems, University of California, Irvine, Irvine, United States
    2. Department of Mathematics, University of California, Irvine, Irvine, United States
    Contribution
    LZ, Conception and design, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.

  3. Christopher V Rackauckas

    1. Center for Complex Biological Systems, University of California, Irvine, Irvine, United States
    2. Department of Mathematics, University of California, Irvine, Irvine, United States
    Contribution
    CVR, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5850-0663

  4. Michelle Digman

    1. Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    2. Center for Complex Biological Systems, University of California, Irvine, Irvine, United States
    3. Department of Biomedical Engineering, University of California, Irvine, Irvine, United States
    Contribution
    MD, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.

  5. Enrico Gratton

    1. Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    2. Center for Complex Biological Systems, University of California, Irvine, Irvine, United States
    3. Department of Biomedical Engineering, University of California, Irvine, Irvine, United States
    Contribution
    EG, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.

  6. Qing Nie

    1. Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    2. Center for Complex Biological Systems, University of California, Irvine, Irvine, United States
    3. Department of Mathematics, University of California, Irvine, Irvine, United States
    Contribution
    QN, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.

  7. Thomas F Schilling

    1. Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    2. Center for Complex Biological Systems, University of California, Irvine, Irvine, United States
    Contribution
    TFS, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    For correspondence
    tschilli@uci.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1798-8695

Funding

National Institutes of Health (P50-GM76516)

  • Julian Sosnik
  • Likun Zheng
  • Christopher V Rackauckas
  • Michelle Digman
  • Enrico Gratton
  • Qing Nie
  • Thomas F Schilling

National Institutes of Health (P41-GM103540)

  • Enrico Gratton
  • Michelle Digman

National Institutes of Health (R01-GM107264)

  • Qing Nie
  • Thomas F Schilling

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors thank Arthur Lander for critical reading of the manuscript and funding, Arul Subramanian and Ines Gehring for assistance with HCR, as well as Scott Fraser, Zeba Wunderlich, Catherine McCusker and members of the Schilling lab for their constructive criticisms. This work was partly supported by grants from the NIH/NIGMS to QN and TS (R01-GM107264), MD and EG (P41-GM103540) and MD, EG, QN, and TS (P50-GM76516).

Ethics

Animal experimentation: The study was performed in strict accordance to the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#2000-2149) of the University of California, Irvine. The renewal of this protocol was approved by the IACUC (Animal Welfare Assurance #A3416.01) on December 11, 2015. All animal experiments were performed on embryos derived from natural breedings and very effort was made to minimize suffering.

Reviewing Editor

  1. Robb Krumlauf, Stowers Institute for Medical Research, United States

Version history

  1. Received: December 24, 2015
  2. Accepted: March 11, 2016
  3. Version of Record published: April 12, 2016 (version 1)

Copyright

© 2016, Sosnik et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Julian Sosnik
  2. Likun Zheng
  3. Christopher V Rackauckas
  4. Michelle Digman
  5. Enrico Gratton
  6. Qing Nie
  7. Thomas F Schilling
(2016)
Noise modulation in retinoic acid signaling sharpens segmental boundaries of gene expression in the embryonic zebrafish hindbrain
eLife 5:e14034.
https://doi.org/10.7554/eLife.14034

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