A framework for quantification and physical modeling of cell mixing applied to oscillator synchronization in vertebrate somitogenesis

In development and disease, cells move as they exchange signals. One example is found in vertebrate development, during which the timing of segment formation is set by a ‘segmentation clock’, in which oscillating gene expression is synchronized across a population of cells by Delta-Notch signaling. Delta-Notch signaling requires local cell-cell contact, but in the zebrafish embryonic tailbud, oscillating cells move rapidly, exchanging neighbors. Previous theoretical studies proposed that this relative movement or cell mixing might alter signaling and thereby enhance synchronization. However, it remains unclear whether the mixing timescale in the tissue is in the right range for this effect, because a framework to reliably measure the mixing timescale and compare it with signaling timescale is lacking. Here, we develop such a framework using a quantitative description of cell mixing without the need for an external reference frame and constructing a physical model of cell movement based on the data. Numerical simulations show that mixing with experimentally observed statistics enhances synchronization of coupled phase oscillators, suggesting that mixing in the tailbud is fast enough to affect the coherence of rhythmic gene expression. Our approach will find general application in analyzing the relative movements of communicating cells during development and disease.Fil: Uriu, Koichiro. Kanazawa University; JapónFil: Bhavna, Rajasekaran. Max Planck Institute of Molecular Cell Biology and Genetics; Alemania. Max Planck Institute for the Physics of Complex Systems; AlemaniaFil: Oates, Andrew C.. Francis Crick Institute; Reino Unido. University College London; Reino UnidoFil: Morelli, Luis Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina. Max Planck Institute for Molecular Physiology; Alemania. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentin

Analysis of Movement of Cellular Oscillators in the Pre-somitic Mesoderm of the Zebrafish Embryo

During vertebrate embryo development, the body axis is subdivided into repeated structures, called somites. Somites bud off from an un-segmented tissue called the pre-somitic mesoderm (PSM) in a sequential and periodic manner, tightly controlled by an in built molecular clock, called the "segmentation clock". According to current understanding, the clock is comprised of: (i) an autonomous cellular oscillator consisting of an intracellular negative feedback loop of Her genes within the PSM cells, (ii) Delta-ligand and Notch-receptor coupling that facilitates synchronization of oscillators among the PSM cells, (iii) Tissue level waves of gene expression that emerge in the posterior PSM and move coherently towards anterior, leading to global arrest of oscillations in the form of somites. However, the movement of cellular oscillators within the PSM before the formation of somitic furrows, a prominent feature of the tissue as observed through this work has not been experimentally considered as a constituent of the segmentation clock so far. Our work aims to incorporate movement of cellular oscillators in the framework of the segmentation clock. It is well known from theoretical studies that the characteristics of relative motion of oscillators affect their synchronization properties and the patterns of oscillations they form. Particularly, theoretical studies by Uriu et al., PNAS (2010) suggest that cell movements promotes synchronization of genetic oscillations. Here, we established experimental techniques and image analysis tools to attain quantitative insight on (i) diffusion co-efficient of cellular oscillators, (ii) dynamics of a population of oscillators, within the PSM aiming towards concomitant understanding of the relationship between movement and synchronization of cellular oscillators. In order to quantitatively relate cellular oscillators and their motion within the PSM, I established imaging techniques that enabled visualization of fluorescenctly labeled nuclei as readouts of cell positions in live embryo, and developed dedicated segmentation algorithm and implemented tracking protocol to obtain nuclei positions over time in 3D space. Furthermore, I provide benchmarking techniques in the form of artificial data that validate segmentation algorithm efficacy and, for the first time proposed the use of transgenic embryo chimeras to validate segmentation algorithm performance within the context of in vivo live imaging of embryonic tissues. Preliminary analysis of our data suggests that there is relatively high cell mixing in the posterior PSM, within the same spatial zone where synchronous oscillations emerge at maximum speed. Also, there are indications of gradient of cell mixing along the anterior-posterior axis of the embryo. By sampling single cell tracks with the help of nuclei markers, we have also been able to follow in vivo protein oscillations at single cell resolution that would allow quantitative characterization of coherence among a population of cellular oscillators over time. Our image analysis work flow allows testing of mutant embryos and perturbation of synchrony dynamics to understand the cause-effect relationship between movement and synchronization properties at cellular resolution. Essentially, through this work, we hope to bridge the time scales of events and cellular level dynamics that leads to highly coordinated tissue level patterns and thereby further our understanding of the segmentation clock mechanism

A deep learning framework for quantitative analysis of actin microridges

Abstract Microridges are evolutionarily conserved actin-rich protrusions present on the apical surface of squamous epithelial cells. In zebrafish epidermal cells, microridges form self-evolving patterns due to the underlying actomyosin network dynamics. However, their morphological and dynamic characteristics have remained poorly understood owing to a lack of computational methods. We achieved ~95% pixel-level accuracy with a deep learning microridge segmentation strategy enabling quantitative insights into their bio-physical-mechanical characteristics. From the segmented images, we estimated an effective microridge persistence length of ~6.1 μm. We discovered the presence of mechanical fluctuations and found relatively greater stresses stored within patterns of yolk than flank, indicating distinct regulation of their actomyosin networks. Furthermore, spontaneous formations and positional fluctuations of actin clusters within microridges were associated with pattern rearrangements over short length/time-scales. Our framework allows large-scale spatiotemporal analysis of microridges during epithelial development and probing of their responses to chemical and genetic perturbations to unravel the underlying patterning mechanisms

I-conotoxin superfamily revisited

The I-conotoxin superfamily (I-Ctx) is known to have four disulfide bonds with the cysteine arrangement C-C-CC-CC-C-C, and the members inhibit or modify ion channels of nerve cells. Recently, Olivera and co-workers (FEBS J. 2005; 272: 4178-4188) have suggested that the previously described I-Ctx should now be divided into two different gene superfamilies, namely, $I_1$ and $I_2$, in view of their having two different types of signal peptides and exhibiting distinct functions. We have revisited the 28 entries presently grouped as I-Ctx in UniProt Swiss-Prot knowledgebase, and on the basis of in silico analysis have divided them into $I_1$ and $I_2$ superfamilies. The sequence analysis has provided a framework for in silico annotation enabling us to carry out computer-based functional characterization of the UniProtKB/TrEMBL entry Q59AA4 from Conus miles and to predict it as a member of the $I_2$ superfamily. Furthermore, we have predicted the mature toxin of this entry and have proposed that it may be an inhibitor of voltage-gated potassium channels

I-conotoxin superfamily revisited

The I-conotoxin superfamily (I-Ctx) is known to have four disulfide bonds with the cysteine arrangement C-C-CC-CC-C-C, and the members inhibit or modify ion channels of nerve cells. Recently, Olivera and co-workers (FEBS J. 2005; 272: 4178-4188) have suggested that the previously described I-Ctx should now be divided into two different gene superfamilies, namely, $I_1$ and $I_2$, in view of their having two different types of signal peptides and exhibiting distinct functions. We have revisited the 28 entries presently grouped as I-Ctx in UniProt Swiss-Prot knowledgebase, and on the basis of in silico analysis have divided them into $I_1$ and $I_2$ superfamilies. The sequence analysis has provided a framework for in silico annotation enabling us to carry out computer-based functional characterization of the UniProtKB/TrEMBL entry Q59AA4 from Conus miles and to predict it as a member of the $I_2$ superfamily. Furthermore, we have predicted the mature toxin of this entry and have proposed that it may be an inhibitor of voltage-gated potassium channels

Correction: Object Segmentation and Ground Truth in 3D Embryonic Imaging

[This corrects the article DOI: 10.1371/journal.pone.0150853.]

Post-processing steps to separate under-segmented objects.

<p>Top panel is a result of the DS algorithm and the bottom panel are the results obtained after post-processing steps for the single stack shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150853#pone.0150853.g001" target="_blank">Fig 1</a>. (a) Stem plot of the volumes of segmented objects obtained after DS algorithm. The black dashed vertical line (at 237 voxels) indicates the empirically calculated ‘Fused object volume’ threshold value above which the objects (with volumes > 160 μm³) were subjected to post-processing steps. Position of the dashed line depends upon the DS algorithm and noise filter parameters and volumes of segmented objects (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150853#pone.0150853.s008" target="_blank">S1 Text</a>). (b) A 3D rendered object from the left side of the black dashed line in (a) correctly segmented by the DS algorithm as indicated with the centroid position in red. (c) An example of 3D rendering of a fused object, as indicated by the position of the centroid in red, from the right side of the black dashed line in (a). (d) Frequency distribution of voxels in <i>x</i>, <i>y</i> and <i>z</i> direction for the correctly segmented nucleus in (b) obtained after DS algorithm. Note that a single nucleus has a unimodal distribution of voxels in each direction. (e) Frequency distribution of voxels in <i>x</i>, <i>y</i> and <i>z</i> direction for the fused case in (c). The distribution in the <i>z</i>-direction indicates 3 peaks marked by asterisks (*), suggesting three nuclei fused in the <i>z</i>-direction. (f) 3D rendered object shown in (b) is post-processed with K-means (centroid position in blue) and GMM (centroid position in green). All the three methods give the same centroid position. (g) 3D rendered object shown in (c) is post-processed with K-means and GMM that find 3 new centroid positions indicated in blue and green thus segregating fused nuclei. (h) Stem plot after DS with post-processing steps. Blue and green stem plots represent segmented volumes after DS with K-means and DS with GMM, respectively. Post-processing steps tend to reduce under-segmentation. (i) Dependence of the average processing time of post-processing steps GMM and K-means on the maximum number of local peaks <i>mnp</i>. Processing time was measured for each single object with a given <i>mnp</i> value. The error bars indicate standard deviations.</p

Assessment of 3D segmentation using synthetic image data.

<p>(a) A stack of slices of a representative synthetic image produced with characteristic SNR and object density. (b) Positions of true centroids of objects (red dots) and centroid positions detected by the Derivatives Sum algorithm (blue dots) in the synthetic image. (c) Dependence of sensitivity and precision of algorithms on the density of objects in synthetic images. SNR = 5. Gaussian filter (<i>σ =</i> 0.5, window size = 5×5 squared pixels) and median filter (window size = 3×3) were used here for de-noising. Parameters in DS are <i>α = β = ε = γ = δ =</i> 1, and <i>σ</i>_<i>g</i> <i>=</i> 1.1. Scale bars = 10 μm.</p

Assessment of 3D segmentation using experimental image data.

<p>(a) Schematic illustration of transplantation experiment. Cells in a donor embryo at blastula stage expressing two histone variants fused to GFP and mCherry (<i>h2Aflv-gfp</i>/ <i>h2Aflv-mCherry</i>) are colored orange. (b) Bright-field image of a 13 somite-stage chimeric embryo. The white box indicates the tailbud. (c) 15 somite-stage chimeric embryo in gfp (left) and mcherry (middle) channels, and merged (right). White box in each image indicates the cropped region (50×50 squared pixels) used for the sensitivity analysis. Inset image is magnification of boxed region. (d) Sensitivity plot over density for five cropped images from four chimeric embryos. Each symbol and error bar indicates the temporal average and standard deviation, respectively, of the sensitivity over 10 time frames for a cropped image. Parameters in DS are <i>α = β = γ = δ</i> = 1, <i>ε =</i> 2, and <i>σ</i>_<i>g</i> <i>=</i> 1.2. De-noising filters; Gaussian filter (<i>σ =</i> 0.5, window size = 5×5 squared pixels) and median filter (window size = 3×3), Lucy-Richardson deconvolution filter with <i>σ =</i> 0.5, non-linear isotropic diffusion filter (<i>κ =</i> 50, <i>n =</i> 5). Both channels for all five cropped images were processed with same parameter values.</p