Global analysis of cell behavior and protein dynamics reveals region-specific roles for Shroom3 and N-cadherin during neural tube closure

  1. Austin T Baldwin
  2. Juliana H Kim
  3. Hyemin Seo
  4. John B Wallingford  Is a corresponding author
  1. Department of Molecular Biosciences, University of Texas at Austin, United States

Abstract

Failures of neural tube closure are common and serious birth defects, yet we have a poor understanding of the interaction of genetics and cell biology during neural tube closure. Additionally, mutations that cause neural tube defects (NTDs) tend to affect anterior or posterior regions of the neural tube but rarely both, indicating a regional specificity to NTD genetics. To better understand the regional specificity of cell behaviors during neural tube closure, we analyzed the dynamic localization of actin and N-cadherin via high-resolution tissue-level time-lapse microscopy during Xenopus neural tube closure. To investigate the regionality of gene function, we generated mosaic mutations in shroom3, a key regulator or neural tube closure. This new analytical approach elucidates several differences between cell behaviors during cranial/anterior and spinal/posterior neural tube closure, provides mechanistic insight into the function of shroom3, and demonstrates the ability of tissue-level imaging and analysis to generate cell biological mechanistic insights into neural tube closure.

Editor's evaluation

This manuscript by Baldwin and colleagues on vertebrate neural tube closure will be of interest to developmental and cell biologists studying tissue morphogenesis as well as human geneticists focusing on neural tube defects. It is timely, as it introduces a new technology for large-scale imaging of cell behaviours in large embryos. Specifically, it uses advanced image analysis to quantitatively describe and correlate active cell behaviours and localization dynamics of key cytoskeletal and adhesion proteins driving a central step of neural tube closure. Data analysis is detailed and followed by careful conclusions.

https://doi.org/10.7554/eLife.66704.sa0

Introduction

Congenital birth defects are the number one biological cause of death for children in the US, and neural tube defects (NTDs) represent the second most common class of human birth defect (Murphy et al., 2018; Wallingford et al., 2013). NTDs represent a highly heterogenous group of congenital defects in which failure of the neural folds to elevate or fuse results in a failure of the skull or spine to enclose the central nervous system (Wallingford et al., 2013). While genetic analyses in both humans and animal models have revealed dozens of genes necessary for normal neural tube closure, several key questions remain.

One central unanswered question relates to the regional heterogeneity of both normal neural tube closure and pathological NTDs. For example, the collective cell movements of convergent extension dramatically elongate the hindbrain and spinal cord of vertebrates, but not the midbrain and forebrain (Nikolopoulou et al., 2017; Wallingford et al., 2013). Accordingly, disruption of genetic regulators of convergent extension such as the planar cell polarity (PCP) genes results in failure of neural tube closure in posterior regions of the neural ectoderm, but not anterior (Kibar et al., 2001; Wang et al., 2006). Conversely, the shroom3 gene is implicated in apical constriction, a distinct cell behavior that drives epithelial sheet bending, and disruption of shroom3 elicits highly penetrant defects in anterior neural tube closure, but only weakly penetrant defects in the posterior (Haigo et al., 2003; Hildebrand and Soriano, 1999). This regional deployment of apical constriction in the anterior and convergent extension in the posterior during neural tube closure is poorly understood.

In addition, the underlying mechanisms of individual cell behaviors necessary for neural tube closure remain incompletely defined. While apical constriction is driven by actomyosin contraction, the precise site of actomyosin action during this process is unclear and constitutes a long-term problem in the field (Martin and Goldstein, 2014). For example, analysis of apical constriction during gastrulation in both Drosophila and Caenorhabditis elegans has shown integration of discrete junctional and medio-apical (‘medial’) populations of actomyosin (Coravos and Martin, 2016; Martin et al., 2009; Roh-Johnson et al., 2012). Recent studies in frog and chick embryos have also described similar pulsed medial actomyosin-based contractions occurring during neural tube closure (Brown and García-García, 2018; Christodoulou and Skourides, 2015; Suzuki et al., 2017), but how those contractions are controlled and how they contribute to tissue-wide cell shape changes during neural tube closure are not known.

For example, Shroom3 is among the more well-defined regulators of apical constriction, being both necessary and sufficient to drive this cell shape change in a variety of cell types, including the closing neural tube (Haigo et al., 2003; Hildebrand, 2005; Plageman et al., 2010; Plageman et al., 2011b). Shroom3 is known to act via Rho kinase to drive apical actin assembly and myosin contraction (Das et al., 2014; Hildebrand, 2005; Nishimura and Takeichi, 2008; Plageman et al., 2011a). However, the relationships between Shroom3 and the medial and junctional populations of actin have not been explored.

An additional outstanding question relates to the interplay of actomyosin contractility and cell adhesion during apical constriction. The classical cadherin Cdh2 (N-cadherin) is essential for apical constriction during neural tube closure in Xenopus (Morita et al., 2010; Nandadasa et al., 2009), and shroom3 displays robust genetic interactions with n-cadherin in multiple developmental processes, including neural tube closure (Plageman et al., 2011b). Moreover, a dominant-negative N-cadherin can disrupt the ability of ectopically expressed Shroom3 to induce apical constriction in MDCK cells (Lang et al., 2014). Nonetheless, it is unclear if or how Shroom3 controls the interplay of N-cadherin and actomyosin during apical constriction. This is an important gap in our knowledge, because despite the tacit assumption that cadherins interact with each other and control actomyosin at cell-cell junctions, N-cadherin displays multiple cell-autonomous activities (Rebman et al., 2016; Sabatini et al., 2011). Intriguingly, several papers now demonstrate that extra-junctional cadherins at free cell membranes can engage and regulate the actomyosin cortex (Ichikawa et al., 2020; Padmanabhan et al., 2017; Sako et al., 1998; Wu et al., 2015).

Finally, though Shroom3 has been extensively studied in the context of cranial apical constriction, the gene is expressed throughout the neural plate (Haigo et al., 2003; Hildebrand and Soriano, 1999) and recent studies have also implicated Shroom family proteins in the control of convergent extension (McGreevy et al., 2015; Nishimura and Takeichi, 2008; Simões et al., 2014). Several studies indicate a genetic and cell biological interplay of Shroom3 and the PCP proteins (Durbin et al., 2020; McGreevy et al., 2015), and one study directly links PCP, apical constriction, and convergent extension (Nishimura et al., 2012). Conversely, some studies also suggest a role for PCP proteins in apical constriction (Ossipova et al., 2015).

Together, these studies highlight the complexity of neural tube closure, which is compounded by the sheer scale of the tissue involved. The neural ectoderm is comprised of hundreds to thousands of cells (depending on organism) and stretches from the anterior to posterior poles of the developing embryo. However, the vast majority of dynamic studies of cell behavior in the neural tube closure, including our own, have focused on small numbers of cells due to constraints of both imaging and image analysis.

Here, we used image-tiling time-lapse confocal microscopy to obtain over 750,000 individual measurements of cell behaviors associated with neural tube closure in Xenopus tropicalis. Using these data, we demonstrate that the cell biological basis of apical constriction differs substantially between the anterior and the posterior neural plate. The data further suggest that the crux of Shroom3 function lies not in actin assembly per se, but rather in the coupling of actin contraction to effective cell surface area reduction. Third, we demonstrate that the control of N-cadherin localization is a key feature of Shroom3 function during neural tube closure. Finally, we demonstrate that the incompletely penetrant posterior phenotypes related to shroom3 loss stem from dysregulation of both actin and N-cadherin localization. Overall, these findings (a) elucidate differences between cell behaviors during cranial/anterior and spinal/posterior neural tube closure, (b) provide new insights into the function of shroom3, an essential neural tube closure gene, and (c) demonstrate the power of large-scale imaging and analysis to generate both cell-level mechanistic insight and new hypotheses for exploring neural tube closure.

Results and discussion

High-content imaging of cell behavior and protein localization during vertebrate neural tube closure

X. tropicalis affords several advantages for imaging neural tube closure, as its cells are large and easily accessible; its culture conditions for imaging are no more complex than synthetic pond water held at room temperature; and its broad molecular manipulability allows examination of diverse fluorescent markers. We developed methods for confocal microscopy and image tiling to collect high-magnification datasets spanning broad regions of the folding neural ectoderm from embryos injected at blastula stages with mRNAs encoding fluorescent reporters (Figure 1A). At the onset of neurulation (approximately Nieuwkoop and Faber, 1994; Nieuwkoop and Faber, 1994, stages 12.5–13), embryos were positioned to image either the anterior or the posterior regions of the neural ectoderm. We then established a pipeline by which cells captured in our movies were segmented using Tissue Analyzer, CSML, and EPySeg (Aigouy et al., 2020; Aigouy et al., 2016; Ota et al., 2018), yielding a map of both the apical cell surfaces and all individual junctions (Figure 1B). Finally, we built pipelines to process these data with Tissue Analyzer (Aigouy et al., 2010; Aigouy et al., 2016) and Fiji (Schindelin et al., 2012) to quantify both cell behaviors and the localization of fluorescent protein reporters across the neural plate and across neurulation.

Figure 1 with 1 supplement see all
Tissue-level imaging and analysis of contractile protein dynamics during neural tube closure in Xenopus.

(A) Schematic of mRNA injections and subsequent imaged regions of the Xenopus tropicalis embryo. (B) Cell segmentation and tracking workflow. Binary segmentation, cell surface tracking, and cell junction tracking were all generated using Tissue Analyzer. (C) Example Xenopus cells with analyzed subcellular domains labeled. Orange label = medial, cyan labels = junctional/junctions. (D) Schematic and N values of whole cell measurements. (E) Schematic and N values of individual cell junction measurements.

With these methods in place, we considered three interrelated problems in neural tube closure biology: First, the incidence and form of NTDs differ widely between the brain and spinal cord (Nikolopoulou et al., 2017; Wallingford et al., 2013), yet our understanding of the dynamic cell behaviors in the two regions remains limited. Second, a unified mechanism for apical constriction has emerged in recent years involving the coordinated action of two discrete populations of actomyosin positioned either at apical cell-cell junctions or the medial apical cell surface (Coravos and Martin, 2016; Martin and Goldstein, 2014; Martin et al., 2009; Roh-Johnson et al., 2012), but the extent to which this model, developed in Drosophila and C. elegans, applies to vertebrates is unknown. Third, N-cadherin is essential for apical constriction in Xenopus (Nandadasa et al., 2009), but its functional interplay with junctional and/or medial actin is unknown. Accordingly, we made movies focused on either the anterior or posterior neural plate during neurulation, imaging the fluorescent actin biosensor LifeAct-RFP (Riedl et al., 2008; Figure 1A, magenta) and N-cadherin-GFP (Figure 1A), and independently quantified the mean fluorescent intensity of junctional and medial populations for both reporters (Figure 1C–D). To account for noise in these measurements, we have smoothed the data within individual cell tracks by averaging the data over a 7-frame window (Figure 1—figure supplement 1A, B).

In total, our dataset is comprised of ~250,000 observations of apical cell surfaces from over 3700 cells and ~580,000 observations from over 13,000 individual cell-cell junctions across nine embryos (Figure 1D, E). Images were collected at a rate of 1 frame/observation per minute over 1–2 hr, spanning roughly stages 13–18. Initial cell sizes and fluorescent varied among cells and embryos due to both natural variation and staging as well as variation introduced via mRNA microinjection. Because our primary interest is in the dynamics of apical constriction, we standardized many of the parameters in our analyses to account for variation in cell size and fluorescent intensity. This standardization involved mean-centering the data for each individual cell track to zero and then dividing the resulting mean-centered values by the standard deviation of each track, such that the standardized parameters are now measured in standard deviations rather than square microns or arbitrary units, allowing for simpler comparisons of overall changes in parameters between cells over time (Figure 1—figure supplement 1A, D).

We first performed an initial test of the validity of our approach, examining our dataset for well-known trends expected for neural epithelial cells during neural tube closure, namely an overall decrease in apical area and an overall increase in apical actin intensity. The heat maps in Figure 2A, B reveal that cells in both anterior and posterior regions generally reduce their apical surface area and increase medial actin intensity, as expected. These overall trends are backed by examination of individual cells, as shown for specific representative cells in Figure 2C. Overall, this analysis suggests that our pipeline is generally effective for quantifying cell shape and actin intensity over time during neural tube closure.

Tissue-level analysis of individual cell behaviors reveals dynamic heterogeneity.

(A) Overall change (Δ) in apical surface area (standardized) across anterior (left) and posterior (right) control embryos. (B) Overall change in medial LifeAct/actin localization (standardized) across anterior (left) and posterior (right) control embryos. Circle and triangle in A and B denote a representative cell for each embryo. Scale bars = 100 µm. (C) Standardized apical surface area (black) and medial actin (red) over time in representative cells from anterior (left/circle) and posterior (right/triangle). s.d. = standard deviation.

Distinct patterns of apical constriction behavior, actin assembly, and N-cadherin localization in anterior and posterior regions of the closing neural tube

Our dataset revealed several interesting trends. First, while bulk measurements showed a decrease in apical area in both anterior and posterior regions over time (Figure 3A), we observed distinct region-specific distributions for these changes. For example, in the anterior, the vast majority of cells displayed significant apical constriction, and this constriction proceeded gradually across neurulation (Figure 3A and A’, left). In the posterior, however, a much smaller proportion of posterior cells constricted and a substantial number actually dilated (Figure 3A, right). Moreover, constriction of cells in the posterior was initiated very late in neurulation and proceeded very rapidly (Figure 3A’, right).

Figure 3 with 1 supplement see all
Cells in the anterior and posterior neural ectoderm both apically constrict but differ in their contractile protein dynamics.

Tissue-level cell size and protein localization dynamics from control embryos in Figure 2. (X) Distribution of overall change (Δ) in displayed parameter (standardized) among cells from control embryos. Horizontal lines on density plots/violins indicate quartiles of distribution. Black circles are individual cells. Statistical comparisons performed by Kolmogorov-Smirnov (KS) test. (X’) 2D density plots of standardized variable versus time for all observations/cells in each control embryo in Figure 2. Green points are measurements from the representative cells denoted in Figure 2. s.d. = standard deviation.

We further observed that both medial and junctional actin intensity generally increased over time in both regions, with their temporal progressions being reciprocal to the changes in apical area described above (Figure 3B, B’, C, and C’, right). Again, these distributions were significantly different between the anterior and posterior regions, with cells in the spine having a significantly more heterogeneous distribution of actin accumulation outcomes (Figure 3B, C, left).

By far the most intriguing results related to the dynamics of N-cadherin localization, for which we observed two surprising patterns. First, in the anterior neural plate N-cadherin accumulated dramatically not only in the junctional region but also in the medial region (i.e. the free apical surface) (Figure 3D, D’, E and E’). Thus, N-cadherin localization closely parallels actin dynamics in the normal anterior neural plate. This result was surprising because classical cadherins such as N-cadherin are typically known for their action at cell-cell junctions. Nonetheless, immunostaining for endogenous N-cadherin in fixed embryos confirmed this medial accumulation in the apical surfaces of anterior neural ectoderm cells (Figure 3—figure supplement 1). Our dataset lacked the time resolution to determine precise patterns of N-cadherin movement during apical constriction, but in Z-projections of highly constricted cells, we observed N-cadherin signal not just coincident with, but also basal to, to the apical actin signal (Figure 4). This result is consistent with the emerging understanding of the cell-autonomous roles for cadherins in both actin organization and endocytosis (Ichikawa et al., 2020; Padmanabhan et al., 2017; Rebman et al., 2016; Sabatini et al., 2011; Sako et al., 1998; Wu et al., 2015).

Figure 4 with 1 supplement see all
N-cadherin localizes both at the apical surface and basally as well.

(A) XY (top row) and XZ (bottom row) projections of N-cadherin-GFP and LifeAct-RFP in the anterior neural ectoderm of a Xenopus tropicalis embryo. (B) XY (top panel) and XZ (bottom panel) projections of NCD-2 (monoclonal α-N-cadherin antibody) in the anterior neural ectoderm of a X. tropicalis embryo. Dashed cyan lines marks the position of the XZ projection.

The medial accumulation of both N-cadherin and actin led us to explore the localization of Shroom3 itself. No antibodies are available for Xenopus Shroom3, and gain-of-function effects during early development preclude analysis of tagged wild-type Shroom3 during neural tube closure. That said, ectopic Shroom3 clearly decorates both junctional and medial regions in diverse epithelial cells (Haigo et al., 2003; Kowalczyk et al., 2021; Lee et al., 2009). To gain insight into Shroom3 localization during neural tube closure, we imaged the localization of the a GFP-tagged Shroom3 construct lacking the c-terminal Rok-binding domain, similar to a construct previously used to explore Shroom dynamics in Drosophila (Farrell et al., 2017; Simões et al., 2014). In movies of the folding neural plate, the construct localized in a pattern essentially identical to actin, accumulating in both junctional and medial regions of the anterior neural plate (Figure 4—figure supplement 1).

Finally, we observed a strikingly different trend in the posterior neural plate, where N-cadherin dynamics did not closely parallel actin dynamics. In fact, neither junctional nor medial N-cadherin displayed significant accumulation in the posterior neural plate during the period of observation (Figure 3D, E), despite robust actin accumulation in this region (Figure 3B, C). Together, these data provide a comprehensive, quantitative description of apical constriction, actin dynamics and N-cadherin localization in the anterior and the posterior neural plate during Xenopus neural tube closure. The data further suggest that the mechanisms linking actin and N-cadherin to apical surface area differ in the two regions.

Mosaic mutation of Shroom3 reveals distinct anterior and posterior phenotypes in the neural ectoderm

The differences in cell behaviors we observed between anterior and posterior neural ectodermal regions reflect the region-specific nature of NTDs in both humans and animal models. To explore the relationships in more detail, we next turned to loss-of-function manipulation of shroom3, which is implicated in human NTDs and has variably penetrant effects on anterior and posterior neural tube closure (Deshwar et al., 2020; Haigo et al., 2003; Hildebrand and Soriano, 1999; Lemay et al., 2015).

F0 mutagenesis using CRISPR has recently emerged as a powerful tool in Xenopus and zebrafish, and mosaic crispants generated by targeted injections allow simultaneous observation of wild-type and crispant cellular phenotypes so that observations are automatically staged and synchronized (Aslan et al., 2017; Kakebeen et al., 2020; Kroll et al., 2021; Szenker-Ravi et al., 2018; Willsey et al., 2020). We therefore designed sgRNAs that effectively targeted the coding region of shroom3, approximately 28 amino acids from the 5’ end of the transcript, such that any indels generated by CRISPR targeting are likely to disrupt all functional domains of the Shroom3 protein (Figure 5—figure supplement 1A).

Using injection into the two dorsal-animal blastomeres at the 8-cell stage to target the neural plate, we demonstrated that our sgRNAs elicited both mutation of the shroom3 locus as well as the anterior neural tube closure defects expected based on results from knockdown in Xenopus using MOs (Haigo et al., 2003), as well as the results in mouse genetic mutants (Hildebrand and Soriano, 1999). As a critical negative control, injections of sgRNA without Cas9 protein had no effect (Figure 5—figure supplement 1C, D).

We next performed more targeted injections to generate mosaic embryos. To do so, we labeled the neural plate by injection of fluorescent reporters into both dorsal blastomeres at the 4-cell stage, and then injected a mixture of shroom3-targeted sgRNA, Cas9 protein, and membrane-BFP mRNA into one dorsal blastomere of 8-cell stage embryos (Figure 5A and see Figure 5—figure supplement 1). We then identified shroom3 crispant cells via membrane-BFP localization (Figure 5B). Because cell junction behavior may be altered at mosaic cell-cell interfaces (i.e. junctions between a control and a crispant cell), we excluded this relatively small number of cells from our analysis. Importantly, this mosaic F0 CRISPR-based approach also generally recapitulated the known phenotype of Shroom3 loss, as we observed gross failure of anterior neural tube closure.

Figure 5 with 3 supplements see all
Disruption of shroom3 via mosaic F0 CRISPR mosaic causes differential apical constriction phenotypes between regions of the neural ectoderm.

(A) Schematic of mosaic F0 CRISPR/Cas9 injections in Xenopus tropicalis embryos. (B) Workflow of identification and analysis of mosaic F0 crispants. (C) Top row, distribution of initial area (square microns) of tracked cells from anterior (left) and posterior (right) embryos. Lower row, distribution of final area (square microns) of tracked cells. (D) Distribution of overall change (Δ) in apical area (standardized) from all cells/embryos. In C and D, horizontal lines on density plots/violins indicate quartiles of distribution. Black circles are individual cells. Statistical comparisons performed by Kolmogorov-Smirnov (KS) test. Cells situated along the mosaic interface were excluded from these analyses. s.d. = standard deviation.

At the level of cell behaviors, we observed a surprising difference in anterior and posterior phenotypes. In the anterior region, shroom3 crispant cells displayed significantly enlarged apical surfaces at the onset of our imaging (~stage 13), and this phenotype grew more severe over time (Figure 5C, left); the majority of cells not only failed to constrict but instead dilated (Figure 5D, left). In the posterior, however, the majority of shroom3 crispant cells still strongly constricted, though collectively they displayed a mildly significant defect in apical constriction (Figure 5C, D, right). Thus, the magnitude of apical constriction defects in shroom3 crispant cells reflects the penetrance of NTDs in the anterior and posterior regions (Haigo et al., 2003; Hildebrand and Soriano, 1999).

Loss of Shroom3 uncouples actin dynamics from N-cadherin localization in the anterior neural ectoderm

Loss of Shroom3 disrupts apical actin assembly in the neural plate (Haigo et al., 2003; McGreevy et al., 2015), but the precise nature of this defect and whether or how it relates to junctional and/or medial actin is unknown. Likewise, N-cadherin is implicated in Shroom3 function and apical constriction (Lang et al., 2014; Nandadasa et al., 2009; Plageman et al., 2011b), but how this relates to actin dynamics is poorly defined. We therefore examined the relationship between apical constriction, actin dynamics, and N-cadherin dynamics, focusing first on the anterior neural plate.

We found that wild-type cells tended to increase both medial and junctional actin localization over time, as expected (Figure 6A–C, left). Consistent with the known role in apical actin accumulation (Haigo et al., 2003; Hildebrand, 2005), shroom3 crispant cells displayed significantly reduced accumulation of both medial and junctional actin (Figure 6B, C, blue violins). However, this effect was surprisingly mild, and in fact, the majority of shroom3 crispant cells actually increased both junctional and medial actin over the period of imaging (Figure 6B, C, blue violins).

Medial actin accumulation drives apical constriction while loss of shroom3 disrupts actin accumulation and constriction in the anterior neural ectoderm.

(A) Representative images of LifeAct/actin localization in control cells (left) and shroom3 crispant cells (right) from the anterior region of the neural ectoderm. Scale bar = 15 µm. (B) Distribution of overall change (Δ) in medial LifeAct/actin (standardized) from anterior cells. (C) Distribution of overall change (Δ) in junctional LifeAct/actin (standardized) from anterior cells. In B and C, horizontal lines on density plots/violins indicate quartiles of distribution, black circles are individual cells, and statistical comparisons performed by Kolmogorov-Smirnov (KS) test. (D and E) 2D distribution of changes in apical area and medial (D) or junctional (E) LifeAct/actin (both standardized). Percentages in white indicate the percentage of total cells in each quadrant. Statistical comparisons performed by Peacock test, a 2D implementation of the KS test. (F and G) 2D density plots of all observations of apical area versus medial (F) or junctional (G) LifeAct/actin for all cells within each group. Red lines indicate best-fit line through the observations. Statistics (r and p) are calculated for Pearson’s correlation. Cells situated along the mosaic interface were excluded from these analyses. s.d. = standard deviation.

In contrast to this surprisingly modest change in actin intensity (Figure 6B, C), bulk measurements revealed that shroom3 crispant cells displayed a profound failure to accumulate both junctional and medial N-cadherin, and in fact the majority of cells actually reduced N-cadherin levels (Figure 6A, D, and E, blue violins). Moreover, this effect was far more pronounced for the medial population of N-cadherin (Figure 6D). Thus, loss of Shroom3 elicits a substantially more severe effect on the dynamics of N-cadherin than of actin, apparently uncoupling the two.

Shroom3 links actin and N-cadherin dynamics to effective apical constriction in the anterior neural ectoderm

To explore these surprising results in more detail, we directly compared changes in apical area with changes in actin and N-cadherin intensity for each cell individually. In 838 control cells, we observed that the vast majority displayed a strong reduction in apical area and a strong increase in both junctional and medial actin intensity (Figure 7A, B). As noted in the bulk statistics above, the majority of 147 shroom3 crispant cells displayed increased actin intensity; however, these crispant cells displayed a bimodal distribution of changes in apical area, with some cells constricting and other cells dilating, yet even cells that increased their apical area after loss of Shroom3 nonetheless accumulated medial and junctional actin (Figure 7A, B). Thus, loss of shroom3 does not lead to loss of apical actin in the anterior neural plate but rather to a reduced accumulation of apical actin.

Medial N-cadherin accumulation is severely disrupted in anterior shroom3 crispant cells that fail to apically constrict.

(A) Representative images of N-cadherin localization in control cells (left) and shroom3 crispant cells (right) from the anterior region of the neural ectoderm. Scale bar = 15 µm. (B) Distribution of overall change (Δ) in medial N-cadherin (standardized) from anterior cells. (C) Distribution of overall change (Δ) in junctional N-cadherin (standardized) from anterior cells. In B and C, horizontal lines on density plots/violins indicate quartiles of distribution, black circles are individual cells, and statistical comparisons performed by Kolmogorov-Smirnov (KS) test. (D and E) 2D distribution of changes in apical area and medial (D) or junctional (E) N-cadherin (both standardized). Percentages in white indicate the percentage of total cells in each quadrant. Statistical comparisons performed by Peacock test, a 2D implementation of the KS test. (F and G) 2D density plots of all observations of apical area versus medial (F) or junctional (G) N-cadherin for all cells within each group. Red lines indicate best-fit line through the observations. Statistics (r and p) are calculated for Pearson’s correlation. Cells situated along the mosaic interface were excluded from these analyses. s.d. = standard deviation.

N-cadherin localization follows a very different trend, with shroom3 crispant cells consistently displaying apical surface dilation over time coupled to a strong reduction in medial N-cadherin intensity over time (Figure 7C). Junctional N-cadherin followed a similar, if less robust, trend (Figure 7D). Thus, loss of shroom3 anteriorly results in a loss of medial and junctional N-cadherin.

An advantage of our large-scale approach is that correlations between parameters provide a more granular view of observed phenotypes. For example, we plotted the correlation between standardized apical area and standardized actin intensity for all cells at all time points (N = ~75 k data points), which revealed a strong negative correlation between apical area and both medial and junctional actin intensity (Figure 8A, B). Interestingly, despite the relatively modest impact on bulk actin accumulation (Figure 6B, C), loss of shroom3 effectively abolished the normally strong correlation between apical area and actin intensity (Figure 8A, B). Likewise, we found that actin intensity was very strongly positively correlated with N-cadherin intensity in both medial and junctional populations, and this correlation was severely weakened by loss of shroom3 (Figure 8C, D).

Actin and N-cadherin accumulation are uncoupled in anterior shroom3 crispant cells.

(A and B) 2D density plots of all observations of medial (A) or junctional (B) LifeAct/actin versus N-cadherin for all cells within each group. Red lines indicate best-fit line through the observations. Statistics (r and p) are calculated for Pearson’s correlation. Cells situated along the mosaic interface were excluded from these analyses. s.d. = standard deviation.

Though further exploration of the issue will be required, our large-scale analysis nonetheless generates two interesting hypotheses. First, they suggest an extra-junctional role for medial N-cadherin in apical constriction in the anterior neural plate. Second, they suggest that shroom3 loss in the anterior neural plate does not prevent actin assembly per se, but rather uncouples medial actomyosin contractility from medial N-cadherin accumulation, and thus uncouples actin assembly from effective apical constriction.

A distinct mode of action for Shroom3 in the posterior neural plate

As noted above, shroom3 crispant cells in the posterior neural ectoderm displayed far milder defects in apical constriction over the period of imaging (Figure 5D, right). Nonetheless, Shroom3 loss can alone elicit low-penetrance spina bifida and can severely exacerbate spina bifida in combination with certain PCP mutants (Hildebrand and Soriano, 1999; McGreevy et al., 2015). We therefore explored our image data for insights into this phenotype. We found that both junctional and medial actin accumulated over the course of our imaging in control cells in the posterior neural ectoderm and as we observed anteriorly, both actin populations still increased in shroom3 crispant cells (Figure 9A–C, right). In striking contrast, N-cadherin intensity decreased, both medially and junctionally, in both control and shroom3 crispant cells (Figure 9A, D, and E). Thus, while actin accumulation was very slightly disrupted in posterior shroom3 cells, N-cadherin dynamics were essentially unaltered in posterior shroom3 cells.

Loss of shroom3 disrupts actin dynamics in the posterior neural ectoderm.

(A) Representative images of LifeAct/actin localization in control cells (left) and shroom3 crispant cells (right) from the posterior region of the neural ectoderm. White asterisks mark the same cell in each embryo. Scale bar = 15 µm. (B) Distribution of overall change (Δ) in medial LifeAct/actin (standardized) from posterior cells. (C) Distribution of overall change (Δ) in junctional LifeAct/actin (standardized) from anterior cells. In (B and C), horizontal lines on density plots/violins indicate quartiles of distribution, black circles are individual cells, and statistical comparisons performed by Kolmogorov-Smirnov (KS) test. (D and E) 2D distribution of changes in apical area and medial (D) or junctional (E) LifeAct/actin (both standardized). Percentages in white indicate the percentage of total cells in each quadrant. Statistical comparisons performed by Peacock test, a 2D implementation of the KS test. (F and G) 2D density plots of all observations of apical area versus medial (F) or junctional (G) LifeAct/actin for all cells within each group. Red lines indicate best-fit line through the observations. Statistics (r and p) are calculated for Pearson’s correlation. Cells situated along the mosaic interface were excluded from these analyses. s.d. = standard deviation.

Considering our data from the anterior neural plate above, these results suggest a fundamentally different relationship between actin, N-cadherin, and apical area in the posterior neural plate. A more granular view of the data revealed two results that reinforced this conclusion. First, the correlations between actin localization and apical area were much weaker in posterior control cells (Figure 10A, B) than in anterior control cells (Figure 8A, B), suggesting that apical constriction is mechanistically different between the anterior and posterior neural ectoderm. Second, loss of Shroom3 disrupted the actin/area correlation in both regions (Figures 8A, B, 10A, B), but this phenotype was much stronger in the anterior region, again consistent with the weaker shroom3 apical constriction phenotype in the posterior region (Hildebrand and Soriano, 1999; McGreevy et al., 2015). Finally, while medial and junctional N-cadherin were both strongly negatively correlated with apical area in wild-type anterior cells (Figure 7F, G), no correlation whatsoever was observed between N-cadherin and apical area in posterior cells (Figure 10C, D).

N-cadherin dynamics are highly heterogenous in the posterior neural ectoderm and poorly correlated with apical constriction.

(A) Representative images of N-cadherin localization in control cells (left) and shroom3 crispant cells (right) from the posterior region of the neural ectoderm. White asterisks mark the same cell in each embryo. Scale bar = 15 µm. (B) Distribution of overall change (Δ) in medial N-cadherin (standardized) from posterior cells. (C) Distribution of overall change (Δ) in junctional N-cadherin (standardized) from posterior cells. In (B and C), horizontal lines on density plots/violins indicate quartiles of distribution, black circles are individual cells, and statistical comparisons performed by Kolmogorov-Smirnov (KS) test. (D and E) 2D distribution of changes in apical area and medial (D) or junctional (E) N-cadherin (both standardized). Percentages in white indicate the percentage of total cells in each quadrant. Statistical comparisons performed by Peacock test, a 2D implementation of the KS test. (F and G) 2D density plots of all observations of apical area versus medial (F) or junctional (G) N-cadherin for all cells within each group. Red lines indicate best-fit line through the observations. Statistics (r and p) are calculated for Pearson’s correlation. Cells situated along the mosaic interface were excluded from these analyses. s.d. = standard deviation.

Together, these results suggest a more complex relationship between actin, N-cadherin, and Shroom3 in the posterior neural plate as compared to the anterior. These results suggest that changes in apical constriction alone are unlikely to explain the role of Shroom3 in posterior NTDs. Instead, the result may reflect the complex interplay of Shroom3-mediated apical constriction and PCP-dependent convergent extension cell behaviors that affect cell-cell junction lengths specifically in the posterior neural plate (e.g. McGreevy et al., 2015; Nishimura et al., 2012).

Loss of Shroom3 elicits a subtle but consistent defect in polarized junction shrinking in the posterior neural plate

Our data suggest that changes in apical surface area are unlikely to explain shroom3-related posterior NTDs, so turned our attention to the 13,000 individual cell-cell junctions tracked in our dataset. We assessed the behavior of each junction by quantifying the change in length over time, and because junction behaviors during epithelial morphogenesis are frequently polarized with respect to the embryonic axes (Pinheiro and Bellaϊche, 2018), we next assigned orientations to all junctions in our dataset. Those with a mean orientation less than 45° were designated anteroposterior (AP), as they represent the anterior face of one cell abutting the posterior face of a neighboring cell; junctions with mean orientations greater than 45° were designated as mediolateral (ML) (Figure 11A).

Figure 11 with 1 supplement see all
Individual junction behaviors are polarized in the posterior neural ectoderm.

(A) Distribution of overall change (Δ) in junction length (standardized) from the posterior neural ectoderm. Horizontal lines on density plots/violins indicate quartiles of distribution, black circles are individual cells, and statistical comparisons performed by Kolmogorov-Smirnov (KS) test. (B) Junction orientation from posterior control embryo from Figure 2. Scale bars = 100 µm. (C) 2D density plots of all observations of mean junction orientation over time versus overall change (Δ) in junction length (standardized) for all junctions within each group. Dashed cyan ellipses indicate areas of altered polarization between control and shroom3 crispant junctions. (D) 2D density plots of all observations of mean junction orientation over time versus overall change (Δ) in junction actin (standardized) for all junctions within each group. (E) 2D density plots of all observations of mean junction orientation over time versus overall change (Δ) in junction N-cadherin (standardized) for all junctions within each group. Percentages in white indicate the percentage of total cells in each quadrant. Statistical comparisons performed by Peacock test, a 2D implementation of the KS test. s.d. = standard deviation.

As a positive control for this dataset, we first examined bulk trends. For example, the vast majority of junctions in the anterior neural plate decreased in length, but shortening junctions displayed no bias to their orientation, consistent with the robust, largely isodiametric apical constriction of these cells (Figure 11—figure supplement 1A, B). Consistent with our data on apical surface area, anterior shroom3 crispant cells displayed a robustly significant defect (p < 2.2 × 10–16), with the majority of junctions actually elongating rather than shortening (Figure 11—figure supplement 1A, left). A very different trend was observed in the posterior, where most junctions shortened but many elongated (Figure 11B, left); shrinking was strongly biased to junction joining AP neighbors, with a reciprocal strong bias for elongation (Figure 11C). The converse pattern was observed for actin accumulation, with increasing actin accumulation negatively correlated to decreasing junction length (Figure 11D). This pattern is consistent with the known convergent extension cell behaviors in the posterior neural tube and our data here reflect the findings of a previous, smaller scale study of this tissue (Butler and Wallingford, 2018) as well as data from fixed mouse embryos (McGreevy et al., 2015), providing confidence in the veracity of the dataset.

Our analysis of posterior shroom3 crispant junctions revealed a barely significant (p = 0.015) change in their bulk behavior (Figure 11B, right). This result reflects our cell-level data (Figure 5D) and reinforces the conclusion that apical constriction defects are likely not sufficient to explain the posterior NTDs caused by lack of Shroom3. Indeed, a similar modest defect was observed in shroom3 mutant mice, and that study also revealed a link to the polarization of planar cell arrangement during neural tube closure (McGreevy et al., 2015).

One possible explanation for such a polarity phenotype is that Shroom3 is directly required for actomyosin contractions that contribute to junction shortening during CE (see Huebner et al., 2021). However, we used Tissue Analyzer to quantify the number of stable neighbor exchanges (‘T1 transitions’) (Figure 10F), and we observed no effect in shroom3 crispant cells (Figure 11G), suggesting that neighbor exchange does not strictly require Shroom3 function. This led us to more carefully explore the effect of Shroom3 loss on polarization of the behaviors of individual junctions.

We found that strongly shrinking junctions were very tightly clustered in the most extreme AP orientation in control cells (i.e. clustered near 0°) (Figure 11C, left). By contrast, in shroom3 crispant cells, shrinking junctions still dominated in the AP quadrant, consistent with previous data from fixed mouse embryos (McGreevy et al., 2015), but importantly, they were spread across a wider range of orientations (i.e. more evenly distributed from ~0° to ~40°) (Figure 11C, cyan ellipses). Histograms of these data provided a more granular view of this subtle, but significant shift (Figure 11H). Elongating junctions were also less polarized in Shroom3 crispants compared to controls (Figure 11D, H). Furthermore, actin dynamics very neatly reflected the shrinking and elongating behaviors in both control and shroom3 crispant cells (Figure 11D).

Thus, loss of shroom3 leads to a subtle but significant shift in the polarization of the junction behaviors that drive convergent extension, a result that could explain the incompletely penetrant posterior NTDs resulting from Shroom3 loss (Haigo et al., 2003; Hildebrand and Soriano, 1999) and that is very consistent with the known genetic interaction of shroom3 and PCP gene mutations (McGreevy et al., 2015). The molecular basis for this interaction remains unclear, but it could relate to the interplay of Shroom3 and N-cadherin. Indeed, we observed a very strong clustering signal for N-cadherin clearance from the most polarized elongating junctions (i.e. ~90°) that was not observed for actin in elongating junctions, and this clustering was significantly diminished in shroom3 crispants (Figure 11E).

Conclusions

Understanding the cellular mechanisms contributing to neural tube closure has long challenged embryologists due to the relatively large number of cells involved and the heterogeneity of their behaviors. Indeed, some of the earliest uses of computer simulation in developmental biology focused on understanding the degree to which neural ectoderm cells constrict their apical surfaces during neural tube closure (Jacobson and Gordon, 1976). Since then, our understanding of neural tube closure has broadly improved. Hundreds of mutations affecting neural tube closure have been identified (Harris and Juriloff, 2010), and the functional interactions between these genes are coming into focus (McGreevy et al., 2015; Murdoch et al., 2014). However, while time-lapse imaging of single cell behaviors in chicks, frogs, and mice have provided key insights (Butler and Wallingford, 2018; Christodoulou and Skourides, 2015; Davidson and Keller, 1999; Galea et al., 2017; Massarwa and Niswander, 2013; Molè et al., 2020; Ossipova et al., 2015; Pyrgaki et al., 2010; Wallingford and Harland, 2002; Williams et al., 2014), our understanding of the cell biology of neural tube closure still lags substantially behind our understanding of the genetics.

Recent research has begun to bridge this gap. In toto imaging has captured neural tube closure as one facet of overall mouse development (McDole et al., 2018), and tissue-scale imaging has revealed the cell biological basis of ciliopathy-related NTDs (Brooks et al., 2020). In a complement to these recent advancements, we have now used high-resolution, but tissue-scale, time-lapse imaging of both actin and N-cadherin followed by cell-tracking analysis to gain several new insights into the process of apical constriction during neural tube closure.

First, we show that anterior and posterior neural ectoderm cells undergo apical constriction to differing degrees and by different constrictive mechanisms. Second, we show that changes in apical area in the anterior neural ectoderm are more strongly correlated with changes in medial actomyosin localization than junctional actomyosin, indicating that contractility driving apical constriction is more likely to be generated at the medial cell surface. Our results on medial actin localization are largely consistent with smaller scale studies in both Xenopus and mice (Brown and García-García, 2018; Christodoulou and Skourides, 2015; Suzuki et al., 2017) and moreover reflect mechanisms described in more detail in the context of Drosophila and C. elegans gastrulation (Martin et al., 2009; Roh-Johnson et al., 2012). Conversely, our data show that both medial and junctional actin localization have similar correlations with apical area in the posterior neural ectoderm, suggesting actomyosin contractility may be more balanced across the medial and junctional domains in this region.

Third, we show that N-cadherin accumulates at the medial surfaces of constricting cells in the anterior neural ectoderm, but that N-cadherin localization poorly correlates with apical constriction in the posterior. Interestingly, our analysis suggests that anterior shroom3 crispant cells fail to constrict less from a lack of actomyosin accumulation and more from an inability to accumulate N-cadherin at the medial surface of cells. This is consistent with a previously reported genetic interaction between shroom3 and N-cadherin (Lang et al., 2014; Li et al., 2021; Plageman et al., 2011b), and suggests that a Shroom3-N-cadherin pathway may drive anterior apical constriction during neural tube closure. The function of this N-cadherin remains to be determined, but two possibilities are suggested by prior studies. First, non-junctional N-cadherin can drive micropinocytosis (Sabatini et al., 2011) in cultured cells, so medial N-cadherin may contribute to the known, Shroom3-dependent endocytosis of the constricting apical surface of neuroepithelial cells (Kowalczyk et al., 2021; Lee and Harland, 2010). Alternatively, medial N-cadherin might act in a manner similar to non-junctional E-cadherin, forming cis-clusters and directing the assembly of the cortical actin cytoskeleton (Wu et al., 2015). Future experiments should be illuminating.

Finally, the study of developmental biology in the 21st century has been marked by explosive increases in the size of experiments – advances in proteomics and transcriptomics are providing an unprecedented depth to our understanding of the molecular workings of cells in embryos. Thus, it is of special importance that we use advances in imaging and data analysis to ask what those cells actually do and how they do it. It’s notable then that the approach described here can be applied to essentially any protein for which reliable fluorescent reporters are available. Many key questions immediately arise from our work, for example, the localization and dynamics of other crucial players such as Myosin II or Rho kinase. In the posterior neural plate expanding previous smaller scale analyses of PCP protein localization (e.g. Butler and Wallingford, 2018) will be of great interest. Finally, the large-scale approach here provides a lower-resolution, but tissue-scale complement to super-resolution imaging advances that are now revealing the subcellular organization of the machinery of morphogenesis (e.g. Huebner et al., 2021). Ultimately, then, the work presented here is significant for providing quantitative insights at tissue scale into the interplay of gene function, protein localization, and cell behavior during a biomedically important process in vertebrate embryogenesis.

Materials and methods

Animals

Wild-type X. tropicalis frogs were obtained from the National Xenopus Resource, Woods Hole, MA (Horb et al., 2019).

Injections

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Wild-type X. tropicalis eggs were fertilized in vitro using sperm from wild-type X. tropicalis males using standard methods (Wlizla et al., 2018).

X. tropicalis embryos were moved to 1/9× MMR +2% Ficoll, then injected in both dorsal blastomeres at the 4-cell stage with 50 pg LifeAct-RFP mRNA and 45 pg Xenopus N-cadherin-GFP mRNA, or 80 pg GFP-Shroom3ΔC-term mRNA. In CRISPR-injected embryos, after the next division to reach 8-cell stage, one dorsal blastomere was injected with 1 ng Cas9 protein (PNA Bio), 250 pg shroom3-targeted sgRNA (target sequence GUAGCCGGAGAGAUCACUUG, Synthego) (Figure 5—figure supplement 1A), and 60 pg membrane(CAAX)-BFP mRNA.

Antibody staining

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X. tropicalis embryos were collected at NF stages 13–17 and devitellinized. Embryos were then fixed in 4% paraformaldehyde in PBS for 30 min at room temperature. Embryos were then washed in PBS + 0.01% Triton X-100 3 times for 20 minutes at room temperature. Fixed embryos were blocked in normal goat serum then stained with monoclonal NCD-2 antibody (Hatta et al., 1987; Hatta and Takeichi, 1986). NCD-2 was diluted 1:10 from supernatant provided by the Developmental Studies Hybridoma Bank, created by the NICHD of the NIH and maintained at The University of Iowa, Department of Biology, Iowa City, IA. Embryos were again washed in PBS + Triton then incubated with a 1:1000 dilution of Invitrogen Alexa Fluor 488 goat α-rat secondary antibody prior to imaging.

CRISPR genotyping

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To test the efficacy of our CRISPR injections in vivo, we injected wild-type X. tropicalis with the above-described Cas9 + sgRNA combination in the following cells and stages: 2× injections into 1-cell stage embryos, 1× injections into each blastomere of 2-cell stage embryos, 1× injections into two blastomeres of the 4-cell stage embryo, and 1× injections into all blastomeres of 4-cell stage embryos (Figure 5—figure supplement 1B). Uninjected embryos that did not receive any Cas9 + sgRNA were used as controls.

Embryos were allowed to grow to approximately NF stage 40 and then were subjected to whole-embryo DNA extraction. PCR products spanning the shroom3 target site were generated from each embryo and separated by capillary electrophoresis for fragment analysis. Fragment analysis data was analyzed in R using the Fragman package (Covarrubias-Pazaran et al., 2016).

Uninjected embryos did not have any indel products at the shroom3 locus and thus produced one sharp peak at 431 base pairs, corresponding to the size of the wild-type shroom3 PCR product (Figure 5—figure supplement 1B). By contrast, CRISPR-injected embryos returned little to no PCR products at this size (Figure 5—figure supplement 1B, dashed red line), indicating that the shroom3 target site was being efficiently cut by Cas9 and repaired by error-prone pathways. The exception to this were the embryos of which only two blastomeres at the 4-cell stage were injected with Cas9 + sgRNA; as expected, fragments of the wild-type size were detected that theoretically correspond to the uninjected blastomere lineages (Figure 5—figure supplement 1B).

Overall, these results indicate that our Cas9 + sgRNA combination efficiently cleaves the shroom3 target site in vivo. However, our F0 mosaic crispants generated by CRISPR injection into one blastomere at the 8-cell stage do not generate enough crispant cells to be detected by whole embryo PCR at later stages of embryonic development.

Imaging

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Injected embryos were held at 25°C until they reached Nieuwkoop and Faber (NF) stage 12.5. At NF stage 12.5, vitelline envelopes were removed from embryos and embryos were allowed to ‘relax’ for 30 min. Embryos were then mounted in imaging chambers and positioned for imaging of either the anterior or posterior neural plate.

Embryos were imaged on a Nikon A1R confocal microscope using the resonant scanner. Image quality, Z-stacking, and XY tiling were optimized to generate optimal 3D images of the neural plate at a rate of 1 frame per minute. Ultimately, movies of nine embryos were of sufficient length and quality for analysis, tissue geometry of the initial frame of each of these embryos is presented in Figure 5—figure supplement 2.

Image analysis

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Raw 3D images were projected to 2D via maximum intensity and underwent initial segmentation of cell boundaries using the FIJI plugin Tissue Analyzer (Aigouy et al., 2010; Aigouy et al., 2016). The segmentation of an initial frame was hand-corrected, and this hand-corrected segmentation was used to train a classifier using the programs CSML and EPySEG (Aigouy et al., 2020; Ota et al., 2018). CSML and EPySEG were used to generate segmentation for subsequent frames, which were then further hand-corrected in Tissue Analyzer.

After hand-correction, Tissue Analyzer was used to track both cell surfaces and cell junctions, then generate a database of measurements of size and fluorescent intensities for each cell and junction over time. Values for medial and junctional localization of imaged markers in cells were calculated as average pixel fluorescence intensity across the entirety of each respective domain (i.e. total fluorescence of a region divided by the area of the region). Similarly, localization of imaged markers to individual junctions was calculated as an average across the entire junction.

For individual junctions, errors in junction length caused by Z-displacement and projection were corrected in Matlab.

Tissue Analyzer databases were imported to R and further analyzed and manipulated primarily using the tidyverse package (Wickham et al., 2019).

Data analysis

Cell tracks shorter than 30 frames and junction tracks shorter than 15 frames were discarded.

Individual cell and junction tracks were smoothed by averaging over a 7 frames/min window (Figure 1—figure supplement 1A, B). Individual cell tracks were further mean-centered and standardized so that variables are measured in standard deviations rather than fluorescence or size units (Figure 1—figure supplement 1C). This standardization allows us to analyze dynamics of cell size and protein localization across a population of cells while controlling for initial size and fluorescence of cells. In an example embryo, cells begin and end tracking with a variety of apical surface areas (Figure 1—figure supplement 1D’), but once the cell tracks are mean-centered and standardized it becomes clear that the cells are behaving similarly at a population level (Figure 1—figure supplement 1D”).

Embryo ‘b’ had a fluorescence anomaly during imaging that resulted in a reduction in overall observed fluorescence followed by a recovery (Figure 5—figure supplement 3A, B). Cells were tracked through the anomaly (Figure 5—figure supplement 3C), but fluorescent values for the frames 23–45 were discarded (Figure 5—figure supplement 3, red dashed box).

Cells were determined to be wild-type versus shroom3 crispant by a membrane-BFP localization threshold specific to each embryo (Figure 5B, middle panel). Crispant calls were then manually annotated in cases along the mosaic interface where thresholding produced crispant calls deemed incorrect.

Individual junctions were determined to be wild-type versus shroom3 crispant versus mosaic interface based on the status of the cells the junction was situated between. Wild-type junctions are situated between two wild-type cells, shroom3 crispant junctions are situated between two shroom3 crispant cells, and mosaic interface junctions are situated between a wild-type and a shroom3 crispant cell (Figure 5B, lower panel).

Junction orientations were corrected so that the ML axis of the embryo was set at 0° and the AP axis of the embryo was set at 90° (Figure 11B).

Cell data parameters

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  • cell_surfaces – frame-by-frame tracked data for cells

  • region: relative region of the neural ectoderm, that is, ‘anterior’ or ‘posterior’

  • movie: label for each individual embryo analyzed

  • track_id_cells: cell tracking label that is unique to cells within an embryo but may be repeated between different embryos

  • minute: time per each individual embryo/movie in minutes

  • center_x_cells: pixel X coordinate of centroid of each cell in each frame

  • center_y_cells: pixel Y coordinate of centroid of each cell in each frame

  • vx_coords_cells: pixel XY coordinates of the vertices of each cell in each frame in X:Y#X:Y format

  • CRISPR: CRISPR status of each cell, that is, ‘control’ or ‘shroom3 crispant’, called based on membrane-BFP localization.

  • control_neighbors: number of cell neighbors that are ‘control’ in each frame

  • crispant_neighbors: number of cell neighbors that are ‘shroom3 crispant’ in each frame

  • apical_area_pixels: cell apical area in pixels (measured within a one pixel constriction of the segmented cell junctions)

  • apical_area_pixels_smoothed: apical_area_pixels averaged over 7 frames, –3 and +3 frame frame in question

  • apical_area_micron_smoothed: apical_area_pixels_smoothed converted to square microns apical_area_standardized: apical_area_pixels_smoothed mean-centered and scaled per cell track (via R ‘scale’ function), measured in standard deviations

  • medial_actin: mean LifeAct-RFP fluorescent intensity at the medial apical domain of each cell in arbitrary units (measured within a one pixel constriction of the segmented cell junctions)

  • medial_actin_smoothed: medial_actin averaged over 7 frames, –3 and +3 frame frame in question

  • medial_actin_standarized: medial_actin_smoothed mean-centered and scaled per cell track (via R ‘scale’ function), measured in standard deviations

  • junctional_actin: mean LifeAct-RFP fluorescent intensity at the junctional domain of each cell in arbitrary units (measured at segmented cell junctions)

  • junctional_actin_smoothed: junctional_actin averaged over 7 frames, –3 and +3 frame frame in question

  • junctional_actin_standarized: junctional_actin_smoothed mean-centered and scaled per cell track (via R ‘scale’ function), measured in standard deviations

  • medial_Ncadherin: mean N-cadherin-GFP fluorescent intensity at the medial apical domain of each cell in arbitrary units (measured within a one pixel constriction of the segmented cell junctions)

  • medial_Ncadherin_smoothed: medial_Ncadherin averaged over 7 frames, –3 and +3 frame frame in question

  • medial_Ncadherin_standarized: medial_Ncadherin_smoothed mean-centered and scaled per cell track (via R ‘scale’ function), measured in standard deviations

  • junctional_Ncadherin: mean N-cadherin-GFP fluorescent intensity at the junctional domain of each cell in arbitrary units (measured at segmented cell junctions)

  • junctional_Ncadherin_smoothed: junctional_Ncadherin averaged over 7 frames, –3 and +3 frame frame in question

  • junctional_Ncadherin_standarized: junctional_Ncadherin_smoothed mean-centered and scaled per cell track (via R ‘scale’ function), measured in standard deviations

  • medial_memBFP: mean membrane(CAAX)-BFP fluorescent intensity at the medial apical domain of each cell in arbitrary units (measured within a one pixel constriction of the segmented cell junctions)

  • medial_memBFP_smoothed: medial_memBFP averaged over 7 frames, –3 and +3 frame frame in question

  • junctional_memBFP: mean membrane(CAAX)-BFP fluorescent intensity at the junctional domain of each cell in arbitrary units (measured at segmented cell junctions)

  • junctional_ memBFP_smoothed: junctional_ memBFP averaged over 7 frames, –3 and +3 frame frame in question

  • cell_surface_stats – summary statistics for cells

  • region: relative region of the neural ectoderm, that is, ‘anterior’ or ‘posterior’

  • movie: label for each individual embryo analyzed

  • track_id_cells: cell tracking label that is unique to cells within an embryo but may be repeated between different embryos

  • CRISPR: CRISPR status of each cell, that is, ‘control’ or ‘shroom3 crispant’, called based on membrane-BFP localization.

  • at_mosaic_interface: TRUE denotes that cell was next to another cell of the other CRISPR type at some point during tracking, that is, control cells next to shroom3 crispant cells and vice versa. FALSE indicates that a cell was only next to cells of the same CRISPR type, that is, control cells next to only other control cells. Cells labeled TRUE were not used in quantitative analyses in this paper.

  • start_area_micron: initial apical area of a cell in square microns (calculated from apical_area_micron_smoothed)

  • end_area_micron: final apical area of a cell in square microns (calculated from apical_area_micron_smoothed)

  • delta_apical_area: final value of apical_area_standardized minus initial value of apical_area_standardized (measured in standard deviations/s.d.)

  • delta_medial_actin: final value of medial_actin_standarized minus initial value of medial_actin_standarized (measured in standard deviations/s.d.)

  • delta_junctional_actin: final value of junctional_actin_standarized minus initial value of junctional_actin_standarized (measured in standard deviations/s.d.)

  • delta_medial_Ncadherin: final value of medial_Ncadherin_standarized minus initial value of medial_Ncadherin_standarized (measured in standard deviations/s.d.)

  • delta_junctional_Ncadherin: final value of junctional_Ncadherin_standarized minus initial value of junctional_Ncadherin_standarized (measured in standard deviations/s.d.)

  • junctions – per minute/junction measurements of junctions

  • region: relative region of the neural ectoderm, that is, ‘anterior’ or ‘posterior’ movie: label for each individual embryo analyzed

  • track_id_junctions: junction tracking label that is unique to junctions within an embryo but may be repeated between different embryos. Junctions are determined as interactions between cells.

  • CRISPR: CRISPR status of each junction based on the cells the junction is between, that is, ‘control’ is between two control cells, ‘shroom3 crispant’ is between two shroom3 crispant cells, and ‘at mosaic interface’ is between a control cell and a shroom3 crispant cell. Junctions ‘at mosaic interface’ were not included in quantitative analyses in this paper.

  • minute: time per each individual embryo/movie in minutes

  • vx_1_x, vx_1_y: pixel XY coordinates of first vertex of junction

  • vx_2_x, vx_2_y: pixel XY coordinates of second vertex of junction

  • actin: mean LifeAct-RFP fluorescent intensity across the junction, measured in arbitrary units actin_smooth:

  • actin averaged over 7 frames, –3 and +3 frame frame in question

  • actin_standardized: actin_smooth mean-centered and scaled per cell track (via R ‘scale’ function), measured in standard deviations

  • Ncadherin: N-cadherin-GFP fluorescent intensity across the junction, measured in arbitrary units

  • Ncadherin_smooth: Ncadherin averaged over 7 frames, –3 and +3 frame frame in question

  • Ncadherin_standardized: Ncadherin_smooth mean-centered and scaled per cell track (via R ‘scale’ function), measured in standard deviations

  • length_px: length of junction in maximum intensity projection measured in pixels

  • length_micron: length_px converted to microns

  • delta_z_micron: Z-distance between first and second vertex of junction, in microns. Calculated by determining where the maximum intensity projection sampled the Z-stack for each vertex in the LifeAct-RFP channel.

  • length_corrected: length_micron corrected for z-distance between vertices using delta_z_micron, measured in microns.

  • length_smooth: length_corrected averaged over 7 frames, –3 and +3 frame frame in question

  • length_standardized: length_smooth mean-centered and scaled per cell track (via R ‘scale’ function), measured in standard deviations

  • orientation: orientation of junction relative to AP axis in degrees, where 0 is aligned with ML axis and 90 is aligned with AP axis.

  • junction_stats – summary statistics for junctions region: relative region of the neural ectoderm, that is, ‘anterior’ or ‘posterior’

  • movie: label for each individual embryo analyzed

  • track_id_junctions: junction tracking label that is unique to junctions within an embryo but may be repeated between different embryos. Junctions are determined as interactions between cells.

  • CRISPR: CRISPR status of each junction based on the cells the junction is between, that is, ‘control’ is between two control cells, ‘shroom3 crispant’ is between two shroom3 crispant cells, and ‘at mosaic interface’ is between a control cell and a shroom3 crispant cell. Junctions ‘at mosaic interface’ were not included in quantitative analyses in this paper.

  • delta_length: final value of length_standarized minus initial value of length_standarized (measured in standard deviations/s.d.)

  • delta_actin: final value of actin_standarized minus initial value of actin_standarized (measured in standard deviations/s.d.)

  • delta_Ncadherin: final value of Ncadherin_standarized minus initial value of Ncadherin_standarized (measured in standard deviations/s.d.)

  • mean_orientation: average of orientation over junction track

Data availability

We have deposited two types of files on Dryad: 'cell_surfaces' and 'junctions' are spreadsheets containing the frame-by-frame measurements of cell/junctions size, location, fluorescent protein localization, and other parameters. 'cell_surface_stats' and 'junction_stats' are spreadsheets of summary statistics generated from the frame-by-frame data describing overall changes in parameters in individual cells and junctions. These data can be downloaded at: https://doi.org/10.5061/dryad.zw3r2289b.

The following data sets were generated
    1. Baldwin AY
    2. Kim J
    3. Seo H
    4. Wallingford J
    (2022) Dryad Digital Repository
    Global analysis of cell behavior and protein localization dynamics reveals region-specific functions for Shroom3 and N-cadherin during neural tube closure.
    https://doi.org/10.5061/dryad.zw3r2289b

References

    1. Horb M
    2. Wlizla M
    3. Abu-Daya A
    4. McNamara S
    5. Gajdasik D
    6. Igawa T
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    9. Noble A
    10. Robert J
    11. James-Zorn C
    12. Guille M
    (2019) Xenopus Resources: Transgenic
    Inbred and Mutant Animals, Training Opportunities, and Web-Based Support. Frontiers in Physiology 10:387.
    https://doi.org/10.3389/fphys.2019.00387
    1. Murphy SL
    2. Xu J
    3. Kochanek KD
    4. Arias E
    (2018)
    Mortality in the United States, 2017
    NCHS Data Brief pp. 1–8.
  1. Book
    1. Nieuwkoop PD
    2. Faber J
    (1994)
    Normal Table of Xenopus Laevis (Daudin): A Systematical and Chronological Survey of the Development from the Fertilized Egg till the End of Metamorphosis
    New York: Garland Pub.
    1. Wlizla M
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    Generation and Care of Xenopus laevis and Xenopus tropicalis Embryos
    Methods in Molecular Biology (Clifton, N.J.) 1865:19–32.

Decision letter

  1. Elke Ober
    Reviewing Editor; University of Copenhagen, Denmark
  2. Marianne E Bronner
    Senior Editor; California Institute of Technology, United States
  3. Elke Ober
    Reviewer; University of Copenhagen, Denmark
  4. Alpha Yap
    Reviewer; University of Queensland, Australia
  5. Jeff Hildebrand
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "Global analysis of cell behavior and protein localization dynamics reveals region-specific functions for Shroom3 and N-cadherin during neural tube closure" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Elke Ober as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and Marianne Bronner as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Α Yap (Reviewer #2); Jeff Hildebrand (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) The authors need to revisit their conclusions, which in places are overextended. For example, it seems too much to conclude that "the primary effect of shroom 3 in the anterior neural plate is …on the coupling of medial actomyosin …with medial N-cadherin accumulation" (p 10, bottom). It is the most obvious change in their current data set, but that data set seems too limited to call it the "primary effect".

Similarly, the manuscript strength is the new analytical approach of molecular and cellular changes at tissue scale rather than in increasing the understanding of neural tube morphogenesis. Therefore, we suggest to better frame the manuscript around this new analytical approach and its capacity to yield data of great richness for understanding morphogenetic processes in vertebrates.

Addressing the points raised below will support the potential of the approach for gaining mechanistic insights and overall strengthen the manuscript.

2) Estimating molecular concentration: Please clarify and address whether the molecular concentration of analyzed proteins has been corrected for change in size of the analyzed area over time. For example, the medial N-cadherin increases with apical constriction (Figure 6), but is this because the apical surface is getting smaller (i.e. same amount of apical N-cadherin, but now more concentrated) or is there an increase in the total amount of protein in the apical surface?

3) Clarify the analysis of medial signals: How do the authors correct for noise in their analyses? Lifeact can also bind G-actin and some of the punctate N-cadherin-GFP could potentially be in vesicles rather than at the apical membrane (see also point 5).

4) The non-junctional distribution of N-cadherin and its dynamic changes during apical constriction represent an exciting result. However, solely using ectopically expressed N-cadherin-GFP to investigate its function is not sufficient, as it may introduce overexpression artifacts. Please provide data corroborating that endogenous N-cadherin behaves similar to the exogenous protein. We appreciate that based on the available reagents (e.g. antibodies) this would likely represent static images. If this proves experimentally not possible, this should at a minimum be addressed in the text.

5) Could the current data be used to assess the trafficking of the N-Cadherin? For instance, can the authors determine if N-cadherin moving from junctional to medial locations, is being trafficked directly to the medial membrane, or being internalized from the medial region, or perhaps some other dynamic behavior. This could help provide more information regarding the mechanistic role of N-cadherin.

6) Does medial N-cadherin co-distribute with Myosin II, ppRLC, or Rho-kinase? Based on previous studies of apical constriction in other model systems this is an attractive assumption. This should be tested experimentally to support and mechanistically corroborate the correlation of molecular events and cell shape changed described in this manuscript. For instance, combine N-cadherin with a MyosinII, a GTP-Rho localization sensor (e.g. Bement lab) or the AHPH system (Piekny and Glotzer, 2008) reporter to generate relevant time-series.

7) Given that non-junctional N-cadherin has been associated with diverse cellular functions apart from adhesion, please discuss its possible role in this current context.

8) Support the functional inactivation of Shroom3: The sequence analysis provided is compelling but actually demonstrating reduced protein would strengthen the method.

What proportion of indels are 3 bp or multiple of 3, could resulting in-frame deletions or monoallelic indels explain for instance the 2 populations observed for instance in Fig5D, E?

Moreover, please clarify which particular domain is targeted by the Shroom3 gRNA employed in this study? How is it expected to impair its function, e.g. complete loss of function, deletion of a specific functional domain? If the latter, could a truncated protein exert partial functions? How would this effect interactions with actin or N-cadherin and relate to the specific phenotypes observed?

9) Shroom3 spatial expression: How is Shroom3 expressed throughout the extent of the anteroposterior neural epithelium, given that it seems to exert different effects in the anterior and posterior parts? Likewise, would be important to see the Shroom3 subcellular distribution in the neural plate to determine if there is a population of Shroom3 in medial positions analogous to N-cadherin.

10) Clarification of sample numbers and data integration of different samples: The samples included in this study and presented in Methods Appendix2 display apparent differences, therefore additional information is required for the number of samples that contribute to each analysis and how data were compared and/or integrated. For instance, the anterior samples show differences in cell size and asymmetries within the tissue. Please explain the reason for this and how is this accounted for when comparing quantifications between samples. This should include how staging between samples was achieved, and the related registration allowing comparison of resulting cell behaviors.

Related to this, there seem to be only two posterior samples containing Shroom3 crispant cells, please describe the variability between samples, similar to above. If only two samples were interrogated, a third sample needs to be included. In general, the sample number per experiment should be greater than two.

11) Describing the data analysis. Please introduce in the Results a few sentences that explain the "standardization" approach that are used to present the data. While this is in the Methods and Appendix, the approach is not one commonly seen, and it would be good to orient readers less familiar with the approach.

12) Facilitating the interpretation of some the 2D density plots in figure 10. Consider an alternative way or simplification of the graphs without breaking the data out into several additional graphs. If difficult, a more detailed description in the methods should be helpful to the reader and included.

13) The 2D density plots in figure 6D and E have such sharp edges; they look artificial. Please check and address whether this is just an issue with the PDF, thresholding, or some other technical issue.

14) Discuss limitations inherent to the approach, including: (i) a relatively limited number of molecular parameters are interrogated (F-actin and N-cadherin). It is possible, for example, that changes in contractility which drive junctional shortening (relevant for the analysis in Figure 10) are due to changes in actin organization (that may not be readily captured by overall measures of quantity) or activity of Myosin II (which is not measured here). Given the scale of the experiments that are involved, it would be technically challenging to interrogate more molecular players at the same time, representing a potential limitation.

(ii) the dynamics are relatively coarse-grained. For example, changes in cadherin levels that occur over hours may not capture changes in molecular turnover.

Reviewer #1:

In this manuscript Baldwin and colleagues investigate a key morphogenetic step of neural tube closure, namely the apical constriction of the neural epithelium. Using Xenopus as a model for live imaging, they monitor the underlying cell behaviours over time, including large-scale quantitative analyses of cell shape changes and subcellular localization of key proteins, actin and N-cadherin. This revealed clear differences in the anterior and posterior neural plate with respect to apical constriction dynamics and cell junction dynamics. The cytoskeletal interactor Shroom3 mediates aspects of these cell behaviours in a region-specific fashion, as revealed by F0 CRISPR/Cas9 mutagenesis. Shroom3 seems to control partially similar actin and N-cadherin localization, however, its loss leads to stronger defects only in anterior neural tube closure. The strength of this study is the highly quantitative nature of the approach: quantifying the morphogenetic behaviours of individual cells at the tissue level, which leads to an integration of previous observations with new insights into region-specific differences of cell behaviours and their correlation with characteristic actin and N-cadherin subcellular localisation. The very interesting observations however require further substantiation by functional experiments, as well as clarification of some experimental details.

The non-junctional distribution of N-cadherin and its dynamic changes during apical constriction represent an exciting result. Given that non-junctional N-cadherin has been associated with diverse functions, please clarify its possible role in this context. If it would be possible to determine its region-specific function(s) without interfering with cell adhesion, would add to the mechanistic understanding.

Which particular domain is targeted by the Shroom3 gRNA employed in this study? How is it expected to impair its function, e.g. complete loss of function, deletion of a specific functional domain? If the latter, could a truncated protein exert partial functions? How would this effect interactions with actin or N-cadherin and relate to the specific phenotypes observed?

What proportion of indels are 3 bp or multiple of 3, could this or monoallelic indels explain for instance the 2 populations observed in Figure 5?

There seem to be two populations of n-cadherin positive cells in the posterior domain, how are they spatially distributed and would this affect morphogenesis on the tissue level?

How is Shroom3 expressed throughout the extent of the anteroposterior neural epithelium? Given that Shroom3 seems to have different effects in the anterior and posterior neural epithelium, information about its spatial distribution and sub cellular localisation would be informative for the interpretation of the results.

The samples included in this study and presented in Methods Appendix2 display apparent differences, therefore additional information is required for the number of samples that contribute to each analysis and how data were compared. For instance, the anterior samples show differences in cell size and asymmetries within the tissue. Please explain the reason for this and how is this accounted for when comparing quantifications between samples?

Along the same lines, there seem to be only two posterior samples containing Shroom3 crispant cells, please describe the variability between samples. If there is variability, a third sample needs to be included in the study.

How was the staging between samples and adjustment between resulting cell behaviours achieved? For instance, there seem to be fewer cells/lower signal at the time of rapid constriction in the posterior neural plate.

Reviewer #2:

Morphogenesis entails cell-level behaviours that generate tissue-level (multicellular) consequences. The challenge of characterizing and dissecting those cellular behaviours in the context of a tissue or organism is made even greater because (a) regional variation is often critical for morphogenetic processes (e.g. convergent extension, tube closure); and (b) cellular level activity is likely to have stochastic components that make it difficult to identify regional patterns of variation. Traditional approaches in cell biology or in the molecular genetic dissection of development have been less well-equipped to deal with these complexities.

To address this issue, Baldwin et al., describe an analysis pipeline that utilizes live optical imaging to capture cellular-level events within a morphogenetically-active tissue. Combining tiling and confocal imaging with large scale image analysis, the authors characterize patterns of cell shape change, subcellular F-actin and N-cadherin accumulation, in the neural tube of Xenopus tropicalis. This addresses the challenge of capturing both cell- and tissue-level behaviours in a relatively large organism. They reveal a regional diversity of behaviours between the anterior and posterior neural tubes, which are also disparately affected when Shroom 3, a known regulator of neural tube development, is depleted.

I think that the major contribution of this paper is to introduce an approach to the large-scale imaging of tissues in a "big" vertebrate. The value is that the approach can extract cellular-level data, combined with regional information at the tissue level. And it can generate large numbers for good statistics. Although this has been done for smaller organisms, less progress has been made for larger samples. Another attraction of the approach is that the authors have used readily-available imaging technology and analysis tools. To be clear, I think that this is a valuable contribution for the field.

I am less certain of how much this paper represents in terms of new "biological" insight. The principal experimental intervention is Shroom3 KO and this changes some of the patterns of correlation (between cell shape change, cadherin concentration and F-actin). But the current analysis doesn't help me better understand which of those changes responsible for the abnormal morphogenesis of the Shroom 3 phenotype. This is for the following reasons (which are inherent features of the approach):

i) A relatively limited number of molecular parameters are interrogated (F-actin and N-cadherin). It is possible, for example, that changes in contractility which drive junctional shortening (relevant for the analysis in Figure 10) are due to changes in actin organization (that may not be readily captured by overall measures of quantity) or activity of Myosin II (which is not measured here). Given the scale of the experiments that are involved, it would be technically challenging to interrogate more molecular players at the same time, but this is a potential limitation that should be acknowledged.

ii) The dynamics are relatively coarse-grained. For example, changes in cadherin levels that occur over hours may not capture changes in molecular turnover.

So, while I think that the paper will make a contribution that could be suitable for eLife, I would ask the authors to consider the following issues.

Specific

1) I think that the authors sometimes draw their bow(s) a little too long. For example, it seems too much to conclude that "the primary effect of shroom 3 in the anterior neural plate is …on the coupling of medial actomyosin …with medial N-cadherin accumulation" (p 10, bottom). It is the most obvious change in their current data set, but that data set seems too limited to call it the "primary effect".

2) Medial signals. How do the authors correct for noise in their analyses? Lifeact can also bind G-actin and some of the punctate N-cadherin-GFP could potentially be in vesicles rather than at the apical membrane.

3) Estimating molecular concentration. I may have missed this, but have the authors also done an analysis where they correct for change in size of the region analysed. For example, the medial N-cadherin increases with apical constriction (Figure 6), but is this because the apical surface is getting smaller (i.e. same amount of apical N-cadherin, but now more concentrated) or is there an increase in the total amount of protein in the apical surface?

4) Focus of the paper. The authors seemed to frame this manuscript as an effort to better understand the morphogenesis of the neural tube. But, as noted above, from that perspective I was disappointed. I think the paper may be better framed as a new analytical approach, which yields data of great richness for future work.

Reviewer #3:

This manuscript by Baldwin, Kim, and Wallingford describes an in-depth, robust analysis of cell morphology, actin dynamics, and N-cadherin distribution in the neural plate of Xenopus tropicalis. This analysis further describes the role of Shroom3 in the regulation or participation in these processes. This work represents an important study of the cellular dynamics associated with neural tube formation and identifies some fundamental differences between the anterior and posterior regions of the neural plate that may contribute to birth defects that occur in different regions of the neural tube. The work presented should have an impact in the areas of cell and tissue morphogenesis, methods for analysis of complex cell dynamics in the context of intact tissues/embryos, epithelial organization, and neural tube formation.

Strengths:

Overall, the conclusions and interpretation are well supported by the detailed analysis of cellular and cytoskeletal dynamics during neural plate morphogenesis. It provides unique and valuable insights into the differences in cellular behavior in the anterior and posterior regions of the neural plate. This approach and level of analysis can help to set a standard for the combined use of Crispr, live cell imaging, and computational analysis to assess the function of specific proteins in dynamic processes in whole embryos.

Weaknesses:

My major concern is with the statement that the data presented advance our understanding of the mechanistic role for actin, Shroom3, N-cadherin, and contractility in apical constriction. This study shows beautiful and compelling correlative data that there is a, actin-Shroom3-N-cadherin network or pathway (or some other type of "interaction") that is important. However, this does not seem to provide a mechanism beyond what has previously been shown in other studies. This, however, does not invalidate the significance of the work presented.

I think some of the points below might help to provide more mechanistic insights, if possible.

1. I have a concern with only using ectopically expressed N-cadherin-GFP to investigate it's function. If possible, it would be useful to have some data indicating that the endogenous N-cadherin behaves in a way that is similar to the exogenous protein. I appreciate these would likely have to be static images and this may not be possible based on the available reagents. At a minimum, it should be addressed in the text if not possible to address experimentally.

2. Is it possible, using the data that has already been collected, to assess the trafficking of the N-Cadherin? For instance, can the authors determine if the N-cadherin is moving from junctional to the medial locations, being trafficked directly to the medial membrane, is it being internalized from the medial region, or perhaps some other dynamic behavior. This could help provide more information regarding the mechanistic role of N-cadherin.

3. Along these same lines, do the authors know if the medial N-cadherin co-distributes with Myosin II, ppRLC, or Rho-kinase? It might be presumed to the case based on previous studies of apical constriction in other model systems, but it would be interesting to see if this is the case.

4. Similarly, it might also be useful to see the Shroom3 distribution in the neural plate to determine if there is a population of Shroom3 in medial positions analogous to N-cadherin. I think the sequence analysis provided is compelling but actually demonstrating reduced protein would strengthen the method.

Additional comments:

I had a difficult time interpreting some the 2D density plots in figure 10. I'm not sure if there is really a better way to simplify it without breaking the data out into several additional graphs. Perhaps a more detailed description in the methods would be helpful to the reader.

I'm curious why the 2D density plots in figure 6D and E have such dramatic edges? These look artificial and I wonder if this is just an issue with the PDF, thresholding, or some other technical issue.

https://doi.org/10.7554/eLife.66704.sa1

Author response

Essential revisions:

1) The authors need to revisit their conclusions, which in places are overextended. For example, it seems too much to conclude that "the primary effect of shroom 3 in the anterior neural plate is …on the coupling of medial actomyosin …with medial N-cadherin accumulation" (p 10, bottom). It is the most obvious change in their current data set, but that data set seems too limited to call it the "primary effect".

Similarly, the manuscript strength is the new analytical approach of molecular and cellular changes at tissue scale rather than in increasing the understanding of neural tube morphogenesis. Therefore, we suggest to better frame the manuscript around this new analytical approach and its capacity to yield data of great richness for understanding morphogenetic processes in vertebrates.

Addressing the points raised below will support the potential of the approach for gaining mechanistic insights and overall strengthen the manuscript.

We have re-written much of the manuscript, focusing on the technical advances here, but also clarifying the distinctions between our findings and our conclusions, which in most cases we have softened.

Furthermore, we have now removed data that we deemed superfluous, thereby shortening and simplifying the manuscript.

2) Estimating molecular concentration: Please clarify and address whether the molecular concentration of analyzed proteins has been corrected for change in size of the analyzed area over time. For example, the medial N-cadherin increases with apical constriction (Figure 6), but is this because the apical surface is getting smaller (i.e. same amount of apical N-cadherin, but now more concentrated) or is there an increase in the total amount of protein in the apical surface?

Yes, we report changes in the mean pixel intensity normalized for area. This has been clarified in the revision on lines 147-149.

3) Clarify the analysis of medial signals: How do the authors correct for noise in their analyses? Lifeact can also bind G-actin and some of the punctate N-cadherin-GFP could potentially be in vesicles rather than at the apical membrane (see also point 5).

To account for "noise" within our data, we have smoothed the data by averaging the data in individual cell tracks over a 7-frame/minute window. We have included a description of this in the main text (lines 149151) and included a diagram in Figure 1 figure supplement 1. As LifeAct cannot strictly distinguish between F-actin and G-actin, we have not distinguished between F-actin and G-actin in the manuscript. We now explicitly describe what we observed and our interpretation thereof. We now also report on the apicobasal position of N-cad relative to actin (lines 197-200, Figure 4) and we discuss the possible mechanisms of action for medial N-cad (Discussion, lines 448-462).

4) The non-junctional distribution of N-cadherin and its dynamic changes during apical constriction represent an exciting result. However, solely using ectopically expressed N-cadherin-GFP to investigate its function is not sufficient, as it may introduce overexpression artifacts. Please provide data corroborating that endogenous N-cadherin behaves similar to the exogenous protein. We appreciate that based on the available reagents (e.g. antibodies) this would likely represent static images. If this proves experimentally not possible, this should at a minimum be addressed in the text.

We agree this is crucial. We therefore examined this issue using immunostaining for endogenous Ncadherin. These experiments, presented in the new Figure 3 figure supplement 1 and lines 194-197 confirm our findings with N-cadherin-GFP.

5) Could the current data be used to assess the trafficking of the N-Cadherin? For instance, can the authors determine if N-cadherin moving from junctional to medial locations, is being trafficked directly to the medial membrane, or being internalized from the medial region, or perhaps some other dynamic behavior. This could help provide more information regarding the mechanistic role of N-cadherin.

We regret that we do not have enough time resolution (1 frame per minute) to resolve the directionality of N-cadherin movement. However, we have shown some new images from our data that provide some insight into the relative apicobasal positioning of actin and N-cadherin (Figure 4), and we discuss the possibility of N-cad endocytosis (lines 197-203 and 448-462).

6) Does medial N-cadherin co-distribute with Myosin II, ppRLC, or Rho-kinase? Based on previous studies of apical constriction in other model systems this is an attractive assumption. This should be tested experimentally to support and mechanistically corroborate the correlation of molecular events and cell shape changed described in this manuscript. For instance, combine N-cadherin with a MyosinII, a GTP-Rho localization sensor (e.g. Bement lab) or the AHPH system (Piekny and Glotzer, 2008) reporter to generate relevant time-series.

We agree that these are interesting experiments, but as the reviewers themselves point out in Point 14 (below), examining these additional markers is extremely daunting given the scale of each experiment here. Thus, we followed the advice in Point 14 and now clearly discuss this limitation of our approach and leave analysis of other markers for a future paper.

7) Given that non-junctional N-cadherin has been associated with diverse cellular functions apart from adhesion, please discuss its possible role in this current context.

The possible roles for medial N-cad are now discussed in lines 448-462 of the discussion.

8) Support the functional inactivation of Shroom3: The sequence analysis provided is compelling but actually demonstrating reduced protein would strengthen the method.

What proportion of indels are 3 bp or multiple of 3, could resulting in-frame deletions or monoallelic indels explain for instance the 2 populations observed for instance in Fig5D, E?

Unfortunately, there are no available antibodies that detect Shroom3 protein in Xenopus (mouse antibodies do not cross-react). We hasten to add, however, that all aspects of the phenotype we observe with shroom3 CRISPR recapitulate known phenotypes not only of shroom3 morphants in Xenopus but also of dominant-negative shroom3 in Xenopus and genetic mutants in mice. This explicitly stated now in the manuscript, and coupled to our sequence analysis, we hope these findings will satisfy the reviewers.

Regarding the proportions of indels and their relationship to phenotypes, we have no way of exploring this possibility. That said, as we now clarify in the revision on lines 236-239 and in Figure 5 figure supplement 1A, our sgRNA targets amino acid ~28 of the ~3000 amino acid Shroom3 protein, making it unlikely that distinct change-of-function mutations could be introduced. We therefore feel it more appropriate not to speculate on the issue.

Moreover, please clarify which particular domain is targeted by the Shroom3 gRNA employed in this study? How is it expected to impair its function, e.g. complete loss of function, deletion of a specific functional domain? If the latter, could a truncated protein exert partial functions? How would this effect interactions with actin or N-cadherin and relate to the specific phenotypes observed?

As noted in point 8, above, the sgRNA targets amino acid ~28 of the ~3000 amino acid Shroom3 protein. This is substantially N-terminal to all defined domains in the protein. This is now stated on lines 236-239 and diagrammed in Figure 5 figure supplement 1A.

9) Shroom3 spatial expression: How is Shroom3 expressed throughout the extent of the anteroposterior neural epithelium, given that it seems to exert different effects in the anterior and posterior parts? Likewise, would be important to see the Shroom3 subcellular distribution in the neural plate to determine if there is a population of Shroom3 in medial positions analogous to N-cadherin.

Xenopus shroom3 is expressed along the entire length of the closing Xenopus neural plate (Haigo et al., Current Biology 2003), as we now indicate on lines 86-88.

As for the protein localization, unfortunately no antibodies that detect Xenopus Shroom3 are available. Compounding this problem, ectopic expression of wild-type Shroom3 causes a severe gain-of-function phenotype in early embryos, eliciting strong apical constriction of blastomeres that precludes analysis of Shroom3-GFP localization at neural plate stages.

That said, ectopically expressed, tagged Shroom3 clearly decorates the entire apical surface in diverse epithelial cell types (see Haigo, 2003; Lee et al., 2009), and this point is now made on lines 204-208 of the revision.

Finally, in a more direct attempt to attempt to address this concern, we took a cue from previous work on Drosophila Shroom, and we expressed a C-terminal (Rok-binding domain) truncation of Xenopus Shroom3.

We found his construct co-accumulates with both medial and junctional actin in the closing neural plate. These data are now discussed on lines 208-214 and shown in Figure 4 figure supplement 1.

10) Clarification of sample numbers and data integration of different samples: The samples included in this study and presented in Methods Appendix2 display apparent differences, therefore additional information is required for the number of samples that contribute to each analysis and how data were compared and/or integrated. For instance, the anterior samples show differences in cell size and asymmetries within the tissue. Please explain the reason for this and how is this accounted for when comparing quantifications between samples. This should include how staging between samples was achieved, and the related registration allowing comparison of resulting cell behaviors.

Differences in cell size and fluorescent intensity arise from many sources: (a) staging, (b) natural variation, (c) variation arising from microinjection of mRNAs, etc. As such, we have focused not so much raw values of size and intensity, but rather in the changes in these values over time. Thanks to our cell tracking paradigm, we are able to mean-center and scale cell size and fluorescence parameters per individual cell track and convert measures of area and fluorescence (i.e. microns and arbitrary units) to standard deviations. Thus both within and between embryos, each cell is analyzed individually for relative changes in parameters over time and then integrated into the overall dataset. We have clarified this in the text on lines 157-164 and is additionally diagrammed in Figure 1 figure supplement 1.

Related to this, there seem to be only two posterior samples containing Shroom3 crispant cells, please describe the variability between samples, similar to above. If only two samples were interrogated, a third sample needs to be included. In general, the sample number per experiment should be greater than two.

We have now included additional videos and analysis.

11) Describing the data analysis. Please introduce in the Results a few sentences that explain the "standardization" approach that are used to present the data. While this is in the Methods and Appendix, the approach is not one commonly seen, and it would be good to orient readers less familiar with the approach.

As described above in response to Point 10, we have improved the description of this method on lines 157164 of the main text and diagrammed the standardization in Figure 1 figure supplement 1.

12) Facilitating the interpretation of some the 2D density plots in figure 10. Consider an alternative way or simplification of the graphs without breaking the data out into several additional graphs. If difficult, a more detailed description in the methods should be helpful to the reader and included.

We have broken out a large part of the data in Figure 10 (now Figure 11) to Figure 11 figure supplement 1 to aid with legibility. These data are now described in more detail and the original plots have now been annotated to guide the reader (Figure 11C, cyan ellipses). In addition, histograms have been extracted from different regions of the plots (Figure 11H) to provide more granular view of specific results.

13) The 2D density plots in figure 6D and E have such sharp edges; they look artificial. Please check and address whether this is just an issue with the PDF, thresholding, or some other technical issue.

This was a technical issue with a background layer on the plots. This background layer has been removed in all density plots in the manuscript.

14) Discuss limitations inherent to the approach, including: (i) a relatively limited number of molecular parameters are interrogated (F-actin and N-cadherin). It is possible, for example, that changes in contractility which drive junctional shortening (relevant for the analysis in Figure 10) are due to changes in actin organization (that may not be readily captured by overall measures of quantity) or activity of Myosin II (which is not measured here). Given the scale of the experiments that are involved, it would be technically challenging to interrogate more molecular players at the same time, representing a potential limitation.

(ii) the dynamics are relatively coarse-grained. For example, changes in cadherin levels that occur over hours may not capture changes in molecular turnover.

Limitation of the approach are now discussed on lines 463-477.

https://doi.org/10.7554/eLife.66704.sa2

Article and author information

Author details

  1. Austin T Baldwin

    Department of Molecular Biosciences, University of Texas at Austin, Austin, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Supervision, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6099-0873
  2. Juliana H Kim

    Department of Molecular Biosciences, University of Texas at Austin, Austin, United States
    Contribution
    Data curation, Formal analysis, Investigation, Software
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6634-4525
  3. Hyemin Seo

    Department of Molecular Biosciences, University of Texas at Austin, Austin, United States
    Contribution
    Investigation, Methodology, Visualization
    Competing interests
    No competing interests declared
  4. John B Wallingford

    Department of Molecular Biosciences, University of Texas at Austin, Austin, United States
    Contribution
    Conceptualization, Funding acquisition, Project administration, Supervision, Writing - original draft, Writing - review and editing
    For correspondence
    wallingford@austin.utexas.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6280-8625

Funding

Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD099191)

  • John B Wallingford

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

Acknowledgements

Special thanks to Pavak Shah and Claire McWhite for assistance with coding and data analysis and to the Wallingford lab for manuscript comments. This work was funded by NICHD Ruth L Kirschstein NRSA F32 HD094521 for AB and R01HD099191 to JW.

Ethics

Approved by IACUC at UT austin: AUP-2021-00167, Expiration date: 08/16/2024.

Senior Editor

  1. Marianne E Bronner, California Institute of Technology, United States

Reviewing Editor

  1. Elke Ober, University of Copenhagen, Denmark

Reviewers

  1. Elke Ober, University of Copenhagen, Denmark
  2. Alpha Yap, University of Queensland, Australia
  3. Jeff Hildebrand

Publication history

  1. Preprint posted: January 19, 2021 (view preprint)
  2. Received: January 20, 2021
  3. Accepted: February 18, 2022
  4. Accepted Manuscript published: March 4, 2022 (version 1)
  5. Version of Record published: April 14, 2022 (version 2)

Copyright

© 2022, Baldwin 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. Austin T Baldwin
  2. Juliana H Kim
  3. Hyemin Seo
  4. John B Wallingford
(2022)
Global analysis of cell behavior and protein dynamics reveals region-specific roles for Shroom3 and N-cadherin during neural tube closure
eLife 11:e66704.
https://doi.org/10.7554/eLife.66704

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    Haikel Dridi et al.
    Research Article Updated

    Age-dependent loss of body wall muscle function and impaired locomotion occur within 2 weeks in Caenorhabditis elegans (C. elegans); however, the underlying mechanism has not been fully elucidated. In humans, age-dependent loss of muscle function occurs at about 80 years of age and has been linked to dysfunction of ryanodine receptor (RyR)/intracellular calcium (Ca2+) release channels on the sarcoplasmic reticulum (SR). Mammalian skeletal muscle RyR1 channels undergo age-related remodeling due to oxidative overload, leading to loss of the stabilizing subunit calstabin1 (FKBP12) from the channel macromolecular complex. This destabilizes the closed state of the channel resulting in intracellular Ca2+ leak, reduced muscle function, and impaired exercise capacity. We now show that the C. elegans RyR homolog, UNC-68, exhibits a remarkable degree of evolutionary conservation with mammalian RyR channels and similar age-dependent dysfunction. Like RyR1 in mammals, UNC-68 encodes a protein that comprises a macromolecular complex which includes the calstabin1 homolog FKB-2 and is immunoreactive with antibodies raised against the RyR1 complex. Furthermore, as in aged mammals, UNC-68 is oxidized and depleted of FKB-2 in an age-dependent manner, resulting in ‘leaky’ channels, depleted SR Ca2+ stores, reduced body wall muscle Ca2+ transients, and age-dependent muscle weakness. FKB-2 (ok3007)-deficient worms exhibit reduced exercise capacity. Pharmacologically induced oxidization of UNC-68 and depletion of FKB-2 from the channel independently caused reduced body wall muscle Ca2+ transients. Preventing FKB-2 depletion from the UNC-68 macromolecular complex using the Rycal drug S107 improved muscle Ca2+ transients and function. Taken together, these data suggest that UNC-68 oxidation plays a role in age-dependent loss of muscle function. Remarkably, this age-dependent loss of muscle function induced by oxidative overload, which takes ~2 years in mice and ~80 years in humans, occurs in less than 2–3 weeks in C. elegans, suggesting that reduced antioxidant capacity may contribute to the differences in lifespan among species.

    1. Cell Biology
    Desiree Schatton et al.
    Research Article

    Proliferating cells undergo metabolic changes in synchrony with cell cycle progression and cell division. Mitochondria provide fuel, metabolites, and ATP during different phases of the cell cycle, however it is not completely understood how mitochondrial function and the cell cycle are coordinated. CLUH is a post-transcriptional regulator of mRNAs encoding mitochondrial proteins involved in oxidative phosphorylation and several metabolic pathways. Here, we show a role of CLUH in regulating the expression of astrin, which is involved in metaphase to anaphase progression, centrosome integrity, and mTORC1 inhibition. We find that CLUH binds both the SPAG5 mRNA and its product astrin, and controls the synthesis and the stability of the full-length astrin-1 isoform. We show that CLUH interacts with astrin-1 specifically during interphase. Astrin-depleted cells show mTORC1 hyperactivation and enhanced anabolism. On the other hand, cells lacking CLUH show decreased astrin levels and increased mTORC1 signaling, but cannot sustain anaplerotic and anabolic pathways. In absence of CLUH, cells fail to grow during G1, and progress faster through the cell cycle, indicating dysregulated matching of growth, metabolism and cell cycling. Our data reveal a role of CLUH in coupling growth signaling pathways and mitochondrial metabolism with cell cycle progression.