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

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 approach elucidates new 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.

Methods
**Updated 7-25-2022 to correct some errors in cell tracking and shroom3 crispant identification, as well as add stretch and orientation values**

All figure references are to the corresponding publication.

Full citations are also in the corresponding publication.

Imaging

Xenopus tropicalis embryos injected with mRNAs encoding LifeAct-RFP and N-cadherin-GFP were held at 25C 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 minutes.  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 9 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

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 frame/minute 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 to 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 mediolateral axis of the embryo was set at 0° and the anteroposterior axis of the embryo was set at 90° (Figure 11B).