Junction Mapper is a novel computer vision tool to decipher cell–cell contact phenotypes

Tight contacts with neighbours to form a cohesive sheet of cells is a fundamental property of multicellular organisms and underpins organ development and function. Conversely, signalling pathways necessary to maintain junctions are often targeted by pathogens and underlie key mechanisms of diseases of the vasculature, heart and different epithelial organs.

Attachment to neighbouring cells has a distinct configuration in different cell types and is dynamically remodelled in homeostasis and diseases. In epithelial tissues, the characteristic cell–cell adhesion site appears as a straight and tense or stiff junction, represented by an apparent contiguous, adjoining staining of E-cadherin receptors (the epithelia-specific cadherin protein). Following stimuli such as growth factor treatment or oncogene expression in epithelial cells, the dynamic nature of cell–cell contacts is manifested in a variety of ways: disturbances in the configuration of the contacting interface between cells, fragmentation of cadherin staining or thinning out of the distribution of receptors from contacting cell borders (Braga et al., 2000; Erasmus et al., 2016; Frasa et al., 2010; Lozano et al., 2008; Nola et al., 2011).

However, a linear junction appearance does not apply to other cell types that also require strong cell–cell attachment. Intercalated discs, specialised junctions in cardiomyocytes, sustain considerable mechanical stress with heart beating (Ehler, 2016; Vermij et al., 2017). Extensive remodelling of the intercalated discs composition (Estigoy et al., 2009) and architecture is observed in cardiac `aging (Sessions and Engler, 2016; Tribulova et al., 2015), diabetes-induced cardiopathies (Adeghate and Singh, 2014), arrythmogenic cardiomyopathies (Calore et al., 2015) and cardiac hypertrophy and failure (Lyon et al., 2015). During vascular homeostasis, endothelial cell–cell contacts may have a similar appearance as epithelial junctions (Dejana and Orsenigo, 2013; Malinova and Huveneers, 2018). Upon stimulation with inflammatory agonists (Radeva and Waschke, 2018), endothelial contacts undergo adjustments to increase permeability, changing from a linear to a zig-zag configuration and the appearance of gaps between cells (Malinova and Huveneers, 2018). Collectively, the above data demonstrate that distinct patterns of junctions are stimulus-dependent and reflect the specific destabilization (or strengthening) of cadherin receptors at contact sites in various cell types.

Despite the extensive scientific progress in our understanding of how cell–cell contacts are modulated, how these distinct phenotypes of junction modulation are fully attained is still unclear. A major road block to furthering mechanistic studies on junction regulation is the restricted capability and efficiency of the quantitative image analysis currently available. Existing imaging platforms (i.e. Cell Profiler) (Carpenter et al., 2006; McQuin et al., 2018) are fantastic resources for cell biologists. For Drosophila or nematode epithelia, several software are available for quantification of morphogenesis that robustly detect cell boundaries during morphogenesis (i.e. during dorsal closure or germ band extension) (Curran et al., 2017; Sumi et al., 2018). In contrast, for mammalian epithelial cells, available systems are not always suitable for the precise delineation of cell borders and the output of current pipelines is mostly morphometric parameters (i.e. cell size, shape, number or texture) (Campbell et al., 2017; McQuin et al., 2018; Yin et al., 2013). Cell–cell borders of mammalian cells are particularly difficult to detect because of cytoplasmic noise, irregular shapes (Held et al., 2011) and variable junction phenotypes, particularly when junctions are severely disrupted. This hitherto prevents an objective approach to analyse regulation of cell–cell contacts of mammalian cells and tissues.

Nevertheless, previous computer vision studies of mammalian junctions report the automated quantification of a single heterotypic junction (e.g. tumour-immune cell contact or host-pathogen contact) (Graus et al., 2014; Merouane et al., 2015), morphometry of mammary gland spheroids (Härmä et al., 2014) or dynamics of VE-cadherin contacts during cell rearrangements in angiogenesis (Bentley et al., 2014). Disruption of cell–cell contacts has been assessed in high-throughput manner by coupling junction segmentation with cell tracker and endothelia stimulation (Seebach et al., 2015), cadherin intensity at junctions (Erasmus et al., 2016) or indirectly, by increased inter-nuclear distance as cells scatter (Loerke et al., 2012).

Notwithstanding these successful studies, available methodology does not enable quantification of distinct patterns of organization of receptors or junction morphometry that are readily identified by the human eye. In addition, manual methods available for junction quantification rely on intensity levels and thresholding, which are not appropriate to detect junction attributes such as alterations in shape, length, fragmentation or continuity of cell–cell contacts. Non-intensity-based attributes of junctions are usually defined visually and/or painstakingly analysed via laborious user-dependent quantification of individual junctions. For example, the switch between a straight to undulated cell–cell contact occurs without apparent changes in receptors levels at contact sites (Otani et al., 2006). In this case, rather than alterations of receptor levels at junctions, it involves impaired signalling of the small GTPase Cdc42 to modulate the amount of contraction, making cell–cell contacts less stiff and tense (Oda et al., 2014; Otani et al., 2006).

To address the above issues, we developed a semi-automated pipeline, Junction Mapper, which can fully capture the distinct patterns of junction perturbation by diverse stimuli in various cell types. It can efficiently (i) identify cell boundaries and cell–cell corners, (ii) describe phenotypes of junction architecture and (iii) quantify parameters that reflect the distribution and organization of junctional markers along the contacting interface. To broaden the software suitability to different models, we validate the robustness of the Junction Mapper software in endothelial cells and cardiomyocytes that show distinct receptor organization and architecture when compared to epithelial cells. The repertoire of parameters distinguishes subtle differences of junction disruption and provides a fingerprint for each stimulus, with insights into modes of action and how efficient and functional junctions are.

We envisage that the analytical capabilities of Junction Mapper will be invaluable for the scientific community to perform quantitative image analysis in mechanistic and translational studies of cell–cell contacts. Most importantly, the generation of tools to facilitate unbiased phenotype identification will be a major step forward to understand how junction dynamics are modulated in homeostasis and pathologies of different tissues.

In normal epithelia, a junction between neighbouring cells usually appears as a straight, taut line, with E-cadherin receptors uniformly distributed along the contacting interface (Figure 1A). Junctions are delimited by corners between three or more cells, where a specialised type of contacts are formed (tricellular junctions) (Figure 1A). Distinct stimuli disrupt the above junction architecture in different ways, from minor reduction in levels to complete removal of adhesion receptors from contacting cells (Figure 1B). Concomitant with changes in levels, junction configuration and architecture are also compromised, which are not always captured by intensity measurements.

Figure 1

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We designed and validated a semi-automated system (Figure 2), Junction Mapper, that builds from our quantitative analysis of images from RNAi screens (Erasmus et al., 2016). Our previous software defines an E-cadherin mask to calculate the intensity specifically around junctions as a percentage of thresholded area of the whole original image. The E-cadherin mask is also used to subtract an ROI from an image of a distinct marker (i.e. F-actin), so that mostly the signal localized at contacts is considered. Junction Mapper implements novel quantification tools (corners, length and area) and a variety of novel primary and secondary parameters expressed per individual junction.

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The main advantage of Junction Mapper is to efficiently segment junctions in a variety of cell types and of different junction patterns, from linear to fragmented or disrupted contacts. The software detects the contacting interface between two cells and obtains a skeletonized edge map (summarised stepwise in Figure 2—figure supplement 1; see Materials and methods). Although semi-automated, users can adjust the outline manually by removing incorrect or adding missing lines, with subsequent refinement of the line location and geometry by the software algorithm (detailed in Appendix 1). A dilation step is applied (user controlled) to define the area to be quantified around the skeletonized map (Figure 2—figure supplement 1). To identify individual junctions for further quantification, the software then automatically identifies each cell–cell corner (i.e. point of contact between three or more cells, see its mathematical definition in Appendix 1). The number and location of corners in each cell can also be manually adjusted by the user (Appendix 1).

Images obtained at different resolutions can be used for analyses in Junction Mapper (Supplementary file 1). However, the highest quality images possible should be used, as resolution may impact the ability to detect individual clusters of the junction marker. For defining the automated skeletonized edge of each image, a suitable signal-to-noise ratio is necessary to ensure enough contrast to differentiate staining at junctions from the cytoplasm (Figure 2—figure supplement 2). The higher the Peak Signal to Noise ratio, the easier it is for the program to automatically delineate the cell outline skeleton. For efficient skeleton definition the ratio should be above 22 dB (Figure 2—figure supplement 2). Using the automated skeleton as a start point, the user can refine the cell outline map by manually drawing or removing lines to close any gaps or correct deviations, particularly in cells with reduced staining at cell–cell contacts (Figure 2—figure supplement 1 to 2). The higher the fragmentation of junctions in the image, the more user input is necessary to define the final skeleton outline for quantification.

User-controlled threshold selection is done by inspection of the image with a slider component on the software interface (Appendix 1). Thresholding aims to reduce background without removing pixels from contact areas. The skeleton obtained is then projected onto the original thresholded image to segment the area of interest and proceed with quantification of each junction for up to two different junctional markers per image (see below). The measurement of the different parameters is performed simultaneously and automatically, in an unbiased manner (Figure 2). The output per junction is produced as an Excel file that contains the selected image, skeleton and the quantification of pre-designed measurements and parameters (see below).

Primary parameters and validation

We envisage that different phenotypes of junction perturbation may require a distinct set of parameters to appropriately capture the disruption features (Figure 1). Towards this goal, we propose a variety of parameters to be used as bona fide readouts and measure information that is based on intensity, area and length of the interface and cell–cell contact (Figure 3A; Appendix 2). The following concepts are defined and used herein. Interface is the contacting membrane between two neighbouring cells and delimited by cell–cell corners. A cell–cell contact or junction is the region of the interface covered by adhesion receptors (e.g. cadherin staining). Junctions may or may not extend to the whole of the interface (corner-to-corner), and can appear fragmented or dotted (Figure 1, Figure 3A). Area is the dilated region around the skeleton and is set to encompass the width of a junctional marker staining (which can vary in thickness). Contour is the length measurement of the outline of the skeleton between defined points (interface or junction). Finally, the straight-line length is defined as the shortest distance (Euclidian distance) between corners that form the boundaries of one junction or interface.

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Data used to validate length- and area-based primary parameters.

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Primary parameters obtain the basic metrics (area, contour and straight-line length) of each interface and junction selected in the monolayer (Figure 3A, Appendix 2). A key innovation of the Junction Mapper is its ability to quantify junction marker staining that is not contiguous and that does not extend to both corners. New parameters assess area or contour occupied by fragments of the junctional staining (Fragmented Junction Contour and Junction Area). To validate the length-based measurements, individual contacts disrupted by H-Ras expression were quantified and, as predicted, decreasing values for the contour of the interface, junction or fragmented junction were revealed for each assessed junction (Figure 3B–C). Although measurements of contacts of control cells also decline between these three parameters, they did not decline to the same extent as the ones of junctions from expressing cells (Figure 3C). The area-based measurements Interface Area and Junction Area of the same junctions followed similar pattern (Figure 3D). We envisage that the primary parameters are useful to show extension or retraction of the contacting interface/junctions, increased fragmentation of the staining of junction marker or fluctuations of global staining intensity at cell–cell contacts.

We next assessed the impact of user-defined settings (dilation and threshold) on the primary parameters. The variable dilation settings define areas for quantification and permits the user to account for different thickness of the staining at cell–cell contacts, wavy or undulated junctions. Increasing the dilated area did not affect the length of contacting interface or fragmented junction (Figure 3—figure supplement 1A–C), but positively correlated with E-cadherin intensity and area (Figure 3—figure supplement 1D). Similarly, larger dilation values better captured the amount of VE-cadherin present in endothelial junctions as they acquire a zig-zag conformation after thrombin stimulation (Figure 3—figure supplement 1E–F,H). A trade-off is necessary between the amount of dilation and thresholding, so that the contribution of cytoplasmic staining is minimized with larger dilation values.

The effect of thresholding was tested using the same images (Figure 3—figure supplement 2A–B,E–F). Of note is that the precise outline of VE-cadherin zig-zag staining was not recognized by the edge map produced (red line, Figure 3—figure supplement 2E–F). Increasing the threshold applied to the original images decreased the measured cadherin area and intensity (Figure 3—figure supplement 2D, H) and the length of junction fragments was severely reduced (Figure 3—figure supplement 2C,G; see definition in Appendix 2). The interface contour values were not affected by thresholding, as the interface is delimited by corners and independent of intensity values (Figure 3—figure supplement 2C,G). Taken together, we concluded that the primary parameters measure the length and area of various features as predicted and are selectively modulated by user-controlled settings in accordance to their definition (Appendix 1).

Secondary parameters

The availability of a repertoire of parameters is a unique advantage of the software for mapping distinct phenotypes. Secondary parameters (Scheme 1, Appendix 2) are derived from the primary metrics above and aim to normalise the measurements to the size of each junction or contacting interface (length or area). The Linearity Index measures how straight an interface between two cells is, as proposed by Takeichi and colleagues (Otani et al., 2006). Coverage Index is a length measurement of the percentage of the interface length that is covered by the junction marker staining and has been previously used manually in the lab (Lozano et al., 2008). Three different parameters quantify the distribution of a junctional marker along the interface. First, Interface Occupancy, measures the area occupied by the marker within the Interface Area of each junction. Second, Junction Protein Intensity per Interface area calculates the fluorescence intensity within the Interface Area. Finally, Cluster Density is the junction protein intensity level within the area delimited by its staining (i.e. Fragmented Junction Area, which considers any fragmentation of the staining; additional parameters are described in Appendix 2). We predict that the secondary parameters are more useful to compare the accumulation or removal of specific markers, their density and relative changes when comparing across different samples.

Scheme 1

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The Coverage Index parameter calculated by Junction Mapper was validated by manual quantification using FIJI on cells expressing active Rac1 (Supplementary file 2A-B). The manual quantification used straight-line measurements (Lozano et al., 2008), rather than the more precise contour measurement by Junction Mapper (Scheme 1). Using either quantification, manual or Junction Mapper, a significant statistical difference was observed between values of control and Rac1-expressing cells (Supplementary file 2C). Lower values were obtained with the manual quantification method as predicted when using straight-lines for measurement. However, when controls values were compared between the two methods, no statistical difference was obtained (Supplementary file 2C).

Thus, the parameters give results as expected from previous manual methods. In addition, the reproducibility of secondary parameters was assessed in independent biological replicates by the same user. Across biological replicates of cells overexpressing active Rac1, the absolute values obtained for each sample were slightly different (Scheme 1B-D). However, the overall result is the same between replicates: a reduction of Coverage Index and Interface Occupancy of E-cadherin at junctions from Rac1 expressing cells (Scheme 1B-D). Comparison of control values between biological replicates (or between Rac1 expressing cells) was not statistically different.

Distinct oncogenes trigger different patterns of junction disruption in epithelia

We addressed whether Junction Mapper parameters could identify distinct features and patterns of junction disruption by different stimuli. We tested images of epithelial cells transfected with oncogenic Rac (H-Ras^G12V; Figure 4A), constitutively activated Rac1 (Rac^Q61L) or activated Src (Src^Y527F; Figure 4—figure supplement 1). Previous visual analysis indicated that perturbation of cell–cell contacts occurs more efficiently between two Rac^Q61L-expressing neighbouring keratinocytes (Braga et al., 1997; Lozano et al., 2008). To validate this quantitatively, junctions were classified into two groups (i) between two expressing cells (ee) and (ii) between one expressing and one non-expressing cell (en). Control junctions from the same image were quantified from cells without oncogenic H-Ras expression. Measurements of the primary parameters (Figure 3) confirmed that junction contour and area were more severely disrupted when two neighbouring cells contained exogenous H-Ras^G12V (Figure 4D–E). The interface of mosaic contacts shared by expressing and non-expressing cells (en) were not significantly different from controls (Figure 4B,C,F).

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Primary parameters data to prepare Figure 4B-F.

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The interface between cells containing oncogenic H-Ras (ee) was significantly longer and larger than control cells (Interface Contour and Interface Area, Figure 4B,C). Irrespective of the longer contacting interface, the contour and the area of E-cadherin stained fragments were considerably altered upon expression of H-Ras^G12V (Fragmented Junction Contour and E-cadherin Area, Figure 4D,E). Finally, the length between cell–cell corners was significantly longer between controls and two adjacent transfected cells (Straight-line Interface Length, Figure 4F). Based on the quantification of the primary parameters, oncogenic H-Ras expression induces a longer contacting interface between cells and a progressive fragmentation of E-cadherin staining.

The primary parameter measurements following expression of activated Rac1^Q61L or Src^Y527F (Figure 4—figure supplement 1) showed distinct alteration profiles when compared between each other and to activated H-Ras (Figure 4). For example, active Src expression did not promote elongation of the contacting interface or an increase in interface area (Figure 4—figure supplement 1H–I), while active Rac1 did not induce fragmentation of cadherin staining (Figure 4—figure supplement 1D–E). The above data may indicate that distinct subsets of parameters can differentiate alterations by different stimuli, thereby providing a unique disruption profile (Figure 5). While data are from one technical replicate only, the high number of junctions quantified per sample is still sufficient to indicate significant differences among groups (Supplementary file 1). However, further experimentation is required to confirm the patterns observed.

Figure 5

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Secondary parameter data on oncogenic junction disruption used for Figure 5 graphs.

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The secondary parameters (Scheme 1) were designed so that the distribution of a junctional marker is normalised to the area or length of the interface or junction between neighbouring cells. Although Intensity is a primary parameter, it was also included here for comparison with other published studies. The patterns of the secondary parameters Interface Occupancy and Cadherin Intensity at Interface Area essentially followed the corresponding primary parameters E-cadherin Area and E-Cadherin Intensity (Figures 4 and 5). Yet, the differences between groups are more apparent in the secondary parameters: data are less scattered with fewer outliers when compared to primary parameters (Figure 5).

We decided to focus on junctions that were shared by two transfected cells (ee), where phenotypes are clearer (Frasa et al., 2010; Lozano et al., 2008). Upon transfection of activated forms of H-Ras, Src or Rac1, a progressive disappearance of E-cadherin from the interface between cells was observed in different patterns (Figure 5A,C,E). When compared to controls, the undulation of the interface was increased among cells expressing H-Ras^G12V or Src^Y527F (higher Interface Linearity Index, Figure 5B,D), but remained unchanged for Rac1^Q61L-perturbed junctions (ee, Figure 5F). These data indicate that, as E-cadherin is removed from junctions, the interface between H-Ras^G12V and Src^Y527F expressing cells becomes less tensile. The percentage of the interface area occupied by E-cadherin receptors was decreased in all samples (Interface Occupancy, Figure 5B,F), but did not reach significance in Src-expressing cells (Figure 5D).

The intensity levels of cadherin at junctions was significantly reduced following transfection of activated H-Ras or Src when measured as raw values (E-cadherin Intensity) or corrected per contacting area between two cells (E-cadherin Intensity per Interface Area) (Figure 5B,D). In contrast, after active Rac1 expression, neither parameter was significantly changed. Consistent with the distinct phenotype of junction perturbation seen in Rac1^Q61L-transfected cells, the density of cadherin clusters was decreased in H-Ras^G12V and Src^Y527F, but slightly augmented in Rac^Q61L.

These data are summarized as a diagram in Scheme 2. We concluded that Junction Mapper quantification can capture the various phenotypes of junction perturbation and collectively define a specific profile for each oncogene. Our preliminary observations suggest that activated Rac1 does not significantly reduce the overall levels of E-cadherin at junctions, since receptor intensity at the interface area is not reduced. Rather, Rac1 activation progressively redistributes receptors at the junction (reduced Interface Occupancy and higher Cluster Density), maintaining straight, linear junctions. In contrast, activated H-Ras or Src disrupt junctions via fragmentation and removal of E-cadherin consistently throughout the contacting interface (decreased in Interface Occupancy and Cluster Density), with concomitant undulation of cell–cell contacts (i.e. reduced tension or stiffness at junctions).

Scheme 2

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Dynamic range of the measurements of cell–cell contact phenotypes

Disassembly of junctions by oncogenes is a severe phenotype, often leading to complete dissolution of contacts. However, other stimuli (i.e. differentiation, protein depletion, growth factor or drug treatment) may induce a milder phenotype that is not easily quantified. We asked whether the Junction Mapper could efficiently detect small changes in E-cadherin levels or distribution under different conditions (Figure 6). Datasets were obtained where the role of actin-regulatory proteins in junction formation was investigated in normal keratinocytes (Erasmus et al., 2016). Depletion of CIP4 (a regulator of cadherin trafficking) (Leibfried et al., 2008; Rolland et al., 2014), VAV2 (an exchange factor for Rho, Rac2 and Cdc42) (Vigil et al., 2010) or EEF1A (an actin bundling protein) (Mateyak and Kinzy, 2010) results in modest, but significant fluctuations in E-cadherin at junctions (20%–30%) using a thresholding method (Erasmus et al., 2016).

Figure 6

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Data from siRNA experiments used in graphs in Figure 6B, D and F.

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When analysed with Junction Mapper, CIP4, VAV2 or EEF1A siRNA did not interfere with the linearity of the contacting interface (Figure 6B,D,F), in line with the appearance of normal, linear junctions. Consistent with our previous findings (Erasmus et al., 2016), a small decrease in E-cadherin intensity was observed in both CIP4- and VAV2-depleted cells (Figure 6B,F), while EEF1A siRNA promoted the unusual phenotype of higher levels of cadherin receptors (Figure 6D). These distinct patterns were also seen when E-cadherin intensity and area were normalized to the interface area (Intensity per Interface Area and Interface Occupancy, respectively). Strikingly, despite the similar reduction in E-cadherin intensity levels following VAV2 and CIP4 depletion, receptors were removed in different ways from junctions. The clusters of cadherin receptors were less dense with lower levels of CIP4, while upon VAV2 siRNA, the density of the clusters slightly increased (Cluster density, Figure 6B,F).

Thus, the discrete changes in junctions result from reduced cadherin levels throughout the contacting interface by lower density of receptor clusters (CIP4 RNAi) or localised E-cadherin removal and redistribution into denser clusters (VAV2 RNAi; Figure 6B,F). In contrast, EEF1A depletion augmented E-cadherin Intensity and Cluster Density along the interface. Taken together, these data highlight the ability of the Junction Mapper to detect phenotypes reproducibly and with good dynamic range. Scheme 3 summarizes the changes and distinct profiles detected by Junction Mapper in our validation experiments.

Scheme 3

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Endothelial junctions and cardiomyocyte intercalated discs

We next asked whether the software would be applicable to endothelial cells and cardiomyocytes that can have junctions differently shaped when compared with epithelial contacts. Both types of junctions are considerably more fragmented than epithelial contacts, and thus it was not clear whether the parameters quantified by the Junction Mapper would be suitable.

Thrombin is a serine protease that stimulates protease-activated receptor (PAR) in endothelial cells to increase vascular permeability in inflammation and injury (Figure 7A) (Kumar et al., 2009). Visually, the junctions of endothelial cells treated with thrombin are quite distinct (Malinova and Huveneers, 2018), but so far it has not been possible to evaluate differences quantitatively. There were no significant changes in the Interface Linearity Index (Figure 7A,B), consistent with the limitation of Junction Mapper to skeletonize zig-zagged junctions and thus measure their length (Figure 3—figure supplement 2E–F).

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Data from endothelial cell stimulation used in graphs in Figure 7B–F.

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However, with appropriate dilation values (Supplementary file 1, Figure 3—figure supplement 1), alterations were detected for area-based measurements. In thrombin-treated cells, VE-cadherin distribution along the interface area (Interface Occupancy), intensity of VE-cadherin staining and normalised intensity per contacting interface were substantially higher (Figure 7C–E). The density of VE-cadherin clusters was also enhanced upon treatment with thrombin (Figure 7F), implying that a higher number of receptors is recruited per contact area. Of note is that the analysis of stimulated endothelial cells, with their typical junction morphology and gaps, was strongly influenced by the user-controlled settings (Figure 7—figure supplement 1A–C). Yet, although raw values of each junction varied with different user settings, the overall trend and conclusion remained the same (Figure 7—figure supplement 1D–F), consistent with our previous comparative analyses (Supplementary files 2–3).

Rat neonatal cardiomyocytes were treated with phenylephrine (PE) as a model to induce hypertrophy (Miragoli et al., 2011; Simpson, 1985). Cells were co-stained with β-catenin as a junctional marker and connexin 43 (Figure 8A), a protein found in gap junctions, a structure necessary for synchronization of cardiomyocyte beating. The appearance of both markers at junctions was considerably fragmented (dotted appearance; Figure 8A), suggesting that parameters that consider the staining area would be the most appropriate.

Figure 8

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Data obtained from cardiomyocyte experiments used in Figure 8 graphs.

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The software used the skeleton and dilated area of β-catenin as a mask as described by Erasmus et al. (2016) to segment the area where connexin 43 was localised. The Interface Linearity Index of β-catenin (Figure 8B) or connexin 43 (Figure 8C) was not significantly altered upon induction of hypertrophy in cellulo. Instead, hypertrophy stimulation promoted higher Interface Occupancy and higher levels of β-catenin and connexin 43 at junctions (raw intensity or corrected by the interface area; Figure 8B,C). However, the density of β-catenin clusters was significantly reduced, while connexin 43 density was augmented in PE-treated cells, in a small but significant manner. Thus, the two markers are modulated differently by hypertrophy stimulation in cellulo. Increased levels of β-catenin are achieved by less dense clusters spread along the interface, while connexin 43 molecules are clustered more densely, suggesting localised stimulation of gap junction formation. These data demonstrate the use of Junction Mapper for multiple cell types and its power to correlate the distribution of different proteins at cell–cell junctions. The data from cardiomyocytes and endothelial cells are summarized in Scheme 4. A heuristic approach on how to define the user-dependent settings and the parameters to use for these cell types are described in Supplementary file 4.

Scheme 4

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Junction configuration and adhesion receptor organization at contact sites are maintained via a complex interplay of distinct cytoskeletal filaments and associated regulatory proteins, with an exquisite regulation by diverse pathways and cellular contractility. At cell–cell contact sites, the challenge remains to translate the regulation of junctional components into functional and appropriately shaped cell–cell contacts. Junction Mapper facilitates the profiling of cell–cell contact behaviour with a variety of novel parameters in a fast, robust and reliable manner. Collectively, the parameter repertoire indicates how effective cell–cell adhesion is, identifies altered patterns of receptor distribution and guides experimental design to unravel the underlying molecular mechanisms.

Junction Mapper provides a semi-automated computer vision solution with broad applicability. The most innovative aspects of Junction Mapper are, first, the measurement of receptor density and occupancy, via the normalization of the junction marker intensity, length and area to the available contacting interface and cell–cell contact area. Second, the automatic quantification of fragmented staining at a junction, which has not been feasible previously and, third, the correlation of the parameters from two junctional markers along the same junction. Underpinning the above aspects is the ability of Junction Mapper to detect cell borders and corners when considerable disruption is observed. Machine learning algorithms certainly have the potential to improve and automate segmentation success, especially of highly fragmented contacts. However, the availability of training image datasets with boundaries manually-generated is still a bottleneck (Häring et al., 2018). One possible use of the software is therefore the generation of precise cell outline skeletons that can be used to train machine-learning algorithms in the future.

While semi-automation has been implemented during the image processing by Junction Mapper, user contribution is necessary and essential to perform the quality control of skeleton outline, corner positioning and setting up the dilation and threshold levels in a given dataset. User bias is particularly relevant in images with very disrupted and irregular junctions, and absolute values are not suitable for comparison across experiments in some cases. However, user bias can be minimized. Analysis of biological replicates or independent analysis by different users show that (i) the secondary parameters are more robust against user bias and (ii) obtained results are similar across replicates when comparing control and treated samples.

The software can detect both major and minor changes at junctions in different experiments. Unexpected and distinct profiles emerge from junction disruption by oncogenes that potently remove E-cadherin from contacts. The junctional defects caused by H-Ras^G12V or Src^Y527F illustrate the novel parameters that measure junction fragmentation and the specific density of E-cadherin in remaining fragments. In contrast, the perturbation profile induced by expression of Rac1^Q61L is not appropriately quantified by intensity alone and may be better assessed by ‘Interface Occupancy’, ‘Coverage Index’ and ‘Cluster Density’. Among the repertoire of pre-defined parameters, we find that a subset is highly likely to assess the features of a particular junction phenotype. Phenotypes not yet analysed could present additional possibilities to improve the Junction Mapper repertoire in future studies.

Junction Mapper analysis confirms mild phenotypes previously observed with manual, threshold-based quantifications of whole images (Erasmus et al., 2016), that indicate either an increase (EEF1A siRNA) or a decrease (CIP4 or VAV2 siRNA) in the intensity levels of E-cadherin. In addition, new Junction Mapper parameters uncover distinct features, that is that EEF1A depleted cells have higher E-cadherin occupancy and augmented cluster density when compared to controls. The profiling with new parameters underscores the potential of Junction Mapper to differentiate among distinct, subtle modes of junction perturbation. Yet, the conceptual significance of such alterations remains to be consolidated in future experiments and with additional biological replicates.

The plasticity of endothelial junctions is well established (Radeva and Waschke, 2018), but their unique responses to different stimuli have been challenging to quantify (Häring et al., 2018). The remarkable zig-zag pattern of thrombin-stimulated endothelial junctions correlates with increased vascular permeability (Malinova and Huveneers, 2018) but it is not recognized by the automated skeleton definition. Using area-based parameters, we find that the contacting interface is occupied more efficiently, with higher density of VE-cadherin receptors at endothelial junctions upon thrombin treatment. Thus, it seems that the increased levels of VE-cadherin at junctions may promote stronger endothelial adhesion, which is relevant to sustain elevated intracellular tension and contractility induced by thrombin stimulation. These results are consistent with the role of mechanical tension in receptor modulation and integrity of multicellular tissues (Liu et al., 2010), and merit further experimentation.

Because of the fragmented and undulated nature of cardiomyocyte contacts (Ehler, 2016; Vermij et al., 2017), quantitative imaging tools specifically designed for intercalated discs have not been available or systematically used. At steady state, our data show that, in control cardiomyocytes, intercalated discs have clusters of cadherin receptors that are far apart. Hypertrophic stimulus in cellulo (neonatal rat cardiomyocytes) potently increases the levels of β-catenin at contacting interfaces, consistent with what was reported in hamster and human hypertrophic hearts (Masuelli et al., 2003). Connexin 43 is a major connexin isoform found in cardiomyocytes and its total protein and mRNA levels are augmented by hypertrophic signals in cellulo (i.e. phenylephrine) (Salameh et al., 2008; Stanbouly et al., 2008) or in human hearts with compensated left-ventricular hypertrophy by pressure-overloading (Kostin et al., 2004). The initial profiling analysis with Junction Mapper suggest that the formation of gap junctions is enhanced after hypertrophy stimulation in cellulo, confirming the broadening of intercalated disc area and higher number of GAP junctions in compensated hypertrophic hearts of human patients (Kostin et al., 2004). Although hypertrophic stimulus increases both β-catenin and connexin 43 levels at intercalated discs, these markers are regulated in distinct ways. The β-catenin cluster density is decreased leading to a more contiguous distribution, while connexin 43 is localised in clusters of higher density.

Clearly, further investigation is necessary to ascertain the profiling and biological significance of the phenotypes observed in different models. The quantification of very fragmented and zig-zagged junctions such as those in endothelia and cardiomyocytes is a challenge that Junction Mapper has begun to address, but improvements in future computation studies are welcomed. The complexity of these junctions also requires more user input to quality control the definition of the skeleton and corners. In addition, the ability of Junction Mapper to quantify junctions in a stratified epithelium, where the added complexity of multiple epithelial cell layers provides an additional challenge, remains to be tested.

We foresee the potential of Junction Mapper in distinct research areas, due to its innovative and in-depth approach to quantify cell–cell contacts. The detailed fine mapping of junction properties forms a basis to distinguish between disassembly mechanisms and infer cellular processes such as intracellular trafficking, receptor clustering or modulation of contraction at junctions. As multiple cellular processes contribute to junction stability, the fingerprinting of junction phenotypes after different stimuli is a powerful tool in pathway inference and guides rescue and translational experiments. Despite its limitations, Junction Mapper’s broad dynamic range, repertoire of novel parameters and applicability to quantify junctions in various cell types will have a significant impact on studies in numerous model systems.

The Junction Mapper code is licensed in github as GNU GENERAL PUBLIC LICENSE. The address is: https://github.com/ImperialCollegeLondon/Junction_Mapper (https://github.com/elifesciences-publications/Junction_Mapper). The software is downloadable as an executable jar file from https://dataman.bioinformatics.ic.ac.uk/junction_mapper/. The image data used in this study has been previously published elsewhere (Erasmus et al., 2016; Huveneer et al., 2012) or are in preparation in separate mechanistic studies (Bruche et al., in preparation). Excel files of the output of parameters and calculations has been provided as source data files online.

1. 1. Adeghate E
   2. Singh J

   (2014) Structural changes in the myocardium during diabetes-induced cardiomyopathy

   Heart Failure Reviews 19:15–23.

   https://doi.org/10.1007/s10741-013-9388-5

   • PubMed
   • Google Scholar
2. 1. Arcelli C
   2. Cordella L
   3. Levialdi S

   (1975) Parallel thinning of binary pictures

   Electronics Letters 11:148.

   https://doi.org/10.1049/el:19750113

   • Google Scholar
3. 1. Bentley K
   2. Franco CA
   3. Philippides A
   4. Blanco R
   5. Dierkes M
   6. Gebala V
   7. Stanchi F
   8. Jones M
   9. Aspalter IM
   10. Cagna G
   11. Weström S
   12. Claesson-Welsh L
   13. Vestweber D
   14. Gerhardt H

   (2014) The role of differential VE-cadherin dynamics in cell rearrangement during angiogenesis

   Nature Cell Biology 16:309–321.

   https://doi.org/10.1038/ncb2926

   • PubMed
   • Google Scholar
4. 1. Braga VM
   2. Machesky LM
   3. Hall A
   4. Hotchin NA

   (1997) The small GTPases rho and rac are required for the establishment of cadherin-dependent cell-cell contacts

   The Journal of Cell Biology 137:1421–1431.

   https://doi.org/10.1083/jcb.137.6.1421

   • PubMed
   • Google Scholar
5. 1. Braga VM
   2. Betson M
   3. Li X
   4. Lamarche-Vane N

   (2000) Activation of the small GTPase rac is sufficient to disrupt cadherin-dependent cell-cell adhesion in normal human keratinocytes

   Molecular Biology of the Cell 11:3703–3721.

   https://doi.org/10.1091/mbc.11.11.3703

   • PubMed
   • Google Scholar
6. 1. Calore M
   2. Lorenzon A
   3. De Bortoli M
   4. Poloni G
   5. Rampazzo A

   (2015) Arrhythmogenic cardiomyopathy: a disease of intercalated discs

   Cell and Tissue Research 360:491–500.

   https://doi.org/10.1007/s00441-014-2015-5

   • PubMed
   • Google Scholar
7. 1. Campbell L
   2. Kumar M
   3. Turner S

   (2017) Using CellProfiler to analyze and quantify vascular morphology

   Methods in Molecular Biology 1544:179–189.

   https://doi.org/10.1007/978-1-4939-6722-3_13

   • PubMed
   • Google Scholar
8. 1. Carpenter AE
   2. Jones TR
   3. Lamprecht MR
   4. Clarke C
   5. Kang IH
   6. Friman O
   7. Guertin DA
   8. Chang JH
   9. Lindquist RA
   10. Moffat J
   11. Golland P
   12. Sabatini DM

   (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes

   Genome Biology 7:R100.

   https://doi.org/10.1186/gb-2006-7-10-r100

   • PubMed
   • Google Scholar
9. 1. Curran S
   2. Strandkvist C
   3. Bathmann J
   4. de Gennes M
   5. Kabla A
   6. Salbreux G
   7. Baum B

   (2017) Myosin II controls junction fluctuations to guide epithelial tissue ordering

   Developmental Cell 43:480–492.

   https://doi.org/10.1016/j.devcel.2017.09.018

   • PubMed
   • Google Scholar
10. 1. Dejana E
    2. Orsenigo F

    (2013) Endothelial adherens junctions at a glance

    Journal of Cell Science 126:2545–2549.

    https://doi.org/10.1242/jcs.124529

    • PubMed
    • Google Scholar
11. 1. Ehler E

    (2016) Cardiac cytoarchitecture — why the “hardware” is important for heart function!

    Biochimica Et Biophysica Acta (BBA) - Molecular Cell Research 1863:1857–1863.

    https://doi.org/10.1016/j.bbamcr.2015.11.006

    • Google Scholar
12. 1. Erasmus JC
    2. Bruche S
    3. Pizarro L
    4. Maimari N
    5. Pogglioli T
    6. Tomlinson C
    7. Lees J
    8. Zalivina I
    9. Wheeler A
    10. Alberts A
    11. Russo A
    12. Braga VM

    (2016) Defining functional interactions during biogenesis of epithelial junctions

    Nature Communications 7:13542.

    https://doi.org/10.1038/ncomms13542

    • PubMed
    • Google Scholar
13. 1. Estigoy CB
    2. Pontén F
    3. Odeberg J
    4. Herbert B
    5. Guilhaus M
    6. Charleston M
    7. Ho JWK
    8. Cameron D
    9. Dos Remedios CG

    (2009) Intercalated discs: multiple proteins perform multiple functions in non-failing and failing human hearts

    Biophysical Reviews 1:43–49.

    https://doi.org/10.1007/s12551-008-0007-y

    • PubMed
    • Google Scholar
14. 1. Ferreira T
    2. Rasband WS

    (2012) ImageJ User Guide - IJ 1

    ImageJ User Guide - IJ 1, 46, https://imagej.net/docs/guide/user-guide.pdf.

    https://imagej.net/docs/guide/user-guide.pdf

    • Google Scholar
15. 1. Frasa MA
    2. Maximiano FC
    3. Smolarczyk K
    4. Francis RE
    5. Betson ME
    6. Lozano E
    7. Goldenring J
    8. Seabra MC
    9. Rak A
    10. Ahmadian MR
    11. Braga VM

    (2010) Armus is a Rac1 effector that inactivates Rab7 and regulates E-cadherin degradation

    Current Biology 20:198–208.

    https://doi.org/10.1016/j.cub.2009.12.053

    • PubMed
    • Google Scholar
16. 1. Graus MS
    2. Pehlke C
    3. Wester MJ
    4. Davidson LB
    5. Steinberg SL
    6. Neumann AK

    (2014) A new tool to quantify receptor recruitment to cell contact sites during host-pathogen interaction

    PLOS Computational Biology 10:e1003639.

    https://doi.org/10.1371/journal.pcbi.1003639

    • PubMed
    • Google Scholar
17. Preprint

    1. Häring M
    2. Großhans J
    3. Wolf F
    4. Eule S

    (2018) Automated segmentation of epithelial tissue using Cycle-Consistent generative adversarial networks

    bioRxiv.

    https://doi.org/10.1101/311373

    • Google Scholar
18. 1. Härmä V
    2. Schukov HP
    3. Happonen A
    4. Ahonen I
    5. Virtanen J
    6. Siitari H
    7. Åkerfelt M
    8. Lötjönen J
    9. Nees M

    (2014) Quantification of dynamic morphological drug responses in 3D organotypic cell cultures by automated image analysis

    PLOS ONE 9:e96426.

    https://doi.org/10.1371/journal.pone.0096426

    • PubMed
    • Google Scholar
19. 1. Held C
    2. Palmisano R
    3. Häberle L
    4. Hensel M
    5. Wittenberg T

    (2011) Comparison of parameter-adapted segmentation methods for fluorescence micrographs

    Cytometry Part A 79A:933–945.

    https://doi.org/10.1002/cyto.a.21122

    • Google Scholar
20. 1. Kostin S
    2. Dammer S
    3. Hein S
    4. Klovekorn WP
    5. Bauer EP
    6. Schaper J

    (2004) Connexin 43 expression and distribution in compensated and decompensated cardiac hypertrophy in patients with aortic Stenosis

    Cardiovascular Research 62:426–436.

    https://doi.org/10.1016/j.cardiores.2003.12.010

    • PubMed
    • Google Scholar
21. 1. Kumar P
    2. Shen Q
    3. Pivetti CD
    4. Lee ES
    5. Wu MH
    6. Yuan SY

    (2009) Molecular mechanisms of endothelial hyperpermeability: implications in inflammation

    Expert Reviews in Molecular Medicine 11:e19.

    https://doi.org/10.1017/S1462399409001112

    • PubMed
    • Google Scholar
22. 1. Lamarche N
    2. Tapon N
    3. Stowers L
    4. Burbelo PD
    5. Aspenström P
    6. Bridges T
    7. Chant J
    8. Hall A

    (1996) Rac and Cdc42 induce actin polymerization and G1 cell cycle progression independently of p65PAK and the JNK/SAPK MAP kinase cascade

    Cell 87:519–529.

    https://doi.org/10.1016/S0092-8674(00)81371-9

    • PubMed
    • Google Scholar
23. 1. Leibfried A
    2. Fricke R
    3. Morgan MJ
    4. Bogdan S
    5. Bellaiche Y

    (2008) Drosophila Cip4 and WASp define a branch of the Cdc42-Par6-aPKC pathway regulating E-cadherin endocytosis

    Current Biology 18:1639–1648.

    https://doi.org/10.1016/j.cub.2008.09.063

    • PubMed
    • Google Scholar
24. 1. Liu Z
    2. Tan JL
    3. Cohen DM
    4. Yang MT
    5. Sniadecki NJ
    6. Ruiz SA
    7. Nelson CM
    8. Chen CS

    (2010) Mechanical tugging force regulates the size of cell-cell junctions

    PNAS 107:9944–9949.

    https://doi.org/10.1073/pnas.0914547107

    • PubMed
    • Google Scholar
25. 1. Loerke D
    2. le Duc Q
    3. Blonk I
    4. Kerstens A
    5. Spanjaard E
    6. Machacek M
    7. Danuser G
    8. de Rooij J

    (2012) Quantitative imaging of epithelial cell scattering identifies specific inhibitors of cell motility and cell-cell dissociation

    Science Signaling 5:rs5.

    https://doi.org/10.1126/scisignal.2002677

    • PubMed
    • Google Scholar
26. 1. Lozano E
    2. Frasa MA
    3. Smolarczyk K
    4. Knaus UG
    5. Braga VM

    (2008) PAK is required for the disruption of E-cadherin adhesion by the small GTPase rac

    Journal of Cell Science 121:933–938.

    https://doi.org/10.1242/jcs.016121

    • PubMed
    • Google Scholar
27. 1. Lyon RC
    2. Zanella F
    3. Omens JH
    4. Sheikh F

    (2015) Mechanotransduction in cardiac hypertrophy and failure

    Circulation Research 116:1462–1476.

    https://doi.org/10.1161/CIRCRESAHA.116.304937

    • PubMed
    • Google Scholar
28. 1. Malinova TS
    2. Huveneers S

    (2018) Sensing of cytoskeletal forces by asymmetric adherens junctions

    Trends in Cell Biology 28:328–341.

    https://doi.org/10.1016/j.tcb.2017.11.002

    • PubMed
    • Google Scholar
29. 1. Masuelli L
    2. Bei R
    3. Sacchetti P
    4. Scappaticci I
    5. Francalanci P
    6. Albonici L
    7. Coletti A
    8. Palumbo C
    9. Minieri M
    10. Fiaccavento R
    11. Carotenuto F
    12. Fantini C
    13. Carosella L
    14. Modesti A
    15. Di Nardo P

    (2003) Beta-catenin accumulates in intercalated disks of hypertrophic cardiomyopathic hearts

    Cardiovascular Research 60:376–387.

    https://doi.org/10.1016/j.cardiores.2003.08.005

    • PubMed
    • Google Scholar
30. 1. Mateyak MK
    2. Kinzy TG

    (2010) eEF1A: thinking outside the ribosome

    Journal of Biological Chemistry 285:21209–21213.

    https://doi.org/10.1074/jbc.R110.113795

    • PubMed
    • Google Scholar
31. 1. McQuin C
    2. Goodman A
    3. Chernyshev V
    4. Kamentsky L
    5. Cimini BA
    6. Karhohs KW
    7. Doan M
    8. Ding L
    9. Rafelski SM
    10. Thirstrup D
    11. Wiegraebe W
    12. Singh S
    13. Becker T
    14. Caicedo JC
    15. Carpenter AE

    (2018) CellProfiler 3.0: next-generation image processing for biology

    PLOS Biology 16:e2005970.

    https://doi.org/10.1371/journal.pbio.2005970

    • PubMed
    • Google Scholar
32. 1. Merouane A
    2. Rey-Villamizar N
    3. Lu Y
    4. Liadi I
    5. Romain G
    6. Lu J
    7. Singh H
    8. Cooper LJ
    9. Varadarajan N
    10. Roysam B

    (2015) Automated profiling of individual cell-cell interactions from high-throughput time-lapse imaging microscopy in nanowell grids (TIMING)

    Bioinformatics 31:3189–3197.

    https://doi.org/10.1093/bioinformatics/btv355

    • PubMed
    • Google Scholar
33. 1. Miragoli M
    2. Moshkov A
    3. Novak P
    4. Shevchuk A
    5. Nikolaev VO
    6. El-Hamamsy I
    7. Potter CMF
    8. Wright P
    9. Kadir SHSA
    10. Lyon AR
    11. Mitchell JA
    12. Chester AH
    13. Klenerman D
    14. Lab MJ
    15. Korchev YE
    16. Harding SE
    17. Gorelik J

    (2011) Scanning ion conductance microscopy: a convergent high-resolution technology for multi-parametric analysis of living cardiovascular cells

    Journal of the Royal Society Interface 8:913–925.

    https://doi.org/10.1098/rsif.2010.0597

    • Google Scholar
34. 1. Nola S
    2. Daigaku R
    3. Smolarczyk K
    4. Carstens M
    5. Martin-Martin B
    6. Longmore G
    7. Bailly M
    8. Braga VM

    (2011) Ajuba is required for rac activation and maintenance of E-cadherin adhesion

    The Journal of Cell Biology 195:855–871.

    https://doi.org/10.1083/jcb.201107162

    • PubMed
    • Google Scholar
35. 1. Oda Y
    2. Otani T
    3. Ikenouchi J
    4. Furuse M

    (2014) Tricellulin regulates junctional tension of epithelial cells at Tricellular contacts through Cdc42

    Journal of Cell Science 127:4201–4212.

    https://doi.org/10.1242/jcs.150607

    • PubMed
    • Google Scholar
36. 1. Otani T
    2. Ichii T
    3. Aono S
    4. Takeichi M

    (2006) Cdc42 GEF tuba regulates the junctional configuration of simple epithelial cells

    The Journal of Cell Biology 175:135–146.

    https://doi.org/10.1083/jcb.200605012

    • PubMed
    • Google Scholar
37. 1. Poiker T
    2. Douglas DH

    (2011) Reflection essay: algorithms for the reduction of the number of points required to represent a digitized line or its caricature

    Classics in Cartography pp. 29–36.

    https://doi.org/10.1002/9780470669488.ch3

    • Google Scholar
38. 1. Radeva MY
    2. Waschke J

    (2018) Mind the gap: mechanisms regulating the endothelial barrier

    Acta Physiologica 222:e12860.

    https://doi.org/10.1111/apha.12860

    • Google Scholar
39. 1. Rolland Y
    2. Marighetti P
    3. Malinverno C
    4. Confalonieri S
    5. Luise C
    6. Ducano N
    7. Palamidessi A
    8. Bisi S
    9. Kajiho H
    10. Troglio F
    11. Shcherbakova OG
    12. Dunn AR
    13. Oldani A
    14. Lanzetti L
    15. Di Fiore PP
    16. Disanza A
    17. Scita G

    (2014) The CDC42-interacting protein 4 controls epithelial cell cohesion and tumor dissemination

    Developmental Cell 30:553–568.

    https://doi.org/10.1016/j.devcel.2014.08.006

    • PubMed
    • Google Scholar
40. 1. Sage D
    2. Unser M

    (2003) Teaching image-processing programming in Java

    IEEE Signal Processing Magazine 20:43–52.

    https://doi.org/10.1109/MSP.2003.1253553

    • Google Scholar
41. 1. Salameh A
    2. Krautblatter S
    3. Baessler S
    4. Karl S
    5. Rojas Gomez D
    6. Dhein S
    7. Pfeiffer D

    (2008) Signal transduction and transcriptional control of cardiac connexin43 up-regulation after alpha ₁-adrenoceptor stimulation

    The Journal of Pharmacology and Experimental Therapeutics 326:315–322.

    https://doi.org/10.1124/jpet.108.136663

    • PubMed
    • Google Scholar
42. 1. Schindelin J
    2. Arganda-Carreras I
    3. Frise E
    4. Kaynig V
    5. Longair M
    6. Pietzsch T
    7. Preibisch S
    8. Rueden C
    9. Saalfeld S
    10. Schmid B
    11. Tinevez JY
    12. White DJ
    13. Hartenstein V
    14. Eliceiri K
    15. Tomancak P
    16. Cardona A

    (2012) Fiji: an open-source platform for biological-image analysis

    Nature Methods 9:676–682.

    https://doi.org/10.1038/nmeth.2019

    • PubMed
    • Google Scholar
43. 1. Seebach J
    2. Taha AA
    3. Lenk J
    4. Lindemann N
    5. Jiang X
    6. Brinkmann K
    7. Bogdan S
    8. Schnittler HJ

    (2015) The CellBorderTracker, a novel tool to quantitatively analyze spatiotemporal endothelial junction dynamics at the subcellular level

    Histochemistry and Cell Biology 144:517–532.

    https://doi.org/10.1007/s00418-015-1357-8

    • PubMed
    • Google Scholar
44. 1. Sessions AO
    2. Engler AJ

    (2016) Mechanical regulation of cardiac aging in model systems

    Circulation Research 118:1553–1562.

    https://doi.org/10.1161/CIRCRESAHA.116.307472

    • PubMed
    • Google Scholar
45. 1. Simpson P

    (1985) Stimulation of hypertrophy of cultured neonatal rat heart cells through an alpha 1-adrenergic receptor and induction of beating through an alpha 1- and beta 1-adrenergic receptor interaction. Evidence for independent regulation of growth and beating

    Circulation Research 56:884–894.

    https://doi.org/10.1161/01.RES.56.6.884

    • PubMed
    • Google Scholar
46. 1. Stanbouly S
    2. Kirshenbaum LA
    3. Jones DL
    4. Karmazyn M

    (2008) Sodium hydrogen exchange 1 (NHE-1) regulates connexin 43 expression in cardiomyocytes via reverse mode sodium calcium exchange and c-Jun NH2-terminal kinase-dependent pathways

    Journal of Pharmacology and Experimental Therapeutics 327:105–113.

    https://doi.org/10.1124/jpet.108.140228

    • PubMed
    • Google Scholar
47. 1. Sumi A
    2. Hayes P
    3. D'Angelo A
    4. Colombelli J
    5. Salbreux G
    6. Dierkes K
    7. Solon J

    (2018) Adherens junction length during tissue contraction is controlled by the mechanosensitive activity of actomyosin and junctional recycling

    Developmental Cell 47:453–463.

    https://doi.org/10.1016/j.devcel.2018.10.025

    • PubMed
    • Google Scholar
48. 1. Tribulova N
    2. Egan Benova T
    3. Szeiffova Bacova B
    4. Viczenczova C
    5. Barancik M

    (2015)

    New aspects of pathogenesis of atrial fibrillation: remodelling of intercalated discs

    Journal of Physiology and Pharmacology 66:625–634.

    • PubMed
    • Google Scholar
49. 1. Vermij SH
    2. Abriel H
    3. van Veen TAB

    (2017) Refining the molecular organization of the cardiac intercalated disc

    Cardiovascular Research 113:259–275.

    https://doi.org/10.1093/cvr/cvw259

    • Google Scholar
50. 1. Vigil D
    2. Cherfils J
    3. Rossman KL
    4. Der CJ

    (2010) Ras superfamily GEFs and GAPs: validated and tractable targets for Cancer therapy?

    Nature Reviews Cancer 10:842–857.

    https://doi.org/10.1038/nrc2960

    • PubMed
    • Google Scholar
51. 1. Yin Z
    2. Sadok A
    3. Sailem H
    4. McCarthy A
    5. Xia X
    6. Li F
    7. Garcia MA
    8. Evans L
    9. Barr AR
    10. Perrimon N
    11. Marshall CJ
    12. Wong ST
    13. Bakal C

    (2013) A screen for morphological complexity identifies regulators of switch-like transitions between discrete cell shapes

    Nature Cell Biology 15:860–871.

    https://doi.org/10.1038/ncb2764

    • PubMed
    • Google Scholar

Author details

1. Helena Brezovjakova

   National Heart and Lung Institute, National Institutes of Health, London, United Kingdom

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Writing - original draft, Performed the formal analyses, software validation and visualization of the data

   Contributed equally with

   Chris Tomlinson

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3554-6084

2. Chris Tomlinson

   Bioinformatics Data Science Group, Faculty of Medicine, Imperial College London, London, United Kingdom

   Contribution

   Software, Supervision, Validation, Methodology, Writing - original draft, Designed the software and computational solutions, Produced the instructions video and website

   Contributed equally with

   Helena Brezovjakova

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8634-6111

3. Noor Mohd Naim

   National Heart and Lung Institute, National Institutes of Health, London, United Kingdom

   Present address

   PAPRSB Institute of Health Sciences, Universiti Brunei, Darussalam, Brunei

   Contribution

   Data curation, Investigation, Methodology, Provided resources (image datasets)

   Competing interests

   No competing interests declared

4. Pamela Swiatlowska

   National Heart and Lung Institute, National Institutes of Health, London, United Kingdom

   Contribution

   Formal analysis, Validation, Visualization, Methodology, Writing - original draft, provided resources (image datasets)

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3705-7495

5. Jennifer C Erasmus

   National Heart and Lung Institute, National Institutes of Health, London, United Kingdom

   Contribution

   Data curation, Supervision, Investigation, Visualization, Methodology, Writing - original draft, provided resources (image datasets) and software validation

   Competing interests

   No competing interests declared

6. Stephan Huveneers

   Department Medical Biochemistry, Amsterdam Cardiovascular Sciences, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Resources, Data curation, Validation, Writing - original draft, Writing - review and editing, Provided datasets

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1091-475X

7. Julia Gorelik

   National Heart and Lung Institute, National Institutes of Health, London, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing, Provided datasets

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1148-9158

8. Susann Bruche

   National Heart and Lung Institute, National Institutes of Health, London, United Kingdom

   Present address

   Department of Anatomy, Physiology and Genetics, Oxford University, Oxford, United Kingdom

   Contribution

   Conceptualization, Software, Supervision, Investigation, Visualization, Methodology, Writing - review and editing, provided resources (image datasets) and software validation

   For correspondence

   susann.bruche@dpag.ox.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5814-7166

9. Vania MM Braga

   National Heart and Lung Institute, National Institutes of Health, London, United Kingdom

   Contribution

   Conceptualization, Software, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   v.braga@imperial.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0546-7163

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