During Drosophila gastrulation, subapical junctions are repositioned toward the apical surface and, as cortical tension increases, are strengthened in a myosin II–dependent manner, which may reflect a mechanosensitive response of junctional complexes to the tension generated by the activation of myosin.

The polarity protein Par3/Bazooka (Baz) has been established as a central component of the apical basal polarity system that determines the position of cell-cell junctions in epithelial cells. Consistent with that view, we show that shortly before gastrulation in Drosophila, Baz protein in the mesoderm is down-regulated from junctional sites in response to Snail (Sna) expression. This down-regulation leads to a specific decrease in adherens junctions without affecting other E-Cadherin pools. However, we further show that, interactions between Baz and junctions are not unidirectional. During apical constriction and the internalization of the mesoderm, down-regulation of Baz is transiently blocked as adherens junctions shift apically and are strengthened in response to tension generated by contractile actomyosin. When such junction remodeling is prevented by down-regulating myosin, Baz is lost prematurely in mesodermal epithelium. During such apical shifts, Baz is initially left behind as the junction shifts position, but then re-accumulates at the new location of the junctions. On the dorsal side of the embryo, a similar pattern of myosin activity appears to limit the basal shift in junctions normally driven by Baz that controls epithelium folding. Our results suggest a model where the sensitivity of Baz to Sna expression leads to the Sna-dependent junction disassembly required for a complete epithelium-mesenchymal transition. Meanwhile this loss of Baz-dependent junction maintenance is countered by the myosin-based mechanism which promotes an apical shift and strengthening of junctions accompanied by a transient re-positioning and maintenance of Baz proteins.

Motivation: While multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One feasible solution is using deep neural networks to model the localization relationship between two proteins so that the localization of a protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflects the modeled relationship. Accordingly, observing the predictions via repeatedly manipulating input localizations is an explainable and feasible way to analyze the modeled relationships between the input and the predicted proteins. Results: We propose a Protein Localization Prediction (PLP) method using a cGAN named Four-dimensional Reslicing Generative Adversarial Network (4DR-GAN) to digitally generate additional channels. 4DR-GAN models the joint probability distribution of imaged and target proteins by simultaneously incorporating the protein localization signals in four dimensions including space and time. Because protein localization often correlates with protein activation state, with accurate PLP, we further propose two novel tools: digital activation (DA) and digital inactivation (DI) to digitally activate and inactivate a protein and observe the response of the predicted protein localization. Compared with genetic approaches, these tools allow precise spatial and temporal control. A comprehensive experiment on four groups of proteins shows that 4DR-GAN achieves higher-quality PLP than Pix2Pix and the DA and DI responses are consistent with the known protein functions. The proposed PLP method helps simultaneously visualize additional proteins and DA and DI provide guidance to study localization-based protein functions.

Motivation While multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One solution is using deep neural networks to model the localization relationship between two proteins so that the localization of one protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflect the modeled relationship. Accordingly, observing the response of the prediction via manipulating input localization could provide an informative way to analyze the modeled relationships between the input and the predicted proteins. Results We propose a Protein Localization Prediction (PLP) method using a cGAN named Four-dimensional Reslicing Generative Adversarial Network (4DR-GAN) to digitally generate additional channels. 4DR-GAN models the joint probability distribution of input and output proteins by simultaneously incorporating the protein localization signals in four dimensions including space and time. Because protein localization often correlates with protein activation state, based on accurate PLP, we further propose two novel tools: digital activation (DA) and digital inactivation (DI) to digitally activate and inactivate a protein, in order to observing the response of the predicted protein localization. Compared with genetic approaches, these tools allow precise spatial and temporal control. A comprehensive experiment on six pairs of proteins shows that 4DR-GAN achieves higher-quality PLP than Pix2Pix, and the DA and DI responses are consistent with the known protein functions. The proposed PLP method helps simultaneously visualize additional proteins, and the developed DA and DI tools provide guidance to study localization-based protein functions. Availability The open-source code is available at https://github.com/YangJiaoUSA/4DR-GAN. Supplementary information Supplementary data are available at Bioinformatics online.

3D fluorescence microscopy of living organisms has increasingly become an essential and powerful tool in biomedical research and diagnosis. An exploding amount of imaging data has been collected, whereas efficient and effective computational tools to extract information from them are still lagging behind. This is largely due to the challenges in analyzing biological data. Interesting biological structures are not only small, but are often morphologically irregular and highly dynamic. Although tracking cells in live organisms has been studied for years, existing tracking methods for cells are not effective in tracking subcellular structures, such as protein complexes, which feature in continuous morphological changes including split and merge, in addition to fast migration and complex motion. In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process. A multi-object tracking method with portion matching is proposed based on 3D segmentation results. The proposed method distills deep feature maps from deep networks, then recognizes and matches objects' portions using an extended search. Experimental results confirm that the proposed method achieves 2.96% higher on consistent tracking accuracy and 35.48% higher on event identification accuracy than the state-of-art methods.

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