Cellular signaling networks are the foundation which determines the fate and function of cells as they respond to various cues and stimuli. The discovery of fluorescent proteins over 25 years ago enabled the development of a diverse array of genetically encodable fluorescent biosensors that are capable of measuring the spatiotemporal dynamics of signal transduction pathways in live cells. In an effort to encapsulate the breadth over which fluorescent biosensors have expanded, we endeavored to assemble a comprehensive list of published engineered biosensors, and we discuss many of the molecular designs utilized in their development. Then, we review how the high temporal and spatial resolution afforded by fluorescent biosensors has aided our understanding of the spatiotemporal regulation of signaling networks at the cellular and subcellular level. Finally, we highlight some emerging areas of research in both biosensor design and applications that are on the forefront of biosensor development.

Genetically Encoded Fluorescent Biosensors Illuminate the Spatiotemporal Regulation of Signaling Networks.

Biosensors and nanoscale analytical tools have shown huge growth in literature in the past 20 years, with a large number of reports on the topic of 'ultrasensitive', 'cost-effective', and 'early detection' tools with a potential of 'mass-production' cited on the web of science Yet none of these tools are commercially available in the market or practically viable for mass production and use in pandemic diseases such as coronavirus disease 2019 (COVID-19) In this context, we review the technological challenges and opportunities of current bio/chemical sensors and analytical tools by critically analyzing the bottlenecks which have hindered the implementation of advanced sensing technologies in pandemic diseases We also describe in brief COVID-19 by comparing it with other pandemic strains such as that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) for the identification of features that enable biosensing Moreover, we discuss visualization and characterization tools that can potentially be used not only for sensing applications but also to assist in speeding up the drug discovery and vaccine development process Furthermore, we discuss the emerging monitoring mechanism, namely wastewater-based epidemiology, for early warning of the outbreak, focusing on sensors for rapid and on-site analysis of SARS-CoV2 in sewage To conclude, we provide holistic insights into challenges associated with the quick translation of sensing technologies, policies, ethical issues, technology adoption, and an overall outlook of the role of the sensing technologies in pandemics

Opportunities and Challenges for Biosensors and Nanoscale Analytical Tools for Pandemics: COVID-19.

https://www.cell.com/developmental-cell/pdf/S1534-5807(20)30400-7.pdf

ERK-Mediated Mechanochemical Waves Direct Collective Cell Polarization.

Although fluorescent reporters and biosensors have become indispensable tools in biological and biomedical fields, fluorescence measurements require external excitation light, thereby limiting their use in thick tissues and live animals. Bioluminescent reporters and biosensors may potentially overcome this hurdle because they use enzyme-catalyzed exothermic biochemical reactions to generate excited-state emitters. This review first introduces the development of bioluminescent reporters, and next, their applications in sensing biological changes in vitro and in vivo as biosensors. Lastly, we discuss chemiluminescent sensors that produce photons in the absence of luciferases. This review aims to explore fundamentals and experimental insights and to emphasize the yet-to-be-reached potential of next-generation luminescent reporters and biosensors.

Development and Applications of Bioluminescent and Chemiluminescent Reporters and Biosensors

Until now, the human expert segments retinal blood vessels manually in fundus images to inspect human retinal-related diseases, such as diabetic retinopathy and vascular occlusion. Recently, many studies were conducted for automatic retinal vessel segmentation from fundus images through supervised and unsupervised methods to minimize user intervention. However, most of them lack in segmentation robustness and cannot optimize loss functions so that results of the segmentation have made lots of fake or thin branches. This article proposes a new conditional generative adversarial network called M-GAN to conduct accurate and precise retinal vessel segmentation by balancing losses through stacked deep fully convolutional networks. It consists of a newly designed M-generator with deep residual blocks for more robust segmentation and an M-discriminator with a deeper network for more efficient training of the adversarial model. In particular, a multi-kernel pooling block is added between the stacked layers to support the scale-invariance of vessel segmentations of different sizes. The M-generator has down-sampling layers to extract features and up-sampling layers to make segmented retinal blood vessel images from the extracted features. The M-discriminator also has a deeper network similar to the down-sampling of the M-generator, but the final layer is constructed as a fully connected layer for decision making. We conduct pre-processing of the input image using automatic color equalization (ACE) to make the retinal vessels of the input fundus image more clear and perform post-processing that makes the vessel branches smooth and reduces false-negatives using a Lanczos resampling method. To verify the proposed method, we used DRIVE, STARE, HRF, and CHASE-DB1datasets and compared the proposed M-GAN with other studies. We measured accuracy, the intersection of union (IoU), F1 score, and Matthews correlation coefficient (MCC) for comparative analysis. Results of comparison proved that the proposed M-GAN derived superior performance than other studies.

/pdf/m-gan-retinal-blood-vessel-segmentation-by-balancing-losses-mor01re7fv.pdf

M-GAN: Retinal Blood Vessel Segmentation by Balancing Losses Through Stacked Deep Fully Convolutional Networks

Genetically encoded biosensors based on the principle of Forster resonance energy transfer comprise two major classes: biosensors based on fluorescence resonance energy transfer (FRET) and those based on bioluminescence energy transfer (BRET). The FRET biosensors visualize signaling-molecule activity in cells or tissues with high resolution. Meanwhile, due to the low background signal, the BRET biosensors are primarily used in drug screening. Here, we report a protocol to transform intramolecular FRET biosensors to BRET-FRET hybrid biosensors called hyBRET biosensors. The hyBRET biosensors retain all properties of the prototype FRET biosensors and also work as BRET biosensors with dynamic ranges comparable to the prototype FRET biosensors. The hyBRET biosensors are compatible with optogenetics, luminescence microplate reader assays, and non-invasive whole-body imaging of xenograft and transgenic mice. This simple protocol will expand the use of FRET biosensors and enable visualization of the multiscale dynamics of cell signaling in live animals.

/pdf/a-platform-of-bret-fret-hybrid-biosensors-for-optogenetics-508taphb1t.pdf

A platform of BRET-FRET hybrid biosensors for optogenetics, chemical screening, and in vivo imaging.

Two decades have passed since the development of the first calcium indicator based on the green fluorescent protein (GFP) and the principle of Forster resonance energy transfer (FRET). During this period, researchers have advanced many novel ideas for the improvement of such genetically encoded FRET biosensors, which have allowed them to expand their targets from small molecules to signaling proteins and physicochemical properties. Although the merits of "genetically encoded" FRET biosensors became clear once various cell lines were established and several transgenic organisms were generated, the road to these developments was not necessarily a smooth one. Moreover, even today the development of new FRET biosensors remains a very labor-intensive, trial-and-error process. Therefore, at this junction, it may be worthwhile to summarize the progress of the FRET biosensor and discuss the future direction of its development and application.Key words: FRET, biosensor, fluorescent protein.

Two decades of genetically encoded biosensors based on förster resonance energy transfer

Genetically-encoded biosensors based on Forster/fluorescence resonance energy transfer (FRET) are versatile tools for studying the spatio-temporal regulation of signaling molecules within not only the cells but also tissues. Perhaps the hardest task in the development of a FRET biosensor for protein kinases is to identify the kinase-specific substrate peptide to be used in the FRET biosensor. To solve this problem, we took advantage of kinase-interacting substrate screening (KISS) technology, which deduces a consensus substrate sequence for the protein kinase of interest. Here, we show that a consensus substrate sequence for ROCK identified by KISS yielded a FRET biosensor for ROCK, named Eevee-ROCK, with high sensitivity and specificity. By treating HeLa cells with inhibitors or siRNAs against ROCK, we show that a substantial part of the basal FRET signal of Eevee-ROCK was derived from the activities of ROCK1 and ROCK2. Eevee-ROCK readily detected ROCK activation by epidermal growth factor, lysophosphatidic acid, and serum. When cells stably-expressing Eevee-ROCK were time-lapse imaged for three days, ROCK activity was found to increase after the completion of cytokinesis, concomitant with the spreading of cells. Eevee-ROCK also revealed a gradual increase in ROCK activity during apoptosis. Thus, Eevee-ROCK, which was developed from a substrate sequence predicted by the KISS technology, will pave the way to a better understanding of the function of ROCK in a physiological context.

A FRET Biosensor for ROCK Based on a Consensus Substrate Sequence Identified by KISS Technology.

Convolutional Neural Network is valid for segmentation of objects in an image. In recent years, it is beginning to be applied to the field of medicine and cell biology. In semantic segmentation, the accuracy has been improved by using single deeper neural network. However, the accuracy is saturated for difficult segmentation tasks. In this paper, we propose a semantic segmentation method by integrating multiple CNNs adaptively. This method consists of a gating network and multiple expert networks. Expert network outputs the segmentation result for an input image. Gating network automatically divides the input image into several sub-problems and assigns them to expert networks. Thus, each expert network solves only the specific problem, and our proposed method is possible to learn more efficiently than single deep neural network. We evaluate the proposed method on the segmentation problem of cell membrane and nucleus. The proposed method improved the segmentation accuracy in comparison with single deep neural network.

/pdf/cell-image-segmentation-by-integrating-multiple-cnns-5e08toplow.pdf

Cell Image Segmentation by Integrating Multiple CNNs

For more than a century, hematoxylin and eosin (H&E) staining has been the de facto standard for histological studies. Consequently, the legacy of histological knowledge is largely based on H&E staining. Due to the recent advent of multi-photon excitation microscopy, the observation of live tissue is increasingly being used in many research fields. Adoption of this technique has been further accelerated by the development of genetically encoded biosensors for ions and signaling molecules. However, H&E-based histology has not yet begun to fully utilize in vivo imaging due to the lack of proper morphological markers. Here, we report a genetically encoded fluorescent marker, NuCyM (Nucleus, Cytosol, and Membrane), which is designed to recapitulate H&E staining patterns in vivo. We generated a transgenic mouse line ubiquitously expressing NuCyM by using a ROSA26 bacterial artificial chromosome (BAC) clone. NuCyM evenly marked the plasma membrane, cytoplasm and nucleus in most tissues, yielding H&E staining-like images. In the NuCyM-expressing cells, cell division of a single cell was clearly observed as five basic phases during M phase by three-dimensional imaging. We next crossed NuCyM mice with transgenic mice expressing an ERK biosensor based on the principle of Forster resonance energy transfer (FRET). Using NuCyM, ERK activity in each cell could be extracted from the FRET images. To further accelerate the image analysis, we employed machine learning-based segmentation methods, and thereby automatically quantitated ERK activity in each cell. In conclusion, NuCyM is a versatile cell morphological marker that enables us to grasp histological information as with H&E staining.Key words: in vivo imaging, histology, machine learning, molecular activity.

Ayako Imanishi

Papers

A platform of BRET-FRET hybrid biosensors for optogenetics, chemical screening, and in vivo imaging.

Two decades of genetically encoded biosensors based on förster resonance energy transfer

A FRET Biosensor for ROCK Based on a Consensus Substrate Sequence Identified by KISS Technology.

Cell Image Segmentation by Integrating Multiple CNNs

A Novel Morphological Marker for the Analysis of Molecular Activities at the Single-cell Level.