Tuning the Sensitivity of Genetically Encoded Fluorescent Potassium Indicators through Structure-Guided and Genome Mining Strategies

Decoding Potassium Homeostasis in Cancer Metastasis and Drug Resistance: Insights from a Highly Selective DNAzyme-Based Intracellular K+ Sensor

Potassium ions (K+) within the tumor microenvironment, along with dysregulation of K+ channels, play critical roles in supporting cancer cell survival and preventing their elimination. Directly monitoring changes in K+ homeostasis within cancer cells is invaluable for understanding these processes. However, achieving high selectivity over other biological metal ions, a detection dynamic range that aligns with intracellular K+ levels, and broad accessibility to research laboratories remain technically challenging for current K+ imaging probes. In this study, we report the in vitro selection of the first K+-specific RNA-cleaving DNAzyme and the development of a K+-specific DNAzyme fluorescent sensor with exceptional selectivity, achieving over 1000-fold selectivity against Na+ and more than 100-fold selectivity over other major biologically relevant metal ions. This sensor has an apparent dissociation constant (105 mM) that is close to the intracellular level of K+, and it has a broad detection range from 21 to 200 mM K+. Using this tool, we reveal a progressive decline in intracellular K+ levels in breast cancer cells with more advanced progression states. Moreover, we demonstrate that elevated extracellular K+ levels interfere with the efficacy of anticancer compounds like ML133 and Amiodarone, suggesting an underappreciated role of microenvironmental K+ in chemoresistance. Notably, blocking the Kir2.1 channel activity restored treatment sensitivity, presenting a potential strategy to overcome chemoresistance in aggressive cancers. These findings underscore the role of K+ homeostasis in tumor progression and support further exploration of ion-channel-targeted cancer therapies.

Colorimetric Detection of Potassium Ions by Electrochromic Thin Film Devices

Potassium ion (K+) detection technology based on electrochemical sensing has proven to be feasible and widely used in physiological real-time monitoring and pathological prediction. Nevertheless, chip integration, wireless communication needs, and specific detection limits present challenges. In this study, high-performance electrochromic materials (W18O49 nanowires) and ion-selective thin films are introduced to present a highly selective, colorimetric K+ biosensor, which demonstrates excellent selectivity in complex ionic environments. The thin film electrochromic electrode offers both colorimetric and electrochemical K+ detection simultaneously from 1 mm to 1000 mm concentration range, achieving up to ≈48% optical transmittance modulation, with a coloration time of 15 s. At the clinically relevant K+ concentration range of 1-10 mm, the electrode displays a linear transmittance modulation sensitivity of ≈0.6% per mm concentration change. Stability tests demonstrate that the colorimetric detection maintains 96% of its efficiency after 100 full concentration change cycles. A compact solid-state sensing device incorporating the electrochromic thin films has also been fabricated, and optically quantified K+ concentration in real-life sweat samples.

Unravelling molecular dynamics in living cells: Fluorescent protein biosensors for cell biology

Genetically encoded, fluorescent protein (FP)-based Förster resonance energy transfer (FRET) biosensors are microscopy imaging tools tailored for the precise monitoring and detection of molecular dynamics within subcellular microenvironments. They are characterised by their ability to provide an outstanding combination of spatial and temporal resolutions in live-cell microscopy. In this review, we begin by tracing back on the historical development of genetically encoded FP labelling for detection in live cells, which lead us to the development of early biosensors and finally to the engineering of single-chain FRET-based biosensors that have become the state-of-the-art today. Ultimately, this review delves into the fundamental principles of FRET and the design strategies underpinning FRET-based biosensors, discusses their diverse applications and addresses the distinct challenges associated with their implementation. We place particular emphasis on single-chain FRET biosensors for the Rho family of guanosine triphosphate hydrolases (GTPases), pointing to their historical role in driving our understanding of the molecular dynamics of this important class of signalling proteins and revealing the intricate relationships and regulatory mechanisms that comprise Rho GTPase biology in living cells.

High-Performance Chemigenetic Potassium Ion Indicator

Potassium ion (K+) is the most abundant metal ion in cells and plays an indispensable role in practically all biological systems. Although there have been reports of both synthetic and genetically encoded fluorescent K+ indicators, there remains a need for an indicator that is genetically targetable, has high specificity for K+ versus Na+, and has a high fluorescent response in the red to far-red wavelength range. Here, we introduce a series of chemigenetic K+ indicators, designated as the HaloKbp1 series, based on the bacterial K+-binding protein (Kbp) inserted into HaloTag7 self-labeled with environmentally sensitive rhodamine derivatives. This series of high-performance indicators features high brightness in the red to far-red region, large intensiometric fluorescence changes, and a range of Kd values. We demonstrate that they are suitable for the detection of physiologically relevant K+ concentration changes such as those that result from the Ca2+-dependent activation of the BK potassium channel.

Molecular Spies in Action: Genetically Encoded Fluorescent Biosensors Light up Cellular Signals

Cellular function is controlled through intricate networks of signals, which lead to the myriad pathways governing cell fate. Fluorescent biosensors have enabled the study of these signaling pathways in living systems across temporal and spatial scales. Over the years there has been an explosion in the number of fluorescent biosensors, as they have become available for numerous targets, utilized across spectral space, and suited for various imaging techniques. To guide users through this extensive biosensor landscape, we discuss critical aspects of fluorescent proteins for consideration in biosensor development, smart tagging strategies, and the historical and recent biosensors of various types, grouped by target, and with a focus on the design and recent applications of these sensors in living systems.

Genetically encoded green fluorescent sensor for probing sulfate transport activity of solute carrier family 26 member a2 (Slc26a2) protein

Genetically encoded fluorescent biosensors became indispensable tools for biological research, enabling real-time observation of physiological processes in live cells. Recent protein engineering efforts have resulted in the generation of a large variety of fluorescent biosensors for a wide range of biologically relevant processes, from small ions to enzymatic activity and signaling pathways. However, biosensors for imaging sulfate ions, the fourth most abundant physiological anion, in mammalian cells are still lacking. Here, we report the development and characterization of a green fluorescent biosensor for sulfate named Thyone. Thyone, derived through structure-guided design from bright green fluorescent protein mNeonGreen, exhibited a large negative fluorescence response upon subsecond association with sulfate anion with an affinity of 11 mM in mammalian cells. By integrating mutagenesis analyses with molecular dynamics simulations, we elucidated the molecular mechanism of sulfate binding and revealed key amino acid residues responsible for sulfate sensitivity. High anion selectivity and sensitivity of Thyone allowed for imaging of sulfate anion transients mediated by sulfate transporter heterologously expressed in cultured mammalian cells. We believe that Thyone will find a broad application for assaying the sulfate transport in mammalian cells via anion transporters and exchangers.

Epilepsy insights revealed by intravital functional optical imaging

Despite an abundance of pharmacologic and surgical epilepsy treatments, there remain millions of patients suffering from poorly controlled seizures. One approach to closing this treatment gap may be found through a deeper mechanistic understanding of the network alterations that underly this aberrant activity. Functional optical imaging in vertebrate models provides powerful advantages to this end, enabling the spatiotemporal acquisition of individual neuron activity patterns across multiple seizures. This coupled with the advent of genetically encoded indicators, be them for specific ions, neurotransmitters or voltage, grants researchers unparalleled access to the intact nervous system. Here, we will review how in vivo functional optical imaging in various vertebrate seizure models has advanced our knowledge of seizure dynamics, principally seizure initiation, propagation and termination.

Probing intracellular potassium dynamics in neurons with the genetically encoded sensor lc-LysM GEPII 1.0 in vitro and in vivo

Neuronal activity is accompanied by a net outflow of potassium ions (K+) from the intra- to the extracellular space. While extracellular [K+] changes during neuronal activity are well characterized, intracellular dynamics have been less well investigated due to lack of respective probes. In the current study we characterized the FRET-based K+ biosensor lc-LysM GEPII 1.0 for its capacity to measure intracellular [K+] changes in primary cultured neurons and in mouse cortical neurons in vivo. We found that lc-LysM GEPII 1.0 can resolve neuronal [K+] decreases in vitro during seizure-like and intense optogenetically evoked activity. [K+] changes during single action potentials could not be recorded. We confirmed these findings in vivo by expressing lc-LysM GEPII 1.0 in mouse cortical neurons and performing 2-photon fluorescence lifetime imaging. We observed an increase in the fluorescence lifetime of lc-LysM GEPII 1.0 during periinfarct depolarizations, which indicates a decrease in intracellular neuronal [K+]. Our findings suggest that lc-LysM GEPII 1.0 can be used to measure large changes in [K+] in neurons in vitro and in vivo but requires optimization to resolve smaller changes as observed during single action potentials.

Fluorescent Protein-Based Sensors for Detecting Essential Metal Ions across the Tree of Life

Genetically encoded fluorescent metal ion sensors are powerful tools for elucidating metal dynamics in living systems. Over the last 25 years since the first examples of genetically encoded fluorescent protein-based calcium indicators, this toolbox of probes has expanded to include other essential and non-essential metal ions. Collectively, these tools have illuminated fundamental aspects of metal homeostasis and trafficking that are crucial to fields ranging from neurobiology to human nutrition. Despite these advances, much of the application of metal ion sensors remains limited to mammalian cells and tissues and a limited number of essential metals. Applications beyond mammalian systems and in vivo applications in living organisms have primarily used genetically encoded calcium ion sensors. The aim of this Perspective is to provide, with the support of historical and recent literature, an updated and critical view of the design and use of fluorescent protein-based sensors for detecting essential metal ions in various organisms. We highlight the historical progress and achievements with calcium sensors and discuss more recent advances and opportunities for the detection of other essential metal ions. We also discuss outstanding challenges in the field and directions for future studies, including detecting a wider variety of metal ions, developing and implementing a broader spectral range of sensors for multiplexing experiments, and applying sensors to a wider range of single- and multi-species biological systems.

Crosstalk between neutrophil extracellular traps and immune regulation: insights into pathobiology and therapeutic implications of transfusion-related acute lung injury

Transfusion-related acute lung injury (TRALI) is the leading cause of transfusion-associated death, occurring during or within 6 hours after transfusion. Reports indicate that TRALI can be categorized as having or lacking acute respiratory distress syndrome (ARDS) risk factors. There are two types of TRALI in terms of its pathogenesis: antibody-mediated and non-antibody-mediated. The key initiation steps involve the priming and activation of neutrophils, with neutrophil extracellular traps (NETs) being established as effector molecules formed by activated neutrophils in response to various stimuli. These NETs contribute to the production and release of reactive oxygen species (ROS) and participate in the destruction of pulmonary vascular endothelial cells. The significant role of NETs in TRALI is well recognized, offering a potential pathway for TRALI treatment. Moreover, platelets, macrophages, endothelial cells, and complements have been identified as promoters of NET formation. Concurrently, studies have demonstrated that the storage of platelets and concentrated red blood cells (RBC) can induce TRALI through bioactive lipids. In this article, recent clinical and pre-clinical studies on the pathophysiology and pathogenesis of TRALI are reviewed to further illuminate the mechanism through which NETs induce TRALI. This review aims to propose new therapeutic strategies for TRALI, with the hope of effectively improving its poor prognosis.

Biosensor-guided discovery and engineering of metabolic enzymes

A variety of chemicals have been produced through metabolic engineering approaches, and enhancing biosynthesis performance can be achieved by using enzymes with high catalytic efficiency. Accordingly, a number of efforts have been made to discover enzymes in nature for various applications. In addition, enzyme engineering approaches have been attempted to suit specific industrial purposes. However, a significant challenge in enzyme discovery and engineering is the efficient screening of enzymes with the desired phenotype from extensive enzyme libraries. To overcome this bottleneck, genetically encoded biosensors have been developed to specifically detect target molecules produced by enzyme activity at the intracellular level. Especially, the biosensors facilitate high-throughput screening (HTS) of targeted enzymes, expanding enzyme discovery and engineering strategies with advances in systems and synthetic biology. This review examines biosensor-guided HTS systems and highlights studies that have utilized these tools to discover enzymes in diverse areas and engineer enzymes to enhance their properties, such as catalytic efficiency, specificity, and stability.

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition

Introduction

In the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain.

Methods

To address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest.

Results and discussion

Experimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models.

Monitoring nutrients in plants with genetically encoded sensors: achievements and perspectives

Understanding mechanisms of nutrient allocation in organisms requires precise knowledge of the spatiotemporal dynamics of small molecules in vivo. Genetically encoded sensors are powerful tools for studying nutrient distribution and dynamics, as they enable minimally invasive monitoring of nutrient steady-state levels in situ. Numerous types of genetically encoded sensors for nutrients have been designed and applied in mammalian cells and fungi. However, to date, their application for visualizing changing nutrient levels in planta remains limited. Systematic sensor-based approaches could provide the quantitative, kinetic information on tissue-specific, cellular, and subcellular distributions and dynamics of nutrients in situ that is needed for the development of theoretical nutrient flux models that form the basis for future crop engineering. Here, we review various approaches that can be used to measure nutrients in planta with an overview over conventional techniques, as well as genetically encoded sensors currently available for nutrient monitoring, and discuss their strengths and limitations. We provide a list of currently available sensors and summarize approaches for their application at the level of cellular compartments and organelles. When used in combination with bioassays on intact organisms and precise, yet destructive analytical methods, the spatiotemporal resolution of sensors offers the prospect of a holistic understanding of nutrient flux in plants.

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Introduction

With the rapid advancement of artificial intelligence (AI) technology, the protection of patient medical image privacy and security has become a critical concern in current research on image privacy protection. However, traditional methods for encrypting medical images have faced criticism due to their limited flexibility and inadequate security. To overcome these limitations, this study proposes a novel chaotic medical image encryption method, called AT-ResNet-CM, which incorporates the attention mechanism fused with the ResNet model.

Methods

The proposed method utilizes the ResNet model as the underlying network for constructing the encryption and decryption framework. The ResNet's residual structure and jump connections are employed to effectively extract profound information from medical images and expedite the model's convergence. To enhance security, the output of the ResNet model is encrypted using a logistic chaotic system, introducing randomness and complexity to the encryption process. Additionally, an attention mechanism is introduced to enhance the model's response to the region of interest within the medical image, thereby strengthening the security of the encrypted network.

Results

Experimental simulations and analyses were conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed method outperforms alternative models in terms of encryption effectiveness, as indicated by a horizontal correlation coefficient of 0.0021 and information entropy of 0.9887. Furthermore, the incorporation of the attention mechanism significantly improves the encryption performance, reducing the horizontal correlation coefficient to 0.0010 and increasing the information entropy to 0.9965. These findings validate the efficacy of the proposed method for medical image encryption tasks, as it offers enhanced security and flexibility compared to existing approaches.

Discussion

In conclusion, the AT-ResNet-CM method presents a promising solution to address the limitations of traditional encryption techniques in protecting patient medical images. By leveraging the attention mechanism fused with the ResNet model, the method achieves improved security and flexibility. The experimental results substantiate the superiority of the proposed method in terms of encryption effectiveness, horizontal correlation coefficient, and information entropy. The proposed method not only addresses the shortcomings of traditional methods but also provides a more robust and reliable approach for safeguarding patient medical image privacy and security.

Disruption of ER ion homeostasis maintained by an ER anion channel CLCC1 contributes to ALS-like pathologies

Although anion channel activities have been demonstrated in sarcoplasmic reticulum/endoplasmic reticulum (SR/ER), their molecular identities and functions remain unclear. Here, we link rare variants of Chloride Channel CLIC Like 1 (CLCC1) to amyotrophic lateral sclerosis (ALS)-like pathologies. We demonstrate that CLCC1 is a pore-forming component of an ER anion channel and that ALS-associated mutations impair channel conductance. CLCC1 forms homomultimers and its channel activity is inhibited by luminal Ca2+ but facilitated by phosphatidylinositol 4,5-bisphosphate (PIP2). We identified conserved residues D25 and D181 in CLCC1 N-terminus responsible for Ca2+ binding and luminal Ca2+-mediated inhibition on channel open probability and K298 in CLCC1 intraluminal loop as the critical PIP2-sensing residue. CLCC1 maintains steady-state [Cl-]ER and [K+]ER and ER morphology and regulates ER Ca2+ homeostasis, including internal Ca2+ release and steady-state [Ca2+]ER. ALS-associated mutant forms of CLCC1 increase steady-state [Cl-]ER and impair ER Ca2+ homeostasis, and animals with the ALS-associated mutations are sensitized to stress challenge-induced protein misfolding. Phenotypic comparisons of multiple Clcc1 loss-of-function alleles, including ALS-associated mutations, reveal a CLCC1 dosage dependence in the severity of disease phenotypes in vivo. Similar to CLCC1 rare variations dominant in ALS, 10% of K298A heterozygous mice developed ALS-like symptoms, pointing to a mechanism of channelopathy dominant-negatively induced by a loss-of-function mutation. Conditional knockout of Clcc1 cell-autonomously causes motor neuron loss and ER stress, misfolded protein accumulation, and characteristic ALS pathologies in the spinal cord. Thus, our findings support that disruption of ER ion homeostasis maintained by CLCC1 contributes to ALS-like pathologies.

Genetically encoded fluorescent sensors for metals in biology

Metal ions intersect a wide range of biological processes. Some metal ions are essential and hence absolutely required for the growth and health of an organism, others are toxic and there is great interest in understanding mechanisms of toxicity. Genetically encoded fluorescent sensors are powerful tools that enable the visualization, quantification, and tracking of dynamics of metal ions in biological systems. Here, we review recent advances in the development of genetically encoded fluorescent sensors for metal ions. We broadly focus on 5 classes of sensors: single fluorescent protein, FRET-based, chemigenetic, DNAzymes, and RNA-based. We highlight recent developments in the past few years and where these developments stand concerning the rest of the field.