Optogenetic Microwell Array Screening System: A High-Throughput Engineering Platform for Genetically Encoded Fluorescent Indicators

Genetically encoded sensors illuminate in vivo detection for neurotransmission: Development, application, and optimization strategies

Limitations in existing tools have hindered neuroscientists from achieving a deeper understanding of complex behaviors and diseases. The recent development and optimization of genetically encoded sensors offer a powerful solution for investigating intricate dynamics such as calcium influx, membrane potential, and the release of neurotransmitters and neuromodulators. In contrast, traditional methods are constrained by insufficient spatial and/or temporal resolution, low sensitivity, and stringent application conditions. Genetically encoded sensors have gained widespread popularity due to their advantageous features, which stem from their genetic encoding and optical imaging capabilities. These include broad applicability, tissue specificity, and non-invasive operation. When combined with advanced microscopic techniques, optogenetics, and machine learning approaches, these sensors have become versatile tools for studying neuronal circuits in intact living systems, providing millisecond-scale temporal resolution and spatial resolution ranging from nanometers to micrometers. In this review, we highlight the advantages of genetically encoded sensors over traditional methods in the study of neurotransmission. We also discuss their recent advancements, diverse applications, and optimization strategies.

Directed Evolution of Fluorescent Genetically Encoded Biosensors: Innovative Approaches for Development and Optimization of Biosensors

Fluorescent protein-based biosensors are indispensable molecular tools in cell biology and biomedical research, providing non-invasive dynamic measurements of metabolite concentrations and other cellular signals. Traditional methods for developing these biosensors rely on rational design, but directed evolution methods offer a more efficient alternative. This review discusses recent advancements in the development of biosensors using directed evolution, including methods for optimizing domain fusions, sequence optimization, and new screening and selection systems. Additionally, the incorporation of machine learning into the directed evolution process is explored, highlighting its potential to enhance the efficiency and cost reduction of biosensor development. Finally, emerging trends in the development of near-infrared biosensors and photochromic sensors are discussed, along with the opportunities presented by de novo design of sensing domains and biosensors.

Principles and Design of Molecular Tools for Sensing and Perturbing Cell Surface Receptor Activity

Cell-surface receptors are vital for controlling numerous cellular processes with their dysregulation being linked to disease states. Therefore, it is necessary to develop tools to study receptors and the signaling pathways they control. This Review broadly describes molecular approaches that enable 1) the visualization of receptors to determine their localization and distribution; 2) sensing receptor activation with permanent readouts as well as readouts in real time; and 3) perturbing receptor activity and mimicking receptor-controlled processes to learn more about these processes. Together, these tools have provided valuable insight into fundamental receptor biology and helped to characterize therapeutics that target receptors.

Dynamics of agonist-evoked opioid receptor activation revealed by FRET- and BRET-based opioid receptor conformation sensors

The opioid receptor family, particularly the µ opioid receptor, are the main drug targets in the management of severe pain. However, their pain-relieving effects are often accompanied by severe adverse effects, underlining the necessity for extensive research on this receptor family. Opioids, the agonists targeting these receptors, differ in their chemical structure and also in their mode of action in different aspects of signaling. Here we introduce novel tools that facilitate the analysis of this receptor family, by the development of FRET- and BRET-based receptor conformation sensors. With these sensors we were able to characterize especially the µ opioid receptor in more detail and reveal a strongly agonist-dependent activation kinetics for this receptor. Moreover, our sensors offer an assay independent from other signaling pathways, thereby minimizing the potential for interfering influences or biases within the system.

Engineering, applications, and future perspectives of GPCR-based genetically encoded fluorescent indicators for neuromodulators

This review explores the evolving landscape of G-protein-coupled receptor (GPCR)-based genetically encoded fluorescent indicators (GEFIs), with a focus on their development, structural components, engineering strategies, and applications. We highlight the unique features of this indicator class, emphasizing the importance of both the sensing domain (GPCR structure and activation mechanism) and the reporting domain (circularly permuted fluorescent protein (cpFP) structure and fluorescence modulation). Further, we discuss indicator engineering approaches, including the selection of suitable cpFPs and expression systems. Additionally, we showcase the diversity and flexibility of their application by presenting a summary of studies where such indicators were used. Along with all the advantages, we also focus on the current limitations as well as common misconceptions that arise when using these indicators. Finally, we discuss future directions in indicator engineering, including strategies for screening with increased throughput, optimization of the ligand-binding properties, structural insights, and spectral diversity.

Machine learning-guided engineering of genetically encoded fluorescent calcium indicators

Here we used machine learning to engineer genetically encoded fluorescent indicators, protein-based sensors critical for real-time monitoring of biological activity. We used machine learning to predict the outcomes of sensor mutagenesis by analyzing established libraries that link sensor sequences to functions. Using the GCaMP calcium indicator as a scaffold, we developed an ensemble of three regression models trained on experimentally derived GCaMP mutation libraries. The trained ensemble performed an in silico functional screen on 1,423 novel, uncharacterized GCaMP variants. As a result, we identified the ensemble-derived GCaMP (eGCaMP) variants, eGCaMP and eGCaMP+, which achieve both faster kinetics and larger ∆F/F0 responses upon stimulation than previously published fast variants. Furthermore, we identified a combinatorial mutation with extraordinary dynamic range, eGCaMP2+, which outperforms the tested sixth-, seventh- and eighth-generation GCaMPs. These findings demonstrate the value of machine learning as a tool to facilitate the efficient engineering of proteins for desired biophysical characteristics.