CASPAM: A Triple-Modality Biosensor for Multiplexed Imaging of Caspase Network Activity

Advancing protein biosensors: redefining detection through innovations in materials, mechanisms, and applications for precision medicine and global diagnostics

Protein biosensors are significant tools in modern diagnostics due to their exceptional sensitivity and specificity in detecting protein biomarkers critical for disease diagnosis, therapeutic monitoring, and biomedical research. Innovations in transduction methods, nanomaterials, and point-of-care system integration have spurred recent advancements in biosensor technology. This summary examines key developments in protein biosensors, focusing on their structure, applications, and future potential. Nanomaterial-enhanced electrochemical biosensors, such as graphene, polyaniline, and carbon nanotubes, offer improved signal transmission due to their large surface area and faster electron transfer rates. Label-free immunosensors activated with gold nanoparticles and MXene-based sensors capable of combined biomarker analysis for detecting ovarian cancer are notable examples. During the COVID-19 pandemic, colorimetric and fluorescence optical biosensors facilitated easier diagnostics. An example of this is the incorporation of SARS-CoV-2 detection technologies into mobile phones. Real-time, label-free tracking with molecular precision is now possible due to the development of new methods, such as CRISPR-based platforms and quartz crystal microbalance (QCM)-based biosensors. This advancement is crucial for effectively managing infectious diseases and cancer. Synthetic fluorescence biosensors increase diagnostics by improving the visualization of protein interactions and cellular communication. Despite these achievements, challenges related to scalability, sustainability, and regulatory compliance remain. Proposed solutions include sustainable biosensor manufacturing, artificial intelligence-enhanced analytics for efficacy evaluation, and multidisciplinary approaches to optimize interaction with decentralised diagnostic systems. This work demonstrates how protein biosensors can advance precision medicine and global health.

Next-Generation Genetically Encoded Fluorescent Biosensors Illuminate Cell Signaling and Metabolism

Genetically encoded fluorescent biosensors have revolutionized the study of cell signaling and metabolism, as they allow for live-cell measurements with high spatiotemporal resolution. This success has spurred the development of tailor-made biosensors that enable the study of dynamic phenomena on different timescales and length scales. In this review, we discuss different approaches to enhancing and developing new biosensors. We summarize the technologies used to gain structural insights into biosensor design and comment on useful screening technologies. Furthermore, we give an overview of different applications where biosensors have led to key advances over recent years. Finally, we give our perspective on where future work is bound to make a large impact.

Detection of ER Stress in iPSC-Derived Neurons Carrying the p.N370S Mutation in the GBA1 Gene

Endoplasmic reticulum (ER) stress is involved in the pathogenesis of many human diseases, such as cancer, type 2 diabetes, kidney disease, atherosclerosis and neurodegenerative diseases, in particular Parkinson's disease (PD). Since there is currently no treatment for PD, a better understanding of the molecular mechanisms underlying its pathogenesis, including the mechanisms of the switch from adaptation in the form of unfolded protein response (UPR) to apoptosis under ER stress conditions, may help in the search for treatment methods. Genetically encoded biosensors based on fluorescent proteins are suitable tools that facilitate the study of living cells and visualization of molecular events in real time. The combination of technologies to generate patient-specific iPSC lines and genetically encoded biosensors allows the creation of cell models with new properties. Using CRISPR-Cas9-mediated homologous recombination at the AAVS1 locus of iPSC with the genetic variant p.N370S (rs76763715) in the GBA1 gene, we created a cell model designed to study the activation conditions of the IRE1-XBP1 cascade of the UPR system. The cell lines obtained have a doxycycline-dependent expression of the genetically encoded biosensor XBP1-TagRFP, possess all the properties of human pluripotent cells, and can be used to test physical conditions and chemical compounds that affect the development of ER stress, the functioning of the UPR system, and in particular, the IRE1-XBP1 cascade.

Maximizing the performance of protein-based fluorescent biosensors

Fluorescent protein (FP)-based biosensors are genetically encoded tools that enable the imaging of biological processes in the context of cells, tissues, or live animals. Though widely used in biological research, practically all existing biosensors are far from ideal in terms of their performance, properties, and applicability for multiplexed imaging. These limitations have inspired researchers to explore an increasing number of innovative and creative ways to improve and maximize biosensor performance. Such strategies include new molecular biology methods to develop promising biosensor prototypes, high throughput microfluidics-based directed evolution screening strategies, and improved ways to perform multiplexed imaging. Yet another approach is to effectively replace components of biosensors with self-labeling proteins, such as HaloTag, that enable the biocompatible incorporation of synthetic fluorophores or other ligands in cells or tissues. This mini-review will summarize and highlight recent innovations and strategies for enhancing the performance of FP-based biosensors for multiplexed imaging to advance the frontiers of research.

Damage sensing mediated by serine proteases Hayan and Persephone for Toll pathway activation in apoptosis-deficient flies

The mechanisms by which the innate immune system senses damage have been extensively explored in multicellular organisms. In Drosophila, various types of tissue damage, including epidermal injury, tumor formation, cell competition, and apoptosis deficiency, induce sterile activation of the Toll pathway, a process that requires the use of extracellular serine protease (SP) cascades. Upon infection, the SP Spätzle (Spz)-processing enzyme (SPE) cleaves and activates the Toll ligand Spz downstream of two paralogous SPs, Hayan and Persephone (Psh). However, upon tissue damage, it is not fully understood which SPs establish Spz activation cascades nor what damage-associated molecules can activate SPs. In this study, using newly generated uncleavable spz mutant flies, we revealed that Spz cleavage is required for the sterile activation of the Toll pathway, which is induced by apoptosis-deficient damage of wing epidermal cells in adult Drosophila. Proteomic analysis of hemolymph, followed by experiments with Drosophila Schneider 2 (S2) cells, revealed that among hemolymph SPs, both SPE and Melanization Protease 1 (MP1) have high capacities to cleave Spz. Additionally, in S2 cells, MP1 acts downstream of Hayan and Psh in a similar manner to SPE. Using genetic analysis, we found that the upstream SPs Hayan and Psh contributes to the sterile activation of the Toll pathway. While SPE/MP1 double mutants show more impairment of Toll activation upon infection than SPE single mutants, Toll activation is not eliminated in these apoptosis-deficient flies. This suggests that Hayan and Psh sense necrotic damage, inducing Spz cleavage by SPs other than SPE and MP1. Furthermore, hydrogen peroxide, a representative damage-associated molecule, activates the Psh-Spz cascade in S2 cells overexpressing Psh. Considering that reactive oxygen species (ROS) were detected in apoptosis-deficient wings, our findings highlight the importance of ROS as signaling molecules that induce the activation of SPs such as Psh in response to damage.

Robust and unbiased estimation of the background distribution for automated quantitative imaging

Background estimation is the first step in quantitative analysis of images. It has an impact on all subsequent analyses, in particular for segmentation and calculation of ratiometric quantities. Most methods recover only a single value such as the median or yield a biased estimation in non-trivial cases. We introduce, to our knowledge, the first method to recover an unbiased estimation of background distribution. It leverages the lack of local spatial correlation in background pixels to robustly select a subset that accurately represents the background. The resulting background distribution can be used to test for foreground membership of individual pixels or estimate confidence intervals in derived quantities.