Editorial: Living Biosensors

Maria Eugenia Inda^*

• Massachusetts Institute of Technology, Cambridge, United States

Editorial: Living Biosensors Precision therapy would be transformative for handling human illnesses in the future. For this, living biosensors that can pinpoint when and where diseases emerge are needed. Rapid advances in bioengineering are enabling us to exploit the information-processing abilities of living cells to do this. Living biosensors are living cells genetically engineered to detect analytes with high sensitivity and specificity to diagnose disease in a cost-effective and noninvasive manner in order to treat it in a controlled fashion.Naturally, cells sense external stimuli in their environment as a means to adapt and survive. By rewiring signaling pathways in these natural systems, new diagnostic and therapeutic systems have been built to sense biomarkers associated with inflammatory, immunologic, and metabolic disorders, some of which have already progressed into clinical trials to gain regulatory approval. For example, living biosensors could be designed to precisely sense by-products of inflammation, and to respond by delivering targeted therapeutics in situ.Why living cells? Using biology to sense biology makes sense, as living biosensors capture the benefits of natural biosensors. By connecting natural transcriptional responses to expression of colorimetric, fluorescent, or bioluminescent reporter genes, living biosensors can continuously sense biomolecules and report this information with the exciting prospect of developing targeted therapies and personalized treatments for many diseases of tomorrow.In this collection, we provide an overview of ongoing efforts in biosensor design, including the current state of tools and highlight translational opportunities, as described below.In an original work, Subach, et al. developed a genetically encoded mercury detector fusing a circularly permutated version of the mNeonGreen fluorescent protein with the merP mercury-binding protein from gram-negative bacteria Shigella flexneri. The developed sensors responded to mercury ions with positive and negative fluorescence changes and they successfully visualized changes in mercury ion concentration in mammalian cultured cells.In the original work of Martsenyuk, et al., the paper investigates the operational stability of lactate biosensors. They constructed an amperometric transducer tailored for lactate measurement. The modeling framework incorporates Brown and Michaelis-Menten kinetics, integrating both distributed and discrete delays to capture the intricate dynamics of lactate sensing. This comprehensive study provides insights into the design and operational characteristics of lactate biosensors, offering a framework for understanding and optimizing their performance in diverse settings.In the original work of Chen, et al., they study the pressing offsets in multi-channel pulse signals. Their results show that increasing the sensor channels can overcome the pressing offsets in radial pulse signal acquisition.In the review, "Emergent Biotechnology Applications in Urology: A Mini Review", Liu, et al. highlight four groundbreaking technologies: whole-cell biosensors, optogenetic interventions for neuromodulation, bioengineered urinary bladder, and 3D bioprinting. Each technology plays a crucial role in enhancing patient care and improving clinical outcomes in urology. Advances in these fields underscore a shift towards precision diagnostics, personalized treatments, and enhanced regenerative strategies, ultimately aiming to enhance patient outcomes and address unmet clinical needs in urological diseases.In the review, "Past, Present and Future of Electrical Impedance Tomography and Myography for Medical Applications: A Scoping Review", Baby, et al. summarize two emerging electrical impedance technologies: myography (EIM) and tomography (EIT). The review details powerful processing algorithms and reconstruction tools for EIT and EIM, examining their strengths and weaknesses. It also summarizes commercial devices and clinical applications: EIT is effective for detecting cancerous tissue and monitoring pulmonary issues, while EIM is used for neuromuscular disease detection and monitoring. The role of machine learning and deep learning in advancing diagnosis, treatment planning, and monitoring is highlighted. This review provides a roadmap for researchers on device evolution, algorithms, reconstruction tools, and datasets, offering clinicians and researchers information on commercial devices and clinical studies for effective use and innovative research.