Reactive oxygen species (ROS), including hydrogen peroxide and peroxynitrite, play dual roles as essential signaling molecules and high-stress markers of cellular damage. However, imaging these volatile species in live biological systems is often hindered by diffusion and poor signal localization. Researchers at UC Berkeley have developed a "tandem" activity-based sensing and labeling strategy that overcomes these challenges. This technology utilizes selective chemical probes that, upon reacting with a specific ROS, undergo a transformation that simultaneously triggers a fluorescent signal and anchors the probe to nearby cellular proteins. By "trapping" the signal at the site of its production, this dual-action mechanism allows for high-resolution, localized imaging of oxidative stress and signaling events within complex cellular environments.

Researchers at the University of California, Davis have developed advanced epigenetic methods and systems that detect and assess developmental risks in embryos caused by maternal stress prior to conception.

Organoid/brain slice immobilization for microelectrode arrays (MEAs) and organoid-on-chip platforms have traditionally depended on hydrogels, harp-style grids, or microfluidic confinement, each with its own set of pros and cons with respect to stability, standardization, and impact on electrophysiology. Hydrogels (e.g., Polyethylene glycol or PEG, extracellular matrix like Matrigel) are widely used to immobilize 3D neural tissues on MEAs. These are known to swell, drift, and alter mechanical microenvironments, which in turn modulate network firing, synchrony, and bursting behavior. Mechanical retention via harp slice grids or similar harp devices is a long-standing practice in acute brain slice and organoid electrophysiology. These devices are typically standardized, fragile, and poorly matched to diverse well and tissue geometries. ​Microfluidic organoid chips and specialized 3D MEAs (e.g., e-Flower, organoid-on-chip platforms) have recently emerged to enable hydrogel-free trapping/encapsulation of organoids for imaging and recordings, but they often require bespoke chip designs and overly complex flow control setups. There is a lack of geometry-agnostic devices for mechanically immobilizing diverse organoids on commercial MEAs that feature consistent stability, uniform and/or tailored contact, and with minimal perturbation of electrophysiological readouts.

Engineered phosphite dehydrogenases enable efficient recycling of noncanonical redox cofactors for sustainable biomanufacturing.

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Engineered phosphite dehydrogenases enable efficient recycling of noncanonical redox cofactors for sustainable biomanufacturing.

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Researchers at the University of California, Davis have developed an imaging scheme for two-photon microscopes enhancing speed and resolution in neuroscience research.

Researchers at the University of California, Davis have developed an AI-powered framework that bridges the gap between predictive feature analysis and actionable interventions by extracting evidence-based recommendations from scientific literature.

Current methods for treating nervous system disorders often rely on generalized approaches that may not optimally address the individual patient's specific pathology, leading to suboptimal outcomes. This innovation, developed by UC Berkeley researchers, provides a method to identify the most critical, or "influential," nodes within a patient's functional connectivity network derived from time-series data of an organ or organ system. The method involves obtaining multiple time-series datasets from an affected organ/system, using them to map the functional connectivity network, and then determining the most influential nodes within that network. By providing this specific and personalized information to a healthcare provider, a treatment can be prescribed that precisely targets the respective organ corresponding to these influential nodes. This personalized, data-driven approach offers a significant advantage over conventional treatments by focusing intervention on the most impactful biological targets, potentially leading to more effective and efficient patient care.

Bioluminescence technology offers highly sensitive and non-invasive imaging in living organisms without the need for external excitation. Naturally occurring luciferases, the enzymes responsible for catalyzing light emission, constrained the full potential of luminescence technology for the past several decades due to their poor protein folding, large size, ATP dependency, and low efficiency.Creation of the next generation of luciferases required breaking free of evolutionary constraints. This work describes the creation of novel bioluminescent enzymes that surpass qualities of native luciferase using AI-powered de novo protein design. These designer luciferase catalysts enable genetic labeling across molecular, cellular, and individual levels in a multiplexed manner, using the same underlying technology.This advancement showcases the design of efficient enzymes from scratch in which our de novo luciferases will enable researchers to study complex biological phenomena effectively.In the last three decades, the development of fluorescent protein families has brought a revolution in the way researchers study biological processes in living cells. However, the dependency on external excitation for FPs introduces inherent drawbacks, such as phototoxicity and autofluorescence background. These especially limit the applications for fluorescent proteins in vivo. Bioluminescence technologies, which rely on an enzyme-catalyzed chemiluminescent reaction of a chromophore substrate to emit photons without the need for external light sources, circumvent these limitations and offer several orders-of-magnitude-higher sensitivity than fluorescence for macro-scale imaging.Practically implementing luciferases as general molecular proges has not progressed as far as fluroescent proteins due to a number of factors. Firefly luciferase (FLuc) is used widely for in vivo imaging, but it is dim, large (61 kDa), and ATP dependent. Gaussia luciferase (GLuc) is brighter than FLuc, but has five disulfide bonds and therefore cannot be used intracellularly. It is also prone to misfolding. Engineered variants of Renilla luciferase (RLuc) and Oplophorus Luciferase (NLuc) are brighter and more stable, but they emit blue light and have poor substrate specificity and therefore are difficult to used in multiplexed applications. LuxSit luciferase (Monod Bio Inc.) is the first de novo designed luciferase and has superior folding fidelity and stability to natural luciferases, but more de novo luciferase species are necessary to meet the needs of researchers.  

Kainate receptors, also known as kainic acid receptors are ionotropic receptors that bind to and are responsive to glutamate in neurons. These were originally identified as being activated by the compund kainic acid, orignally isolated from algae. Postsynaptic kainate receptors are involved in excitatory neurotransmission while presynaptic kainate receptors are involved in inhibitory neurotransmission. Kainic acid is a potentially very useful compound but very difficult to synthesize. As a result, there are very few pharmacological tool compounds to study kainate receptors and none that are readily tunable to install labeling compounds. 

Traditional environmental policymaking often struggles to efficiently target interventions to achieve multiple, complex air quality goals simultaneously across a geographic area. This innovation, developed by UC Berkeley researchers, addresses this challenge by providing a sophisticated, multi-objective optimization method for targeted reduction of air pollution. The method generates a comprehensive mitigation pathway by integrating several modules: a forward module to model pollutant concentrations, a target concentration surface that defines the policy goals, a prioritization module to assess uncertainty and importance via a prioritization covariance matrix, and a Bayesian inversion module to estimate optimum emissions required to meet the target. This systematic, data-driven approach culminates in a mitigation pathway that guides the performance of specific pollution control measures, offering a significant advantage over conventional, less targeted policy-making by ensuring resources are directed where they will have the maximum environmental impact.