Ultra-fast genetically encoded sensor for precise real-time monitoring of physiological and pathophysiological peroxide dynamics.

Hydrogen Peroxide (H2O2) is a central oxidant in redox biology due to its pleiotropic role in physiology and pathology. However, real-time monitoring of H2O2 in living cells and tissues remains a challenge. We address this gap with the development of an optogenetic hydRogen perOxide Sensor (oROS), leveraging the bacterial peroxide binding domain OxyR. Previously engineered OxyR-based fluorescent peroxide sensors lack the necessary sensitivity and response speed for effective real-time monitoring. By structurally redesigning the fusion of Escherichia coli (E. coli) ecOxyR with a circularly permutated green fluorescent protein (cpGFP), we created a novel, green-fluorescent peroxide sensor oROS-G. oROS-G exhibits high sensitivity and fast on-and-off kinetics, ideal for monitoring intracellular H2O2 dynamics. We successfully tracked real-time transient and steady-state H2O2 levels in diverse biological systems, including human stem cell-derived neurons and cardiomyocytes, primary neurons and astrocytes, and mouse brain ex vivo and in vivo. These applications demonstrate oROS's capabilities to monitor H2O2 as a secondary response to pharmacologically induced oxidative stress and when adapting to varying metabolic stress. We showcased the increased oxidative stress in astrocytes via Aß-putriscine-MAOB axis, highlighting the sensor's relevance in validating neurodegenerative disease models. Lastly, we demonstrated acute opioid-induced generation of H2O2 signal in vivo which highlights redox-based mechanisms of GPCR regulation. oROS is a versatile tool, offering a window into the dynamic landscape of H2O2 signaling. This advancement paves the way for a deeper understanding of redox physiology, with significant implications for understanding diseases associated with oxidative stress, such as cancer, neurodegenerative, and cardiovascular diseases.

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.

Structure-guided engineering of a fast genetically encoded sensor for real-time H2O2 monitoring.

Hydrogen Peroxide (H2O2) is a central oxidant in redox biology due to its pleiotropic role in physiology and pathology. However, real-time monitoring of H2O2 in living cells and tissues remains a challenge. We address this gap with the development of an optogenetic hydRogen perOxide Sensor (oROS), leveraging the bacterial peroxide binding domain OxyR. Previously engineered OxyR-based fluorescent peroxide sensors lack the necessary sensitivity or response speed for effective real-time monitoring. By structurally redesigning the fusion of Escherichia coli (E. coli) ecOxyR with a circularly permutated green fluorescent protein (cpGFP), we created a novel, green-fluorescent peroxide sensor oROS-G. oROS-G exhibits high sensitivity and fast on-and-off kinetics, ideal for monitoring intracellular H2O2 dynamics. We successfully tracked real-time transient and steady-state H2O2 levels in diverse biological systems, including human stem cell-derived neurons and cardiomyocytes, primary neurons and astrocytes, and mouse neurons and astrocytes in ex vivo brain slices. These applications demonstrate oROS's capabilities to monitor H2O2 as a secondary response to pharmacologically induced oxidative stress, G-protein coupled receptor (GPCR)-induced cell signaling, and when adapting to varying metabolic stress. We showcased the increased oxidative stress in astrocytes via Aß-putriscine-MAOB axis, highlighting the sensor's relevance in validating neurodegenerative disease models. oROS is a versatile tool, offering a window into the dynamic landscape of H2O2 signaling. This advancement paves the way for a deeper understanding of redox physiology, with significant implications for diseases associated with oxidative stress, such as cancer, neurodegenerative disorders, and cardiovascular diseases.

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

Genetically encoded fluorescent indicators (GEFIs) are protein-based optogenetic tools that change their fluorescence intensity when binding specific ligands in cells and tissues. GEFI encoding DNA can be expressed in cell subtypes while monitoring cellular physiological responses. However, engineering GEFIs with physiological sensitivity and pharmacological specificity often requires iterative optimization through trial-and-error mutagenesis while assessing their biophysical function in vitro one by one. Here, the vast mutational landscape of proteins constitutes a significant obstacle that slows GEFI development, particularly for sensors that rely on mammalian host systems for testing. To overcome these obstacles, we developed a multiplexed high-throughput engineering platform called the optogenetic microwell array screening system (Opto-MASS) that functionally tests thousands of GEFI variants in parallel in mammalian cells. Opto-MASS represents the next step for engineering optogenetic tools as it can screen large variant libraries orders of magnitude faster than current methods. We showcase this system by testing over 13,000 dopamine and 21,000 opioid sensor variants. We generated a new dopamine sensor, dMASS1, with a >6-fold signal increase to 100 nM dopamine exposure compared to its parent construct. Our new opioid sensor, µMASS1, has a ∼4.6-fold signal increase over its parent scaffold's response to 500 nM DAMGO. Thus, Opto-MASS can rapidly engineer new sensors while significantly shortening the optimization time for new sensors with distinct biophysical properties.

Machine Learning Ensemble Directed Engineering of Genetically Encoded Fluorescent Calcium Indicators.

In this study, we focused on the transformative potential of machine learning in the engineering of genetically encoded fluorescent indicators (GEFIs), protein-based sensing tools that are critical for real-time monitoring of biological activity. GEFIs are complex proteins with multiple dynamic states, rendering optimization by trial-and-error mutagenesis a challenging problem. We applied an alternative approach using 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. We used the trained ensemble to perform an in silico functional screen on 1423 novel, uncharacterized GCaMP variants. As a result, we identified the novel ensemble-derived GCaMP (eGCaMP) variants, eGCaMP and eGCaMP+, that achieve both faster kinetics and larger fluorescent responses upon stimulation than previously published fast variants. Furthermore, we identified a combinatorial mutation with extraordinary dynamic range, eGCaMP2+, that outperforms the tested 6th, 7th, and 8th generation GCaMPs. These findings demonstrate the value of machine learning as a tool to facilitate the efficient pre-screening of mutants for functional characteristics. By leveraging the learning capabilities of our ensemble, we were able to accelerate the identification of promising mutations and reduce the experimental burden associated with trial-and-error mutagenesis. Overall, these findings have significant implications for optimizing GEFIs and other protein-based tools, demonstrating the utility of machine learning as a powerful asset in protein engineering.

Incorporation of sensing modalities into de novo designed fluorescence-activating proteins.

Through the efforts of many groups, a wide range of fluorescent protein reporters and sensors based on green fluorescent protein and its relatives have been engineered in recent years. Here we explore the incorporation of sensing modalities into de novo designed fluorescence-activating proteins, called mini-fluorescence-activating proteins (mFAPs), that bind and stabilize the fluorescent cis-planar state of the fluorogenic compound DFHBI. We show through further design that the fluorescence intensity and specificity of mFAPs for different chromophores can be tuned, and the fluorescence made sensitive to pH and Ca2+ for real-time fluorescence reporting. Bipartite split mFAPs enable real-time monitoring of protein-protein association and (unlike widely used split GFP reporter systems) are fully reversible, allowing direct readout of association and dissociation events. The relative ease with which sensing modalities can be incorporated and advantages in smaller size and photostability make de novo designed fluorescence-activating proteins attractive candidates for optical sensor engineering.

Publisher Correction: Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Structural basis for ion selectivity and engineering in channelrhodopsins.

Channelrhodopsins have become an integral part of modern neuroscience approaches due to their ability to control neuronal activity in targeted cell populations. The recent determination of several channelrhodopsin X-ray structures now enables us to study their function with unprecedented molecular precision. We will discuss how these insights can guide the engineering of the ion conducting pathway to increase its selectivity for Cl-, Ca2+, and K+ ions and improve the overall conductance. Engineering such channelrhodopsins would further increase their utility in neuroscience research and beyond by controlling a wider range of physiological events. To thoroughly address this issue, we compare channelrhodopsin structures with structural features of voltage and ligand-gated K+, Cl- and Ca2+ channels and discuss how these could be implemented in channelrhodopsins.

A microfluidic biochip platform for electrical quantification of proteins.

Sepsis, an adverse auto-immune response to an infection often causing life-threatening complications, results in the highest mortality and treatment cost of any illness in US hospitals. Several immune biomarker levels, including Interleukin 6 (IL-6), have shown a high correlation to the onset and progression of sepsis. Currently, no technology diagnoses and stratifies sepsis progression using biomarker levels. This paper reports a microfluidic biochip platform to detect proteins in undiluted human plasma samples. The device uses a differential enumeration platform that integrates Coulter counting principles, antigen specific capture chambers, and micro size bead based immunodetection to quantify cytokines. This microfluidic biochip was validated as a potential point of care technology by quantifying IL-6 from plasma samples (n = 29) with good correlation (R2 = 0.81) and agreement (Bland-Altman) compared to controls. In combination with previous applications, this point of care platform can potentially detect cell and protein biomarkers simultaneously for sepsis stratification.

Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis.

Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention.