Near-infrared catecholamine nanosensors for high spatiotemporal dopamine imaging

Near-infrared nanosensors enable optical imaging of oxytocin with selectivity over vasopressin in acute mouse brain slices

Oxytocin plays a critical role in regulating social behaviors, yet our understanding of its function in both neurological health and disease remains incomplete. Real-time oxytocin imaging probes with spatiotemporal resolution relevant to its endogenous signaling are required to fully elucidate oxytocin's role in the brain. Herein, we describe a near-infrared oxytocin nanosensor (nIROXT), a synthetic probe capable of imaging oxytocin in the brain without interference from its structural analogue, vasopressin. nIROXT leverages the inherent tissue-transparent fluorescence of single-walled carbon nanotubes (SWCNT) and the molecular recognition capacity of an oxytocin receptor peptide fragment to selectively and reversibly image oxytocin. We employ these nanosensors to monitor electrically stimulated oxytocin release in brain tissue, revealing oxytocin release sites with a median size of 3 µm in the paraventricular nucleus of C57BL/6 mice, which putatively represents the spatial diffusion of oxytocin from its point of release. These data demonstrate that covalent SWCNT constructs, such as nIROXT, are powerful optical tools that can be leveraged to measure neuropeptide release in brain tissue.

Rationally Designed Functionalization of Single-Walled Carbon Nanotubes for Real-Time Monitoring of Cholinesterase Activity and Inhibition in Plasma

Enzymes play a pivotal role in regulating numerous bodily functions. Thus, there is a growing need for developing sensors enabling real-time monitoring of enzymatic activity and inhibition. The activity and inhibition of cholinesterase (CHE) enzymes in blood plasma are fluorometrically monitored using near-infrared (NIR) fluorescent single-walled carbon nanotubes (SWCNTs) as probes, strategically functionalized with myristoylcholine (MC)- the substrate of CHE. A significant decrease in the fluorescence intensity of MC-suspended SWCNTs upon interaction with CHE is observed, attributed to the hydrolysis of the MC corona phase of the SWCNTs by CHE. Complementary measurements for quantifying choline, the product of MC hydrolysis, reveal a correlation between the fluorescence intensity decrease and the amount of released choline, rendering the SWCNTs optical sensors with real-time feedback in the NIR biologically transparent spectral range. Moreover, when synthetic and naturally abundant inhibitors inhibit the CHE enzymes present in blood plasma, no significant modulations of the MC-SWCNT fluorescence are observed, allowing effective detection of CHE inhibition. The rationally designed SWCNT sensors platform for monitoring of enzymatic activity and inhibition in clinically relevant samples is envisioned to not only advance the field of clinical diagnostics but also deepen further understanding of enzyme-related processes in complex biological fluids.

Near infrared fluorescent nanosensors for high spatiotemporal oxytocin imaging

Oxytocin is a neuropeptide thought to play a central role in regulating social and emotional behavior. Current techniques for neuropeptide imaging are generally limited in spatial and temporal resolution, real-time imaging capacity, selectivity for oxytocin over vasopressin, and application in young and non-model organisms. To avoid the use of endogenous oxytocin receptors for oxytocin probe development, we employed a protocol to evolve purely synthetic molecular recognition on the surface of near-infrared fluorescent single-walled carbon nanotubes (SWCNT) using single-stranded DNA (ssDNA). This probe reversibly undergoes up to a 172% fluorescence increase in response to oxytocin with a K d of 4.93 μM. Furthermore, this probe responds selectively to oxytocin over oxytocin analogs, receptor agonists and antagonists, and most other neurochemicals. Lastly, we show our probe can image synaptic evoked oxytocin release in live mouse brain slices. Optical probes with the specificity and resolution requisite to image endogenous oxytocin signaling can advance the study of oxytocin neurotransmission for its role in both health and disease.

Nanozyme-Enhanced Electrochemical Biosensors: Mechanisms and Applications

Nanozymes, as innovative materials, have demonstrated remarkable potential in the field of electrochemical biosensors. This article provides an overview of the mechanisms and extensive practical applications of nanozymes in electrochemical biosensors. First, the definition and characteristics of nanozymes are introduced, emphasizing their significant role in constructing efficient sensors. Subsequently, several common categories of nanozyme materials are delved into, including metal-based, carbon-based, metal-organic framework, and layered double hydroxide nanostructures, discussing their applications in electrochemical biosensors. Regarding their mechanisms, two key roles of nanozymes are particularly focused in electrochemical biosensors: selective enhancement and signal amplification, which crucially support the enhancement of sensor performance. In terms of practical applications, the widespread use of nanozyme-based electrochemical biosensors are showcased in various domains. From detecting biomolecules, pollutants, nucleic acids, proteins, to cells, providing robust means for high-sensitivity detection. Furthermore, insights into the future development of nanozyme-based electrochemical biosensors is provided, encompassing improvements and optimizations of nanozyme materials, innovative sensor design and integration, and the expansion of application fields through interdisciplinary collaboration. In conclusion, this article systematically presents the mechanisms and applications of nanozymes in electrochemical biosensors, offering valuable references and prospects for research and development in this field.

Engineered Glucose Oxidase-Carbon Nanotube Conjugates for Tissue-Translatable Glucose Nanosensors

Continuous and non-invasive glucose monitoring and imaging is important for disease diagnosis, treatment, and management. However, glucose monitoring remains a technical challenge owing to the dearth of tissue-transparent glucose sensors. In this study, we present the development of near-infrared fluorescent single-walled carbon nanotube (SWCNT) based nanosensors directly functionalized with glucose oxidase (GOx) capable of immediate and reversible glucose imaging in biological fluids and tissues. We prepared GOx-SWCNT nanosensors by facile sonication of SWCNT with GOx in a manner that-surprisingly-does not compromise the ability of GOx to detect glucose. Importantly, we find by using denatured GOx that the fluorescence modulation of GOx-SWCNT is not associated with the catalytic oxidation of glucose but rather triggered by glucose-GOx binding. Leveraging the unique response mechanism of GOx-SWCNT nanosensors, we developed catalytically inactive apo-GOx-SWCNT that enables both sensitive and reversible glucose imaging, exhibiting a ΔF/F0 of up to 40 % within 1 s of exposure to glucose without consuming the glucose analyte. We finally demonstrate the potential applicability of apo-GOx-SWCNT in biomedical applications by glucose quantification in human plasma and glucose imaging in mouse brain slices.

Nanoreporter Identifies Lysosomal Storage Disease Lipid Accumulation Intracranially

Dysregulated lipid metabolism contributes to neurodegenerative pathologies and neurological decline in lysosomal storage disorders as well as more common neurodegenerative diseases. Niemann-Pick type A (NPA) is a fatal neurodegenerative lysosomal storage disease characterized by abnormal sphingomyelin accumulation in the endolysosomal lumen. The ability to monitor abnormalities in lipid homeostasis intracranially could improve basic investigations and the development of effective treatment strategies. We investigated the carbon nanotube-based detection of intracranial lipid content. We found that the near-infrared emission of a carbon nanotube-based lipid sensor responds to lipid accumulation in neuronal and in vivo models of NPA. The nanosensor detected lipid accumulation intracranially in an acid sphingomyelinase knockout mouse via noninvasive near-infrared spectroscopy. This work indicates a tool to improve drug development processes in NPA, other lysosomal storage diseases, and neurodegenerative diseases.

Identifying Neural Signatures of Dopamine Signaling with Machine Learning

The emergence of new tools to image neurotransmitters, neuromodulators, and neuropeptides has transformed our understanding of the role of neurochemistry in brain development and cognition, yet analysis of this new dimension of neurobiological information remains challenging. Here, we image dopamine modulation in striatal brain tissue slices with near-infrared catecholamine nanosensors (nIRCat) and implement machine learning to determine which features of dopamine modulation are unique to changes in stimulation strength, and to different neuroanatomical regions. We trained a support vector machine and a random forest classifier to decide whether the recordings were made from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS) and find that machine learning is able to accurately distinguish dopamine release that occurs in DLS from that occurring in DMS in a manner unachievable with canonical statistical analysis. Furthermore, our analysis determines that dopamine modulatory signals including the number of unique dopamine release sites and peak dopamine released per stimulation event are most predictive of neuroanatomy. This is in light of integrated neuromodulator amount being the conventional metric used to monitor neuromodulation in animal studies. Lastly, our study finds that machine learning discrimination of different stimulation strengths or neuroanatomical regions is only possible in adult animals, suggesting a high degree of variability in dopamine modulatory kinetics during animal development. Our study highlights that machine learning could become a broadly utilized tool to differentiate between neuroanatomical regions or between neurotypical and disease states, with features not detectable by conventional statistical analysis.

Monitoring the Formation of Fibrin Clots as Part of the Coagulation Cascade Using Fluorescent Single-Walled Carbon Nanotubes

Blood coagulation is a critical defense mechanism against bleeding that results in the conversion of liquid blood into a solid clot through a complicated cascade, which involves multiple clotting factors. One of the final steps in the coagulation pathway is the conversion of fibrinogen to insoluble fibrin mediated by thrombin. Because coagulation disorders can be life-threatening, the development of novel methods for monitoring the coagulation cascade dynamics is of high importance. Here, we use near-infrared (NIR)-fluorescent single-walled carbon nanotubes (SWCNTs) to image and monitor fibrin clotting in real time. Following the binding of fibrinogen to a tailored SWCNT platform, thrombin transforms the fibrinogen into fibrin monomers, which start to polymerize. The SWCNTs are incorporated within the clot and can be clearly visualized in the NIR-fluorescent channel, where the signal-to-noise ratio is improved compared to bright-field imaging in the visible range. Moreover, the diffusion of individual SWCNTs within the fibrin clot gradually slows down after the addition of thrombin, manifesting a coagulation rate that depends on both fibrinogen and thrombin concentrations. Our platform can open new opportunities for coagulation disorder diagnostics and allow for real-time monitoring of the coagulation cascade with a NIR optical signal output in the biological transparency window.

Second Near-Infrared (NIR-II) Window for Imaging-Navigated Modulation of Brain Structure and Function

For a long time, optical imaging of the deep brain with high resolution has been a challenge. Recently, with the advance in second near-infrared (NIR-II) bioimaging techniques and imaging contrast agents, NIR-II window bioimaging has attracted great attention to monitoring deeper biological or pathophysiological processes with high signal-to-noise ratio (SNR) and spatiotemporal resolution. Assisted with NIR-II bioimaging, the modulation of structure and function of brain is promising to be noninvasive and more precise. Herein, in this review, first the advantage of NIR-II light in brain imaging from the interaction between NIR-II and tissue is elaborated. Then, several specific NIR-II bioimaging technologies are introduced, including NIR-II fluorescence imaging, multiphoton fluorescence imaging, and photoacoustic imaging. Furthermore, the corresponding contrast agents are summarized. Next, the application of various NIR-II bioimaging technologies in visualizing the characteristics of cerebrovascular network and monitoring the changes of the pathology signals will be presented. After that, the modulation of brain structure and function based on NIR-II bioimaging will be discussed, including treatment of glioblastoma, guidance of cell transplantation, and neuromodulation. In the end, future perspectives that would help improve the clinical translation of NIR-II light are proposed.

Identifying Neural Signatures of Dopamine Signaling with Machine Learning

The emergence of new tools to image neurotransmitters, neuromodulators, and neuropeptides has transformed our understanding of the role of neurochemistry in brain development and cognition, yet analysis of this new dimension of neurobiological information remains challenging. Here, we image dopamine modulation in striatal brain tissue slices with near infrared catecholamine nanosensors (nIRCat) and implement machine learning to determine which features of dopamine modulation are unique to changes in stimulation strength, and to different neuroanatomical regions. We trained a support vector machine and a random forest classifier to determine whether recordings were made from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS) and find that machine learning is able to accurately distinguish dopamine release that occurs in DLS from that occurring in DMS in a manner unachievable with canonical statistical analysis. Furthermore, our analysis determines that dopamine modulatory signals including the number of unique dopamine release sites and peak dopamine released per stimulation event are most predictive of neuroanatomy yet note that integrated neuromodulator amount is the conventional metric currently used to monitor neuromodulation in animal studies. Lastly, our study finds that machine learning discrimination of different stimulation strengths or neuroanatomical regions is only possible in adult animals, suggesting a high degree of variability in dopamine modulatory kinetics during animal development. Our study highlights that machine learning could become a broadly-utilized tool to differentiate between neuroanatomical regions, or between neurotypical and disease states, with features not detectable by conventional statistical analysis.

Real-Time Monitoring of Neurotransmitters in the Brain of Living Animals

Neurotransmitters, as important chemical small molecules, perform the function of neural signal transmission from cell to cell. Excess concentrations of neurotransmitters are often closely associated with brain diseases, such as Alzheimer's disease, depression, schizophrenia, and Parkinson's disease. On the other hand, the release of neurotransmitters under the induced stimulation indicates the occurrence of reward-related behaviors, including food and drug addiction. Therefore, to understand the physiological and pathological functions of neurotransmitters, especially in complex environments of the living brain, it is urgent to develop effective tools to monitor their dynamics with high sensitivity and specificity. Over the past 30 years, significant advances in electrochemical sensors and optical probes have brought new possibilities for studying neurons and neural circuits by monitoring the changes in neurotransmitters. This Review focuses on the progress in the construction of sensors for in vivo analysis of neurotransmitters in the brain and summarizes current attempts to address key issues in the development of sensors with high selectivity, sensitivity, and stability. Combined with the latest advances in technologies and methods, several strategies for sensor construction are provided for recording chemical signal changes in the complex environment of the brain.

Monitoring the Activity and Inhibition of Cholinesterase Enzymes using Single-Walled Carbon Nanotube Fluorescent Sensors

Cholinesterase enzymes are involved in a wide range of bodily functions, and their disruption is linked to pathologies such as neurodegenerative diseases and cancer. While cholinesterase inhibitors are used as drug treatments for diseases such as Alzheimer and dementia at therapeutic doses, acute exposure to high doses, found in pesticides and nerve agents, can be lethal. Therefore, measuring cholinesterase activity is important for numerous applications ranging from the search for novel treatments for neurodegenerative disorders to the on-site detection of potential health hazards. Here, we present the development of a near-infrared (near-IR) fluorescent single-walled carbon nanotube (SWCNT) optical sensor for cholinesterase activity and demonstrate the detection of both acetylcholinesterase and butyrylcholinesterase, as well as their inhibition. We show sub U L^-1 sensitivity, demonstrate the optical response at the level of individual nanosensors, and showcase an optical signal output in the 900-1400 nm range, which overlaps with the biological transparency window. To the best of our knowledge, this is the longest wavelength cholinesterase activity sensor reported to date. Our near-IR fluorescence-based approach opens new avenues for spatiotemporal-resolved detection of cholinesterase activity, with numerous applications such as advancing the research of the cholinergic system, detecting on-site potential health hazards, and measuring biomarkers in real-time.

Optical Sensing Strategies for Probing Single-Cell Secretion

Measuring cell secretion events is crucial to understand the fundamental cell biology that underlies cell-cell communication, migration, proliferation, and differentiation. Although strategies targeting cell populations have provided significant information about live cell secretion, they yield ensemble profiles that obscure intrinsic cell-to-cell variations. Innovation in single-cell analysis has made breakthroughs allowing accurate sensing of a wide variety of secretions and their release dynamics with high spatiotemporal resolution. This perspective focuses on the power of single-cell protocols to revolutionize cell-secretion analysis by allowing real-time and real-space measurements on single live cell resolution. We begin by discussing recent progress on single-cell bioanalytical techniques, specifically optical sensing strategies such as fluorescence-, surface plasmon resonance-, and surface-enhanced Raman scattering-based strategies, capable of in situ real-time monitoring of single-cell released ions, metabolites, proteins, and vesicles. Single-cell sensing platforms which allow for high-throughput high-resolution analysis with enough accuracy are highlighted. Furthermore, we discuss remaining challenges that should be addressed to get a more comprehensive understanding of secretion biology. Finally, future opportunities and potential breakthroughs in secretome analysis that will arise as a result of further development of single-cell sensing approaches are discussed.

Letting the little light of mind shine: Advances and future directions in neurochemical detection

Synaptic transmission via neurochemical release is the fundamental process that integrates and relays encoded information in the brain to regulate physiological function, cognition, and emotion. To unravel the biochemical, biophysical, and computational mechanisms of signal processing, one needs to precisely measure the neurochemical release dynamics with molecular and cell-type specificity and high resolution. Here we reviewed the development of analytical, electrochemical, and fluorescence imaging approaches to detect neurotransmitter and neuromodulator release. We discussed the advantages and practicality in implementation of each technology for ease-of-use, flexibility for multimodal studies, and challenges for future optimization. We hope this review will provide a versatile guide for tool engineering and applications for recording neurochemical release.

Synthetic nanosensors for imaging neuromodulators

Neuromodulation plays a critical role in regulating brain function and its dysregulation is implicated in the pathogenesis of numerous neurological and psychiatric disorders. However, only in the last few years have optical tools become available to probe the spatial and temporal profiles of neuromodulator signaling, including dopamine, with the requisite resolution to uncover mechanisms of neuromodulation. In this review, we summarize the current state of synthetic nanomaterial-based optical nanosensors for monitoring neurotransmission with high spatial and temporal resolution. Specifically, we highlight how synthetic nanosensors can elucidate the spatial distribution of neuromodulator release sites and report the temporal dynamics and spatial diffusion of neuromodulator release. Next, we outline advantages and limitations of currently available nanosensors and their recent application to imaging endogenous dopamine release in brain tissue. Finally, we discuss strategies to improve nanosensors for in vivo use, with implications for translational applications.