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.

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.

Near-infrared catecholamine nanosensors for high spatiotemporal dopamine imaging.

Dopamine neuromodulation of neural synapses is a process implicated in a number of critical brain functions and diseases. Development of protocols to visualize this dynamic neurochemical process is essential to understanding how dopamine modulates brain function. We have developed a non-genetically encoded, near-IR (nIR) catecholamine nanosensor (nIRCat) capable of identifying ~2-µm dopamine release hotspots in dorsal striatal brain slices. nIRCat is readily synthesized through sonication of single walled carbon nanotubes with DNA oligos, can be readily introduced into both genetically tractable and intractable organisms and is compatible with a number of dopamine receptor agonists and antagonists. Here we describe the synthesis, characterization and implementation of nIRCat in acute mouse brain slices. We demonstrate how nIRCat can be used to image electrically or optogenetically stimulated dopamine release, and how these procedures can be leveraged to study the effects of dopamine receptor pharmacology. In addition, we provide suggestions for building or adapting wide-field microscopy to be compatible with nIRCat nIR fluorescence imaging. We discuss strategies for analyzing nIR video data to identify dopamine release hotspots and quantify their kinetics. This protocol can be adapted and implemented for imaging other neuromodulators by using probes of this class and can be used in a broad range of species without genetic manipulation. The synthesis and characterization protocols for nIRCat take ~5 h, and the preparation and fluorescence imaging of live brain slices by using nIRCats require ~6 h.

Mitigation of Carbon Nanotube Neurosensor Induced Transcriptomic and Morphological Changes in Mouse Microglia with Surface Passivation.

Single-walled carbon nanotubes (SWCNT) are used in neuroscience for deep-brain imaging, neuron activity recording, measuring brain morphology, and imaging neuromodulation. However, the extent to which SWCNT-based probes impact brain tissue is not well understood. Here, we study the impact of (GT)6-SWCNT dopamine nanosensors on SIM-A9 mouse microglial cells and show SWCNT-induced morphological and transcriptomic changes in these brain immune cells. Next, we introduce a strategy to passivate (GT)6-SWCNT nanosensors with PEGylated phospholipids to improve both biocompatibility and dopamine imaging quality. We apply these passivated dopamine nanosensors to image electrically stimulated striatal dopamine release in acute mouse brain slices, and show that slices labeled with passivated nanosensors exhibit higher fluorescence response to dopamine and measure more putative dopamine release sites. Hence, this facile modification to SWCNT-based dopamine probes provides immediate improvements to both biocompatibility and dopamine imaging functionality with an approach that is readily translatable to other SWCNT-based neurotechnologies.

Imaging striatal dopamine release using a nongenetically encoded near infrared fluorescent catecholamine nanosensor.

Neuromodulation plays a critical role in brain function in both health and disease, and new tools that capture neuromodulation with high spatial and temporal resolution are needed. Here, we introduce a synthetic catecholamine nanosensor with fluorescent emission in the near infrared range (1000-1300 nm), near infrared catecholamine nanosensor (nIRCat). We demonstrate that nIRCats can be used to measure electrically and optogenetically evoked dopamine release in brain tissue, revealing hotspots with a median size of 2 µm. We also demonstrated that nIRCats are compatible with dopamine pharmacology and show D2 autoreceptor modulation of evoked dopamine release, which varied as a function of initial release magnitude at different hotspots. Together, our data demonstrate that nIRCats and other nanosensors of this class can serve as versatile synthetic optical tools to monitor neuromodulatory neurotransmitter release with high spatial resolution.

Review Article: Tools and trends for probing brain neurochemistry.

The brain is composed of complex neuronal networks that interact on spatial and temporal scales that span several orders of magnitude. Uncovering how this circuitry gives rise to multifaceted phenomena such as perception, memory, and behavior remains one of the grand challenges in science today. A wide range of investigative methods have been developed to delve deeper into the inner workings of the brain, spanning the realms of molecular biology, genetics, chemistry, optics, and engineering, thereby forming a nexus of discovery that has accelerated our understanding of the brain. Whereas neuronal electrical excitability is a hallmark property of neurons, chemical signaling between neurons-mediated by hundreds of neurotransmitters, neuromodulators, hormones, and other signaling molecules-is equally important, but far more elusive in its regulation of brain function for motor control, learning, and behavior. To date, the brain's neurochemical state has been interrogated using classical tools borrowed from analytical chemistry, such as liquid chromatography and amperometry, and more recently, newly developed fluorescent sensors. Here, the authors review advances in the development of functional fluorescent probes that are beginning to expand their understanding of the neurochemical basis of brain function alongside device-based analytical tools that have already made extensive contributions to the field. The emphasis herein is on the paradigms of probe and device development, which follow certain design principles unique to the interrogation of brain chemistry.

Conditional Network Assembly and Targeted Protein Retention via Environmentally Responsive, Engineered ß-Roll Peptides.

Stimulus-responsive biomaterials have applications in many areas of biotechnology, such as tissue engineering, drug delivery, and bioelectrocatalysis. The intrinsically disordered repeat-in-toxin (RTX) domain is a conformationally dynamic peptide that gains ß-roll secondary structure when bound to calcium ions. A smart hydrogel platform was constructed by genetically fusing two rationally designed mutant RTX domains: first, a mutant peptide with hydrophobic interfaces capable of calcium-dependent network assembly, and second, another mutant that conditionally binds the model target protein lysozyme. In this way, the calcium-induced control over the secondary structure of the ß-roll peptide was exploited to regulate both the cross-linking and lysozyme-binding functionalities. The constructed biomaterial exhibited calcium-dependent gelation and target molecule retention, and erosion experiments showed that ß-roll peptides with a higher affinity for lysozyme produced more robust hydrogel networks. This work demonstrates the use of RTX domains for introducing two useful features simultaneously, network cross-linking and target protein binding, and that the calcium-dependent regulation of these systems can be useful for controlling bulk self-assembly and controlled release capabilities.