abstract: Calcium imaging is a well-established, non-invasive or minimally technique designed to study the electrical signaling neurons. Calcium regulates the release of gliotransmitters in astrocytes. Analyzing astrocytic calcium transients can provide significant insights into mechanisms such as neuroplasticity and neural signal modulation. In the past decade, numerous methods have been developed to analyze in-vivo calcium imaging data that involves complex techniques such as overlapping signals segregation and motion artifact correction. The hypothesis used to detect calcium signal is the spatiotemporal sparsity of calcium signal, and these methods are unable to identify the passive cells that are not actively firing during the time frame in the video. Statistics regarding the percentage of cells in each frame of view can be critical for the analysis of calcium imaging data for human induced pluripotent stem cells derived neurons and astrocytes. The objective of this research is to develop a simple and efficient semi-automated pipeline for analysis of in-vitro calcium imaging data. The region of interest (ROI) based image segmentation is used to extract the data regarding intensity fluctuation caused by calcium concentration changes in each cell. It is achieved by using two approaches: basic image segmentation approach and a machine learning approach. The intensity data is evaluated using a custom-made MATLAB that generates statistical information and graphical representation of the number of spiking cells in each field of view, the number of spikes per cell and spike height. / Dissertation/Thesis / Masters Thesis Biomedical Engineering 2019

In the brain, glycogen, the storage form of glucose, is exclusively localized in astrocytes (Magistretti and Allaman, 2015). Glycogenolysis leads to the production of L-lactate, which is shuttled to neurons for ATP production. Interestingly, L-lactate was recently shown to be not only a source of energy, but also a signaling molecule to neurons. This was demonstrated through the inhibition of L-lactate production or transport in an inhibitory avoidance paradigm, where the rodents developed amnesia. This inhibition of memory consolidation was rescued by L-lactate and not by equicaloric glucose emphasizing that L-lactate acts as a signaling molecule as well (Suzuki et al., 2011). A recent study in our laboratory suggests that the action of L-lactate takes place through a cascade of molecular events via the modulation of N-methyl-D-aspartate receptor (NMDAR) activity (Yang et al., 2014). Since NADH produced similar results to those seen with L-lactate, it was hypothesized that the action of the latter is based on altering the redox state of the cell, in particular in view of the fact that redox-sensitive sites are present on the NMDAR. However, the precise molecular mechanism underlying the apparent change in the NMDAR activity is not fully elucidated. The objective of this study is to explore those mechanisms.

NMDA receptors are the most studied receptors in the field of neuroscience and are known to play an important role in development and plasticity. These receptors exhibit different kinetics depending on their subunit composition. NR2A and NR2B are the predominating NMDAR subunits in the brain. These receptors localize to synapses where they interact with other proteins including CaMKIIα, an abundant kinase which plays an important role in synaptic plasticity. Although CaMKIIα is known to bind to all types of NMDARs, it exhibits a higher affinity to NR2B compared to NR2A subunits. Studies have shown that lactate acts as a signaling molecule promoting the expression of genes related to synaptic plasticity via NMDARs activation. However, the mechanism describing how lactate exerts these effects is not well understood. We hypothesize that the redox state change, resulting from the metabolic conversion of lactate to pyruvate, may promote the interaction between CaMKIIα and NMDARs, thereby potentiating NMDARs activity. To tackle this question, we used a pharmacogenetics model consisting of NMDARs expressing HEK293 cells in the presence or absence of CaMKIIα. To monitor NMDARs activity, we use the ratio-metric calcium dye Fura-2 in calcium imaging experiments. We report that L-lactate decreases the peak responses of the NR2A and NR2B NMDARs in the absence of CaMKII expression. Upon CaMKII presence, we found that lactate prolongs the activation period of GluN2B as observed during the washout period and modestly increase the peak response of GluN2A NMDARs. Interestingly, we confirm that expressing CaMKIIα in control (no lactate) HEK cells significantly augmented NR2B but not NR2A NMDARs. We also report that pyruvate was able to increase peak responses of both NR2A and NR2B NMDARs in the absence of CaMKII, while it only increased the NR2A-NMDAR peak responses in the presence of CaMKII. These results suggest that lactate exerts a neuroprotective effect in the absence of CaMKII and it slightly boosts NR2B NMDARs activity when CaMKII is expressed, possibly favoring plasticity. Moreover, data obtained with pyruvate indicates that in our HEK cell model pyruvate affects the NMDARs in a manner independent of the presence of CaMKII through an alternative mechanism.

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The brain is a complex system that seamlessly solves intricate problems with unprecedented efficiency. Part of the brain’s task is gathering sensory information from its environment, processing and representing it in a highly efficient manner. One of the key mechanisms used by sensory pathways is to process information by its context, disregarding redundancies and selectively focusing on novelty (deviance/change detection). A quantitative measure for how well the brain can detect novel stimuli is measured with the oddball paradigm and the mismatch negativity component (MMN). Deficits in context modulation and reduced MMN components are associated with mental disorders such as schizophrenia. Typically, oddball studies are done with coarse recording methods like EEG and MEG, and the network response dynamics underlying novelty detection is still unclear. In this work we used two-photon calcium imaging in awake mice listening to acoustic oddball stimuli, and recorded from large populations of neurons in primary and secondary auditory cortex (AC). We analyzed single cell and population representations of contextual information and found robust context modulation across all recorded AC regions. Responses to redundant stimuli were strongly suppressed while those to novel stimuli amplified. Furthermore, responses to identical stimuli in deviant, neutral, or redundant contexts were encoded by distinct populations of neurons, indicating an even stronger context encoding than seen in average population activity. Finally, we found that stimulus complexity also has an effect on where and to what extent context information is most robustly expressed. A2 was the most engaged in context processing for simple tones, while for complex frequency gratings A1 was doing novelty detection to the largest extent. My results provide a circuit basis for novelty detection in the auditory cortex, as a stepping stone to understand how processing of sensory stimuli is carried out by the brain.

Two-photon microscopy has become a widely adopted tool for functional Calcium imaging in neuroscience research. Due to the decreased scattering at near-infrared wavelengths, two-photon excitation improves penetration depth and image contrast in the mouse brain over single-photon excitation. However, the imaging acquisition is usually performed in a laser-scanning approach, which restricts the system’s spatiotemporal bandwidth, allowing only a limited number of neurons to be captured from a 2D image plane. This dissertation focuses on the development of a single-objective lens, light-sheet excitation, two-photon microscopy approach (2P-SCAPE) that dramatically improves the system’s bandwidth over laser-scanning. The spatial multiplexing provided by light-sheet excitation resolved the trade-off between imaging speed and signal-to-noise ratio in laser-scanning. The single objective lens oblique illumination also frees up the sample space for in vivo experiments. When combined with the state-of-the-art scientific CMOS/intensified CMOS camera, 2P-SCAPE enabled high spatiotemporal bandwidth imaging of biological tissues from hundred MHz to GHz. The first aim of the dissertation was to investigate the feasibility of two-photon light-sheet excitation given constraints such as power, signal, photodamage sources. An optimized excitation strategy was derived for laser parameters, light-sheet parameters. The performance of a near-infrared light-sheet was also investigated in a silico model. The second aim was to design and develop the 2P-SCAPE system. The imaging bandwidth and resolution of the system were improved with iterative system optimizations, including an optimized excitation strategy, dispersion management, collection throughput improvement, extended depth of focus illumination. The third aim was to apply the 2P-SCAPE system to many mouse brain and zebrafish samples for high spatiotemporal imaging of neural activities. Several spatiotemporal unmixing processing methods were applied to illustrate the rich information captured with the system. Finally, two alternative approaches to increase the penetration depth of SCAPE with NIR excitation were investigated. Proof-of-concept experiments in mouse brains also suggest they improved penetration depths over single-photon blue excitation.

Calcium (Ca2+), a crucial effector for many biological systems, has been associated with diseases such as cardiovascular disease, Alzheimer’s, Parkinson’s, cancer, and osteoporosis. It is important to develop calcium sensors to measure intracellular Ca2+ dynamics at various biological and pathological states. Our lab has engineered such probes by designing a Ca2+ binding site into fluorescent proteins such as Enhanced Green Fluorescent Protein (EGFP) and mCherry. In this thesis, we aim to improve optical properties and metal binding properties of green EGFP-based sensor CatchER and mCherry based red sensors by site-directed mutagenesis and protein engineering, various spectroscopic methods and cell imaging. The green EGFP-based sensor CatchER, with a Ca2+ binding pocket charge of -5, displays the greatest affinity for Ca2+ and has the greatest fluorescence intensity change with Ca2+ when compared to its variants with a less negative binding pocket charge. In addition, we have also designed several SR/ER targeting CatchER variants using Ryanodine receptor and Calnexin transmembrane domains. These constructs were shown to display a strong presence in the SR/ER lumen and further designed for a new luminal orientation. Further, we have shown that the optical properties of two red calcium sensors can be significantly improved by modifying the local environment of the chromophore.

Recent improvements in high performance fluorescent sensors and scientific CMOS cameras enable optical imaging of neural networks at a much larger scale. Our lab has demonstrated the ability of wide-field calcium-imaging (using GCaMP6f) to capture the concurrent dynamic activity from hundreds to thousands of neurons over millimeters of brain tissue in behaving mice. The expansiveness of the neuronal network captured by the system requires innovation in data analysis methods. This thesis explores data analysis techniques to extract dynamics of hippocampal neural network containing a large number of individual neurons recorded using GCaMP6, while mice were learning a classical eye puff conditioning behavior. GCaMP6 fluorescence signals in each neuron is first considered one dimension, and each dataset thus contains hundreds to thousands dimensions. To understand the network structure, we first performed dimension reduction technique to examine the low-dimension evolution of the neural trajectory using Gaussian Process Factor Analysis, which smooths across dimensions, while extracting the low dimension representation. Because of the slow time course of GCaMP6 signals, the Factor Analysis was biased to the long lasting decay phase of the signal that does not represent neural activities. We found that it is critical to first estimate the spike train inference prior to application of dimension reduction, such as using the Fast Nonnegative Deconvolution method. While the low-dimension presentation described intriguing features in the neural trajectories that paralleled the learning behavior of the animal, to further quantify the network changes we directly examined the network in the high dimension space. We calculated the changes in the distance of the network trajectory over time in the high dimension space without any filtering, and compared across different phases of the behavioral states. We found that the speed of the trajectory in the high dimension space is significantly higher when animal learned the task, and the trajectory travelled much further away from baseline during the delay phase of the conditioning behavior. Together, these results demonstrate that dimension reduction analysis technique and the network trajectory within the non-reduced high dimension space can capture evolving features of neural networks recorded using calcium imaging. While this thesis concerns the hippocampal dynamics during learning, such data analysis techniques are expected to be broadly applicable to other behaviorally relevant networks.

Steroid hormones are known to have significant effects on a wide variety of biological processes. In particular, they serve as critical modulators of neural function and behavior and play critical roles in stress responses and neurologic disorders. Until recently the biological actions of steroid hormones were believed to operate primarily through activation of cognate nuclear hormone receptors or the allosteric modulation of ion channels (Majewskaet al., 1986). However, new signaling pathways involving G-protein coupled receptors (GPCRs) for steroid hormones have been recently identified in multiple different species, implicating steroid hormones in direct fast modulation of intracellular signaling and in turn behavior (Thomas et al., 2006, Gabor et al., 2015). In mammals G protein-coupled estrogen receptor 1 (GPER), also known as G protein-coupled receptor 30 (GPR30), is expressed throughout the body including in the nervous system and has been suggested to play a variety of roles in health and behavior (Prossnitz and Barton, 2011). Despite recent progress in this area from studies using rodent models, the mechanisms underlying "non-genomic” actions of steroids remain largely elusive. This gap in our understanding presents a significant scientific and clinical challenge to a comprehensive view of the role of steroid hormones in regulating both neural function, behavior and overall health of the organism. To understand the mechanisms for this unconventional steroid signaling we sought to use a simpler system to explore the functions of GPCR’s for steroid hormones. In 2005, Peter Evans’s group identified DopEcR, a unique GPCR in Drosophila melanogaster, which responds to ecdysone—the major steroid hormone in insects (Srivastava et al. 2005). This unconventional GPCR for steroid hormones is particularly interesting because it is a dual receptor that also responds to a structurally dissimilar compound, dopamine. DopEcR is preferentially expressed in the nervous system and has recently been implicated in modulating multiple behaviors including starvation-induced enhancement of sugar sensitivity (Inagaki et al., 2012), experience-dependent courtship suppression, habituation of the giant fiber pathway (Ishimoto et al., 2013) and ethanol-induced sedation (Petruccelli et al. 2016) in flies. DopEcR also plays a role in perception of sex pheromones in moths (Abrieux et al., 2013). More recently the mammalian GPCR for estrogen GPER has also been found to bind dopamine indicating that this unique attribute may be more prevalent among these novel GPCRs for steroids (Evans et al. 2013). Despite these previous findings, we still know little about how GPCRs for steroids modulate neurons at the cellular level and how they modulate behaviors. Therefore we sought to forge a more comprehensive understanding of the function of steroid signaling by characterizing DopEcR function in neuronal and behavioral modulation through GPCR’s. To characterize DopEcR’s function we looked at the consequences of DopEcR signaling at three levels: behavior, neuronal morphology and finally physiology. Because changes steroid hormones levels are often associated with environmental stressors we assayed the role of DopEcR in a stress related behavior: starvation-induced sleep suppression and hyperactivity. To look at DopEcR’s role in neuronal physiology we used bioluminescent calcium imaging to measure its effect on the stimulated calcium response in a brain structure critical for behavior. Finally we used principal clock neurons in the brain (PDF+ l-LNv neurons) as a model to examine DopEcR’s role in modulating plasticity and neuronal structure. In our present work described in Chapter 2, we found that the D1-like receptor, DopR1, modulates sleep and activity independent of starvation while DopEcR plays a role in mediating starvation-induced sleep suppression and enhanced activity. We found that knocking down EGFR in a DopEcR mutant background restored starvation induced changes in behavior, suggesting that DopEcR normally suppresses EGFR signaling to suppress sleep under starvation. In Chapter 4, we show that the nicotine-induced Ca2+-response was selectively enhanced in the medial lobes either in DopEcR mutant or in flies with DopEcR selectively knocked down within the MBs. Using a pharmacological approach, we show that the endogenous ligands of DopEcR mediated two different responses in the MBs: the steroid ligand ecdysone enhances activity in the calyx and cell body region, whereas monoaminergic ligand dopamine reduced activity in the medial lobes. In Chapter 5, we find that reducing DopEcR in PDF neurons results in reduced basal levels of bouton numbers. The reduction in bouton number is independent of cAMP signaling but instead relies on inhibition of EGFR signaling. Signifying that DopEcR may modulate EGFR associated signaling to make changes in the in the brain. These results demonstrate that DopEcR is able to modulate neuronal excitability, physical structure of neurons and the behavior of the organism. Interestingly it also indicates that DopEcR’s different ligands, dopamine and ecdysone, may have unique and spatially distinct effects on different brain structures or within the same structure. Overall, this study provides a solid foundation for understanding the roles and action mechanisms of GPCR-mediated steroid signaling in regulation of neural development, physiology and behavior.

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