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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Expression of polysialylated neural cell adhesion molecules on adult stem cells after neuronal differentiation of inner ear spiral ganglion neurons

Highlights: • PolySia expressed on neurons primarily during early stages of neuronal development. • PolySia–NCAM is expressed on neural stem cells from adult guinea pig spiral ganglion. • PolySia is a biomarker that modulates neuronal differentiation in inner ear stem cells. - Abstract: During brain development, polysialylated (polySia) neural cell adhesion molecules (polySia–NCAMs) modulate cell–cell adhesive interactions involved in synaptogenesis, neural plasticity, myelination, and neural stem cell (NSC) proliferation and differentiation. Our findings show that polySia–NCAM is expressed on NSC isolated from adult guinea pig spiral ganglion (GPSG), and in neurons and Schwann cells after differentiation of the NSC with epidermal, glia, fibroblast growth factors (GFs) and neurotrophins. These differentiated cells were immunoreactive with mAb’s to polySia, NCAM, β-III tubulin, nestin, S-100 and stained with BrdU. NSC could regenerate and be differentiated into neurons and Schwann cells. We conclude: (1) polySia is expressed on NSC isolated from adult GPSG and on neurons and Schwann cells differentiated from these NSC; (2) polySia is expressed on neurons primarily during the early stage of neuronal development and is expressed on Schwann cells at points of cell–cell contact; (3) polySia is a functional biomarker that modulates neuronal differentiation in inner ear stem cells. These new findings suggest that replacement of defective cells in the inner ear of hearing impaired patients using adult spiral ganglion neurons may offer potential hope to improve the quality of life for patients with auditory dysfunction and impaired hearing disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Synchronous firing patterns of induced pluripotent stem cell-derived cortical neurons depend on the network structure consisting of excitatory and inhibitory neurons

Highlights: • Induced pluripotent stem cells yielded excitatory and inhibitory cortical neurons. • Neurons spontaneously fired and functionally matured on microelectrode arrays. • Firing patterns depended on the in vitro neuronal network structure. The balance between glutamate-mediated excitation and GABA-mediated inhibition is critical to cortical functioning. However, the contribution of network structure consisting of the both neurons to cortical functioning has not been elucidated. We aimed to evaluate the relationship between the network structure and functional activity patterns in vitro. We used mouse induced pluripotent stem cells (iPSCs) to construct three types of neuronal populations; excitatory-rich (Exc), inhibitory-rich (Inh), and control (Cont). Then, we analyzed the activity patterns of these neuronal populations using microelectrode arrays (MEAs). Inhibitory synaptic densities differed between the three types of iPSC-derived neuronal populations, and the neurons showed spontaneously synchronized bursting activity with functional maturation for one month. Moreover, different firing patterns were observed between the three populations; Exc demonstrated the highest firing rates, including frequent, long, and dominant bursts. In contrast, Inh demonstrated the lowest firing rates and the least dominant bursts. Synchronized bursts were enhanced by disinhibition via GABA{sub A} receptor blockade. The present study, using iPSC-derived neurons and MEAs, for the first time show that synchronized bursting of cortical networks in vitro depends on the network structure consisting of excitatory and inhibitory neurons.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2008

Closing the Phenotypic Gap between Transformed Neuronal Cell Lines in Culture and Untransformed Neurons

Studies of neuronal dysfunction in the central nervous system (CNS) are frequently limited by the failure of primary neurons to propagate in vitro. Neuronal cell lines can be substituted for primary cells but they often misrepresent normal conditions. We hypothesized that a dimensional (3-D) cell culture system would drive the phenotype of transformed neurons closer to that of untransformed cells. In our studies comparing 3-D versus 2-dimensional (2-D) culture, neuronal SH-SY5Y (SY) cells underwent distinct morphological changes combined with a significant drop in their rate of cell division. Expression of the proto-oncogene N-myc and the RNA binding protein HuD was decreased in 3-D culture as compared to standard 2-D conditions. We observed a decline in the anti-apoptotic protein Bcl-2 in 3-D culture, coupled with increased expression of the pro-apoptotic proteins Bax and Bak. Moreover, thapsigargin (TG)-induced apoptosis was enhanced in the 3-D cells. Microarray analysis demonstrated significantly differing mRNA levels for over 700 genes in the cells of each culture type. These results indicate that a 3-D culture approach narrows the phenotypic gap between neuronal cell lines and primary neurons. The resulting cells may readily be used for in vitro research of neuronal pathogenesis.

Myers, Tereance A.↗
DOE OSTI2018

Neuron-specific knockdown of Drosophila PDHB induces reduction of lifespan, deficient locomotive ability, abnormal morphology of motor neuron terminals and photoreceptor axon targeting

Highlights: • Drosophila CG11876 (dPDHB) is a homolog of human pyruvate dehydrogenase beta, PDHB. • Neuron-specific knockdown of dPDHB causes locomotive defects and reduced life span. • Neuron-specific knockdown of dPDHB causes structural defects in NMJ. • Neuron-specific knockdown of dPDHB induces mitochondrial fragmentation in brain. • Knockdown of dPDHB induces a rough eye and aberrant photoreceptor axon targeting. Pyruvate dehydrogenase complex deficiency (PDCD) is a common primary cause of defects in mitochondrial function and also can lead to peripheral neuropathy. Pyruvate dehydrogenase E1 component subunit beta (PDHB) is a subunit of pyruvate dehydrogenase E1, which is a well-known component of PDC. In Drosophila melanogaster, the CG11876 (dPDHB) gene is a homolog of human PDHB. In this study, we established a Drosophila model with neuron-specific knockdown of dPDHB to investigate its role in neuropathy pathogenesis. Knockdown of dPDHB in pan-neurons induced locomotor defects in both larval and adult stages, which were consistent with abnormal morphology of the motor neuron terminals at neuromuscular junctions and mitochondrial fragmentation in brains. Moreover, neuron-specific knockdown of dPDHB also shortened the lifespan of adult flies. In addition, flies with knockdown of dPDHB manifested a rough eye phenotype and aberrant photoreceptor axon targeting. These results with the Drosophila model suggest the involvement of PDHB in peripheral neuropathy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Energy-efficient Mott activation neuron for full-hardware implementation of neural networks

To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, in this work, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal–oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.

electrical and electronic engineering↗
DOE OSTI2023

CRISPR/Cas9 Directed Reprogramming of iPSC for Accelerated Motor Neuron Differentiation Leads to Dysregulation of Neuronal Fate Patterning and Function

Neurodegeneration causes a significant disease burden and there are few therapeutic interventions available for reversing or slowing the disease progression. Induced pluripotent stem cells (iPSCs) hold significant potential since they are sourced from adult tissue and have the capacity to be differentiated into numerous cell lineages, including motor neurons. This differentiation process traditionally relies on cell lineage patterning factors to be supplied in the differentiation media. Genetic engineering of iPSC with the introduction of recombinant master regulators of motor neuron (MN) differentiation has the potential to shorten and streamline cell developmental programs. We have established stable iPSC cell lines with transient induction of exogenous LHX3 and ISL1 from the Tet-activator regulatory region and have demonstrated that induction of the transgenes is not sufficient for the development of mature MNs in the absence of neuron patterning factors. Comparative global transcriptome analysis of MN development from native and Lhx-ISL1 modified iPSC cultures demonstrated that the genetic manipulation helped to streamline the neuronal patterning process. However, leaky gene expression of the exogenous MN master regulators in iPSC resulted in the premature activation of genetic pathways characteristic of the mature MN function. Dysregulation of metabolic and regulatory pathways within the developmental process affected the MN electrophysiological responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

The primed SNARE–complexin–synaptotagmin complex for neuronal exocytosis

Synaptotagmin, complexin, and neuronal SNARE (soluble N -ethylmaleimide sensitive factor attachment protein receptor) proteins mediate evoked synchronous neurotransmitter release, but the molecular mechanisms mediating the cooperation between these molecules remain unclear. Here we determine crystal structures of the primed pre-fusion SNARE–complexin–synaptotagmin-1 complex. These structures reveal an unexpected tripartite interface between synaptotagmin-1 and both the SNARE complex and complexin. Simultaneously, a second synaptotagmin-1 molecule interacts with the other side of the SNARE complex via the previously identified primary interface. Mutations that disrupt either interface in solution also severely impair evoked synchronous release in neurons, suggesting that both interfaces are essential for the primed pre-fusion state. Ca 2+ binding to the synaptotagmin-1 molecules unlocks the complex, allows full zippering of the SNARE complex, and triggers membrane fusion. In conclusion, the tripartite SNARE–complexin–synaptotagmin-1 complex at a synaptic vesicle docking site has to be unlocked for triggered fusion to start, explaining the cooperation between complexin and synaptotagmin-1 in synchronizing evoked release on the sub-millisecond timescale.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Development of Lipid-Coated Semiconductor Nanosensors for Recording of Membrane Potential in Neurons

In the past decade, optical imaging methods have significantly improved our understanding of the information processing principles in the brain. Although many promising tools have been designed, sensors of membrane potential are lagging behind the rest. Semiconductor nanoparticles are an attractive alternative to classical voltage indicators, such as voltage-sensitive dyes and proteins. Such nanoparticles exhibit high sensitivity to external electric fields via the quantum-confine Stark effect. Here we report the development of semiconductor voltage-sensitive nanorods (vsNRs) that self-insert into the neuronal membrane. To facilitate interaction of the nanorods with the membrane, we functionalized their surface with the lipid mixture derived from brain extract. We describe a workflow to detect and process the photoluminescent signal of vsNRs after widefield time-lapse recordings. We also present data indicating that vsNRs are feasible for sensing membrane potential in neurons at a single-particle level. This shows the potential of vsNRs for the detection of neuronal activity with unprecedentedly high spatial and temporal resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Reconstruction of neuronal input through modeling single-neuron dynamics and computations

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

BIN1 protein isoforms are differentially expressed in astrocytes, neurons, and microglia: neuronal and astrocyte BIN1 are implicated in tau pathology

Background: Identified as an Alzheimer’s disease (AD) susceptibility gene by genome wide-association studies, BIN1 has 10 isoforms that are expressed in the Central Nervous System (CNS). The distribution of these isoforms in different cell types, as well as their role in AD pathology still remains unclear. Methods: Utilizing antibodies targeting specific BIN1 epitopes in human post-mortem tissue and analyzing mRNA expression data from purified microglia, we identified three isoforms expressed in neurons and astrocytes (isoforms 1, 2 and 3) and four isoforms expressed in microglia (isoforms 6, 9, 10 and 12). The abundance of selected peptides, which correspond to groups of BIN1 protein isoforms, was measured in dorsolateral prefrontal cortex, and their relation to neuropathological features of AD was assessed. Results: Peptides contained in exon 7 of BIN1’s N-BAR domain were found to be significantly associated with ADrelated traits and, particularly, tau tangles. Decreased expression of BIN1 isoforms containing exon 7 is associated with greater accumulation of tangles and subsequent cognitive decline, with astrocytic rather than neuronal BIN1 being the more likely culprit. These effects are independent of the BIN1 AD risk variant. Conclusions: Exploring the molecular mechanisms of specific BIN1 isoforms expressed by astrocytes may open new avenues for modulating the accumulation of Tau pathology in AD. Keywords: BIN1 isoforms, Alzheimer’s disease, Amyloid, Tau, Microglia, Astrocytes, Neurons

Taga, Mariko↗
NASA NTRS1999

Firing behavior of vestibular neurons during active and passive head movements: vestibulo-spinal and other non-eye-movement related neurons

The firing behavior of 51 non-eye movement related central vestibular neurons that were sensitive to passive head rotation in the plane of the horizontal semicircular canal was studied in three squirrel monkeys whose heads were free to move in the horizontal plane. Unit sensitivity to active head movements during spontaneous gaze saccades was compared with sensitivity to passive head rotation. Most units (29/35 tested) were activated at monosynaptic latencies following electrical stimulation of the ipsilateral vestibular nerve. Nine were vestibulo-spinal units that were antidromically activated following electrical stimulation of the ventromedial funiculi of the spinal cord at C1. All of the units were less sensitive to active head movements than to passive whole body rotation. In the majority of cells (37/51, 73%), including all nine identified vestibulo-spinal units, the vestibular signals related to active head movements were canceled. The remaining units (n = 14, 27%) were sensitive to active head movements, but their responses were attenuated by 20-75%. Most units were nearly as sensitive to passive head-on-trunk rotation as they were to whole body rotation; this suggests that vestibular signals related to active head movements were cancelled primarily by subtraction of a head movement efference copy signal. The sensitivity of most units to passive whole body rotation was unchanged during gaze saccades. A fundamental feature of sensory processing is the ability to distinguish between self-generated and externally induced sensory events. Our observations suggest that the distinction is made at an early stage of processing in the vestibular system.

Non-NASA Center↗
NASA NTRS2008

Spiking Neurons for Analysis of Patterns

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Huntsberger, Terrance↗