A Single-Neuron: Current Trends and Future Prospects

Soft Bioelectronics Using Nanomaterials and Nanostructures for Neuroengineering

The identification of neural networks for large areas and the regulation of neuronal activity at the single-neuron scale have garnered considerable attention in neuroscience. In addition, detecting biochemical molecules and electrically, optically, and chemically controlling neural functions are key research issues. However, conventional rigid and bulky bioelectronics face challenges for neural applications, including mechanical mismatch, unsatisfactory signal-to-noise ratio, and poor integration of multifunctional components, thereby degrading the sensing and modulation performance, long-term stability and biocompatibility, and diagnosis and therapy efficacy. Implantable bioelectronics have been developed to be mechanically compatible with the brain environment by adopting advanced geometric designs and utilizing intrinsically stretchable materials, but such advances have not been able to address all of the aforementioned challenges.Recently, the exploration of nanomaterial synthesis and nanoscale fabrication strategies has facilitated the design of unconventional soft bioelectronics with mechanical properties similar to those of neural tissues and submicrometer-scale resolution comparable to typical neuron sizes. The introduction of nanotechnology has provided bioelectronics with improved spatial resolution, selectivity, single neuron targeting, and even multifunctionality. As a result, this state-of-the-art nanotechnology has been integrated with bioelectronics in two main types, i.e., bioelectronics integrated with synthesized nanomaterials and bioelectronics with nanoscale structures. The functional nanomaterials can be synthesized and assembled to compose bioelectronics, allowing easy customization of their functionality to meet specific requirements. The unique nanoscale structures implemented with the bioelectronics could maximize the performance in terms of sensing and stimulation. Such soft nanobioelectronics have demonstrated their applicability for neuronal recording and modulation over a long period at the intracellular level and incorporation of multiple functions, such as electrical, optical, and chemical sensing and stimulation functions.In this Account, we will discuss the technical pathways in soft bioelectronics integrated with nanomaterials and implementing nanostructures for application to neuroengineering. We traced the historical development of bioelectronics from rigid and bulky structures to soft and deformable devices to conform to neuroengineering requirements. Recent approaches that introduced nanotechnology into neural devices enhanced the spatiotemporal resolution and endowed various device functions. These soft nanobioelectronic technologies are discussed in two categories: bioelectronics with synthesized nanomaterials and bioelectronics with nanoscale structures. We describe nanomaterial-integrated soft bioelectronics exhibiting various functionalities and modalities depending on the integrated nanomaterials. Meanwhile, soft bioelectronics with nanoscale structures are explained with their superior resolution and unique administration methods. We also exemplified the neural sensing and stimulation applications of soft nanobioelectronics across various modalities, showcasing their clinical applications in the treatment of neurological diseases, such as brain tumors, epilepsy, and Parkinson's disease. Finally, we discussed the challenges and direction of next-generation technologies.

What do brain oscillations tell about the human sense of smell?

Brain activity may manifest itself as oscillations which are repetitive rhythms of neuronal firing. These local field potentials can be measured via intracranial electroencephalography (iEEG). This review focuses on iEEG used to map human brain structures involved in olfaction. After presenting the methodology of the review, a summary of the brain structures involved in olfaction is given, followed by a review of the literature on human olfactory oscillations in different contexts. A single case is provided as an illustration of the olfactory oscillations. Overall, the timing and sequence of oscillations found in the different structures of the olfactory system seem to play an important role for olfactory perception.

Massively Parallel High-Throughput Single-Cell Patterning and Large Biomolecular Delivery in Mammalian Cells Using Light Pulses

The recent advancements of single-cell analysis have significantly enhanced the ability to understand cellular physiology when compared to bulk cellular analysis. Here a massively parallel single-cell patterning and very large biomolecular delivery is reported. Micro-pillar polydimethyl siloxane stamp with different diameters (40-100 µm with 1 cm × 1 cm patterning area) is fabricated and then imprint distinct proteins and finally pattern single-cell to small clusters of cells depending on the micro-pillar diameters. The maximum patterning efficiency is achieved 99.7% for SiHa, 96.75% for L929, and 98.6% for MG63 cells, for the 100 µm micro-pillar stamp. For intracellular delivery of biomolecules into the patterned cells, a titanium micro-dish device is aligned on top of the cells and exposed by infrared light pulses. The platform successfully delivers small to very large biomolecules such as PI dyes (668 Da), dextran 3000 Da, siRNA (20-24 bp), and large size enzymes (464 KDa) in SiHa, L929 and MG63 cells. The delivery efficiency for PI dye, Dextran 3000, siRNA, and enzyme for patterned cells are ≈95 ± 3%, 97 ± 1%, 96 ± 1% and 94 ± 3%, with cell viability of 98 ± 1%. Thus, the platform is compact, robust, easy for printing, and potentially applicable for single-cell therapy and diagnostics.

Single neurons on microelectrode array chip: manipulation and analyses

Chips-based platforms intended for single-cell manipulation are considered powerful tools to analyze intercellular interactions and cellular functions. Although the conventional cell co-culture models could investigate cell communication to some extent, the role of a single cell requires further analysis. In this study, a precise intercellular interaction model was built using a microelectrode array [microelectrode array (MEA)]-based and dielectrophoresis-driven single-cell manipulation chip. The integrated platform enabled precise manipulation of single cells, which were either trapped on or transferred between electrodes. Each electrode was controlled independently to record the corresponding cellular electrophysiology. Multiple parameters were explored to investigate their effects on cell manipulation including the diameter and depth of microwells, the geometry of cells, and the voltage amplitude of the control signal. Under the optimized microenvironment, the chip was further evaluated using 293T and neural cells to investigate the influence of electric field on cells. An examination of the inappropriate use of electric fields on cells revealed the occurrence of oncosis. In the end of the study, electrophysiology of single neurons and network of neurons, both differentiated from human induced pluripotent stem cells (iPSC), was recorded and compared to demonstrate the functionality of the chip. The obtained preliminary results extended the nature growing model to the controllable level, satisfying the expectation of introducing more elaborated intercellular interaction models.

Co-variations of cerebral blood volume and single neurons discharge during resting state and visual cognitive tasks in non-human primates

To better understand how the brain allows primates to perform various sets of tasks, the ability to simultaneously record neural activity at multiple spatiotemporal scales is challenging but necessary. However, the contribution of single-unit activities (SUAs) to neurovascular activity remains to be fully understood. Here, we combine functional ultrasound imaging of cerebral blood volume (CBV) and SUA recordings in visual and fronto-medial cortices of behaving macaques. We show that SUA provides a significant estimate of the neurovascular response below the typical fMRI spatial resolution of 2mm3. Furthermore, our results also show that SUAs and CBV activities are statistically uncorrelated during the resting state but correlate during tasks. These results have important implications for interpreting functional imaging findings while one constructs inferences of SUA during resting state or tasks.

Active fault tolerant deep brain stimulator for epilepsy using deep neural network

Millions of people around the world are affected by different kinds of epileptic seizures. A deep brain stimulator is now claimed to be one of the most promising tools to control severe epileptic seizures. The present study proposes Hodgkin-Huxley (HH) model-based Active Fault Tolerant Deep Brain Stimulator (AFTDBS) for brain neurons to suppress epileptic seizures against ion channel conductance variations using a Deep Neural Network (DNN). The AFTDBS contains the following three modules: (i) Detection of epileptic seizures using black box classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), (ii) Prediction of ion channels conductance variations using Long Short-Term Memory (LSTM), and (iii) Development of Reconfigurable Deep Brain Stimulator (RDBS) to control epileptic spikes using Proportional Integral (PI) Controller and Model Predictive Controller (MPC). Initially, the synthetic data were collected from the HH model by varying ion channel conductance. Then, the seizure was classified into four groups namely, normal and epileptic due to variations in sodium ion-channel conductance, potassium ion-channel conductance, and both sodium and potassium ion-channel conductance. In the present work, current controlled deep brain stimulators were designed for epileptic suppression. Finally, the closed-loop performances and stability of the proposed control schemes were analyzed. The simulation results demonstrated the efficacy of the proposed DNN-based AFTDBS.

HH model based smart deep brain stimulator to detect, predict and control epilepsy using machine learning algorithm

BACKGROUND

Epilepsy is the most common neurological disorder in the world. To control epilepsy, deep brain stimulation is one of the widely accepted treatment techniques. However, conventional deep brain stimulation technique provides continuous stimulation without optimizing the stimulation parameters, resulting in adverse side effects and unexpected death. Hence, understanding the dynamic behavior of brain neural networks at a cellular level is required for patient-specific epilepsy treatment. Considering the underlying mechanism of a single neuronal shift in the brain neural network, computational model-based techniques have a new face for healthcare, which aims to develop effective medical devices for preclinical investigations.

NEW METHOD

This paper discusses the design of a Smart Deep Brain Stimulator (SDBS) using the Hodgkin-Huxley (HH) conductance-based cellular model of brain neurons to automatically detect, predict and regulate epilepsy against patient-specific conditions. Epileptic activity is simulated as a spike train of action potential due to sodium and potassium channel conductance variations in the single-neuron HH model. The proposed SDBS consists of three components:- i) seizure detection using bagging and boosting-based ensemble machine learning classifiers, ii) channel conductance prediction using Long Short Term Memory-Recurrent Neural Network (LSTM-RNN) based Deep Neural Network (DNN) for updating model parameters of brain neuron, and iii) model-based intelligent control of epileptic seizure with Nonlinear Autoregressive Moving Average-L2 (NARMA-L2) Controller and Nonlinear Model Predictive Controller (NMPC).

RESULTS

For effective treatment, improving the overall accuracy and efficiency of SDBS is essential. For epilepsy detection, the ensemble bagging machine learning algorithm provides better accuracy of 92.7% compared to the ensemble boosting algorithm. LSTM-RNN deep neural network model with four layers predicts the variations in channel conductance with Root Mean Square Error (RMSE) of 0.00568 and 0.009081 for sodium and potassium channel conductance, respectively. From the closed-loop performances of SDBS with an intelligent control scheme, it is observed that SDBS with NMPC provides efficient and accurate stimulation with minimum energy consumption. From a stability point of view, SDBS with NMPC provides better stability than SDBS with NARMA-L2 Controller.

COMPARISON WITH EXISTING METHOD

The proposed SDBS is designed to generate accurate stimulation pulses for epilepsy patients with specific conditions depending on the neuronal activity of a single neuron. Moreover, it will also adapt to the dynamic condition of epilepsy patients. The existing deep brain stimulator continuously provides stimulation pulses without adapting to the patient's conditions.

CONCLUSION

The proposed SDBS could provide patient-specific treatment based on sodium/potassium channel conductance variations of brain neurons. It will help increase the use of deep brain stimulation techniques and reduce sudden death. Furthermore, the proposed technique will be extended to neural network models with larger neuronal populations to improve the practical feasibility.

FISHing for Chromosome Instability and Aneuploidy in the Alzheimer's Disease Brain

Fluorescence in situ hybridization (FISH) is the method of choice for visualizing chromosomal DNA in post-mitotic cells. The availability of chromosome-enumeration (centromeric), site-specific, and multicolor-banding DNA probes offers opportunities to uncover genomic changes, at the chromosomal level, in single interphase nuclei. Alzheimer's disease (AD) has been associated repeatedly with (sub)chromosome instability and aneuploidy, likely affecting the brain. Although the types and rates of chromosome instability in the AD brain remain a matter of debate, molecular cytogenetic analysis of brain cells appears to be important for uncovering mechanisms of neurodegeneration. Here, we describe a FISH protocol for studying chromosome instability and aneuploidy in the AD brain.

Online Seizure Prediction System: A Novel Probabilistic Approach for Efficient Prediction of Epileptic Seizure with iEEG Signal

Background:

1% of people around the world are suffering from epilepsy. It is, therefore crucial to propose an efficient automated seizure prediction tool implemented in a portable device that uses the electroencephalogram (EEG) signal to enhance epileptic patients’ life quality.

Methods:

In this study, we focused on time-domain features to achieve discriminative information at a low CPU cost extracted from the intracranial electroencephalogram (iEEG) signals of six patients. The probabilistic framework based on XGBoost classifier requires the mean and maximum probability of the non-seizure and the seizure occurrence period segments. Once all these parameters are set for each patient, the medical decision maker can send alarm based on well-defined thresholds.

Results:

While finding a unique model for all patients is really challenging, and our modelling results demonstrated that the proposed algorithm can be an efficient tool for reliable and clinically relevant seizure forecasting. Using iEEG signals, the proposed algorithm can forecast seizures, informing a patient about 75 minutes before a seizure would occur, a period large enough for patients to take practical actions to minimize the potential impacts of the seizure.

Conclusion:

We posit that the ability to distinguish interictal intracranial EEG from pre-ictal signals at some low computational cost may be the first step towards an implanted portable semi-automatic seizure suppression system in the near future. It is believed that our seizure prediction technique can conceivably be coupled with treatment techniques aimed at interrupting the process even prior to a seizure initiates to develop.

Distributed interfacing by nanoscale photodiodes enables single-neuron light activation and sensory enhancement in 3D spinal explants

Among emerging technologies developed to interface neuronal signaling, engineering electrodes at the nanoscale would yield more precise biodevices opening to progress in neural circuit investigations and to new therapeutic potential. Despite remarkable progress in miniature electronics for less invasive neurostimulation, most nano-enabled, optically triggered interfaces are demonstrated in cultured cells, which precludes the studies of natural neural circuits. We exploit here free-standing silicon-based nanoscale photodiodes to optically modulate single, identified neurons in mammalian spinal cord explants. With near-infrared light stimulation, we show that activating single excitatory or inhibitory neurons differently affects sensory circuits processing in the dorsal horn. We successfully functionalize nano-photodiodes to target single molecules, such as glutamate AMPA receptor subunits, thus enabling light activation of specific synaptic pathways. We conclude that nano-enabled neural interfaces can modulate selected sensory networks with low invasiveness. The use of nanoscale photodiodes can thus provide original perspective in linking neural activity to specific behavioral outcome.

Prefrontal Neuronal Activities During Active Retrieval of Information From Long-Term Memory

Abstract. Single-neuron studies performed in the primate prefrontal cortex (PFC) revealed that retaining information in working memory (WM) is associated with sustained firing during the delay period in a match-to-sample task. On the other hand, single-neuron studies using a pair-association task have shown that retrieving information from long-term memory (LTM) is related to two kinds of neural activities: decreasing activity representing information linked to the sample stimulus and increasing activity predicting information for the forthcoming matching stimulus. To further examine neuronal behavioral patterns during LTM retrieval, we used a partial correlation coefficient analysis to analyze single-neuron activities in the PFC while monkeys performed the visual pair-association task. Results showed that, for most of the task-related neurons, firing activity depicted information from the sample stimulus. Nevertheless, some neurons showed an opposite pattern, this is, increasing activity during the delay period, possibly indicating a prospective memory coding from LTM. Interestingly, both activities seem to be present at different degrees as the delay period progresses. Together, these results unveil a new aspect of PFC neurons when retrieving unseen information from LTM.

GLP-1 Suppresses Feeding Behaviors and Modulates Neuronal Electrophysiological Properties in Multiple Brain Regions

The glucagon-like peptide-1 (GLP-1) plays important roles in the regulation of food intake and energy metabolism. Peripheral or central GLP-1 suppresses food intake and reduces body weight. The electrophysiological properties of neurons in the mammalian central nervous system reflect the neuronal excitability and the functional organization of the brain. Recent studies focus on elucidating GLP-1-induced suppression of feeding behaviors and modulation of neuronal electrophysiological properties in several brain regions. Here, we summarize that activation of GLP-1 receptor (GLP-1R) suppresses food intake and induces postsynaptic depolarization of membrane potential and/or presynaptic modulation of glutamatergic or GABAergic neurotransmission in brain nuclei located within the medulla oblongata, pons, mesencephalon, diencephalon, and telencephalon. This review may provide a background to guide future research about the cellular mechanisms of GLP-1-induced feeding inhibition.

Microfluidic platforms for single neuron analysis

Single-neuron actions are the basis of brain function, as clinical sequelae, neuronal dysfunction or failure for most of the central nervous system (CNS) diseases and injuries can be identified via tracing single-neurons. The bulk analysis methods tend to miscue critical information by assessing the population-averaged outcomes. However, its primary requisite in neuroscience to analyze single-neurons and to understand dynamic interplay of neurons and their environment. Microfluidic systems enable precise control over nano-to femto-liter volumes via adjusting device geometry, surface characteristics, and flow-dynamics, thus facilitating a well-defined micro-environment with spatio-temporal control for single-neuron analysis. The microfluidic platform not only offers a comprehensive landscape to study brain cell diversity at the level of transcriptome, genome, and/or epigenome of individual cells but also has a substantial role in deciphering complex dynamics of brain development and brain-related disorders. In this review, we highlight recent advances of microfluidic devices for single-neuron analysis, i.e., single-neuron trapping, single-neuron dynamics, single-neuron proteomics, single-neuron transcriptomics, drug delivery at the single-neuron level, single axon guidance, and single-neuron differentiation. Moreover, we also emphasize limitations and future challenges of single-neuron analysis by focusing on key performances of throughput and multiparametric activity analysis on microfluidic platforms.

Pulsed laser assisted high-throughput intracellular delivery in hanging drop based three dimensional cancer spheroids

Targeted intracellular delivery of biomolecules and therapeutic cargo enables the controlled manipulation of cellular processes. Laser-based optoporation has emerged as a versatile, non-invasive technique that employs light-based transient physical disruption of the cell membrane and achieves high transfection efficiency with low cell damage. Testing of the delivery efficiency of optoporation-based techniques has been conducted on single cells in monolayers, but its applicability in three-dimensional (3D) cell clusters/spheroids has not been explored. Cancer cells grown as 3D tumor spheroids are widely used in anti-cancer drug screening and can be potentially employed for testing delivery efficiency. Towards this goal, we demonstrated the optoporation-based high-throughput intracellular delivery of a model fluorescent cargo (propidium iodide, PI) within 3D SiHa human cervical cancer spheroids. To enable this technique, nano-spiked core-shell gold-coated polystyrene nanoparticles (ns-AuNPs) with a high surface-to-volume ratio were fabricated. ns-AuNPs exhibited high electric field enhancement and highly localized heating at an excitation wavelength of 680 nm. ns-AuNPs were co-incubated with cancer cells within hanging droplets to enable the rapid aggregation and assembly of spheroids. Nanosecond pulsed-laser excitation at the optimized values of laser fluence (45 mJ cm-2), pulse frequency (10 Hz), laser exposure time (30 s), and ns-AuNP concentration (5 × 1010 particles per ml) resulted in the successful delivery of PI dye into cancer cells. This technique ensured high delivery efficiency (89.6 ± 2.8%) while maintaining high cellular viability (97.4 ± 0.4%), thereby validating the applicability of this technique for intracellular delivery. The optoporation-based strategy can enable high-throughput single cell manipulation, is scalable towards larger 3D tissue constructs, and may provide translational benefits for the delivery of anti-cancer therapeutics to tumors.

Microfluidic Based Physical Approaches towards Single-Cell Intracellular Delivery and Analysis

The ability to deliver foreign molecules into a single living cell with high transfection efficiency and high cell viability is of great interest in cell biology for applications in therapeutic development, diagnostics, and drug delivery towards personalized medicine. Various physical delivery methods have long demonstrated the ability to deliver cargo molecules directly to the cytoplasm or nucleus and the mechanisms underlying most of the approaches have been extensively investigated. However, most of these techniques are bulk approaches that are cell-specific and have low throughput delivery. In comparison to bulk measurements, single-cell measurement technologies can provide a better understanding of the interactions among molecules, organelles, cells, and the microenvironment, which can aid in the development of therapeutics and diagnostic tools. To elucidate distinct responses during cell genetic modification, methods to achieve transfection at the single-cell level are of great interest. In recent years, single-cell technologies have become increasingly robust and accessible, although limitations exist. This review article aims to cover various microfluidic-based physical methods for single-cell intracellular delivery such as electroporation, mechanoporation, microinjection, sonoporation, optoporation, magnetoporation, and thermoporation and their analysis. The mechanisms of various physical methods, their applications, limitations, and prospects are also elaborated.

Hybrid Electrical and Optical Neural Interfaces

Neural interfaces bridge the nervous system and the outside world by recording and stimulating neurons. Combining electrical and optical modalities in a single, hybrid neural interface system could lead to complementary and powerful new ways to explore the brain. It has gained robust and exciting momentum recently in neuroscience and neural engineering research. Here, we review developments in the past several years aiming to achieve such hybrid electrical and optical microsystem platforms. Specifically, we cover three major categories of technological advances: transparent neuroelectrodes, optical neural fibers with electrodes, and neural probes/grids integrating electrodes and microscale light-emitting diodes. We discuss examples of these probes tailored to combine electrophysiological recording with optical imaging or optical neural stimulation of the brain and possible directions of future innovation.

Single-Cell Analysis

Cells are known to be the most fundamental building block of life[...].