Experimental evidence, both in vitro and in vivo, has indicated cardioprotective effects of extracellular vesicles (EVs) derived from various cell types, including induced pluripotent stem cell-derived cardiomyocytes. The biological effects of EV secretion, particularly in the context of ischemia and cardiac electrophysiology, remain to be fully explored. Therefore, the goal of this study was to unveil the effects of exosome (EXO)-mediated cell-cell signaling during hypoxia by employing a simulated preconditioning approach on human-induced pluripotent stem cell-derived cardiomyocytes (hIPSC-CMs). Electrophysiological activity of hIPSC-CMs was measured using a multielectrode array (MEA) system. A total of 16 h of hypoxic stress drastically increased the beat period. Moreover, hIPSC-CMs preconditioned with EXOs displayed significantly longer beat periods compared with non-treated cells after 16 h of hypoxia (+15.7%, p < 0.05). Furthermore, preconditioning with hypoxic EXOs resulted in faster excitation-contraction (EC) coupling compared with non-treated hIPSC-CMs after 16 h of hypoxia (-25.3%, p < 0.05). Additionally, microRNA (miR) sequencing and gene target prediction analysis of the non-treated and pre-conditioned hIPSC-CMs identified 10 differentially regulated miRs and 44 gene targets. These results shed light on the intricate involvement of miRs, emphasizing gene targets associated with cell survival, contraction, apoptosis, reactive oxygen species (ROS) regulation, and ion channel modulation. Overall, this study demonstrates that EXOs secreted by hIPSC-CM during hypoxia beneficially alter electrophysiological properties in recipient cells exposed to hypoxic stress, which could play a crucial role in the development of targeted interventions to improve outcomes in ischemic heart conditions.

The use of anesthetic agents in the management of fish in fish farming or ornamental fish breeding aims to minimize stress and promote animal welfare. Therefore, this study aims to investigate behavioral, electrocardiographic, and ventilatory characteristics of tambaquis exposed to anesthetic baths with etomidate. The study was conducted with juvenile tambaquis (27.38 ± 3.5g) n = 99, at etomidate concentrations of 2-4 mg.L -1, analyzing induction and anesthetic recovery behavior (experiment I), electrocardiogram (experiment II), and opercular movement (experiment III). Fish exposed to high concentrations of etomidate reached the stage of general anesthesia faster, however, the recovery time was longer, characterizing a dose-dependent relationship. Cardiorespiratory analyzes demonstrated a reduction in heart rate (69.19%) and respiratory rate (40.70%) depending on the concentration of etomidate used during anesthetic induction. During the recovery period, there was cardiorespiratory reversibility to normality. Therefore, etomidate proved to be safe as an anesthetic agent for this species at concentrations of 2 to 3 mg.L -1 for short-term anesthesia, but at higher doses the animals showed slow reversibility of anesthesia in a gradual manner and without excitability. The hemodynamic effect due to the rapid decrease in heart rate includes a negative factor of using higher concentrations of etomidate for Colossome macropomum anesthesia.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by \'electrophysiology-invisible\' signals. These findings offer a novel perspective on our understanding of RSN interpretation.
The brain contains many cells known as neurons that send and receive messages in the form of electrical signals. The neurons in different regions of the brain must coordinate their activities to enable the brain to operate properly. Researchers often use a method called resting-state functional magnetic resonance imaging (rsfMRI) to study how different areas of the brain work together. This method indirectly measures brain activity by detecting the changes in blood flow to different areas of the brain. Regions that are working together will become active (that is, have higher blood flow and corresponding rsfMRI signal) and inactive (have lower blood flow and a lower rsfMRI signal) at the same time. These coordinated patterns of brain activity are known as “resting-state brain networks” (RSNs). Previous studies have identified RSNs in many different situations, but we still do not fully understand how these changes in blood flow are related to what is happening in the neurons themselves. To address this question, Tu et al. performed rsfMRI while also measuring the electrical activity (referred to as electrophysiology signals) in two distinct regions of the brains of rats. The team then used the data to generate maps of RSNs in those brain regions. This revealed that rsfMRI signals and electrophysiology signals produced almost identical maps in terms of the locations of the RSNs. However, the electrophysiology signals only contributed a small amount to the changes in the local rsfMRI signals over time at the same recording site. This suggests that RSNs may arise from cell activities that are not detectable by electrophysiology but do regulate blood flow to neurons. The findings of Tu et al. offer a new perspective for interpreting how rsfMRI signals relate to the activities of neurons. Further work is needed to explore all the features of the electrophysiology signals and test other methods to compare these features with rsfMRI signals in the same locations.

The peptide/histidine transporter PHT1 (SLC15A4) is expressed in the lysosomal membranes of immune cells where it plays an important role in metabolic and inflammatory signaling. PHT1 is an H+-coupled/histidine symporter that can transport a wide range of oligopeptides, including a variety of bacterial-derived peptides. Moreover, it enables the scaffolding of various metabolic signaling molecules and interacts with key regulatory elements of the immune response. Not surprisingly, PHT1 has been implicated in the pathogenesis of autoimmune diseases such as systemic lupus erythematosus (SLE). Unfortunately, the pharmacological development of PHT1 modulators has been hampered by the lack of suitable transport assays. To address this shortcoming, a novel transport assay based on solid-supported membrane-based electrophysiology (SSME) is presented. Key findings of the present SSME studies include the first recordings of electrophysiological properties, a pH dependence analysis, an assessment of PHT1 substrate selectivity, as well as the transport kinetics of the identified substrates. In contrast to previous work, PHT1 is studied in its native lysosomal environment. Moreover, observed substrate selectivity is validated by molecular docking. Overall, this new SSME-based assay is expected to contribute to unlocking the pharmacological potential of PHT1 and to deepen the understanding of its functional properties.

Enhanced electrical activity in detrusor smooth muscle (DSM) cells is a key factor in detrusor overactivity which causes overactive bladder pathological disorders. Transient receptor potential melastatin-4 (TRPM4) channels, which are calcium-activated cation channels, play a role in regulating DSM electrical activities. These channels likely contribute to depolarizing the DSM cell membrane, leading to bladder overactivity. Our research focuses on understanding TRPM4 channel function in the DSM cells of mice, using computational modeling. We aimed to create a detailed computational model of the TRPM4 channel based on existing electrophysiological data. We employed a modified Hodgkin-Huxley model with an incorporated TRP-like current to simulate action potential firing in response to current and synaptic stimulus inputs. Validation against experimental data showed close agreement with our simulations. Our model is the first to analyze the TRPM4 channel\'s role in DSM electrical activity, potentially revealing insights into bladder overactivity. In conclusion, TRPM4 channels are pivotal in regulating human DSM function, and TRPM4 channel inhibitors could be promising targets for treating overactive bladder.

Inducible pluripotent stem cell-derived human β-like cells (BLCs) hold promise for both therapy and disease modeling, but their generation remains challenging and their functional analyses beyond transcriptomic and morphological assessments remain limited. Here, we validate an approach using multicellular and single-cell electrophysiological tools to evaluate function of BLCs from pioneer protocols that can be easily adapted to more differentiated BLCs. The multi-electrode arrays (MEAs) measuring the extracellular electrical activity revealed that BLCs, like primary β-cells, are electrically coupled and produce slow potential (SP) signals that are closely linked to insulin secretion. We also used high-resolution single-cell patch clamp measurements to capture the exocytotic properties, and characterize voltage-gated sodium and calcium currents, and found that they were comparable with those in primary β- and EndoC-βH1 cells. The KATP channel conductance is greater than in human primary β-cells, which may account for the limited glucose responsiveness observed with MEA. We used MEAs to study the impact of the type 2 diabetes-protective SLC30A8 allele (p.Lys34Serfs50*) and found that BLCs with this allele have stronger electrical coupling activity. Our data suggest that BLCs can be used to evaluate the functional impact of genetic variants on β-cell function and coupling.
UNASSIGNED:

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.

暂无摘要。

The complexity of information processing in the brain requires the development of technologies that can provide spatial and temporal resolution by means of dense electrode arrays paired with high-channel-count signal acquisition electronics. In this work, we present an ultra-low noise modular 512-channel neural recording circuit that is scalable to up to 4096 simultaneously recording channels. The neural readout application-specific integrated circuit (ASIC) uses a dense 8.2 mm × 6.8 mm 2D layout to enable high-channel count, creating an ultra-light 350 mg flexible module. The module can be deployed on headstages for small animals like rodents and songbirds, and it can be integrated with a variety of electrode arrays. The chip was fabricated in a TSMC 0.18 µm 1.8 V CMOS technology and dissipates a total of 125 mW. Each DC-coupled channel features a gain and bandwidth programmable analog front-end along with 14 b analog-to-digital conversion at speeds up to 30 kS/s. Additionally, each front-end includes programmable electrode plating and electrode impedance measurement capability. We present both standalone and in vivo measurements results, demonstrating the readout of spikes and field potentials that are modulated by a sensory input.

Brain organoid is a three-dimensional (3D) tissue derived from stem cells such as induced pluripotent stem cells (iPSCs) embryonic stem cells (ESCs) that reflect real human brain structure. It replicates the complexity and development of the human brain, enabling studies of the human brain in vitro. With emerging technologies, its application is various, including disease modeling and drug screening. A variety of experimental methods have been used to study structural and molecular characteristics of brain organoids. However, electrophysiological analysis is necessary to understand their functional characteristics and complexity. Although electrophysiological approaches have rapidly advanced for monolayered cells, there are some limitations in studying electrophysiological and neural network characteristics due to the lack of 3D characteristics. Herein, electrophysiological measurement and analytical methods related to neural complexity and 3D characteristics of brain organoids are reviewed. Overall, electrophysiological understanding of brain organoids allows us to overcome limitations of monolayer in vitro cell culture models, providing deep insights into the neural network complex of the real human brain and new ways of disease modeling. [BMB Reports 2024; 57(7): 311-317].