Channel Clustering Research Articles

A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus.

Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here, we propose a spatial autocorrelation method based on Local Moran's I coefficient to differentiate signal, background, and noise in any type of image. The method, originally described for geoinformatics, does not require a predefined intensity threshold or teaching algorithm for image segmentation and allows quantitative comparison of samples obtained in different conditions. It utilizes relative intensity as well as spatial information of neighboring elements to select spatially contiguous groups of pixels. We demonstrate that Moran's method outperforms threshold-based method in both artificially generated as well as in natural images especially when background noise is substantial. This superior performance can be attributed to the exclusion of false positive pixels resulting from isolated, high intensity pixels in high noise conditions. To test the method's power in real situation, we used high power confocal images of the somatosensory thalamus immunostained for Kv4.2 and Kv4.3 (A-type) voltage-gated potassium channels in mice. Moran's method identified high-intensity Kv4.2 and Kv4.3 ion channel clusters in the thalamic neuropil. Spatial distribution of these clusters displayed strong correlation with large sensory axon terminals of subcortical origin. The unique association of the special presynaptic terminals and a postsynaptic voltage-gated ion channel cluster was confirmed with electron microscopy. These data demonstrate that Moran's method is a rapid, simple image segmentation method optimal for variable and high noise conditions.

The Role of Cholesterol in M2 Clustering and Viral Budding Explained.

The influenza A M2 homotetrameric channel consists of four transmembrane (TM) and four amphipathic helices (AHs). This viral proton channel is suggested to form clusters in the catenoid budding neck areas in raft-like domains of the plasma membrane, resulting in cell membrane scission and viral release. The channel clustering environment is rich in cholesterol. Previous experiments have shown that cholesterol significantly contributes to lipid bilayer undulations in viral buds. However, a clear explanation of membrane curvature from the distribution of cholesterol around the M2TM-AH clusters is lacking. Using coarse-grained molecular dynamics simulations of M2TM-AH in bilayers, we observed that M2 channels form specific, C2-symmetric, clusters with conical shapes driven by the attraction of their AHs. We showed that cholesterol stabilized the formation of M2 channel clusters by filling and bridging the conical gap between M2 channels at specific sites in the N-termini of adjacent channels or via the C-terminal region of TM and AHs, with the latter sites displaying a longer interaction time and higher stability. The potential of mean force calculations showed that when cholesterols occupy the identified interfacial binding sites between two M2 channels, the dimer is stabilized by 11 kJ/mol. This translates to the cholesterol-bound dimer being populated by almost 2 orders of magnitude compared to a dimer lacking cholesterol. We demonstrated that the cholesterol-bridged M2 channels can exert a lateral force on the surrounding membrane to induce the necessary negative Gaussian curvature profile, which permits spontaneous scission of the catenoid membrane neck and leads to viral buds and scission.

Establishment of a Serum-Free Human iPSC-Derived Model of Peripheral Myelination.

Myelination and the formation of nodes of Ranvier are essential for the rapid conduction of nerve impulses along axons in the peripheral nervous system (PNS). While many animal-based and serum-containing models of peripheral myelination have been developed, these have limited ability when it comes to studying genetic disorders affecting peripheral myelination. We report a fully induced pluripotent stem cell (iPSC)-derived human model of peripheral myelination using Schwann cells (SCs) and motoneurons, cultured in a serum-free medium on patterned and nonpatterned surfaces. Results demonstrated iPSC-derived SC-expressed early growth response protein 2 (Egr2), a key transcription factor for myelination, and after ∼30 days in coculture, hallmark features of myelination, including myelin segment and node of Ranvier formation, were observed. Myelin segments were stained for the myelin basic protein, which surrounded neurofilament-stained motoneuron axons. Clusters of voltage-gated sodium channels flanked by paranodal protein contactin-associated protein 1, indicating node of Ranvier formation, were also observed. High-resolution confocal microscopy allowed for 3D reconstruction and measurement of myelin g-ratios of myelin segments, with an average g-ratio of 0.67, consistent with reported values in the literature, indicating mature myelin segment formation. This iPSC-based model of peripheral myelination provides a platform to investigate numerous PNS diseases, including Charcot-Marie Tooth disorder, Guillian-Barre syndrome, chronic inflammatory demyelinating polyneuropathy, and antimyelin-associated glycoprotein peripheral neuropathy, with the potential for greater translatability to humans for improving the applicability for drug-screening programs.

Modulation of heteromeric glycine receptor function through high concentration clustering.

Ion channels are targeted by many drugs for treating neurological, musculoskeletal, renal and other diseases. These drugs bind to and alter the function of individual channels to achieve desired therapeutic effects. However, many ion channels function in high concentration clusters in their native environment. It is unclear if and how clustering modulates ion channel function. Human heteromeric glycine receptors (GlyRs) are the major inhibitory neurotransmitter receptors in the spinal cord and are active targets for developing chronic pain medications. We show that the α2β heteromeric GlyR assembles with the master postsynaptic scaffolding gephyrin (GPHN) into micron-sized clustered at the plasma membrane after heterologous expression. The inhibitory trans- synaptic adhesion protein neuroligin-2 (NL2) further increases both the cluster sizes and GlyR concentration. The apparent glycine affinity increases monotonically as a function of GlyR concentration but not with cluster size. We also show that ligand re-binding to adjacent GlyRs alters kinetics but not chemical equilibrium. A positively charged N- terminus sequence of the GlyR β subunit was further identified essential for glycine affinity modulation through clustering. Taken together, we propose a mechanism where clustering enhances local electrostatic potential, which in turn concentrates ions and ligands, modulating the function of GlyR. This mechanism is likely universal across ion channel clusters found ubiquitously in biology and provides new perspectives in possible pharmaceutical development.

Hispidol Regulates Behavioral Responses to Ethanol through Modulation of BK Channels: A Novel Candidate for the Treatment of Alcohol Use Disorder.

Alcohol use disorder (AUD) is the most common substance use disorder and poses a significant global health challenge. Despite pharmacological advances, no single drug effectively treats all AUD patients. This study explores the protective potential of hispidol, a 6,4'-dihydroxyaurone, for AUD using the Caenorhabditis elegans model system. Our findings demonstrate that hispidol-fed worms exhibited more pronounced impairments in thrashes, locomotory speed, and bending amplitude, indicating that hispidol exacerbated the detrimental effects of acute ethanol exposure. However, hispidol significantly improved ethanol withdrawal behaviors, such as locomotory speed and chemotaxis performance. These beneficial effects were absent in slo-1 worms (the ortholog of mammalian α-subunit of BK channel) but were restored with the slo-1(+) or hslo(+) transgene, suggesting the involvement of BK channel activity. Additionally, hispidol increased fluorescence intensity and puncta in the motor neurons of slo-1::mCherry-tagged worms, indicating enhanced BK channel expression and clustering. Notably, hispidol did not alter internal ethanol concentrations, suggesting that its action is independent of ethanol metabolism. In the mouse models, hispidol treatment also demonstrated anxiolytic activity against ethanol withdrawal. Overall, these findings suggest hispidol as a promising candidate for targeting the BK channel in AUD treatment.

Glucocorticoids Rapidly Modulate CaV1.2-Mediated Calcium Signals through Kv2.1 Channel Clusters in Hippocampal Neurons.

The precise regulation of Ca2+ signals plays a crucial role in the physiological functions of neurons. Here, we investigated the rapid effect of glucocorticoids on Ca2+ signals in cultured hippocampal neurons from both female and male rats. In cultured hippocampal neurons, glucocorticoids inhibited the spontaneous somatic Ca2+ spikes generated by Kv2.1-organized Ca2+ microdomains. Furthermore, glucocorticoids rapidly reduced the cell surface expressions of Kv2.1 and CaV1.2 channels in hippocampal neurons. In HEK293 cells transfected with Kv2.1 alone, glucocorticoids significantly reduced the surface expression of Kv2.1 with little effect on K+ currents. In HEK293 cells transfected with CaV1.2 alone, glucocorticoids inhibited CaV1.2 currents but had no effect on the cell surface expression of CaV1.2. Notably, in the presence of wild-type Kv2.1, glucocorticoids caused a decrease in the surface expression of CaV1.2 channels in HEK293 cells. However, this effect was not observed in the presence of nonclustering Kv2.1S586A mutant channels. Live-cell imaging showed that glucocorticoids rapidly decreased Kv2.1 clusters on the plasma membrane. Correspondingly, Western blot results indicated a significant increase in the cytoplasmic level of Kv2.1, suggesting the endocytosis of Kv2.1 clusters. Glucocorticoids rapidly decreased the intracellular cAMP concentration and the phosphorylation level of PKA in hippocampal neurons. The PKA inhibitor H89 mimicked the effect of glucocorticoids on Kv2.1, while the PKA agonist forskolin abrogated the effect. In conclusion, glucocorticoids rapidly suppress CaV1.2-mediated Ca2+ signals in hippocampal neurons by promoting the endocytosis of Kv2.1 channel clusters through reducing PKA activity.

Distinct active zone protein machineries mediate Ca2+ channel clustering and vesicle priming at hippocampal synapses.

Action potentials trigger neurotransmitter release at the presynaptic active zone with spatiotemporal precision. This is supported by protein machinery that mediates synaptic vesicle priming and clustering of CaV2 Ca2+ channels nearby. One model posits that scaffolding proteins directly tether vesicles to CaV2s; however, here we find that at mouse hippocampal synapses, CaV2 clustering and vesicle priming are executed by separate machineries. CaV2 nanoclusters are positioned at variable distances from those of the priming protein Munc13. The active zone organizer RIM anchors both proteins but distinct interaction motifs independently execute these functions. In transfected cells, Liprin-α and RIM form co-assemblies that are separate from CaV2-organizing complexes. At synapses, Liprin-α1-Liprin-α4 knockout impairs vesicle priming but not CaV2 clustering. The cell adhesion protein PTPσ recruits Liprin-α, RIM and Munc13 into priming complexes without co-clustering CaV2s. We conclude that active zones consist of distinct machineries to organize CaV2s and prime vesicles, and Liprin-α and PTPσ specifically support priming site assembly.

An evolutionarily conserved AnkyrinG-dependent motif clusters axonal K2P K+ channels.

The evolution of ion channel clustering at nodes of Ranvier enabled the development of complex vertebrate nervous systems. At mammalian nodes, the K+ leak channels TRAAK and TREK-1 underlie membrane repolarization. Despite the molecular similarities between nodes and the axon initial segment (AIS), TRAAK and TREK-1 are reportedly node-specific, suggesting a unique clustering mechanism. However, we show that TRAAK and TREK-1 are enriched at both nodes and AIS through a common mechanism. We identified a motif near the C-terminus of TRAAK that is necessary and sufficient for its clustering. The motif first evolved among cartilaginous fish. Using AnkyrinG (AnkG) conditional knockout mice, CRISPR/Cas9-mediated disruption of AnkG, co-immunoprecipitation, and surface recruitment assays, we show that TRAAK forms a complex with AnkG and that AnkG is necessary for TRAAK's AIS and nodal clustering. In contrast, TREK-1's clustering requires TRAAK. Our results expand the repertoire of AIS and nodal ion channel clustering mechanisms and emphasize AnkG's central role in assembling excitable domains.

The components of an electrical synapse as revealed by expansion microscopy of a single synaptic contact.

Most nervous systems combine both transmitter-mediated and direct cell-cell communication, known as 'chemical' and 'electrical' synapses, respectively. Chemical synapses can be identified by their multiple structural components. Electrical synapses are, on the other hand, generally defined by the presence of a 'gap junction' (a cluster of intercellular channels) between two neuronal processes. However, while gap junctions provide the communicating mechanism, it is unknown whether electrical transmission requires the contribution of additional cellular structures. We investigated this question at identifiable single synaptic contacts on the zebrafish Mauthner cells, at which gap junctions coexist with specializations for neurotransmitter release and where the contact unequivocally defines the anatomical limits of a synapse. Expansion microscopy of these single contacts revealed a detailed map of the incidence and spatial distribution of proteins pertaining to various synaptic structures. Multiple gap junctions of variable size were identified by the presence of their molecular components. Remarkably, most of the synaptic contact's surface was occupied by interleaving gap junctions and components of adherens junctions, suggesting a close functional association between these two structures. In contrast, glutamate receptors were confined to small peripheral portions of the contact, indicating that most of the synaptic area functions as an electrical synapse. Thus, our results revealed the overarching organization of an electrical synapse that operates with not one, but multiple gap junctions, in close association with structural and signaling molecules known to be components of adherens junctions. The relationship between these intercellular structures will aid in establishing the boundaries of electrical synapses found throughout animal connectomes and provide insight into the structural organization and functional diversity of electrical synapses.

Multicenter integration analysis of TRP channels revealed potential mechanisms of immunosuppressive microenvironment activation and identified a machine learning-derived signature for improving outcomes in gliomas.

This study aimed to explore the mechanisms of transient receptor potential (TRP) channels on the immune microenvironment and develop a TRP-related signature for predicting prognosis, immunotherapy response, and drug sensitivity in gliomas. Based on the unsupervised clustering algorithm, we identified novel TRP channel clusters and investigated their biological function, immune microenvironment, and genomic heterogeneity. Invitro and invivo experiments revealed the association between TRPV2 and macrophages. Subsequently, based on 96 machine learning algorithms and six independent glioma cohorts, we constructed a machine learning-based TRP channel signature (MLTS). The performance of the MLTS in predicting prognosis, immunotherapy response, and drug sensitivity was evaluated. Patients with high expression levels of TRP channel genes had worse prognoses, higher tumor mutation burden, and more activated immunosuppressive microenvironment. Meanwhile, TRPV2 was identified as the most essential regulator in TRP channels. TRPV2 activation could promote macrophages migration toward malignant cells and alleviate glioma prognosis. Furthermore, MLTS could work independently of common clinical features and present stable and superior prediction performance. This study investigated the comprehensive effect of TRP channel genes in gliomas and provided a promising tool for designing effective, precise treatment strategies.

YouTube as an information source on politics and current affairs: Supply- and demand-side perspectives

This study examines YouTube channels reporting on French news and current affairs. After identifying the relevant channels, the study identifies the top 10 viewed videos of each channel during the year 2023. Manual annotation has been conducted for content, presentation format, engagement style, and position regarding the government and mainstream media. Each channel has also been coded for its business and communication strategies. Correspondence analysis is used to extract the most emblematic news content and presentation styles, while hierarchical clustering is used to categorize the channels into clusters of different genres. From the demand-side perspective, the study also examines the network of users commenting on the top 10 videos to assess the audience overlap between the identified clusters. . Five clusters of alternative news channels are identified, differing in presentation, style, and partisanship. Network analysis showed audience overlaps, notably between satire, alternative, and re-information clusters. Most channels earn revenue through branding and cross-media activities, occasionally supporting each other. These clusters act as mediators between politics, entertainment, and re-information, serving diverse audiences. Uncertainty remains regarding future dynamics and competition with established news outlets.

Uncertainty assessment in unsupervised machine-learning methods for deepwater channel seismic facies using outcrop-derived 3D models and synthetic seismic data

Unsupervised machine-learning (ML) techniques have been widely applied to analyze seismic reflection data, including the identification of seismic facies and structural features. However, interpreting the resulting clusters often relies on geoscientists’ expertise, necessitating a robustness assessment of these methods. To evaluate their reliability, synthetic data generated from an actual outcrop model are used to demonstrate how two unsupervised methods, self-organizing maps (SOMs) and generative topographic maps (GTMs), cluster deepwater channel-related seismic facies and then measure the associated error. Six seismic attributes, comprising root-mean-square amplitude, instantaneous envelope, peak magnitude, and spectral decomposition frequencies at 20, 40, and 55 Hz, served as input variables. Geobodies are assigned to each cluster formed, and the error in facies clustering is quantified by comparing the actual 3D model with the facies grouped by ML methods on a voxel-by-voxel basis. This allowed for error quantification and the computation of metrics such as F1 score and accuracy through correlation matrices. Key findings reveal that (1) GTM and SOM exhibit similar performance, with a clustering configuration of 81 for GTM slightly outperforming others. (2) Error rates are approximately 2% for the predominant facies (background shale) but significantly higher for individual channel-related facies, suggesting that channel clusters might represent multiple facies. (3) Resolution and imbalanced data distribution impact seismic facies predictability, resulting in nonuniqueness in cluster generation. (4) Using synthetic seismic data proved valuable for experimenting with different unsupervised MLs, highlighting the need for assessing uncertainty in these methods, given their implications for crucial economic decisions reliant on reservoir interpretation, modeling, and volumetric estimations.

Assessing neural markers of attention during exposure to construction noise using machine learning classification of electroencephalogram data

Construction noise is one of the leading causes of attention impairment both for workers within construction sites and individuals in their direct vicinity. Distraction caused by construction noise can significantly affect productivity of people in the surrounding area. In addition, lack of adequate attention by workers in construction sites can increase the risk of mistakes potentially increasing work-related accidents. This study highlights the feasibility of using electroencephalogram (EEG) data for detecting distractions caused by construction noise. EEG data were collected from 23 participants while they completed a standard attention test (Go-NoGo) during exposure to construction noise. Both frequency-based features and non-linear features were extracted from the EEG signal and subject-independent machine learning models were applied. The results showed that channel FC5, with an F1 score of 68.41 % and an accuracy score of 70.28 % presented the best performance among all channels and the defined channel clusters. These findings will help inform the best features and channels to select for developing new purpose-built EEG devices with a smaller number of channels that are suited for distraction detection due to noise exposure in construction sites.

Spatial interactions impact on Ca-driven synaptic plasticity: An ionic cable theory perspective

We extend our previous paper on deriving an approximate analytical solution of a nonlinear cable equation by including other ion channels in neurons and calcium dynamics based on reaction-diffusion dynamics that lead to a system of nonlinear cable equations. Here, excitable dendrite possesses clusters of voltage-activated ion channels that are discretely distributed as point sources or hotspots of transmembrane current along a continuous cable structure of fixed length. Single and/or trains of action potentials and spatially distributed synaptic inputs drive the depolarisation and activate sparsely distributed voltage-dependent calcium channels. This leads to calcium influx and diffusion in the cable. Here, time-dependent analytical solutions were obtained by applying a perturbation expansion of the non-dimensional voltage (Φ) and non-dimensional calcium (ΦCa) and then solving the resulting set of integral equations. We use this framework to gain insights into calcium-driven synaptic plasticity in dendrites. Many previous studies have traditionally focused on the local impact of calcium on whether the synapse's strength is increased (potentiated) or decreased (depressed). Only recently have studies focusing on heterosynaptic plasticity been gaining popularity, and here we ask the question of how a local plasticity rule is influenced by the spatially and temporally distributed synaptic inputs. Specifically, we focus on how synaptic inputs and calcium influx impact a calcium-derived temporal learning window for spike-timing-dependent plasticity (STDP) at nearby sites to assess the nature of the resulting distance-dependent interaction on the associated learning window at the synapse of interest.

CaV1.3 channel clusters characterized by live-cell and isolated plasma membrane nanoscopy

A key player of excitable cells in the heart and brain is the L-type calcium channel CaV1.3. In the heart, it is required for voltage-dependent Ca2+-signaling, i.e., for controlling and modulating atrial cardiomyocyte excitation-contraction coupling. The clustering of CaV1.3 in functionally relevant channel multimers has not been addressed due to a lack of stoichiometric labeling combined with high-resolution imaging. Here, we developed a HaloTag-labeling strategy to visualize and quantify CaV1.3 clusters using STED nanoscopy to address the questions of cluster size and intra-cluster channel density. Channel clusters were identified in the plasma membrane of transfected live HEK293 cells as well as in giant plasma membrane vesicles derived from these cells that were spread on modified glass support to obtain supported plasma membrane bilayers (SPMBs). A small fraction of the channel clusters was colocalized with early and recycling endosomes at the membranes. STED nanoscopy in conjunction with live-cell and SPMB imaging enabled us to quantify CaV1.3 cluster sizes and their molecular density revealing significantly lower channel densities than expected for dense channel packing. CaV1.3 channel cluster size and molecular density were increased in SPMBs after treatment of the cells with the sympathomimetic compound isoprenaline, suggesting a regulated channel cluster condensation mechanism.

Neural tracking of natural speech in children in relation to their receptive speech abilities

Receptive speech is the ability to understand speech addressed to a person. It is a crucial process for a child’s cognitive development. We examine the relationship between receptive speech and neural tracking of natural speech in 52 children aged 3–8 years to infer the neurophysiological mechanisms underlying speech development. We registered a 32-channel electroencephalogram (EEG) while children listened to narrative audio stories. The temporal response function (TRF) approach was used to study neural tracking features at acoustic and semantic levels. We found a strong positive correlation between the TRF prediction accuracy values that demonstrate the magnitude of neural tracking, and receptive speech abilities measured by the Preschool Language Scales (PLS-5). Topographic analysis of these correlations showed significant clusters of EEG channels in the right temporal region for acoustic tracking, and in the left fronto-central and right parieto-occipital regions for semantic tracking. We assume that these results reflect the development of the brain systems necessary for speech comprehension. To sum up, we suggest that the TRF measures are easy-to-assess neurophysiological markers of receptive speech development in children.

How the discovery of Cold Noise delayed the production of ATLAS ITk strip tracker modules by a year

The construction of the ATLAS strip tracker barrel will require the assembly of 12,000 barrel detector modules over the course of 3.5 years. In 2022, during the module pre-production phase, modules were found to display clusters of noisy channels outside required specifications when tested at operating temperatures (-35°C), called “Cold Noise”. Extensive investigations into the cause and mechanism of Cold Noise interrupted pre-production and occupied most barrel module assembly sites. This contribution presents an overview of the year-long investigations into Cold Noise, the final identification of the underlying mechanism and necessary changes for the transition to production.

A two-stage transformer based network for motor imagery classification

Brain-computer interfaces (BCIs) are used to understand brain functioning and develop therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in rehabilitating motor dysfunction and advancing motor imagery applications. For motor imagery, electroencephalogram (EEG) signals are used to classify the subject's intention of moving a body part without actually moving it. This paper presents a two-stage transformer-based architecture that employs handcrafted features and deep learning techniques to enhance the classification performance on benchmarked EEG signals. Stage-1 is built on parallel convolution based EEGNet, multi-head attention, and separable temporal convolution networks for spatiotemporal feature extraction. Further, for enhanced classification, in stage-2, additional features and embeddings extracted from stage-1 are used to train TabNet. In addition, a novel channel cluster swapping data augmentation technique is also developed to handle the issue of limited samples for training deep learning architectures. The developed two-stage architecture offered an average classification accuracy of 88.5 % and 88.3 % on the BCI Competition IV-2a and IV-2b datasets, respectively, which is approximately 3.0 % superior over similar recent reported works.

Comparing ANOVA and PowerShap Feature Selection Methods via Shapley Additive Explanations of Models of Mental Workload Built with the Theta and Alpha EEG Band Ratios

Background: Creating models to differentiate self-reported mental workload perceptions is challenging and requires machine learning to identify features from EEG signals. EEG band ratios quantify human activity, but limited research on mental workload assessment exists. This study evaluates the use of theta-to-alpha and alpha-to-theta EEG band ratio features to distinguish human self-reported perceptions of mental workload. Methods: In this study, EEG data from 48 participants were analyzed while engaged in resting and task-intensive activities. Multiple mental workload indices were developed using different EEG channel clusters and band ratios. ANOVA’s F-score and PowerSHAP were used to extract the statistical features. At the same time, models were built and tested using techniques such as Logistic Regression, Gradient Boosting, and Random Forest. These models were then explained using Shapley Additive Explanations. Results: Based on the results, using PowerSHAP to select features led to improved model performance, exhibiting an accuracy exceeding 90% across three mental workload indexes. In contrast, statistical techniques for model building indicated poorer results across all mental workload indexes. Moreover, using Shapley values to evaluate feature contributions to the model output, it was noted that features rated low in importance by both ANOVA F-score and PowerSHAP measures played the most substantial role in determining the model output. Conclusions: Using models with Shapley values can reduce data complexity and improve the training of better discriminative models for perceived human mental workload. However, the outcomes can sometimes be unclear due to variations in the significance of features during the selection process and their actual impact on the model output.

Postsynaptic β1spectrin maintains Na+ channels at the neuromuscular junction.

Spectrins function together with actin as obligatory subunits of the submembranous cytoskeleton. Spectrins maintain cell shape, resist mechanical forces, and stabilize ion channel and transporter protein complexes through binding to scaffolding proteins. Recently, pathogenic variants of SPTBN4 (β4spectrin) were reported to cause both neuropathy and myopathy. Although the role of β4spectrin in neurons is mostly understood, its function in skeletal muscle, another excitable tissue subject to large forces, is unknown. Here, using a muscle specific β4spectrin conditional knockout mouse, we show that β4spectrin does not contribute to muscle function. In addition, we show β4spectrin is not present in muscle, indicating the previously reported myopathy associated with pathogenic SPTBN4 variants is neurogenic in origin. More broadly, we show that α2, β1 and β2spectrins are found in skeletal muscle, with α2 and β1spectrins being enriched at the postsynaptic neuromuscular junction (NMJ). Surprisingly, using muscle specific conditional knockout mice, we show that loss of α2 and β2spectrins had no effect on muscle health, function or the enrichment of β1spectrin at the NMJ. Muscle specific deletion of β1spectrin also had no effect on muscle health, but, with increasing age, resulted in the loss of clustered NMJ Na+ channels. Together, our results suggest that muscle β1spectrin functions independently of an associated α spectrin to maintain Na+ channel clustering at the postsynaptic NMJ. Furthermore, despite repeated exposure to strong forces and in contrast to neurons, muscles do not require spectrin cytoskeletons to maintain cell shape or integrity. KEY POINTS: The myopathy found in pathogenic human SPTBN4 variants (where SPTBN4 is the gene encoding β4spectrin) is neurogenic in origin. β1spectrin plays essential roles in maintaining the density of neuromuscular junction Nav1.4 Na+ channels. By contrast to the canonical view of spectrin organization and function, we show that β1spectrin can function independently of an associated α spectrin. Despite the large mechanical forces experienced by muscle, we show that spectrins are not required for muscle cell integrity. This is in stark contrast to red blood cells and the axons of neurons.