Neuronal Models Research Articles

Polyglot entrainment for higher dimensional neuronal models

The entrainment of biological oscillators is a classic problem in the field of dynamical systems and synchronization. This paper explores a novel type of entrainment mechanism referred to as polyglot entrainment [Khan et al., “The emergence of polyglot entrainment responses to periodic inputs in vicinities of Hopf bifurcations in slow–fast systems,” Chaos 32, 063137 (2022)] (multiple disconnected 1:1 regions for a range of forcing amplitude) for higher dimensional nonlinear systems. Polyglot entrainment has been recently explored only in two-dimensional slow–fast models in the vicinity of Hopf bifurcations (HBs). Heading toward generality, in this research, we investigate the phenomenon of polyglot entrainment in higher-dimensional conductance-based models including the four-dimensional Hodgkin-Huxley model and its reduced three-dimensional version. We utilize dynamical systems tools to uncover the mechanism of entrainment and geometric structure of the null surfaces to explore the conditions for the existence of polyglot entrainment in these models. In light of our findings, in the vicinity of HB, when an unforced system acts as a damped oscillator and the fixed point is located near a cubic-like manifold, polyglot entrainment is observed.

«The Trouble with Bias» in Neural Networks: Conflict and Ethical Challenges

This article is devoted to the analysis of ethical and conflict challenges related to the trouble with bias in neural networks. The necessity of a correct, scientifically based explication of the phenomenon of bias is postulated in order to build models correcting this problem as a necessary element of the software development process based on artificial intelligence algorithms. The history of the development of neural networks is considered from the origin of the idea of a mechanical organism to the construction of modern models of an artificial neuron. The most significant characteristics of modern neural networks are highlighted: architecture, weights and offsets, activation functions, inferences, and learning methods.A detailed description of natural language as a neural network learning resource is given, and programming in natural language is analyzed. The specificity of the natural language of the neural network as a set of linguistic practices reflecting the entire digitized experience of mankind, including stereotypes, inequalities, hate speech and other phenomena, ultimately producing the trouble with bias, is emphasized.Considerable attention is paid to the analysis of the phenomena of “politics classification”, “power discourse”, “cultural violence” in the context of the search for methodological foundations of natural language filtering and censorship strategies in the process of constructing a neural network.Separately, it is emphasized how the errors in neural networks are reflected in the existing ethical and conflict studies debates around the problem of artificial intelligence. It is concluded that the current assessment of the moral aspects of the problem does not imply granting neural networks the status of a moral agent and places the ethical expertise of the product on its developers. It is particularly noted that the conflict aspect of the trouble with bias lies in its recognition exclusively in relation to groups that have now acquired the “sensitive” status of discriminated against as a result of social conflicts.In conclusion, the paper substantiates the urgent need to optimize artificial intelligence in order to reduce the destructive potential of the trouble with bias, which necessarily implies the modification of social relations in the broader context of the struggle of excluded groups for the right to be recognized as discriminated against.

Dynamics and synchronization of the Morris-Lecar model with field coupling subject to electromagnetic excitation

In this study, we extend the Morris-Lecar (M-L) neuron model to design a neuronal network model with field effects. Using Lyapunov theory and the master stability function to evaluate the stability of synchronous manifolds. The Hamilton energy function of a single neuron is derived using Helmholtz's theorem to equivalently describe its internal field energy, and bioelectric activities are analyzed in conjunction with synchronization error functions. A comprehensive analysis was conducted using various numerical methods, including time series diagrams, bifurcation diagrams, two-parameter plane synchronization error diagrams, and similarity function diagrams. The observations indicate that variations in conductivity and equilibrium potential directly impact the firing patterns and synchronization states of neurons. In various coupling methods, an increase in coupling strength induces different degrees of oscillation within the coupled system. The effects of three coupling methods on the synchronization of the neuronal network are examined using spatiotemporal evolution diagrams and energy evolution diagrams. Global synchronization error and synchronization factors are introduced to quantify the level of synchrony. Numerical results indicate that an appropriate coupling strength and ionic conductance enhance network synchrony. Numerical results indicate that appropriate coupling strength promotes network synchrony. The insights gained from this study contribute to providing a more coherent framework for constructing neuronal network models under field-coupled conditions, thereby enhancing the understanding of fundamental principles in neuroscience and offering new perspectives for the treatment and rehabilitation of neurological disorders.

Velocity coding in the central brain of bumblebees.

Moving animals experience wide-field optic flow due to the displacement of the retinal image during motion. These cues provide information about self-motion and are important for flight control and stabilization, and for more complex tasks like path integration. While in honeybees and bumblebees the use of wide-field optic flow in behavioral tasks is well investigated, little is known about the underlying neuronal processing of these cues. Furthermore, there is a discrepancy between the temporal frequency tuning observed in most motion-sensitive neurons described so far from the optic lobe of insects and the velocity tuning that has been shown for many behaviors. Here we investigated response properties of motion-sensitive neurons in the central brain of bumblebees. Extracellular recordings allowed us to present a large number of stimuli to probe the spatiotemporal tuning of these neurons. We presented moving gratings that simulated either front-to-back or back-to-front optic flow and found three response types. Direction-selective responses of one of the groups matched those of TN-neurons, which provide optic flow information to the central complex, while the other groups contained neurons with purely excitatory responses that were either selective or non-selective for stimulus direction. Most recorded units showed velocity-coding properties at lower angular velocities, but showed spatial frequency dependent responses at higher velocities. Based on behavioral data, neuronal modelling work has previously predicted the existence of non direction-selective neurons with such properties. Our data now provides physiological evidence for these neurons and shows that neurons with TN-like properties exhibit a similar velocity-dependent coding.

An electrodiffusive network model with multicompartmental neurons and synaptic connections.

Most computational models of neurons assume constant ion concentrations, disregarding the effects of changing ion concentrations on neuronal activity. Among the models that do incorporate ion concentration dynamics, simplifications are often made that sacrifice biophysical consistency, such as neglecting the effects of ionic diffusion on electrical potentials or the effects of electric drift on ion concentrations. A subset of models with ion concentration dynamics, often referred to as electrodiffusive models, account for ion concentration dynamics in a way that ensures a biophysical consistent relationship between ion concentrations, electric charge, and electrical potentials. These models include compartmental single-cell models, geometrically explicit models, and domain-type models, but none that model neuronal network dynamics. To address this gap, we present an electrodiffusive network model with multicompartmental neurons and synaptic connections, which we believe is the first compartmentalized network model to account for intra- and extracellular ion concentration dynamics in a biophysically consistent way. The model comprises an arbitrary number of "units," each divided into three domains representing a neuron, glia, and extracellular space. Each domain is further subdivided into a somatic and dendritic layer. Unlike conventional models which focus primarily on neuronal spiking patterns, our model predicts intra- and extracellular ion concentrations (Na+, K+, Cl-, and Ca2+), electrical potentials, and volume fractions. A unique feature of the model is that it captures ephaptic effects, both electric and ionic. In this paper, we show how this leads to interesting behavior in the network. First, we demonstrate how changing ion concentrations can affect the synaptic strengths. Then, we show how ionic ephaptic coupling can lead to spontaneous firing in neurons that do not receive any synaptic or external input. Lastly, we explore the effects of having glia in the network and demonstrate how a strongly coupled glial syncytium can prevent neuronal depolarization blocks.

Tufm lactylation regulates neuronal apoptosis by modulating mitophagy in traumatic brain injury.

Lactates accumulation following traumatic brain injury (TBI) is detrimental. However, whether lactylation is triggered and involved in the deterioration of TBI remains unknown. Here, we first report that Tufm lactylation pathway induces neuronal apoptosis in TBI. Lactylation is found significantly increased in brain tissues from patients with TBI and mice with controlled cortical impact (CCI), and in neuronal injury cell models. Tufm, a key factor in mitophagy, is screened and identified to be mostly lactylated. Tufm is detected to be lactylated at K286 and the lactylation inhibits the interaction of Tufm and Tomm40 on mitochondria. The mitochondrial distribution of Tufm is then inhibited. Consequently, Tufm-mediated mitophagy is suppressed while mitochondria-induced neuronal apoptosis is increased. In contrast, the knockin of a lactylation-deficient TufmK286R mutant in mice rescues the mitochondrial distribution of Tufm and Tufm-mediated mitophagy, and improves functional outcome after CCI. Likewise, mild hypothermia, as a critical therapeutic method in neuroprotection, helps in downregulating Tufm lactylation, increasing Tufm-mediated mitophagy, mitigating neuronal apoptosis, and eventually ameliorating the outcome of TBI. A novel molecular mechanism in neuronal apoptosis, TBI-initiated Tufm lactylation suppressing mitophagy, is thus revealed.

Human iPSC-derived MSCs induce neurotrophic effects and improve metabolic activity in acute neuronal injury models.

Mesenchymal stromal cell (MSC) therapy has regenerative potentials to treat various pathological conditions including neurological diseases. MSCs isolated from various organs can differentiate into specific cell types to repair organ damages. However, their paracrine mechanisms are predicted to predominantly mediate their immunomodulatory, pro-angiogenic, and regenerative properties. While preclinical studies highlight the significant potential of MSC therapy in mitigating neurological damage from stroke and traumatic brain injury, the variability in clinical trial outcomes may stem from the inherent heterogeneity of somatic MSCs. Accumulating evidence has demonstrated that induced pluripotent stem cells (iPSCs) are an ideal alternative resource for the unlimited expansion and biomanufacturing of MSCs. Thus, we investigated how iPSC-derived MSCs (iMSCs) influence properties of iPSC-derived neurons. Our findings demonstrate that the secretome from iMSCs possesses neurotrophic effects, improving neuronal survival and promoting neuronal outgrowth and synaptic activity in vitro Additionally, the iMSCs enhance metabolic activity via mitochondrial respiration in neurons, both in vitro and in mouse models. Glycolytic pathways also increased following the administration of iMSC secretome to iPSC-derived neurons. Consistently, in vivo experiments showed that intravenous administration of iMSCs compensated for the elevated energetic demand in male mice with irradiation-induced brain injury by restoring synaptic metabolic activity during acute brain damage. 18F-FDG PET imaging also detected an increase in brain glucose uptake following iMSC administration. Together, our results highlight the potential of iMSC-based therapy in treating neuronal damage in various neurological disorders, while paving the way for future research and potential clinical applications of iMSCs in regenerative medicine.Significance Statement Regenerative biotherapeutics using MSCs have emerged as a promising intervention for treating various neurological diseases. Our study explored the potential beneficial effects of human iPSC-derived MSCs (iMSCs) on neurons. We demonstrated that molecules secreted into the culture medium by iMSCs enhance regenerative capabilities by improving neuronal survival, growth, and metabolic activity, as well as synaptic functions, in human iPSC-derived neurons. Mouse experiments also suggested the potential of iMSC therapy to mitigate synaptic mitochondrial dysfunction and enhance brain glucose uptake during acute radiation-induced brain injury, steps that contribute to restoring normal neuronal function. Our results highlight that iMSCs may be a promising alternative cell product for treating neuronal damage, overcoming the inconsistent efficacy of somatic MSCs due to cell variability.

Neuronal Models for the Optimization of Inventory, Waste and Informational Flow

The present study aims to identify and put into practice certain solutions for economic improvement using the Lean Six Sigma (LSS) methodology within companies from the wood processing industry. The main objectives refer to estimating those effects, which are generated by the informational flows in terms of the decision-making process and to identify those areas corresponding to the manufacturing process, which can be improved by using this methodology. The research strategy is based on the use of a questionnaire during 2019-2021, which has addressed to over 150 most important companies from the wood processing industry. In developing the neuronal models for the improvement of stock, the informational flow of the management accounting, costs and for the reduction of the overstock risk in relationship with the waste management was discovered by applying the LSS methodology that the issues were related to inadequate quality records, formality, procedures, and management systems.

Statistical method accounts for microscopic electric field distortions around neurons when simulating activation thresholds.

Notwithstanding advances in computational models of neuromodulation, there are mismatches between simulated and experimental activation thresholds. Transcranial Magnetic Stimulation (TMS) of the primary motor cortex generates motor evoked potentials (MEPs). At the threshold of MEP generation, whole-head models predict macroscopic (at millimeter scale) electric fields (50-70 V/m) which are considerably below conventionally simulated cortical neuron thresholds (200-300 V/m). We hypothesize that this apparent contradiction is in part a consequence of electrical field warping by brain microstructure. Classical neuronal models ignore the physical presence of neighboring neurons and microstructure and assume that the macroscopic field directly acts on the neurons. In previous work, we performed advanced numerical calculations considering realistic microscopic compartments (e.g., cells, blood vessels), resulting in locally inhomogeneous (micrometer scale) electric field and altered neuronal activation thresholds. Here we combine detailed neural threshold simulations under homogeneous field assumptions with microscopic field calculations, leveraging a novel statistical approach. We show that, provided brain-region specific microstructure metrics, a single statistically derived scaling factor between microscopic and macroscopic electric fields can be applied in predicting neuronal thresholds. For the cortical sample considered, the statistical methods match TMS experimental thresholds. Our approach can be broadly applied to neuromodulation models, where fully coupled microstructure scale simulations may not be practical.

Theoretical framework for learning through structural plasticity.

A growing body of research indicates that structural plasticity mechanisms are crucial for learning and memory consolidation. Starting from a simple phenomenological model, we exploit a mean-field approach to develop a theoretical framework of learning through this kind of plasticity, capable of taking into account several features of the connectivity and pattern of activity of biological neural networks, including probability distributions of neuron firing rates, selectivity of the responses of single neurons to multiple stimuli, probabilistic connection rules, and noisy stimuli. More importantly, it describes the effects of stabilization, pruning, and reorganization of synaptic connections. This framework is used to compute the values of some relevant quantities used to characterize the learning and memory capabilities of the neuronal network in training and testing procedures as the number of training patterns and other model parameters vary. The results are then compared with those obtained through simulations with firing-rate-based neuronal network models.

Angiotensin-(1-7) relieves behavioral defects and α-synuclein expression through NEAT1/miR-153-3p axis in Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, whose characteristic pathology involves progressive deficiency of dopaminergic neurons and generation of Lewy bodies (LBs). Aggregated and misfolded α-synuclein (α-syn) is the major constituent of LBs. As the newly discovered pathway of renin-angiotensin system (RAS), Angiotensin-(1-7) (Ang-(1-7)) and receptor Mas have attracted increasing attentions for their correlation with PD, but underlying mechanisms remain not fully clear. Based on above, this study established PD models of mice and primary dopaminergic neurons with AAV-hα-syn(A53T), then discussed the effects of Ang-(1-7)/Mas on α-syn level and neuronal apoptosis for these models combined with downstream long non-coding RNA (lncRNA) and microRNA (miRNA). Results showed that Ang-(1-7) alleviated behavioral impairments, rescued dopaminergic neurons loss and lowered α-syn expression in substantia nigra of hα-syn(A53T) overexpressed PD mice. We also discovered that Ang-(1-7) decreased level of α-syn and apoptosis in the hα-syn(A53T) overexpressed dopaminergic neurons through lncRNA NEAT1/miR-153-3p axis. Moreover, miR-153-3p level in peripheral blood is found negatively correlated with that of α-syn. In conclusion, our work not only showed neuroprotective effect and underlying mechanisms for Ang-(1-7) on α-syn in vivo and vitro, but also brought new hope on miR-153-3p and NEAT1 for diagnosis and treatment in PD.

Brain-derived neurotrophic factor modulation in response to oxidative stress and corticosterone: role of scopolamine and mirtazapine

Major Depressive Disorder (MDD) is a very complex disease, challenging to study and manage. The complexities of MDD require extensive research of its mechanisms to develop more effective therapeutic approaches. Crucial in the context of this disease is the role of brain-derived neurotrophic factor (BDNF) signaling pathway. AimThis manuscript aims to explore the complex relationship between MDD and BDNF signaling pathway, focusing on how BDNF is modulated in response to oxidative stress and corticosterone, known to be altered in MDD and contributing to the pathology of the disorder, when treated with scopolamine and mirtazapine. MethodsTo assess BDNF levels after the different treatment conditions, rat hippocampal slices and mice primary hippocampus and cortical cell culture were analyzed by immunofluorescence and Western blot. Key findingsBoth mirtazapine and scopolamine under stress conditions induced by hydrogen peroxide (H2O2) and corticosterone, had a significant impact on BDNF levels, and this was distinct in different neuronal models. Mirtazapine, especially when combined with H2O2, altered BDNF expression. Scopolamine when combined with both stressors also altered BDNF levels. However, its effects varied depending on the specific neuronal model and stress condition. In accordance with BDNF results, phosphorylated tropomyosin receptor kinase B (pTrkB) presented increased activation when neuronal cells subjected to stress were treated with mirtazapine or scopolamine. SignificanceCollectively, this study highlights the complex connection between these compounds, stress conditions, and BDNF/TrkB modulation, supporting the potential therapeutic effects of scopolamine and mirtazapine in modulating BDNF levels, even in stressful conditions.

Compact Physical Implementation of Spiking Neural Network Using Ambipolar WSe2 n-Type/p-Type Ferroelectric Field-Effect Transistor.

Spiking neural networks (SNNs) are attracting increasing interests for their ability to emulate biological processes, offering energy-efficient computation and event-driven processing. Currently, no devices are known to combine both neuronal and synaptic functions. This study presents an experimental demonstration of an ambipolar WSe2 n-type/p-type ferroelectric field-effect transistor (n/p-FeFET) integrated with ferroelectric Hf0.5Zr0.5O2 (HZO) to achieve both volatile and nonvolatile properties in a single device. The nonvolatile n-FeFET, driven by the stable ferroelectric properties of HZO, exhibits highly linear synaptic behavior. In contrast, the volatile p-FeFET, influenced by electron self-compensation in the ambipolar WSe2, enables self-resetting leaky-integrate-and-fire neurons. Integrating neuronal and synaptic functions in the same device allows for compact neuromorphic computing applications. Additionally, simulations of SNNs using experimentally calibrated synaptic and neuronal models achieved a 93.8% accuracy in MNIST digit recognition. This innovative approach advances the development of SNNs with high biomimetic fidelity and reduced hardware costs.

Mechanism of Mettl14 regulating AIM2 inflammasome activation and neuronal apoptosis and pyroptosis in spinal cord injury by mediating PPARγ m6A methylation.

Spinal cord injury (SCI) represents a destructive pathological and neurological state. Methyltransferase-like 14 (Mettl14)-mediated m6A modification links to spinal cord injury (SCI), and we explored its mechanism. SCI mouse models were subjected to si-Mettl14 and si-negative control treatments and mouse behavior, pathological condition and apoptosis assessments. The oxygen/glucose deprivation (OGD)-induced spinal cord neuronal cell models were processed with si-Mettl14 and si-peroxisome proliferator-activated receptor γ (PPARγ) plasmids, and pcDNA3.1-YTHDF2 or synthetic dsDNA Poly(dA: dT), followed by viability and apoptosis evaluation by MTT and flow cytometry. Levels of Mettl14, PPARγ, and YTHDF2 mRNAs and proteins, AIM2 inflammasome activation-associated and pyroptosis marker proteins, PPARγ m6A methylation and pyroptosis-related inflammatory factors were determined by RT-qPCR, Western blot, Me-RIP and ELISA, with PPARγ mRNA stability and YTHDF2-PPARγ interaction assessed. Mettl14 and PPARγ m6A modification levels rose in SCI spinal cord tissues, while PPARγ levels dropped. Mettl14 knockdown dampened m6A modification, up-regulated PPARγ levels, weakened neuronal apoptosis, and ameliorated SCI in mice. OGD down-regulated PPARγ and accelerated OGD-induced neuronal apoptosis and pyroptosis via inducing Mettl14-mediated m6A modification. Mettl14 amplified PPARγ mRNA degradation and down-regulated PPARγ by mediating m6A methylation via the YTHDF2-dependent pathway. Mettl14 silencing-mediated PPARγ m6A methylation mitigated OGD-induced neuronal apoptosis and pyroptosis by inactivating AIM2 inflammasome. Mettl14 triggered activated AIM2 inflammasomes, promoted neuronal apoptosis and pyroptosis, and worsened SCI in SCI mice via mediating PPARγ m6A methylation. Mettl14 regulates AIM2 inflammasome activation, and redounds to spinal cord neuronal apoptosis and pyroptosis in SCI by mediating m6A methylation of PPARγ.

Vibration-resonance chimeras in coupled excitable systems with heterogeneous aperiodic excitations

In this work, we present a novel chimera-like state known as the vibration-resonance chimera (VRC) state, which is induced by heterogeneous aperiodic (HA) excitations and emerges within the optimal range of amplitude for the HA excitations. We investigate the dynamic behaviors of the FitzHugh-Nagumo model in relation to HA excitations and coupling strength in parameter spaces, identifying specific regions where VRC states exist and analyzing the transition mechanisms between different dynamic behaviors. Using basin stability measures, we find that the mean period of HA excitations affects the multi-stable states of neural systems. Specifically, the model excited by HA excitations with a larger mean period is more likely to exhibit multi-stable states. Additionally, we explore the effects of two levels of heterogeneity in HA excitations, namely amplitude heterogeneity and period heterogeneity, on VRC states. Amplitude heterogeneity can suppress the occurrence of VRC states, whereas period heterogeneity can promote their generation. This work provides valuable insights into complex system dynamics, enhancing our understanding of chimera states in neuronal models and their potential applications in neural networks.

Calibration of stochastic, agent-based neuron growth models with approximate Bayesian computation.

Understanding how genetically encoded rules drive and guide complex neuronal growth processes is essential to comprehending the brain's architecture, and agent-based models (ABMs) offer a powerful simulation approach to further develop this understanding. However, accurately calibrating these models remains a challenge. Here, we present a novel application of Approximate Bayesian Computation (ABC) to address this issue. ABMs are based on parametrized stochastic rules that describe the time evolution of small components-the so-called agents-discretizing the system, leading to stochastic simulations that require appropriate treatment. Mathematically, the calibration defines a stochastic inverse problem. We propose to address it in a Bayesian setting using ABC. We facilitate the repeated comparison between data and simulations by quantifying the morphological information of single neurons with so-called morphometrics and resort to statistical distances to measure discrepancies between populations thereof. We conduct experiments on synthetic as well as experimental data. We find that ABC utilizing Sequential Monte Carlo sampling and the Wasserstein distance finds accurate posterior parameter distributions for representative ABMs. We further demonstrate that these ABMs capture specific features of pyramidal cells of the hippocampus (CA1). Overall, this work establishes a robust framework for calibrating agent-based neuronal growth models and opens the door for future investigations using Bayesian techniques for model building, verification, and adequacy assessment.

Reverse-engineered models reveal differential membrane properties of autonomic and cutaneous unmyelinated fibers.

Unmyelinated C-fibers constitute the vast majority of axons in peripheral nerves and play key roles in homeostasis and signaling pain. However, little is known about their ion channel expression, which controls their firing properties. Also, because of their small diameters (~ 1 μm), it has not been possible to characterize their membrane properties using voltage clamp. We developed a novel library of isoform-specific ion channel models to serve as the basis functions of our C-fiber models. We then developed a particle swarm optimization (PSO) framework that used the isoform-specific ion channel models to reverse engineer C-fiber membrane properties from measured autonomic and cutaneous C-fiber conduction responses. Our C-fiber models reproduced experimental conduction velocity, chronaxie, action potential duration, intracellular threshold, and paired pulse recovery cycle. The models also matched experimental activity-dependent slowing, a property not included in model optimization. We found that simple conduction responses, characterizing the action potential, were controlled by similar membrane properties in both the autonomic and cutaneous C-fiber models, but complicated conduction response, characterizing the afterpotenials, were controlled by differential membrane properties. The unmyelinated C-fiber models constitute important tools to study autonomic signaling, assess the mechanisms of pain, and design bioelectronic devices. Additionally, the novel reverse engineering approach can be applied to generate models of other neurons where voltage clamp data are not available.

Mittag–Leffler Fractional Stochastic Integrals and Processes with Applications

We study Mittag–Leffler (ML) fractional integrals involved in the solution processes of a system of coupled fractional stochastic differential equations. We introduce the ML fractional stochastic process as a ML fractional stochastic integral with respect to a standard Brownian motion. We provide some representation formulas of solution processes in terms of Mittag–Leffler fractional integrals and processes. Computable expressions of the mean functions and of the covariances of such processes are specifically given. The application in neuronal modeling is provided, and all involved functions and processes are specifically determined. Numerical evaluations are carried out and some results are shown and discussed.

Advances in the cell biology of the trafficking and processing of amyloid precursor protein: impact of familial Alzheimer's disease mutations.

The production of neurotoxic amyloid-β peptides (Aβ) is central to the initiation and progression of Alzheimer's disease (AD) and involves sequential cleavage of the amyloid precursor protein (APP) by β- and γ-secretases. APP and the secretases are transmembrane proteins and their co-localisation in the same membrane-bound sub-compartment is necessary for APP cleavage. The intracellular trafficking of APP and the β-secretase, BACE1, is critical in regulating APP processing and Aβ production and has been studied in several cellular systems. Here, we summarise the intracellular distribution and transport of APP and its secretases, and the intracellular location for APP cleavage in non-polarised cells and neuronal models. In addition, we review recent advances on the potential impact of familial AD mutations on APP trafficking and processing. This is critical information in understanding the molecular mechanisms of AD progression and in supporting the development of novel strategies for clinical treatment.

Unraveling Axonal Transcriptional Landscapes: Insights from iPSC-Derived Cortical Neurons and Implications for Motor Neuron Degeneration.

Neuronal function and pathology are deeply influenced by the distinct molecular profiles of the axon and soma. Traditional studies have often overlooked these differences due to the technical challenges of compartment specific analysis. In this study, we employ a robust RNA-sequencing (RNA-seq) approach, using microfluidic devices, to generate high-quality axonal transcriptomes from iPSC-derived cortical neurons (CNs). We achieve high specificity of axonal fractions, ensuring sample purity without contamination. Comparative analysis revealed a unique and specific transcriptional landscape in axonal compartments, characterized by diverse transcript types, including protein-coding mRNAs, RNAs encoding ribosomal proteins (RPs), mitochondrial-encoded RNAs, and long non-coding RNAs (lncRNAs). Previous works have reported the existence of transcription factors (TFs) in the axon. Here, we detect a set of TFs specific to the axon and indicative of their active participation in transcriptional regulation. To investigate transcripts and pathways essential for central motor neuron (MN) degeneration and maintenance we analyzed KIF1C-knockout (KO) CNs, modeling hereditary spastic paraplegia (HSP), a disorder associated with prominent length-dependent degeneration of central MN axons. We found that several key factors crucial for survival and health were absent in KIF1C-KO axons, highlighting a possible role of these also in other neurodegenerative diseases. Taken together, this study underscores the utility of microfluidic devices in studying compartment-specific transcriptomics in human neuronal models and reveals complex molecular dynamics of axonal biology. The impact of KIF1C on the axonal transcriptome not only deepens our understanding of MN diseases but also presents a promising avenue for exploration of compartment specific disease mechanisms.