Biology subjects

Maxion, A.

Publications and source records attributed to Maxion, A..

4 recordsLinked to original sources

bioRxiv2025

A Hybrid Knowledge- and Data-driven Model for Automatic Assessment of Chemically Induced Spiking Patterns in C-fiber Microneurography

Analyzing temporal spike patterns in C-fibers recorded via microneurography is challenging due to the use of a single recording electrode, waveform variability, and high similarity of spike shapes across neurons, limiting the interpretation of sensory coding, such as pain and itch. We present a computational pipeline combining peak detection and supervised classification for spike sorting to improve the analysis of discharges, identified through activity-dependent conduction velocity changes. In the knowledge-driven step, we extract spike templates from electrically evoked spikes obtained during low-frequency stimulation and focus on the "best" template as the fiber of interest. Spike detection is further restricted to intervals showing activity-dependent latency shifts, substantially reducing the search space compared to unsupervised clustering. In the data-driven steps, we systematically evaluate three feature sets and machine learning models: One-class SVM, SVM, and XGBoost. For the evaluation, we created a specialized stimulation protocol, providing reliable ground truth labels for all electrically evoked spikes, allowing precise spike time-locking. Compared to Spike2 software, our approach achieved higher F1-scores and reduced false positives, indicating improved spike sorting. Although XGBoost achieved the highest median F1-scores, optimal performance was dependent on individual combinations of feature sets and models for each recording. In some recordings with many nerve fibers and a low signal-to-noise ratio, reliable sorting was not feasible. This highlights the necessity to determine sortability and optimal configurations for individual recordings. To illustrate the potential of our approach to sensory spike train analysis, we present a proof-of-concept application of the pipeline to chemically induced C-fiber activity. These findings represent an important step toward reliable analysis of activity associated with pain and itch signaling.

neuroscience↗

bioRxiv2025

Modeling the Memory of Unmyelinated Axons: Integration of a Data-Driven Approach with Physiological Memory Concept

In this study, we present a simplified and resource-efficient computational model to predict activity-dependent conduction velocity changes of action in unmyelinated axons. This model serves as a complementary tool to Hodgkin-Huxley models. Our approach is based on the concept of "memory," where the speed of subsequent action potentials is modulated by prior activity. We utilized microneurography data from 95 mechano-insensitive C-fibers of healthy human participants, including both sexes, across various stimulation protocols to optimize model parameters. The model incorporates linear long-term and non-linear short-term memory components. By convolving the history of recorded action potentials with the memory function, the model can effectively predict the propagation speed of subsequent action potentials with low mean squared errors for the proposed one-dimensional and two-dimensional memory functions. This computational framework provides insights into the dynamics of unmyelinated axons under varying conditions and thus in signal processing along the axon and the short-term memory of axons. The models rapid computation times make it suitable for real-time applications in electrophysiological experiments. Significance StatementThis study introduces a novel model for simulating activity-dependent conduction velocity changes in unmyelinated axons, which crucially shape signal processing during conduction. While traditional Hodgkin-Huxley (HH) models offer valuable physiological insights, they are computationally intensive and challenging to adapt due to numerous parameters. Our approach leverages the concept of fiber "memory," capturing how prior activity shapes conduction. It requires fewer parameters and therefore makes it easier to fit to diverse datasets, including patient data. Importantly, our model is highly efficient and suitable for real-time applications, enabling rapid simulation and analysis that are not feasible with HH models.

neuroscience↗

bioRxiv2024

Molecular architecture of human dermal sleeping nociceptors

Human dermal sleeping nociceptors display ongoing activity in neuropathic pain, affecting 10% of the population. Despite advances in rodents, a molecular marker for these mechano-insensitive C-fibers (CMis) in human skin remains elusive, preventing targeted therapy. In this translational Patch-seq study, we combine single-cell transcriptomics following electrophysiological characterization with single-nucleus and spatial transcriptomics from pigs and humans. We functionally identified CMis in pig sensory neurons with patch-clamp using adapted protocols from human microneurography. We identified oncostatin-M-receptor (OSMR) and somatostatin (SST) as marker genes for CMis. Following dermal injection in healthy human volunteers, oncostatin-M, the ligand of OSMR, exclusively modulates CMis. We identified the entire molecular architecture of human dermal sleeping nociceptors, providing new therapeutic targets and the basis for a mechanistic understanding of neuropathic pain. One Sentence SummaryWe identify the molecular architecture and specifically OSMR and SST as molecular markers for human dermal sleeping nociceptors, key players in the generation of neuropathic pain. Short versionIn this Patch-seq study, we identify OSMR and SST as molecular markers for human dermal sleeping nociceptors, key players in the generation of neuropathic pain.

neuroscience↗

bioRxiv2023

Biophysics of sodium channels during subthreshold depolarization in vitro and in silico

Voltage-gated sodium channels (VGSC) in the peripheral nervous system shape action potentials (AP) and thereby support the detection of sensory stimuli. Most of the nine mammalian VGSC subtypes are expressed in nociceptors, but predominantly, three are linked to several human pain syndromes: while Nav1.7 is suggested to be a (sub-)threshold channel, Nav1.8 is thought to support the fast AP upstroke. Nav1.9, as it produces large persistent currents, is attributed a role in determining the resting membrane potential. We characterized gating of Nav1.1-Nav1.3 and Nav1.5-Nav1.9 in manual patch clamp with focus on the AP subthreshold depolarization phase. Nav1.9 exhibited the most hyperpolarized activation while its fast inactivation resembled the depolarized inactivation of Nav1.8. For some VGSCs (e.g., Nav1.1 and Nav1.2), a positive correlation between ramp current and window current was detected. Using a modified Hodgkin-Huxley model which accounts for the time needed for inactivation to occur, we used the acquired data to simulate two nociceptive nerve fiber types (an A{delta}-and a mechano-insensitive C-nociceptor) containing VGSC conductances according to published human RNAseq data. Our simulations suggest that Nav1.9 is supporting both the AP upstroke and its shoulder. A reduced threshold for AP generation was induced by enhancing Nav1.7 conductivity or shifting its activation to more hyperpolarized potentials, as observed in Nav1.7-related pain disorders. Here, we provide a comprehensive, comparative functional characterization of VGSCs relevant in nociception and describe their gating with Hodgkin-Huxley-like models, which can serve as a tool to study their specific contributions to AP shape and sodium channel-related diseases. DisclaimerParts of this study were published as a preprint on bioRxiv: Koster, P.A., T. Stiehl, J. Tigerholm, A. Maxion, B. Namer, and A. Lampert. 2023. Biophysics of sodium channels during subthreshold depolarization in vitro and in silico. bioRxiv. doi.org/10.1101/2023.09.03.556095 (Preprint posted September 6, 2023) SummarySubthreshold gating of seven sodium channels (Nav1.1-3, Nav1.5-8) is determined by manual patch clamp and, together with Nav1.9, integrated into a computer model of an A{delta}-and a mechano-insensitive nociceptor (CMi). Simulations reveal contribution of Nav1.9 to the action potential upstroke and shoulder and prove useful for Nav1.7-related disease modelling.

physiology↗