Biology subjects

Maltsev, A. V.

Publications and source records attributed to Maltsev, A. V..

11 recordsLinked to original sources

bioRxiv2026

Proton tunneling at the ryanodine receptor Ca2+ activation site provides temperature-invariant noise for robust Ca2+-induced Ca2+ release

Ryanodine receptor (RyR)-mediated Ca2+-induced Ca2+ release (CICR) is a regenerative trigger-to-release mechanism used in diverse cell types. RyRs amplify small Ca2+ signals into large, localized surges that drive autonomous oscillation, excitation-contraction coupling, pulsatile secretion, neurotransmitter release, or memory formation. Robust RyR function depends on the probability of RyR recruitment remaining within an operating range as physiological conditions change. This raises a basic question: what stabilizes the microscopic Ca2+-sensitive opening step of RyR across temperature and cellular state? Here we tested that proton tunneling at the conserved RyR2 Ca2+-activation site contributes a temperature-stable stochastic component to channel opening. To this end we developed a multiscale quantum-structural pipeline that estimates the per-channel modulation amplitude from cryo-EM geometry under stated assumptions. We applied this pipeline to a rabbit sinoatrial node cell model with explicit Ca2+ release unit architecture. Holding this RyR quantum noise constant across 25-37 {circ}C preserves pacemaker rhythmicity at low temperature, whereas a Q10-scaled classical noise results in irregular pacemaking activity as amplitude falls below the coherence-resonance range. Experimentally in isolated rabbit sinoatrial node cells, Ca2+ transient rate increased with temperature, but inter-transient interval variability did not, consistent with our numerical model predictions. These results support a model in which proton tunneling stabilizes the regenerative RyR-mediated CICR step. In autonomous oscillators, such as sinoatrial node cells, this manifests as robust rhythmicity. In triggered systems, it is expected to stabilize CICR gain, release synchrony, and release fidelity. The importance of this shared microscopic mechanism is evidenced by the conservation of the Ca2+-activation site across RyR isoforms for >600 million years of evolution. Significance StatementThis work suggests that RyR-based CICR networks can harness quantum-conditioned stochasticity. Proton tunneling at a conserved RyR activation site contributes a temperature-stable source of microscopic variability, and CICR amplifies that variability into robust physiological output. In pacemaker cells, this appears as stable rhythmic timing. More broadly, it provides a concrete route by which atomic-scale quantum events can influence macroscopic cellular function.

biophysics↗

bioRxiv2026

DeepBranchAI: A Novel Cascade Workflow Enabling Accessible 3D Branching Network Segmentation

Three-dimensional branching networks exist throughout biological, natural, and man-made systems as pathways through volumetric space. Segmentation is required to correctly reconstruct the networks in whole or in part for analysis. This presents a unique challenge as minor voxel misclassifications can cause sporadic connectivity shifts, whereby connected elements appear to disconnect (false negatives) or to even become amplified (false positives). Addressing this topological vulnerability requires the generation of 3D models since 2D slice-by-slice approaches cannot maintain connectivity across x, y, and z axes. Yet tracking 3D architecture demands substantially more analytical resources than using a 2D strategy as generating volumetric annotations requires extraordinary amounts of expert time to manually annotate. This creates a fundamental annotation bottleneck: with sparse training data available, deep learning models tend to overfit available volumes and fail to generalize to novel volumes. We present a cascade training workflow that overcomes this bottleneck through a positive feedback loop in which trained models become annotation aids for subsequent volumes. The workflow begins with random forests that generate initial drafts from minimal labels, followed by expert refinement that cycle ever closer to the ground truth. As refined data accumulates, training transitions from 2D to 3D architectures, which systematically expand sparse datasets into comprehensive training sets. The outcome is a 3D nnU-Net model optimized for topology-preserving segmentation. We dub our resulting model DeepBranchAI. Training validation on heavily branching mitochondrial networks, generated by focused ion beam scanning electron microscopy (FIB-SEM, 15nm voxel resolution) achieved Dice Similarity Coefficient (DSC) = 0.942 across 5-fold cross-validation. Transfer learning to vascular networks (VESSEL12 dataset, CT volumes, 30,000-fold voxel size difference) achieved 97.05% accuracy against ground truth, validating that learned features represent domain-general topological principles. This workflow reduces annotation time from months to weeks while transforming sparse initial labels into robust training sets. Complete implementation, trained weights, and validation code are provided open source.

bioinformatics↗

bioRxiv2025

A New Fight-or-Flight Pacemaker Mechanism via Ryanodine Receptor Abundance and Superclustering

The sinoatrial node is the primary cardiac pacemaker. Individual sinoatrial node cells (SANCs) generate spontaneous rhythmic action potentials (APs) that initiate each heartbeat. The mechanism of SANC automaticity and its modulation by autonomic nervous system are based on the coupled function of molecules of both the cell membrane (ion channels, exchangers, and pumps) and the sarcoplasmic reticulum (SR), which generates rhythmic local Ca releases (LCRs). While LCRs are generated by ryanodine receptors (RyRs), the molecular-scale RyR network structure remains unknown. Here we performed single-molecule localization of RyRs via direct Stochastic Optical Reconstruction Microscopy (dSTORM) in rabbit SANCs in basal conditions and 5 minutes after {beta}-adrenergic receptor ({beta}AR) stimulation by isoproterenol. RyRs form clusters of various sizes, with a mean density of 67.7{+/-}13.2 RyR/m2. (Mean{+/-}SEM, 6 cells). While the majority of cluster sizes ranged from 3 to 32 RyRs, each cell had a few substantially larger clusters (>76 RyRs), dubbed superclusters. {beta}AR stimulation significantly increased the RyR density to 119.1{+/-}22.6 RyR/m2 (8 cells, p<0.05) and created more superclusters. Our new numerical SANC model showed that superclustering substantially decreased the AP cycle length (APCL) by creating Ca release hotspots that initiated larger LCRs under any condition. Increasing RyR density prolonged APCL in the basal state but shortened APCL during {beta}AR stimulation. With no change in RyR network, {beta}AR stimulation of only SR Ca pump and ion currents shortened APCL on average from 414.9 to 284 ms. When realistic higher RyR density and superclustering were added to the model, APCL further shortened to 231.9 ms. Thus, dynamic nanoscale changes in RyR network provide a new powerful pacemaker mechanism. This mechanism may help explain athletic bradycardia at rest and high exercise heart rates, with its deterioration contributing to the age-related loss of heart rate reserve. Author summaryThe heartbeat begins in the sinoatrial node, the hearts natural pacemaker. During stress or exercise, pacemaker cells accelerate their firing rate via {beta}-adrenergic ("fight-or-flight") stimulation. Although Ca signals are known to drive this acceleration via electrogenic Na/Ca exchanger, how the underlying ryanodine receptor (RyR) Ca release channels are organized at the nanoscale and how this organization changes during stimulation remain unclear. Here, we used super-resolution microscopy (dSTORM) to visualize individual RyRs in rabbit pacemaker cells. We found that RyRs form heterogeneous clusters that include rare, very large "superclusters." {beta}-adrenergic stimulation increased overall RyR density, enlarged clusters, and markedly increased the number of superclusters. To understand how these structural changes affect pacemaker function, we developed a computational sinoatrial node cell model in which each RyR was represented explicitly and arranged according to our imaging data. Numerical model simulations revealed that superclusters act as Ca-release hotspots that fire early during diastolic depolarization and recruit neighboring RyR clusters to fire via Ca-induce-Ca-release mechanism. When combined with {beta}-adrenergic enhancement of Ca cycling and membrane currents, the superclustering and RyR abundance synergistically accelerated action potential firing. Together, our results identified dynamic nanoscale RyR network reorganization as a key structural mechanism in fight-or-flight response.

biophysics↗

bioRxiv2024

Unique spatiotemporal synchronization solutions of heterogeneous local Ca2+ dynamics underlie the formation of each impulse that emerges from the cardiac sinoatrial node

We used both linear and nonlinear analyses to determine how information processing within and among incessant heterogeneous local Ca2+ oscillation (LCO) results in formation of rhythmic impulses in mouse SAN ex vivo. Phase analysis delineated a network of functional pacemaker cell clusters, distinguished by their LCO amplitudes, kinetics, and phases. Cross-talk of LCO dynamics within the network culminated in rhythmic SAN global Ca2+ transients (CaTs), having a mean rate and rhythm identical to that recorded by the reference sharp electrode in the right atria, indicating that CaTs are induced by global SAN electrical impulses. Initial conditions of each impulse and subsequent LCO ensemble evolution during an impulse differed from each other, associated with an apparent stochastic process (carrying a degree of uncertainty) within network. A small pacemaker cluster located near the crista terminalis (CT) exhibited the highest degrees of intrinsic power, earliest rotor-like energy transfer, most frequent point-to-point instability, earliest acrophase, greatest impulse-to-impulse variability within the SAN functional cluster network. Unique, variable small-world network Ca2+ information sharing within and among all clusters during initial and terminal impulse phases, created a unique solution for each impulse, while preserving the identity of each cluster (a highly efficient form of information processing at low wiring and energy costs). Cross-recurrence analysis verified that LCO dynamics within the small cluster near CT were more stochastic and less deterministic than those of the other clusters, indicating that this small cluster took the lead in the initiation of the SAN impulse and that the others followed.

cell biology↗

bioRxiv2024

Cardiac Pacemaker Cells Harness Stochastic Resonance to Ensure Fail-Safe Operation at Low Rates Bordering on Sinus Arrest

BACKGROUNDThe sinoatrial node (SAN) is the primary pacemaker of the heart. Recent high-resolution imaging showed that synchronized action potentials (APs) exiting the SAN emerge from heterogeneous signals, including subthreshold signals in non-firing (dormant) cells. This raises a new question in cardiac biology: how do these signals contribute to heartbeat generation? Here, we tested the hypothesis that pacemaker cells harness stochastic resonance to ensure fail-safe operation, especially at low rates bordering on sinus arrest. METHODSMembrane potential and Ca signals were measured using perforated-patch recordings in rabbit SAN cells exposed to sine-wave or white-noise currents. Additionally, we imaged Ca signals in intact mouse SAN tissue and performed multiscale model simulations at the subcellular, cellular, and tissue levels. RESULTSIn addition to classical synchronized Ca transients, SAN tissue exhibited heterogeneous local Ca signals of different kinetics. Noise currents, mimicking the heterogeneous natural cell environment, restored AP firing in dormant cells and substantially improved the rate and rhythm of those firing infrequently and irregularly. The benefit followed a bell-shaped curve: the performance improved but then declined, demonstrating a hallmark of stochastic resonance. Rhythmic AP generation in response to sine-wave currents of different frequencies defined a resonance spectrum in SAN cells, reflecting their ability to respond via stochastic resonance to specific frequency components embedded in noise. Cholinergic stimulation shifted the resonance spectrum and responses to noise toward lower frequencies across all amplitudes tested, rendering cells unresponsive to higher-frequency signals while enabling more effective processing of slower signals. Both the numerical models and simultaneous recordings of membrane potential and Ca dynamics demonstrated that stochastic resonance is amplified by coupled electrical and Ca signaling, enhancing AP generation at low noise levels. Adding noise currents to the cell and tissue models allowed firing under conditions where they otherwise would have stopped. CONCLUSIONSSAN cells harness stochastic resonance amplified by coupled membrane-Ca signaling to ensure rhythmic heartbeat initiation, especially at low rates. This new signaling mechanism could help avoid sinus arrest when heart slows but noise increases, such as during parasympathetic stimulation, bradyarrhythmia, or aging.

biophysics↗

bioRxiv2024

Structure-Function Relationship of the Ryanodine Receptor Cluster Network in Sinoatrial Node Cells

The rate of spontaneous action potentials (APs) generated by sinoatrial node cells (SANC) is regulated by local Ca2+ release (LCR) from the sarcoplasmic reticulum via Ca2+ release channels (ryanodine receptors, RyRs). LCR events propagate and self-organize within the network of RyR clusters (Ca release units, CRUs) via Ca-induced-Ca-release (CICR) that depends on CRU sizes and locations: while larger CRUs generate stronger release signals, the networks topology governs signal diffusion and propagation. This study used super-resolution structured illumination microscopy to image the 3D network of CRUs in rabbit SANC. The peripheral CRUs formed a spatial mesh, reflecting the cell surface geometry. Two distinct subpopulations of CRUs were identified within each cell, with size distributions conforming to a two component Gamma mixture model. Furthermore, neighboring CRUs exhibited repulsive behavior. Functional properties of the CRU network were further examined in a novel numerical SANC model developed using our experimental data. Model simulations revealed that heterogeneities in both CRU sizes and locations facilitate CICR and increase AP firing rate in a cooperative manner. However, these heterogeneities reduce the effect of {beta}-adrenergic stimulation in terms of its relative change in AP firing rate. The presence of heterogeneities in both sizes and locations allows SANC to reach higher absolute AP firing rates during {beta}-adrenergic stimulation. Thus, the CICR facilitation by heterogeneities in CRU sizes and locations regulates and optimizes cardiac pacemaker cell operation under various physiological conditions. Dysfunction of this optimization could be a key factor in heart rate reserve decline in aging and disease.

biophysics↗

bioRxiv2023

A novel conceptual model of heart rate autonomic modulation based on a small-world modular structure and heterogeneous innervation of the sinoatrial node

The current theory of cardiac pacemaker rate modulation by the autonomic nervous system is based on the concept that a primary pacemaker cell or a group of cells in the center of the sinoatrial node (SAN) can change its AP firing rate within a broad range, driving respective myocardium contractions to commensurate body demands. Experimental data show, however, that pacemaker cells are extremely heterogeneous, with different areas of the SAN or cell clusters specializing to drive APs at specific rates. Thus, higher heart rates under stress are mainly driven by cell clusters in superior SAN, whereas low rates by cell clusters in inferior SAN, with basal state rates generated somewhere in the middle of the node. Cells within different clusters feature different intrinsic electrophysiological and Ca cycling properties, sympathetic and parasympathetic innervation, and vasculature, thereby supporting effective shift of the system to an optimal rate (under given conditions) accompanied by respective shifts in the leading pacemaker site. Thus, the popular single-cell-based pacemaker theory does not capture this complex emerging paradigm of pacemaker function of SAN tissue revealed by recent experimental studies. Here we propose a more realistic, conceptual model of heart rate autonomic modulation based on these studies. Our new model (the gear model) simulates the SAN as a brain-like structure featuring a small world of loosely connected clusters (functional modules) of tightly coupled cells, modeled as Maltsev-Lakatta coupled-clock system. One module (the higher chronotropic gear) generates higher AP rates in basal state and under {beta}-adrenergic stimulation, but its activity is strongly suppressed by parasympathetic stimulation. The other module (the lower gear) generates lower rates and has low sensitivity to parasympathetic stimulation. Such modular, gear-like system reproduces the respective shifts of the leading pacemaker site observed experimentally and features a wide range of rate modulation and robust function whilst conserving energy. In perspective, future refinement and application of this new pacemaker tissue mechanism will provide better understanding of cardiac pacemaker function, its deterioration in aging and disease, and ultimately the creation of new therapies to treat sick sinus syndrome and other SAN function-related cardiac arrythmias.

biophysics↗

bioRxiv2022

Meaningful Local Signaling in SA Node Center Identified by Random Matrix Theory

AO_SCPLOWBSTRACTC_SCPLOWThe sinoatrial node (SAN) is the pacemaker of the heart. Recently calcium signals, believed to be crucially important in rhythm generation, have been imaged in intact SAN and shown to be heterogeneous in various regions of the SAN and shown to be heterogeneous in various regions of the SAN with a lot of analysis relying on visual inspection rather than mathematical tools. Here we apply methods of random matrix theory (RMT) developed for financial data and various biological data sets including {beta}-cell collectives and EEGs to analyse correlations in SAN calcium signals using eigenvalues and eigenvectors of the correlation matrix. We use principal component analysis (PCA) to locate signalling modules corresponding to localization properties the eigenvectors corresponding to high eigenvalues. We find that the top eigenvector captures the common response of the SAN to action potential. In some cases, the eigenvector corresponding to the second highest eigenvalue yields a pacemaker region whose calcium signals predict the action potential. Furthermore, using new analytic methods, we study the relationship between covariance coefficients and distance, and find that even inside the central zone, there are non-trivial long range correlations, indicating intercellular interactions in most cases. Lastly, we perform an analysis of nearest-neighbor eigenvalue distances and find that it coincides with universal Wigner surmise under all available experimental conditions, while the number variance, which captures eigenvalue correlations, is sensitive to experimental conditions. Thus RMT application to SAN allows to remove noise and the global effects of the action potential and thereby isolate the local and meaningful correlations in calcium signalling.

biophysics↗

bioRxiv2022

The Heart's Pacemaker Mimics Brain Cytoarchitecture and Function: Autonomic innervation, a peripheral glial cell web, and a novel S100B expressing interstitial cell type impart structural and functional complexity to the sinoatrial node

ObjectivesThis study sought to describe the 3D cytoarchitecture of sinoatrial node tissue, including autonomic innervation, peripheral glial cells, and pacemaker cells. BackgroundThe sinoatrial node of the heart produces rhythmic action potentials (AP), generated via calcium signaling within and among pacemaker cells. Our previous work has described the SAN as composed of an HCN4-expressing pacemaker cell meshwork, which merges with a network of CX43+/F-actin+ cells. It is also known that sympathetic and parasympathetic innervation from epicardial ganglia create an autonomic plexus in the sinoatrial node, which modulates heart rate and rhythm. However, the anatomical details of the interaction of this plexus with the pacemaker cell meshwork have yet to be described. Methods3D confocal laser-scanning microscopy of triple immunolabeled SAN whole mount preparations with combinations of antibodies for HCN4, S100B, GFAP, ChAT or VAChT, and TH, and transmission electron microscopy (TEM). ResultsThe SAN exhibited heterogeneous autonomic innervation, which was accompanied by a web of peripheral glial cells (PGCs). Further, we identified a novel S100B+/GFAP- interstitial cell population, with unique morphology and distinct distribution pattern, creating complex interactions with other cell types in the node. TEM images showed a similar population of cells, here identified as telocytes, which appeared to secrete vesicles towards pacemaker cells. Application of S100B protein to SAN preparations induced distinct changes in rhythmogenic calcium signaling. ConclusionsThe autonomic plexus and its associated peripheral glial cell web, a novel network of S100B expressing interstitial cells resembling telocytes, and a meshwork of HCN4+ cells interact to impart structural complexity to the sinoatrial node. Summary Table [Table 1][Table 1]

physiology↗

bioRxiv2021

Functional heterogeneity of cell populations increases robustness of pacemaker function in a numerical model of the sinoatrial node tissue

Each heartbeat is initiated by specialized pacemaker cells operating within the sinoatrial node (SAN). While individual cells within SAN tissue exhibit substantial heterogeneity of their electrophysiological parameters and Ca cycling, the role of this heterogeneity for cardiac pacemaker function remains mainly unknown. Here we investigated the problem numerically in a 25x25 square grid of coupled-clock Maltsev-Lakatta cell models and tested the hypothesis that functional heterogeneity of cell populations increases robustness of SAN function. The tissue models were populated by cells with different degree of heterogeneity of the two key model parameters of the coupled-clock system, maximum L-type Ca current conductance (gCaL) and sarcoplasmic reticulum Ca pumping rate (Pup). Our simulations showed that in the areas of Pup-gCaL parametric space at the edge of the system stability where action potential (AP) firing was absent or dysrhythmic in tissues populated by identical cells, rhythmic AP generation was rescued in tissues populated by cells with uniformly random distributions of gCaL or Pup (but keeping the same average values). This effect to increase robust AP generation was synergistic with respect to heterogeneity in both gCaL and Pup and was further strengthened by clustering of cells with higher gCaL or Pup. The effect of functional heterogeneity was not due to a simple summation of activity of intrinsically firing cells naturally present in SAN; rather AP firing cells locally and critically interacted with non-firing/dormant cells. When firing cells prevailed, they recruited many dormant cells to fire, strongly enhancing overall SAN function. And vice versa, prevailing dormant cells suppressed AP firing in cells with intrinsic automaticity and halted SAN automaticity.

systems biology↗

bioRxiv2019

Automated methods for 3D Segmentation of Focused Ion Beam-Scanning Electron Microscopic Images

Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) is an imaging approach that enables analysis of the 3D architecture of cells and tissues at resolutions that are 1-2 orders of magnitude higher than that possible with light microscopy. The slow speeds of data collection and analysis are two critical problems that limit more extensive use of FIB-SEM technology. Here, we present a robust method that enables rapid, large-scale acquisition of data from tissue specimens, combined with an approach for automated data segmentation using machine learning, which dramatically increases the speed of image analysis. We demonstrate the feasibility of these methods through the 3D analysis of human muscle tissue by showing that our process results in an improvement in speed of up to three orders of magnitude as compared to manual approaches for data segmentation. All programs and scripts we use are open source and are immediately available for use by others.\n\nImpact StatementThe high-throughput, easy-to-use and versatile segmentation pipeline described in our manuscript will enable rapid, large-scale statistical analysis of sub-cellular structures in tissues.

cell biology↗