Biophysical modelling of intrinsic cardiac nervous system neuronal electrophysiology based on single-cell transcriptomics.

IF 4.7 2区 医学 Q1 NEUROSCIENCES Journal of Physiology-London Pub Date : 2025-03-12 DOI:10.1113/JP287595
Suranjana Gupta, Michelle M Gee, Adam J H Newton, Lakshmi Kuttippurathu, Alison Moss, John D Tompkins, James S Schwaber, Rajanikanth Vadigepalli, William W Lytton
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Abstract

The intrinsic cardiac nervous system (ICNS), termed as the heart's 'little brain', is the final point of neural regulation of cardiac function. Studying the dynamic behaviour of these ICNS neurons via multiscale neuronal computer models has been limited by the sparsity of electrophysiological data. We developed and analysed a computational library of neuronal electrophysiological models based on single neuron transcriptomic data obtained from ICNS neurons. Each neuronal genotype was characterized by a unique combination of ion channels identified from the transcriptomic data, using a cycle threshold cutoff that ensured the electrical excitability of the neuronal models. The parameters of the ion channel models were grounded based on passive properties (resting membrane potential, input impedance and rheobase) to avoid biasing the dynamic behaviour of the model. Consistent with experimental observations, the emergent model dynamics showed phasic activity in response to the current clamp stimulus in a majority of neuronal genotypes (61%). Additionally, 24% of the ICNS neurons showed a tonic response, 11% were phasic-to-tonic with increasing current stimulation and 3% showed tonic-to-phasic behaviour. The computational approach and the library of models bridge the gap between widely available molecular-level gene expression and sparse cellular-level electrophysiology for studying the functional role of the ICNS in cardiac regulation and pathology. KEY POINTS: Computational models were developed of neuron electrophysiology from single-cell transcriptomic data from neurons in the heart's 'little brain': the intrinsic cardiac nervous system. The single-cell transcriptomic data were thresholded to select the ion channel combinations in each neuronal model. The library of neuronal models was constrained by the passive electrical properties of the neurons and predicted a distribution of phasic and tonic responses that aligns with experimental observations. The ratios of model-predicted conductance values are correlated with the gene expression ratios from transcriptomic data. These neuron models are a first step towards connecting single-cell transcriptomic data to dynamic, predictive physiology-based models.

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Journal of Physiology-London
Journal of Physiology-London 医学-神经科学
CiteScore
9.70
自引率
7.30%
发文量
817
审稿时长
2 months
期刊介绍: The Journal of Physiology publishes full-length original Research Papers and Techniques for Physiology, which are short papers aimed at disseminating new techniques for physiological research. Articles solicited by the Editorial Board include Perspectives, Symposium Reports and Topical Reviews, which highlight areas of special physiological interest. CrossTalk articles are short editorial-style invited articles framing a debate between experts in the field on controversial topics. Letters to the Editor and Journal Club articles are also published. All categories of papers are subjected to peer reivew. The Journal of Physiology welcomes submitted research papers in all areas of physiology. Authors should present original work that illustrates new physiological principles or mechanisms. Papers on work at the molecular level, at the level of the cell membrane, single cells, tissues or organs and on systems physiology are all acceptable. Theoretical papers and papers that use computational models to further our understanding of physiological processes will be considered if based on experimentally derived data and if the hypothesis advanced is directly amenable to experimental testing. While emphasis is on human and mammalian physiology, work on lower vertebrate or invertebrate preparations may be suitable if it furthers the understanding of the functioning of other organisms including mammals.
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