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As in other excitable tissues, two classes of electrical signals are of fundamental importance to the functioning of smooth muscles: junction potentials, which arise from neurotransmission and represent the initiation of excitation (or in some instances inhibition) of the tissue, and spikes or action potentials, which represent the accomplishment of excitation and lead on to contractile activity. Unlike the case in skeletal muscle and in neurons, junction potentials and spikes in smooth muscle have been poorly understood in relation to the electrical properties of the tissue and in terms of their spatiotemporal spread within it. This owes principally to the experimental difficulties involved in making precise electrical recordings from smooth muscles and also to two inherent features of this class of muscle, ie, the syncytial organization of its cells and the distributed innervation they receive, which renders their biophysical analysis problematic. In this review, we outline the development of hypotheses and knowledge on junction potentials and spikes in syncytial smooth muscle, showing how our concepts have frequently undergone radical changes and how recent developments hold promise in unraveling some of the many puzzles that remain. We focus especially on computational models and signal analysis approaches. We take as illustrative examples the smooth muscles of two organs with distinct functional characteristics, the vas deferens and urinary bladder, while also touching on features of electrical functioning in the smooth muscles of other organs.

Action potential (AP) profiles vary based on the cell type, with cells of the same type typically producing APs with similar shapes. But in certain syncytial tissues, such as the smooth muscle of the urinary bladder wall, even a single cell is known to exhibit APs with diverse profiles. The origin of this diversity is not currently understood, but is often attributed to factors such as syncytial interactions and the spatial distribution of parasympathetic nerve terminals. Thus, the profile of an action potential is determined by the inherent properties of the cell, and influenced by its biophysical environment. The analysis of an AP profile, therefore, holds potential for constructing a biophysical picture of the cellular environment. An important feature of any AP is its depolarization to threshold, termed the AP foot, which holds information about the origin of the AP. Currently, there exists no established technique for the quantification of the AP foot. In this study we explore several possible approaches for this quantification, namely exponential fitting, evaluation of the radius of curvature, triangulation altitude, and various area based methods. We have also proposed a modified area-based approach, C(X,Y), which quantifies foot convexity as the area between the AP foot and a predefined line. We assess the robustness of the individual approaches over a wide variety of signals, mimicking AP diversity. The proposed C(X,Y) method is demonstrated to be superior to the other approaches, and we demonstrate its application on experimentally recorded AP profiles. The study reveals how the quantification of the AP foot could be related to the nature of the underlying synaptic activity, and help shed light on biophysical features such as the density of innervation, proximity of varicosities, size of the syncytium, or the strength of intercellular coupling within the syncytium. The work presented here is directed towards exploring these aspects, with further potential towards clinical electrodiagnostics by providing a better understanding of whole-organ biophysics.

Background: Urinary bladder detrusor smooth muscle cells exhibit spontaneous electrical activities comprising various signal types. Aims and Objectives: This article introduces and analyzes a rare category of signals observed in such activity, named spontaneous depolarization-linked hyperpolarization (sDLH). Materials and Methods: A mouse model was used in the study, where all the occurrences of sDLHs were pooled together from multiple intracellular recording sessions. Four features – (i) resting membrane potential (RMP) (R, in mV), (ii) depolarization amplitude (D, in mV), (iii) hyperpolarization amplitude (H, in mV), and (iv) time course of the hyperpolarization (T, in ms) – were evaluated from all sDLHs. Results: The analysis of results indicated that (a) the signals appear more frequently in cells with higher RMP, (b) the depolarization amplitudes seem to be distributed randomly and have no correlation with other features, (c) hyperpolarization amplitudes show two distinct clusters and exhibit strong correlation with the RMP, and (d) time course of hyperpolarization phase shows no distinct groups and is distributed in a window larger than that of any other signals seen in the intracellular recordings. With the help of the results obtained from the analysis, a hypothesis for the biophysical origin of these signals is proposed. Conclusions: This needs to be tested experimentally, and if proved right, would help extend the boundaries of our current understanding of the detrusor smooth muscle system.

Unlike other excitable cells, the urinary bladder smooth muscle cells exhibit a wide variety of spontaneous Action Potential (sAP) shapes. The complex network of gap-junction coupled smooth muscle cells and innervations makes it difficult to comprehend the origin of such a shape diversity. It is expected that the shape of the sAPs observed intracellularly from a smooth muscle cell give hints about the syncytial properties of the tissue in its neighborhood. In this work, four major features - foot convexity, after hyperpolarization, after depolarization, and extended after hyperpolarization - which define the shape of sAPs were identified. Algorithms were designed to automatically detect the presence of each features. The proposed algorithms were able to detect the features with accuracies greater than 70%. Further, a scheme to label the sAPs based on the presence/absence of the features is also proposed. The labels could be used to classify the sAPs into different groups.

The wide variety of action potential shapes observed in the spontaneous electrical activities of the the urinary bladder smooth muscle tissue (detrusor) is difficult to comprehend. the Experimental exploration of the same is near impossible because of the small size of the smooth muscle cells and the heterogeneous syncytial arrangement of interconnected smooth muscle cells and their complex innervation. In this paper, an approach to uncover the biophysical environment of a smooth muscle cell based on the analysis of the intracellularly recorded spontaneous electrical activities is presented. From the recordings taken from 20 smooth muscle cells, templates were formed which represent the types of shapes seen in the spontaneous activity of the detrusor. The signals expressed by the individual muscle cells are then classified into different groups represented by the above templates. The statistics of the signals in each group, expressed by the cells are then used to classify the cells types. Four categories of cells are thus identified in this study. It is expected that the cells belonging to a single category have identical biophysical environments. Based on the types of shapes and their frequencies expressed by the cells belonging to each of the categories, some properties of the biophysical environment could be inferred.

Voiding of the urinary bladder depends on contraction of the smooth muscle of its wall, known as detrusor muscle, and this in turn relies on electrical signals generated in the muscle cells. The exact shapes of these signals contain important information about bladder biophysics, but are poorly understood. We present an analysis of detrusor signals using a vector quantization technique based on a modified k-means clustering algorithm for the automatic detection and classification of the signals. We find that our procedure is able to sort the signals from a mixed pool into three predefined classes with an overall sensitivity of 0.9 and a specificity of 0.97. The various features of the signals belonging to an example class are evaluated for inter-feature correlation, and these correlations appear to be consistent with certain hypotheses about the mechanism of generation of the signals. Our work offers a novel approach to analyzing intracellularly recorded signals and inferring muscle biophysics at the cellular level.