Biology subjects

Maleckar, M. M.

Publications and source records attributed to Maleckar, M. M..

6 recordsLinked to original sources

bioRxiv2023

Dual effects of the small-conductance Ca2+-activated K+ current on human atrial electrophysiology and Ca2+-driven arrhythmogenesis: an in silico study

By sensing changes in intracellular Ca2+, small-conductance Ca2+-activated K+ (SK) channels dynamically regulate the dynamics of the cardiac action potential (AP) on a beat-to-beat basis. Given their predominance in atria vs. ventricles, SK channels are considered a promising atrial-selective pharmacological target against atrial fibrillation (AF), the most common cardiac arrhythmia. However, the precise contribution of SK current (ISK) to atrial arrhythmogenesis is poorly understood, and may potentially involve different mechanisms that depend on species, heart rates, and degree of AF-induced atrial remodeling. Both reduced and enhanced ISK have been linked to AF. Similarly, both SK channel up- and downregulation have been reported in chronic AF (cAF) vs. normal sinus rhythm (nSR) patient samples. Here, we use our multi-scale modeling framework to obtain mechanistic insights into the contribution of ISK in human atrial myocyte electrophysiology. We simulate several protocols to quantify how ISK modulation affects the regulation of AP duration (APD), Ca2+ transient, refractoriness, and occurrence of alternans and delayed afterdepolarizations (DADs). Our simulations show that ISK activation shortens the APD and atrial effective refractory period, limits Ca2+ cycling, and slightly increases the propensity for alternans in both nSR and cAF conditions. We also show that increasing ISK counteracts DAD development by reducing the coupling between transmembrane potential and intracellular Ca2+. Taken together, our results suggest that increasing ISK in human atrial myocytes could promote reentry, while protecting against triggered activity. Depending on the leading arrhythmogenic mechanism, ISK inhibition may thus be a beneficial or detrimental anti-AF strategy.

physiology↗

bioRxiv2019

Improved computational identification of drug response using optical measurements of human stem cell derived cardiomyocytes in microphysiological systems

Cardiomyocytes derived from human induced pluripotent stem cells hold great potential for drug screening applications. However, their usefulness is limited by the relative immaturity of cells electro-physiological properties as compared to native cardiomyocytes in the adult human heart. In this work, we extend and improve on methodology to address this limitation, building on previously introduced computational procedures which predict drug effects for mature cells based on changes in optical measurements of action potentials and Ca2+ transients made in stem cell derived cardiac microtissues. This methodology quantifies ion channel changes through the inversion of data into a mathematical model, and maps this response to a mature phenotype through the assumption of functional invariance of fundamental intracellular and membrane channels during maturation.\n\nHere we utilize an updated action potential model to represent both immature and mature cells, apply an IC50-based model of dose-dependent drug effects, and introduce a continuation-based optimization algorithm for analysis of dose escalation measurements using five drugs with known effects. The improved methodology can identify drug induced changes more efficiently, and quantitate important metrics such as IC50 in line with published values. Consequently, the updated methodology is a step towards employing computational procedures to elucidate drug effects in mature cardiomyocytes for new drugs using stem cell-derived experimental tissues.

biophysics↗

bioRxiv2019

DLITE: Dynamic Local Intercellular Tension Estimation

The shape of cell-cell interfaces and the forces resulting from actomyosin interactions, mem-brane tension, or cell-cell adhesion are closely coupled. For example, the tight junction protein, ZO-1, forms a link between the force-bearing actin cortex and the rest of the tight junction protein (TJP) complex, regulating epithelial cell differentiation and the flux of solutes across epithelia. Here we introduce a method for Dynamic Local Intercellular Tension Estimation (DLITE) to computationally infer the evolution of cell-cell forces from a mechanical model of collective cell behaviour. This builds upon prior work in the field (CellFIT, Brodland et al., PloS one 9.6 (2014): e99116). We validate our estimated forces against those predicted by Surface Evolver simulations. Inferred tensions of a cell colony rearranging over time correlate better with the ground truth for our method (DLITE) than for prior methods intended for single time-points. DLITE is robust to both skeletonization errors and topological changes. Finally, we used DLITE in WTC-11 human induced pluripotent stem (hIPS) cells endogenously expressing ZO-1 GFP to find that major topo-logical changes in cell connectivity, e.g. mitosis, can result in an increase in tension. This suggests a correlation between the dynamics of cell-cell forces and colony rearrangement.

biophysics↗

bioRxiv2018

Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy

Understanding living cells as integrated systems, a challenge central to modern biology, is complicated by limitations of available imaging methods. While fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, slow, and damaging to cells. Here, we present a label-free method for predicting 3D fluorescence directly from transmitted light images and demonstrate that it can be used to generate multi-structure, integrated images.

cell biology↗

bioRxiv2017

Building a 3D Integrated Cell

We present a conditional generative model for learning variation in cell and nuclear morphology and predicting the location of subcellular structures from 3D microscopy images. The model generalizes well to a wide array of structures and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of the approach by producing and evaluating photo-realistic 3D cell images using the generative model, and show that the conditional nature of the model provides the ability to predict the localization of unobserved structures, given cell and nuclear morphology. We additionally explore the models utility in a number of applications, including cellular integration from multiple experiments and exploration of variation in structure localization. Finally, we discuss the model in the context of foundational and contemporary work and suggest forthcoming extensions.

cell biology↗

bioRxiv2017

Three dimensional cross-modal image inference: label-free methods for subcellular structure prediction

Fluorescence microscopy has enabled imaging of key subcellular structures in living cells; however, the use of fluorescent dyes and proteins is often expensive, time-consuming, and damaging to cells. Here, we present a tool for the prediction of fluorescently labeled structures in live cells solely from 3D brightfield microscopy images. We show the utility of this approach in predicting several structures of interest from the same static 3D brightfield image, and show that the same tool can prospectively be used to predict the spatiotemporal position of these structures from a bright-field time series. This approach could also be useful in a variety of application areas, such as cross-modal image registration, quantification of live cell imaging, and determination of cell state changes.

cell biology↗