Explore the vast range of titles available.

Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux. Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

Cellular electrophysiology underpins fields from basic science in neurology, cardiology, and oncology to safety critical applications for drug safety testing, risk assessment of rare mutations, and models based on cellular electrophysiology data even guide clinical interventions. Patch‐clamp voltage clamp is the gold standard for measuring ionic current dynamics that explain cellular electrophysiology, but recordings can be influenced by artifacts introduced by the measurement process. A computational approach is developed, validated through electrical model cell experiments, to explain and predict intricate artifacts in voltage‐clamp experiments. Applied to various cardiac fast sodium current measurements, the model resolved artifacts in the experiments by coupling observed current with simulated membrane voltage, explaining some typically observed shifts and delays in recorded currents. It is shown that averaging data for current‐voltage relationships can introduce biases comparable to effect sizes reported for disease‐causing mutations. The computational pipeline provides improved assessment and interpretation of voltage‐clamp experiments, correcting, and enhancing understanding of ion channel behavior. Patch‐clamp experiments are fundamental to the measurement and understanding of ion currents that underlie physiological functions from neural activity to muscle contraction. Via a mathematical model of the experimental apparatus, this article highlights how artifact effects can be as large as some reported mutation and drug effects, and enables computational models to allow for artifacts when calibrating to experimental data.

Cellular identity is often inferred from molecular markers, while function is measured independently, obscuring how these dimensions align at single-cell resolution. In cardiomyocytes, this disconnect is especially limiting, as calcium dynamics and subtype markers are typically assessed in bulk or separate cells then averaged across populations. In human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), this gap has limited our ability to determine whether heterogeneity in electrophysiology and calcium handling reflects noise, maturation, or structured biological states. This lack of clarity is due in part to the lack of methods that directly link live functional measurements with molecular identity at single-cell resolution. Here, we introduce CARBONITE (Calcium Recordings Before Identification by Expression), a scalable single-cell imaging framework that pairs live calcium dynamics with protein expression and spatial phenotyping. Using high-content imaging in 96-well plates, CARBONITE integrates per-cell calcium transient features with immunofluorescent identification applied to cardiomyocyte subtype markers, enabling quantitative functional-molecular mapping within the same cells. Applying CARBONITE to mixed human induced pluripotent stem cell derived cardiomyocyte (iPSC-CM) populations reveals heterogeneity in calcium transient dynamics and marker expression within individual wells. Canonical atrial and ventricular markers capture only a subset of functional variability. Notably, calcium transient shape segregates cells into two discrete functional states that exhibit perinuclear ANP (atrial marker) enrichment and nucleation state (i.e., mono- vs. binucleation). Notably, these groups show know relationship to MYL2 (ventricular marker) expression. Binucleated cells are more likely to exhibit a spike-like calcium transient, identifying nucleation as a dominant and previously underappreciated axis of cardiomyocyte identity influencing calcium function. Together, these results establish CARBONITE as a functional multimodal single-cell platform that reveals organizational principles of cell identity, providing a foundation for dissecting functional niches in development and disease.

Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux (https://github.com/saucermanlab/Netflux). Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

As a renewable, easily accessible, human-derived in vitro model, human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) are a promising tool for studying arrhythmia-related factors, including cardiotoxicity and congenital proarrhythmia risks. An oft-mentioned limitation of iPSC-CMs is the abundant cell-to-cell variability in recordings of their electrical activity. Here, we develop a new method, rapid ionic current phenotyping (RICP), that utilizes a short (10 s) voltage clamp protocol to quantify cell-to-cell heterogeneity in key ionic currents. We correlate these ionic current dynamics to action potential recordings from the same cells and produce mechanistic insights into cellular heterogeneity. We present evidence that the L-type calcium current is the main determinant of upstroke velocity, rapid delayed rectifier K+ current is the main determinant of the maximal diastolic potential, and an outward current in the excitable range of slow delayed rectifier K+ is the main determinant of action potential duration. We measure an unidentified outward current in several cells at 6 mV that is not recapitulated by iPSC-CM mathematical models but contributes to determining action potential duration. In this way, our study both quantifies cell-to-cell variability in membrane potential and ionic currents, and demonstrates how the ionic current variability gives rise to action potential heterogeneity. Based on these results, we argue that iPSC-CM heterogeneity should not be viewed simply as a problem to be solved but as a model system to understand the mechanistic underpinnings of cellular variability.As a renewable, easily accessible, human-derived in vitro model, human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) are a promising tool for studying arrhythmia-related factors, including cardiotoxicity and congenital proarrhythmia risks. An oft-mentioned limitation of iPSC-CMs is the abundant cell-to-cell variability in recordings of their electrical activity. Here, we develop a new method, rapid ionic current phenotyping (RICP), that utilizes a short (10 s) voltage clamp protocol to quantify cell-to-cell heterogeneity in key ionic currents. We correlate these ionic current dynamics to action potential recordings from the same cells and produce mechanistic insights into cellular heterogeneity. We present evidence that the L-type calcium current is the main determinant of upstroke velocity, rapid delayed rectifier K+ current is the main determinant of the maximal diastolic potential, and an outward current in the excitable range of slow delayed rectifier K+ is the main determinant of action potential duration. We measure an unidentified outward current in several cells at 6 mV that is not recapitulated by iPSC-CM mathematical models but contributes to determining action potential duration. In this way, our study both quantifies cell-to-cell variability in membrane potential and ionic currents, and demonstrates how the ionic current variability gives rise to action potential heterogeneity. Based on these results, we argue that iPSC-CM heterogeneity should not be viewed simply as a problem to be solved but as a model system to understand the mechanistic underpinnings of cellular variability.

The gene regulatory networks (GRNs) that control early heart formation are beginning to be understood, but lineage-specific GRNs remain largely undefined. We investigated networks controlled by the vital transcription factor MEF2C, with a time course of single-nucleus RNA- and ATAC-sequencing in wild-type and -null embryos. We identified a \"posteriorized\" cardiac gene signature and chromatin landscape in the absence of MEF2C. Integrating our multiomics data in a deep learning-based model, we constructed developmental trajectories for each of the outflow tract, ventricular, and inflow tract segments, and alterations of these in -null embryos. We computationally identified segment-specific MEF2C-dependent enhancers, with activity in the developing zebrafish heart. Finally, using inferred GRNs we discovered that the -null heart malformations are partly driven by increased activity of the nuclear hormone receptor NR2F2. Our results delineate lineage-specific GRNs in the early heart tube and provide a generalizable framework for dissecting transcriptional networks governing developmental processes.

While atrial septal defects (ASDs) and atrial fibrillation (AF) present differently, there is evidence that they share some genetic basis. Here, we used directed differentiation of human induced pluripotent stem cells into atrial or ventricular cardiomyocytes (CMs) to delineate gene regulatory networks (GRNs) that define each identity. We uncovered accessible chromatin regions, transcription factor motifs and key regulatory nodes specific to, or shared by, both CM types, including the transcription factor , which is linked to genetic susceptibility of ASDs and AF in humans. Complete loss resulted in a near absence of atrial CMs with a concomitant increase in the abundance of other cell types. Reduced dosage of TBX5 in human atrial CMs caused cellular, electrophysiologic and molecular phenotypes consistent with features of atrial CM dysfunction. This included dose-dependent aberrant accessibility of many chromatin regions and perturbation of gene regulatory networks of atrial CM identity. These results suggest that, in addition to stemming from ion channel or extracellular matrix dysfunction, atrial diseases such as ASDs or AF may result from disruptions of atrial CM identity.

Cellular electrophysiology is the foundation of many fields, from basic science in neurology, cardiology, oncology to safety critical applications for drug safety testing, clinical phenotyping, etc. Patch-clamp voltage clamp is the gold standard technique for studying cellular electrophysiology. Yet, the quality of these experiments is not always transparent, which may lead to erroneous conclusions for studies and applications. Here, we have developed a new computational approach that allows us to explain and predict the experimental artefacts in voltage-clamp experiments. The computational model captures the experimental procedure and its inadequacies, including: voltage offset, series resistance, membrane capacitance and (imperfect) amplifier compensations, such as series resistance compensation and supercharging. The computational model was validated through a series of electrical model cell experiments. Using this computational approach, the artefacts in voltage-clamp experiments of cardiac fast sodium current, one of the most challenging currents to voltage clamp, were able to be resolved and explained through coupling the observed current and the simulated membrane voltage, including some typically observed shifts and delays in the recorded currents. We further demonstrated that the typical way of averaging data for current-voltage relationships would lead to biases in the peak current and shifts in the peak voltage, and such biases can be in the same order of magnitude as those differences reported for disease-causing mutations. Therefore, the presented new computational pipeline will provide a new standard of assessing the voltage-clamp experiments and interpreting the experimental data, which may be able to rectify and provide a better understanding of ion channel mutations and other related applications.

New therapeutic compounds go through a preclinical drug cardiotoxicity screening process that is overly conservative and provides limited mechanistic insight, leading to the misclassification of potentially beneficial drugs as proarrhythmic. There is a need to develop a screening paradigm that maintains this high sensitivity, while ensuring non-cardiotoxic compounds pass this phase of the drug approval process. In this study, we develop an in vitro-in silico pipeline using human induced stem-cell derived cardiomyocytes (iPSC-CMs) to address this problem. The pipeline includes a model-guided optimization that produces a voltage-clamp (VC) protocol to determine drug block of seven cardiac ion channels. Such VC data, along with action potential (AP) recordings, were acquired from iPSC-CMs before and after treatment with a control solution or a low-, intermediate-, or high-risk drug. We identified significant AP prolongation (a proarrhythmia indicator) in two high-risk drugs and, from the VC data, determined strong ion channel blocks that led to the AP changes. The VC data also uncovered an undocumented funny current (If) block by quinine, which we confirmed with experiments using a HEK-293 expression line. We present a new approach to cardiotoxicity screening that simultaneously evaluates proarrhythmia risk (e.g. AP prolongation) and mechanism (e.g. channel block) from iPSC-CMs. Competing Interest Statement The authors have declared no competing interest.

The transcription factor MEF2C plays a critical role in the development of the linear heart tube, but the specific transcriptional networks controlled by MEF2C remain largely undefined. To address this, we performed combined single-nucleus RNA-and ATAC-sequencing on wild type and MEF2C-null embryos at distinct stages of development. We identified a broadly “posteriorized” cardiac gene signature and chromatin landscape throughout the heart tube in the absence of MEF2C. By integrating our gene expression and chromatin accessibility data in a deep-learning based model, we were able to construct developmental trajectories for each of the outflow tract, ventricular, and inflow tract lineages and determined how each of these segment-specific trajectories were distinctly altered in the MEF2C-null embryos. We computationally identified potential segment-specific MEF2C-dependent enhancers, and from these candidates, identified novel enhancers with activity in the developing heart tube using transgenesis in zebrafish. Finally, using inferred gene regulatory networks we discovered a genetic interaction between Mef2c and the atrial nuclear hormone receptor Nr2f2 , revealing that the MEF2C-null heart malformations are partly driven by a transcriptional network with increased NR2F2 activity. These studies not only provide a rich description of the genomic regulation of early heart tube development, but provide a generalizable framework for using genetic mutants to dissect the transcriptional networks that govern developmental processes.