Biology subjects

Pijnappels, D. A.

Publications and source records attributed to Pijnappels, D. A..

4 recordsLinked to original sources

bioRxiv2025

Atypical collective oscillatory activity in cardiac tissue uncovered by optogenetics

Many biological processes emerge as frequency-dependent responses to trains of external stimuli. Heart rhythm disturbances, i.e. cardiac arrhythmias, are important examples as they are often triggered by specific patterns of preceding stimuli. In this study, we investigated how ectopic arrhythmias can be induced by external stimuli in cardiac tissue containing a localised area of depolarisation. Using optogenetic in vitro experiments and in silico modelling, we systematically explored the dynamics of these arrhythmias, which are characterized by local oscillatory activity, by gradually altering the degree of depolarization in a predefined region. Our findings reveal a bi-stable system, in which transitions between oscillatory ectopic activity and a quiescent state can be precisely controlled, i.e. by adjusting the number and frequency of propagating waves through the depolarized area oscillations could be turned on or off. These frequency-dependent responses arise from collective mechanisms involving stable, non-self-oscillatory cells, contrasting with the typical role of self-oscillations in individual units within biophysical systems. To further generalize these findings, we demonstrated similar frequency selectivity and bi-stability in a simplified reaction-diffusion model. This suggests that complex ionic cell dynamics are not required to reproduce these effects; rather, simpler non-linear systems can replicate similar behaviour, potentially extending beyond the cardiac context.

biophysics↗

bioRxiv2023

Creation of predictive cardiac excitation models at the tissue scale with machine learning in augmented state space

The quick and easy creation of fast in-silico models of excitable media is, for instance, needed for patient-specific predictions in diagnostics and decision making in cardiac electrophysiology. We here present a model creation pipeline that not only generates new models quickly, but also only requires data from one easily measurable spatio-temporal variable. These data may, for instance, be an optical voltage mapping recording of the electrical waves in cardiac muscle tissue controlling the heart beat. We use exponential moving averages and compute standard deviations to extract additional states from this one variable to span a sparse discretised state space. The standard deviation in a neighbourhood can be used as a proxy for the gradient. To this augmented state space, we fit a simple polynomial model to predict the evolution of this one state variable. For optical voltage mapping data of human atrial myocyte monolayers electrically stimulated by stochastic burst pacing, the data-driven model is able to describe the excitation and recovery of the system, as well as wave propagation. The data-driven model is also able to predict spiral waves only based on data from focal waves. In contrast to conventional models, with our model creation pipeline new models can be generated in a matter of hours from experiment to fitting, rather than months or years. HighlightsO_LIModels of excitation waves can be created in minutes from experiment to fitting. C_LIO_LIOne variable in space and time is sufficient to create a working excitation model. C_LIO_LIA polynomial can predict excitation waves based on useful extracted features. C_LIO_LISpiral waves in heart muscle tissue can be predicted from focal wave data. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/540314v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@2165acorg.highwire.dtl.DTLVardef@9a1bfeorg.highwire.dtl.DTLVardef@1a3cd50org.highwire.dtl.DTLVardef@fc5698_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2020

Dynamic Loading of Human Engineered Heart Tissue Enhances Contractile Function and Drives Desmosome-linked Disease Phenotype

The role mechanical forces play in shaping the structure and function of the heart is critical to understanding heart formation and the etiology of disease but is challenging to study in patients. Engineered heart tissues (EHTs) incorporating human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes have the potential to provide insight into these adaptive and maladaptive changes in the heart. However, most EHT systems are unable to model both preload (stretch during chamber filling) and afterload (pressure the heart must work against to eject blood). Here, we have developed a new dynamic EHT (dyn-EHT) model that enables us to tune preload and have unconstrained fractional shortening of >10%. To do this, 3D EHTs are integrated with an elastic polydimethylsiloxane (PDMS) strip that provides mechanical pre- and afterload to the tissue in addition to enabling contractile force measurements based on strip bending. Our results demonstrate in wild-type EHTs that dynamic loading is beneficial based on the magnitude of the forces, leading to improved alignment, conduction velocity, and contractility. For disease modeling, we use hiPSC-derived cardiomyocytes from a patient with arrhythmogenic cardiomyopathy (ACM) due to mutations in desmoplakin. We demonstrate that manifestation of this desmosome-linked disease state requires the dyn-EHT conditioning and that it cannot be induced using 2D or standard 3D EHT approaches. Thus, dynamic loading strategy is necessary to provoke a disease phenotype (diastolic lengthening, reduction of desmosome counts, and reduced contractility), which are akin to primary endpoints of clinical disease, such as chamber thinning and reduced cardiac output. Single Sentence SummaryDevelopment of a dynamic mechanical loading platform to improve contractile function of engineered heart tissues and study cardiac disease progression.

bioengineering↗

bioRxiv2018

Optogenetics enables real-time spatiotemporal control over spiral wave dynamics in an excitable cardiac system

Propagation of non-linear waves is key to the functioning of diverse biological systems. Such waves can organize into spirals, rotating around a core, whose properties determine the overall wave dynamics. Theoretically, manipulation of a spiral wave core should lead to full spatiotemporal control over its dynamics. However, this theory lacks supportive evidence (even at a conceptual level), making it thus a long-standing hypothesis. Here, we propose a new phenomenological concept that involves artificially dragging spiral waves by their cores, to prove the afore-mentioned hypothesis in silico, with subsequent in vitro validation in optogenetically-modified monolayers of rat atrial cardiomyocytes. We thereby connect previously established, but unrelated concepts of spiral wave attraction, anchoring and unpinning to demonstrate that core manipulation, through controlled displacement of heterogeneities in excitable media, allows forced movement of spiral waves along pre-defined trajectories. Consequently, we impose real-time spatiotemporal control over spiral wave dynamics in a biological system.

bioinformatics↗