Cherry, E. M. | bioRxiv Science

bioRxiv2023

Beyond Alternans: Detection of Higher-Order Periodicity in Ex-Vivo Human Ventricles Before Induction of Ventricular Fibrillation

BackgroundRepolarization alternans, defined as period-2 oscillation in the repolarization phase of the action potentials, is one of the cornerstones of cardiac electrophysiology as it provides a mechanistic link between cellular dynamics and ventricular fibrillation (VF). Theoretically, higher-order periodicities (e.g., period-4, period-8,...) are expected but have very limited experimental evidence. MethodsWe studied explanted human hearts, obtained from the recipients of heart transplantation at the time of surgery, using optical mapping technique with transmembrane voltage-sensitive fluorescent dyes. The hearts were stimulated at an increasing rate until VF was induced. The signals recorded from the right ventricle endocardial surface just before the induction of VF and in the presence of 1:1 conduction were processed using the Principal Component Analysis and a combinatorial algorithm to detect and quantify higher-order dynamics. ResultsA prominent and statistically significant 1:4 peak (corresponding to period-4 dynamics) was seen in three of the six studied hearts. Local analysis revealed the spatiotemporal distribution of higher-order periods. Period-4 was localized to temporally stable islands. Higher-order oscillations (period-5, 6, and 8) were transient and primarily occurred in arcs parallel to the activation isochrones. DiscussionWe present evidence of higher-order periodicities and the co-existence of such regions with stable non-chaotic areas in ex-vivo human hearts before VF induction. This result is consistent with the period-doubling route to chaos as a possible mechanism of VF initiation, which complements the concordant to discordant alternans mechanism. The presence of higher-order regions may act as niduses of instability that can degenerate into chaotic fibrillation.

biophysics↗

bioRxiv2019

Cell fate forecasting: a data assimilation approach to predict epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT) is a fundamental biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-{beta} (TGF{beta}) is a major and potent inducer of this cellular transition, which is comprised of transitions from an epithelial state to an intermediate or partial EMT state, then to a mesenchymal state. Using computational models to predict state transitions in a specific experiment is inherently difficult for many reasons, including model parameter uncertainty and the error associated with experimental observations. In this study, we demonstrate that a data-assimilation approach using an ensemble Kalman filter, which combines limited noisy observations with predictions from a computational model of TGF{beta}-induced EMT, can reconstruct the cell state and predict the timing of state transitions. We used our approach in proof-of-concept \"synthetic\" in silico experiments, in which experimental observations were produced from a known computational model with the addition of noise. We mimic parameter uncertainty in in vitro experiments by incorporating model error that shifts the TGF{beta} doses associated with the state transitions. We performed synthetic experiments for a wide range of TGF{beta} doses to investigate different cell steady state conditions, and we conducted a parameter study varying several properties of the data-assimilation approach, including the time interval between observations, and incorporating multiplicative inflation, a technique to compensate for underestimation of the model uncertainty and mitigate the influence of model error. We find that cell state can be successfully reconstructed in synthetic experiments, even in the setting of model error, when experimental observations are performed at a sufficiently short time interval and incorporate multiplicative inflation. Our study demonstrates a feasible proof-of-concept for a data assimilation approach to forecasting the fate of cells undergoing EMT.\n\nAuthor summaryEpithelial-mesenchymal transition (EMT) is a biological process in which an epithelial cell loses core epithelial-like characteristics, such as tight cell-to-cell adhesion, and gains core mesenchymal-like characteristics, such as an increase in cell motility. EMT is a multistep process, in which the cell undergoes transitions from epithelial state to a partial or intermediate state, and then from a partial state to a mesenchymal state. In this study, we apply data assimilation to improve prediction of these state transitions. Data assimilation is an approach well known in the weather forecasting community, in which experimental observations are iteratively combined with predictions from a dynamical model to provide an improved estimation of both observed and unobserved system states. We show that this data assimilation approach can reconstruct cell state measurements and predict state transition dynamics using noisy observations, while minimizing the error produced by the limitations and imperfections of the dynamical model.

cell biology↗