SUMMARY: The plasma membrane and accompanying cortex serve as major hubs of signal transduction and cytoskeletal activities that collectively regulate cell physiological processes such as migration, polarity, macropinocytosis, phagocytosis, and cytokinesis. Yet, dynamically tracking membrane-cortex associated protein or lipid kinetics from live-cell image series remains challenging, primarily due to the difficulty of accurately extracting and aligning the cell boundary between consecutive frames as the cell continuously deforms and moves. Here, we present Membrane Kymograph Generator, a cross-platform software that accepts multichannel time-lapse live-cell fluorescent imaging datasets and automates boundary tracking, inter-frame alignment, and intensity sampling along the boundary. The software implements a rotational offset minimization algorithm that aligns boundaries across consecutive frames by exhaustively searching for the optimal angular shift that minimizes point-to-point distances, while handling variations in boundary point counts due to cell shape changes. The software outputs kymographs representing spatiotemporal dynamics of membrane-associated proteins or biosensors, allows users to fine-tune visualization parameters through an interactive interface, and provides built-in correlation analysis tools for multi-channel datasets. Furthermore, a native Python API enables programmatic usage for batch processing and further downstream analysis. Validation tests demonstrated that the Membrane Kymograph Generator accurately tracks, visualizes, and quantitates the spatial kinetics of a wide array of membrane proteins and lipid biosensors over extended time periods, in a variety of cell types including Dictyostelium amoeba, human neutrophils, mouse macrophages, and mammalian cancer cells. The GUI-based software is user-friendly, requires no technical expertise, and significantly reduces the manual effort required for kymograph generation and analysis while ensuring high accuracy and reproducibility. AVAILABILITY AND IMPLEMENTATION: Membrane Kymograph Generator is free and open-source, licensed under GNU General Public License 3.0 or later. It can be installed on both x86-64 and AArch64/ARM64 computers running Windows, macOS, or any standard Linux distribution. The software is distributed as standalone installer files and portable executables targeting specific architectures and operating systems, requiring no dependency resolution. The source code, documentation/wiki, installers, portable binaries, and test data are freely available at https://github.com/tatsatb/membrane-kymograph-generator. The software can also be installed via PIP (package ID: membrane-kymograph, https://pypi.org/project/membrane-kymograph) and accessed programmatically via a built-in Python API. The source code is also archived on Zenodo (DOI: 10.5281/zenodo.20318834).

In the social amoeba Dictyostelium, cell motility is regulated through a signal transduction excitable network that interfaces with the cytoskeleton to control actin polymerization patterns. In turn, the cytoskeleton influences the signaling machinery via several feedback loops, but the nature and function of this feedback remain poorly understood. In this study, we use computational models to discern the essential role of complementary positive and negative feedback loops in polarizing cells. We contrast two potential mechanisms for the negative feedback: local inhibition and global inhibition. Our results indicate that both mechanisms can stabilize the leading edge and inhibit actin polymerization in other sites, preventing multipolarity. While some experimental perturbations align more closely with the local inhibition model, statistical analyses reveal its limited polarization potential within a wide excitability range. Conversely, global inhibition more effectively suppresses secondary and tertiary leading-edge formation, making it a more robust polarization mechanism. This raises an intriguing question: if local inhibition better replicates experimental observations but is less effective for polarization than local excitation and global inhibition, could there be a supplementary mechanism enhancing its polarization potential? To address this, we propose a novel mechanism involving the dynamic partitioning of back molecules which enhances communication between the front and back of the cell and can be leveraged by local feedback interactions between the cytoskeleton and signal transduction to improve polarization efficiency.

During the last decade, a consensus has emerged that the stochastic triggering of an excitable system drives pseudopod formation and subsequent migration of amoeboid cells. The presence of chemoattractant stimuli alters the threshold for triggering this activity and can bias the direction of migration. Though noise plays an important role in these behaviors, mathematical models have typically ignored its origin and merely introduced it as an external signal into a series of reaction-diffusion equations. Here we consider a more realistic description based on a reaction-diffusion master equation formalism to implement these networks. In this scheme, noise arises naturally from a stochastic description of the various reaction and diffusion terms. Working on a three-dimensional geometry in which separate compartments are divided into a tetrahedral mesh, we implement a modular description of the system, consisting of G-protein coupled receptor signaling (GPCR), a local excitation-global inhibition mechanism (LEGI), and signal transduction excitable network (STEN). Our models implement detailed biochemical descriptions whenever this information is available, such as in the GPCR and G-protein interactions. In contrast, where the biochemical entities are less certain, such as the LEGI mechanism, we consider various possible schemes and highlight the differences between them. Our simulations show that even when the LEGI mechanism displays perfect adaptation in terms of the mean level of proteins, the variance shows a dose-dependence. This differs between the various models considered, suggesting a possible means for determining experimentally among the various potential networks. Overall, our simulations recreate temporal and spatial patterns observed experimentally in both wild-type and perturbed cells, providing further evidence for the excitable system paradigm. Moreover, because of the overall importance and ubiquity of the modules we consider, including GPCR signaling and adaptation, our results will be of interest beyond the field of directed migration.

Chemotaxis involves the coordinated action of separable but interrelated processes: motility, gradient sensing, and polarization. We have hypothesized that these are mediated by separate modules that account for these processes individually and that, when combined, recreate most of the behaviors of chemotactic cells. Here, we describe a mathematical model where the modules are implemented in terms of reaction-diffusion equations. Migration and the accompanying changes in cellular morphology are demonstrated in simulations using a mechanical model of the cell cortex implemented in the level set framework. The central module is an excitable network that accounts for random migration. The response to combinations of uniform stimuli and gradients is mediated by a local excitation, global inhibition module that biases the direction in which excitability is directed. A polarization module linked to the excitable network through the cytoskeleton allows unstimulated cells to move persistently and, for cells in gradients, to gradually acquire distinct sensitivity between front and back. Finally, by varying the strengths of various feedback loops in the model we obtain cellular behaviors that mirror those of genetically altered cell lines.