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.