An integrated workflow for 2D and 3D posture analysis during vestibular system testing in mice.

Introduction: Posture extraction from videos is fundamental to many real-world applications, including health screenings. In this study, we extend the utility and specificity of a well-established protocol, the balance beam, for examining balance and active motor coordination in adult mice of both sexes. Objectives: The primary objective of this study is to design a workflow for analyzing the postures of mice walking on a balance beam. Methods: We developed new tools and scripts based on the FluoRender architecture, which can interact with DeepLabCut (DLC) through Python code. Notably, twenty input videos were divided into four feature point groups (head, body, tail, and feet), based on camera positions relative to the balance beam (left and right), and viewing angles (90° and 45° from the beam). We determined key feature points on the mouse to track posture in a still video frame. We extracted a standard walk cycle (SWC) by focusing on foot movements, which were computed by a weighted average of the extracted walk cycles. The correlation of each walk cycle to the SWC was used as the weight. Results: We learned that positions of the camera angles significantly improved the performance of 2D pose estimation (90°) and 3D (45°). Comparing the SWCs from age-matched mice, we found a consistent pattern of supporting feet on the beam. Two feet were consistently on the beam followed by three feet and another three feet in a 2-3-3 pattern. However, this pattern can be mirrored among individual subjects. A subtle phase shift of foot movement was also observed from the SWCs. Furthermore, we compared the SWCs with speed values to reveal anomalies in mouse walk postures. Some anomalies can be explained as the start or finish of the traversal, while others may be correlated to the distractions of the test environment, which will need further investigation. Conclusion: Our posture analysis workflow improves the classical behavioral testing and analysis, allowing the detection of subtle, but significant differences in vestibular function and motor coordination.

Distinctive mechanisms of epilepsy-causing mutants discovered by measuring S4 movement in KCNQ2 channels.

Neuronal KCNQ channels mediate the M-current, a key regulator of membrane excitability in the central and peripheral nervous systems. Mutations in KCNQ2 channels cause severe neurodevelopmental disorders, including epileptic encephalopathies. However, the impact that different mutations have on channel function remains poorly defined, largely because of our limited understanding of the voltage-sensing mechanisms that trigger channel gating. Here, we define the parameters of voltage sensor movements in wt-KCNQ2 and channels bearing epilepsy-associated mutations using cysteine accessibility and voltage clamp fluorometry (VCF). Cysteine modification reveals that a stretch of eight to nine amino acids in the S4 becomes exposed upon voltage sensing domain activation of KCNQ2 channels. VCF shows that the voltage dependence and the time course of S4 movement and channel opening/closing closely correlate. VCF reveals different mechanisms by which different epilepsy-associated mutations affect KCNQ2 channel voltage-dependent gating. This study provides insight into KCNQ2 channel function, which will aid in uncovering the mechanisms underlying channelopathies.