Optical interrogation of cellular electrical activity is a crucial tool for understanding how cells function and communicate in complex networks. Scientists often use voltage-sensitive dyes to measure the excitability of cells, but these dyes can interfere with cellular function. Label-free techniques offer a way to measure electrical activity without using external probes. In this study, researchers found that second-harmonic generation from live cells is highly sensitive to changes in transmembrane potential by electrode control, making it a promising label-free approach to measure electrical activity in more complex cellular networks. This research provides a promising framework for a non-invasive label-free tool to measure electrical activity in cells.
Calcium imaging is a widely used technique in neuroscience permitting the simultaneous monitoring of electro- physiological activity of hundreds of neurons at single cell resolution. Identification of neuronal activity requires rapid and reliable image analysis techniques, especially when neurons fire and move simultaneously over time. Traditionally, image segmentation is performed to extract individual neurons in the first frame of a calcium sequence. Thereafter, the mean intensity is calculated from the same region of interest in each frame to infer calcium signals. However, when cells move, deform and fire, this segmentation on its own generates artefacts and therefore biased neuronal activity. Therefore, there is a pressing need to develop a more efficient cell tracking technique. We hereby present a novel vision-based cell tracking scheme using a thin-plate spline deformable model. The thin-plate spline warping is based on control points detected using the Fast from Accelerated Segment Test descriptor and tracked using the Lucas-Kanade optical flow. Our method is able to track neurons in calcium time-series, even when there are large changes in intensity, such as during a firing event. The robustness and efficiency of the proposed approach is validated on real calcium time-lapse images of a neuronal population.
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