Sharp and Fast Dynamic Extraction and Tracking of Emitted Cellular Transients

Understanding neural correlates of brain function in neuroscience now largely involves detecting and analyzing transient signals from fluorescent sensors. Imaging technologies such as confocal and two-photon microscopy, along with onboard miniscopes, enable the visualization of neural activities and capture dynamic signals both ex vivo and in vivo . This includes monitoring Ca ²⁺ transients via the expression of genetically encoded sensors such as GCaMP in specific brain cells. Additionally, the advent of GPCR-based neurotransmitter sensors allows for imaging the release of neurotransmitters including glutamate and GABA, as well as neuromodulators such as dopamine or noradrenaline. These approaches however generate large, high-dimensional, spatiotemporally complex datasets, presenting significant challenges for signal detection and analysis. To overcome these challenges, we developed a versatile pipeline of Dynamic Extraction and Tracking of Emitted Cellular Transients (DETECT), which combines background denoising, object segmentation, and multi-object tracking. Our user-friendly, Python-based GUI offers a low-resource platform for efficient data analysis. Validated across various imaging modalities and biological models, DETECT provides a robust and comprehensive solution for analyzing complex imaging datasets in neuroscience research.