@article {69422,
	title = {Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals},
	journal = {bioRxiv},
	year = {2024},
	month = {2024 May 07},
	abstract = {<p>Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful patterns embedded within complex and rich data sources. Here, we introduce Activity Quantification and Analysis (AQuA2), a fast, accurate and versatile data analysis platform built upon advanced machine learning techniques. It decomposes complex live imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a range of biosensors (calcium, norepinephrine, ATP, acetylcholine, dopamine), cell types (astrocytes, oligodendrocytes, microglia, neurons), organs (brains and spinal cords), animal models (zebrafish and mouse), and imaging modalities (confocal, two-photon, light sheet). As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, and distinct sensorimotor signal propagation patterns in the mouse spinal cord.</p>
},
	doi = {10.1101/2024.05.02.592259},
	url = {https://www.biorxiv.org/content/early/2024/05/06/2024.05.02.592259},
	author = {Xuelong Mi and Alex Bo-Yuan Chen and Daniela Duarte and Erin Carey and Charlotte R. Taylor and Philipp N. Braaker and Mark Bright and Rafael G. Almeida and Jing-Xuan Lim and Virginia M. Rutten and Wei Zheng and Mengfan Wang and Michael E. Reitman and Yizhi Wang and Kira E. Poskanzer and David A. Lyons and Axel Nimmerjahn and Misha B. Ahrens and Guoqiang Yu}
}
