Kavli Affiliate: Kira Poskanzer
| Authors: Xuelong Mi, Alex Bo-Yuan Chen, Daniela Duarte, Erin Carey, Charlotte R. Taylor, Philipp N. Braaker, Mark Bright, Rafael G. Almeida, Jing-Xuan Lim, Virginia M. Rutten, Wei Zheng, Mengfan Wang, Michael E. Reitman, Yizhi Wang, Kira E. Poskanzer, David A. Lyons, Axel Nimmerjahn, Misha B. Ahrens and Guoqiang Yu
| Summary:
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. AQuA2 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 of AQuA2 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.