Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals.

Journal: Cell
Published Date: Apr 8, 2025

Abstract

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 spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. 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 wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord.

Authors

  • Xuelong Mi
    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
  • Alex Bo-Yuan Chen
    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA; Graduate Program in Neuroscience, Harvard Medical School, Boston, MA 02115, USA.
  • Daniela Duarte
    Waitt Advanced Biophotonics Center, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
  • Erin Carey
    Waitt Advanced Biophotonics Center, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
  • Charlotte R Taylor
    Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
  • Philipp N Braaker
    Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh BioQuarter, Edinburgh EH16 4SB, UK.
  • Mark Bright
    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
  • Rafael G Almeida
    Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh BioQuarter, Edinburgh EH16 4SB, UK.
  • Jing-Xuan Lim
    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
  • Virginia M S Ruetten
    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Gatsby Computational Neuroscience Unit, UCL, London W1T 4JG, UK.
  • Yizhi Wang
    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
  • Mengfan Wang
    School of Chemical Engineering and Technology, State Key Laboratory of Chemical Engineering, Tianjin University Tianjin 300350 P. R. China mwang@tju.edu.cn.
  • Weizhan Zhang
    Department of Automation, Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.
  • Wei Zheng
    School of Computer Engineering, Jinling Institute of Technology, Nanjing, 211169, China. zhengwei@jit.edu.cn.
  • Michael E Reitman
    Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
  • Yongkang Huang
    IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; New Cornerstone Science Laboratory, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China.
  • Xiaoyu Wang
    Department of Statistics Florida State University Tallahassee, FL, USA.
  • Lei Li
    Department of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
  • HanFei Deng
    State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200032, China.
  • Song-Hai Shi
    IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; New Cornerstone Science Laboratory, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China.
  • Kira E Poskanzer
    Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA; Kavli Institute for Fundamental Neuroscience, San Francisco, CA, USA.
  • David A Lyons
    Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh BioQuarter, Edinburgh EH16 4SB, UK.
  • Axel Nimmerjahn
    Waitt Advanced Biophotonics Center, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA. Electronic address: animmerj@salk.edu.
  • Misha B Ahrens
    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.
  • Guoqiang Yu

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