دورية أكاديمية
Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals.
| العنوان: | Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals. |
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| المؤلفون: | Mi X; Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.; These authors contributed equally., Chen AB; 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.; These authors contributed equally., Duarte D; Waitt Advanced Biophotonics Center, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA., Carey E; Waitt Advanced Biophotonics Center, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA., Taylor CR; Department of Biochemistry & Biophysics, University of California, San Francisco, CA, USA.; Neuroscience Graduate Program, University of California, San Francisco, CA, USA., Braaker PN; Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh BioQuarter, Edinburgh EH16 4SB, UK., Bright M; Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA., Almeida RG; Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh BioQuarter, Edinburgh EH16 4SB, UK., Lim JX; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA., Ruetten VMS; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.; Gatsby Computational Neuroscience Unit, UCL, London W1T 4JG, USA., Zheng W; Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA., Wang M; Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA., Reitman ME; Department of Biochemistry & Biophysics, University of California, San Francisco, CA, USA.; Neuroscience Graduate Program, University of California, San Francisco, CA, USA., Wang Y; Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA., Poskanzer KE; Department of Biochemistry & Biophysics, University of California, San Francisco, CA, USA.; Neuroscience Graduate Program, University of California, San Francisco, CA, USA.; Kavli Institute for Fundamental Neuroscience, San Francisco, CA, USA., Lyons DA; Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh BioQuarter, Edinburgh EH16 4SB, UK., Nimmerjahn A; Waitt Advanced Biophotonics Center, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA., Ahrens MB; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA., Yu G; Department of Automation, Tsinghua University, Beijing 100084, China.; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.; Lead contact. |
| المصدر: | BioRxiv : the preprint server for biology [bioRxiv] 2024 Jun 01. Date of Electronic Publication: 2024 Jun 01. |
| نوع المنشور: | Journal Article; Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE |
| مستخلص: | 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, and imaging modalities. 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. Competing Interests: DECLARATION OF INTERESTS The authors declare no competing interests. |
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| معلومات مُعتمدة: | R01 MH110504 United States MH NIMH NIH HHS; R01 MH121446 United States MH NIMH NIH HHS; U19 NS123719 United States NS NINDS NIH HHS; United Kingdom WT_ Wellcome Trust; R01 NS099254 United States NS NINDS NIH HHS; U19 NS112959 United States NS NINDS NIH HHS |
| فهرسة مساهمة: | Keywords: AQuA2; astrocytes; cell interaction analysis; functional units; glial cells; image analysis; machine learning; molecular spatiotemporal signals; neurons; time-lapse imaging |
| تواريخ الأحداث: | Date Created: 20240520 Latest Revision: 20240613 |
| رمز التحديث: | 20240613 |
| مُعرف محوري في PubMed: | PMC11100599 |
| DOI: | 10.1101/2024.05.02.592259 |
| PMID: | 38766026 |
| قاعدة البيانات: | MEDLINE |
| DOI: | 10.1101/2024.05.02.592259 |
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