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Published April 8, 2023 | Version v2
Preprint Open

A biologist's guide to the field of quantitative bioimaging

  • 1. Broad Institute of MIT and Harvard, Cambridge MA, USA; Department: Imaging Platform
  • 2. Live Cell Imaging Laboratory, Calvin, Phoebe and Joan Snyder Institute for Chronic Diseases, and Department of Physiology and Pharmacology, University of Calgary
  • 3. Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden, Germany, Department: National Center for Tumor Diseases, University Cancer Center, NCT-UCC,
  • 4. U of Minnesota Twin Cities, Minneapolis MN; Department: University of Minnesota Informatics Institute
  • 5. MicRoN Core, Harvard Medical School, Boston, MA, USA
  • 6. European Bioinformatic Institute (EMBL-EBI), European Molecular Biology Laboratory (EMBL), Cambridge, UK
  • 7. Baylor College of Medicine, Optical Imaging & Vital Microscopy (OiVM) Core, Houston, TX, USA
  • 8. Rockefeller University, Mammalian Cell Biology and Development, New York, NY, USA
  • 9. Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
  • 10. University of Wisconsin-Madison
  • 11. University of WIsconsin-Madison


Technological advancements in biology and microscopy have empowered a transition from bioimaging as an observational method to a quantitative one. However, as biologists are adopting quantitative bioimaging and these experiments become more complex, researchers need additional expertise to carry out this work in a rigorous and reproducible manner. Here we provide a navigational guide for experimental biologists to understand quantitative bioimaging from sample preparation through image acquisition, image analysis, and data interpretation. We discuss the interconnectedness of these steps, and for each, we provide general recommendations, key questions to consider, and links to high-quality open access resources for further learning. This synthesis of information will empower biologists to plan and execute rigorous quantitative bioimaging experiments efficiently.


Funding was provided by the National Institutes of Health (NIH COBA P41 GM135019 to BAC and KWE). This project has been made possible in part by grant number 2020-225720 to BAC from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation. VU is supported by EMBL internal funding. ELE is supported by a Morgridge Postdoctoral Fellowship.



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Journal article: 10.1371/journal.pbio.3002167 (DOI)