A biologist's guide to planning and performing quantitative bioimaging experiments.


Journal

PLoS biology
ISSN: 1545-7885
Titre abrégé: PLoS Biol
Pays: United States
ID NLM: 101183755

Informations de publication

Date de publication:
06 2023
Historique:
medline: 29 6 2023
pubmed: 27 6 2023
entrez: 27 6 2023
Statut: epublish

Résumé

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. This Essay provides a navigational guide for experimental biologists to aid understanding of quantitative bioimaging from sample preparation through to 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.

Identifiants

pubmed: 37368874
doi: 10.1371/journal.pbio.3002167
pii: PBIOLOGY-D-22-02784
pmc: PMC10298797
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

e3002167

Subventions

Organisme : NIGMS NIH HHS
ID : P41 GM135019
Pays : United States

Informations de copyright

Copyright: © 2023 Senft et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Rebecca A Senft (RA)

Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.

Barbara Diaz-Rohrer (B)

Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.

Pina Colarusso (P)

Live Cell Imaging Laboratory, University of Calgary, Calgary, Alberta, Canada.

Lucy Swift (L)

Live Cell Imaging Laboratory, University of Calgary, Calgary, Alberta, Canada.

Nasim Jamali (N)

Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.

Helena Jambor (H)

National Center for Tumor Diseases, University Cancer Center, NCT-UCC, Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden, Dresden, Germany.

Thomas Pengo (T)

Informatics Institute, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America.

Craig Brideau (C)

Live Cell Imaging Laboratory, University of Calgary, Calgary, Alberta, Canada.

Paula Montero Llopis (PM)

MicRoN Core, Harvard Medical School, Boston, Massachusetts, United States of America.

Virginie Uhlmann (V)

European Bioinformatic Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, United Kingdom.

Jason Kirk (J)

Optical Imaging & Vital Microscopy Core, Baylor College of Medicine, Houston, Texas, United States of America.

Kevin Andrew Gonzales (KA)

Mammalian Cell Biology and Development, Rockefeller University, New York, New York, United States of America.

Peter Bankhead (P)

Edinburgh Pathology, Centre for Genomic and Experimental Medicine, and CRUK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom.

Edward L Evans (EL)

Morgridge Institute and University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

Kevin W Eliceiri (KW)

Morgridge Institute and University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

Beth A Cimini (BA)

Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.

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Classifications MeSH