SpotitPy: a semi-automated tool for object-based co-localization of fluorescent labels in microscopy images.
Co-localization
Fluorescent microscopy
Image analysis
Quantification
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
21 Oct 2022
21 Oct 2022
Historique:
received:
18
05
2022
accepted:
16
10
2022
entrez:
22
10
2022
pubmed:
23
10
2022
medline:
26
10
2022
Statut:
epublish
Résumé
In fluorescence microscopy, co-localization refers to the spatial overlap between different fluorescent labels in cells. The degree of overlap between two or more channels in a microscope may reveal a physical interaction or topological functional interconnection between molecules. Recent advances in the imaging field require the development of specialized computational analysis software for the unbiased assessment of fluorescently labelled microscopy images. Here we present SpotitPy, a semi-automated image analysis tool for 2D object-based co-localization. SpotitPy allows the user to select fluorescent labels and perform a semi-automated and robust segmentation of the region of interest in distinct cell types. The workflow integrates advanced pre-processing manipulations for de-noising and in-depth semi-automated quantification of the co-localized fluorescent labels in two different channels. We validated SpotitPy by quantitatively assessing the presence of cytoplasmic ribonucleoprotein granules, e.g. processing (P) bodies, under conditions that challenge mRNA translation, thus highlighting SpotitPy benefits for semi-automatic, accurate analysis of large image datasets in eukaryotic cells. SpotitPy comes in a command line interface or a simple graphical user interphase and can be used as a standalone application. Overall, we present a novel and user-friendly tool that performs a semi-automated image analysis for 2D object-based co-localization. SpotitPy can provide reproducible and robust quantifications for large datasets within a limited timeframe. The software is open-source and can be found in the GitHub project repository: ( https://github.com/alexiaales/SpotitPy ).
Sections du résumé
BACKGROUND
BACKGROUND
In fluorescence microscopy, co-localization refers to the spatial overlap between different fluorescent labels in cells. The degree of overlap between two or more channels in a microscope may reveal a physical interaction or topological functional interconnection between molecules. Recent advances in the imaging field require the development of specialized computational analysis software for the unbiased assessment of fluorescently labelled microscopy images.
RESULTS
RESULTS
Here we present SpotitPy, a semi-automated image analysis tool for 2D object-based co-localization. SpotitPy allows the user to select fluorescent labels and perform a semi-automated and robust segmentation of the region of interest in distinct cell types. The workflow integrates advanced pre-processing manipulations for de-noising and in-depth semi-automated quantification of the co-localized fluorescent labels in two different channels. We validated SpotitPy by quantitatively assessing the presence of cytoplasmic ribonucleoprotein granules, e.g. processing (P) bodies, under conditions that challenge mRNA translation, thus highlighting SpotitPy benefits for semi-automatic, accurate analysis of large image datasets in eukaryotic cells. SpotitPy comes in a command line interface or a simple graphical user interphase and can be used as a standalone application.
CONCLUSIONS
CONCLUSIONS
Overall, we present a novel and user-friendly tool that performs a semi-automated image analysis for 2D object-based co-localization. SpotitPy can provide reproducible and robust quantifications for large datasets within a limited timeframe. The software is open-source and can be found in the GitHub project repository: ( https://github.com/alexiaales/SpotitPy ).
Identifiants
pubmed: 36271369
doi: 10.1186/s12859-022-04988-1
pii: 10.1186/s12859-022-04988-1
pmc: PMC9587566
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
439Subventions
Organisme : HORIZON EUROPE Excellent Science
ID : GA 812830
Organisme : HORIZON EUROPE Excellent Science
ID : GA 812829
Organisme : HFRI
ID : 06204
Informations de copyright
© 2022. The Author(s).
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