dc.contributor.author
Schmied, Christopher
dc.contributor.author
Ebner, Michael
dc.contributor.author
Samsó, Paula
dc.contributor.author
Veen, Rozemarijn van der
dc.contributor.author
Haucke, Volker
dc.contributor.author
Lehmann, Martin
dc.date.accessioned
2024-11-04T14:14:44Z
dc.date.available
2024-11-04T14:14:44Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/45493
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-45205
dc.description.abstract
Background
Eukaryotic cells are highly compartmentalized by a variety of organelles that carry out specific cellular processes. The position of these organelles within the cell is elaborately regulated and vital for their function. For instance, the position of lysosomes relative to the nucleus controls their degradative capacity and is altered in pathophysiological conditions. The molecular components orchestrating the precise localization of organelles remain incompletely understood. A confounding factor in these studies is the fact that organelle positioning is surprisingly non-trivial to address e.g., perturbations that affect the localization of organelles often lead to secondary phenotypes such as changes in cell or organelle size. These phenotypes could potentially mask effects or lead to the identification of false positive hits. To uncover and test potential molecular components at scale, accurate and easy-to-use analysis tools are required that allow robust measurements of organelle positioning.
Results
Here, we present an analysis workflow for the faithful, robust, and quantitative analysis of organelle positioning phenotypes. Our workflow consists of an easy-to-use Fiji plugin and an R Shiny App. These tools enable users without background in image or data analysis to (1) segment single cells and nuclei and to detect organelles, (2) to measure cell size and the distance between detected organelles and the nucleus, (3) to measure intensities in the organelle channel plus one additional channel, (4) to measure radial intensity profiles of organellar markers, and (5) to plot the results in informative graphs. Using simulated data and immunofluorescent images of cells in which the function of known factors for lysosome positioning has been perturbed, we show that the workflow is robust against common problems for the accurate assessment of organelle positioning such as changes of cell shape and size, organelle size and background.
Conclusions
OrgaMapper is a versatile, robust, and easy-to-use automated image analysis workflow that can be utilized in microscopy-based hypothesis testing and screens. It effectively allows for the mapping of the intracellular space and enables the discovery of novel regulators of organelle positioning.
en
dc.format.extent
17 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Segmentation
en
dc.subject
Image analysis
en
dc.subject
Data analysis
en
dc.subject
Organelle position
en
dc.subject.ddc
500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie
dc.title
OrgaMapper: a robust and easy-to-use workflow for analyzing organelle positioning
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
220
dcterms.bibliographicCitation.doi
10.1186/s12915-024-02015-8
dcterms.bibliographicCitation.journaltitle
BMC Biology
dcterms.bibliographicCitation.number
1
dcterms.bibliographicCitation.volume
22
dcterms.bibliographicCitation.url
https://doi.org/10.1186/s12915-024-02015-8
refubium.affiliation
Biologie, Chemie, Pharmazie
refubium.affiliation.other
Institut für Chemie und Biochemie
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
1741-7007
refubium.resourceType.provider
WoS-Alert