ClearFinder: a Python GUI for annotating cells in cleared mouse brain

Abstract Background Tissue clearing combined with light-sheet microscopy is gaining popularity among neuroscientists interested in unbiased assessment of their samples in 3D volume. However, the analysis of such data remains a challenge. ClearMap and CellFinder are tools for analyzing neuronal activ...

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Main Authors: Stefan Pastore, Philipp Hillenbrand, Nils Molnar, Irina Kovlyagina, Monika Chanu Chongtham, Stanislav Sys, Beat Lutz, Margarita Tevosian, Susanne Gerber
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Bioinformatics
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Online Access:https://doi.org/10.1186/s12859-025-06039-x
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author Stefan Pastore
Philipp Hillenbrand
Nils Molnar
Irina Kovlyagina
Monika Chanu Chongtham
Stanislav Sys
Beat Lutz
Margarita Tevosian
Susanne Gerber
author_facet Stefan Pastore
Philipp Hillenbrand
Nils Molnar
Irina Kovlyagina
Monika Chanu Chongtham
Stanislav Sys
Beat Lutz
Margarita Tevosian
Susanne Gerber
author_sort Stefan Pastore
collection DOAJ
description Abstract Background Tissue clearing combined with light-sheet microscopy is gaining popularity among neuroscientists interested in unbiased assessment of their samples in 3D volume. However, the analysis of such data remains a challenge. ClearMap and CellFinder are tools for analyzing neuronal activity maps in an intact volume of cleared mouse brains. However, these tools lack a user interface, restricting accessibility primarily to scientists proficient in advanced Python programming. The application presented here aims to bridge this gap and make data analysis accessible to a wider scientific community. Results We developed an easy-to-adopt graphical user interface for cell quantification and group analysis of whole cleared adult mouse brains. Fundamental statistical analysis, such as PCA and box plots, and additional visualization features allow for quick data evaluation and quality checks. Furthermore, we present a use case of ClearFinder GUI for cross-analyzing the same samples with two cell counting tools, highlighting the discrepancies in cell detection efficiency between them. Conclusions Our easily accessible tool allows more researchers to implement the methodology, troubleshoot arising issues, and develop quality checks, benchmarking, and standardized analysis pipelines for cell detection and region annotation in whole volumes of cleared brains.
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institution Kabale University
issn 1471-2105
language English
publishDate 2025-01-01
publisher BMC
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series BMC Bioinformatics
spelling doaj-art-de4d87bcabbe4524b5e024f3274f129c2025-01-26T12:54:55ZengBMCBMC Bioinformatics1471-21052025-01-0126111310.1186/s12859-025-06039-xClearFinder: a Python GUI for annotating cells in cleared mouse brainStefan Pastore0Philipp Hillenbrand1Nils Molnar2Irina Kovlyagina3Monika Chanu Chongtham4Stanislav Sys5Beat Lutz6Margarita Tevosian7Susanne Gerber8Institute for Human Genetics, University Medical Center Johannes Gutenberg UniversityInstitute of Physiological Chemistry, University Medical Center Johannes Gutenberg UniversityInstitute for Human Genetics, University Medical Center Johannes Gutenberg UniversityInstitute of Physiological Chemistry, University Medical Center Johannes Gutenberg UniversityLeibniz Institute for Resilience ResearchInstitute for Human Genetics, University Medical Center Johannes Gutenberg UniversityInstitute of Physiological Chemistry, University Medical Center Johannes Gutenberg UniversityInstitute of Physiological Chemistry, University Medical Center Johannes Gutenberg UniversityInstitute for Human Genetics, University Medical Center Johannes Gutenberg UniversityAbstract Background Tissue clearing combined with light-sheet microscopy is gaining popularity among neuroscientists interested in unbiased assessment of their samples in 3D volume. However, the analysis of such data remains a challenge. ClearMap and CellFinder are tools for analyzing neuronal activity maps in an intact volume of cleared mouse brains. However, these tools lack a user interface, restricting accessibility primarily to scientists proficient in advanced Python programming. The application presented here aims to bridge this gap and make data analysis accessible to a wider scientific community. Results We developed an easy-to-adopt graphical user interface for cell quantification and group analysis of whole cleared adult mouse brains. Fundamental statistical analysis, such as PCA and box plots, and additional visualization features allow for quick data evaluation and quality checks. Furthermore, we present a use case of ClearFinder GUI for cross-analyzing the same samples with two cell counting tools, highlighting the discrepancies in cell detection efficiency between them. Conclusions Our easily accessible tool allows more researchers to implement the methodology, troubleshoot arising issues, and develop quality checks, benchmarking, and standardized analysis pipelines for cell detection and region annotation in whole volumes of cleared brains.https://doi.org/10.1186/s12859-025-06039-xTissue clearingAtlas alignmentCell count3D volumetric imaging
spellingShingle Stefan Pastore
Philipp Hillenbrand
Nils Molnar
Irina Kovlyagina
Monika Chanu Chongtham
Stanislav Sys
Beat Lutz
Margarita Tevosian
Susanne Gerber
ClearFinder: a Python GUI for annotating cells in cleared mouse brain
BMC Bioinformatics
Tissue clearing
Atlas alignment
Cell count
3D volumetric imaging
title ClearFinder: a Python GUI for annotating cells in cleared mouse brain
title_full ClearFinder: a Python GUI for annotating cells in cleared mouse brain
title_fullStr ClearFinder: a Python GUI for annotating cells in cleared mouse brain
title_full_unstemmed ClearFinder: a Python GUI for annotating cells in cleared mouse brain
title_short ClearFinder: a Python GUI for annotating cells in cleared mouse brain
title_sort clearfinder a python gui for annotating cells in cleared mouse brain
topic Tissue clearing
Atlas alignment
Cell count
3D volumetric imaging
url https://doi.org/10.1186/s12859-025-06039-x
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