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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Format: | Article |
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BMC
2025-01-01
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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. |
format | Article |
id | doaj-art-de4d87bcabbe4524b5e024f3274f129c |
institution | Kabale University |
issn | 1471-2105 |
language | English |
publishDate | 2025-01-01 |
publisher | BMC |
record_format | Article |
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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