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

• Published in: BMC bioinformatics Vol. 26; no. 1; pp. 24 - 13
• Main Authors: Pastore, Stefan, Hillenbrand, Philipp, Molnar, Nils, Kovlyagina, Irina, Chongtham, Monika Chanu, Sys, Stanislav, Lutz, Beat, Tevosian, Margarita, Gerber, Susanne
• Format: Journal Article
• Language: English
• Published: London BioMed Central 21.01.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: 3D volumetric imaging; Accessibility; Algorithms; Animals; Annotations; Antibodies; Atlas alignment; Bioinformatics; Biomedical and Life Sciences; Brain - cytology; Brain cells; Brain research; Cell count; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data analysis; Fluorescence microscopy; Graphical user interface; Imaging, Three-Dimensional - methods; Life Sciences; Light; Light sheets; Measurement; Methods; Mice; Microarrays; Microscopy; Neurons - cytology; Observations; Python (Programming language); Software; Statistical analysis; Tissue clearing; Troubleshooting; User interface; User-Computer Interface; Visualization; Germany; United States > US; Tissue clearing; 3D volumetric imaging; Atlas alignment; Cell count
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-025-06039-x

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.