Chongtham, M. C. | Search bioRxiv

bioRxiv2024

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

BackgroundTissue 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. ResultsWe 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 report significant differences in total cell counts between CellFinder and ClearMap when cross-analyzing the same samples, underscoring the need for optimizing reproducibility within the field. ConclusionsOur 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.

bioinformatics↗

bioRxiv2020

Isolation of nuclei and downstream processing of cell-type-specific nuclei from micro-dissected mouse brain regions - techniques and caveats.

The mammalian brain consists of several structurally and functionally distinct regions equipped with an equally complex cell-type system. Due to its relevance in uncovering disease mechanisms, the study of cell-type-specific molecular signatures of different brain regions has increased. The rapid evolution of newer and cheaper sequencing techniques has also boosted the interest in cell-type-specific epigenetic studies. In fact, the nucleus holds most of the cells epigenetic information and is quite resistant to tissue dissociation processes as compared to cells. As such, nuclei are continually preferred over cells for epigenetic studies. However, the isolation of nuclei from cells is still a biochemically complex process, with every step affecting downstream results. Therefore, it is necessary to use protocols that fit the experimental design to yield nuclei of high quality and quantity. However, the current protocols are not suitable for nuclei isolation of small volumes of micro-dissected brain regions from individual mouse brains. Additionally, the caveats associated with centrifugation steps of nuclei extraction and the effects of different buffers have not been thoroughly investigated. Therefore, in this study, we describe an iodixanol based density gradient ultracentrifugation protocol suitable for micro-dissected brain regions from individual mice using ArccreERT2 (TG/WT).R26CAG-Sun1-sfGFP-Myc (M/WT or M/M). This mouse model shows sfGFP expression (sfGFP+) in the nuclear membrane of specific stimulus activated cells, thereby providing a good basis for the study - nuclei isolation and separation of cell-type-specific nuclei. The study also introduces new tools for rapid visualization and assessment of quality and quantity of nascent extracted nuclei. These tools were then used to examine critical morphological features of nuclei derived from different centrifugation methods and the use of different buffers to uncover underlying effects. Finally, to obtain cell-type-specific nuclei (sfGFP+ nuclei) from the isolated nuclei pool of high viscosity, an optimized protocol for fluorescence activated nuclei sorting (FANS) was established to speed up sorting. Additionally, we present a 1% PFA protocol for fixation of isolated nuclei for long term microscopic visualization.

cell biology↗