Selective isolation of mouse glial nuclei optimized for reliable downstream omics analyses
Bibliographic record
Abstract
ABSTRACT Background Isolation of cell types of interest from the brain for molecular applications presents several challenges, including cellular damage during tissue dissociation or enrichment procedures, and low cell number in the tissue in some cases. Techniques have been developed to enrich distinct cell populations using immunopanning or fluorescence activated cell/nuclei sorting. However, these techniques often involve fixation, immunolabeling and DNA staining steps, which could potentially influence downstream omics applications. New Method Taking advantage of readily available genetically modified mice with fluorescent-tagged nuclei, we describe a technique for the purification of cell-type specific brain nuclei, optimized to decrease sample preparation time and to limit potential artefacts for downstream omics applications. We demonstrate the applicability of this approach for the purification of glial cell nuclei and show that the resulting cell-type specific nuclei obtained can be used effectively for omics applications, including ATAC-seq and RNA-seq. Results We demonstrate excellent enrichment of fluorescently-tagged glial nuclei, yielding high quality RNA and chromatin. We identify several critical steps during nuclei isolation that help limit nuclei rupture and clumping, including quick homogenization, dilution before filtration and loosening of the pellet before resuspension, thus improving yield. Sorting of fluorescent nuclei can be achieved without fixation, antibody labelling, or DAPI staining, reducing potential artifactual results in RNA-seq and ATAC-seq analyses. We show that reproducible glial cell type-specific profiles can be obtained in transcriptomic and chromatin accessibility assays using this rapid protocol. Comparison with existing methods Our method allows for rapid enrichment of glial nuclei populations from the mouse brain with minimal processing steps, while still providing high quality RNA and chromatin required for reliable omics analyses. Conclusions We provide a reproducible method to obtain nucleic material from glial cells in the mouse brain with a quick and limited sample preparation. Highlights - Fast and easy isolation and sorting of glial nuclei from the mouse brain - Reproducible and versatile processing of enriched nuclei for omics applications
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".