More is better: A simple antibody-based strategy for recovering all major mouse brain cell types from multiplexed single-cell RNAseq samples
Bibliographic record
Abstract
ABSTRACT Single-cell RNA sequencing (scRNAseq) is a powerful yet costly technique for studying cellular diversity within the complexity of organs and tissues. Here, we sought to establish an effective multiplexing strategy for the adult mouse brain that could allow multiple experimental groups to be pooled into a single sample for sequencing, reducing costs, increasing data yield, and eliminating batch effects. We first describe an optimized cold temperature single-cell dissociation protocol that permits isolation of a high yield and viability of brain cells from the adult mouse. Cells isolated using this protocol were then screened by flow cytometry using a panel of antibodies, allowing identification of a single antibody, anti-Thy1.2, that can tag the vast majority of isolated mouse brain cells. We then used this primary antibody against a “universal” neural target, together with secondary antibodies carrying sample-specific oligonucleotides and the BD Rhapsody single-cell system and show that multiple adult mouse brain samples can be pooled into a single multiplexed run for scRNAseq. Bioinformatic analyses enable efficient demultiplexing of the sequenced pooled brain sample, with high tagging efficiency and precise annotation and clustering of brain cell populations. The efficiency and flexibility of the cell dissociation protocol and the two-step multiplexing strategy simplifies experimental design, optimizes reagent usage, eliminates sequencing batch effects and reduces overall experimental costs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".