A simple and fast method for the isolation of untouched mouse panDCs from spleen (100.43)
Bibliographic record
Abstract
Abstract Dendritic cells (DCs) control immune responses through their robust antigen presenting activity. Steady-state mouse spleen contains three major DC subsets with distinct functional and phenotypic properties including CD8+ and CD8− conventional DCs (cDCs), and plasmocytoid DCs (pDCs). These subsets are present at a very low frequency (< 4%). Typically, elaborate purification protocols such as FACS-based cell sorting or expansion in culture are needed to obtain enough DCs for subsequent studies. Here, we describe a negative selection method to isolate all DC subsets (panDC) from mouse spleen. This method uses immunomagnetic, column-free cell separation technology (EasySepTM). Briefly, non-DCs are labeled for depletion with biotinylated antibodies and cross-linked to magnetic particles using bispecific antibody complexes. The unwanted cells are then removed using an EasySepTM magnet. The selection steps can be fully automated using RoboSepTM . The panDCs are assessed by flow cytometry and defined as Lin−CD11c+ (cDCs) or Lin−CD11cloPDCA-1+ (pDCs). PanDC purities of 80 ± 7% (n=8) are achieved. The rare pDCs are enriched 36-fold with purity of 11.4 ± 1.4% as compared to 0.3 ± 0.1% in the start spleen. Both CD8+ and CD8− cDC subsets are represented in the cDC fraction. The freshly isolated DCs are not activated but upregulate maturation markers upon stimulation with LPS. This method enables fast, easy isolation of panDCs required for immune regulation studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.006 | 0.007 |
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".