An efficient new column‐free immunomagnetic isolation method for mouse CD4+CD25+ regulatory T cells
Bibliographic record
Abstract
Mouse CD4 + CD25 + regulatory T cells (Tregs) are extremely difficult to isolate due to their rareness and the absence of a unique marker that differentiates them from other cell types. Their isolation typically requires at least two steps, often including sorting by FACS, which can be time consuming and very expensive. We thus sought to develop a simple and efficient method for Treg isolation using column‐free immunomagnetic cell separation (EasySep®). The resulting two‐step method begins with depletion of non‐CD4 + T cells, and is followed by selection on CD25 whereby both CD4 + CD25 + and CD4+CD25 neg T cell fractions can be isolated to a high level of purity (90.3 ± 3.4% CD4 + CD25 + , n=14). This new method proved to be highly efficient as cell output was routinely well over 10e06 Tregs per spleen. Intracellular flow cytometric analysis of FOXP3 expression clearly demonstrated that isolated CD4 + CD25 + T cell fractions were highly enriched for FOXP3 expressing cells (n=4). In vitro culture assays also showed that isolated CD4 + CD25 + T cells were able to strongly suppress CD4 + CD25 neg T cell proliferation responses (n=3). We are currently working towards fully automating the CD25 positive selection step using the RoboSep® cell separator. This will add further convenience to a method that enables the efficient isolation of highly purified CD4 + CD25 + FOXP3 + Tregs from mouse lymphoid tissues.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.003 | 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".