Isolation of highly purified mouse CD4+CD25+FoxP3+ T regulatory cells in less than 45 minutes (IRC4P.476)
Bibliographic record
Abstract
Abstract Regulatory T cells (Tregs), a subset of lymphocytes, play a key role in maintaining peripheral tolerance, preventing autoimmune diseases and limiting chronic inflammatory diseases. They can be broadly classified into natural or adaptive (induced) Tregs. Natural Tregs (CD4+CD25+FoxP3+), develop in the thymus and emigrate to the periphery to help maintain immune homeostasis. Adaptive Tregs can be induced from CD25-negative naïve CD4+ T cells in the thymus and acquire CD25 (IL-2R alpha) and FoxP3 expression in the periphery after adequate antigenic stimulation. They are typically induced by chronic allergic inflammation and disease processes, such as autoimmunity. The isolation of highly purified Tregs is essential for advancing research in this field. To date this has been achieved by lengthy protocols or flow-based cell sorting. We have developed a rapid column-free one-step immunomagnetic cell separation method (EasySep™) to isolate Tregs (natural and induced) from mouse splenocytes in less than 45 minutes. CD25-PE-labelled cells are bound to magnetic particles using antibody complexes and separated using an EasySep™ magnet. The procedure can be automated using RoboSep™. Starting from 2.2±0.4% Tregs, purities of 84±3% (mean±SD, n=20) CD4+CD25+FoxP3+ cells can be achieved. This kit provides a new tool to study the mechanisms by which Tregs exert their influence, which has broad implications for the development of cell-based therapies for autoimmune disease and cancer.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".