Isolation of functional murine naive and memory T cell subsets in as little as 15 minutes (P3387)
Bibliographic record
Abstract
Abstract Effective cellular immunity requires naïve CD4+ and CD8+ T cells to undergo activation, proliferation and differentiation into various effector cell populations following antigen exposure. CD4+ and CD8+ T cell activation induces the production of cytokines and cytolytic effector proteins which function to coordinate immune responses or directly eliminate infected cells. While most effector T cells become senescent and apoptotic, some effector T cells further differentiate into memory T cells which can provide long lasting immunity. Although the signals that regulate these processes have been widely investigated, continued efforts will eventually construct a definitive model for the role of T cells in adaptive immunity. In many cases, highly purified cells are required for these studies and current methods for their isolation from mice require either lengthy protocols or flow based cell sorting. We have recently developed three new column-free, immunomagnetic cell isolation kits to isolate untouched naïve and memory CD4+ T cells, and naïve CD8+ T cells in as little as 15 minutes. Purities of up to 98% can be achieved as assessed by CD44 and CD62L expression using the manual EasySep™ pour-off method or the fully automated RoboSep™ cell separator. The purified naïve and memory T cells can be immediately used for downstream assays and are fully functional as assessed by in vitro proliferation and cytokine production assays.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".