CD2 costimulation as a method to improve mouse T cell activation by soluble antibody complexes
Bibliographic record
Abstract
Abstract Immunology research using mouse T cells requires cell activation that is often achieved using anti-CD3/CD28 antibodies immobilized (imm) on beads or cultureware. This immobilization can be tedious, leading us to investigate mouse T cell activation using soluble (sol) antibody complexes. Purified mouse T cells were activated by immCD3/CD28 or solCD3/CD28 complexes for 2 days, resulting in an average of 85% and 68% CD25+ T cells by flow cytometry, respectively (n = 12). However, adding CD2 to the solCD3/CD28 complex increased the %CD25+ T cells to 99% after 2 days (n = 12), suggesting that CD2 enhances activation. Purified T cells activated by solCD3/CD28/CD2 complexes showed improved fold expansion (28-fold) after 7 - 8 days compared to activation by immCD3/CD28 (15-fold) or solCD3/CD28 (18-fold) complexes (n = 17). As well, purified CD4+ T cells activated by solCD3/CD28 complexes exhibited increased fold expansion from 1- to 20-fold after 8 days by adding CD2 to the complexes (n = 3). Lastly, we investigated how activation methods impact a 6-day polarization of naive CD4+ T cells to T helper subsets. Cells activated by solCD3/CD28 complexes resulted in 53% Th1, 29% Th2, or 67% Treg cells, whereas solCD3/CD28/CD2 complexes resulted in 77% Th1, 24% Th2, or 79% Treg cells (n = 6). In summary, these soluble ImmunoCult™ Mouse T Cell Activators effectively activate, expand, and polarize mouse T cell subsets, which is enhanced by CD2 costimulation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".