Performance of serum-supplemented and serum-free media in IFNγ Elispot Assays for human T cells
Bibliographic record
Abstract
The choice of serum for supplementation of media for T cell assays and in particular, Elispot has been a major challenge for assay performance, standardization, optimization, and reproducibility. The Assay Working Group of the Cancer Vaccine Consortium (CVC-CRI) has recently identified the choice of serum to be the leading cause for variability and suboptimal performance in large international Elispot proficiency panels. Therefore, a serum task force was initiated to compare the performance of commercially available serum-free media to laboratories' own medium/serum combinations. The objective of this project was to investigate whether a serum-free medium exists that performs as well as lab-own serum/media combinations with regard to antigen-specific responses and background reactivity in Elispot. In this way, a straightforward solution could be provided to address the serum challenge. Eleven laboratories tested peripheral blood mononuclear cells (PBMC) from four donors for their reactivity against two peptide pools, following their own Standard Operating Procedure (SOP). Each laboratory performed five simultaneous experiments with the same SOP, the only difference between the experiments was the medium used. The five media were lab-own serum-supplemented medium, AIM-V, CTL, Optmizer, and X-Vivo. The serum task force results demonstrate compellingly that serum-free media perform as well as qualified medium/serum combinations, independent of the applied SOP. Recovery and viability of cells are largely unaffected by serum-free conditions even after overnight resting. Furthermore, one serum-free medium was identified that appears to enhance antigen-specific IFNgamma-secretion.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".