Improving Reliability of Immunological Assays by Defining Minimal Criteria for Cell Fitness
Bibliographic record
Abstract
Human PBMC-based assays are often used as biomarkers for the diagnosis and prognosis of disease, as well as for the prediction and tracking of response to biological therapeutics. However, the development and use of PBMC-based biomarker assays is often limited by poor reproducibility. Complex immunological assays can be further complicated by variation in cell handling before analysis, especially when using cryopreserved cells. Variation in postthaw viability is further increased if PBMC isolation and cryopreservation are done more than a few hours after collection. There is currently a lack of evidence-based standards for the minimal PBMC viability or "fitness" required to ensure the integrity and reproducibility of immune cell-based assays. In this study, we use an "induced fail" approach to examine the effect of thawed human PBMC fitness on four flow cytometry-based assays. We found that cell permeability-based viability stains at the time of thawing did not accurately quantify cell fitness, whereas a combined measurement of metabolic activity and early apoptosis markers did. Investigation of the impact of different types and levels of damage on PBMC-based assays revealed that only when cells were >60-70% live and apoptosis negative did biomarker values cease to be determined by cell fitness rather than the inherent biology of the cells. These data show that, to reproducibly measure immunological biomarkers using cryopreserved PBMCs, minimal acceptable standards for cell fitness should be incorporated into the assay protocol.
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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 | 0.031 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".