Centrally Determined Standardization of Flow Cytometry Methods Reduces Interlaboratory Variation in a Prospective Multicenter Study
Bibliographic record
Abstract
OBJECTIVES: Flow cytometry (FC) aids in characterization of cellular and molecular factors involved in pathologic immune responses. Although FC has potential to facilitate early drug development in inflammatory bowel disease, interlaboratory variability limits its use in multicenter trials. Standardization of methods may address this limitation. We compared variability in FC-aided quantitation of T-cell responses across international laboratories using three analytical strategies. METHODS: Peripheral blood mononuclear cells (PBMCs) were isolated from three healthy donors, stimulated with phorbol 12-myristate 13-acetate and ionomycin at a central laboratory, fixed, frozen, and shipped to seven international laboratories. Permeabilization and staining was performed in triplicate at each laboratory using a common protocol and centrally provided reagents. Gating was performed using local gating with a local strategy (LGLS), local gating with a central strategy (LGCS), and central gating (CG). Median cell percentages were calculated across triplicates and donors, and reported for each condition and strategy. The coefficient of variation (CV) was calculated across laboratories. Between-strategy comparisons were made using a two-way analysis of variance adjusting for donor. RESULTS: Mean interlaboratory CV ranged from 1.8 to 102.1% depending on cell population and gating strategy (LGLS, 4.4-102.1%; LGCS, 10.9-65.6%; CG, 1.8-20.9%). Mean interlaboratory CV differed significantly across strategies and was consistently lower with CG. CONCLUSIONS: Central gating was the only strategy with mean CVs consistently lower than 25%, which is a proposed standard for pharmacodynamic and exploratory biomarker 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 | 0.119 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".