Fluorescent Cell Barcoding of Peripheral Blood Mononuclear Cells for High‐Throughput Assessment of Vaccine‐Induced T Cell Responses in Low‐Volume Research Samples
Bibliographic record
Abstract
T cell responses are rarely measured in large-scale human vaccine studies due to the sample volumes required, as well as the logistical, technical, and financial challenges associated with available assays. Fluorescent cell barcoding has been proposed in other contexts to allow for more high-throughput flow cytometry-based assays. Here, we aimed to expand on existing barcoding approaches to develop a reagent and sample-sparing assay for in-depth assessment of T cell responses to vaccine antigens. By using various concentrations of two fixable viability dyes in a matrix format, up to 25 samples that were pooled and acquired together could be successfully deconvoluted based on their unique fluorescent signature. This fluorescent cell barcoding approach was then combined with extracellular and intracellular staining to identify functional (i.e., producing at least one cytokine) and polyfunctional (i.e., producing multiple cytokines) T cells in response to vaccine antigen stimulation. As a proof-of-concept, we plated just 200,000 peripheral blood mononuclear cells (PBMC) per condition, and by staining and acquiring only two pooled samples, we were able to detect rare antigen-specific T cell responses in eight donors to four stimulants each. The frequencies of antigen-induced cytokine-positive cells detected in barcoded samples with 200,000 input PBMC were strongly correlated with those detected in non-barcoded samples from the same donors with 1 million input PBMC, demonstrating the validity of this approach. In conclusion, by reducing the number of PBMC needed by five-fold, and the volume of staining reagents needed by 25-fold, this assay has widespread potential applications to human vaccine studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".