Fluorescent Cell Barcoding of Peripheral Blood Mononuclear Cells for High‐Throughput Assessment of Vaccine‐Induced T Cell Responses in Low‐Volume Research Samples
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it