Profiling antibody responses by multiparametric analysis of primary B cells
Bibliographic record
Abstract
Determining the efficacy of a vaccine generally relies on measuring neutralizing antibodies in sera. This measure cannot elucidate the mechanisms responsible for the development of immunological memory at the cellular level, however. Quantitative profiles that detail the cellular origin, extent, and diversity of the humoral (antibody-based) immune response would improve both the assessment and development of vaccines. Here, we describe a novel approach to collect multiparametric datasets that describe the specificity, isotype, and apparent affinity of the antibodies secreted from large numbers of individual primary B cells (approximately 10(3)-10(4)). The antibody/antigen binding curves obtained by this approach can be used to classify closely related populations of cells using algorithms for data clustering, and the relationships among populations can be visualized graphically using affinity heatmaps. The technique described was used to evaluate the diversity of antigen-specific antibody-secreting cells generated during an in vivo humoral response to a series of immunizations designed to mimic a multipart vaccination. Profiles correlating primary antibody-producing cells with the molecular characteristics of their secreted antibodies should facilitate both the evaluation of candidate vaccines and, broadly, studies on the repertoires of antibodies generated in response to infectious or autoimmune diseases.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".