Single-cell Sequencing of Circulating Human Plasmablasts during <i>Staphylococcus aureus</i> Bacteremia
Bibliographic record
Abstract
Staphylococcus aureus is the major cause of healthcare-associated infections, including life-threatening conditions as bacteremia, endocarditis, and implant-associated infections. Despite adequate antibiotic treatment, the mortality of S. aureus bacteremia remains high. This calls for different strategies to treat this infection. In past years, sequencing of Ab repertoires from individuals previously exposed to a pathogen emerged as a successful method to discover novel therapeutic monoclonal Abs and understand circulating B cell diversity during infection. In this paper, we collected peripheral blood from 17 S. aureus bacteremia patients to study circulating plasmablast responses. Using single-cell transcriptome gene expression combined with sequencing of variable heavy and light Ig genes, we retrieved sequences from >400 plasmablasts revealing a high diversity with >300 unique variable heavy and light sequences. More than 200 variable sequences were synthesized to produce recombinant IgGs that were analyzed for binding to S. aureus whole bacterial cells. This revealed four novel monoclonal Abs that could specifically bind to the surface of S. aureus in the absence of Ig-binding surface SpA. Interestingly, three of four mAbs showed cross-reactivity with Staphylococcus epidermidis. Target identification revealed that the S. aureus-specific mAb BC153 targets wall teichoic acid, whereas cross-reactive mAbs BC019, BC020, and BC021 target lipoteichoic acid. All mAbs could induce Fc-dependent phagocytosis of staphylococci by human neutrophils. Altogether, we characterize the active B cell responses to S. aureus in infected patients and identify four functional mAbs against the S. aureus surface, of which three cross-react with S. epidermidis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".