Human SARS-CoV-2 challenge uncovers differences in local and systemic cellular responses associated with protection from infection and distinct infection states
Bibliographic record
Abstract
Despite the rapid global COVID-19 research effort, our understanding of how and why some people go on to develop an infection upon SARS-CoV-2 viral exposure, whilst others do not, remains limited. The world’s first Human COVID-19 Challenge Study offers a unique opportunity to delineate the differences in the cellular dynamics between abortive, transient, and sustained infection groups. Matched nasopharyngeal and blood samples were collected at 6-7 timepoints (pre-infection to 28 days post-infection) from 16 healthy seronegative individuals inoculated with pre-alpha SARS-CoV-2 and processed for multi-omic single cell analysis (n=181). Our data revealed dozens of rapid and highly dynamic changes in cell type proportions and cellular response states in the epithelium and immune cells associated with specific timepoints and infection status. We observed an interferon response in blood that preceded that in the nasopharynx, with a rapid local infiltration of immune cells in transiently infected participants. Several early anti-viral ciliated cell responses were also identified, with non-productive infections in the resident T cells and macrophages. A high expression of HLA-DQA2 pre-inoculation was associated with protection. Lastly, we described a subpopulation of acutely activated T cells, clonally expanded and carrying convergent SARS-CoV-2 TCR motifs. Overall, our detailed time series data (covid19cellatlas.org) can serve as a “Rosetta stone” for epithelial and immune cell responses and reveals early dynamic responses associated with protection from infection, providing insights to further aid treatment and vaccine design.
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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) | 0.002 | 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".