Spatiotemporal-multimodal integration reveals BCG-induced skin-blood crosstalk
Bibliographic record
Abstract
Abstract Tuberculosis (TB) remains the leading cause of infectious death. The Bacille Calmette-Guérin (BCG) vaccine has been the only licensed vaccine available for TB prevention. Despite BCG being administered intradermally for over a century to >100 million individuals annually, the molecular events in the skin following BCG administration have not been investigated; as a result, measurable correlates of protection that could predict vaccine effectiveness already early after vaccination are lacking. Here we show that BCG immediately (within one day after vaccination) induces dynamic molecular waves that drive the acute human host response across space (layers of the skin and systemically in blood) and time (days). Integration of this data across space and time identified robust networks of interactive modules related to immune surveillance (e.g. Langerhans cells), cell trafficking (e.g. endothelial cells, ITGB5 ), and trained immunity (e.g. neutrophils, macrophages, γδ-T cell). Importantly, not only were we able to identify BCG-activated pathways associated with ‘trained immunity’ such as mTOR signaling and glycolysis/gluconeogenesis, we were able to pinpoint the time-point and precise location (skin layer) of the initial activation of theses pathways. Combining tissue biopsies of human skin (spatial genomics) with ‘liquid biopsies’ (cell-free blood plasma RNASeq) following BCG vaccination our data both confirmed known evidence (e.g. prominent γδ-T cell induction at the site of BCG administration; negative correlation of blood vs tissue myeloid-derived suppressor cells), but also generated promising new leads such as baseline levels of B cells, platelets and nuocytes in the skin prior to BCG administration predict eventual outcome, and that these predictive differences in baseline cellular composition can be captured non-invasively using high resolution images of the site of injection (dermatoscopy). Given this data represents the first holistic view of the acute molecular response to BCG in the skin in a human population at medium to high TB risk, we anticipate our findings of the immediate/early events following BCG vaccination, including non-invasive predictive assessment will support acceleration of vaccine development in the fight against TB.
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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 | 0.000 |
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".