Dictionary of immune responses to 86 cytokines <i>in vivo</i> at single-cell resolution
Bibliographic record
Abstract
Abstract Cytokines mediate cell-cell communication in the immune system and represent important therapeutic targets. A myriad of studies have underscored their central role in immune function, yet we lack a global view of the cellular responses of each immune cell type to each cytokine. To address this gap we created the Immune Dictionary – a compendium of single-cell transcriptomic profiles of over 20 cell types in response to each of 86 cytokines in murine lymph nodes in vivo, representing over 1,500 cytokine-cell type combinations. A cytokine-centric view of the dictionary revealed that most cytokines induced highly cell type-specific responses. For example, the inflammatory cytokine IL-1β induced distinct gene programs in almost every cell type. A cell type-centric view of the dictionary identified multiple cytokine-driven cellular polarization states in each immune cell type, including previously uncharacterized states such as an IL-18-induced polyfunctional NK cell state. Based on the dictionary, we developed companion software, Immune Response Enrichment Analysis (IREA), for assessing cytokine activities and immune cell polarization from gene expression data, and applied it to reveal cytokine networks in tumors following immune checkpoint blockade therapy. Our dictionary generates new hypotheses for cytokine functions, illuminates pleiotropic effects of cytokines, expands our knowledge of activation states of each immune cell type, and provides a framework to deduce the roles of specific cytokines and cell-cell communication networks in any immune response. This work was supported by the NIH Grant RM1HG006193 and an Adelson Medical Research Foundation Grant to N.H. This work was also supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Doctoral Fellowship, Whitaker Health Sciences Fund Fellowship, and Wellington and Irene Loh Fund Fellowship to A.C., and NCI Research Specialist Award (R50CA251956) to S.L.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".