Single-cell systems-level analysis of human Toll-like receptor activation defines a chemokine signature in patients with systemic lupus erythematosus
Bibliographic record
Abstract
BACKGROUND: Activation of Toll-like receptors (TLRs) induces inflammatory responses involved in immunity to pathogens and autoimmune pathogenesis, such as in patients with systemic lupus erythematosus (SLE). Although TLRs are differentially expressed across the immune system, a comprehensive analysis of how multiple immune cell subsets respond in a system-wide manner has not been described. OBJECTIVE: We sought to characterize TLR activation across multiple immune cell subsets and subjects, with the goal of establishing a reference framework against which to compare pathologic processes. METHODS: Peripheral whole-blood samples were stimulated with TLR ligands and analyzed by means of mass cytometry simultaneously for surface marker expression, activation states of intracellular signaling proteins, and cytokine production. We developed a novel data visualization tool to provide an integrated view of TLR signaling networks with single-cell resolution. We studied 17 healthy volunteer donors and 8 patients with newly diagnosed and untreated SLE. RESULTS: Our data revealed the diversity of TLR-induced responses within cell types, with TLR ligand specificity. Subsets of natural killer cells and T cells selectively induced nuclear factor κ light chain enhancer of activated B cells in response to TLR2 ligands. CD14(hi) monocytes exhibited the most polyfunctional cytokine expression patterns, with more than 80 distinct cytokine combinations. Monocytic TLR-induced cytokine patterns were shared among a group of healthy donors, with minimal intraindividual and interindividual variability. Furthermore, autoimmune disease altered baseline cytokine production; newly diagnosed untreated SLE patients shared a distinct monocytic chemokine signature, despite clinical heterogeneity. CONCLUSION: Mass cytometry defined a systems-level reference framework for human TLR activation, which can be applied to study perturbations in patients with inflammatory diseases, such as SLE.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".