HIGH-DIMENSIONAL ANALYSIS OF HUMAN REGULATORY T CELLS USING MASS CYTOMETRY
Bibliographic record
Abstract
Abstract Regulatory T cells (Tregs) are critical for maintenance of peripheral tolerance in a variety of tissues. Evidence that Tregs seem to be a phenotypically diverse population that varies between individuals and tissues suggests that there may be functionally-specialized subsets of Tregs which vary depending on location and/or disease state. We developed a new mass cytometry-based method to measure FOXP3 in combination with 20 other parameters to characterize the phenotype of human Tregs in different tissues, and to enable analysis of antigen-specific cells. We first developed a protocol to detect FOXP3, the Treg lineage-defining transcription factor, via mass cytometry. We found this optimized protocol is more sensitive than commercially-available methodology to detect FOXP3 by mass cytometry and is compatible with staining of other intracellular targets such as cytokines and other transcription factors. To ask how Treg populations differ depending on location and disease state, we stained mononuclear cells from adult peripheral blood, pediatric thymus and cord blood, as well as synovial fluid from pediatric subjects with juvenile idiopathic arthritis with our Treg-specific mass cytometry panel. The resulting data were analyzed using several bioinformatics approaches including viSNE and revealed diversity in the heterogeneity and phenotype of Tregs depending on their origin. The ability to stain FOXP3 using mass cytometry will facilitate the further characterization of Tregs in health versus disease and help us understand how Tregs are functionally specialized in the context of different tissues and disease.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".