Imaging Mass Cytometry in Psoriatic Disease Reveals Immune Profile Heterogeneity in Skin and Synovial Tissue
Bibliographic record
Abstract
Imaging mass cytometry is a technology that enables comprehensive analysis of cellular phenotypes at the tissue level. We performed a multiparameter characterization of structural and immune cell populations in psoriatic skin and synovial tissue samples aimed at characterizing immune cell differences in patients with psoriasis and psoriatic arthritis. A panel of 33 antibodies was used to stain selected immune and structural cell populations. Imaging mass cytometry data were segmented into single cells on the basis of combinations of antibody stains. Single cells were then clustered into cell categories on the basis of prespecified markers. The spatial relationships of different cell populations were assessed using neighborhood analysis. Among all cell types in the skin and synovium, lymphoid cells accounted for the most prevalent cell type. T cells and macrophages were the most prevalent immune cell type in the synovium, and B cells and NK cells were also identified. Neighborhood analysis showed high correlation between synovial T cells, B cells, macrophages, dendritic cells, and neutrophils, suggesting spatial organization. Innate and adaptive immune cells can be reliably identified using imaging mass cytometry in the skin and synovium. Interpatient heterogeneity exists in tissue cell populations. Imaging mass cytometry provides opportunities for exploring in depth the underlying immunological mechanisms driving psoriasis and psoriatic arthritis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".