Defining T Cell Subsets in Human Tonsils Using ChipCytometry
Bibliographic record
Abstract
Abstract ChipCytometry is a multiplex imaging method that can be used to analyze either cell suspensions or tissue sections. Images are acquired by iterative cycles of immunostaining with fluorescently labeled Abs, followed by photobleaching, which allows the accumulation of multiple markers on a single sample. In this study, we explored the feasibility of using ChipCytometry to identify and phenotype cell subsets, including rare cell types, using a combination of tissue sections and single-cell suspensions. Using ChipCytometry of tissue sections, we successfully demonstrated the architecture of human palatine tonsils, including the B and T cell zones, and characterized subcompartments such as the B cell mantle and germinal center zone, as well as intrafollicular PD1-expressing CD4+ T cells. Additionally, we were able to identify the rare tonsillar T cell subsets, mucosal-associated invariant T (MAIT) and γδ-T cells, within tonsil tissue. Using single-cell suspension ChipCytometry, we further dissected human tonsillar T cell subsets via unsupervised clustering analysis as well as supervised traditional manual gating. We were able to show that PD1+CD4+ T cells are comprised of CXCR5+BCL6high follicular Th cells and CXCR5−BCL6mid pre–follicular Th cells. Both supervised and unsupervised analysis approaches identified MAIT cells in single-cell suspensions, confirming a phenotype similar to that of blood-derived MAIT cells. In this study, we demonstrate that ChipCytometry is a viable method for single-cell suspension cytometry and analysis, with the additional benefit of allowing phenotyping in a spatial context using tissue sections.
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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 | 0.000 |
| 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) | 0.002 | 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".