Topology of multiple antigen expression in tonsillar lymphoid follicles and stroma analyzed by imaging mass cytometry.
Bibliographic record
Abstract
Abstract Investigation of co-distribution of 35 biomarkers simultaneously (major cell type and immune-oncology, stromal and tissue architecture markers, as well as cell proliferation and nuclear markers) in normal lymphoid sections using Imaging Mass Cytometry (Qing et al, Cytometry: Part A, 2017) is presented. Tonsil tissue revealed primary lymph follicles as dense clusters of lymphocytes expressing B-cell markers, surrounded by CD3, CD4, and CD8 T-cells. Secondary lymph follicles could be distinguished by proliferating B-cells with high Ki-67 expression. Strong expression of Bcl-6 was detected in germinal-center B-cells. Tonsillar FoxP3 CD8 T-cells exhibit a Treg phenotype with high CTLA-4 and CD45RO. CD68+ mac/mono were found in germinal centers and rarely in stroma. Squamous epithelium of crypts was beta-catenin positive and infiltrated by PD-L1+ T-cells, while PD-1+ cells were also found within the follicle centers. Images acquired on the Hyperion™ Imaging System (Fluidigm Inc.) were compared to immunofluorescence (IF) of sequential sections stained with the same antibodies (Ab) conjugated to fluorophores (FL). For direct comparison of IF and IMC, dual tagged Abs were created by attaching a metal tag first (Maxpar®, Fluidigm Inc.), then conjugating to FL using click chemistry. CD3 and CD19 were labeled with metals and FL, and tested in combination with the full panel on 8 μm frozen tonsil sections. CD3+ and CD19+ lymphs were identified by IF first, followed by IMC analysis. Results show that IMC is comparable to IF images and allows identification, characterization and localization of cell populations and tissue architecture in the absence of autofluorescence, photobleaching, with low background for acquisition of up to 50 targets.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".