Abstract 5755: Expansion of the known functional diversity of human T cells achieved due to the unprecedented resolution of intracellular proteins by mass cytometry (CyTOF)
Bibliographic record
Abstract
Abstract Measuring the functional signatures of immune cells comprehensively, spanning the inflammatory (Th1/Th17) and immunosuppressive (Th2/Treg) lineages, provides key insights into several facets of cancer research and therapy. Single-cell detection of cytokines from diverse functional lineages is required to determine mechanisms underlying success/failure of checkpoint blockade, define immunosuppressive activity of the tumor-resident cell subsets and identify immunological biomarkers that predict clinical outcomes. Powerful cytometric research tools, such as fluorescence cytometry, enable important intracellular measurements of functional potential, such as cytokines, phosphorylation events and transcription factors in single cells. However, expanding the number of targets detected per cell by these methods has limitations in commercial availability of compatible conjugates and the technology’s resolving capacity for rare subsets, specifically due to spectral signal overlap and autofluorescence. Many readouts, including cytokines of the Th2/Treg lineages (IL-5, IL-10 and IL-13), have also been notoriously difficult to reproducibly detect in human cells using fluorescence-based cytometry. We predicted that mass cytometry may overcome such limitations and enable better signal resolution for such applications. We evaluated three small (11-12-plex) panels using a full-spectrum flow cytometer and the CyTOF™ XT mass cytometer to address this. Each panel was comprised of surface and intracellular analytes (cytokines, phospho-epitopes or transcription factors) and designed to minimize potential impact of spectral overlap on the resolution of spectral flow data. PBMC were stimulated, split and stained with either fluorochrome- (Cytek Aurora) or metal-conjugated antibodies (CyTOF). Datasets were analyzed by PhenoGraph clustering and visualized with opt-SNE to determine cellular functional diversity. Overall, data collected on the CyTOF XT system demonstrated superior resolution for many intracellular readouts, including cytokines IL-10 and IL-13, with stimulation-specific events only detected using CyTOF technology. Additionally, improved signal:noise (S/N) provided better resolution of phospho-activation events and transcription factor expression, particularly TOX and Tbet. Further, better S/N with CyTOF technology enabled more accurate population clustering using PhenoGraph, and more distinct functional signatures resulted from mass cytometry datasets compared with fluorescent counterparts. CyTOF XT mass cytometry platform clarifies understanding a sample's immune signatures in unsupervised analysis. Our findings indicate that the CyTOF XT platform could serve as a catalyst for seminal discoveries in immune profiling to drive therapeutic design and advanced disease monitoring in cancer. Citation Format: Laura Polanco, Michael J. Cohen, Erika L. Smith-Mahoney, Ling Wang, David King, Madison Bailey, Christina Loh, Anna C. Belkina, Amedeo J. Cappione, Jennifer E. Snyder-Cappione. Expansion of the known functional diversity of human T cells achieved due to the unprecedented resolution of intracellular proteins by mass cytometry (CyTOF) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5755.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".