Abstract 78: Superior resolution of intracellular markers with CyTOF reveals greater functional diversity of human T cells
Bibliographic record
Abstract
Abstract Measuring functional signatures of immune cells in a comprehensive manner, spanning the inflammatory (Th1/Th17) and immunosuppressive (Th2/Treg) lineages, would provide key insights into several facets of cancer research and therapy. Determining mechanisms underlying success/failure of checkpoint blockade, defining immunosuppressive activity of the tumor-resident cell subsets, and identifying immunological biomarkers that predict clinical outcomes, could all be achievable if detection of cytokines spanning many lineages among individual cells was in reach. To achieve this goal, powerful cytometric research tools are required. Cell phenotyping panels are expanding and often now include measurement of intracellular markers of functional potential, such as cytokines, phosphorylation events, and transcription factors. Many of these readouts, including cytokines of the Th2/Treg lineages (IL-5, IL-10, and IL-13), have been notoriously difficult to detect in human cells using fluorescence-based cytometry. Large panels with multiplex intracellular markers are subject to challenges with autofluorescence and spillover spread, thus limiting fluorochrome capacity and reducing resolving power for rare subsets even with full spectrum flow cytometers. For such applications, mass cytometry may overcome such limitations and enable better signal resolution. To address this, we evaluated three small (13-plex) panels using a full spectrum flow cytometer and the CyTOF XT™ mass cytometer. 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. PBMCs were stimulated, split, and stained with either fluorochrome- (Cytek® Aurora) or metal-conjugated antibodies (CyTOF XT). Data sets were analyzed by PhenoGraph clustering and visualized with opt-SNE to determine cellular functional diversity. Overall, data collected on the CyTOF XT demonstrated superior resolution for many intracellular readouts, including cytokines IL-5, IL-10, and IL-13. Also, when better signal/noise enabled accurate clustering, a higher number of immune cell clusters possessing distinct functional signatures resulted from mass cytometry datasets as compared with fluorescent counterparts. In summary, the CyTOF XT mass cytometry platform offers superior signal resolution compared to spectral flow for a range of intracellular targets. Use of mass cytometry translates to the identification of more diverse functional subsets and thus a clearer understanding of the immune signatures present in a sample in the context of unbiased 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: Erika L. Smith-Mahoney, Michael J. Cohen, Anna C. Belkina, Thirumahal Selvanantham, David King, Christina Loh, Amedeo J. Cappione, Jennifer E. Snyder-Cappione. Superior resolution of intracellular markers with CyTOF reveals greater functional diversity of human T cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 78.
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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 | 0.001 | 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) | 0.011 | 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".