164 A 50-marker mass cytometry intracellular cytokine staining panel: unprecedented resolution enables unrivaled detection of functional diversity present among human immune cell subsets
Bibliographic record
Abstract
Background Understanding mechanisms of immune evasion to therapeutics or during responses to cancer, autoimmunity and infectious diseases requires high-dimensional functional profiling at the single-cell level. Measurement of functional immune cell signatures that include both inflammation and immunosuppression provides key insights into several facets of cancer research and therapy. If single-cell detection of cytokines spanning many cell lineages was possible, 1) determining mechanisms underlying success/failure of checkpoint blockade, 2) defining immunosuppressive activity of the tumor-resident cell subsets and 3) identifying immunological biomarkers that predict clinical outcomes would be within reach. Based on previous demonstration of superior signal resolution for intracellular readouts when compared with fluorescent cytometry, we used CyTOF™ technology to achieve this goal of high-parameter cross-functional profiling. Through the application of this orthogonal technology, we circumvent the limitations of fluorescence-based cytometry, such as signal spillover, autofluorescence, compensation errors and spectral unmixing complications.Methods A 50-marker CyTOF panel that achieves comprehensive phenotyping of immune subpopulations with detection of 20-plus intracellular cytokines spanning Th1, Th2, Th17 and Treg lineages for functional profiling was developed. PBMC were cultured with an array of stimulation conditions, stained and acquired on the CyTOF XT system. Datasets were analyzed using PhenoGraph clustering and visualized with opt-SNE to determine cellular functional diversity.Results Our findings show clear detection of IL-5-, IL-10- and IL-13-producing cells, found in surprising co-expression patterns with pro-inflammatory cytokines such as IFNγ. More significantly, we were able to detect expression of several secretory analytes that are historically difficult to identify by flow cytometry at a single-cell level, including the immunosuppressive cytokine TGF-β. Furthermore, deeper analyses of functional immune capacity unveiled unique, previously unidentified subpopulations of potential interest.Conclusions Mass cytometry, in concert with this comprehensive cytokine profiling panel, provides a far wider lens of visualization of the functional diversity of human immune cells than has been achieved previously. We predict that using this panel, novel immune regulatory mechanisms that abate/prevent cellular responses in tumors will be revealed. Furthermore, given the ease of panel design and customization offered by mass cytometry, this panel provides a scaffold for easily constructing additional panels to address research-specific needs. In sum, our findings indicate that the CyTOF XT platform is well positioned as a catalyst for seminal discoveries in immune profiling to drive therapeutic design and advanced disease monitoring in cancer.For Research Use Only. Not for use in diagnostic procedures.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".