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Record W4409629286 · doi:10.1158/1538-7445.am2025-2191

Abstract 2191: A 50-marker mass cytometry intracellular cytokine staining panel: unprecedented resolution enables unrivaled detection of functional diversity present among human immune cell subsets

2025· article· en· W4409629286 on OpenAlexaff
Laura Polanco, Michael J. Cohen, Erika L. Smith-Mahoney, Lauren J. Tracey, Amedeo Cappione, Anna C. Belkina, David King, Jennifer Snyder‐Cappione

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsIntegrity Testing Laboratory (Canada)
Fundersnot available
KeywordsMass cytometryImmune systemBiologyCytokineFlow cytometryIntracellularImmunologyCell biologyComputational biologyGeneticsPhenotypeGene

Abstract

fetched live from OpenAlex

Abstract 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. We used CyTOF™ technology to achieve this goal of high-parameter cross-functional profiling to avoid the limitations of fluorescence-based cytometry, such as signal spillover, autofluorescence, compensation errors and spectral unmixing complications. A 50-marker CyTOF panel that achieves comprehensive profiling of immune subpopulations with detection of 20-plus intracellular cytokines spanning Th1, Th2, Th17 and Treg lineages 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. 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 a number of secretory analytes that are historically difficult to identify by flow cytometry at a single-cell level, including the immunosuppressive cytokine TGF-β. Mass cytometry, in concert with this new expanded cytokine panel, provides a far wider lens of visualization of the functional diversity of human immune cells than has been achieved previously. We predict that through the use of this panel, novel immune regulatory mechanisms that abate/prevent cellular responses in tumors will be revealed. 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. Citation Format: Laura Polanco,1 Michael J. Cohen,2 Erika L. Smith-Mahoney,1 Lauren Tracey,2 Christina Loh,2 Amedeo J. Cappione,2 Anna C. Belkina,1 David King,2 Jennifer E. Snyder-Cappione1. A 50-marker mass cytometry intracellular cytokine staining panel: unprecedented resolution enables unrivaled detection of functional diversity present among human immune cell subsets [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 2191.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.311
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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