Abstract 78: Superior resolution of intracellular markers with CyTOF reveals greater functional diversity of human T cells
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».