Abstract 4461: Optimized human immunophenotyping panels enhance the flexibility for high-dimensional flow cytometry analysis with CyTOF
Notice bibliographique
Résumé
Abstract The immune system plays a pivotal role in the pathogenesis of cancer. Beyond its importance in leukemia and lymphoma, scientists now understand that the immune system is involved in virtually all malignancies. Heterogeneity of cell phenotypes within a tumor microenvironment, including blood cell phenotypes, is a hallmark of cancer that is poorly understood. Resolution of these phenotypes is essential to direct therapeutic development. CyTOF® technology has pioneered the field of high-dimensional flow cytometry through the use of isotopically pure metal-tagged antibodies and a highly sensitive mass cytometer to enable 50-plus-parameter analysis. Easy panel design without the need for compensation controls or issues of autofluorescence allows for comprehensive single-cell analysis in complex biological samples and is particularly well suited for revealing the intricacies of oncogenesis. Maxpar® OnDemand reagents were recently introduced for CyTOF to increase flexibility and facilitate larger panel design in a short period of time. To this end, lineage markers that have strong, reliable expression were reassigned to optimal metal isotopes to reserve the high-sensitivity lanthanide channels for more difficult-to-detect markers with low endogenous expression, common in immuno-oncology studies. In this study, we aimed to test the newly released Maxpar OnDemand™ Antibodies to create an optimized immune phenotyping panel and maximize further customization for CyTOF. Individual antibody performance was compared between the new and existing antibodies with different metal tags. Human peripheral blood mononuclear cells (PBMC) and fresh whole blood (WB) were stained to confirm equivalent capabilities for population gating. Importantly, we demonstrated the power of these antibodies to generate reproducible, impactful data by creating functional human immunophenotyping panels. A 20-marker human phenotyping panel was tested in PBMC and WB. We identified major immune cell populations including T cells, B cells, granulocytes, natural killer (NK) cells, and monocytes. Furthermore, the addition of CD45RO to the panel enhanced the delineation of naive, memory and effector T cell subsets. Additional myeloid markers were included to resolve the complexity within this compartment. With the updated placement of key lineage markers, the optimized phenotyping panel is ideal for future immuno-oncology studies as it can be easily expanded to interrogate cell cycle proteins, cytokines, cell signaling proteins, and oncogenic transcription factors. This work demonstrates the capability of CyTOF for robust, high-parameter immunophenotyping and a high degree of flexibility to expand our understanding of the complex processes underlying carcinogenesis and response to therapy. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Lauren J. Tracey, Michael Cohen, Christina Loh. Optimized human immunophenotyping panels enhance the flexibility for high-dimensional flow cytometry analysis with CyTOF. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4461.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».