Abstract 4461: Optimized human immunophenotyping panels enhance the flexibility for high-dimensional flow cytometry analysis with CyTOF
Bibliographic record
Abstract
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".