Abstract 79: Enabling fast and convenient immune profiling of fresh and long-term stabilized human whole blood samples with CyTOF
Bibliographic record
Abstract
Abstract Accurate phenotyping of immune cells in whole blood (WB) from patients with cancer is critical for making disease prognoses and monitoring clinical efficacy of immunotherapies. Fresh WB must be analyzed within 24 hours of collection to minimize changes in cellular composition. However, WB collection and cytometric analysis are often performed at different sites, which can result in sample processing delays. Several WB preservation reagents have been developed to address this challenge, including PROT1 (Smart Tube Inc.) and Cytodelics Whole Blood Cell Stabilizer (Cytodelics). However, not all antibody panels are compatible with these reagents. A CyTOF® panel was developed to be compatible with these commercial WB stabilizers and for use in pharmaceutical and clinical research. CyTOF flow cytometry uses metal-tagged antibodies to identify cellular and functional phenotypes. Advantages and features of CyTOF technology enable rapid design and application of 50-plus-marker panels and convenient workflows in which samples can be stained and acquired in a single tube. Compensation is not required since CyTOF flow cytometry has low signal spillover and no autofluorescence. Moreover, antibody cocktails and stained samples can be frozen for later use and acquisition. Thus, CyTOF technology overcomes major hurdles of fluorescence-based cytometry and provides a streamlined and flexible workflow in clinical research. The CyTOF panel contains 20 antibodies to identify over 30 immune cell populations. For easy customization, there are more than 30 additional open channels to analyze markers of interest. The panel works with fresh and stabilized WB samples and is amenable to different staining and acquisition workflows. To show the flexibility of sample staining and stabilization, WB from three healthy donors was assessed using two stabilization workflows. First, fresh WB samples were stained with the antibody panel, followed by PROT1 or Cytodelics stabilization and storage at -80 °C. The second workflow involved immediate stabilization/fixation of WB with PROT1 or Cytodelics and storage at -80 °C. Subsequently, the samples for the second workflow were thawed, surface stained, and acquired. To reduce technical variability from staining, the antibodies were pooled together and frozen at -80 °C as single-use aliquots. Furthermore, all samples were barcoded and acquired as a single tube to reduce variability from sample acquisition. The CyTOF panel developed for broad immune profiling is compatible with WB stabilizers, which overcomes traditional and logistical challenges with WB processing and acquisition. Furthermore, freezing antibody cocktails is a unique feature of CyTOF flow cytometry, ensuring batch-to-batch consistency in clinical research. Thus, CyTOF workflows enable swift and convenient analysis of WB samples. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Michael Cohen, Shakir Hasan, Stephen Li, Christina Loh. Enabling fast and convenient immune profiling of fresh and long-term stabilized human whole blood samples with CyTOF [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 79.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".