Abstract 1667: A robust human immune profiling assay using CyTOF technology and automated data analysis software
Bibliographic record
Abstract
Abstract Immune monitoring is an essential method for quantifying changes in immune cell population numbers and states over time in health and disease. A cornerstone in translational and clinical research, it is frequently used in the investigation of chronic inflammation, infectious disease, autoimmune diseases, and cancer. The diversity of immune cell populations demands a highparameter approach to more fully and efficiently quantify these changes. Mass cytometry, which utilizes CyTOF® technology, is a single-cell analysis platform that uses metal-tagged antibodies to resolve over 40 markers in a single tube of sample without the need for compensation. It is an ideal solution for routine enumeration of immune cell populations. However, development of a robust, highly multiplexed assay requires panel optimization as well as standardization of instrument setup and an analysis pipeline. We have developed a sample-to-answer solution for human immune profiling using mass cytometry. It includes an optimized 30-marker immune profiling panel provided in a lyophilized, single-tube format, validated SOPs for human whole blood and PBMC staining, an instrument data acquisition template, instructions for data acquisition on a Helios™ system, and automated software for data analysis. The software analyzes FCS 3.0 files generated with the kit; automatically reports cell counts, percentage calculations, and staining intensity; and produces graphical elements such as histograms, dot plots, and a Cen-se′™ (t-SNE variant) graph for 36 immune cell populations. Here we present assay analytical validation data on repeatability, reproducibility, software precision, and software accuracy. We also present a performance comparison of the lyophilized material and a liquid formulation of the same antibodies and clones used in the lyophilized format. This assay provides a robust, complete solution for broad immune profiling using mass cytometry that reduces sources of variability and subjectivity in sample preparation and data analysis. Citation Format: Stephen K. Li, Daniel Majonis, C. Bruce Bagwell, Benjamin C. Hunsberger, Vladimir I. Baranov, Olga I. Ornatsky. A robust human immune profiling assay using CyTOF technology and automated data analysis software [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1667.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".