Enhancements in mass cytometry for multiplexing samples and increasing cell detection rate (TECH3P.938)
Bibliographic record
Abstract
Abstract Mass Cytometry uniquely enables high dimensional single cell proteomic analysis for systems-level discovery and comprehensive functional profiling applications. At the core of the technology are the ICP ion source and a fast elemental analyzer designed for metal-conjugated affinity reagents. We show how recent advancements in reagents, sample introduction system and the ion source have simplified data acquisition and improved data quality with the mass cytometry platform. Up to 20 samples from an experiment are barcoded with palladium-based cell tagging reagents, combined into one tube, and stained and run as one sample. This multiplexing improves data quality by eliminating sample-to-sample staining and running variation, increases experimental throughput by reducing the number of tubes that need to be processed and run, and reduces reagent consumption. Since each barcode consists of a unique set of 3 Pd isotopes, the de-barcoding algorithm also filters out most doublets (with more than 3 isotopes). The multiplexed sample is run on the CyTOF using the novel large volume sample introduction system. We will discuss results of modifications to the sample introduction system and the inductively coupled plasma ion source, which enable analysis of large samples at high cell detection rate. Examples of barcoded PBMCs from multiple donors, stained with high dimensional antibody panels that combine deep phenotyping with multiple cytokine or signaling targets will be demonstrated.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".