Barcoding of Live Human Peripheral Blood Mononuclear Cells for Multiplexed Mass Cytometry
Bibliographic record
Abstract
Mass cytometry is developing as a means of multiparametric single-cell analysis. In this study, we present an approach to barcoding separate live human PBMC samples for combined preparation and acquisition on a cytometry by time of flight instrument. Using six different anti-CD45 Ab conjugates labeled with Pd104, Pd106, Pd108, Pd110, In113, and In115, respectively, we barcoded up to 20 samples with unique combinations of exactly three different CD45 Ab tags. Cell events carrying more than or less than three different tags were excluded from analyses during Boolean data deconvolution, allowing for precise sample assignment and the electronic removal of cell aggregates. Data from barcoded samples matched data from corresponding individually stained and acquired samples, at cell event recoveries similar to individual sample analyses. The approach greatly reduced technical noise and minimizes unwanted cell doublet events in mass cytometry data, and it reduces wet work and Ab consumption. It also eliminates sample-to-sample carryover and the requirement of instrument cleaning between samples, thereby effectively reducing overall instrument runtime. Hence, CD45 barcoding facilitates accuracy of mass cytometric immunophenotyping studies, thus supporting biomarker discovery efforts, and it should be applicable to fluorescence flow cytometry as well.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".