Development of multiplexed flow cytometry‐based red blood cell antibody screen and identification assays
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The purpose of this study was to develop a high-throughput method of performing red blood cell antibody screens and identification by utilizing flow cytometry and intracellular dyes to allow a multiplexed assay where three-cell screens can be performed in a single test well and 11-cell panels in three test wells. MATERIALS AND METHODS: Reagent red blood cells were labelled using Violet Proliferation Dye 450 (V450) and Oregon Green fluorescent dyes, which bind intracellular proteins to allow up to four cells to be interrogated in a single test well. Sixteen 3-cell screen panels and ten 11-cell identification panels were tested using sera with known antibody specificity. Antibody binding was detected using secondary anti-immunoglobulin G and anti-immunoglobulin M fluorescently labelled antibodies. RESULTS: Intracellular dyes allowed clear separation of the different screen and identification panel test cells. Three distinct populations of V450+, Oregon Green+ and negative for both stains were demonstrated in the screening panel and an additional double positive for V450 and Oregon Green was utilized to include a fourth cell in the identification panel testing to increase throughput. A total of 158 screen or identification panel RBC/serum combinations were tested against different known antibodies, and expected results were obtained with 100% concordance. CONCLUSION: This study demonstrates the successful development of a high-throughput multiplexed flow cytometry-based red cell antibody screen and identification panel assays. This method could be implemented in clinical laboratories to complement existing antibody detection methods. The multiplexing enabled via intracellular staining could be utilized to further augment other flow cytometry-based transfusion assays.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".