The use of intracellular dyes to create a multiplexed flow cytometry‐based red blood cell phenotyping assay
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Flow cytometry can be used to phenotype red blood cell antigens, allowing for high-throughput testing while using low reagent volumes. This article utilizes intracellular dyes to pre-label red blood cells to further multiplex flow cytometry-based red blood cell antigen phenotyping. MATERIALS AND METHODS: Red blood cells were pre-labelled using the intracellular dyes V450 and Oregon Green. These dyes are detected fluorescently via flow cytometry. Four combinations of intracellular staining were used to allow four patient or donor red blood cells to be analysed in a single test well. Antigen phenotyping was then performed via flow cytometry using a previously described method. RESULTS: The intracellular dyes showed uniform staining when measured in mean fluorescence intensity and allowed the red blood cells to be clearly distinguished from one another. The presence or absence of red blood cell antigens was determined with 100% accuracy. CONCLUSION: The use of intracellular dyes allowed a fourfold increase in the throughput of our previously described flow cytometry-based red blood cell antigen phenotyping method. The described method allows up to 48 patients to be simultaneously phenotyped using a single 96-well microplate. Furthermore, additional fluorescent dyes could potentially increase the throughput exponentially.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".