Diagnosing PNH with FLAER and multiparameter flow cytometry
Bibliographic record
Abstract
BACKGROUND: PNH is an acquired hematopoietic stem cell disorder leading to a partial or absolute deficiency of all glycophosphatidyl-inositol (GPI)-linked proteins. The classical approach to diagnosis of PNH by cytometry involves the loss of at least two GPI-linked antigens on RBCs and neutrophils. While flow assays are more sensitive and specific than complement-mediated lysis or the Hams test, they suffer from several drawbacks. Bacterial aerolysin binds to the GPI moiety of cell surface GPI-linked molecules and causes lysis of normal but not GPI-deficient PNH cells. FLAER is an Alexa488-labeled inactive variant of aerolysin that does not cause lysis of cells. Our goals were to develop a FLAER-based assay to diagnose and monitor patients with PNH and to improve detection of minor populations of PNH clones in other hematologic disorders. METHODS: In a single tube assay, we combined FLAER with CD45, CD33, and CD14 allowing the simultaneous analysis of FLAER and the GPI-linked CD14 structure on neutrophil and monocyte lineages. RESULTS: Comparison to standard CD55 and CD59 analysis showed excellent agreement. Because of the higher signal to noise ratio, the method shows increased sensitivity in our hands over single (CD55 or CD59) parameter analysis. Using this assay, we were able to detect as few as 1% PNH monocytes and neutrophils in aplastic anemia, that were otherwise undetectable using CD55 and CD59 on RBC's. We also observed abnormal FLAER staining of blast populations in acute leukemia. In these cases, the neutrophils stained normally with FLAER, while the gated CD33bright cells failed to express normal levels of CD14 and additionally showed aberrant CD45 staining and bound lower levels of FLAER. CONCLUSION: FLAER combined with multiparameter flow cytometry offers an improved assay for diagnosis and monitoring of PNH clones and may have utility in detection of unsuspected myeloproliferative disorders.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 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".