Cutting through the weeds: Evaluation of a novel adsorption with crossmatch cells and elution protocol to sharpen HLA antibody identification by the single antigen bead assay
Bibliographic record
Abstract
The single antigen bead (SAB) assay is the most used test for the identification of HLA specific antibodies pre- and post-transplant. Nevertheless, detection of spurious reactivities remains a recognized assay limitation. In addition, the presence of weak reactivity patterns can complicate unacceptable antigen assignment. This work presents the evaluation of the adsorption with crossmatch cells and elution (AXE) technique, which was designed to help differentiate weak HLA specific antibodies targeting native antigens from spurious and background SAB assay reactivity. The AXE protocol uses selected donor cells to adsorb HLA specific antibodies from sera of interest. Bound antibodies are then eluted off washed cells and identified using the SAB assay. Only antibodies targeting native HLA are adsorbed. Assay evaluation was performed using five cell donors and pooled positive control serum. AXE efficiency was determined by comparing SAB reactivity of adsorbed/eluted antibody to that of the antibodies in unadsorbed sera. A robust efficiency was seen across a wide range of original MFI for donor specific antibodies (DSA). A higher absorption/elution recovery was observed for HLA class I antigens vs. class II. Locus-specific variation was also observed, with high-expression HLA loci (HLA-A/B/DR) providing the best recovery. Importantly, negligible reactivity was detected in the last wash control, confirming that AXE eluates were not contaminated with HLA antibody carry-over. Donor cells incubated with autologous and DSA-containing allogeneic sera showed that AXE selectively adsorbed HLA antibodies in a donor antigen-specific manner. Importantly, antibodies targeting denatured epitopes or other non-HLA antigens were not detected by AXE. AXE was particularly effective at distinguishing weak HLA antibodies from background reactivity. When combined with epitope analysis, AXE enhanced precise identification of antibody-targeted eplets and even facilitated the characterization of a potential novel eplet. Comparison of AXE to flow cytometric crossmatching further revealed that AXE was a more sensitive technique in the detection of weak DSA. Spurious reactivities on the current SAB assay have a deleterious impact on the assignment of clinically relevant HLA specificities. The AXE protocol is a novel test that enables users to interrogate reactive patterns of interest and discriminate HLA specific antibodies from spurious reactivity.
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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".