Evaluation of multiplexed liquid glycan Array (LiGA) for serological detection of glycan-binding antibodies
Bibliographic record
Abstract
We test the performance of the multiplexed liquid glycan array (LiGA) technology in serological assays. Specifically, we use LiGA to detect ABO blood group antibodies in human serum. This LiGA, which we name ABO-LiGA, contains ABO blood group trisaccharide glycans with an ethylazido aglycone conjugated to groups of ten multi-barcoded M13 particles carrying dibenzocyclooctyne (DBCO) on p8 proteins. ELISA clonal binding assays to anti-A/B antibodies confirmed the functional performance of ABO-clones and aligned with next-generation sequencing (NGS) of the mixed clones. Multiple DNA-barcoded technical replicates in LiGA allow for quantification of reproducibility and robustness as determined by the Z'-score using NGS. We then tested ABO-LiGA for specific detection of IgG and IgM anti-A and anti-B IgG and IgM antibodies in human serum samples. Comparison of antibody binding responses in sera from 31 healthy donors to ABO-LiGA with an ABO-Luminex-based method revealed consistent responses to LiGA-ABO but also minor deficiencies of ABO-LiGA such as low robustness of the current assay format and a limited ability to detect low intensity antibody responses. Some results point to undesired interactions of serum antibodies with small-footprint glycans conjugated to phage via the bulky DBCO moiety. This report illuminates the path for future development of LiGA-based serological assays and suggests the need to develop alternative methods for conjugating glycans to phage to avoid liabilities of the hydrophobic DBCO moiety.
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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 | 0.001 |
| 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".