Comparative analysis of LAG3 antibodies shows differential binding patterns by flow cytometry
Bibliographic record
Abstract
BACKGROUND: LAG3 is an immune checkpoint molecule with emerging therapeutic use. Expression of LAG3 is well studied on T cells, but the proportion of LAG3-expressing cells varies greatly by study and its comparative expression between non-T cells is lacking. METHODS/OBJECTIVES: This study uses flow cytometry to compare surface LAG3 expression between T cells, NK cells, B cells, pDCs and monocytes of healthy donors. This study also compares three monoclonal LAG3 antibodies to a commonly used polyclonal LAG3 antibody on ex vivo and PHA-blasts from healthy donors and LAG3+ and LAG3- cell lines. RESULTS: LAG3 was most highly expressed on classical and intermediate monocytes (25 % and 32 %, respectively), while LAG3 expression on B cells, NK cells and iNKT cells was negligible. Notably, the polyclonal antibody stained a higher proportion of all cell types than the monoclonal antibodies, which had similar staining patterns to one another. Further study using LAG3+ and LAG3- cell lines showed greater specificity and similar sensitivity of the monoclonal antibody T47-530 than the polyclonal antibody. CONCLUSION: Monocytes may represent an unappreciated source of LAG3 and target of LAG3 checkpoint inhibitors. Furthermore, the discrepancies between monoclonal and polyclonal LAG3 antibodies warrants consideration when designing future studies and interpreting past studies, and may explain discrepancies in the literature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".