Abstract A058: nELISA high-throughput protein profiling captures immune secretomes for high-resolution phenotypic screens
Bibliographic record
Abstract
Abstract Immune phenotypes can be extremely diverse and complex, posing both a challenge to understand them and an opportunity to target them with advanced immune engineering approaches. Unfortunately, proteomics tools that capture the breadth of possible immune responses lack the throughput and affordability to rapidly profile the large sample numbers generated to identify and characterize optimally engineered therapeutic candidates. To overcome this issue, we developed the nELISA: a high-throughput miniaturized ELISA capable of quantifying >275 cytokines, proteases, immune receptors, and growth factors, at 10x-reduced cost compared to previous tools. We applied the nELISA to a model high-throughput screen for the discovery and characterization of immune-modulatory perturbations. We ran the largest PBMC secretome screen to date, in which ∼10,000 PBMC samples were profiled in 1 week, at a throughput of 1536 samples/day. Cells were treated with various inflammatory stimuli, and were further perturbed with a selected library of 80 recombinant protein perturbagens. The broad nELISA content enabled us to capture disease phenotypes and donor variability, as well as distinguish between perturbations with similar effects on single markers but vastly different overall phenotypes. For example, IFN gamma was potently induced by >12 perturbagens with very different effects on other cytokines. Thus, IL-23 had almost no impact other than to induce IFN gamma; in contrast, IL-15 also induced IL-1 beta, TNF alpha, CXCL9, CXCL10, and CCL5, whereas IFN beta impacted the expression of >20 cytokines in addition to IFN gamma. Interestingly, we also identified perturbagens that could substitute for another, while avoiding deleterious effects. Thus, IL-1 Receptor antagonist shared all of the immunosuppressive effects of IFN beta, without inducing IFN gamma and CXCL10, supporting its use as a replacement for Type I interferons in certain indications such as multiple sclerosis. These findings highlight the ability of the nELISA to capture a wide range of immune phenotypes at a throughput and cost amenable to immune engineering studies. Citation Format: Alyssa Rosenbloom, Nathaniel Robichaud, Grant Ongo, Milad Dagher. nELISA high-throughput protein profiling captures immune secretomes for high-resolution phenotypic screens [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr A058.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".