Protocol for affinity enrichment of polyclonal autoantibodies from human plasma
Bibliographic record
Abstract
Autoantibodies (AAbs) contribute to various immune-mediated diseases and are valuable biomarkers for diagnosis, classification, and disease activity. Here, we present a protocol for the affinity enrichment of AAbs from human plasma samples. We describe steps to generate a human cell line lysate, which is immobilized on Sepharose beads for affinity enrichment of AAbs. We then detail the quality-control procedure of verifying autoreactivity of AAb fractions. This protocol has potential application in functional and proteomic analyses of AAbs. For complete details on the use and execution of this protocol, please refer to Hagadorn et al. 1 • Protocol for affinity enrichment of AAbs from human plasma samples • Immobilization of human cell lysate on Sepharose beads for AAb capture • Affinity enrichment of AAbs using lysate-conjugated Sepharose beads • Verification procedures for assessing AAb enrichment efficiency Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Autoantibodies (AAbs) contribute to various immune-mediated diseases and are valuable biomarkers for diagnosis, classification, and disease activity. Here, we present a protocol for the affinity enrichment of AAbs from human plasma samples. We describe steps to generate a human cell line lysate, which is immobilized on Sepharose beads for affinity enrichment of AAbs. We then detail the quality-control procedure of verifying autoreactivity of AAb fractions. This protocol has potential application in functional and proteomic analyses of AAbs.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.027 |
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".