A rapid in vitro methodology for simultaneous target discovery and antibody generation against functional cell subpopulations
Bibliographic record
Abstract
Cell surface antigen discovery is of great interest for biomedical research both for isolation of rare cell populations and therapeutic targeting. We developed a rapid, cost-effective, fully in vitro technology which facilities the simultaneous target discovery and human antibody generation on the surface of virtually any cell population of interest. We apply our technique to human colorectal cancer-initiating cells (CICs) and identify hundreds of unique human antibodies. We characterized the top three antibody candidates targeting these CICs and identify their protein targets as integrin α7 (ITGA7), HLA-A1 and integrin β6 (ITGB6). We demonstrate that these antibodies can be used to isolate self-renewing colorectal CICs, and that the integrin α7 antibody can prospectively identify glioblastoma brain tumor initiating cells as well as human muscle stem cells. We also demonstrate that genetic ablation of integrin β6 impedes colorectal CIC function. The methodology can be readily applied to other cell populations including stem cells, cancer, or immune cells to facilitate the rapid identification of novel targets and simultaneous generation of potent and specific antibodies with therapeutic potential.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".