Profiling protein–protein interactions of single cancer cells with<i>in situ</i>lysis and co-immunoprecipitation
Bibliographic record
Abstract
Heterogeneity in a tumor allows a small portion of cancer cells to survive and regrow upon targeted cancer therapy, eventually leading to cancer relapse. Such drug-resistant cells often exhibit dynamic adaptation of their signaling pathways at the level of protein-protein interactions (PPIs). To probe the rewiring of signaling pathways and the heterogeneity across individual cancer cells, we developed a single-cell version of the co-immunoprecipitation (co-IP) analysis that examines the amount and PPIs of target proteins immunoprecipitated from individual cells. The method captures cancer cells at predefined locations using a microfluidic chip, pulls down target proteins on the surface using antibodies, and lyses the captured cells in situ. Then, subsequent addition of eGFP-labeled downstream proteins enables the determination of the corresponding PPIs for the minimal amount of target proteins sampled from a single cell. We applied the technique to probe epidermal growth factor receptors (EGFRs) in PC9 lung adenocarcinoma cells. The results reveal that the strength of EGFR PPIs can be largely uncorrelated with the expression level of EGFRs in single cells. In addition, the individual PC9 cells showed markedly different patterns of PPIs, indicating a high heterogeneity in EGFR signaling within a genetically homogeneous population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".