Fast and efficient isolation of human EGFR-positive cells using EasySep&[trade]
Bibliographic record
Abstract
Abstract Epidermal growth factor receptor (EGFR) is a cell surface receptor normally expressed on most epithelial cells and some hematopoietic cells. Mutation and overexpression of EGFR is a key driver of many human cancers, including lung, breast, as well as colorectal cancers. Circulating tumour cells (CTCs) can also express EGFR, where enrichment of EGFR+ CTCs can provide promising liquid biopsy and prognostic values. EGFR-positive cells can be difficult to isolate because they are present in many tissue types at a wide range of frequencies. We have developed a simple method to isolate human EGFR-positive cells to address this challenge. A549 human lung adenocarcinoma cells expressing EGFR were spiked into human PBMCs at defined frequencies as a model system. Starting with a single-cell suspension with 10% EGFR+ cells, the EGFR-expressing cells were labeled with an antibody complex that linked the EGFR-expressing cells to magnetic particles, then separated using an EasySep™ magnet. Using this method, EGFR+ cells were enriched from 9.9 ± 3.3% to 95.0 ± 2.8% (mean ± SD; n = 14). Protocols have been optimized for different sample sizes and different EGFR+ cell starting frequencies. EasySep™-isolated EGFR+ lung adenocarcinoma cells were viable and proliferative, with fold expansion and Ki67 expression comparable to untreated cells. EasySep™ Human EGFR Positive Selection Kit enables simple and easy isolation of EGFR-positive cells in 15 minutes, facilitating EGFR and cancer research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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".