Comparison of Fixation Methods for the Detection of Claudin 1 and E-Cadherin in Breast Cancer Cell Lines by Immunofluorescence
Bibliographic record
Abstract
The tight junction membrane protein claudin 1 and the adherens junction protein E-cadherin play critical roles in cell-cell communication and in cell signaling. As a result, their protein levels and distribution in cancer have been a focus of cancer researchers in recent years. The loss of sensitivity to contact inhibition and the establishment of invasive properties in cancer are thought to be a result of the mislocalization of these membrane proteins to the cytoplasm. However, reports on their distribution and levels have been inconsistent. It is therefore critical that the techniques used to determine the cellular localization of these proteins be both consistent and reliable. This study was undertaken to determine the optimal fixation method, methanol or formalin, for the detection of claudin 1 and E-cadherin by immunofluorescence in five different human breast cancer cell lines. Both methods exhibited staining of the cell membrane and cytoplasm, but the strongest and most distinct signals were obtained using methanol fixation. Interestingly, cell-specific differences were also observed that appeared to be associated with levels of claudin 1 and E-cadherin as seen by Western blotting. Therefore, when evaluating cellular localization of the junction proteins claudin 1 and E-cadherin, expression level and cell type differences must be considered.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".