Murine Stem Cell–Based Retrovirus Production for Marking Primary Mouse Mammary Cells for Metastasis Studies
Bibliographic record
Abstract
Since the introduction of retroviral vector technology, permanent genetic marking of cells has considerably contributed to the understanding of different physiological and disease processes in vivo. Recent marking strategies aim to elucidate the contribution of cells on the clonal level, and the advent of fluorescent proteins has opened new avenues for the in vivo analysis of gene-marked cells. Gene-modified cells are easily identifiable (e.g., via the introduced fluorescent protein) within whole organ structures, allowing one to measure the contribution of transduced cells to malignant outgrowth. In our laboratory, we use the tetracycline-inducible system to study oncogene cooperation in metastatic progression. We use bicistronic retroviruses expressing the tetracycline transactivator (tTA) and the candidate gene (MIT-gene) or the tTA alone (MIT-Rx) to infect primary mammary cells from mice harboring tetracycline-inducible transgenes. This allows for constitutive expression of the candidate gene and tTA-dependent expression of the inducible oncogene. We also use MIG-based vectors, which allow for constitutive expression of the candidate gene and a green fluorescent protein. Here we describe how to produce retroviral particles carrying both MIT- and MIG-based vectors. Because of the fragility of the retroviral envelope, we do not attempt to concentrate the virus, and we directly use packaging cell media to infect primary epithelial cells (either normal or tumor). Infected cells can be transplanted into recipient mice to investigate metastatic colonization.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".