Rapid Isolation of untouched mouse MDSCs
Bibliographic record
Abstract
Abstract Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of cells that regulate immune responses in cancer, chronic infections and inflammatory conditions. In an individual with a tumor burden, MDSCs accumulate in peripheral lymphoid organs and within the tumor microenvironment. In several human cancers, and in mouse tumor models, the presence of high numbers of MDSCs correlates with tumor growth, metastasis and an overall poor prognosis. Approaches that impair the activity or deplete MDSCs have shown promise in animal models. Thus, MDSCs are an attractive target for therapeutic intervention. We have developed an immunomagnetic method for the isolation of untouched mouse MDSCs. Using CyTOF analysis of cell surface antigens on spleen cells from tumor bearing mice, we developed a cocktail of antibodies that targets non-MDSCs for removal using a column-free, negative selection protocol. Our method isolates CD45+CD11b+Gr1+ cells with the following purity and recovery from spleen, BM and blood: MDSCs (CD11b+Gr1+) from tumor-bearing miceTissuePurity (±SD)Recovery (±SD)Spleen94 ± 2.1%50 ± 12%Blood99 ± 0.6%55 ± 7.0%Bone marrow96 ± 1.0%68 ± 5.6% (naïve) As a functional test, naïve mouse splenocytes were labeled with a proliferation dye and the T cells were activated with anti-CD3 and anti-CD28 antibodies. MDSCs isolated from tumor-bearing mice were added at different ratios and after 3 days of culture proliferation of CD4+ and CD8+ T cells was assessed by flow cytometry. This assay showed a dose-dependent suppression of both CD4+ and CD8+ T cells by the isolated MDSCs. Our new EasySep™ Mouse MDSC (CD11b+Gr1+) Isolation Kit offers a fast and easy method that could facilitate MDSC discoveries that impact a wide range of diseases.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".