Functional Analysis of Tumor-Infiltrating Myeloid Cells by Flow Cytometry and Adoptive Transfer
Bibliographic record
Abstract
The tumor-infiltrating myeloid cell compartment represents a heterogeneous population of broadly immunosuppressive cells that have been exploited by the tumor to support its growth. Their accumulation in tumor and secondary lymphoid tissue leads to the suppression of antitumor immune responses and is thus a target for therapeutic intervention. As it is known that the local cytokine milieu can dictate the functional programming of tumor-infiltrating myeloid cells, strategies have been devised to manipulate the tumor microenvironment (TME) to express a cytokine landscape more conducive to antitumor myeloid cell activity. To evaluate therapy-induced changes in tumor-infiltrating myeloid cells, this paper will outline the procedure to dissociate intradermal/subcutaneous tumor tissue from solid tumor-bearing mice in preparation for leukocyte recovery. Strategies for flow cytometric analysis will be provided to enable the identification of heterogeneous myeloid populations within isolated leukocytes and the characterization of unique myeloid phenotypes. Lastly, this paper will describe a means of purifying viable myeloid cells for functional assays and determining their therapeutic value in the context of adoptive transfer.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".