Isolation of Tumor-Infiltrating Leukocytes from Mouse Tumors
Bibliographic record
Abstract
Abstract Cell-based immunotherapy is being evaluated in various types of cancer and it is one of the most rapidly growing and promising areas of cancer research. Tumor-infiltrating leukocytes (TILs) consist of highly diverse leukocyte subsets with major roles in cancer immune surveillance. Due to their relatively low frequency, tumor heterogeneity, and the abundance of tissue debris in tumor samples, it is difficult to isolate or analyze TILs with sensitivity and precision. To address this challenge, we have developed a simple method for isolating CD45+ TILs from mouse tumors. Performance was evaluated in three commonly used mouse models, namely the B16 melanoma, CT26 colon carcinoma, and 4T1 mammary tumor models. Solid tumors were induced by subcutaneous implantation of B16, CT26.WT, and 4T1 cancer cell lines into syngeneic recipients. Starting with a single-cell suspension, TILs from tumor samples were labeled with an antibody complex that links CD45+ cells to magnetic particles, then separated using an EasySep™ magnet. Using this method, TILs were enriched from 17.5 +/− 5.8% to 90.1 +/− 6.0% (n = 13) from B16 tumors, 27.9 +/− 9.4% to 74.2 +/− 12.3 % (n = 9) from CT26 tumors, and 39.5 +/− 8.0% to 87.5 +/− 3.7% (n = 6) from 4T1 tumors. The protocol can be easily modified to achieve higher purity or recovery as required by adjusting the addition volume of the antibody complex or particles. Importantly, major immune subsets including T cells, B cells, and myeloid cells are recovered after isolation. The EasySep™ Mouse CD45 TIL Isolation Kit allows researchers to isolate leukocytes from tumors with ease, improving the TIL downstream workflow. Furthering our understanding of TILs will be essential for developing effective immunotherapeutic strategies.
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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 | 0.001 |
| 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".