Isolation of mouse CD45 positive leukocytes from tissues
Bibliographic record
Abstract
Abstract Immune cell function is often tissue-specific, therefore isolating cells from their tissue microenvironment is necessary to better understand their role in health and disease. This can be challenging in the presence of non-immune, tissue-derived cells and cellular debris from tissue dissociation. These factors reduce the leukocyte start frequency, which can prolong the isolation process and limit the identification of small but critical leukocyte subsets. To overcome these obstacles, we developed a simple and rapid selection method to enrich for CD45+ leukocytes from mouse tissues. Starting with a single cell suspension, cells are labelled with an antibody complex that links CD45+ cells to magnetic particles and following magnetic separation, the isolated CD45+ cells are ready for use. Using healthy mouse lung tissue as an example, CD45+ leukocytes were enriched from 63.5 ± 9.4% to 97.1 ± 1.2% purity, and the recovery of viable CD45+ cells was 37.7 ± 14.5% (mean ± SD, n=37). Preliminary testing on tumors from a mouse 4T1 breast tumor model resulted in efficient enrichment of tumor-infiltrating leukocytes. Importantly, the composition of immune subsets from both healthy lung tissues and tumor samples was maintained following CD45 selection. To demonstrate functionality, T cells within the CD45 selected population from spleen were able to upregulate CD25 and CD69, and proliferate upon stimulation. In as little as 20 minutes, the EasySep™ Mouse CD45 Isolation Kit allows researchers to easily enrich for functional leukocytes from tissue samples. Examining immune cells directly from healthy and tumor tissues will enhance our understanding of how these cells function and aid in developing new approaches to combat disease progression.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".