Isolation of human CD45+ leukocytes from tissues and human tumor xenografts in humanized mice
Bibliographic record
Abstract
Abstract The study of immune cell function in non-lymphoid tissue and tumors promises to elucidate novel strategies to treat immune disorders, infectious diseases, and cancer. To address the challenge of isolating leukocytes from complex and variable tissues and tumors, we have developed a new protocol to isolate particle-free, human CD45+ leukocytes. Using the EasySep™ Release Human CD45 Positive Selection Kit, leukocytes are labeled with antibody complexes linked to magnetic particles and separated using an EasySep™ magnet. The magnetic particles are then removed from the desired cells by resuspension in EasySep™ Release Buffer and a final magnetic separation. To assess performance, NRG-3GS mice were first engrafted with human CD34+ cells followed by xenotransplant with human breast (MDA-MB-231) or ovarian (SKOV3) cancer cell lines. In humanized mouse lungs, bone marrow and spleen, the starting and isolated human CD45+ frequency ranges were 6.0 – 57.2% and 90.9 – 99.4%, respectively (n = 3). Starting with human tumor xenografts, tumor infiltrating leukocytes were enriched from a starting range of 0.4 – 18.0% to 76.6 – 92.7% (n = 4). The final immune cell frequencies are representative of the starting population, and further separation of immune subsets can be achieved with additional downstream isolation. Humanized mouse models of clinical disease are instrumental in furthering our understanding of complex mechanisms of disease progression and resolution. This new kit for the isolation of human immune cells from tissues and tumors will facilitate further examination of the roles of immunity in disease and the evaluation of immune-based treatment 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 | 0.001 | 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".