A fast and robust protocol for immunomagnetic isolation of human CD45+ leukocytes from samples with variable start frequencies
Bibliographic record
Abstract
Abstract The study of immune cell function in non-lymphoid tissue and tumors has emerged as an exciting research area, with the promise to provide novel strategies for the treatment of immune disorders, infectious diseases and cancer. A major challenge in the field is the isolation of leukocytes from tissues due to the complexity of starting samples that can be highly variable in frequency and subset composition. We describe a new 28 minute protocol optimized for the isolation of particle-free human CD45+ leukocytes from varied sample sources. 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 isolated cells are then released from particles by resuspension in EasySep™ Release Buffer and a final magnetic separation step. Performance was assessed using human leukocytes spiked into a single cell suspension of mouse thymocytes across a range of frequencies to simulate the variable leukocyte content in tissue and tumor samples. Human CD45+ purities were 95.4 ± 4.3% from start ranges of 0.5–2.0% (n=15) and 99.1 ± 0.7% from start frequencies of ≥10% (n=18). The isolated cells are compatible with further downstream isolation and are functional, as demonstrated by T cell proliferation and IFN-gamma production assays. We have also assessed our CD45+ isolation kit from tissues of humanized NSG mice, expanding the application of our protocol to highly relevant disease and cancer models. The EasySep™ Release Human CD45 Positive Selection Kit provides a robust method for high purity isolation from diverse tissues and starting frequencies, generating particle-free cells ready for downstream evaluation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".