Quick and Easy Isolation of Immune Cells From Large-Volume Samples
Bibliographic record
Abstract
Abstract Large-scale cell isolation is commonly performed in labs and core facilities for cell banking and drug discovery, and as a critical step in cell therapy manufacturing. Current methods can be a significant bottleneck in a lab’s workflow, often requiring a full day for sample processing and cell isolation. To address this need, we have developed two methods: manual column-free isolation using the Easy 250 EasySep™ magnet, and automated cell isolation in a closed system using RoboSep™-C. The Easy 250 magnet allows users to isolate cells from a full leukopak of up to 20 billion cells in under 30 minutes by simply pipetting out their target cells. Negative selection protocols have been optimized to achieve 95.3% T cell, 97.2% CD4+ T cell, 91.9% CD8+ T cell, 99.4% B cell, 96.5% NK cell, and 91.5% monocyte purities. Positive selection protocols obtain 95.4% CD3+ cell, 94.4% CD4+ T cell, 93.9% CD8+ T cell, and 96.2% CD14+ cell purities. RoboSep™-C automates this cell isolation procedure, along with the cell washing steps for sample preparation, in as little as 50 minutes. The instrument features a scale tower, pump and clamp modules to direct fluid through defined paths, and a magnet for separation of labelled cells. The system uses a sterile single-use tubing set that incorporates a cartridge for cell washing and concentration, and a magnet chamber for cell separation. Starting with fresh leukopaks, we obtained 95.9% T cell, 95.8% CD4+ T cell, and 89.6% CD8+ T cell purities following negative selection, and 92.0% CD4+ T cell and 89.5% CD8+ T cell purities following positive selection. These approaches offer efficient and user-friendly cell isolation that allow researchers to scale up their operations, and can be easily integrated upstream of existing workflows.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".