Automated Large-Scale T Cell Isolation in a New Closed Cell Separation System
Bibliographic record
Abstract
Abstract Large-scale T cell isolation is commonly performed in a wide range of laboratory settings, including for cell therapy and drug discovery research, as well as in core facilities as part of cell manufacturing and banking. However, current methods can be a significant bottleneck in a lab’s workflow, often requiring a full day just for sample processing and cell isolation. Furthermore, many applications have stringent requirements for purity, sterility, and standardization of the cell isolation procedure. To address these needs, we have developed RoboSep™-C, an instrument for efficient and automated cell isolation in a closed system. RoboSep™-C automates the established EasySep™ technology for immunomagnetic cell separation to enable isolation of untouched T cells, CD4+ T cells, or CD8+ T cells from leukapheresis samples. To set up the system, the user follows the on-screen prompts to install a sterile single-use tubing set and load the recommended medium, cell isolation reagents, and starting leukopak. The instrument then performs all necessary cell processing steps including sample washing, cell labeling, magnetic separation, and cell concentration in a 50-minute protocol. Starting with samples ranging from 2.5 to 20 billion nucleated cells, we obtained purities of 95.4 ± 4.1%, 95.7 ± 2.5%, and 89.3 ± 3.5% for T cell (n=16), CD4+ T cell (n=14), and CD8+ T cell (n=12) isolation, respectively (mean ± SD). The recoveries of the isolated T, CD4+ T, and CD8+ T cells were 64.2 ± 12.4%, 67.0 ± 8.2%, and 54.3 ± 13.5%, respectively. Automated isolation of high-purity T cells with RoboSep™-C enables researchers to scale up their operations and can be easily integrated upstream of existing T cell expansion, genome editing, and cryopreservation protocols.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".