Immunomagnetic enrichment of human ILC2 cells from peripheral blood leukopak
Bibliographic record
Abstract
Abstract Group 2 innate lymphoid cells (ILC2s) are essential for airway tissue repair, protection against helminth infection, metabolic homeostasis, and the development of allergic airway diseases. They are widely distributed throughout the human body in lung, intestine, palatine tonsils, skin and peripheral blood, but are extremely rare within a given tissue. In healthy individuals, ILC2s comprise 0.01–0.04% of leukocytes in the peripheral blood. Currently, flow cytometric cell (FACS) sorting is the only method to purify ILC2s, which is time-consuming due to their low frequency. We have adapted EasySep™ to pre-enrich ILC2s from peripheral blood leukopaks, and thereby shorten subsequent FACS analysis and sorting times. Non-ILC2 cells in an unprocessed leukopak were immunomagnetically labeled using a cocktail of antibody complexes and magnetic particles. The sample was then placed in a magnet for 10 minutes, the labeled cells were retained, and the unlabeled cells, containing the ILC2 population, were recovered. ILC2s were enriched from 5 x108unprocessed cells in 30 minutes. Enriched cells were counted and evaluated by FACS. ILC2s, defined as lineage negative (CD1a− CD3− CD11c− CD14− CD19− CD34− CD123− TCRαβ− TCRγδ− BDCA2− FcɛR1−), CRTH2+ CD161+CD127+, were enriched 268±167 fold, with 74± 34% recovery (means ± SD, n=7). EasySep™ pre-enrichment of ILC2s reduced subsequent FACS analysis and sorting time compared to non-enriched samples. Importantly, ILC2s enriched with EasySep™ were functional, as shown by production of the type 2 cytokine IL-13 upon stimulation with IL-33 and IL-2. Overall, EasySep™ is a simple and rapid method to pre-enrich ILC2s from leukopak for subsequent FACS analysis or sorting and functional analyses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".