Fast and efficient enrichment of functional ILC2 from human whole blood
Bibliographic record
Abstract
Abstract Group 2 innate lymphoid cells (ILC2) are a functionally distinct subset of recently identified immune cells with important roles in type-2 immunopathologies such as allergies, asthma, helminth infections and other metabolic diseases. Studying these rare cells is challenging due to a lack of specific surface markers, and currently multicolor flow cytometric analysis and cell sorting are the only methods to characterize and isolate ILC2s. However, the scarcity of these cells makes flow cytometry time-consuming, expensive and often results in low purities and recoveries. Thus, better approaches for effective identification and isolation are essential to further understanding of ILC2 biology and function. We have developed a rapid and efficient method for enrichment of human ILC2 from whole blood. In brief, non-ILC2 cells in whole blood were crosslinked to red blood cells already present in the sample using RosetteSep™. The sample was then layered over Lymphoprep in a SepMate™ tube, spun at 1200 x g for 10 minutes (min), and the untouched, desired cells simply poured off. Cells were washed once and were then ready for subsequent analysis. Starting with only 0.01 – 0.07% in whole blood, ILC2 were enriched 350 ± 220 fold to 8.2 ± 6.8% in 35 min (means ± SD, n=17). Subsequent cell sorting from these pre-enriched samples was faster and yielded higher purity ILC2 than sorting from non-enriched controls (n=3, p<0.05 paired t test). Sorted ILC2, both pre-enriched and non-enriched controls, were cultured and stimulated, and secreted similar high levels of IL-13 as assessed by ELISA, indicating that these cells are functional. In summary, ILC2 pre-enrichment improves sorting efficiency, increases ILC2 purity, and maintains ILC2 functionality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".