Efficient Enrichment of Functional ILC Subsets from Human PBMC by Immunomagnetic Selection
Bibliographic record
Abstract
Abstract Innate lymphoid cells (ILCs) are exceedingly rare but important regulators of homeostatic and disease-associated immune processes. The frequency of ILCs in peripheral blood of healthy humans is ~0.07% of CD45+ leukocytes. ILCs lack specific cell surface markers but can be divided into distinct subsets (ILC1, 2 and 3) based on their differential expression of effector cytokines and master transcription factors. Currently, cell sorting is the most widely used method to isolate ILCs, but it is time consuming, expensive and often results in low purities and recoveries. Pre-enrichment of ILCs would allow for reduced sorting times and improved purities. Accordingly, we have developed a fast immunomagnetic negative selection method to pre-enrich all ILCs subsets from human leukapheresis samples. Briefly, unwanted cells are labelled with antibodies and magnetic particles and placed into an EasySep™ magnet. Unwanted cells are retained in the magnet and the enriched ILC fraction is simply poured off into a new tube. We find that total ILCs (defined as Lineage− CD45+ CD127+) are enriched from a frequency of 0.01 – 0.23% (n=28) to a final frequency of 17 – 86%, an enrichment of 200 – 1500 fold with virtually no loss of ILCs. ILC1 were enriched from 0.01 – 0.2% to 4.5 – 14%. ILC2 were enriched from 0.01 – 0.1% to 5.8 – 51% and ILC3 were enriched from 0.01 – 0.1% to 6 – 16%. This pre-enrichment drastically decreases sort time, allowing sorting over 3.7 × 105 ILCs from 2 × 109 PBMCs in only 12 minutes. Sorted cells maintained their functionality; when stimulated, ILC1s produced IFNγ, ILC2s secreted IL-13 and ILC3s produced IL-22. Our newly developed method of ILC pre-enrichment should aid human ILC research by enabling their rapid isolation when combined with cell sorting
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".