Flow cytometric analysis of innate lymphoid cells: challenges and solutions
Bibliographic record
Abstract
Introduction The three groups of helper innate lymphoid cells (ILCs), namely ILC1, ILC2 and ILC3, have been identified by flow cytometry by combinations of cell surface markers. Here, we review various ways ILCs are currently identified, focusing on potential problems and their solutions. The first step to identify all ILCs is to exclude other lymphocytes and myeloid cells by their lineage-specific markers (Lin). However, the Lin cocktail varies in various studies, and the definition of Lin- population containing ILCs is often ambiguous, resulting in contamination of Lin + cells, particularly T cells. Method We have designed combinations of cell surface markers to identify ILC populations in various tissues of B6 mice by flow cytometry. To minimize T cell contamination, TCR/CD3ϵ antibodies were used separately from the Lin cocktail. ILCs identified by surface markers are confirmed by the expression of the transcription factors GATA3, RORγt, T-bet and Eomes. Result ILC1s in the B6 mouse liver are identified by Lin-NKp46 + NK1.1 + TCR/CD3ϵ − CD49a + CD49b − . However, defining ILC1s in other tissues remains a challenge. ILC2s in the lung are identified by Lin − TCR/CD3ϵ − Thy1 + CD127 + ST2 + whereas ILC2s in the small intestine and liver are identified by Lin − TCR/CD3ϵ − Thy1 + GATA3 + RORγt − . ILC3s in B6 mouse spleen, liver, lung and small intestine are identified by Lin − TCR/CD3ϵ − Thy1 + CD127 + RORγt + . Discussion The ILC population is heterogeneous and the strategies to identify ILCs have to be designed for each ILC population and tissue. Excluding T cells in all cases is crucial, and a combination of transcription factors GATA3, RORγt, T-bet, and Eomes should be used to identify ILCs. Using CD3ϵ/TCRs in a different fluorochrome not in Lin cocktail minimizes contamination of T cells specifically identify individual ILC populations in various tissues.
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 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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".