Stages and Transcriptional Regulation of Innate Lymphoid Cell Development
Bibliographic record
Abstract
In recent years it has become apparent that innate lymphoid cells (ILCs) play major roles in infectious, inflammatory and allergic diseases as well as tissue homeostasis. However many of the models used to study the requirement for ILCs, also have effects on other cells of the immune system, particularly T cells. This deficit in ILC-specific deletion models, highlights a need to better understand ILC development for the identification of ILC specific master regulators. Our lab has recently identified a committed ILC precursor in the bone marrow and fetal liver based on the high expression of the NKT master regulator PLZF. This precursor gives rise to all known ILC lineages, ILC1, 2, and 3, but not LTi or NK. However the stages of development and factors involved in the emergence of the PLZF expressing precursor and factors involved in the trifurcation of the ILC precursor into ILC1, 2, and 3 remain to be identified. We use single-cell multiplex qPCR of defined ILC precursors to assess their transcriptional profiles and establish a hierarchy of ILC development and ILC developmental transcription factors. We identified early clusters of precursors that corresponded to αLP that expressed Id2, Nfil3 and Tox, but lacked expression of ILC lineage specific markers. We also identified a transitional cluster of precursors that marked the bifurcation of ILCP and LTiP and interestingly expressed high levels of Sox4 and Runx1, which heretofore have not been well studied in ILC development. We also identified the ILCP as the stage of lineage trifurcation of ILCs, a process which we show to occur through multi-lineage priming. These results are the basis for a blueprint of ILC development and provide insight into the differentiation of ILC lineages. The precise definition of ILC developmental precursors and intermediates will allow for the identification of novel regulators and the development of improved models to study the function of ILCs and their importance in human disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".