“Just one word, plastic!”: Controversies and caveats in innate lymphoid cell plasticity
Bibliographic record
Abstract
Innate lymphoid cells (ILCs) are frontline immune effectors involved in the early stages of host defense and maintenance of tissue homeostasis, particularly at mucosal surfaces such as the intestine, lung, and skin. Canonical ILCs are described as tissue-resident cells that populate peripheral tissues early in life and respond appropriately based on environmental exposure and their anatomical niche and tissue microenvironment. Intriguingly, there are accumulating reports of ILC "plasticity" that note the existence of non-canonical ILCs that exhibit distinct patterns of master transcription factor expression and cytokine production profiles in response to tissue inflammation. Yet this concept of ILC-plasticity is controversial due to several confounding caveats that include, among others, the independent large-scale recruitment of new ILC subsets from distal sites and the local, in situ, differentiation of uncommitted resident precursors. Nevertheless, the ability of ILCs to acquire unique characteristics and adapt to local environmental cues is an attractive paradigm because it would enable the rapid adaptation of innate responses to a wider array of pathogens even in the absence of pre-existing 'prototypical' ILC responder subsets. Despite the impressive recent progress in understanding ILC biology, the true contribution of ILC plasticity to tissue homeostasis and disease and how it is regulated remains obscure. Here, we detail current methodologies used to study ILC plasticity in mice and review the mechanisms that drive and regulate functional ILC plasticity in response to polarizing signals in their microenvironment and different cytokine milieus. Finally, we discuss the physiological relevance of ILC plasticity and its implications for potential therapeutics and treatments.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.021 |
| 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".