Immunometabolic analysis of primary murine Group 2 Innate Lymphoid Cells: a robust step-by-step approach
Bibliographic record
Abstract
Abstract Group 2 Innate Lymphoid Cells (ILC2s) have recently been shown to exert key regulatory functions in innate and adaptive immune response networks that drive the establishment and progression of type 2 immunity and its associated pathologies. Although mainly tissue resident, ILC2s and their crosstalk within tissue microenvironments influences both local and systemic metabolism. In turn, the metabolic status shapes the diverse ILC2 phenotypes and effector functions. Hence, deciphering the metabolic networks of ILC2s is essential in understanding ILC2’s roles in health as well as pathophysiologies. Here we detail a framework of experimental approaches to study key immunometabolic states of primary murine ILC2s and link them to phenotypes and functionality. Utilizing flow cytometry, Single Cell ENergetIc metabolism by ProfilIng Translation inhibition (SCENITH) as well as the Seahorse platform we provide a framework that allows in-depth analysis of cellular bioenergetic states to determine the immunometabolic wiring of ILC2. Linking immunometabolic states and networks to ILC2 phenotypes and effector functions will allow in-depth studies that assess the potential of novel pharmaceutics to alter ILC2 functionality in experimental and clinical settings.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".