Immunometabolic analysis of primary murine group 2 innate lymphoid cells: a robust step-by-step approach
Bibliographic record
Abstract
Group 2 Innate Lymphoid Cells (ILC2s) have recently been shown to exert key regulatory functions in both innate and adaptive immune response networks that drive the establishment and progression of type 2 immunity. Although mainly tissue resident, ILC2s and their crosstalk within tissue microenvironments influence metabolism at both the local and systemic levels. In turn, the energetic demand and metabolic status within these systems shape the diverse phenotypes and effector functions of ILC2s. Deciphering these metabolic networks in ILC2s is therefore essential in understanding their various roles in health as well as their associated pathophysiologies. Here we detail a framework of experimental approaches to study key immunometabolic states of primary murine ILC2s and link them to unique phenotypes and their corresponding functionality. Utilizing flow cytometry, Single Cell ENergetic metabolism by profilIng Translation inHibition (SCENITH), and the Seahorse platform we provide a framework that allows in-depth analysis of cellular bioenergetic states to determine the immunometabolic wiring of ILC2s. Connecting immunometabolic states and networks to ILC2 phenotypes and effector functions with this method will allow future in-depth studies to assess the potential of novel pharmaceutics in altering ILC2 functionality in 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) | 0.001 | 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 | 0.001 |
| 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".