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Record W4242821206 · doi:10.1084/20182293

Single-cell analysis of RORα tracer mouse lung reveals ILC progenitors and effector ILC2 subsets

2019· article· en· W4242821206 on OpenAlexaff
Maryam Ghaedi, Mona Orangi, Itziar Martínez-González, Lisa L. Wei, Xiaoxiao Lu, Arundhoti Das, Alireza Heravi‐Moussavi, Marco A. Marra, Fumio Takei

Bibliographic record

VenueThe Journal of Experimental Medicine · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsCanada's Michael Smith Genome Sciences CentreTerry Fox Research InstituteUniversity of British Columbia
Fundersnot available
KeywordsInnate lymphoid cellRAR-related orphan receptor gammaBiologyPopulationProgenitor cellInterleukin-7 receptorImmunologyLungOrphan receptorCell biologyProinflammatory cytokineStem cellTranscription factorInflammationT cellInnate immune systemInternal medicineMedicineGeneticsImmune systemGene

Abstract

fetched live from OpenAlex

Lung group 2 innate lymphoid cells (ILC2s) drive allergic inflammation and promote tissue repair. ILC2 development is dependent on the transcription factor retinoic acid receptor–related orphan receptor (RORα), which is also expressed in common ILC progenitors. To elucidate the developmental pathways of lung ILC2s, we generated RORα lineage tracer mice and performed single-cell RNA sequencing, flow cytometry, and functional analyses. In adult mouse lungs, we found an IL-18Rα+ST2− population different from conventional IL-18Rα−ST2+ ILC2s. The former was GATA-3intTcf7EGFP+Kit+, produced few cytokines, and differentiated into multiple ILC lineages in vivo and in vitro. In neonatal mouse lungs, three ILC populations were identified, namely an ILC progenitor population similar to that in adult lungs and two distinct effector ILC2 subsets that differentially produced type 2 cytokines and amphiregulin. Lung ILC progenitors might actively contribute to ILC-poiesis in neonatal and inflamed adult lungs. In addition, neonatal lung ILC2s include distinct proinflammatory and tissue-repairing subsets.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.254
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Experimental MedicineSame topicIL-33, ST2, and ILC PathwaysFrench-language works237,207