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Record W4391930853 · doi:10.1016/j.devcel.2024.02.003

Stem cell migration drives lung repair in living mice

2024· article· en· W4391930853 on OpenAlexaff
Maurizio Chioccioli, Shuyu Liu, Sumner Magruder, Aleksandra Tata, Lucia Borriello, John E. McDonough, Arvind Konkimalla, Sang‐Hun Kim, Jessica Nouws, David G. Gonzalez, Brian Traub, Xianjun Ye, Tao Yang, David Entenberg, Smita Krishnaswamy, Caroline Hendry, Naftali Kaminski, Purushothama Rao Tata, Maor Sauler

Bibliographic record

VenueDevelopmental Cell · 2024
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsMcMaster University
FundersNational Center for Advancing Translational SciencesNational Heart, Lung, and Blood InstituteVeracyteNational Cancer InstituteYale UniversityNational Institutes of HealthUnited Therapeutics Corporation
KeywordsBiologyStem cellCell biologyMotilityCell migrationRegeneration (biology)PhenotypeIn vivoCellCell typeEx vivoGeneticsGene

Abstract

fetched live from OpenAlex

Tissue repair requires a highly coordinated cellular response to injury. In the lung, alveolar type 2 cells (AT2s) act as stem cells to replenish both themselves and alveolar type 1 cells (AT1s); however, the complex orchestration of stem cell activity after injury is poorly understood. Here, we establish longitudinal imaging of AT2s in murine intact tissues ex vivo and in vivo in order to track their dynamic behavior over time. We discover that a large fraction of AT2s become motile following injury and provide direct evidence for their migration between alveolar units. High-resolution morphokinetic mapping of AT2s further uncovers the emergence of distinct motile phenotypes. Inhibition of AT2 migration via genetic depletion of ArpC3 leads to impaired regeneration of AT2s and AT1s in vivo. Together, our results establish a requirement for stem cell migration between alveolar units and identify properties of stem cell motility at high cellular resolution.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.001

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.

Opus teacher head0.025
GPT teacher head0.320
Teacher spread0.294 · 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

Citations25
Published2024
Admission routes1
Has abstractyes

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