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Record W4410930202 · doi:10.1172/jci183796

PIEZO1 mediates mechanical reprogramming of neutrophils for proangiogenic specialization in the lung

2025· article· en· W4410930202 on OpenAlexaff
Jin Wang, Wenying Zhao, Wenjuan Bai, Dong Dong, Hui Wang, Xin Qi, Ajitha Thanabalasuriar, Youqiong Ye, Tian‐Le Xu, Paul Kubes, Bin Li, Jing Wang

Bibliographic record

VenueJournal of Clinical Investigation · 2025
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsUniversity of CalgaryMcGill University
FundersNational Natural Science Foundation of China
KeywordsReprogrammingLungCell biologyBiologyImmunologyChemistryMedicineCellInternal medicineGenetics

Abstract

fetched live from OpenAlex

Neutrophils are the most abundant immune cells that constantly patrol or marginate inside vascular beds to support immune homeostasis. The extent to which neutrophils undergo reprogramming in response to the changes in vascular architecture and the resultant biological implications of such adaptations remain unclear. Here, we performed intravital imaging and transcriptional profiling to investigate neutrophil behavior across different tissues. Our findings revealed that neutrophils had significant deformability and spontaneous calcium signaling while navigating through the narrow pulmonary vessels. Pulmonary neutrophils exhibited unique transcriptional profiles and were specialized for proangiogenic functions. We found that the mechanosensitive ion channel Piezo-type mechanosensitive ion channel component 1 (PIEZO1) was essential for neutrophil reprogramming. Deletion of Piezo1 in neutrophils ablated the lung-specific proangiogenic transcriptional signature and impaired capillary angiogenesis in both physiological and pathological conditions. Collectively, these data show that mechanical adaptation of neutrophils within the pulmonary vasculature drives their reprogramming in the lungs and promotes pulmonary vascular homeostasis.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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)0.0000.000

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 head0.083
GPT teacher head0.407
Teacher spread0.324 · 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 designObservational
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

Citations14
Published2025
Admission routes1
Has abstractyes

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