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Single-cell Proteome Profiling Reveals Distinct Immunological Patterns in the Lungs of Patients With Acute Respiratory Distress Syndrome

2025· article· en· W4410273688 on OpenAlexaff
Shuyang Zhang, Sebastiaan C M Joosten, L.S. Boers, Helene B. van den Heuvel, Juan J. García‐Vallejo, Tom van der Poll, Jan Willem Duitman, Lieuwe D. J. Bos

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedicineAcute respiratory distressProfiling (computer programming)ProteomeRespiratory distressRespiratory systemARDSImmunologyIntensive care medicineLungBioinformaticsInternal medicineSurgery

Abstract

fetched live from OpenAlex

Abstract Rationale: Acute Respiratory Distress Syndrome (ARDS) is a life-threatening condition caused by diverse etiologies, typically characterized by inflammation and immune dysregulation in the lungs. A comprehensive understanding of the host immune response is essential for identifying distinct ARDS subphenotypes and has significant implications for developing targeted therapeutic interventions. However, existing studies primarily focus on immune responses in the blood. Understanding of local functional heterogeneity has largely relied on transcriptomic data, which gives a comprehensive image but may not translate into phenotypic differences at the protein level. This limitation restricts in-depth insights into immune cell functions within the lung microenvironment. In this study, we describe the alveolar immune cell composition and function at single cell level, and test differences between etiologies and between survivors and non-survivors. Methods: In this observational cohort study, bronchoalveolar lavage (BAL) fluid was collected repeatedly from mechanically ventilated patients admitted to the intensive care unit (ICU). BAL leukocytes were isolated and analyzed using high-dimensional cytometry by time-of-flight (CyTOF). A 50-marker panel was used to assess the maturity, activation states and targeted therapeutic potential of immune cells. Results: A total of 128 BAL fluid samples from 91 intubated patients were analyzed. Of these patients, 75 (82.4%) patients had ARDS, 64 (70.3%) had pneumonia, and 68 (74.7%) patients survived at 28 days post-intubation. The relative abundance of immune cell subsets did not correlate with disease etiology or survival with a predominance of immature neutrophils (CD10-) throughout all disease stages. CD64 expression was higher in neutrophils from patients with pneumonia compared to those without pneumonia (p<0.001). PD-1 expression on T cells was also higher in pneumonia patients (p<0.001). Macrophage activation, indicated by several activation markers (pJNK, iNOS, CD163), was negatively associated with mortality in ARDS patients. Similarly, neutrophils showed a decrease in activation markers (CD64, CD11b, CD45RO) in non-survivors. Conclusion: Despite the heterogenous etiology of ARDS, patients share similar frequencies of immune cell population in the lungs. However, the activation states of these immune cells vary depending on the presence of pneumonia and are associated with mortality.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.271
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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