Multi-omics profiling reveals phenotypic and functional heterogeneity of neutrophils in COVID-19
Bibliographic record
Abstract
Abstract Background Accumulating evidence has revealed unexpected phenotypic heterogeneity and diverse functions of neutrophils in several diseases. Coronavirus disease (COVID-19) can alter the leukocyte phenotype based on disease severity, including neutrophil activation in severe cases. However, the plasticity of neutrophil phenotypes and their relative impact on COVID-19 pathogenesis has not been well addressed. This study aimed to identify and validate the heterogeneity of neutrophils in COVID-19 and evaluate the phenotypic alterations for each subpopulation. Methods We analyzed public single-cell RNA-seq, bulk RNA-seq, and human plasma proteome data from healthy donors and patients with COVID-19 to investigate neutrophil subpopulations and their response to disease pathogenesis. Results We identified eight neutrophil subtypes, namely C1–C8, and found that they exhibited distinct features, including activation signatures and multiple enriched pathways. The neutrophil subtype C4 (DEFA1/1B/3+) associated with severe and fatal disease. Bulk RNA-seq and proteome dataset analyses using a cellular deconvolution approach validated the relative abundances of neutrophil subtypes and the expansion of C4 (DEFA1/1B/3+) in severe COVID-19 patients. Cell– cell communication analysis revealed representative ligand-receptor interactions among the identified neutrophil subtypes. Notably, the C4 (DEFA1/1B/3+) fraction showed transmembrane receptor expression of CD45 and CAP1 as well as the secretion of pro-platelet basic protein (PPBP). We further demonstrated the clinical potential of PPBP as a novel diagnostic biomarker for severe COVID-19. Conclusion Our work has great value in terms of both clinical and public health as it furthers our understanding of the phenotypic and functional heterogeneity of neutrophils and other cell populations in multiple diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".