Mass Spectrometry‐Based Quantitative Proteomics of Murine‐Derived Polymorphonuclear Neutrophils
Bibliographic record
Abstract
Polymorphonuclear cells (PMNs or neutrophils) are the most abundant leukocyte in humans and represent an essential component of the innate immune system. The ability of neutrophils to initiate an immediate and non-specific host response against invading microbial species is the key to determining the outcome of infection. Neutrophils produce and secrete a plethora of immunomodulatory proteins, including major granule proteins and cytokines, as well as various enzymes, which regulate adherence, phagocytosis, chemotaxis, and cell survival. Historically, characterization of neutrophils and their roles during infection have relied on genetic and phenotypic analyses, as well as biochemical assays. However, recent advances in mass spectrometry-based proteomic workflows and technological platforms have supported the comprehensive profiling of neutrophil-associated immune responses in consideration of cellular factors and secreted proteins. Given the critical role of neutrophils in maintaining and regulating innate immune function, comprehensive profiling of their response to infection is imperative to ensuring host survival. Here, we briefly discuss the role of neutrophils in host-defense and describe methods to purify neutrophils from murine samples and comprehensively profile their proteomes. © 2019 by John Wiley & Sons, Inc.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".