Quantitative Proteomic Profiling of Murine Ocular Tissue and the Extracellular Environment
Bibliographic record
Abstract
Mass spectrometry-based proteomics provides a robust and reliable method for detecting and quantifying changes in protein abundance among samples, including cells, tissues, organs, and supernatants. Physical damage or inflammation can compromise the ocular surface permitting colonization by bacterial pathogens, commonly Pseudomonas aeruginosa, and the formation of biofilms. The interplay between P. aeruginosa and the immune system at the site of infection defines the host's ability to defend against bacterial invasion and promote clearance of infection. Profiling of the ocular tissue following infection describes the nature of the host innate immune response and specifically the presence and abundance of neutrophil-associated proteins to neutralize the bacterial biofilm. Moreover, detection of unique proteins produced by P. aeruginosa enable identification of the bacterial species and may serve as a diagnostic approach in a clinical setting. Given the emergence and prevalence of antimicrobial resistant bacterial strains, the ability to rapidly diagnose a bacterial infection promoting quick and accurate treatment will reduce selective pressure towards resistance. Furthermore, the ability to define differences in the host immune response towards bacterial invasion enhances our understanding of innate immune system regulation at the ocular surface. Here, we describe murine ocular infection and sample collection, as well as outline protocols for protein extraction and mass spectrometry profiling from corneal tissue and extracellular environment (eye wash) samples. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Murine model of ocular infection Basic Protocol 2: Murine model sample collection Basic Protocol 3: Protein extraction from eye wash Basic Protocol 4: Protein extraction from corneal tissue Basic Protocol 5: Mass spectrometry-based proteomics and bioinformatics from eye wash and corneal tissue samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".