Analysis of Crosstalk Between Pathogens and Immune System in Human Airway Mucus via Machine Learning‐Enhanced DIA Mass Spectrometry

We demonstrate excellent depth of proteome coverage and experimental repeatability for low-abundance pathogen proteomes in human airway secretions via data-independent acquisition liquid chromatography tandem mass spectrometry leveraging machine learning for spectral analysis. The host's sputum proteome was also profiled, allowing inferences of immune defense mechanisms against pathogens. This proof-of-principle study shows the opportunity to gain insights into respiratory disease burdens and bacterial virulence by directly analyzing clinical specimens and the potential for biomarker discovery and pharmacodynamic response monitoring in interventional studies related to respiratory tract infections.