In-silico immune cell deconvolution of the airway proteomes of infants with pneumonia reveals a link between reduced airway eosinophils and an increased risk of mortality

Charles J Sande; Jacqueline M Waeni; James M Njunge; Martin N Mutunga; Elijah Gicheru; Nelson K Kibinge; Agnes Gwela

A potential way to address these shortcomings is to monitor changes in marker proteins that are uniquely or predominantly expressed by specific immune cell phenotypes and to use this information to infer the dynamics of the underlying cell types. We have recently described a high-resolution mass-spectrometry-based proteomics approach for characterizing the total proteome of the infant airway to a depth of more than 1,000 proteins 10, 11 . The airway proteome characterized by this technique represents an unbiased snapshot of the underlying cell populations and can be leveraged to infer changes in the frequencies of the contributing cell phenotypes. Here, we describe an in-silico immune cell phenotype deconvolution approach where we use protein markers that are uniquely or predominantly expressed by specific immune cell subsets to infer the dynamics of those phenotypes in airways of children who survived or died from pneumonia.