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The prevalence of invasive fungal infections is vastly increasing and is correlated with unacceptably high mortality rates, most commonly reported in individuals with immunodeficient predispositions1. Cryptococcus neoformans is a notorious opportunistic fungal pathogen capable of intracellular survival within host macrophage cells. Inadequate antifungal intervention results in fungal dissemination and life-threatening manifestations of cryptococcal meningitis and meningoencephalitis2,3. The global increase in immunocompromised status has demanded a parallel increase in the use of antifungal agents, in which many fungal species, including C. neoformans, have increasingly evolved resistance towards4,5,6. Therefore, it is imperative to implement robust and efficient technologies to answer vital biological questions regarding host defence response and microbial pathogenesis.
The new age of technological advancement in mass spectrometry (MS), including the generation of powerful computational and bioinformatic pipelines, provides the foundation for an integrative vision for large-scale analysis of host-pathogen research7,8. Conventional pathogenesis-driven proteomic analysis commonly profiles the view of infection from either the host or pathogen perspective, including comprehensive methodologies such as protein correlation profiling, affinity chromatography combined with proteomics, and interactomics9. Investigations into the virulence of dangerous pathogens in a host system are of immense clinical importance; however, the application of a dual perspective analysis in a single experiment was formerly considered unattainable. For example, the pathogen’s perspective towards infection is often overwhelmed by highly abundant host proteins resulting in reduced sensitivity for the detection of low-abundant fungal proteins7. Furthermore, the high sample complexity invites many targets to investigate in a single experimental system and proves challenging to elucidate mechanisms of action for a specific pathogen protein.
Bottom-up proteomics is a popular MS technique that enables manageable sample preparation, in which peptides are generated by sequence-specific enzymatic digestion followed by liquid chromatography separation, identification, and quantification by MS10,11. Here, we present a method demonstrating a data-dependent acquisition strategy purposed to achieve an unbiased coverage of an infection-based proteome or ‘infectome’. Specifically, label-free quantification (LFQ) sheds the dependence on chemical or metabolic labels for robust and accurate identification of protein level changes across multiple proteomes, reducing sample handling and processing steps12,13. This universal application interrogates produced proteins at a given moment within a cell independent of any expected protein production; thus, novel insights may be discovered that are critical to infection.
The workflow described herein is optimized to explore protein level changes of C. neoformans during infection-mimicking conditions with host immune cells (Figure 1). Rather than relying on the isolation and separation of cell types, this approach extracts the host and pathogen proteome together, and utilizes bioinformatic separation using two organism-specific databases to distinguish species-specific protein production. This method offers advantages for an unlimited number of samples to be processed without the extra costly preparation steps necessary in isotope-based labelling studies or fractionation. Furthermore, this workflow supports optimized protein extraction protocols transferable to a wide range of fungal and bacterial pathogens capable of targeting and infecting host immune cells. Overall, this protocol outlines the steps to complete an unbiased protein extraction and sample processing for high-resolution MS, followed by data and statistical analysis, capable of providing a wealth of knowledge of fungal proteins significant for infection combined with comprehensive profiling of the host defense response.