Stochastic assembly of biomacromolecular complexes: impact and implications on charge interpretation in native mass spectrometry.

Native mass spectrometry is a potent method for characterizing biomacromolecular assemblies. A critical aspect to extracting accurate mass information is the correct inference of the ion ensemble charge states. While a variety of experimental strategies and algorithms have been developed to facilitate this, virtually all approaches rely on the implicit assumption that any peaks in a native mass spectrum can be directly attributed to an underlying charge state distribution. Here, we demonstrate that this paradigm breaks down for several types of macromolecular protein complexes due to the intrinsic heterogeneity induced by the stochastic nature of their assembly. Utilizing several protein assemblies of adeno-associated virus capsids and ferritin, we demonstrate that these particles can produce a variety of unexpected spectral appearances, some of which appear superficially similar to a resolved charge state distribution. When interpreted using conventional charge inference strategies, these distorted spectra can lead to substantial errors in the calculated mass (up to ∼5%). We provide a novel analytical framework to interpret and extract mass information from these spectra by combining high-resolution native mass spectrometry, single particle Orbitrap-based charge detection mass spectrometry, and sophisticated spectral simulations based on a stochastic assembly model. We uncover that these mass spectra are extremely sensitive to not only mass heterogeneity within the subunits, but also to the magnitude and width of their charge state distributions. As we postulate that many protein complexes assemble stochastically, this framework provides a generalizable solution, further extending the usability of native mass spectrometry in the characterization of biomacromolecular assemblies.

Sensitive bioluminescence imaging of fungal dissemination to the brain in mouse models of cryptococcosis.

Cryptococcus neoformans is a leading cause of fungal brain infection, but the mechanism of dissemination and dynamics of cerebral infection following pulmonary disease are poorly understood. To address these questions, non-invasive techniques that can study the dynamic processes of disease development and progression in living animal models or patients are required. As such, bioluminescence imaging (BLI) has emerged as a powerful tool to evaluate the spatial and temporal distribution of infection in living animals. We aimed to study the time profile of the dissemination of cryptococcosis from the lung to the brain in murine models by engineering the first bioluminescent C. neoformans KN99α strain, expressing a sequence-optimized red-shifted luciferase. The high pathogen specificity and sensitivity of BLI was complemented by the three-dimensional anatomical information from micro-computed tomography (µCT) of the lung and magnetic resonance imaging (MRI) of the brain. These non-invasive imaging techniques provided longitudinal readouts on the spatial and temporal distribution of infection following intravenous, intranasal or endotracheal routes of inoculation. Furthermore, the imaging results correlated strongly with the fungal load in the respective organs. By obtaining dynamic and quantitative information about the extent and timing of brain infections for individual animals, we found that dissemination to the brain after primary infection of the lung is likely a late-stage event with a timeframe that is variable between animals. This novel tool in Cryptococcus research can aid the identification of host and pathogen factors involved in this process, and supports development of novel preventive or therapeutic approaches.