Ultrahigh-Sensitivity Mass Spectrometry for Scalable Proteomics

The protein composition of a cell, or proteome, responds dynamically to external and internal environments, and provides fundamental information on biomolecular mechanisms. However, research in these primary effectors in cellular function is constrained by the lack of high-sensitivity mass spectrometry (MS) approaches necessary to characterize proteomic changes, such as aging effects across neurons, subtypes of neurons, or neuronal subcompartments and in/between samples at trace abundance, such as in the cerebrospinal fluid. At the biological level, aging results from accumulated cellular damage over time, and has been associated with deterioration of synaptic composition, dysregulation of excitatory/inhibitory balance of neuronal circuits, and deleterious proteolytic processing. Here, a new proteomics platform, termed scalable Single-Cell Mass Spectrometry (SCeMaS), will be developed and validated on biological models of proteins and protein fragments associated with these biomolecular effects of aging via three independent aims. Each aim is designed to address contemporary technical limitations that have historically hindered deep MS `omics and integration with functional tools of research on aging, such as electrophysiology. This project strategically leverages invertebrate and vertebrate biological models based on their (i) practical advantages to facilitate specific aspects of technology development, refinement, and validation as well as (ii) established use in various contexts of research on aging. Giant identified neurons of the crayfish are chosen to advance the technology of proteome collection and processing with reduced protein/peptide losses than presently feasible. Specific single neurons will be patched in the mouse to downscale the approach through development of a sample-enrichment method. To enable recording of live neurons in these models and microanalysis of proteome degradation in the CSF, a specialized chemical approach will be introduced to remove salt interferences, thus ushering MS proteomics and electrophysiology to a one-step process. Using limited populations of cell lines modeling Alzheimer's disease and neuronal subcompartments, MS sequencing will be advanced by developing a “smart” data acquisition method that alleviates current bottlenecks in bandwidth. SCeMaS will be validated using each of these biological models, where representative biomarkers are known and compared to healthy adult samples, aging neural proteomes are expected to be less diverse and show disrupted stoichiometries and/or homeostasis consistent with abnormal protein production, aggregation, and proteostasis. This project includes a multidisciplinary team that combines strengths in instrumental bioanalytical chemistry, aging, neuroscience, biochemistry, and bioinformatics to identify proteomic changes during healthy aging, accelerated aging in progeria, and in neurodegenerative disease. SCeMaS will enable scalable and deep MS-based proteomics-peptidomics under physiological conditions, thus expanding the bioanalytical toolbox of research on aging in these and other biological models.