Biology subjects

Syka, J. E. P.

Publications and source records attributed to Syka, J. E. P..

3 recordsLinked to original sources

bioRxiv2026

Defining state-selective lipid binding to brain GPCRs - introducing REVEAL

Membrane lipids are central regulators of G protein-coupled receptor (GPCR) function. Defining receptor-specific lipid interactions in native, fully modified mammalian systems remains challenging. Extensive post-translational modifications generate heterogeneous proteoforms that confound conventional mass spectrometry approaches. Here we introduce REVEAL (REceptor enVironment Elucidation by Activated Lipid-release), an automated native top-down mass spectrometry strategy that discriminates specifically bound lipids from background. Applied here to intact, heterogeneous mammalian membrane protein complexes, incubated with a brain polar lipid extract (>1000 components), we define receptor-specific lipid-binding for two neuronal class C GPCRs. We show that agonism remodels lipid occupancy, selectively enriching a reduced repertoire of bound lipids. Plasmalogen lipids emerge as persistent binders across all conformational states of the metabotropic glycine receptor and are preferentially depleted under oxidative stress, implying a protective role at the receptor surface. These findings position lipids as dynamic regulators of both function and response to the cellular redox environment.

biophysics↗

bioRxiv2024

Boosting the Sensitivity of Quantitative Single-Cell Proteomics with Activated Ion-Tandem Mass Tags (AI-TMT)

Single-cell proteomics is a powerful approach to precisely profile protein landscapes within individual cells toward a comprehensive understanding of proteomic functions and tissue and cellular states. The inherent challenges associated with limited starting material in single-cell analyses demands heightened analytical sensitivity. Just as advances in sample preparation maximize the amount of material that makes it from the cell to the mass spectrometer, we strive to maximize the number of ions that make it from ion source to the detector. In isobaric tagging experiments, limited reporter ion generation limits quantitative accuracy and precision. The combination of infrared photoactivation and ion parking circumvents the m/z dependence inherent in HCD, maximizing reporter generation and avoiding unintended degradation of TMT reporter molecules in a method we term activated ion-tandem mass tags (AI-TMT). The method was applied to single-cell human proteomes using 18-plex TMTpro, resulting in a 4-5-fold increase in reporter ion signal on average compared to conventional SPS-MS3 approaches. AI-TMT enables faster duty cycles, higher throughput, and increased peptide identification and quantification. Comparative experiments showcase 4-5-fold lower injection times for AI-TMT, providing superior sensitivity without compromising accuracy. In all, AI-TMT enhances the sensitivity and dynamic range of proteomic experiments and is compatible with other techniques, including gas-phase fractionation and real-time searching, promising increased gains in the study of cellular heterogeneity and disease mechanisms.

biochemistry↗

bioRxiv2023

Exposing the molecular heterogeneity of glycosylated biotherapeutics

Glycosylated biotherapeutics are an emerging class of drugs with high molecular heterogeneity, which can affect their safety and efficacy. Characterizing this heterogeneity is crucial for drug development and quality assessment, but existing methods are limited in their ability to analyze intact glycoproteins. Here, we present a new approach to glycoform fingerprinting that uses proton-transfer charge-reduction with gas-phase fractionation to analyze intact glycoproteins by mass spectrometry. The method provides a detailed landscape of the intact molecular weights present in biotherapeutic protein preparations in a single experiment and offers insights into glycoform composition when coupled with a suitable bioinformatic strategy. We tested the approach on various biotherapeutic molecules, including Fc-fusion, VHH-fusion, and peptide-bound MHC class II complexes to demonstrate efficacy in measuring the proteoform-level diversity of biotherapeutics. Notably, we inferred the glycoform distribution for hundreds of molecular weights for the eight-times glycosylated fusion drug IL22-Fc, enabling correlations between glycoform sub-populations and the drugs pharmacological properties. Our method is broadly applicable and provides a powerful tool to assess the molecular heterogeneity of emerging biotherapeutics.

biochemistry↗