Biology subjects

Hinkle, J. D.

Publications and source records attributed to Hinkle, J. D..

5 recordsLinked to original sources

bioRxiv2026

Defining glycoproteoform landscapes through an integrated glycoproteomics approach enabled by high-resolving power proton transfer charge reduction tandem mass spectrometry

Glycan heterogeneity is a fundamental property of glycoproteins. A holistic understanding of glycan modification states is critical to translating glycoproteome regulation to biological function, but the high degree of glycosite-level heterogeneity leads to technical challenges in measuring glycoproteoforms. Common bottom-up glycoproteomics provide some insights but cannot recapitulate the full ensemble of glycoproteoforms from glycopeptide measurements alone. Promising efforts to profile masses of intact glycoproteins have recently explored data-independent acquisition (DIA) coupled with proton-transfer charge reduction (PTCR) or electron-capture-induced charge reduction mass spectrometry (MS). While valuable for generating broad glycoproteoform mass distributions, these approaches have remained limited in their ability to generate discrete glycoproteoform mass measurements, largely because they rely on low-resolving power measurements and deconvolution that does not account for isotopic information. Here, we develop a DIA-PTCR workflow that couples high-resolving power (Rp ~240,000 at m/z 200) tandem mass spectra with an open-source processing suite to define glycoproteoform populations within 20 ppm mass accuracy thresholds. We demonstrate the glycoproteoform characterization capabilities of this platform using a collection of glycoproteins with well-described translational interests (EpCAM, TIGIT, CD40, PDL1, and CD24). With a focus on EpCAM, we showcase how intact glycoproteoform masses acquired using our high-resolving power DIA-PTCR (hRp-DIA-PTCR) approach can be integrated with bottom-up intact glycoproteomics and Direct-Mass Technology (i.e., Orbitrap-based charge-detection MS) acquisitions to inform structural and biological insights. Altogether, our hRp-DIA-PTCR method extends the current capabilities of intact glycoprotein analyses by enabling robust characterization of isotopically resolved proteoforms and facilitating deep biological interpretation of glycosylation heterogeneity. Our open-source informatics platform includes a GUI-based tool called PTsliCR to clean PTCR spectra directly from DIA-PTCR raw files and a deconvolution R package called IsoTrac, both of which are freely available on GitHub at https://github.com/riley-research.

systems biology↗

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↗

bioRxiv2025

Predicting High-Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Recent advancements in molecular Mass Spectrometry Imaging (MSI) have sparked interest in integrating high spatial resolution methods with molecular mass-spectrometry-based chemical imaging. Fusion-based algorithms have proven effective in generating high spatial-resolution molecular mass spectra. However, a significant challenge stems from the differing physical mechanisms underlying image generation and data upsampling techniques, potentially leading to discrepancies in integrated information channels. Integrating physical constraints into data processing workflows is essential to tackle this issue. In this study, we propose an innovative approach that merges data from Fourier transform ion cyclotron resonance (FTICR), time- of-flight matrix-assisted laser desorption/ionization (MALDI-ToF), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging techniques. By leveraging FT-ICRs unparalleled spectral resolution and ToF-SIMSs exceptional spatial resolution, we achieve submicron spatial resolution, enabling the observation of intact molecular species with remarkable spectral precision. Canonical correlation analysis is employed to incorporate physical constraints. Through sophisticated image processing and machine learning techniques, the results of this fusion hold significant promise for advancing our comprehension of complex systems and unveiling concealed molecular intricacies.

neuroscience↗

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↗