Mass spectrometry-based chemical proteomics is a powerful method to analyze proteins labelled by small molecules to identify protein targets of active compounds and to profile protein post-translational modifications. The throughput and high protein input for chemical proteomics workflows has been often a limiting factor for application of the technology for specialized and difficult to culture cell lines. The high protein input was necessary to gain significant difference of noise to signal ratio in proteomics readout. Here, we describe a general chemical proteomics workflow, which is performed in 96-well plate and necessitate only 25 μg of protein input to profile post-translationally modified proteins including abundant O-GlcNAcylated proteins as well as low abundant AMPylated proteins. The workflow integrates advances in Cu(I)-catalyzed azide-alkyne cycloaddition to minimize chemical side-reactivity of the ‘click reaction’ and data-independent acquisition mode during LC-MS/MS measurement. An iterative optimization of protein clean-up on carboxylate-coated paramagnetic beads led to significant saving of the beads usage and lowers the unspecific protein background that resulted in sensitivity gain.
Improvements enabling quantification of site-specific modifications, including post-translational modifications and covalent compound-protein interactions spanning diverse pathways are described.
Steven R. Shuken, Geordon A. Frere, Charlotte R. Beard et al.· Nature Communications· 0 citations
Liquid chromatography-mass spectrometry (LC-MS)-based proteomics of trace-level samples, such as tens of cells or spatially resolved tissue regions, offers unique biological insights but is often constrained by the requirement for specialized, costly instrumentation. In this study, we developed a scalable workflow for the deep proteomic analysis of low- to ultralow-input samples by systematically optimizing a widely adopted Orbitrap and UHPLC platform to maximize sensitivity, precision, and throughput. This optimized workflow identified over 5600 proteins from 5 ng of peptides and 3400 proteins from 20 sorted cells, achieving a throughput of 30 analyses per day while maintaining deep proteome coverage and high quantitative reproducibility. Furthermore, by applying this method to spatially resolved proteomics, we identified over 6100 proteins from microscale regions of interest (ROIs) within a formalin-fixed, paraffin-embedded (FFPE) tissue. A data-driven normalization strategy was employed to correct for variable cellularity across tissue regions, effectively revealing intratumor heterogeneity and distinct molecular and functional signatures, including pathway activations not apparent in parallel spatial transcriptomic analysis. Ultimately, this accessible, high-performance method substantially lowers the instrumentation barrier for the deep proteomic profiling of trace-level biological samples.
Dongyoon Shin, Sumin Lee, S. Yang et al.· Analytical Chemistry· 0 citations
High-throughput shotgun proteomics is often hindered by the incompatibility of detergents with mass spectrometry (MS), making sample preparation a critical bottleneck for accuracy and robustness. This challenge is amplified in muscle proteomics, where the high dynamic range of protein abundance requires highly efficient and reproducible workflows to capture low-abundance proteins. To address this, we performed a systematic benchmarking of five preparation methods—stacking-gel (SG), tube-gel (TG), solid-phase extraction (SPE), filter-aided sample preparation (FASP), and suspension traps (S-TRAP)—using the sarcoplasmic fraction of pig muscle. The protein extracts obtained were subjected to label-free semi-quantitative proteomic analysis using high-performance nano-liquid chromatography coupled to tandem MS. Qualitative and quantitative results were compared using bioinformatics and biostatistics tools. Our study identified 530 proteins with significant variations across methods. S-TRAP provided the highest identification depth, capturing the broadest proteome coverage. Conversely, the TG method demonstrated superior quantitative reproducibility, essential for detecting subtle physiological changes. For translational research, such as meat quality science or muscle-related clinical models, our findings imply that S-TRAP is the preferred choice for discovery-phase proteomics (biomarker hunting), while TG or SG should be prioritized for high-precision validation studies.
D. Viala, R. Maillet, A. Delavaud et al.· Omics· 0 citations
The advantages of top-down proteomics (TDP) in the characterization of proteoforms, resulting from genetic variations, alternative splicing, and post-translational modifications (PTMs), have been well documented. However, TDP applications on limited samples have been less explored, and no direct comparison with the bottom-up proteomics (BUP) approach for the same scarce amounts of samples has been conducted to date. In this work, we processed ∼100–1000 HeLa cells using bottom-up and top-down workflows and subjected sample volumes equivalent to ∼25 and up to ∼250 HeLa cells to liquid chromatography–mass spectrometry (LC–MS)-based TDP and BUP analyses. Porous layer open-tubular (PLOT) columns were used for the separation of intact proteins in TDP MS, while traditional bead-packed columns were used for the BUP workflow. Up to 500 proteoforms and nearly 1300 proteins from cell lysates equivalent to ∼25 HeLa cells were identified in TDP and BUP, respectively. Interestingly, among all the unambiguously identified proteins from both ∼25 HeLa and ∼250 HeLa cell lysates in TDP, ∼20–30% were not identified in BUP under the same sample loading, suggesting significant complementarity between TDP and BUP approaches. Additionally, biologically relevant PTMs (e.g., acetylation, phosphorylation, and methylation) were reliably characterized in TDP as different proteoforms. We anticipate that TDP, enhanced by ultralow-flow PLOT chromatography columns coupled to MS, could be a supplementary or an alternative approach for limited-sample analysis, as it eliminates the need for protein digestion and minimizes sample cleanup, enabling rapid sample preparation while preserving proteoform information.
Yunfan Gao, Michal Greguš, James C. Kostas et al.· Analytical Chemistry· 0 citations
