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Key research themes
1. How can gel-free proteomics workflows improve sample preparation efficiency and proteome coverage compared to gel-based methods?
This theme explores advancements and optimizations in gel-free (bottom-up) proteomics sample preparation workflows, emphasizing methods that enhance protein recovery, reproducibility, throughput, and proteome coverage. Gel-free approaches circumvent limitations of gel-based separation, such as laborious handling and protein losses. Improving digestion efficiency, minimizing sample loss, and integrating automated or miniaturized preparation steps are critical to handling limited or single-cell samples and achieving high-sensitivity proteomic analyses.
Key finding: This study introduces the water droplet-in-oil digestion (WinO) method that minimizes protein and peptide losses during digestion by reducing contact surface area with containers. Compared to conventional in-solution... Read more
Key finding: The authors developed the proteoCHIP platform enabling automated processing of up to 192 single cells with final volumes in the nanoliter range to minimize adsorption losses in bottom-up proteomics sample preparation. This... Read more
Key finding: The surfactant-assisted one-pot (SOP-MS) method utilizes an MS-compatible surfactant to reduce adsorptive sample loss with a simplified all-in-one sample processing workflow for label-free single-cell MS proteomics. It... Read more
Key finding: This paper introduces 'promor,' an R package facilitating downstream analysis of label-free proteomics data frequently generated in gel-free workflows. Its comprehensive pipeline supports data filtering, normalization, and... Read more
2. What advances in top-down gel-free proteomics enable enhanced proteoform identification, quantification, and proteome coverage?
This theme covers the development of gel-free top-down proteomics methodologies, including novel acquisition algorithms, instrumentation adaptations, and bioinformatic tools designed to analyze intact proteins and their proteoforms directly. The focus is on overcoming challenges such as complex charge state distributions, large search spaces of proteoforms due to PTMs, and improved data acquisition strategies to increase proteome coverage and proteoform-level identifications, which complement gel-based top-down approaches and gel-free bottom-up proteomics.
Key finding: While primarily a bottom-up review, this paper clarifies the historical reliance on gel-based separation techniques like 2D-PAGE and underscores the challenges that motivate gel-free top-down approaches, such as capturing... Read more
Key finding: The authors present 'Informed-Proteomics,' an open-source software suite addressing critical bioinformatic challenges in top-down gel-free proteomics data analysis. Its ProMex module improves LC-MS feature detection,... Read more
Key finding: FLASHIda is a machine learning-driven real-time data acquisition algorithm tailored for gel-free top-down proteomics. It enhances proteoform diversity by selecting high-quality precursor ions, mitigating redundancies inherent... Read more
Key finding: This review articulates recent advances in gel-free top-down proteomics, including improvements in intact protein separations and high-performance mass spectrometry instruments. It highlights that current platforms achieve... Read more
3. How do advanced mass spectrometry data acquisition strategies, especially data-independent acquisition (DIA), enhance gel-free proteomics identification and quantification?
This theme investigates novel MS acquisition modes such as data-independent acquisition (DIA) that provide improved reproducibility, sensitivity, and quantitative accuracy in gel-free bottom-up proteomics. DIA circumvents limitations of traditional data-dependent acquisition (DDA) by comprehensively fragmenting peptide ions without stochastic precursor selection. The development of spectral libraries and computational analysis algorithms tailor DIA workflows for large-scale proteome mapping, biomarker discovery, and clinical applications, driving gel-free proteomics to new quantitative and analytical heights.
Key finding: This paper reviews the progression from DDA to DIA, highlighting DIA’s comprehensive and reproducible peptide sampling in gel-free LC-MS/MS proteomics. DIA overcomes DDA’s limitations by eliminating stochastic precursor ion... Read more
Key finding: The study explores MS1-only MS proteomics in negative ion mode as a gel-free approach to improve ionization efficiency of acidic peptides, which are underrepresented in typical positive-mode analyses. Using the DirectMS1... Read more