Deep Proteomics
Deep proteomics is an advanced technology in proteomics, focusing on the comprehensive analysis of protein diversity, expression levels, post-translational modifications, and interactions within biological samples. Unlike traditional proteomic methods, deep proteomics employs highly sensitive and high-resolution mass spectrometry, alongside optimized techniques for sample preparation, separation, and data analysis. This approach allows for a more complete and precise characterization of the proteome in various biological systems. In life sciences, proteins are essential molecules that facilitate cellular functions, with significant variations in protein composition and modification patterns across different tissues, cell types, and physiological conditions. Traditional proteomics often struggles with the detection sensitivity and coverage required to analyze low-abundance proteins or dynamic protein networks in complex samples. Deep proteomics addresses these challenges with strategies including ultra-high performance liquid chromatography (UHPLC) for separation, high-resolution tandem mass spectrometry (HR-MS/MS), single-cell proteomics, and sophisticated bioinformatics analyses, enabling deeper insights into biological systems. For instance, in neuroscience, it can elucidate neuronal protein expression profiles and their dynamics, shedding light on the molecular underpinnings of neurodegenerative diseases such as Alzheimer's and Parkinson's. In oncology, it helps identify pivotal protein alterations within the tumor microenvironment, offering potential targets for precision therapies.
The workflow of deep proteomics involves several critical steps: efficient sample preparation, protein digestion, multidimensional separation, mass spectrometry, and data analysis. Initially, protein extraction tailored to specific biological samples (e.g., tissue lysis, membrane protein enrichment, or single-cell analysis) is employed to minimize sample loss and enhance the detection of low-abundance proteins. Multistage separation techniques, such as high pH reverse-phase chromatography and hydrophilic interaction chromatography, are used to fractionate complex protein samples, reducing complexity and enhancing detection coverage. During mass spectrometry, high-resolution platforms like Orbitrap, TOF-MS, or ion mobility spectrometry (TIMS-MS) are utilized for precise protein identification and quantification. Advanced data processing methods, integrating deep learning and machine learning with multi-omics analysis, facilitate the detailed mapping of protein networks and their biological roles.
As single-cell proteomics, multi-omics integration, and high-throughput computing technologies advance, deep proteomics is reaching new heights of detail. Single-cell proteomics uncovers protein expression at the cellular level, revealing heterogeneity and dynamic variations among cells. In immunology, it can distinguish protein expression in different immune cell subsets, elucidating the molecular basis of immune responses. Multi-omics integration, which combines data from genomics, transcriptomics, metabolomics, and proteomics, constructs comprehensive models of biological networks, offering a holistic view of disease mechanisms. Furthermore, the integration of artificial intelligence and deep learning enhances proteomics data processing, pattern recognition, and functional prediction, accelerating the discovery and application of valuable biological insights. MtoZ Biolabs, leveraging advanced proteomics platforms, offers high-throughput, high-sensitivity proteomics services. We provide comprehensive solutions, including low-abundance protein detection, post-translational modification analysis, multi-omics integration, and precision medicine research.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
Related Services
How to order?
