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    Single Cell Phosphoproteomics Service

      Single cell phosphoproteomics is a technique that studies protein phosphorylation at the single-cell level. By combining high-sensitivity mass spectrometry and advanced sample enrichment methods, it captures phosphorylated proteins and their post-translational modifications within individual cells. Single cell phosphoproteomics typically includes single-cell isolation, phosphopeptide enrichment, liquid chromatography separation, mass spectrometry detection, data processing, and signaling pathway analysis. This technology provides precise phosphorylation dynamics and offers molecular-level insights into intercellular signaling.

       

      Single cell phosphoproteomics is critical for uncovering signaling heterogeneity, adaptive resistance mechanisms, and the regulatory mechanisms of complex diseases. Compared to bulk-level studies, single cell phosphoproteomics resolves the issue of signal masking caused by population heterogeneity, identifies low-abundance phosphorylation events and dynamic regulatory networks, and pinpoints key disease-associated phosphorylation sites and drug targets, thereby advancing precision medicine and personalized therapy.

       

      MtoZ Biolabs offers advanced single cell phosphoproteomics service designed to explore protein phosphorylation at the single cell level with unparalleled precision. Utilizing cutting-edge mass spectrometry platforms and optimized phosphopeptide enrichment techniques, our single cell phosphoproteomics service provides comprehensive insights into phosphorylation dynamics and signaling pathways within individual cells. From single-cell isolation to data processing and pathway analysis, our end-to-end workflow ensures high-quality results tailored to diverse research needs.

       

      Service Advantages 

      1. High Sensitivity and Resolution  

      MtoZ Biolabs' single cell phosphoproteomics service utilizes advanced mass spectrometry platforms to precisely detect low-abundance phosphoproteins and phosphorylation events at the single-cell level, which is critical for studying cellular signaling dynamics.  

       

      2. Comprehensive Signaling Pathway Analysis  

      Our single cell phosphoproteomics service provides in-depth insights into phosphorylation-mediated signaling pathways, revealing early changes in cellular signaling networks that are crucial for understanding disease mechanisms and adaptive responses.  

       

      3. Customized Solutions  

      Backed by extensive expertise, MtoZ Biolabs' single cell phosphoproteomics service offers tailored workflows to address challenges in single-cell analysis, including sample complexity and data interpretation, enabling the identification of actionable targets and therapeutic strategies.  

       

      Applications

      1. Signal Pathway Analysis: Through single cell phosphoproteomics services, the dynamic changes of phosphorylation modifications in different cells can be analyzed, revealing key nodes and regulatory mechanisms in signaling pathways.

       

      2. Disease Mechanism Research: Utilizing single cell phosphoproteomics services, abnormal phosphorylation events in disease-related cells (such as tumor cells or immune cells) can be detected, identifying molecular markers associated with disease progression.

       

      3. Drug Mechanism of Action Research: Single cell phosphoproteomics services can be used to evaluate the regulatory effects of drugs on cellular signaling pathways, detecting phosphorylation changes at the single-cell level to validate drug targets and mechanisms of action.

       

      4. Cell Differentiation and Developmental Regulation: Through single cell phosphoproteomics service, phosphorylation patterns during cell differentiation or development can be analyzed, revealing regulatory networks and key factors related to cell fate determination.

       

      Case Study

      Case 1: Single cell phosphoproteomics resolves adaptive signaling dynamics and informs targeted combination therapy in glioblastoma

      Intratumoral signaling network heterogeneity in glioblastoma (GBM) is a key factor in targeted therapy resistance. This study utilized single-cell phosphoproteomics to analyze a patient-derived in vivo GBM model resistant to mTOR kinase inhibitors (mTORki). Early changes in signaling coordination were detected within 2.5 days of treatment, uncovering mechanisms of adaptive resistance. This approach identified combination therapies, including unconventional drug combinations, that achieved complete tumor suppression. Single cell phosphoproteomics service resolves early signaling network changes, providing critical data for developing combination therapies to overcome drug resistance.

       

      MtoZ Biolabs-Single Cell Phosphoproteomics Service1

      Wei, W. et al. Cancer Cell. 2016.

      Figure 1. Single Cell Proteomic Analysis of GBM39 Tumors

       

      Case 2: Mapping Nanoscale-To-Single-Cell Phosphoproteomic Landscape by Chip-DIA

      This study proposed a Chip-DIA strategy that combines a microfluidic-based phosphoproteomic chip (iPhosChip) with data-independent acquisition mass spectrometry (DIA-MS), achieving ultrasensitive nanoscale-to-single-cell phosphoproteomic profiling. Chip-DIA successfully mapped the single-cell phosphoproteomic landscape, detecting thousands of phosphopeptides from 10 to 1013 cells. The approach revealed druggable phosphorylation sites and phosphorylation-mediated networks in lung cancer, providing new insights into cytoskeleton remodeling and resistance mechanisms to EGFR therapy. It also stratified mixed-lineage adenocarcinoma-squamous carcinoma subtypes and suggested alternative therapies for late-stage patients, showcasing its potential to guide therapeutic strategies in precision oncology. Single cell phosphoproteomics service enables the detection of low-abundance phosphorylation events and the analysis of signaling heterogeneity in diseases, providing critical data for understanding drug resistance mechanisms and identifying potential therapeutic targets.

       

      MtoZ Biolabs-Single Cell Phosphoproteomics Service2

      Muneer, G. et al. Adv Sci (Weinh). 2024.

      Figure 2. Characteristics of Phosphopeptides Identified in Single Cells by Chip-DIA

       

      FAQ

      Q1: How does MtoZ Biolabs accurately identify and quantify phosphorylation modifications in different cell types or states through single cell phosphoproteomics service to reveal key regulatory mechanisms in signaling pathways? 

      Answer: Through its single cell phosphoproteomics service, MtoZ Biolabs combines advanced mass spectrometry technologies (e.g., LC-MS/MS) with single-cell sample processing techniques to precisely analyze phosphorylated proteins at the single-cell level. First, optimized cell lysis methods are used to extract target proteins from individual cells, followed by phosphorylation peptide enrichment techniques (such as immunoprecipitation or TiO2 enrichment) to specifically capture phosphorylated peptides. These phosphorylated peptides are then separated via liquid chromatography (LC) and subjected to qualitative and quantitative analysis using high-resolution mass spectrometry (MS/MS). To enhance sensitivity, highly efficient ion sources and high-resolution mass spectrometers (such as the Q Exactive or Orbitrap systems) are employed. Coupled with specific database searches and data analysis methods, phosphorylation modifications can be accurately identified and quantified even in complex backgrounds. This workflow effectively reveals the dynamic changes in phosphorylation modifications within single cells, aiding in the analysis of regulatory mechanisms in signaling pathways and their variations under different physiological states.

       

      Deliverables

      1. Comprehensive Experimental Details

      2. Materials, Instruments, and Methods

      3. Relevant Liquid Chromatography and Mass Spectrometry Parameters

      4. The Detailed Information of Single Cell Phosphoproteomics

      5. Mass Spectrometry Image

      6. Raw Data

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