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Proteomics

Proteomics focuses on proteins—the executors of life activities that play a crucial role in organismal transcription regulation. It analyzes the composition, expression levels, and modification states of all dynamically changing proteins in tissues or cells, addressing the significant impact of proteome abundance dynamics on various life processes. Widely applied in medicine, agriculture, and animal husbandry. Qualitative Proteomics utilizes HPLC-MS/MS protein identification technology to identify samples including gel strips, IP, and CO-IP/Pull down samples. Quantitative Proteomics achieves accurate quantification and identification of all proteins expressed by a genome or in a complex mixed system. Current quantitative proteomics technologies are mainly categorized into labeled (TMT) and label-free (Label Free, DIA, PRM) approaches. BMKGENE provides multi-platform and multi-technology proteomics solutions.


Service Details

Bioinformatics

Demo Results

Featured Publication

Features

●Qualitative Proteomics: Uses LC-MS/MS to identify protein composition in complex samples (SDS-PAGE gel strips, IP, Co-IP, Pull-down). Advantages: No sample number limit, fast detection, simple sample processing, high throughput, and capability to detect low-abundance proteins.

● Label Free Quantitative Proteomics: Non-labeled technology with individual sample detection. Quantifies proteins by comparing peptide signal intensities in mass spectrometry data. Advantages: Simple operation, high throughput (no sample number limit), and wide applicability (suitable for "presence/absence" differential protein comparison across species).

● DIA Quantitative Proteomics: Adopts data-independent acquisition (DIA) mode, scanning all ions in segmented windows to capture complete ion information. Advantages: Better repeatability, higher protein coverage, and more accurate quantification than DDA mode (TMT/Label Free), ideal for large-sample studies.

● 4D-Label Free Quantitative Proteomics/4D-DIA Quantitative Proteomics: Based on Bruker timsTOF mass spectrometer, adding ion mobility (collision cross-section) to traditional 3D separation. Advantages: Improved ion utilization and accuracy, comprehensive enhancement in coverage depth, sensitivity and throughput; higher identification depth than traditional 3D method.

● Astral Label Free Quantitative Proteomics/Astral DIA Quantitative Proteomics: Based on OrbitrapTM AstralTM high-resolution mass spectrometer. Advantages: Higher throughput (100+ samples/day with 8-min gradient), deeper coverage (8,000+ proteins detected in Hela cells in 8 min), and higher sensitivity (less sample required).

● TMT Quantitative Proteomics: Uses 18 isotope tags to label peptides for simultaneous detection of 18 samples. Advantages: High stability (minimal instrument stability interference, small system error) and high sensitivity (fractionation reduces sample complexity, improving low-abundance protein detection).

● PRM Targeted Proteomics: High-resolution targeted technology for selective detection of target proteins/peptides. Advantages: Enables relative/absolute quantification and verification of quantitative proteomics results; no antibody restriction, wider applicability than Western Blot and ELISA.

Advantages

● Advanced Equipment: Equip with both conventional proteomics platforms and advanced high-depth mass spectrometers (e.g., 4D, Astral).

● Stable Detection: Rigorous quality control systems ensure consistent and stable testing processes.

● Reliable Results: Simultaneous qualitative and quantitative analysis delivers relative expression, molecular weight, abundance and other key data for each group.

● High Automation: Automated LC-MS systems enable fast analysis and excellent separation performance.

Service Specifications

Metabolomics Research in Medicine-04(1)
Comparison of Non-targeted Proteomics Techniques
  Label-free DIA TMT
Labeling NO NO YES
Data Scanning Mode DDA DDA DIA DDA
Reproducibility Lower High High
Sensitivity Lower High High
Multiplexing NO YES NO YES
Application Comparing protein presence/absence Large-scale samples Analysis of different batches of samples Comparing differential protein expression
in the same species and tissue
Accuracy ★★(4D/Astral DlA ★★★) ★★
Detection Count ★★(4D/Astral D|A★★★) ★★

Sample Requirements

Are you wondering whether your samples meet our criteria? Click here to get our latest sample requirements .

Service Work Flow

sample delivery

Sample Collection

Pilot experiment

Metabolite Extraction

1

Enzymatic digestion

2

Separation

Library Preparation

Data Acquisition

Data analysis

Data Analysis

Data delivery-05

Data Delivery


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  • Qualitative Proteomics

    1. Protein solution/gel: qualitative result table
    2. Database Search Results
      2.1 List of peptide segments matched in the database search
      2.2 List of proteins matched in the database search
      2.3 List of modifications matched in the database search (phosphorylation, ubiquitination, etc.)
    3. Raw data

     

    Quantitative Proteomics (Label-Free/DIA/TMT)

    1. Data Preprocessing
      1.1 Protein Database Search
    2. Protein Expression Analysis
      2.1 Principal Component Analysis (PCA)
      2.2 Relative Standard Deviation (RSD)
      2.3 K-means Trend Distribution
      2.4 Reproducibility Evaluation
      2.5 Protein Expression Heatmap
    3. Functional Annotation
      3.1 GO Functional Annotation
      3.2 KEGG Functional Annotation
      3.3 COG Functional Annotation
      3.4 GOslim Functional Annotation
      3.5 Pfam Protein Structure Domain Annotation
    4. Protein Differential Analysis (biological replicates ≥ 3)
      4.1 Protein Differential Analysis Results
      4.2 Distribution of Differential Protein Fold Changes (FC)
      4.3 Statistical Analysis of Differential Protein Quantity
      4.4 Differential Protein Volcano Plot
      4.5 Differential Protein Clustering Heatmap
      4.6 Differential Protein GO Functional Annotation and Enrichment Analysis
      4.7 Differential Protein KEGG Functional Annotation and Enrichment Analysis
      4.8 Differential Protein COG Functional Annotation
      4.9 Differential Protein GOslim Annotation and Enrichment Analysis
      4.10 Differential Protein Structure Domain Annotation and Enrichment Analysis
    5. Protein Network Interaction Analysis
    6. Protein Reactome Pathway Annotation
    7. Signal Peptide Prediction
    8. Subcellular Localization of Proteins

     

    Quantitative Proteomics (PRM)

    1. PRM Protein Quantification Results
      1.1 Overview of Quantification Results
      1.2 Distribution of Fragment Ion Peak Areas for Peptide Segments

    Quantitative Proteomics
    * Protein Expression Analysis

    图片1

    ↑Principal Component Analysis (PCA) 

    图片2

     Relative Standard Deviation (RSD)

    图片3
      ↑K-means Trend Distribution

     图片4
      ↑Correlation Analysis of Protein Expression Levels

    图片5
      ↑ Protein Expression Heatmap

    * Functional Annotation

    图片6
      ↑GO Functional Annotation

    图片7
       KEGG Functional Annotation 

    图片8

    ↑ COG Functional Annotation

    图片9
      ↑ GOslim Functional Annotation

    图片10
      ↑ Pfam Protein Structure Domain Annotation
    * Protein Network Interaction Analysis

     图片11

    ↑ Protein Network Interaction Analysis

    * Signal Peptide Prediction

     图片12

    ↑ Signal Peptide Prediction

    * Subcellular Localization of Proteins

    图片13

    ↑Subcellular Localization of Proteins

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