Proteins are vital molecules involved in numerous cellular processes, and carry out important functions such as cell growth, material transportation, and signal transduction. Therefore, understanding protein expressions is crucial to studying vital cell functions. The quantitative comparison of protein abundance across a large number of biological or patient samples represents an important proteomics challenge that needs to be addressed for proteomics discovery applications.
Using our DIA MS technologies, we provide a variety of discovery proteomics services, including protein identification, protein expression level quantification, post-translational modification identification, protein interaction analysis, and large-scale phosphorylation proteomics analysis. Our discovery proteomics will help researchers accelerate their research progress, shorten the research cycle, and provide them with the key insights they need to move forward. And we are interested in developing and implementing new proteomics methods to study unique scientific projects.
Our label-free DIA quantitative proteomics strategy is designed to quantify as many proteins as possible in a dynamic range, which can analyze up to 9,000 proteins per sample under different conditions and identify significantly regulated proteins. This platform is ideal for detecting the differences of multiple samples in protein abundance and low abundance proteins.
a) SWATH
b) SRM™
c) MSX-DIA
d) PCT-DIA
e) GPF-DIA
Discovery Proteomics strategy uses DIA technique, which divides the whole scanning range of mass spectrum into several windows instead of selecting, segmenting and detecting all ions of each window at high speed in a cycle. All ion fragments in the sample can be obtained without omission, which greatly improves the protein utilization. Then data is analyzed with the database built by OpenSWATH and Skyline softwares.
Problems to be Solved | Bioinformatics Analysis |
Quality Assessment of Protein | SWATH Data Analysis |
Protein Comparison of Different Samples | Multivariate PCA Analysis |
Protein Statistical Analysis | Venn Diagram |
Volcano Plot | |
Functional Annotation | KEGG Annotation |
GO Annotation | |
COG Annotation | |
Clustering Analysis | Hierarchical Clustering |
K-Means Clustering | |
Network Analysis | STRING Analysis |
AB SCIEX Triple-TOF 5600-plus, Q-Exactive, Orbitrap Fusion
Based on our special protein extraction technology, we can quickly extract proteins from various samples and design personalized experimental schemes according to different experimental purposes. Specific requirements are as follows:
Sample Type | Protein | Cell | Animal Tissue | Plant Tissue | Blood | Urine | Serum | Microbes |
Quantify | 100 ug | 1×107 cells | 1 g | 200 mg | 1 mL | 2 mL | 0.2-0.5 mL | Dry weighed: 200 mg |
Note:
1) Biomarker discovery and validation
2) Target discovery
3) Pathological study of disease
4) Genetic association study
5) Microorganisms proteomics research
6) Crop proteomics research
7) Quantitative comparison of proteomes in multiple biological samples
The study aimed to establish a rapid and reliable Data-Independent Acquisition (DIA) workflow for comprehensive mapping of urinary proteomes. The research focused on biomarker discovery in pediatric emergency room (ER) patients experiencing abdominal pain, utilizing a urine-based approach for efficient and clinically relevant analysis.
The research utilized 87 urine samples from pediatric emergency room patients presenting with abdominal pain. The diverse sample set included cases of ovarian cysts, urinary tract infections (UTIs), and a control group representing other abdominal pain causes and indeterminate cases.
DIA Method Optimization:
Spectral Library Selection:
Highly Reproducible Peptide Detection and Quantitation:
Comprehensive Urinary Proteome Coverage:
Workflow. (A) Generation of spectral library. (B) DIA sample acquisition.
DIA Workflow Efficiency:
Biomarker Discovery Study:
Comprehensive Urinary Proteome Coverage:
Validation of workflow. (A) Influence of spectral library. (B) Number of peptide and protein identifications in replicate runs. (C) Quantification precision. (D) Protein %CV in relation to protein abundance.
Overview of DIA data set.
Reference:
4D Proteomics with Data-Independent Acquisition (DIA)
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×Specializing in proteomics, Creative Proteomics offers cutting-edge protein analysis services. Our distinctive approach revolves around harnessing the power of DIA technology, enabling us to deliver precise and comprehensive insights that drive advancements in research and industry.