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Proteomics

The Explore HT is a multiplex, high-throughput protein biomarker analysis platform which measures the relative concentration of more than 5400 proteins from minute amounts of sample. The Explore HT utilizes Olink’s Proximity Extension Assay (PEA) technology, which offers high sensitivity and specificity, coupled with Illumina for readout on next-generation sequencing.

Advantages

  • Multiplex: Rapid detection of 5,400 protein markers in a sample. Able to support population scale studies.
  • High sensitivity and wide dynamic range: Detection sensitivity up from fg/mL to mg/ml. The dynamic range spans 10 logs, and the full coverage of high, medium and low abundance proteins.
  • Low input sample volume: Each assay requires only 2μL of sample matrix.
  • Data reproducibility: no need for technical repetition, far better than traditional methods such as mass spectrometry.
  • Multiple omics: Multiple omics data can be obtained through an NGS sequencer.
  • Complementary to Mass spectrometry: Olink can concurrently detect low abundance proteins, missed out by mass spectrometry due to sample preparation steps which removes albumin and other high abundance proteins, and loses low abundance proteins, especially in the case of blood samples.
  • High concordance with other orthogonal protein assays such as ELISA.

There are numerous examples showing how assays run in Olink correlates very well with standard single ELISAs and other antibody based assays.

Correlation between conventional ELISA and Olink for CXCL-101

Correlation between Olink and SIMOA for NFL1

Sample Requirement

Sample Type

Sample Amount

Storage / transport condition

Others

Serum/Plasma

50 µL or more in 1.5mL tubes, or in 96 wellplate

Please include a sufficient amount of dry ice and send by frozen delivery, to avoid sample thawing during shipping.

Store sample at -80℃ immediately after extraction. Hemolysis may affect analysis results.

Other sample matrix

CSF, lysates,

*Please contact us for more information

*Please contact us for more information.

Workflow

From sample preparation, library preparation, sequencing and data quality control, to bioinformatics analysis, Novogene provides high-quality products and professional services. Each step is performed in agreement with a high scientific standard and meticulous design to ensure high-quality research results.

Gain access to more tools and technical information:

  • Access the complete Explore HT assay list here.
  • Explore top publications using Olink’s panels for protein biomarker discovery.
  • Sign up for Olink Insight to get access to the pathway browser, study size calculator, and more proteomics tools, and stay connected with a global proteomics community.

Reference:

Featured publications of Proteomics

Data Normalization and Quality Control Using NPX Values

NPX, Normalized Protein eXpression, is Olink’s arbitrary unit which is in Log2 scale. Following sequencing, the raw data is converted to counts, assigning an integer value to each assay-sample combination based on the detected copy numbers. These raw data counts are converted into NPX values, enabling the identification of protein level changes within different sample sets, and establishing protein signatures. In general, a higher NPX value refer to a higher protein concentration.

The x-axis represents the sample names. The y-axis represents the NPX values. A red bar indicates a QC warning sample, and a blue bar represents QC pass sample.


Principal Component Analysis of Protein Expression

Principal component analysis (PCA) is a statistical technique for analyzing large dataset by reducing the dimensionality of the dataset. In this analysis, PCA is conducted on the NPX data file, and all samples are plotted as a scatterplot along the selected principal components. It can be useful for showing that samples separate, or cluster based on variables of interest. In addition, it can be used to identify potential outlier samples.

Dots in different colors represent different groups. The closer the distance between samples, the higher the similarity between samples.


Hierarchical Clustering Heatmap of Protein Expression

Hierarchical clustering is a method used to get an overview of data, and to identify subgroups of similar samples or proteins based on protein profiles. This analysis is often visualized through a heatmap, where similar proteins or samples are positioned adjacent to each other. The protein expression levels are then represented using a color gradient on the heatmap.

Red color indicates proteins with higher NPX expression levels, and blue color indicates proteins with lower NPX expression levels. The color ranging from red to blue indicates that NPX values range from large to small.


Volcano Plot of Differential Protein Expression

The comparison of protein expression level among groups are performed using t-test or ANOVA and the result is visualized using volcano plot.

The x-axis shows the NPX.difference between two groups, and the y-axis shows the -log(p-value) value(base 10). NPX.different= NPX.group1 – NPX.group2. Red dots represent significant differential expressed protein. Blue dots represent insignificant differential expressed proteins.


Go Enrichment Analysis

GO is the abbreviation of Gene Ontology (http://www.geneontology.org/), which is a major bioinformatics classification system to unify the presentation of gene properties across all species. It includes three main branches: cellular component, molecular function, and biological process. GO terms with padj < 0.05 are significant enrichment.

The x-axis represents the GO Term, and the y-axis indicates the level of significance of enrichment for each GO Term, expressed as -log10(padj). Different functional categories are represented by varying colors.