Proteomics Research Blog

Knowledge about protein expression levels is of utmost importance to predict toxic effects, estimate adverse effects of drug candidates and to identify new drug targets. Sciomics has a highly optimised range of protein profiling services available. These help you to speed up your research and generate important data at lower cost through a highly-parallel protein expression level analysis using our Scio-Discover Antibody Microarray Platform. Furthermore, you may link to and verify your Genomics and Transcriptomics data with protein expression levels.

Applications:

• Pre-clinical Toxicology Studies
  • rodents such as mice* and rats*
  • cell culture models
  • tissue culture models
• Drug target identification and verification
• Mechanistic studies for various indications
• Profile cytokine and inflammatory status
• Protein Biomarker identification and verification

Key advantages:

• High multiplexity (more than 1,400 proteins in parallel)
• Low sample consumption
• Investigation of human and murine samples*
• Fast turnaround time, as low as two weeks**
• No need for laborious depletion or fractionation of your samples
• High sensitivity (as sensitive as an ELISA or better)
• High reproducibility (CV < 10%)
• Various sample types can be investigated
  • Plasma or Serum
  • fresh-frozen tissue samples
  • Cell culture samples
  • Cerebrospinal fluid (CSF)

Overview
Scio-Discover     Biomarker
Discovery     Contact
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* Antibody array is designed to target human proteins. Due to a high sequence homology of vast majority of proteins it was often successfully applied to study murine, rat and ovine models as well  [Thorenz A. et al. (2018), Peiro J. et al. (2018), Reichman H. et al. (2019), Raquel F. et al. (2020)].

** Depending on study design and sample numbers.

You have identified a compound with the desired effect ?

You are looking for its targets ?

Which additional pathways are affected ?

Which proteins are altered in a diseased tissue ?

Get a complete picture of protein expression in multiple pathways !

In-depth knowledge about protein expression levels is crucial for the discovery and evaluation of new drug targets. Our Scio-Discover service is fully immuno-based and covers proteins involved in key disease and signaling pathways as well as secreted proteins. Only minimal sample amounts are required for highly parallel protein analysis. There is no need for sample fractionation or depletion. Our platform is capable of processing most biological samples without extensive sample preparation, saving valuable time and resources.

Applications

• Drug target identification and verification studies
• Mechanistic studies for various indications
• Correlation of next-gen sequencing (NGS) data with protein expression levels
• Profile cytokine and inflammatory status of your samples

Key Advantages

• Low sample consumption, for example*
  • Plasma sample: 5µl
  • Tissue: 1mg (fresh-frozen)
  • Cells: 100,000
  • CSF: 5µl
• Investigation of human, murine and rat samples**
• Fast turnaround time, as little as two weeks***
• No need for laborious sample depletion or fractionation
• High sensitivity (as sensitive as or more sensitive than ELISA)
• High reproducibility (CV < 10%)
• Various sample types can be analyzed
  
  • Blood serum or plasma
  • Tissue
  • Cell culture
  • Cerebrospinal fluid (CSF)

Service Features

• Study design
• Protein extraction and purification
• Protein quantification
• Protein labelling with high-performance fluorescent dyes
• Automated array incubation and handling 
• Array scanning and data acquisition
• Data analysis according to your requirements
• Customized study report

Overview
Scio-Discover     Biomarker
Discovery     Contact
us

*Minimum quantity. These values may vary depending on the sample type, study design, storage conditions. and additional parameters.

_{** Antibody array is designed to target human proteins. Due to a high sequence homology of vast majority of proteins it was often successfully applied to study murine, rat and ovine models as well  [Thorenz A. et al. (2018), Peiro J. et al. (2018), Reichman H. et al. (2019), Raquel F. et al. (2020)].}

_{*** Depending on study design and sample numbers.}