Notes on Sensitive Proximity Extension Assay

Sensitive Proximity Extension Assay

Overview

The Sensitive Proximity Extension Assay measures protein profiles from small blood samples (less than one drop). It uses matched pairs of antibodies with unique DNA tags to achieve high specificity and scalability.

Key Principles

  • Antibody Binding: Matched pairs of antibodies, each carrying a unique DNA tag, bind to their corresponding proteins within the sample.
  • Hybridization: When the antibodies bind in close proximity, the attached DNA tags are brought close enough to hybridize.
  • Specificity: The requirement for dual antibody binding and DNA barcoding ensures high specificity. Non-specific binding does not result in signal generation because the DNA tags cannot hybridize.
  • Signal Generation: Only correctly hybridized tags are extended into amplicons, each with a unique sequence for each protein.

OLINK Target (qPCR)

  • Readout: Uses qPCR for a digital readout.
  • Scalability: Can simultaneously run 4848 or 9696 assays on 9696 samples.
  • Quantification: The number of qPCR cycles correlates with the protein concentration in the sample.
  • Data Processing: Software processes the data to report relative protein concentrations in each sample.

OLINK Explore (NGS)

  • Readout: Uses Next-Generation Sequencing (NGS).
  • Scalability: Can simultaneously run 15361536 assays on 9696 samples.
  • Amplification: DNA amplicons are amplified using standard PCR.
  • Sequence Design: Sequences include adapters for correct sequencing direction and unique barcodes to identify the protein and sample.
  • Cluster Generation: Adapters bind to matching DNA primers on a surface, and amplicons are extended in cycles, creating small clusters with identical sequences. Each cluster represents a specific protein from a specific blood sample.

Real-Time Biology Status

  • Sequencing: During sequencing, the fluorophore for each DNA base is registered as a digital signal.
  • Quantification: The number of digital signals corresponds to the concentration of each protein biomarker in each sample.
  • Protein Profiling: Statistical tools are used to identify and verify individual protein profiles, which are unique like a fingerprint.
  • Applications:
    • Patient stratification
    • Disease and treatment prediction
    • Understanding of biological processes
    • Discovery of new drug targets

Significance

  • The technology can generate powerful data from a very small amount of blood (less than one drop).