The expansion includes a His-tag protein quantification kit and sensors
Life sciences technology company Abselion has launched a His-tag protein quantification kit and sensors for its assay portfolio Amperia.
The new assay runs in a premix competition format, within Amperia’s software-controlled workflow, to quantify His-tagged proteins directly from crude or purified samples without specialist training.
“The His-tag launch is one of the first affinity-based, electrochemical, automated kits for crude samples and marks an important step in extending Amperia into one of the most widely used areas of protein science,” said Abselion founder and CEO Dr Ruizhi Wang.
Amperia is a benchtop platform uses redox electrochemical detection for the quantification of biomolecules without optics or fluidics.
The assay was developed with GenScript’s The His Tag monoclonal antibodies to provide a ready-to-use solution for measurement of His-tagged proteins. His-tags are the most commonly used affinity tags for supporting purification and detecting recombinant proteins expressed in bacterial, insect and mammalian systems.
“By combining GenScript’s trusted antibody technology with our ready-to-use design, we are giving researchers faster, more dependable ways to quantify His-tagged proteins directly from complex samples,” said GenScript senior director, protein and antibody research and development Higgins Qin.
Quantification often involves Elisa, gels, or spectrophotometric methods, which can be time-consuming, variable, or less effective, particularly when working with crude samples. Abselion’s tagged protein quantification kit offers an alternative suitable for drug discovery, structural biology, therapeutic research and bioprocess development workflows.
The kit includes pre-coated sensor strips, assay plates, detection reagents and buffers. The electrochemical sensor strips are pre-coated with GenScript’s validated antibodies immobilised on the sensor surface. The antibodies offer broad reactivity across common His-tag variants and expression systems while reducing manual handling through the pre-coating method.
The intention is to generate higher quality data more quickly and reliably across varying workflows.
