Vanderbilt University scientists noticed an amazing aspect of graphene in a recent publication in Nature Communications: Nerve cell membranes pulled in more cholesterol when grown on graphene than when grown on glass alone. Why is this important? Because cholesterol is a neurotransmitter, and more neurotransmitter means stronger signals and more neuronal firing between cells, which could greatly impact neurological diseases. See our blog post for more details!
What if you didn’t have to run your experiment in a cold room, but could instead bring the cold room to your assay platform… and stay perfectly comfortable doing so? Agile R100 can be used on a bed of ice to maintain the stability of your protein! See our blog post for more details on our GPCR characterization experiment, performed on a bed of ice.
Ahoy, Mateys! We had our company Halloween party last week, pirate style. Hosted at the house of one of our scientists, it was complete with a handmade shipwreck. Read more details about our company personality in our blog post!
We have a shiny new toy – our new AI Analyzer by Nanotronics! It’s the first QA tool ever used to quality check mass-manufactured graphene biosensors, and it’s making our QA process go 20 times faster than it would manually. Read more details in our blog post!
As a scientist, you have preferred, go-to methods for testing molecular interactions, such as surface plasmon resonance (SPR), Bio-Layer Interferometry (BLI), ELISA, or fluorescence labeling. So, what is the best method to use if you’re measuring a small molecule therapeutic drug interacting with a targeted protein?
One way of characterizing a system is to consider only its inputs and outputs with no concern for its inner workings. This opaque system is called a black box, and black boxes are useful shortcuts when you need fast answers. However, when your input doesn’t give you the correct output, then what do you do? As a scientist, knowing how the black box works and how to modify it can help you have confidence in the results.
Scientists have been dealing with biomolecules for decades, and several strategies have been developed to reduce background signals that contribute to poor assay performance. Even better, new technologies are being released to market that make the most finicky aspects of blocking a thing of the past.
Reference samples can consume sensor cells and precious biomaterial, and any difference between the reference run and experiment run adds inaccuracy to the data. Isn’t getting accurate data hard enough already? Reduce or eliminate bulk responses from solvent effects in your measurements!