You’ve spent budget for cell culture, for cell prep, for analysis… and it’s all for naught because your read-outs are all over the place. You can barely see a pattern, but it’s just below significance. You’re close, but the experiment is failed and the budget is wasted.

VAIDR helps you reduce noise in your read-outs by reducing noise in the cells going into your experiments: VAIDR uses non-destructive, high-throughput imaging and sophisticated machine learning algorithms to identify faulty samples. Get solid results by excluding these samples either from your analysis or even from cell prep.

Use VAIDR to image and analyze samples before and after treatment to derive changes in cell quality and integrate these additional read-outs with those of your main assay to make the most of your budget and increase your chance of finding the signal in the noise.

You’ve done your experiments and got fascinating results. You know the prep and analysis were perfect because you’ve done them and checked them twice. But do you know that the results are even due to your prep? Have you checked that your cells were the same before you treated them, beyond a quick glance through the microscope? Would you be able to convince a reviewer or customer if they asked? That could be a question that might invalidate all your hard work.

VAIDR uses bright-field microscopy and sophisticated AI algorithms to objectively quantify quality in cell cultures. It automatically tracks image data, metadata, and quality-related read-outs so you can document and demonstrate that cells were the same before treatments and different after. You can rest easy and be sure of your results.

You’re nearing the end of a large omics research project. At 90% completion, while the results are being assembled, it becomes clear that the cells were much less morphologically uniform than in initial trials. Control wells are not really comparable across plates, casting doubt on the validity of the entire campaign.

VAIDR helps you spot trouble early on by establishing a morphological quality baseline. Any deviation is detected quickly and cost-efficiently by the combination of label-free imaging and powerful machine-learning analysis. Spot emerging problems early on and react quickly to either stop a doomed project or save a troubled project while there is still time.