Mica - Imaging Microhub
Mica is the world’s first imaging Microhub. More than a highly automated microscope, it unites widefield and confocal imaging in a sample protecting, incubating environment. With the simple push of a button, you have everything you need - all in one place - to supercharge fluorescence imaging workflows and get meaningful scientific results faster.
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What if every scientist could access spatial information?
Mica empowers every researcher to move from set up to beautifully visualized results and analysis efficiently, accurately, and confidently. Now you can focus on your science, not figuring out your microscope.
Access for all
Everyone can now leverage microscopy to make more discoveries. Mica provides a clear sample overview and allows you to easily change observation conditions with just a few clicks.
- 85% fewer steps to the first image
- 33% less time to the first image
- 50% of the training time
No constraints
The Microhub has everything you need to enable discoveries, unified in one easy-to-use system.
- Simultaneously capture all 4 labels or different structures in a single acquisition for both widefield and confocal, without ever moving your sample. This overcomes the spatiotemporal mismatch between labels of moving objects during sequential acquisition.
- Select from multiple imaging modalities, including widefield, confocal, THUNDER imaging, LIGHTNING, Z-stacks, time-lapse and more. Seamlessly move from fast overview to high resolution when required by your experiment
- Achieve physiological-like conditions throughout your experiments
Radically simplified workflows
Intelligent automation and AI-supported analysis enables greater efficiency and a faster track to publication.
- Reduce over 60% of process steps through system intelligence
- Reduce time and effort from sample to insight by simplifying your entire workflow
- Enable 100% reproducibility and repeatability throughout your experiment
Key applications include fluorescence multi-well plate assays, 3D tissue imaging and long-term timelapses.
