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Leveraging the Power of Spatial Context
Industry Insight

Leveraging the Power of Spatial Context

Leveraging the Power of Spatial Context
Industry Insight

Leveraging the Power of Spatial Context


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As a greater number of scientists are seeking spatial context in their research, the need to utilize complex imaging is growing. However, obtaining expert microscopy results can require vast amounts of training and specialist knowledge, leaving it inaccessible to many.


Today, Leica Microsystems launched Mica, the world’s first Microhub. This new imaging solution aims to democratize microscopy, giving researchers the ability to utilize complex imaging regardless of their level of experience.


To learn more about Mica, how it simplifies microscopy workflows, and some of the areas it could impact the most, Technology Networks spoke to James O’Brien, vice president life sciences at Leica Microsystems.


Ash Board (AB): Mica is described as the world’s first Microhub, can you explain what a Microhub is?


James O’Brien (JO): We decided to call it a Microhub because Mica is so much more than a microscope. A Microhub stands alone as a single easy-to-use digital imaging and analysis hub that guides users from setup to acquisition and to relevant insights. The best analogy we can use to explain what a Microhub is, would be to compare it to an airport hub that brings together passengers and guides them to their destination. In the same way, Mica brings all the users of the lab and their experiments together and guides them to their experimental destinations. Technologies to cover the majority of workflows are together in the Microhub. Mica contains not just widefield and confocal microscope technology, but also combines incubation, machine learning software, automation tools and unique hyperspectral unmixing techniques to automate the imaging workflow in a single product.


AB: Mica eliminates the need for specialist microscopy expertise, how is this achieved?


JO: When setting up your microscope for imaging, you go through an iterative process of multiple steps to get to the first image – and this is not even thinking about making the final image acquisition. Compared to a conventional microscope, Mica reduces the setup steps by over 85% until you reach the first image and a third less time to the overall imaging result. This is accomplished through the automated optimization of imaging parameters that is effortlessly executed using the OneTouch button in Mica. This is the key to making microscopy accessible.



AB: How does the workflow on Mica compare to standard microscopy?


JO: One of the keys benefits Mica will be known for are the radically simplified workflows. Mica will reduce over 60% of the process steps through system intelligence, bringing you from sample to discovery much faster. In a basic confocal experiment with a conventional microscope, we’ve counted 24 steps that you need to perform to take you from setup to results. When using Mica, this workflow is radically simplified into as little as eight steps, reducing the time and effort needed to go from sample to insight.


We have also simplified post acquisition steps. Mica’s pixel classifier that is powered by our AI-based Aivia technology can be easily trained to automatically segment your image simply by marking the object you want to measure. Now you can select which values you want to compare and directly create a visual representation. Additionally, the model generated by your training can be easily transferred and ensures repeatability and reproducibility. You can even enhance existing models through further training.


AB: In what fields do you see Mica having the biggest impact?


JO: We believe Mica will have an impact across all research areas in life science.  In all areas, the questions of researchers are becoming more complex while striving for clinical relevance. It is critical to answer these questions in spatial context to get to a deeper understanding. Mica is making microscopy accessible so researchers from any background can leverage the power of spatial context. Take for instance translational research, where functional and structural information need to be correlated in context. An example of this is when utilizing a drug uptake assay where the internalization of the drug is monitored, and the cellular response analyzed. Mica is making these assays accessible.



James O’Brien was speaking to Dr. Ash Board, Editorial Director at Technology Networks.

  

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Ash Board PhD
Ash Board PhD
Editorial Director
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