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SmartLab All-in-one automation, digitalization, and miniaturization

Laboratories have to analyze and interpret an ever-increasing number of samples for research and diagnostic services. At the same time, they are required to quickly deliver quality results. In this interview, Dr. Felix Lenk explains how this can be achieved through laboratory automation.

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(Bild: © TU Dresden)

Dr. Lenk, what is the objective of the SmartLab Systems research group?

Dr. Felix Lenk
Dr. Felix Lenk
(Bild: © TU Dresden)

Dr. Felix Lenk: We explore three key research areas: first, the automated handling of chemicals, consumables and lab automation in general, second, the automated 2D and 3D image analysis of biological samples, and third, miniaturized measurement technology.

For us, the SmartLab, also called the laboratory of the future, is a term that sums up these technologies - automation, digitalization, and miniaturization.

What projects are you working on as it relates to applications in biomedicine and biotechnology?

Lenk: We use the PetriJet platform to screen for new antibiotic agents. Researchers use a zone of inhibition test for identification: When a microorganism produces an antibiotic substance, it is combined in a co-culture with another organism that triggers the production. This might be yeast cells or bacteria. A circular area forms around the organism where the substance is effective. The size of the zone of inhibition relates to the level of antimicrobial activity present in the sample.

The optical PetriJet allows high-throughput screening because we have to combine different types of organisms, always in multiple cultures. The platform can process culture dishes individually, open them, pour and cultivate the contents in a new culture dish. It also takes images of the process flow under different light exposure and from different perspectives and measures the zone of inhibition. This enables us to non-invasively screen a wide range of microorganisms and select new antibiotic candidates.

What is the current state of digitalization in the laboratory? What role do networking, the Internet of Things and big data play in this setting?

Lenk: This is best described by our “five-tiered approach to digital transformation”. Tier One refers to sensors that record short-latency and high-frequency laboratory processes. Tier Two is a sensor network. This means that the sensors must be able to deliver data via wireless or wired connection. Tier Three pertains to how this data is stored in a database structure even for long periods of time and is called the "data lake".

Tier Four refers to the "structured data lake". This turns the database structure into a kind of network. Terms and measurement data are thus put into correlation. This gives data context. And finally, Tier Five means making the data human-interpretable. Researchers seek answers to questions such as “Is this a great antibiotic candidate?” or “How do I change culture media for optimized growth?” If you capture data with three dimensions, it often makes it difficult for humans to understand because you can no longer display it in a diagram. Neural networks can process this data. It can be visualized with tools such as mixed reality headsets, voice output, or assistive technologies.

What’s next for this area?

Lenk: So far, point solutions have worked well to improve processes and data management, though interfaces are slightly more problematic. For example, when a pharmaceutical company moves a project from development to production, the process often has to be revamped at the pilot or production scale.

While you have the specific formula composition, there is no information about the form factor of the lab reactor, the power input, or the stirring speed. The biggest challenge is to create interfaces to allow all data transfer. To ensure connectivity and a network approach, standards are being created including the OPC-UA initiative by SPECTARIS e.V., or the SiLA Consortium (Standardization in Lab Automation) in Switzerland.

Connectivity also entails difficult adjustments. It is comparatively easy to map the actual workflow analysis and create requirement specifications for the target status. Many believe adjustments can be easily made during ongoing lab operations, clearly underestimating the difficulty of the implementation process. Yet many devices are incompatible because of the lack of standards. Each application requires individual driver programming. Joint standards and interfaces would make transition phases more effective, easier, and faster.

The interview was conducted by Timo Roth and translated from German by Elena O'Meara.

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