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The four biggest trends in lab automation this year

publication date: Jul 25, 2022
author/source: Russell Green, Lead Product Manager for Drug Discovery and SynBio, Automata


Russell Green, Lead Product Manager for Drug Discovery and SynBio at Automata outlines the four biggest trends in lab automation this year

Around the world, labs are full of highly trained scientists eager to make breakthrough discoveries. However, tedious tasks and shallow levels of data are currently preventing them from utilising their knowledge to its fullest capacity. At SLAS Europe last month, it was evident that empowering these scientists was top of the agenda, with a number of key trends emerging based around helping teams to innovate – no matter where they are in their automation journey.


1. Automated contextualisation: working smart with data

We can expect to see a greater focus on the quality of the data collected by automated means. Being able to collect, analyse and learn from data is at the heart of innovation today and often it is this desire to generate high-quality data that causes labs to first look at automation as a solution.

Automation can generate a greater quantity of high-quality data, enabling scientists to draw better conclusions from their studies. However, there is a risk of labs creating a ‘data lake’ with masses of information that isn’t contextualised – and without context, data means nothing.

As a result, labs are no longer focusing just on the speed and scale of data accumulation but are pre-empting the depth of data they will receive and how it can be organised, contextualised, and then acted upon. Contextualising data involves putting related information together, which makes it easier to digest and interpret. It also means data is reusable and accessible for other scientists.

We are also seeing a move towards more data sharing within organisations, as the benefits of collaboration are being recognised. With cloud software, scientists can take detailed and contextualised data, such as genotype patterns, and use it to work together on projects, bringing in knowledge from all over the world.


2. Optimising end-to-end workflows

Labs often invest in a single piece of equipment, which can be a bulky piece of hardware that automates a singular part of a bigger workflow and consider themselves to have ‘automated’.

Sometimes, this isn’t closely aligned to the goal that a lab is trying to achieve. Or the tool may be an excellent solution for a single task, but risks taking up large amounts of space without maximising the role it can play across an entire workflow. It can also be seen as hard to adapt to the ever-changing workflows of a research environment.

To make the most of automation, we need to think about end-to-end workflows. By breaking down a workflow to smaller component processes and using robotics to link each of those, lab technicians can transform previously clunky, step-by-step systems into one continuous flow that increases both scale and precision. A continuous flow prevents the hold-up that can happen when only a small part of a system is automated and maximises the capabilities of equipment to produce high quality results without manual intervention.


3. Accelerating automation in cell culture labs

In cell culture labs, there are many labour intensive and repetitive tasks such as plating, feeding and splitting cells – making them an ideal target for automation. While this has been the goal for the industry for many years, there have been numerous challenges holding back the shift, from cell culture research being considered too difficult to automate to the right technology not being readily available. However, recent advances mean the industry is beginning to replace slower, more cumbersome tools and incorporate automation into larger workflows.

As a result, scientists can streamline previously slow tasks such as media exchange and get more accurate results in less time. With robots able to find the best time to feed cells and outside of the 9 to 5, scientists can ensure their cell culture is at its most efficient.

A good example of this is to look at the greater focus on commercial applications for cell culture labs, such as lab-grown meat and leather. These new products demand faster turnaround times and the ability to scale, while still requiring high levels of precision.


4. Modular automation to maximise lab space and innovation

Lab space is highly valuable, and devices used in them are often large and expensive. To maximise the footprint of a lab and an efficient workspace, the answer lies in an interoperable and modular model.

Traditionally, when robotics has been integrated into workflows, labs have been set up in an inwardly facing way that does not allow for alterations or interaction between humans and robots. By opening out the space and allowing scientists to use the robots to their full capabilities, labs can have the flexibility to grow and change while still optimising their processes.

Using a modular system, with parts being added or switched depending on need, means there is greater scope for innovation, and gives scientists the opportunity to change how they work without having to dismantle an entire workflow.


Breaking down barriers with automation

Revolutionary science has always been reliant on the worldview that anything can be made possible – from ground-breaking treatments for rare diseases to synthetically grown meat. However, when it comes to the physical processes used to make such discoveries, this forward-looking mindset is not always present. Labs still hesitate over the decision to automate, feeling they are alone in a risky procedure, even though these changes are not as daunting as they seem. By building relationships with automation partners and embracing the rich applications of this technology, scientists can continue to use their knowledge to make the impossible possible.


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