Approaches to automation

The lab of the future is in reach – but it relies on end-to-end automation, explains Russell Green.

For the past 20 years, the design of a typical lab hasn’t changed. Lab benches are set up in the same way, and scientists take on a lot of manual work – moving samples between pieces of equipment, plate reformatting, barcoding tubes and more.

There has been the introduction of automation in more recent years, but this has mostly involved automating highly specialised benchtop instruments, such as liquid handlers or plate readers, any only cutting down a small amount of the hands-on workload for lab scientists. As long as they still need to move samples from instrument to instrument, they will spend hours of time on work that could be replaced by designing new experiments and analysing important data.

So, what do scientists need instead? Automation hardware and software are the answers to reducing repetitive manual tasks and dramatically increasing the walkaway time involved in experiments. If labs can adopt fully automated processes, scientists are also able to work together – whether in the same office or across the world from one another – while also scaling the number of experiments they can run, without compromising accuracy or repeatability.

Efficiently run labs that gift time back to scientists so they can spend time working on valuable tasks that ultimately lead to faster discoveries.

Reduce workload and boost output

By adding robotics, entire workflows can be connected, automated, and optimised for productivity, as multiple pieces of equipment can be linked together in an end-to-end workflow. In addition to increasing the throughput of a lab, fully automated workflows also ensure accurate results. By allowing every piece of equipment to operate concurrently and carry out a variety of different processes, labs can achieve much closer to 100% capacity than when carrying out manual processes, without compromising on accuracy.

And the impact is evident. For example, Automata works with a genomics organisation that in one year has reduced manual touchpoints in one of its workflows by 88% and increased throughput from 16,000 to 50,000. It also supported a genomics testing lab in increasing its capacity – a key challenge in the life science industry. Originally, the lab could only process 96 microplates in the DNA library preparation, but with end-to-end automation, it now processes 384 – a remarkable increase of 300%.

Software also plays a key role in effective lab automation. For example, a digital platform can replicate how a lab’s workflow operates run, allowing scientists to visualise, test and – where necessary – adapt workflows before implementing them in real-life experiments. This means that everyone in the lab can be certain that assays re being run accurately, without ever having to make hardware adjustments. With less time spent on manually correcting workflows, scientists are freed up to spend more time running effective experiments and generating valuable data.

By integrating the digital and physical workflow, it’s also possible to monitor and manage experiments remotely. Previously, scientists had to be in the lab for most of their day to keep track of how experiments were progressing. With automation, they can instead be confident that the scientific process is always happening correctly and win back the time previously spent keeping a watchful eye, to use it for more skills tasks instead.

Software can also help harvest data from experiments and automatically upload them to a digital platform, streamlining the analysis process and minimising the risk of manual errors.

Given that many labs still rely on manual processes, there is a risk of samples being mislabelled, data recorded incorrectly, and siloed workflows due to poor visibility of results. These factors can impede or derail scientific research, incurring costs in both money and time.

Technologies such as Linq Cloud – an easy-to-use cloud-based software, connect a lab’s physical and digital environment, enabling life science organisations to scale. In addition to enabling users to quickly draft, visualise and adapt workflows to the needs of each individual experiment – automatic data uploads ensure accurately recorded and sharable results.

Make automation flexible

Every life science organisation is unique, and each lab has different goals ranging from gene editing to the development of cell-cultured meats and materials. And these goals will change over time too. Therefore, automation cannot take a ‘one-size-fits-all’ approach, it must be flexible and customisable to accommodate every workflow.

This flexibility becomes especially important at a time when the demand for lab space in the UK is at an all-time high. We are witnessing the conversion of spaces such as unused retail stores and offices into labs, spaces which weren’t originally designed to include lab equipment. So the more flexible hardware, such as automated benches can be, the more effective these repurposed spaces can be.

To achieve this flexibility and futureproof labs, a modular approach to equipment is key. This will allow for the addition of more benches to expand processes and safeguard the investments of lab science organisations. Integrated software can also play a pivotal role in this regard. For example, when the needs of a lab evolve, hardware can be swapped in and out to seamlessly deliver one unified ecosystem.

For larger organisations with labs spanning multiple countries, it is crucial to make sure workflows run consistently everywhere, and the results achieved can be replicated in any lab. Although the physical lab spaces may vary across locations, cloud-based lab automation software ensures that the workflows running in them and the data collected are identical in every disparate lab.

As well as anywhere, using the cloud makes it possible to run, monitor and even modify experiments at any time. As a result, workflows don’t have to be manually stopped and started from within the lab, and scientists can enjoy far more freedom in managing their working week. This is a particular benefit when organisations are working with highly demanding samples, like when growing cells. Scientists no longer need to be physically present in the lab during late hours or weekends, as they can remotely monitor the status of cells and customise experiments to start at the most opportune time. This ensures optimal attention and care for the samples without compromising scientists’ work-life balance.

By using cloud-based lab automation and remote monitoring capabilities, organisations can streamline operations, increase flexibility, and enhance the efficiency of their scientific research across multiple locations.

Automation in the lab has already come a long way in recent years, but scientists are still spending too much time to manual processes. As a result, the ability to scale processes and output is hampered, and the risk of manual errors in the lab grows.

By harnessing the power of integrated hardware and software, scientists can allocate their time to designing new experiments and drawing conclusions from automatically harvested data whilst simultaneously ensuring the smooth operation of ongoing experiments. This represents the lab of the future, and with automation it can be achieved.

Russell Green is with Automata.

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