The four biggest trends in lab automation this year

1. Advancement of automation in cell culture labs
In cell culture labs, tasks such as feeding, splitting and planting cells are highly repetitive and labour intensive, making them a perfect target for automation. While this has been an objective for the industry for many years, there have been several problems holding this back, for example, the right technology being unavailable and cell culture research being too difficult to automate. Despite this, recent developments have meant that slower tools are being replaced with automation.

As a result, scientists can streamline previously time-consuming tasks and get more accurate results in significantly less time. Scientists can be sure that their cell culture is efficient as robots find the optimum time to feed cells outside of the 9 to 5.

2. Automated contextualisation
We can assume to see a heightened focus on the quality of data collected by automated means. At the heart of innovation is collecting, analysing and learning from data. This desire to generate high-quality data often leads labs to seek out automation as a solution. It can generate a greater quantity of higher quality data, allowing better conclusions to be drawn from their studies.

To reduce the risk of creating a ‘data lake’ with masses of information that isn’t contextualised, labs must pre-empt the depth of data they will receive and how it can be organised and contextualised. Contextualising data involves putting related information together, making it easier to interpret and digest, in addition to ensuring the data is reusable and accessible for other scientists.

3. Optimising innovation and lab space using modular automation
Lab space is precious, and devices used in them are, most of the time, expensive and large. The answer lies in a modular and interoperable model to maximise the footprint of a lab.

Traditionally, labs have been set up in an inwardly facing way that does not allow for interaction between robots and humans or modifications when robotics have previously been integrated into workflows. By opening the space and allowing scientists to utilise the robots to their full capacities, labs can have the flexibility to evolve and change while still enhancing their procedures.

Using a modular system, with parts being substituted or added depending on need, results in a broader scope for innovation, in addition to giving scientists the chance to alter how they work without dismantling an entire workflow.

4. Maximising end-to-end workflows
Labs are often investing in a single piece of equipment, which can be a bulky piece of hardware that automates a smaller of a larger workflow and consider themselves to have ‘automated’.

We need to think about end-to-end workflows in order to make the most of automation. By breaking down a workflow into smaller component processes and utilising robotics to connect each of those, lab technicians can convert previously clunky systems into one nonstop flow that significantly surges precision and scale. A continuous flow maximises the competencies of equipment to create high-quality results without manual interference.