Laboratory automation is a rapidly evolving field, with new technologies and approaches being developed all the time. As such, it can be challenging to keep up with the latest trends and developments in the field. In this blog post, we will take a look at some of the latest trends in laboratory automation in academic institutions, highlighting some of the key technologies and approaches that are being used to improve the efficiency, accuracy, and reproducibility of experiments. From the increasing adoption of robotics and artificial intelligence, to the integration of lab equipment and the use of cloud computing, these trends are transforming the way research is being done in academic labs around the world.
There are several trends in laboratory automation that are currently being seen in academic institutions:
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Increased adoption of robotics: There has been a growing trend towards the use of robots in academic labs, particularly in areas such as liquid handling and sample preparation. These robots can improve the accuracy and consistency of experiments, as well as increase the speed at which they can be performed. One of our favourite devices in this field is the d2 by UK Robotics Ltd.
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Use of artificial intelligence and machine learning: There is also a growing trend towards the use of artificial intelligence and machine learning in laboratory automation. These technologies can be used to optimize experimental protocols, analyze data, and even predict outcomes. We're seeing a lot of uptake of these methods in microscopy & image analysis and neurobehaviour, two areas of research that have historically high labour costs. Perhaps one of the most revolutionary advancements in this area in the context of neuroscience research was DeepLabCut (DLC). DLC has a thriving community of open-source developers and its success has seen its researchers funded by the Chan Zuckerberg Initiative.
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Integration of lab equipment: Another trend is the integration of different types of lab equipment into automated systems, allowing for the seamless transfer of data between devices and the automation of complex protocols. Have you ever had to wait until the end of the day for your samples to finish running, or your reagents to finish mixing, only to then spend a small amount of time transferring said samples into another machine/centrifuge/other long-running machine process? What if you miss a key window of time within this process because life gets in the way? That's where integration of lab equipment helps, by having them talk to each other directly, the only thing you need to do is set the experiment up and wait for the results at the end.
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Increased use of cloud computing: The use of cloud computing in laboratory automation is also on the rise, allowing researchers to access and analyze data from anywhere and at any time, and with far greater computing power than is feasible to have in the office or at home. Being able to share code blocks with colleagues seamlessly with Google Colab has revolutionised the way data is analysed. No longer is it necessary to set up difficult-to-install graphics card drivers in each of your dedicated machine learning workstations. Now, it's as easy as sharing a document via email.
In conclusion, the trends in laboratory automation in academic institutions are focused on improving the efficiency, accuracy, and reproducibility of experiments, as well as reducing the time and resources required to perform them. From the increasing adoption of robotics and artificial intelligence, to the integration of lab equipment and the use of cloud computing, these trends are transforming the way research is being done in academic labs around the world. As the field of laboratory automation continues to evolve, it will be interesting to see how these trends continue to shape the future of research in academia.
It's worth noting that while these trends are certainly exciting and have the potential to significantly impact the way research is conducted, they are not without their challenges. For example, the adoption of new technologies can be expensive, and there may be a learning curve involved in getting up to speed with these tools. Additionally, there are concerns about the potential impact of automation on job security and the role of humans in the research process. These are important issues that will need to be carefully considered as the field of laboratory automation continues to evolve.