LIMS & Lab Automation


Rapid COVID-19 testing enabled by advanced laboratory automation and LIMS


Kevin Smith, Senior Director Global Services and Support, Digital Science, Thermo Fisher Scientifi c


Over the last decade, laboratory automation has proven to be critical, and its benefi ts, such as increased throughput, standardisation and reproducibility, have been recognised by organisations worldwide. The importance of automation has especially come to light during the COVID-19 pandemic, where it has enabled laboratories to operate effi ciently in a challenging climate. First, it can minimise the number of people required on-site, which helps to reduce transmission risks and overcome the shortage of trained technicians, and second, it facilitates the rapid scale up of diagnostic testing. This article reviews the importance of automation and the role of digital solutions within automated science. It goes on to explore how automated processes featuring laboratory information management systems (LIMS) software have minimised disruption from the pandemic, and allowed COVID-19 testing to be quickly scaled up to meet demand.


Advantages of automation


Automation has many benefi ts for a laboratory. One of the most widely acknowledged advantages is the ability to achieve high levels of throughput and processing that are not possible with manual methods. However, there are important additional benefi ts to note, for example, minimising human interaction reduces the risk of errors, resulting in improved data quality, integrity and reproducibility. By reducing the hands-on time needed on basic repetitive tasks, automation also frees scientists up for higher value activities without interruptions. Furthermore, automation provides fl exibility for laboratories to scale operations to meet current workfl ow and future capacity needs.


There are also other advantages of automation that may be initially overlooked. For example, a positive impact of higher throughput and reduced errors means that laboratories can reduce time spent on re-testing samples, and, therefore, shorten research timelines and iteration cycles. Additionally, the higher quality data generated can more easily be used for artifi cial intelligence (AI) and machine learning (ML) applications to drive further insights.


Automation can take place on any level, from simple tasks to complex systems. Initially used to allow high throughput screening in drug discovery, automation’s benefi ts are now also being recognised in other areas, for example in QA/QC, production environments and precision medicine. It has many applications across different stages of the pharmaceutical industry and most recently, implementation has been rising in diagnostic testing for COVID-19.


Despite its many advantages, one of main barriers to entry for automation, until now, is that it can cause disruption during implementation. However, the benefi ts outweigh the time and effort for implementation since without automation, discovery and progress can be stifl ed, and laboratories will not be able to keep up with the competition and continue to meet customer expectations. Laboratories that embraced automation prior to the pandemic have been able to better respond to the challenges posed and return to full productivity more quickly. Indeed, during the COVID-19 pandemic, laboratory automation has increasingly been viewed in a strategic way to assure business continuity.


The importance of digital systems Automated science is often regarded as being built on three foundational pillars:


1. Physical automation, the hardware that includes tools such as analytical instruments, robotic sample handling and automated reagent supply.


2. Data infrastructure, encompassing laboratory information management systems (LIMS) to manage samples and data, electronic laboratory notebooks (ELNs), dedicated software connectivity tools for bi-directional data transfer, and internet capable devices (internet of things, IoT). Essentially the entire infrastructure that enables the generation of standardisable, sharable data and makes it available for wider use.


3. AI and ML, deep learning technologies that take large volumes of data and turn them into the insights that drive discovery and push the science forward [1,2].


The complete benefi ts of laboratory automation are fully realised when combining the three elements above. Here, digitalisation is the critical link between these pillars, with digital tools connecting the physical instruments to the advanced analytical tools. LIMS has a key role to play in collecting, centralising and managing data, automating processes, and delivering connectivity and data integrity to provide a strong foundation for AI and ML. For example, LIMS manage Standard Operating Processes for the analytical instruments and collect large amounts of high-quality experimental and operational data, storing it in a manageable way so that it can be analysed in deep learning.


LIMS software handles data securely and comprehensively to ensure data integrity and traceability, which is crucial for complying with regulations and guaranteeing product/ result quality. LIMS can also allow laboratories to automate processes, such as reagent re-stocking or fl agging when instrument maintenance may be required. Lab automation specifi c software solutions, such as Thermo Scientifi c Momentum workfl ow software, enable connection to external applications such as LIMS, ELNs and other platforms to streamline data management and tracking. This connectivity also enables users to connect and synchronise applications across multiple different laboratory sites.


Underlying digital transformation is the concept of FAIR data, key in today’s laboratory environment. FAIR data is fi ndable, accessible, interoperable and reusable, and it is these key attributes that make it so valuable. This concept goes beyond instruments simply talking to one another. It means data must be fi ndable and accessible between systems and scientists, and be of suffi cient quality to be re-used with confi dence. Data management through digitalisation supports the achievement of FAIR data.


There is no doubt that digitalisation is key to enabling automated science. The IoT provides tools to generate huge volumes of research data and metadata, including operational, environmental and inventory. Processes are honed and integrity increases as manual steps, such as data transcription, are removed. Integrated physical and digital automation allows facilities to collaborate under standardised conditions. When this is achieved, scientists can obtain reliable high-quality data that can be shared and accessed across different platforms by all who need it, while feeding machine learning applications.


Rising to the challenges of COVID-19


The COVID-19 pandemic has created huge challenges for laboratories worldwide as they have to maintain social-distancing guidelines and maintain continued operation with fewer people on site.


Moreover, there are further challenges for diagnostic laboratories in particular as they face intense pressure for increased capacity and expanded services to accommodate COVID-19 testing. In turn, this has placed additional demands on data management systems as scientists need to increase throughput while tracking the samples’ journeys and accurately reporting results.


Laboratories using automated tools have been able to rise to these challenges. A study entitled ‘High-speed large-scale automated isolation of SARS-CoV-2 from clinical samples using miniaturised co-culture coupled to high-content screening’ [3], has proven that automation is indeed enabling rapid testing with minimal human interaction, which reduces the risk of contamination. The paper describes how scientists developed a new high-throughput isolation strategy using novel technologies for rapid and automated isolation of SARS-CoV-2.


Automation in action Figure 1: The three pillars of automation.


Achieving high throughput workfl ows and data integrity and traceability are especially important for enabling diagnostic laboratories to scale up services and expand testing capabilities in response to COVID-19. Yet, expanding high throughput testing capabilities without scaling workfl ows in the digital space can limit the effi ciencies automation offers and potentially compromise the integrity of results. During the global pandemic, there are


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