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Introducing the Lab of the Future

Jul 14, 2023Jul 14, 2023

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Looking around a research lab today, what do you see? Researchers extracting data from hundreds of pieces of stand-alone equipment before manually adding them to a spreadsheet (introducing errors as they go). Those spreadsheets are split across multiple computers, with researchers struggling to clean and analyze each of them. You are likely to see each researcher working as an individual silo using hand-written lab books, with little regard for sustainability, lab- or energy-efficiency or cost reduction. 70% of lab workers’ time is wasted on administrative tasks, preparation work, finding and cleaning data and reporting.1 Isn’t there a better way of doing science?

In this article we introduce you to the lab of the future, what you should expect to find in it and what impacts it will have on scientific research and industry.

The term “lab of the future” is a collective name for the technologies that will feature and enable the next generation of researchers. Let’s take a look at how the lab will run.

In the lab of the future, data collection will be enabled by plug-and-play devices within the internet of things in semi-autonomous lab processes. All data and metadata yielded from analysis and experimentation will feed directly from instruments and researchers into a single, managed data ecosystem, which will become the indelible record and remain available for further analysis.

Automation and robotics will handle all samples, chemicals and equipment. Sensors will record lab activity and ensure supply of reagents and samples, empowering scientists to work on their area of expertise – research and adding value.

The lab of the future will integrate virtual and physical technology, and may be shared between teams and across organizations, both in person and remotely.

Visualization tools, like augmented reality, will enhance what researchers see with digital information, such as safety procedures and batch numbers.

Supply chain issues will be predicted and resolved automatically by machine learning models, and systems tasked with delivering specific results to improve their performance, becoming more efficient as they go.

The lab of the future will also have much greater regard for sustainability, efficiency of the lab and of energy, and will source environmentally responsible products made with fewer toxic materials.

So how is all this going to be achieved, and what technologies will be needed to make this a reality?

The innovations of the lab of the future can be divided into seven broad groups:

Through linking people, equipment, consumables, systems and data you make information more accessible, allowing for reliable, detailed searches on-demand. Researchers and other staff should have confidence in your data, and as such, data should be of high quality – it should be FAIR2 (findable, accessible, interoperable and reusable). Laboratory information management systems (LIMS) and electronic lab notebooks (ELNs) are critical in this regard.3

A LIMS is a type of software housed on a server designed to improve lab productivity and efficiency by keeping track of data associated with samples, experiments, lab workflows and instruments. It’s a tool that allows you to actively manage all lab processes from instrument maintenance and samples to people and consumables, reducing human error and increasing efficiency.4

An ELN is a piece of software made to gather and organize information and notes, allowing research scientists to document research, experiments and procedures performed in a laboratory, and to collect, organize and share their data seamlessly.5

As an example, the Human Tissue Resource Center (HTRC) of the University of Chicago’s Department of Pathology adopted a LIMS solution that integrates an interface designed for a scalable production environment. It collects and tracks biobanking data such as patient information, diagnosis, organ site and linked pathology reports.

Until recently, a LIMS could be a really expensive option; employing server specialists and tech personnel can be a drain on resources, and LIMS was only really a viable solution for the largest companies.

Now there are cloud-based LIMS, where all this is done for you – a provider hosts it externally. No up-front costs, no tech staff to employ, and security and regulatory compliance are built in as standard.

With a cloud-based solution, though, comes increased risks, such as cyber breaches. It is critical that a lab is protected from cyberattacks and that data remains secure, that the usage is compliant and at the same time transparent to authorized stakeholders. This awareness has made security paramount for many cloud providers.

The Miami Project to Cure Paralysis is a pioneering spinal cord injury research center that runs a lab at the University of Miami. While their lab is relatively small, their 20+ research scientists perform high-content screening of genes and compounds to explore ways of making nerve cells grow better, and the amount of data they generate is overwhelming.

They transitioned first from a paper-based system to a self-hosted LIMS solution, then to a cloud-based LIMS solution.

“Upgrades are now automatic, and the Miami Project remains on the cutting edge. For a little lab, that is a huge advantage” says Vance Lemmon, a professor of neurological surgery at the lab.6

In the digital lab, there is improved instrument inter-connectedness due to the internet of things (IoT). This permits data to be collected more efficiently, enabling pharmaceutical and life science companies to control product quality, optimize lab asset performance, lab workflows and research throughput acceleration and ultimately improving patient outcomes.

The IoT refers to the integration of smart devices across the lab landscape via internet-connected sensors or software. The availability of affordable and connected technology is allowing laboratories to optimize their operations and combine instruments and data more efficiently.7 This inevitably generates large amounts of data (volume) from different sources (variety) as close to real-time as possible (velocity). These “three Vs of big data” and the ability to transform a tsunami of data into actionable results is key to understanding the challenges of (big) data management.8

Additionally, sustainability is another priority for the life sciences industry. Labs are able to minimize exposure to hazards, reduce waste, increase lab efficiency and reduce costs by using energy-efficient instruments and equipment and sourcing environmentally responsible products that are manufactured using fewer toxic materials.

For example, many labs are now using low energy LED bulbs rather than traditional lighting, and are even switching to mercury-free microscopy.

Realizing that certain chemicals, like formalin, ethyl alcohol and xylene can be recycled, distilled or filtered can help formulate a strategy to prevent chemical waste from being a part of the lab's waste stream – and can also reduce costs.One not-for-profit organization have introduced a vendor neutral environmental impact factor labeling scheme – ACT (accountability, consistency, and transparency) – similar to the food nutritional labeling scheme we see on our supermarket shelves. The ACT scheme aims to provide information about the environmental impact of manufacturing, using, and disposing of a product and its packaging, making it easier to choose safe, sustainable products in the lab.9

Once your data and workflows are automated and optimized, you’ll be able to use these advanced analytics modalities to drive actionable insights, enabling you to make intelligent, data-informed business decisions.

There are two main modes of AI; predictive and goal-based.

In predictive mode, AI can be used to anticipate, for instance, when issues may arise in supply chain management and automatically implement solutions.

In goal-based mode, you can instruct an intelligent system of your desired outcome and allow it to learn iteratively how to achieve that result. This is of particular importance in the area of robotics, but can also lead AI to select the optimal experiment to do next. Recently, for example, a UK-based company has arguably solved one of the greatest challenges that biology has faced for several decades – the ‘protein folding problem’ – using an AI system known as AlphaFold to predict and determine a protein’s structure and shape.10

Laboratory automation is usually classified according to the degree of instrument integration:11

Many labs are now combining advanced analytics with automation and robotics for faster and more focused scientific discovery. For example, labs can use robotics and automated systems to generate high-quality data, which is interpreted by ML and used by AI to select the next optimal experiment to conduct.12 Equally, R&D labs have been using AI algorithms and automation in these same closed loop approaches to identify, synthesize and validate novel molecules. For example, using a ML approach and gene expression data, researchers at the Centre for Molecular Medicine in Oslo, Norway, found novel biomarkers and potential drug targets for rare soft tissue sarcoma.13

Virtual reality (VR) has been gaining acceptance in the lab, with promising projects in place, such as the use of VR headsets to navigate microscopy data and conduct colocalization analysis.16

And from VR is emerging augmented reality (AR), which is now expanding out of the gaming world and into labs. Unlike VR, AR doesn’t disconnect people from the real world, but instead enhances it by adding extra information.

AR can be used to help with life science research, training, international collaborations and regulatory compliance.

With AR, trainers could monitor researchers remotely, seeing and hearing their environment and offering advice in real time. Research labs could build their own training programs, incorporating AR and AI to streamline the induction processes of new researchers and ensure adherence to standard operating procedures.

One of the greatest values that AR could bring to research labs is the ability to capture quality control data in real-time, allowing researchers to make on the spot decisions to maintain quality.

AR tools can also assist with compliance by providing interactive protocols, recording data automatically, and including time stamps of all tasks – even monitoring researchers’ actions and only unlocking subsequent steps once each task is performed correctly.

As part of the United Nations Industrial Development Organization’s (UNIDOs) efforts to promote industry and modern smart technology, in 2020 they set up a virtual assessment of the Ghanaian government’s Food and Drugs Authority (FDA) Cosmetic Laboratory in Accra.17

They used AR to enable an international expert on laboratory analysis, based in Rome, to exchange video, audio and data, to provide technical support and to assess the physical infrastructure, equipment, and human capacities to the laboratory in Accra. The analyst in the FDA Cosmetic Laboratory was able to receive instructions directly from the international expert and execute the tasks required.

The UNIDO project management team in Vienna, Austria, also participated in the live session, and after conducting the virtual visit the expert team concluded that the FDA Cosmetic Laboratory is ready to receive support from UNIDO in its accreditation process.

Organizations undergoing a digital transformation will experience the optimization of processes and throughput, alongside a concomitant increase in quality control and compliance.18

The digital lab will be far more automated, and as a consequence will contain fewer scientists. The skillsets of the people working in the lab will therefore be critical in its operation. The researchers that remain will experience more open, co-working spaces for collaboration and teamwork.

An increased focus on technologies and data standards such as IoT and FAIR will ensure that data privacy and security remain paramount in securing the lab’s digital assets, and a greater attention to environmentally responsible products will make for a more sustainable, efficient and cost-effective lab.

Finally, novel technologies continue to adapt and emerge. Only 10 years ago, ML, AR and voice recognition technologies were at an embryonic stage of development, and while they are still in their infancy, their utility continue to develop at an astonishing pace. It would be folly to attempt to predict how they will be used a decade from now, or predict what new, as-yet unheard-of technologies will emerge.

While the future might be difficult to predict, what is fair to say is that the future of the lab, while continuing to evolve, is an exciting one.

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