The coronavirus pandemic has upended work routines worldwide, as many businesses shut down, and many others instituted work-from-home policies. Now, as much of the world looks at strategies for ending lockdowns and returning to places of work, there are new hurdles to overcome. For scientists, social distancing measures, while necessary for maintaining public health, have put a premium on lab time as facilities aren’t able to operate at the capacity that they did pre-COVID.
Adapting to this new reality won’t be easy, but with innovative thinking and improving technologies, there is great potential to revolutionize labs for a safe return. Advancements in digital R&D technology can facilitate improved experiment execution, data management and collaboration, data analysis, experimental design and innovation, and interoperability. Additionally, by increasing lab automation capabilities, scientists can continue their research without being physically present. This allows them to focus on innovation, decide which questions to explore, and even accelerate research processes. In this article, we will discuss some specific technologies for a safe and productive return to the lab.
To ensure social distancing, labs will need to use a staggered approach for assigning lab time. Because of this, it will be increasingly difficult for scientists to run their experiments manually. However, automatic lab equipment, such as Beckman Coulter’s Biomek 4000 Automated Liquid Handler, can run experiments without the need for a scientist to be physically present or even actively involved. By automating routine processes, a scientist can minimize their lab time while seamlessly continuing their research.
Automated lab equipment becomes even more useful with network connectivity. Automation systems, such as those offered by Laboperator, connect devices and lab equipment to a centralized platform, allowing the automation of more complex workflows. As an added benefit, scientists can operate networked lab equipment from anywhere. This technology enables scientists to monitor experiments, and even initiate new ones, without ever stepping foot in the lab.
Automated experiments and accelerated research will result in increasingly large volumes of data that require proper transcription, storage, and access. As research is trending away from siloed labs toward a more interdisciplinary approach with multiple collaborators, scientists need improved methods for storing and accessing project data. Lab data management has been a predominantly analog process, writing in paper notebooks and manually transcribing data. Not only is the process inefficient and error-prone, but it also makes it very difficult to share data with colleagues who are not in the same room.
Benchling addresses these issues by automatically capturing data collected by instruments directly into an electronic lab notebook (ELN). Digitizing the process eliminates the possibility of transcription errors, and records data in a ready-to-share format. Scientists can also focus on carrying out their experiments without having to worry about documenting results. Additionally, while handwritten notes have to be copied manually and are prone to destruction, digital records can be quickly copied and backed up any number of times to ensure they are never lost.
Preventing the large volumes of data that automation can consistently generate from bottlenecking projects will require innovation in the field of data analysis as well. Genedata has developed an AI that uses deep neural networks to analyze captured data and keep backlogs from accumulating. This technology reduces image analysis time significantly and allows for automation of screening and analysis workflows, providing scientists more time to focus on innovation and designing new experiments, and allowing them to scale and conduct larger, more complex research projects.
Once scientists complete their current projects, digital R&D technology can continue to assist scientists in designing new ones. With limited lab capacity, efficient and effective experimental design is even more critical. One aspect of R&D that is notoriously inefficient is selecting appropriate reagents for experiments. In fact, one-third of experiment irreproducibility can be attributed to inappropriate reagents. With unreliability in available reagent data, scientists often have to perform multiple validation experiments before starting their actual research. Under the circumstances this is an even greater problem, thus scientists must find a way to conduct experiments with a higher rate of success.
BenchSci offers an AI solution for faster, more accurate reagent selection using proprietary machine learning algorithms trained by Ph.D. biologists to extract and organize pertinent experimental data from publications. With BenchSci, scientists can quickly hone in on the appropriate reagent for their specific experimental context, without spending precious lab time testing and validating multiple reagents only to find they are inappropriate for the experiment.
Scientific initiative groups have also formed to facilitate the adoption of technology in the lab and increase interoperability. One such group, the SiLA Consortium, aims to establish standards for communicating data within independent systems. Connecting R&D equipment and software from different providers and transferring data between them is often difficult or impossible, creating a significant barrier to research. Time is lost while finding workarounds, and sometimes functioning equipment has to be replaced because it’s no longer compatible. Standardizing software for lab equipment would resolve these issues by significantly improving connectivity between devices and allowing scientists to focus their shortened lab time on advancing their projects instead of troubleshooting.
As our rapidly changing world continues to present us with new challenges, technology continues to be one of our most valuable assets in overcoming them. The utilization of digital R&D solutions will be instrumental, not only for a safe return to the lab during COVID times but also for overcoming current and future productivity challenges and improving research efficiency. Experiment execution, data management and collaboration, data analysis, experiment design, and interoperability all stand to benefit from digital R&D innovation.
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