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With the advent of automation and digitalization, tools and technologies supporting research in the biological sciences are evolving rapidly. In this article, I explore the transformative impacts of new technologies on science laboratories.
Automation
One of the most impactful changes in modern research laboratories is the increased use of automation.1 Robotic devices can precisely and precisely perform routine tasks such as pipetting, mixing, and measuring, which is especially useful for high-throughput screening and next-generation sequencing experiments.
Automated tools reduce the risk of human error and free researchers from repetitive manual tasks, reducing work-related injuries such as carpal tunnel and stiff neck syndrome.2 The introduction of automation increases reproducibility, experimentation, and data collection rates. With less time spent at the bench, scientists can devote themselves to greater intellectual pursuits.
AI-powered data analysis
As the complexity of experiments increases, the amount of data generated also increases. Artificial intelligence (AI) algorithms help researchers analyze and interpret their data, identifying hidden patterns and correlations.3 Additionally, machine learning (ML) allows machines to “learn” features of data sets, allowing algorithms to adapt and make predictions.
Therefore, AI can help automate data analysis, allowing investigators to work more efficiently and accurately. AI algorithms can identify outliers in large data sets or detect new compounds. One specific example of how scientists are currently using this approach is to improve the sequence design of therapeutic mRNA.4
Laboratory connection
The interconnectedness of laboratory operations allows for better management of assets, resources and personnel. Monitoring equipment such as incubators and freezers, even remotely, ensures that samples and experiments are protected, ensures high-quality results, and allows for regulatory compliance.
Furthermore, other laboratory equipment, such as automated sequencers and data analysis software, can send their output directly to electronic notebooks (ELNs), making traditional paper notebooks a thing of the past. Using digital notebooks, researchers can easily edit results, find previous experiments, and compare data from related experiments. Furthermore, ELNs produce consistent and comparable documentation, enabling researchers to easily share their data with collaborators.5
Data digitization
As data grows in size and becomes more complex, storing it on local servers or hard drives becomes less practical. Cloud storage allows researchers to securely store their data and access it around the world.6 This allows for greater collaboration between researchers, as they can share data quickly and efficiently.
Along with the increasing availability of cloud storage options, concerns about privacy, confidentiality, and Internet security are growing. This is especially true of ideas related to patent and proprietary discoveries. To address these concerns, specialized encryption programs are being implemented across research institutes and workshops on confidentiality and data sharing are being held to educate researchers and make them aware of local legislation, regulations and institute policies.
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Sustainability
Laboratories have large carbon footprints. Single-use plastics are widely used and some experiments consume and generate hazardous materials. Intensive power use is also common in laboratories where instruments run around the clock. Experts estimate that the energy needs of an average laboratory are three times that of an office space of the same size. Furthermore, the contribution of plastic is believed to be 2% of the world's total plastic waste.7
A paradigm shift is taking place, with energy-efficient appliances, better-maintained equipment and facilities becoming a priority. In addition, laboratory suppliers implement bulk ordering, streamlined packaging, and container recycling, while replacing plastic and toxic reagents with environmentally friendly counterparts, when available.8 Conscious waste disposal is also strictly enforced by institutes – laboratories often have a pre-appointed person responsible for waste disposal – further emphasizing the importance of laboratory sustainability.
Accessibility
Making research accessible to people with visible and non-visible disabilities is another important consideration for laboratories moving forward. To make this possible, multiple disciplines need to collaborate to make laboratories accessible to people with different needs.9
For example, older buildings must include ramps and stair lifts to assist people with certain physical disabilities, while new building designs must plan for these features from the beginning. Modular laboratory furniture should be manufactured to accommodate all occupants, and audio and visual alerts should also be installed throughout the laboratory to keep all scientists safe in the event of an emergency.10 In addition, automation reduces the necessity of hands-on work, while new accessibility software tools increase inclusion of scientists with disabilities.11
References
- Hayase S, Katayama O, Hata T, et al. Full automation of COPMAN using LabDroid enables high-throughput and sensitive detection of SARS-CoV-2 RNA in wastewater as a key indicator. Total environmental science. 2023;881:163454. doi:10.1016/j.scitotenv.2023.163454
2. Al-Hilali M, Balkhi HH, Valenius L. Carpal tunnel syndrome among laboratory technicians and its relationship to personal and ergonomic factors at work. The occupation of health. 2017;59(6):513-520. doi:10.1539/joh.16-0279-OA
3. Brito AJ, Matos Felipe P, Mourao J, Moreira S. SYNPRED: Predicting drug combination effects in cancer using different synergy metrics and ensemble learning. Gigascience. 2022;11:GEAC087. doi:10.1093/gigascience/giac087
4. Castillo-Hair SM, Seelig G. Machine learning to design next-generation mRNA therapeutics. ACC Chemistry Res. 2022;55(1):24-34. doi:10.1021/acs.accounts.1c00621
5. Elberskirch L, Bender K, Reifler N, et al. Digital research data: from analysis of existing standards to the scientific basis of a standard metadata scheme in nanosafety. Part of the fiber Toxicol. 2022;19(1):1. doi:10.1186/s12989-021-00442-x
6. Krumm N. Organizational and technical security considerations for laboratory cloud computing. J Appl Lab Med. 2023;8(1):180-193. doi:10.1093/jalem/jfac118
7. Farley M, Nicolet BP. Reusing laboratory instruments reduces the equivalent CO2 footprint and operating costs. One plus. 2023;18(4):e0283697. doi:10.1371/journal.pone.0283697
8. Freeland B, McCarthy E, Balakrishnan R, et al. A review of polylactic acid as an alternative material for single-use laboratory components. Materials (Basel). 2022;15(9):2989. doi:10.3390/ma15092989
9. Bernard MA. Promoting disability inclusion in the scientific workforce SWD at the National Institutes of Health. Diversity science. Published July 21, 2021. Accessed May 26, 2023. https://diversity.nih.gov/blog/2021-07-21-advancing-disability-inclusion-scientific-workforce
10. Smith SB, Ross AD, Pagano T. Chemical and biological research with deaf and hard-of-hearing students and professionals: ensuring a safe and successful laboratory environment. J Chem Health SAF. 2016;23(1):24-31. doi:10.1016/j.jchas.2015.03.002
11. Ellis LD. Design laboratory spaces that are accessible to people with disabilities. Harvard T Chan School of Public Health. Published June 14, 2021. Accessed May 26, 2023. https://www.hsph.harvard.edu/ecpe/designing-accessible-labs-for-people-with-disabilities/
