The era of automation defines modern laboratory medicine. Lab automation plays a central role in many fields, drastically changing the workplace and leading to higher efficiency and reduced human errors.[1] Continue reading to learn more about the role of laboratory automation in genomic research.
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What is Lab Automation?
Laboratory automation was originally introduced by the AutoAnalyzer, which involved performing continuous flow analysis, as well as the Robot Chemist, which automated conventional manual analytical steps. Several generations of stand-alone analyzers have changed the scope of laboratory medicine, and improved efficiency has reduced human errors. Higher throughput has also provided larger assay menus.[1]
The progression of automation has also resulted in overcoming the need to do laborious steps within the pre-analytical phase, which is time-consuming, involving sample identification, sorting, and centrifugation, as well as steps in the post-analytical phase, such as specimen storage and archiving. Total laboratory automation has included several instruments that run parallel through a robotic tracking system and artificial intelligence that links all stages of the analytical process.[1]
As a result of increased automation, there are many benefits, including a reduction in cost and laboratory errors, as well as improved performance and efficiency. Additionally, the automation of manual, laborious steps enables technicians to take on more skilled responsibilities, allowing them to gain more knowledge in different areas, such as microscopy and plate interpretation.[1]
How is Lab Automation Used in Genomics?
Laboratory automation has significantly impacted different fields, including analytical advancements in genetics and genomics. An example of an automation-related advancement in genomics includes the development and progression of next-generation and single-cell sequencing, which has revolutionized this field as well as transcriptomics for whole genome DNA and RNA sequencing with both high throughput and cost reduction.[1]
This paradigm shift from bulk tissue analysis to single-cell sequencing has resulted from technological advancements over the past twenty years, with quantitative microarray technology being able to measure genome-wide DNA and RNA in the 1990s; however, this technology required too much material input for single-cell analysis.[1]
Additionally, whole transcriptome and whole genome amplification were developed between 1990 and the early 2000s to amplify both genome-wide DNA and RNA in order to overcome the challenges of polymerase chain reaction (PCR).[1]
Later technologies include next-generation sequencing (NGS), which enabled genome-wide DNA and RNA sequencing, which significantly increased throughput with improved cost efficiency compared to sequencing techniques used previously, such as in the Human Genome Project.[1]
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During the Human Genome Project, an approach called shotgun sequencing was developed to sequence longer sections of DNA; this approach enabled genomic DNA to be broken down either enzymatically or mechanically into small pieces and then cloned into sequencing vectors to allow DNA fragments to be sequenced individually. This process enabled the entire human genome to be sequenced and interestingly, the essence of the massive parallel sequencing that is used in NGS has been adapted from shotgun sequencing.[2]
Novel NGS technologies can read DNA templates randomly throughout the entire genome by breaking the genome into smaller fragments and then ligating the pieces of DNA to designated adaptors that are randomly read during DNA synthesis.[2]
While the automated process of NGS has enabled the progression of many samples being sequenced simultaneously through massively parallel sequencing, the read length achieved is significantly shorter than Sanger sequencing, with current NGS technology providing 50-500 continuous base pair reads.
However, this is a significant limitation as advancements in developing NGS technologies through single-molecule sequencing can address these challenges. Advances may also surpass Sanger sequencing, with the capacity to read many continuous kilo-base pairs.[2]
Another example of high throughput genomic technology includes microarray technology, which has led to significant accomplishments in genetic linkage, association studies, DNA copy numbers, and gene expression analysis. The progress of genomics technology has the potential to provide revolutionary advancements in human health, with automated technologies enabling genomic studies into genes involved with molecular pathways for targeting various diseases and disorders.[2]
An Overview of the Genomics Market
Global laboratory automation use in the genomics market was valued at 1.89 billion USD in 2023 and is expected to increase to 3.39 billion USD in 2029, with a compound annual growth rate of 12.43% during the forecast period.[3]
The development of next-generation sequencing has led to innovative scientific progress in genomics, which has increased the computational capacity of researchers and scientists, further driving the market.[3] Widespread automation has enabled companies to increase their output as well as their profit.[4]
Overall, automated advancements in genomics are critical for identifying genetic involvement in human disorders and have increased comprehension of many genes and genomic regions related to the pathogenesis of diseases.[2]
Additionally, the progression in genomic analysis has led to significant medical advancements, with laboratory automation enabling innovative flexibility, increased throughput, and cost-efficiency. This has also led to an increase in genetic testing businesses due to DNA sequencing technologies being more affordable and available to the wider population.[3]
The future of genomic research holds great potential for the comprehension and possible treatment of various human diseases, with automation driving revolutionary change through enabling higher throughput as well as increasing the speed and reliability of patient-specific data for clinicians while also reducing cost.[2,4]
References and Further Reading
- Wilson S, Steele S, Adeli K. Innovative technological advancements in laboratory medicine: Predicting the lab of the future. Biotechnology & Biotechnological Equipment. 2022;36(sup1). doi:10.1080/13102818.2021.2011413
- Zhang J, Chiodini R, Badr A, Zhang G. The impact of next-generation sequencing on Genomics. Journal of Genetics and Genomics. 2011;38(3):95-109. doi:10.1016/j.jgg.2011.02.003
- Lab automation in genomics market size & share analysis - industry research report - growth trends. Lab Automation In Genomics Market Size & Share Analysis - Industry Research Report - Growth Trends. Accessed January 11, 2024. https://www.mordorintelligence.com/industry-reports/lab-automation-in-genomics-market.
- Holland I, Davies JA. Automation in the Life Science Research Laboratory. Frontiers in Bioengineering and Biotechnology. 2020;8. doi:10.3389/fbioe.2020.571777
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