With greater speed, lower costs, and higher throughput than first generation methods, next generation sequencing (NGS) has catapulted biological research into a new era. Whole genome sequencing, epigenomics, metagenomics, and microbiomics are just a few of the many NGS applications of today, facilitating advances in epidemiology, cancer diagnostics and drug discovery, and vastly improving our understanding of how living organisms use genetic information to survive and reproduce in constantly changing environments.
With a wide range of sequencing instruments and service providers on the market, NGS is fast becoming accessible to labs of all sizes and on varying budgets. While sequencing and data analyses are usually automated, the upstream NGS steps, namely sample processing and library preparation are often carried out manually. Fortunately, with some effort and optimization these steps can be easily automated on a pipetting robot, saving the user many potential headaches. Here, we take a look at our top 5 reasons to automate your NGS workflow.
1. Scalability
If you’ve ever tried to process large numbers of NGS-bound samples by hand, the potential to scale up should get you excited about automation. The exact capabilities of the robot will be model-dependent, but in most cases you will be able to process up to 96 samples simultaneously, significantly reducing the time you would spend on manually processing the same number.
Automation also saves you the dreaded hassle of having to process samples in batches, which is often a necessity in manual setups due to restrictions imposed by centrifuge size, the risk of sample degradation (e.g., when working with RNA), and the maximum number of samples that a person feels comfortable with handling at any one time. Even more important than avoiding hassle, fewer batches means less or no batch-to-batch variation.
2. Innovation
Scaling up your NGS experiments through automation will inevitably allow you to address novel research questions that are hardly feasible with manual setups. Here are just a few examples across a range of NGS applications:
Drug discovery: Screen large panels of novel compounds for desired transcriptional or epigenetic changes in disease material (e.g., cell lines), instead of trying to cherry-pick compounds that might have therapeutically-relevant effects.
Microbiomics: Profile large enough sample sets to get a picture of the ‘normal’ microflora in your species of choice. This will be an impossible task if you can only ever process 12 or 24 samples at a time.
Plant breeding: Large-scale identification of agronomically-important genes and genetic markers to aid genomic selection and genetic engineering towards developing novel breeds with highly desirable traits.
Disease research and diagnostics: Screen large numbers of clinical samples for discovery of novel disease-relevant genes or biomarkers.
Environmental monitoring: Assess ecosystem health via qualitative and quantitative sequence analysis of its invertebrate inhabitants.
As well as increasing innovation in your own research, the higher throughput should also facilitate increased collaboration with other laboratories, opening doors to new projects and discoveries that would otherwise by very challenging, if at all possible.
3. Consistent Data Quality
Of the entire NGS workflow, sample processing, library construction, and downstream processing steps such as PCR amplification and library normalization are particularly prone to human error. The protocols are lengthy with many pipetting steps with changing reagent volumes, and often call for sample transfer to new tubes along the way. Incorrect labeling, contamination, pipetting errors, or the slightest deviation from a protocol can significantly impact the resulting data.
Even if human error was completely unavoidable, batch-to-batch variation and variations in sample handling among staff are inevitable. Monitoring these errors and variations in an NGS workflow is impractical, if at all possible. Automated sample and library preparation alleviates this problem, providing better process control, an accurate and efficient workflow, and better run-to-run repeatability independently of staff changes, thus providing reliability and consistent data quality whether you have 1 or 1,000 samples.
4. Increased Walk-Away Time
This one is not exactly black and white because you will need to invest some time and effort during the start up phase to ensure that your pipetting robot is performing the protocols down to a T. If you are lucky, you may be able to find a suitable script for your robot from its vendor or the supplier of your chosen nucleic acid extraction and library preparation kits, many of whom collaborate with automation vendors to develop automation-ready protocols.
In any case, once your automated setup is up and running you can enjoy spending your precious laboratory time on planning new critical experiments instead of repetitive sample and library preparation. Automation should also reduce the effort needed to train staff in many different protocols, freeing up even more resources as well as saving staff from repetitive strain injury caused by excessive pipetting.
5. Cost Reduction
Depending on your current resources, there might be a considerable outlay during the setup and optimization phase, for example, you will need to get your hands on a pipetting robot if your lab doesn’t already have one. You will also need to spend time programming the robot to perform the various protocols. However, once your automated setup is up and running, the time saved on staff training and sample handling, and the avoidance of material and reagent loss due to human error will likely translate to significant cost savings, especially if NGS is a regular and critical part of your research.
Can You Automate Yourself out a Job?
Don’t worry, even the best automated setups do not completely eliminate the need for human intervention. You will still need to incorporate quality control checks between key steps to ensure that the robot is performing in accordance with protocols. You should also consider including reference standards that allow you to monitor the entire workflow, for example, mock microbial communities or samples with well-defined sequence profiles. Although the nature of these controls will be application-dependant, an ideal control should allow you to monitor sample lysis and nucleic acid extraction efficiency, nucleic acid quantification and efficiency of downstream processing steps (e.g., cDNA synthesis), library quantification and overall library quality. You can read more about reference standards for NGS in this recent review article.
Let us know what other headaches have you avoided by automating your NGS workflow, and feel free to reach out to our automation specialists with any questions, at [email protected]
References:
Hardwick SA, Deveson IW, Mercer TR. 2017. Reference standards for next-generation sequencing. Nat. Rev. Genet. 18(8):473-484. doi: 10.1038/nrg.2017.44.
