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Investing in qPCR Automation: Time & Money Saved is Research Gained

Written by Nila Lê | Jul 17, 2024 1:30:00 PM

Quantitative polymerase chain reaction (qPCR) has become an indispensable tool in modern research. It allows scientists to precisely quantify DNA in real-time by tracking fluorescence signals from tagged DNA molecules1. This technique is pivotal in studying gene expression, detecting genetic variations, and identifying pathogens with high sensitivity, making it essential in genetics, oncology, and drug development, among other research areas2,3

However, the numerous manual, low-volume liquid handling steps in qPCR workflows are time-consuming and prone to errors, creating significant bottlenecks in workflows. Here, we uncover the true cost of manual qPCR workflows and explore how qPCR automation can save you time and money, making it a worthwhile investment for any lab that regularly performs qPCR.

The True Cost of Manual qPCR Workflows

Lab personnel often assume that the initial equipment purchase, such as a PCR machine, is the most significant expense in setting up a new workflow. However, this overlooks the substantial costs associated with running workflows manually, which can be categorized into three main areas: time, labor, and errors.

Time Constraints

Manual qPCR workflows are highly time-consuming, mostly owing to the high number of pipetting steps, as scientists can only process one sample at a time. This limits throughput and prevents scientists from being able to scale workflows in line with the ever-changing demands of the project, delaying research progress. 

Labor Costs

Another important consideration when quantifying the true cost of manual qPCR workflows is the cost of labor. Often, repetitive pipetting tasks are performed by highly trained scientists such as postdoctoral research associates in academia or senior scientists in industry. Employing highly skilled professionals for low-skill activities is an inefficient use of resources and, put simply, a waste of money. 

Hidden Costs of Errors

In addition to the obvious costs of time and labor, the hidden costs of errors in manual pipetting tasks are often overlooked4. The repetitive nature of manual qPCR workflows and the small volumes involved make them prone to human error and cross-contamination. These errors can necessitate costly and time-consuming repeat experiments, wasting reagents and delaying project timelines.

How Automation Saves Time and Money

The true costs of manual qPCR workflows are often far higher than anticipated. Fortunately, time, labor, and error costs can all be substantially reduced through qPCR automation with tools like the I.DOT Liquid Handler (Fig. 1). 

Figure 1. The I.DOT Non-Contact Dispenser has numerous built-in features that support effective qPCR automation

Increased Throughput

Automation enables the simultaneous processing of numerous qPCR samples, greatly accelerating research. Automated systems, such as robotic liquid handlers, can run multiple qPCR reactions in parallel, improving throughput compared to manual processing. This allows researchers to analyze more samples in less time, speeding up experimental timelines and data generation4.

Reduced Labor Costs

qPCR automation frees researchers from repetitive, manual tasks, allowing them to focus on higher-value and, let’s face it, more interesting activities like data analysis and experimental design. This shift not only streamlines processes but also leads to significant savings in labor costs.

Minimized Errors

Automated liquid handling systems ensure precise and consistent reagent dispensing, minimizing pipetting inaccuracies and cross-contamination. This precision leads to fewer failed experiments and reduces the need for repeat runs, conserving valuable reagents and samples. By maintaining high-quality and reproducible results, qPCR automation minimizes waste and maximizes research efficiency5.

Additional Benefits of qPCR Automation

Beyond the significant time and cost savings, qPCR automation offers some notable additional benefits:

  • Improved Data Quality. qPCR automation ensures consistent pipetting, resulting in reliable, reproducible, and high-quality data that is not only easy to analyze but also easy to trust6.
  • Enhanced Workflow Consistency. qPCR automation enhances workflow consistency, providing comparable results regardless of who performs the experiments and ensuring reliability across different users7.
  • Reduced Risk of Repetitive Strain Injury (RSI). qPCR automation significantly reduces the risk of RSI by eliminating the burden of repetitive manual pipetting tasks, protecting researchers from strain and injury (Fig. 2).

Figure 2. Repetitive manual pipetting tasks put users at risk of RSI, which can be overcome by qPCR automation. (Source)

Making the Investment Worthwhile

Understanding the benefits of qPCR automation is one thing, but ensuring the investment is worthwhile requires careful consideration to achieve a good long-term return on investment. To make sure the automated system purchase is suitable for your lab, consider the following factors:

  1. Workflow Needs & Budget. The first step is to align your lab's workflow needs with the capabilities of available systems. Evaluate the requirements and throughput of your experiments alongside your budget to select a system that meets your specific demands without overspending.
  2. Ease of Use & Training. Labs can also benefit from opting for a user-friendly system with an intuitive interface and minimal training requirements. This is particularly important for labs with limited qPCR automation experience, as it reduces the time needed for users to become proficient with the system.
  3. Scalability & Integration. Finally, it is important to choose a system that can adapt to the lab's evolving needs. Ensure it can handle increased workloads and integrate with other equipment, providing a flexible, future-proof solution that grows with your research.

Conclusion

Investing in qPCR automation transforms laboratory workflows by saving time and money while enhancing research outcomes. Automation increases sample throughput, reduces labor costs, and minimizes human errors, leading to more efficient and reliable experiments.

Additionally, automated systems improve data quality and consistency, reduce the risk of RSI, and ensure scalability to meet future research needs. By carefully selecting an appropriate automated pipetting system, labs can achieve substantial long-term returns on investment through improved productivity and cost-effectiveness.

Stop wasting time and money on manual qPCR workflows! Unleash the power of qPCR automation with the I.DOT Liquid Handler and book a demo today.

References

  1. Jalali M, Zaborowska J, Jalali M. The Polymerase Chain Reaction. In: Basic Science Methods for Clinical Researchers. Elsevier; 2017:1-18. doi:10.1016/B978-0-12-803077-6.00001-1
  2. Lorenz TC. Polymerase Chain Reaction: Basic Protocol Plus Troubleshooting and Optimization Strategies. J Vis Exp. 2012;(63):3998. doi:10.3791/3998
  3. Schmittgen TD, Zakrajsek BA, Mills AG, Gorn V, Singer MJ, Reed MW. Quantitative Reverse Transcription–Polymerase Chain Reaction to Study mRNA Decay: Comparison of Endpoint and Real-Time Methods. Anal Biochem. 2000;285(2):194-204. doi:10.1006/abio.2000.4753
  4. Taylor SC, Nadeau K, Abbasi M, Lachance C, Nguyen M, Fenrich J. The Ultimate qPCR Experiment: Producing Publication Quality, Reproducible Data the First Time. Trends Biotechnol. 2019;37(7):761-774. doi:10.1016/j.tibtech.2018.12.002
  5. Kuang J, Yan X, Genders AJ, Granata C, Bishop DJ. An overview of technical considerations when using quantitative real-time PCR analysis of gene expression in human exercise research. Kalendar R, ed. PLOS ONE. 2018;13(5):e0196438. doi:10.1371/journal.pone.0196438
  6. Karlen Y, McNair A, Perseguers S, Mazza C, Mermod N. Statistical significance of quantitative PCR. BMC Bioinformatics. 2007;8(1):131. doi:10.1186/1471-2105-8-131
  7. Ruiz-Villalba A, Ruijter JM, Van Den Hoff MJB. Use and Misuse of Cq in qPCR Data Analysis and Reporting. Life. 2021;11(6):496. doi:10.3390/life11060496