Life science research is going high-throughput, and there is no turning back.

Sequencing, cell culture, hybridization assays, and cell screening — to name a few — are all moving toward generating more data in less time, which requires automation.

For smaller labs, the concept of automating routine tasks at the research bench is an attractive possibility. However, it may seem out of reach due to a variety of factors, including cost, space, and training requirements. Fortunately, automation technologies have advanced a great deal in the last decade, making high-value, entry-level automated liquid handling platforms and other systems significantly smaller, less expensive, and easier to use.

Automation platforms can simplify routine laboratory tasks in any research program, and the benefits don’t end there.

 

Increase Efficiency

For most labs, personnel costs are the highest line-item expense in a research budget. Maximizing the efficiency of laboratory staff is paramount to the success and productivity of a commercial lab or research program.

It is important to note that a lab doesn’t have to be fully automated to benefit from lab automation. The most obvious targets for automation are repetitive, laborious tasks, such as pipetting and liquid handling, that could be delegated to an automated system. Critically, this recouped time can be used for higher-level data analysis and other laboratory tasks. Increasing productivity is just one of the many ways automated liquid handlers and other automated systems provide a return on investment (ROI, Figure 1).

Less obvious targets for automation and informatics, however, are the amount of time required for experiment planning and routine data analysis. Software is also available to assist staff with the proper design of routine experiments in the lab and is included with automated liquid handling and other automation platforms.

An important consideration for labs interested in automation is the increased amount of data generated by more automated, high-throughput systems. Increased data generation may result in laboratory staff being buried in data or test results without the analysis tools to cope with the increased workload. Rather than increasing the number of staff dedicated to data analysis, labs can incorporate higher-throughput informatics solutions to eliminate data bottlenecks in the lab and increase the productivity of the lab as a whole.

 

 

Enhance Reproducibility

For many small labs, particularly in academia, more repetitive and laborious tasks have been delegated to undergraduates or other students with little to no training or experience in life sciences research for little or no compensation.

 

 

 

 

Figure 1.A flowchart illustrating the return on investment (ROI) of automation and informatics platforms in the research lab.

 

 

While this strategy may seem cost-effective at first, data generated by less experienced and less costly staff may carry a higher price tag in the long run. Issues with data reproducibility, accuracy, and failed experiments waste both time and money, decreasing the productivity of the lab. Labs can lose months’ worth of work, for example, if a mouse colony is improperly genotyped and used in highly technical experiments down the road.

The COVID-19 pandemic highlighted the necessity of next-generation sequencing (NGS) for diagnostic testing and epidemiological studies, and staffing shortages reinforced the role automation can play in productivity bottlenecks. Labs tasked with around-the-clock processing, testing, and analysis of specimens can particularly benefit from automation by facilitating:

• Sample processing, testing, and analysis with fewer staff members
• Staffing labs with personnel with less specialized training
• Improved sample quality
• Less sample retesting

Importantly, these benefits can extend to smaller labs that could benefit simply from after-hours testing or improved, more consistent sample prep and data reproducibility. Automation allows these labs to increase productivity without the burden of hiring staff for more repetitive and labor-intensive aspects of lab research.

 

Decrease Waste

Life science research reagents are increasingly expensive. Automated sample prep and liquid handling can help a research program’s bottom line by reducing the amount of waste caused by minor laboratory reagent spills.

Improved data quality, accuracy, and reproducibility also decrease the number of replicates required to statistically power a research study, improving experiment efficiency. This decreases the number of tips required for pipetting, the amount of reagents needed for a study, and the amount of time required to complete an experiment.

 

 

Figure 2 illustrates how reducing data error can increase the power of experimental studies. In the left panel, the differences in individual data measurements are so large that data points overlap between the control and experimental groups. In contrast, the data points in the right panel have lower variability, and there is no overlap in data measurements between the control and experimental groups.

 

The lack of overlap between measurements in the two groups decreases the likelihood that the differences observed between the two groups occurred solely by chance. In other words, decreasing measurement error – through automation or other means — increases the statistical significance of an experimental result.

Enhancing the statistical power of experiments also improves the likelihood of a study detecting smaller effects of an experiment variable, whereas lower-powered studies will only catch larger, more obvious effects (Figure 3). Ultimately, incorporating automation into laboratory workflows and decreasing measurement variability can help studies detect experimental effects that might otherwise remain undiscovered.

Figure 3. A diagram using a visual analogy to describe how experiments with higher statistical power can detect smaller experimental effects compared to experiments with lower statistical power. (https://cxl.com/blog/statistical-power/)

 

 

Decrease Repetitive Strain Injury

Let’s face it: people have elbows and wrists that wear out and can’t be replaced. Robots have mechanical joints and parts that wear out that can easily be replaced.

Repetitive strain injury is a real issue in laboratories that depend on repetitive, laborious motions as part of their normal workflow. These injuries also tend to occur in staff who perform these motions the most often over the longest period of time — the most experienced and oftentimes most knowledgeable personnel of the lab.

The National Institute for Occupational Safety and Health (NIOSH) estimates that occupational musculoskeletal disorders, including repetitive strain injuries, cost the U.S. between $13 and $20 billion annually. The median time away from work for a repetitive strain injury is estimated at 18 days. A total of 55% of repetitive strain injuries in the study affect the wrist.

OSHA’s occupational injuries and illnesses cost estimator estimates that a single case of carpal tunnel syndrome in the workplace costs an employer $30,930 in direct costs and $34,023 in indirect costs, totaling $64,953. For a commercial lab with a 3% profit margin, the company must increase sales by an additional $2,165,100 to cover the total cost of the single repetitive strain injury.

The losses incurred by labs due to repetitive strain injuries are real and significant, and disproportionately affect staff with the most experience on the job. Fortunately, automation can eliminate the risk of repetitive motion injuries in the lab while increasing worker productivity and decreasing experimental waste and measurement variability.

 

 

Stay Competitive

Perhaps most importantly, life sciences research and commercial labs are becoming increasingly high-throughput, requiring automation of common, repetitive tasks and data analysis to maintain the productivity and cost efficiency of other labs.

The National Institutes of Health and other granting agencies have taken notice.

• Sanger sequencing has been relegated to applications requiring only the most accurate DNA sequencing, such as diagnosing gene mutations in a single gene
• Next-generation sequencing is currently magnitudes of order cheaper and faster than traditional Sanger sequencing
• Linkage analysis studies, previously used to pinpoint causative gene mutations, have largely fallen to the wayside in favor of exon or whole-genome sequencing
• Mass spectrometry has all but replaced the Western blot to identify differential expression of proteins

Granting agencies pay close attention to which laboratories embrace new technologies to improve the efficiency of diagnostics, experiments, and overall lab productivity, and this is often reflected in grant and other funding feedback. This includes the use of automated liquid handling systems that can be programmed to perform any task a technician would do with a hand-held pipettor and different sized pipette tips (Figure 4). This automation frees up personnel time for other lab priorities and data analysis while delegating pipetting to a fast, reliable, and precise automated dispenser.

 

Figure 4. Common laboratory tasks that can be automated to improve lab efficiency and productivity.

 

Challenges of Automation

 

Cost

Many labs incorrectly assume that the upfront cost of automated technologies is simply too high to justify their purchase. However, advances in technology and competition between vendors have significantly decreased the cost of many automated liquid handling systems and the investment required to improve throughput in the laboratory.

Smaller semi-automated liquid handling systems, for instance, can cost as little as $5,000, whereas more complex, fully-automated systems can cost as much as $500,000. Importantly, there is an automated system available for nearly every pipetting task.

 

Infrastructure

Advances in technology have also decreased the amount of in-house infrastructure required for lab automation. Virtually all systems include a computer to program workflows. Many high-level computing resources have been shifted to cloud-based technologies that reduce the dependence on on-site server space or in-house computing power. The majority of liquid handlers additionally provide their own refrigeration.

 

Footprint

While automation may decrease the personnel requirements for certain repetitive laboratory tasks, it may take up valuable research space in already crowded quarters. Over time, the footprint of automated liquid handling systems has decreased significantly. In fact, the footprint of most systems is dictated by the size and number of plates to run in a given workflow. Liquid handling system vendors offer a variety of solutions for labs requiring more standard high throughput solutions versus systems that require 24/7 operation with minimal human intervention.

 

Supplies

Many automated systems are designed for compatibility with a variety of different consumable brands rather than just a single, in-house brand. With increased competition in the lab automation market, vendors are aware that seemingly harmless changes, such as changing the brand of pipette tip, can alter the results of highly sensitive experiments. Increased competition also ensures that the labs are able to choose the supplies they prefer based on both quality and price.

 

Heat and Noise

Laboratory equipment is notorious for heat and noise. Thankfully, advances in technology have improved cooling efficiency in automated liquid handling systems, decreasing their noise and heat output. The precise tip fitting of automated handlers may also decrease noise in the lab compared to the loud, forceful fitting of tips onto manual multichannel pipettors.

 

 

Key Takeaways

The requirements and workflows for every lab are different, making opportunities for automation vary from lab to lab. In order to determine how automation will impact your lab’s efficiency and productivity, it is important to consider the following questions (Figure 5):

• Which lab workflows can benefit from automation?
• How do laboratory staff feel automation would affect their workday and productivity?
• Does our lab want to automate several workflows or start with just one? Which workflows should we prioritize?
• Which automation solutions are available for our workflow? What solutions fit our budget?
• Have other labs used the same systems? What are the pros and cons of each system?

Asking these questions up front will help managers understand the needs of laboratory staff and the priorities of the program, department, or company as a whole. Additionally, researching the different systems available for liquid handling and other automation can build confidence in larger line-item purchases.

Critically, life sciences research is moving toward higher and higher throughput techniques and technologies, requiring labs to adapt or suffer lower efficiency and productivity compared to their more automated counterparts. Smaller labs, in particular, no longer need to fear the up-front investment in automated liquid handling systems due to their decreasing price, increasing flexibility and smaller footprints of high-value, entry-level systems like the ARI Scorpion. Investments in laboratory automation can reap significant rewards in data quality and overall efficiency both now and in the future.