An interesting outcome of the COVID-19 pandemic has been increased attention and interest in laboratory automation across labs in the life sciences. Why? Perhaps years of sustained public interest in all things related to the life sciences drove labs to analyze their own workflows. It’s impossible to say for sure, but the exact reasons likely varied from lab to lab.
For labs testing thousands of swab samples each day for the presence of SARS-CoV-2, automation significantly increased throughput compared to manual methods. People were anxious for results, and automation kept sample backlogs at a minimum during times of surging submissions.
For some labs, staff was top of mind. Even beyond infectious diseases, labs wanted to keep their staff safe. Automation helped minimize manual intervention and risk of exposure. With robots taking over repetitive tasks, the risk of strain and injury was virtually eliminated. On top of this, without having to spend time on low value tasks in the lab, users were free to focus on higher value tasks such as data interpretation, all while working remotely.
Data integrity was another critical factor for labs. With highly precise, accurate, and repeatable robotics, the risks of variability and error are greatly diminished. During a time of great uncertainty due to the COVID-19 pandemic, automation provided a welcome source of confidence.
Once reserved for sample management and high throughput screening applications, lab automation is now widespread across the entire drug discovery continuum. But how is automation defined?
Is it:
If you answered yes to any of the bullets above, you’d be right!
At the upcoming SLAS 2023 conference, the mecca of the lab automation, you’ll hear a plethora of different descriptions from various vendors regarding how they define a lab automation solution.
Indeed, automation is a broad term that can be applied in multiple ways. At the same time, all viable automation solutions must contain at least three basic components: hardware, software, and peopleware.
In this three-part blog series, we discuss the three tenets of lab automaton, starting with hardware.
Hardware, tools, machines, devices. These terms all describe the visible, tangible, and very necessary elements of laboratory work. Any hardware that removes a task or set of tasks from the physical hands of a scientist (and to-do list) can be considered lab automation.
This can be represented by plate sealers, dispensers, washers, liquid handlers, and a wide range of other devices that are commonly found in the laboratory.
When it comes to lab automation hardware in the life sciences, there are varying degrees of products that can be grouped as shown in the figure below.
Naturally, the degree of manual labor and automation performed should be inversely proportional.
For instance, the broad spectrum of assays that are currently performed and the variety of experimental procedures available to accomplish each type of task requires that a correspondingly wide range of devices be made available. Even at the point of use, a given assay requires the implementation of multiple devices within the overall workflow.
But as you move from a manual to a semi-automated workflow, and then finally into a fully automated process, your automation will no longer be limited by the number, variety, or performance of the individual pieces of hardware, but by what your lab is attempting to accomplish.
As we highlighted earlier, higher throughput, decreased variability, time savings, and user safety are all common goals.
Consider the following when researching hardware for your automated workflow:
Solicit feedback from those experienced with the hardware in an automated workflow. Although their needs and circumstances may vary from yours, their experience may highlight nuances that aren’t apparent in a spec sheet or during a demonstration.
Test the device in your lab environment using actual samples instead of blanks or mock samples. In a truly validated format, you can gauge if the proposed solutions actually address your scientific requirements.
Ensure that the device(s) you intend to use fit within your footprint parameters. It goes without saying that real estate in any lab is typically at a premium, so make sure that space is being utilized wisely.
Choose adaptable hardware. Change is constant in our daily lives, and never more so than in science. The research you performed five years ago has likely evolved into something entirely different today. The same holds true for work you perform five years in the future. Make sure your device can keep up with these changes.
Use the device for its intended purpose. Can a thermal cycler be used as a general-purpose incubator? Probably. Should it be? Absolutely not. Can an automated liquid handler be used as a total automation solution? One can try, but as we detail below, natural liquid handler limitations render this impractical.
Let’s consider a hypothetical example where a scientist wants to decrease variability in their research process. They know that automated liquid handlers are more precise, accurate, consistent, and repeatable at transferring liquids compared to manual pipetting methods.
After exploring several automated liquid handlers, they select one that they feel is well suited to achieve their goals at the time of purchase. The key phrase here is “at the time”.
In the rush and excitement of getting a new piece of hardware, future needs were not considered.
Fast-forward twelve to twenty-four months. The liquid handler is operating as expected and used daily, and the scientist now starts to consider further ways to improve the now semi-automated assay workflow.
For instance, the team is interrupting their daily priorities at irregular intervals to transfer plates from the liquid handler to a plate reader. By integrating a plate reader onto the liquid handler’s deck and integrating a storage carousel adjacent to the liquid handler to hold plates and consumables, the team could avoid these interruptions.
After a consideration process, two new pieces of hardware are selected and integrated with the automated liquid handler. The impact is immediate and welcome. The team is focusing more time on priorities such as data analysis, and more sources of variability, including inconsistent incubation times, are removed from the workflow.
Fast-forward another twelve to eighteen months, and the updated semi-automated workflow is a productive facet of the lab. The scientist wants to further improve the workflow by integrating an incubator and a washer/dispenser device with the liquid handling workstation.
Herein lies the problem. Automated liquid handlers aren’t designed to accommodate scalability in device integration. The scientist used up all the available space in the last enhancement round. So, when it comes to further workflow efficiencies, it’s game over for this lab. Because future considerations were not accounted for in the initial stages of their automation implementation, the automation is not fully scalable and limited by the physical constraints of the lab.
Liquid handlers are excellent tools when it comes to aspirating and dispensing (transferring) reagents from one tube or plate to another. This is the technology’s primary raison d'être.
Liquid handlers are not, however, designed to become automation systems. Below are several reasons why:
By understanding the capabilities and limitations of each device intended for use in the lab, you and your team can carefully map the evolution of your lab’s automation hardware. Doing so will surely positively impact your scientific research today and tomorrow.
In closing, I would like to thank several individuals for taking the time to connect with me and discuss this topic over the past several years. Your time and feedback helped craft this blog. Huge thanks to: Tim Dawes, Jonathan Schneeweis, Matt Humes, Paul Harper, Sam Michael.
Join us in the next installment of this blog series when we discuss software as the second tenet of lab automation.
Revision: BL-DIG-230215-01_RevD