Sample preparation (“prep”) is a tedious, time-consuming task but a necessary part of nearly every analytical workflow, regardless of industry or laboratory type.
Sample prep involves collecting, treating, and manipulating a physical substance before subjecting it to some operation, usually instrumental analysis. Some samples, like pure liquids taken from reagent bottles, hardly require preparation at all. Generally speaking, the intricacy of the preparative workflow is the product of the sample’s complexity, the analytic specificity, and the ability of the instrument to discriminate from nontarget substances within the sample.
A typical manual sample preparation workflow consists of gathering labware and reagents; calibrating measurement, delivery, and analytical systems; preparing solvents and reagents; recording relevant identifiers (lot numbers, expiration dates, weights, concentrations); labeling containers; weighing; calculating; filling; obtaining standards; and completing physical/ mechanical operations such as filtering, grinding, or sonicating. Each of these steps involves multiple operations on its own. For example, glassware must be cleaned, dried, and moved around the lab, while standards need to be created, tested, and rendered into usable form through dilution, titration, and dispensing. An additional layer of recordkeeping and compliance applies to regulated laboratories.
The more complex and numerous the samples, the more critical become informatics, sample tracking, and knowledge handling. Most automated sample prep systems are connected to a laboratory information management system or are accessible through an electronic laboratory notebook, but often these require a level of “middleware” intermediary software to enable communication between system and computers.
High-caliber analysis modes such as GC- and LC-MS have raised the quality standard for sample prep. As a result, laboratories view sample preparation as a bottleneck in terms of cost and worker hours.
Yet according to estimates from Agilent Technologies (Wilmington, DE), 70 to 80 percent of prep work is still performed manually. Given the concentration of automation in very high-throughput venues, it is safe to say that close to 90 percent of all labs still engage in sample prep. Reasons for not automating include acquisition and operating costs, system complexity and steep learning curves, and perceived lack of system reliability and support.
“Users expect the same robustness, usability, and uptime from sample preparation as from their analytical instruments,” says Peter Mrozinski, product manager for workflow automation at Agilent. “They believe that constant tweaking at the low end and steep learning curves at the high end defeat the purpose of automation.”
Lab automation developed from the growth of high-throughput screening in the pharmaceutical industry and then was revived by the Human Genome Project. Automation has recently shifted from screening toward sample preparation, which caused the trend away from large, complex, integrated systems to smaller, more compact, dedicated workstations.
In a review published in Bioanalysis (2011; 3(13), 1415–1418, Jim Shen of Merck Research Laboratories (Summit, NJ) writes that “the key to improve throughput for sample preparation in a modern laboratory is to attack any bottlenecks that may exist in the process. While balancing budgetary concerns, training, and complexity of automation, laboratories should automate as much or as little of their existing processes [as] their comfort level with technology [allows].”
Shen’s specific recommendations include heavy investment in automation such as parallel extraction/processing, liquid-handling robotics, online extraction/ chromatography, chromatography multiplexing, and ultrahigh-pressure LC to improve turnaround and data quality.
The business case for automation is nearly identical for every workflow and boils down to greater consistency, fewer errors, and freeing up workers for other tasks. Very high-throughput genomics, proteomics, and medical laboratories would not exist without automation. These markets are adequately served by large, high-end systems costing hundreds of thousands of dollars. But as the number of samples decreases, nagging issues persist about learning curves, fear of automation, cost, lack of familiarity with automated workflows, and ignorance of what is possible. In other words, automation is not always an easy sell.
“Most people don’t appreciate how significant a problem sample prep poses for laboratories,” notes Zoe Grosser, Ph.D., director of marketing at Horizon Technology (Salem, NH). “There’s a lot of talk about the instrumentation and analytical methods. Sample prep is key to obtaining good results, yet it has been ignored over the years. Usually the most inexperienced people are assigned sample prep to learn their craft. It makes sense to improve an area where so much error occurs and to which so little attention has been paid.”