The benefits of integrated systems for managing both samples and experimental data: An opportunity for labs in universities and government research institutions to lead the way

Traditionally three kinds of software have been used by commercial labs to manage physical things, like samples, and abstract things, i.e. information in electronic form like spreadsheets and thumbnails of images. The first kind of software, Electronic lab notebooks (ELNs,) has been used to manage information.

Terminological confusion, however, besets discussion about the other two kinds of software [1]. The term Laboratory Information Management Systems (LIMS) is sometimes used in a broad sense to mean "Software applications used to automate the routine operations of a laboratory" [2], or instrument data management. It is also used, however, to specifically include sample management. Thus the Wikipedia entry on LIMS starts by stating, "The core function of LIMS is the management of samples" [3]. The confusion arises because although some LIMS do indeed deal with sample management in addition to assisting with the automation of other functions like QA, QC, and workflow, there is a third category of software, usually referred to as sample or inventory management software, that deals only with managing samples. Unlike ELN or LIMS, there is no generally accepted acronym to describe this third kind of software. Sample/inventory management software is often included in lists of LIMS vendors, with no distinction made between them and the fully fledged LIMS which handle a full range of lab automation functions, including various forms of instrument data management.

Recently there has been considerable discussion about the coming together of LIMS and ELNs. This discussion often glosses over the terminological confusion noted above. When you look beneath the surface, what is being pointed to is the emergence of systems that are able to handle both general data management and sample management [4]. I.e. what is converging with ELNs is really the sample/inventory management function, handled by LIMS and sample/inventory management software with ELNs, rather than the other aspect of LIMS, instrument data management.

This article looks at the needs driving the push for integration of sample management and management of experimental data, and the developments in software that are beginning to make this integration possible. It discusses the different practices, needs and usage of software in commercial labs, where usage of ELNs and fully fledged LIMS is pervasive, and labs in academic settings and small-to-medium size biotechs, which are just beginning to look at ELNs and LIMS. The article concludes that these smaller labs should find it easier to adopt the new integrated solutions which are becoming available because these labs do not face the complication of attempting to make the new solution work together with a legacy ELN and/or LIMS. There is thus an opportunity for labs in universities and government research institutions, which have been slow to move from paper to electronic record keeping, to leapfrog better resourced commercial labs and lead the way in adopting the new generation of tools which permit integrated management of samples and experimental data.

Notwithstanding these barriers, a number of factors are driving the need to integrate the two core activities of the lab -- managing samples and conducting and documenting experiments.

An important background factor is the ever increasing amount of data to process and ever mounting pressure to process it faster. This puts a premium on more efficient management of information generally. It is bad enough if your lab does not have efficient mechanisms for managing sample data and/or managing experimental data at a time when the number and complexity of both kinds of data is continually expanding. But given that samples are relevant to most of the experiments being carried out, it is even worse if you manage these two kinds of data separately.

A second factor is the increasing amount of time spent by researchers online as a percentage of total time spent at the computer. This is true in terms of time spent working-a recent study of seven life sciences labs noted that most now use wikis to share general information like meeting notes and non-confidential things like protocols, and that the scientists in the labs were big users of Google, for search, but also of Google Docs, and of online databases [7]. The growing popularity of Mendeley for reference management is an example of adoption of a specialist online tool for researchers. Researchers also are spending more time online outside of work, where scientists are just as likely as others to use Google for search, Facebook for communicating with friends and family, etc.

The changing landscape of tools that researchers use at work and outside work, and the changing pattern of how people are using these tools, is giving rise to a third factor, changing expectations about how information should be discovered and managed, and the kinds of tools that are appropriate-and necessary-to do this efficiently. Expectations are not only changing, they are being raised. When you are used to Google and Facebook it seems odd not be able to, say, link information about a particular sample used in an experiment to the write up of that experiment. This trend is accelerating as a new generation of postdocs, graduate students and undergraduates, who are comfortable with online tools and take for granted their ongoing rapid development, take active roles in labs.

A fourth factor, mirroring the increase in data and its growing, complexity, is the decrease in the cost of accessing tools to manage the data. 10 years ago, LIMS and ELNs cost $10,000s-$100,000s and were beyond the reach of virtually all academic labs. Five years ago, low cost ELNs and sample/inventory management software began appearing on the market. Today free-admittedly simple-ELNs and sample/inventory management software, is becoming available, and fully featured ELNs and sample/inventory management systems are available for as little as $1,000. LIMS with instrument automation capability are the exception to this trend, and remain prohibitively expensive for virtually all academic labs.

A fifth factor arises from the role samples play and the way in which they are used in experimental research. Take an antibody, for example. It might begin life as a sample the lab brings in or creates. Its original characteristics are recorded. In many cases it will be aliquoted. Then the aliquots are processed or analyzed, and the changes they undergo are also recorded, and analyzed. The aliquots and what has happened to them may be compared with others and what happened to them, and this process will be examined in the broader context of what else was going on in the experiment. Does the management of data relating to the sample fall under the category of sample management or experimental data management? Both, of course! The distinction between the two is entirely artificial, and only arose because of the lack of tools that allowed the sample/aliquots and their history to be viewed in the context of the experiment(s) in which they were used.

Integrated management of samples and experimental data will bring important benefits to academic labs. These include:

Labs in universities and government research institutions have been slow to adopt electronic lab notebooks and sample management software. There are a variety of reasons for this, prominent among them inertia and the difficulty of making decisions in a consensual environment, and cost. As noted above, the availability of affordable solutions is rapidly removing cost as a factor.

Precisely because labs in publicly funded environments have not had the resources until now to purchase the expensive commercial ELNs and LIMS that have been widely adopted by the pharmaceutical industry, these labs do not face the complication of attempting to make the new solution work together with a legacy ELN and/or LIMS. Somewhat ironically, this now opens an opportunity for smaller labs, which have been slow to move from paper to electronic record keeping, to leapfrog better resourced commercial labs and lead the way in adopting the new generation of tools which permit integrated management of samples and experimental data, and from that a range of tangible benefits to conducting research.