Why sample management should be every lab's first consideration

When considering the needs of a modern laboratory, it is common to first think of the core lab equipment and then software for experimental data capture and analysis. Clearly, these are necessary facets of a laboratory, but to overlook the importance of a foundational sample management software platform would be a mistake.

Too often, the tracking of samples is left to spreadsheets — yet the one currency common to all laboratories is samples.

Samples at the centre of laboratory operations

Samples can be defined as the materials being tested, analysed or studied in research or experiments. They are frequently unique or hard to replace, and may indeed be irreplaceable (as opposed to other laboratory inventory such as reagents). They might include clinical materials obtained from study participants, drug candidates, stability or degradation samples in testing, compound libraries for screening, cell lines in cryogenic storage, samples obtained for QC or diagnostic tests — the list goes on.

The key thing about samples is that they are directly linked to experimental or study outcomes and test results. Given this definition, it becomes clear that the proper management of such samples should be at the core of everything a laboratory does. Information about these samples is consumed by multiple upstream and downstream processes, and Figure 1 shows the breadth of just some of the laboratory applications that rely on accurate sample information:

Figure 1: Sample management at the centre of laboratory operations. For a larger image, click here.

Why good sample management matters

Poor sample management can lead to significant issues in a laboratory setting. Ineffective sample tracking leads to misplacing or losing samples and inaccurate inventory, leading to wasted time finding samples for an experiment. Poor sample data integrity can cause inconsistent results, reducing the research reproducibility and credibility as well as increasing costs and lowering overall productivity. Ineffective sample storage management leads to cluttered storage, sample degradation, wastage of labile samples, and increased energy needs due to inefficient use of space. Ultimately, the above could result in regulatory compliance failures that could even halt research activities and damage a laboratory’s reputation.

Given the importance of good sample management, what software capabilities are needed to avoid the pitfalls? Here is a quick guide to some of the top features that you should be looking for:

Audit trail

Every sample event should be tracked in a 21CFR11 compliant or equivalent audit trail — including who, what and when. This ensures a detailed history of every sample for compliance or experimental reproducibility. It also allows laboratories to track down problems such as missing samples or liquid handler misfires and acts as a permanent record of a sample’s location history, such as which freezer it used to be stored in, when it was disposed, or where it was sent to.

Sample genealogy

Accurate tracking of sample lineage from tube-to-tube or well-to-well transfers between plates, including the lineage between materials when one substance is derived from another, is essential. This capability facilitates the repeatability of experiments by ensuring that the same source sample is used. Maintaining clear records of splits and derivations is particularly important for human sample tracking compliance purposes.

Barcode management

Every sample is assigned a unique barcode from a managed range, and pre-barcoded samples are verified for uniqueness. Any sample in the lab can be identified unambiguously with a scan, and errors from manual labelling and misidentification are eliminated.

Storage management

This relates to the ability to model the hierarchy of sample storage — including freezers, incubators, cupboards and specific locations within these. It reduces the need for manual searches, decreases equipment costs by freeing up storage space from outdated samples and improves overall sample organisation.

Data validation

Data validation prevents incorrect or conflicting data from being imported and ensures that all data are of the correct data type. Consistent and accurate data make it easier to locate samples using simple and consistent terminology.

Expiry date management

Tracking the date at which action should be taken on a sample — whether disposal, archiving or requalification — reduces the risk of using expired samples that might compromise compliance. It also makes it easy to perform regular housekeeping to dispose of expired samples efficiently.

Accurate tracking of sample amounts and concentrations

Knowing the inventory and quantity of samples available is essential, ideally updated automatically as samples are used. This makes it easier to confirm material availability for experiments and to quickly locate samples with both the amount and concentration required for specific assays.

Management of diverse container types

Supporting a variety of container types — such as tubes, vials, flasks, microtitre plates or tissue blocks/slides — ensures accurate recording for easier identification and more efficient storage utilisation. This capability also enables laboratories to leverage sample automation effectively, as automated stores and liquid handlers will expect specific container types.

API and integration capabilities

Programmatic access to sample data and integration with other software platforms is increasingly critical. By linking foundational sample data with other applications, laboratories can easily associate experimental results and metadata with physical samples, ensuring accurate analyses.

Keeping pace with future developments

The increasing trend towards collaborative research provides further challenges for sample management. Samples may be sent out for testing or archival at remote sites, perhaps to be shipped onsite on demand. These sample movements need to be tracked, so consideration should be given to whether this is a future critical capability for your operations.

Artificial intelligence (AI) is already making its way into laboratories, and this trend is accelerating. AI relies on high-quality data and, by providing access to accurate sample inventory data, could lead to game-changing process improvements, particularly when integrated with laboratory automation. Such process improvements might include:

• Enhanced experiment planning and scheduling — taking into account sample availability and equipment needs.
• Automation of stock monitoring and replenishment.
• Sample insights based on analytical results tied to those samples — eg, identifying suspect samples or differential results due to variation in assay plate layouts.
• Prediction of workflow times for different sample processing operations.
   

This list could go on. A discussion of AI in laboratory operations deserves an article of its own. Suffice it to say that AI will transform the way sample data is used and analysed, leading to more efficient laboratory operations and research — but only if the sample data is accessible and of high quality.

In summary

Effective sample tracking is crucial for ensuring the success of research and development, enhancing operational efficiency and regulatory compliance. It ensures the accuracy and reproducibility of results while preparing the lab for technological advances, such as AI, and can help streamline collaborative research efforts.

Whether a laboratory is managing millions of samples in 1536-well plates in HTS libraries, small numbers of clinical study samples or anything in between, all samples have value and the tracking of them is fundamental to the success of research, development or testing processes.

As life sciences R&D embraces increasingly diverse sample types, growing throughput and automation adoption, what might once have been possible to track manually soon becomes an impossibility. As a laboratory grows in size and scope to meet these challenges, having the right foundations of a sample management platform that supports scalability, integration with automation platforms and the management of diverse modalities enables this evolution.

In conclusion, a robust sample management platform forms the backbone of efficient, compliant and high-quality laboratory operations, accelerating research and improving experimental outcomes.

*Marcus Oxer is a Domain Solution Manager at Titian Software, provider of Mosaic — a sample management platform for life sciences.

Top image credit: iStock.com/busracavus