Often, laboratory errors originate before analysis starts. From collection and transfer to storage and environmental exposure, the preanalytical phase quietly determines whether a sample remains fit for purpose. Rachel Sully discusses how improving outcomes depends less on correcting results, and more on designing workflows, infrastructure and handling practices that protect integrity from the outset.

Most laboratory workflows are built around the moment of analysis – instruments are calibrated, methods are validated and performance is closely monitored. Yet by the time a sample reaches the analyser, much of its fate has already been decided. The conditions under which it was collected, transferred, stored and handled will have shaped its composition – sometimes subtly, sometimes irreversibly. On this basis, the analytical phase can be thought of as revealing problems, rather than causing them.

Sample integrity can be conceived as the state of biological specimen or chemical sample that remains in its original, unaltered condition from collection through to analysis. It is not a single attribute but a combination of physical, chemical, biological and temporal aspects that ensures the preservation of a sample. To the eye, it may appear unchanged yet be undergoing degradation at a molecular level.

Evidence consistently demonstrates that the majority of laboratory errors arise before analysis begins, within what is broadly termed the pre-analytical phase – Vermeersch et al. (2021) state that pre-analytical errors are responsible for 60-70% of all laboratory mistakes. This includes everything from preparation and sample collection, to labelling, transport and storage.

Each step presents opportunities for variation: inconsistent collection techniques, delays in transit, temperature changes, misidentification or inadequate mixing. Individually these seem minor. However, collectively they determine whether a sample remains fit for purpose.

Common points of failure for sample integrity include sample collection, transport, storage, labelling, and environmental conditions. Time, temperature, humidity, light exposure and mechanical disturbance all play a role in silent degradation, rarely causing clear errors in results. Instead, they introduce variability – small deviations that accumulate into noise, bias or false confidence in apparently precise data.

Sample collection

Making available test request forms can help to gather all the required information for the correct, clear labelling and processing of collected samples. Establishing criteria for non-representative sampling can prevent the introduction of bias before analysis has even begun and can be prevalent in industrial and QC laboratories.

Defining a sample collection process and preparing instructions for the personnel collecting the samples can ensure uniformity. Clinical laboratories often experience hemolysis of their samples during collection and transport, caused by poor phlebotomy techniques, incorrect tubes or rough handling. This can lead to false increases in the analyte such as potassium, enzymes, etc.

Pre-analytical errors are responsible for 60-70% of all laboratory mistakes. This includes everything from preparation and sample collection, to labelling, transport and storage… Individually these seem minor. However, collectively they determine whether a sample remains fit for purpose

Once the samples reach the laboratory, it should be verified that they are labelled correctly and record the samples in a log. A rejection criterion should be established and any samples that meet it should be disposed of immediately to prevent any contamination or mix-ups. Ensuring there are no delays in the processing of samples helps prevent metabolite drift and RNA degradation which can occur if the samples are not stabilised quickly.

Sample transport

Often samples are collected outside of the laboratory and need to be transported for processing and testing. Irrespective of the distance of transport, it must be managed carefully to maintain sample integrity. Ensuring a sample is transported efficiently, preventing degradation, involves determining the mode of transport, the classification of a package, the correct materials for packaging the sample and ensuring the package is correctly labelled and contains the correct documentation.

One study found that non-received samples were 3.7% of errors, typically occurring during handoffs between wards, couriers and laboratories. Misidentification and labelling errors were also identified as core pre-analytical failures, alongside transport and handling issues. These simple errors accounted for 40-70% of unsuitable samples. The Centres for Disease Control and Prevention (CDC) provides detailed information on how to package a sample to maintain its integrity (see references).

Sample storage

Policies detailing the description of what samples are to be stored, their retention time and the location of storage should be developed. Storing samples in an organised manner, creating an inventory and regularly monitoring them can help to ensure there are no errors in labelling or mix-up of samples.

It is vital that samples are disposed of when their retention time is reached. Repeated freeze-thaw cycles should be noted, as over time this can affect the integrity of a sample – repeated cycles alter protein structure and biomarker levels, which is documented widely in biobanking literature.

These points of failure are well understood, and in many cases entirely predictable. What is less often acknowledged is that they align with the everyday tools and practices used in routine laboratory work. The same systems that introduce variability are also the points at which control can be established.

Weighing, pipetting, labelling, transport and temperature management are not separate technical concerns; they are the practical mechanisms through which sample integrity is either preserved or lost.

Seen in this light, preventing error is not about adding checks at the end of the process, but about how consistently and effectively these routine operations are designed and executed from the outset.

Weighing discipline

Weighing is often treated as a simple preparatory step, but in reality, it is highly sensitive to environmental conditions and operator practice. Factors to consider when weighing include air currents causing balance instability; temperature differences creating convection currents; static electricity affecting small masses; vibration from nearby equipment; and drift from insufficient equilibration time.

Errors introduced here propagate through the entire workflow. A small weighing bias becomes a systematic concentration error, not a random fluctuation – one that no downstream precision can correct. Good practice includes controlling weighing environments; allowing samples to equilibrate to room conditions before weighing; routine calibration and verification with traceable standards; and minimising handling time/exposure.

Liquid handling

Liquid handling is one of the most common sources of hidden variability, particularly in research and assay-based environments. Key risks during liquid handling include operatordependent variability (angle, speed and technique of pipetting); inconsistent aspiration/ dispensing; air bubbles or incomplete dispensing; incorrect pipette calibration; and temperature differences affecting liquid viscosity.

Misidentification and labelling errors were also identified as core pre-analytical failures, alongside transport and handling issues. These simple errors accounted for 40-70% of unsuitable samples. The Centres for Disease Control and Prevention (CDC) provides detailed information on how to package a sample to maintain its integrity

Volume errors directly alter concentrations, meaning the sample being analysed is no longer what was intended. This is especially critical in qPCR, ELISA or drug assays. Good practice includes regular pipette calibration and maintenance; standardised pipetting techniques across teams; use of appropriate tips and pre-wetting methods; and automation where reproducibility is critical.

Cold chain management

Temperature is one of the most powerful – and often invisible – drivers of sample degradation. Often delays between collection and refrigeration/freezing; temperature changes during transport; inconsistent freezer performance or monitoring; and repeated freeze-thaw cycles cause degradation.

Temperature affects a lot of parameters, especially enzyme activity, protein stability and metabolite levels. Damage is often irreversible and undetectable, leading to results that appear valid but are misleading. Good practice includes defined temperature ranges for each sample type; continuous temperature monitoring using data loggers; validated transport systems such as insulated packaging; and aliquoting samples to avoid repeated freeze-thaw.

Reagent stability

Sample integrity is only part of the equation – reagents themselves can introduce variability if not properly controlled. Key risks include degradation due to light, temperature or time; lot-to-lot variation; contamination during repeated use; and improper storage or reconstitution. Unstable reagents create the illusion of analytical error when the issue is actually upstream. Good practice includes clear storage requirements; tracking expiry and openvial stability; minimising repeated freeze-thaw or exposure; and lot validation for critical assays.

Labelling and tracking systems

A sample without a reliable identity is effectively compromised, regardless of its physical condition. Primary pitfalls include mislabelled or unlabelled samples; duplicate identifiers; manual transcription errors; and breaks in chain of custody. Misidentification is not recoverable through analysis, representing a total loss of integrity, not just reduced quality. Good practice includes barcode-based identification systems; electronic tracking (LIMS); standardised labelling at point of collection; and minimising manual data entry.

Transport and handling systems

Much of the pre-analytical phase occurs outside the laboratory, where control is of the weakest. Threats to integrity include mechanical stress; delays between collection and processing; uncontrolled environmental exposure; and multiple handoffs between staff. Integrity depends on systems spanning outside multiple locations and teams. Good practice includes defined transport protocols and timelines; training beyond the laboratory; validated transport methods; and clear ownership of samples at each stage.

Across laboratory workflows, sample integrity is rarely lost through a single obvious failure. More often, it is eroded through small, cumulative deviations. By the time a sample reaches the analyser, these effects are already embedded, shaping results in ways that are difficult to detect and impossible to reverse. For this reason, sample integrity cannot be assured through training alone. While technique matters, human performance will always vary, particularly across complex, multi-step processes involving different people and environments. Where systems are weak – whether in transport, storage, labelling or environmental control – variability becomes inevitable.

Seen in this light, sample integrity is fundamentally an infrastructure problem. Reliable results depend on how effectively laboratories design and control the entire preanalytical pathway, from collection to analysis. Without that foundation, even the most precise instruments will produce results that are consistent, but not necessarily correct.

• Dr Rachel Sully is senior scientist at OmniSpirant Therapeutics

References:

1 Laboratory Quality Management System (LQMS) (2011), World Health Organisation (WHO), https://iris.who.int/server/api/core/ bitstreams/0772adf4-743a-48bb-843b-7ef4c2dc6896/content

2 Standards, Clinical and Laboratory Standards Institute (CLSI), https://clsi.org/shop/standards/?page=1&sortBy=Date:%20Newest%20First (Accessed 29th March 2026)

3 Specimen Storage and Shipping Guidance, Centres for Disease Control and Prevention (CDC), https://www.cdc.gov/laboratory/specimen-submission/pdf/specimen-packing-and-shipping-guidanceinfectious-diseases-laboratories.pdf (Accessed 29th March 2026)

4 How to meet ISO15189:2012 pre-analytical requirements in clinical laboratories? A consensus document by the ELFM WG-PRE, Vermeersch et al. (2021), Clin. Chem. Lab. Med. 2021: 59(6): 1047- 1061. DOI: 10.1515/cclm-2020-1859 https://www.degruyterbrill.com/document/doi/10.1515/cclm-2020-1859/pdf?licenseType=free

5 Preanalytical errors in clinical laboratory testing at a glance: source and control measures, Nordin et al. (2024), Cureus 2024: 16(3): e57243. DOI: 10.7759/cureus.57243 https://pmc.ncbi.nlm.nih.gov/articles/PMC10981510/pdf/cureus-0016-00000057243.pdf