Encyclopedia of Lipidomics

Living Edition
| Editors: Markus R. Wenk

Liquid Extraction: Automated Extraction

  • Marcus StåhlmanEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-94-007-7864-1_97-1


Liquid Extraction Initial Investment Cost Liquid Extraction Method Robotic Setup Great Structural Diversity 
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Sample preparation, sample analysis, and data evaluation are three important parts of a comprehensive lipid characterization of biological samples. While analysis and evaluation have, to a large extent, been automated, sample preparation is still often performed manually. However, since liquid extraction methods are the most commonly used methods for sample preparation, the development of advanced pipetting robots now makes it possible to integrate this part of the analysis into the high-throughput workflows of the modern lipid laboratory.

Liquid extraction is a process where analytes (in our case lipids) of interest are extracted from the matrix by the addition of one or several solvents. Because of the great structural diversity of lipids, which results in a wide range in polarity, the choice of solvents depends heavily on the lipids of interest and the analytical approach. Sometimes a single addition of a pure solvent is sufficient, while in some cases a more elaborate combination of solvents are required to achieve high recovery and sufficiently pure extracts.

Pipetting Robots

Pipetting robots can be obtained from several manufacturers and come in many different forms. Some robots have only one tip and can only handle one sample at a time, while some can work in the 1536-well footprint. Some use plastic tips while some have non-exchange tips of an inert material. Some have integrated vortex mixing, evaporation stations and even centrifugation. Another important issue is that the complexity of the robot interface differs between manufacturers. For optimal performance, some robots require the user to be good at programming while others use a more simplified “drag-and-drop” interface. To conclude, robots exist in many different forms and formats, and the choice of robot most suitable for a given application depends on many factors including sample format, pipetting technique, and software.

Automation of One-Phase Liquid Extraction Methods

Automated one-phase liquid extraction methods, which when working with plasma or serum are sometimes called protein precipitation protocols, were developed early on in the pharmaceutical industry mainly driven by high-throughput requirements. However, for the pharmaceutical industry, the methods were not developed for lipid extraction but mainly to extract drugs for quantification using mass spectrometry. Actually, phospholipids and sphingomyelins are often viewed as unwanted contaminants and accused of being responsible for ion suppression effects that can cause potential disturbances when using high-performance liquid chromatography coupled to mass spectrometry LC-MS.

One-phase liquid extractions can be performed using a number of different solvent systems. Methanol is often used and will by itself be able to extract many lipids (such as bile acids and phospholipids) with high recovery. By adding chloroform or butanol (Alshehry et al. 2015) to the methanol, a solvent mixture will be generated that allows the extraction of a more comprehensive range of lipids.

The degree of automation used for a one-phase liquid extraction can vary depending on the needs but also on the capabilities of the robotic setup. In the simplest methods, only the addition of solvent and transferring of supernatant are made by the robot. The other steps, such as vortex mixing and centrifugation, require manual intervention. On the other hand, a more comprehensive robotic setup will be able to do all of these steps and more (Fig. 1).
Fig. 1

Simplified scheme describing the liquid extraction workflow. The degree of automation for performing a robot-assisted liquid extraction will vary depending on the setup. All pipetting robots will be able to handle solvent additions and transfers (basic performance marked green above). However, for full automation, the robot must be equipped with an integrated vortex mixer (marked orange) and in some case even a centrifugation module (marked red). For LLE, the processes within the dotted line can be repeated for increased recovery

1This addition creates a two-phase system

Automation of Liquid/Liquid Extraction (LLE) Methods

For some applications it is desirable to perform LLE procedures mainly to increase the purity of the extract. In LLE a second solvent/solvent mixture is added to the one-phase system to create an immiscible mix of two solvent systems (a two-phase system). The lipids of interest will be enriched in one of the phases, and the unwanted, often more polar substances such as carbohydrates, salts, and amino acids will end up in the other phase. Two of the most commonly used LLE methods in lipid research are the Folch and the Bligh and Dyer extraction methods, which both contain chloroform (Folch et al. 1957; Bligh and Dyer 1959).

Compared to single-phase liquid extraction method, for which a large number of methods exists, automated LLE methods for lipid extraction have not been commonly reported in the literature. However, an automated Folch extraction method performed in the 96-well format has been described for plasma/serum (Stahlman et al. 2009). The drawbacks with the Folch method, in terms of automation, are that it has a relatively high solvent to sample ratio and that the lipids become enriched in the lower phase. An alternative method for automated LLE of plasma/serum is the BUME method which uses a non-chlorinated solvent system resulting in a lower solvent/sample ratio and with a lipid-enriched upper phase (Lofgren et al. 2012). The two methods (automated Folch and BUME) are both fully automated. This means that samples are placed on the robot deck in a 96-well format; the robot is started and 60–90 min later the lipid extracts are ready for collection. Using the BUME method, the need to perform centrifugation can be avoided since there will be a relatively fast and spontaneous phase separation (Fig. 1). In addition, the debris and protein precipitate will be located in the lower polar phase and in the interphase and will not interfere with the transfer of lipid extracts to new vials.

Another example of an LLE method that has the potential of replacing the old chloroform-based extraction methods has been published by Matyash et al. (2008). In this protocol a mixture of methyl-tert-butyl ether (MTBE) and methanol is used for the initial one-phase extraction. After addition of water, a two-phase system is created with the lipids enriched in the upper phase, which makes the method suitable for automation. Rigorous testing showed that the protocol delivered similar or better recoveries of the investigated lipid classes compared to the Folch or Bligh and Dyer methods.

Reasons for Automation

The inclusion of automated liquid extraction methods in the analytical workflows has several advantages. One of the more important factors being that it can increase sample throughput. It can also work unattended allowing time to be spent elsewhere. Other factors can also be of importance. For example, robot-assisted pipetting is performed with high accuracy and precision, which will generate reproducible data and reduce the chance of human error. In addition, pipetting robot will alleviate the strain on the neck and shoulders associated with manual pipetting. The major drawback with using robotic setups is the high initial investment cost and an increased cost for consumables (tips). Difficulties in setting up protocols can also be a factor that needs to be considered.


Liquid extractions are important techniques that are frequently used in lipid research. While still often performed manually, the introduction of pipetting robots has made automation possible. The number of robot models and manufacturers is large, and care must be taken to define the needs before acquiring a pipetting robot for the laboratory.



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© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Wallenberg Laboratory, Department of Molecular and Clinical MedicineSahlgrenska Academy, University of GothenburgGothenburgSweden