Making mixtures forms an integral part of science. Mixtures range in complexity, from simple buffers to complex mixtures with numerous components such as media compositions or PCR mastermixes.

This technical note outlines a workflow where mixtures were made by specifying the mixture composition and the order of liquid addition using the Make Mixtures element. The workflow was executed on three liquid handlers: a Gilson PIPETMAX 268, a Hamilton Microlab STAR and a Tecan Freedom EVO. When using the Make Mixtures element, Synthace automatically calculates mixture volumes when target and stock concentrations are provided to prevent human-error. This workflow has been used to validate liquid handling accuracy for the volumes of liquid (tartrazine) transferred for mixtures, and provides a starting point for liquid handling optimization should it be required.

Within this workflow (Figure 1), six mixtures (Mix 1-6) were made over two plates (Plate 1 and Plate 2). Each mixture was made from three representative components: (1) diluent (reverse osmosis (RO) water); (2) additive (RO Water), and; (3) tartrazine, where the target concentration of tartrazine was varied in each mixture ranging from 0.2-0.1 mM in a total final volume of 200 µl.

Figure 1: Make Mixture workflow in Synthace. This workflow describes the method for making mixtures over three automation devices. All liquids required to make the mixtures are defined in the Define Liquids And Plates element whilst the mixture definitions are described within the Make Mixtures element.

Eight replicates of each mixture were prepared on two replicate plates (16 replicates in total) to demonstrate intra- and interplate variability respectively (Figure 2). This same workflow is executed over three devices, namely a Gilson PIPETMAX 268, a Hamilton Microlab STAR and a Tecan Freedom EVO.

Figure 2. Execution Details Preview in Synthace. Users can simulate their workflows, preview and verify their experiments in silico prior to physical execution, demonstrated here on the Hamilton Microlab STAR.

Labware information and estimated run times for each of the liquid handling devices is shown in Table 1. The workflow was run on all three devices on the same day. Tartrazine absorbance was measured using a BMG Labtech CLARIOstar microplate reader at 425 nm (Tartrazine 𝜆max), and baseline subtraction performed with absorbance at 620 nm. Data was processed and graphs were generated by a python script in Jupyter Notebook and can be accessed from the Downloads section at the end of this article.

Table 1: Liquid handling information

Independent measurements, and intra- and interplate variability for making mixtures with different concentrations of tartrazine as one of the components of the mixture were calculated for samples prepared across the different devices tested (Figure 3).

The average intraplate coefficient of variance (CV) was 3.68% (Glison PIPETMAX 268), 1.66% (Hamilton Microlab STAR) or 3.23% (Tecan Freedom EVO), while average interplate CV was 1.26% (Glison PIPETMAX 268), 1.37% (Hamilton Microlab STAR) and 0.86% (Tecan Freedom EVO).

Figure 3: Absorbance data for mixtures prepared across three devices. Corrected absorbance values of tartrazine for individual mixtures with decreasing concentrations made by liquid handlers; (A) Gilson PIPTETMAX 268, (B) Hamilton Microlab STAR and (C) Tecan Freedom EVO. Each data point represents an individual replicate for the mixture made per plate. Coefficient of variation (% CV) for each set of replicates on each plate, (D) intraplate variation, and across both plates, (E) interplate variation. Dashed horizontal lines represent Synthace internal validation standards for the % CV maximum thresholds.

The following file contains the workflow that was discussed in this technical note. To download the file, right-click the button, then select Save as.

After you download the file, complete the following steps.

The following file contains the raw data that was discussed in this technical note. To download the file, right-click a button, then select Save as.

If you want to process the raw data in the way that was discussed in this technical note, download the Jupyter Notebook or Python scripts. You must edit the scripts to point at the directory that contains the raw data.