Process Automation in Biomedical Research

Robotic screening platform

© dkfz.de

The large-scale approaches that are carried out in the division necessitate the automation of processes in order to achieve the throughput that is needed to allow for a systematic acquisition of data and information in biochemical and cell-biological experiments. For example, this applies to the set up of enzymatic reactions (e.g. quantitative PCR, sequencing reactions), the purification of nucleic acids (e.g. plasmid preparations), and the set up and application of (genome-wide) cell-based screens.

We have available a Biomek FX pipeting robot that is contained in a sterile housing, and is equipped with a cell culture incubator, automated centrifuge, a hotel, and a luminescence plate reader. Further equipment includes robots for plasmid preparation (96well format), several non-contact spotting devices to produce protein arrays, automated DNA sequencers, and quantitative real-time PCR instruments. Data acquisition in cell-based assays is with fluorescence and luminescence plate readers, flow cytometry instruments (FACS Array/FACS Calibur), or high-content screening microscopy (MDS ImageXpress). LIMS systems for the tracking of samples during the experimental processes are developed.The automation unit is key to the success of the experimental approaches carried out in the functional genomics and proteomics groups.

Automation of wound-healing scratch assay

Collective migration is an important cellular trait, which is intensely studied by both basic and translational researchers. Investigation of the underlying mechanisms necessitates high-throughput assays and computational algorithms capable of generating reproducible quantitative measurements of cell migration.

In collaboration with the groups of Yosef Yarden and Eytan Domany at the Weizmann Institute of Science, we have worked out an automated solution for performing and analyzing wound-healing scratch assays. The scratches are generated using the robot described above.

For full automation of the assay, we developed a desktop tool that can be used easily by any researcher, to quantify both fluorescent and phase-contrast images produced in the course of commonly used gap closure ("scratch," "wound healing") collective migration assays. The software has a simple graphical interface that allows the user to tune the relevant parameters and process large numbers of images (or movies). The output contains segmented images and the numerical values inferred from them, allowing easy quantitative analysis of the results (Zeisel 2013).

Application of qCMA software to fluorescent microscopy images from scratch assays

Application of qCMA software to fluorescent microscopy images from scratch assays
© Journal of Biomolecular Screening

upper panel: Experiment shown is MCF10A cells treated with siRNA oligonucleotides growing as a monolayer in a 96-well plate. At t = 0, a scratch is made by a robotically operated pin followed by stimulation with epidermal growth factor (EGF). The results of analysis of the three control oligonucleotides are presented: siCTRL (negative control), siEGFR (inhibits migration), and siCSNK1G2 (accelerates migration). The presentation has the form of average migration distance (AMD) versus time (see left panel). AMD is the difference between the initial value of average gap width (AGW) and its value at time t. Each oligonucleotide has several repeats, and the heavy solid line and error bars represent the mean and the standard error, respectively. In the right panel, segmented images are shown for one of the repeats of each condition.

lower panel: Application to phasecontrast microscopy. MCF10A cells were grown as a monolayer around a plastic insert (ibidi, Martinsried, Germany, wound-healing assay). At t = 0, cells were stimulated by EGF. Left panel shows the results for seven repeats (light lines); the heavy solid line and error bars present the mean and the standard error, respectively. The right panel shows segmented images for three representative wells. Note that despite the very different light conditions in the three repeats, the results are highly similar between wells. In particular, the lower row of images shows a typical set of images that are difficult to analyze, where the light on the two sides (left/right) is very different and the signal is weak (Zeisel 2013).

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