Cell biological experiments are slow to perform. In most cases every step is done manually, ranging from cell culture work to microscopy sessions. Also, the evaluation of experiments and data analysis are seldomly automated. This limits the throughput of cell biological experiments.
On the other hand, "OMICS" technologies such as proteomic screens, next generation RNA/DNA analysis and high-content screens generate a plethora of interesting candidates. Unfortunately, due to their limited processing capacity, researchers can only cherry pick interesting candidates, leaving the vast majority unstudied. Clearly, we need to accelerate our current experimental workflows. This will allow us to increase the speed with which we make new discoveries.
We are integrating various automation steps into cell biological experiments to speed them up. At the moment we utilize multiplexed high-content imaging in combination with automated image and data analysis workflows. Furthermore, we are integrating automated cell culture and sample preparation steps to establish flexible automation pipelines. We apply this scalable approach to elucidate cellular mechanisms underlying the development of hypercholesterolemia.
Hypercholesterolemia, a high blood cholesterol concentration, is a major risk factor for cardiovascular disease. In Europe, more than 130 million people are living with hypercholesterolemia. Even though the cell biology of hypercholesterolemia has been studied during the past decades, we lack insight into cellular processes contributing to disease progression in individual persons and how they are affected by genetic variation. It is our goal to provide functional insight into cellular disease mechanisms in a personalized manner and to pinpoint the effects of genetic variants. We aim to use this information to reduce the impact of hypercholesteromia for individual persons and society.