CellProfiler and KNIME: open source tools for high content screening.

High content screening (HCS) has established itself in the world of the pharmaceutical industry as an essential tool for drug discovery and drug development. HCS is currently starting to enter the academic world and might become a widely used technology. Given the diversity of problems tackled in academic research, HCS could experience some profound changes in the future, mainly with more imaging modalities and smart microscopes being developed. One of the limitations in the establishment of HCS in academia is flexibility and cost. Flexibility is important to be able to adapt the HCS setup to accommodate the multiple different assays typical of academia. Many cost factors cannot be avoided, but the costs of the software packages necessary to analyze large datasets can be reduced by using Open Source software. We present and discuss the Open Source software CellProfiler for image analysis and KNIME for data analysis and data mining that provide software solutions which increase flexibility and keep costs low.

Image analysis in high-content screening.

The field of High Content Screening (HCS) has evolved from a technology used exclusively by the pharmaceutical industry for secondary drug screening, to a technology used for primary drug screening and basic research in academia. The size and the complexity of the screens have been steadily increasing. This is reflected in the fact that the major challenges facing the field at the present are data mining and data storage due to the large amount of data generated during HCS. On the one hand, technological progress of fully automated image acquisition platforms, and on the other hand advances in the field of automated image analysis have made this technology more powerful and more accessible to less specialized users. Image analysis solutions for many biological problems exist and more are being developed to increase both the quality and the quantity of data extracted from the images acquired during the screens. We highlight in this review some of the major challenges facing automatic high throughput image analysis and present some of the software solutions available on the market or from academic open source solutions.