Standardization of high content imaging and informatics

Single Cell and Population Level Analysis of HCA Data

High Content Analysis instrumentation has undergone tremendous hardware advances in recent years. It is now possible to obtain images of hundreds of thousands to millions of individual objects, across multiple wells, channels, and plates, in a reasonable amount of time. In addition, it is possible to extract dozens, or hundreds, of features per object using commonly available software tools. Analyzing this data provides new challenges to the scientists. The magnitude of these numbers is reminiscent of flow cytometer, where practitioners have long been taking what effectively amounted to very low resolution, multi-parametric measurements from individual cells for many decades. Flow cytometrists have developed a wide range of tools to effectively analyze and interpret these types of data. This chapter will review the techniques used in flow cytometry and show how they can easily and effectively be applied to High Content Analysis.

Drug screening in 3D in vitro tumor models: overcoming current pitfalls of efficacy read-outs

There is cumulating evidence that in vitro 3D tumor models with increased physiological relevance can improve the predictive value of pre-clinical research and ultimately contribute to achieve decisions earlier during the development of cancer-targeted therapies. Due to the role of tumor microenvironment in the response of tumor cells to therapeutics, the incorporation of different elements of the tumor niche on cell model design is expected to contribute to the establishment of more predictive in vitro tumor models. This review is focused on the several challenges and adjustments that the field of oncology research is facing to translate these advanced tumor cells models to drug discovery, taking advantage of the progress on culture technologies, imaging platforms, high throughput and automated systems. The choice of 3D cell model, the experimental design, choice of read-outs and interpretation of data obtained from 3D cell models are critical aspects when considering their implementation in drug discovery. In this review, we foresee some of these aspects and depict the potential directions of pre-clinical oncology drug discovery towards improved prediction of drug efficacy.