High Content Screening has become an invaluable tool for pharmaceutical research laboratories the world over. In order to chart the progress of this important technology over recent years, as well as its applications, we spoke to a selection of industry experts. Read what the technology users and vendors have to say about its value and the future of HCS.
High content screening (HCS) is based on subcellular imaging using automated microscopy, in combination with automated image analysis. High content screening was first introduced over a decade ago as one of the promising new technologies, intended to address the bottleneck of secondary assays in the development of new drugs. Since then, the application has rapidly expanded throughout the entire drug discovery process, from target identification and validation, through to lead optimisation and detailed investigation of the mode of action.
Creating the molecular tools to combat human disease and infection remains the cornerstone activity of the pharmaceutical industry. The methodologies employed to discover new drugs has continually evolved as new biological techniques have emerged; nevertheless the development of each novel compound is still only realised after many years of careful research, and a detailed analysis of its specific target.
High Content Screening (HCS) is becoming increasingly utilised as an early drug-discovery and basic research tool for defining the functions of genes, proteins and other biomolecules in normal and abnormal cellular functions. HCS involves the integration of a number of preparation steps which include; cell-sample preparation, fluorescent labelling, image acquisition, image processing, image analysis, information management and knowledge mining.
One of the chief incentives for the use of high content screening (HCS) approaches is the data rich return one gets from an individual assay. However, conventional methods for hit selection and activity determination are not well suited to handling multi-parametric data. Tools borrowed from the genomics area have been applied to HCS data, but there are important differences between the two data types that are driving the development of novel statistical approaches for HCS data analysis. This article will describe the use of techniques such as principal component analysis, classification trees, neural networks and random forests, as well as recently published approaches for the identification and classification of compound profiles resulting from HCS assays.
High-content screening (HCS) is defined as multiplexed functional screening based on imaging multiple markers (e.g. nuclei, mitochondria etc.) in the physiologic context of intact cells by extraction of multicolour fluorescence information1. It is based on a combination of advanced fluorescence-based reagents, modern liquid handling devices, automated imaging systems and data processing, as well as sophisticated image analysis software.
Data management has become one of the central issues in High Content Screening (HCS) as it has high potential within predictive toxicity assessments. In particular, HCS applying automated microscopy requires a technology and system which is capable of storing and analying vast amounts of image and numeric data. HCS data includes comprehensive information about the bioactive molecules, the targeted genes and images, as well as their extracted data matrices after acquisition. Here we describe a bioinformatics solution HCS LIMS (Laboratory Information Management System) for the management of data from different screening microscopes. Additionally, the data handling approaches used in HCS for image converting, compression and archiving of images are discussed.
High content screening (HCS) has now become integrated into all aspects of drug discovery from target identification and validation to hit generation and lead optimisation through to toxicological profiling. In neuroscience, the ability to perform automated neurite outgrowth and neuronal morphology screening has been a significant driver of HCS implementation. This approach has evolved significantly from relatively simple measures of total neurite length to detailed multi-parametric characterisation of neuronal morphology.
The statins (3-hydroxy-3-methylglutaryl coenzyme A [HMG-CoA] reductase inhibitors) are drugs that inhibit cholesterol biosynthesis by blocking the formation of the cholesterol precursor mevalonate. Statins are the most effective cholesterol-lowering agents available and are considered the first line of treatment for most patients with high serum cholesterol levels.
Advances in optical imaging methods, personal computer power and cell/molecular biology methodology have merged to form the field of ‘Cellomics’1 also referred to as High Content Cellular Imaging (HCCI). HCCI is a powerful and flexible cell-based assay platform that has the potential to shorten cycle times by broadly impacting the Drug Discovery process from Target Validation/Lead Generation through in vitro support of Clinical Candidates. This article provides an overview of HCCI, contrasts it with conventional cell based assay modalities, and provides general examples of the technology’s effects on the Drug Discovery process at Eli Lilly and Company.
Molecular technologies such as genomics and proteomics have brought in a thorough make-over to early stage drug discovery. The strategic spotlight from the genomics technologies has gradually shifted focus to the cellular domain where the entire drug target interaction takes place. As a result, cell based screening provides promising potential to yield safer and non-toxic drugs. With the compelling need to improve the quality of hits that occur during the screening phase, it is critical that therapeutically relevant targets are identified, consequently bringing in savings on time and R&D costs.
HCS has been implemented as a key technology to address complex biology associated with CNS drug targets. This review will describe a new generation of HCS assays including multiplexed HCS assays with biochemical markers, novel techniques for studying receptor internalisation and the application of HCS to neural network cultures that have facilitated CNS drug discovery.
High content imaging (HCI), the combination of automated fluorescence microscopy with quantitative image analysis, has been opening new dimensions in cytometry. This article gives an overview on the growing spectrum of applications and an outlook on the future use of this still rapidly developing technology.
High-Content Analysis (HCA) provides a drug discovery tool capable of rapid screening of drug effects in pharmacologically relevant cell culture systems. Interest in HCA has been increasing during the past few years. This reflects the confidence that HCA-technology has established due to the stability and reliability offered to the drug discovery process. HCA offers the capability to support an experienced and open minded cell biologist in challenging the current limits of cell biology. HCA is a versatile tool providing statistically secured data of cellular and subcellular events, respectively.
In drug discovery for CNS diseases, the use of complex neural cell culture systems offers many advantages. Innovations in high content screening enable us to identify compounds which affect key cell biological properties in such cultures. We can bridge the divide between kinetic and endpoint screening by use of another novel technology, RNAi.
