High-Content Screening

The term High-Content Screening (HCS) was first coined by Giuliano and co-workers in their article ‘High-Content Screening: A New Approach to Easing Key Bottlenecks in the Drug Discovery Process’ and defined as ‘High-content screens automate the extraction of multicolour fluorescence information derived from specific fluorescence-based reagents incorporated into cells’ (1). In their article, they described also the first automated microscope enabling larger-scale imaging. Although, typically automated microscopy is applied in HCS, also multi-parametric FACS has been brought meanwhile to high-throughput (ref. 2), and leaders in the field include it when talking about high-content screening. Other multi-parametric screening formats might be considered as well. A typical work-flow and instrumental setting is displayed in figure 1.

Fig. 1

Fig. 1. Typical setting of a high-content screening process as it was applied in 2008 at the central high-content screening facility TDS of the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany. (ref. 3)

High-content assays rely on reading multiple parameters in the physiological context of intact cells simultaneously. There are critical features and obvious advantages:

- Sub-population analysis

- Analysis on individual cell level and/or at sub-cellular level

- Multiple parameters per cell

- Parametersmay be in relation to each other

- Spatial (and temporal) resolution

- Low false-positive / false-negative rates

- High physiological relevance (e.g. drug responses are not limited to a single cellular target)

- Higher sensitivity compared to many homogeneous assays can be observed

- Images and/or data sets can be mined later again for additional information not considered yet.

However, there are also limitations that challenge:

- High variability

- Lower throughput

- Miniaturisation

- Assay development times prolonged

- Major financial investment

- Challenging image analysis

- Huge amount of data: major challenge to IT infrastructure.

In the context of drug discovery, compound screening has been entering a new level. Mechanistically very specific assays can be developed that allow screening in the physiological context of a cell and considers additional parameters early on during the primary screen: cell permeability, compound stability and cytotoxicity. We observe broad application of HCS throughout the drug discovery process, supporting target identification and validation, (high-throughput) screening, hit profiling, hit-to-lead optimisation, or the preclinical development.

Three major types of imagers are used currently. Laser scanning cytometers scan entire plates ideally with multiple lasers to allow multiple colour detection. The speed is quite high at 3-15min per plate while the resolution is equivalent to a 10x objective. Therefore, resolution is some-what limited. Most instruments have a normal inverse microscope inserted and allow imaging at various magnifications, at epifluorescent or transmitted light mode. A motorised stage and autofocus system(s) allow unsupervised image acquisition. The one camera allows simultaneous detection of one colour while multi-colour images result from subsequent images while automatically filters are exchanged between the shots. However, this lowers the throughput. Some instruments allow insertion of a spinning disc to receive confocal images. However, the majority of the light gets lost, and exposition times have to be significantly adjusted. High energy or multiple lamps are installed to tackle this limitation. Some confocal systems exist that are empowered by laser lights. Multiple lasers and cameras allow simultaneous imaging at several colours in parallel. Such instruments reach relative high speed and can generate more than 50.000 multi-colour images per 24 hours.

After generating such amounts of images, nobody will visit and analyse all of them by eye. Therefore, from beginning of the automated microscopy technology development, image analysis tools were developed and automated. Meanwhile most instruments come with their own image analysis software. Mainly ‘canned’ algorithms are included that allow most popular assays with correlating parameters to be evaluated. Certain adjustments can be done easily. However, if needs and expectations are higher, programming skills are requested. A number of software packages are available for the advanced, even some freeware.

A major challenge is data management and data analysis. A serious IT infrastructure has to be in place, and capacities for analysis. At a later stage, data mining might be added to bring own data into context of data produced by others.

References:

(1) Giuliano et al.: High-Content Screening: A New Approach to Easing Key Bottlenecks in the Drug Discovery Process. J Biomol Screen 1997, 2:249-259.

(2) Edwards et al.: Flow cytometry for high-throughput, high-content screening. Curr Opin Chem Biol 2004, 8:392-398.

(3) Krausz, E.: Academic Screening Facilities at the Interface of Academia and Industry. HUGO Workshops in Genomic Sciences: High Content Cellular Screening, Human Genome Organisation (HUGO), November 12-14, 2008, Singapore.

Recommended further reading:

Götte M, Gabriel D: Image-Based High-Content Screening in Drug Discovery. In Drug Discovery and Development - Present and Future, Dr. Izet Kapetanović (Ed.), 2011, ISBN: 978-953-307-615-7, InTech, Available from: http://www.intechopen.com/books/drug-discovery-and-development-present-andfuture/image-based-high-content-screening-in-drug-discovery

Haney, S.A. (ed.): High Content Screening: Science, Techniques and Applications. Wiley ( Hoboken, New Jersey), (2008).

Shorte, S.L., Frischknecht, F. (eds.): Principles and Practice: Imaging Cellular and Molecular Biological Functions. Springer-Verlag ( Berlin Heidelberg, Germany), (2007).

Taylor DL, Haskins JR, Giuliano KA (eds.): High Content Screening: A Powerful Approach to Systems Cell Biology and Drug Discovery. Meth Mol Biol (2007), Vol. 356.

Inglese, J (ed.): Measuring Biological Responses with Automated Microscopy. Meth Enzymol (2006), Vol. 414.