Researchers are turning more and more to high-content imaging for help with drug discovery, clinical trials, dosing strategies, and studies of cellular interactions and functions. And 3-D imaging could take these applications even further. Although not yet king, high-content imaging is increasingly important in the life sciences. By allowing the use of cells as the basic unit in rapid and highly parallel biological research, high-content imaging also is helping scientists increase their understanding of how cells function and interact. “It’s really about being able to have more in-depth answers to biological questions,” said Mark A. Collins, director of marketing for cell science at Thermo Fischer Scientific Inc.’s Cellomics division in Pittsburgh. Using machine learning, PhenoLOGIC™ software classifies cells in high-content images. Courtesy of PerkinElmer. Thanks to advances in photonics, algorithms, probes and automation, researchers can now generate and analyze reams of image data. On the horizon is the ability to do so in three dimensions, in combination with spectroscopy, or in other ways to tease out even more information. Currently, high-content imaging is done on the organelles inside cells, cells themselves, tissues and complete organisms like zebra fish or Caenorhabditis elegans. In all cases, imaging of a great many items is done, with a variety of parameters captured in an automated fashion. It’s not unusual for an assay to generate hundreds of thousands of numeric values and images. Hence, everything depends upon the quality and reproducibility of the image. Better imaging starts with better lighting. For instance, there have been advances so that LEDs can now replace filters and white-light sources, Collins said. Since its introduction late in 2009 by Thermo Fisher, most customers have taken the LED option, he added. Among the benefits are the elimination of bulb changes, very stable illumination and the ability to dial the intensity up or down as needed. The latter allows the light to be set so that cells are not disturbed or damaged, a particularly important consideration in extended live cell assays. LEDs also may make it possible to control illumination on a color basis or even for each location in a 96- or 384-well plate. Data from high-content screening has to be mined for knowledge. For example, Collins pointed to the company’s recently introduced assay for predictive liver toxicity testing. Drugs under development that fail often do so because they injure the liver. Using high-content imaging to detect five or six biomarkers of damage enables a better than 90 percent accurate toxicity prediction, he said. Doing this early in product development could save a drug company hundreds of millions of dollars a year, and it can do so while potentially replacing animals with cell-based alternatives. “It’s a hardware, software and wetware, or reagents, platform designed to do toxicity assays. You might consider it to be a virtual lab rat,” Collins said of the product. He added that this and other customized systems are lower in cost and complexity than general-purpose high-content imaging systems. Synapses from primary rat cortical cultures acquired on Thermo Scientific Arrayscan VTI HCS. ©Thermo Fisher Scientific. Waltham, Mass.-based PerkinElmer Inc. also has put photonic advances to use. Jacob G. Tesdorpf, the company’s director of high-content instruments and applications, noted that the latest cameras offer 14-bit resolution over relatively large fields of view, making it possible to acquire finer gradations of data over a wider area. A challenge confronting vendors is increased multiplexing, with today’s standard being three to four channels. Investigators, however, would like to go to as many as six, which may require the development of new fluorescent reporters. For multiplexing, the signal from fluorophores should overlap as little as possible. What’s more, it is best if the fluorescence is in the red or near-infrared. Longer wavelength excitation and emission decreases light-induced cellular damage, a plus when cell vitality may influence important biomarkers. Longer wavelengths also penetrate cells and tissues more easily, leading to better images. Fluorescent probe development is, therefore, an active area. But software advances may be as, or even more, important, Tesdorpf said. Scientists using high-content imaging are typically life sciences experts. They know how to grow cells, tissues and animals and how to classify them into groups. Generally, they want tools that can handle complex image analysis without requiring software expert knowledge. PerkinElmer has made it easier for users of its high-content imaging systems to accomplish this. Researchers combine high-content imaging with fluorescence correlation spectroscopy (fluctuation measurement right) to track protein interactions and diffusion within a cell. Courtesy of Winfried Wiegraebe, Stowers Institute for Medical Research. “Basically, the user defines a training set by pointing and clicking. The software applies machine learning to it and then is able to classify cells from a whole set of images,” Tesdorpf said. He noted that dealing with the amount of data generated by high-content imaging is an area where ongoing improvement is needed. In that, the field benefits from progress being made elsewhere. Suitable statistical methods and associated software algorithms may be one way to handle the mountain of data. The quantity of data could be going up, courtesy in part to efforts of researchers around the world. For instance, a team from the Stowers Institute for Medical Research in Kansas City, Mo., has developed high-content screening based on fluorescence correlation spectroscopy, which previously had been done manually. The researchers automated the process, cutting the time to interrogate a cell from 10 or so minutes down to 10 seconds. They used transmitted light to determine cell boundaries, segmented the cell into sections via software, then followed the fluctuating fluorescence in the cell. The correlation statistics allowed them to measure protein interactions, diffusion properties and local concentrations in cells in a 96-well plate, and their custom setup enabled them to do this in an automated fashion. In a February 2011 SPIE proceedings paper, the researchers reported that they had measured the local concentration and diffusion properties of 4000 different proteins in yeast. Team leader Winfried Wiegraebe, head of microscopy at Stowers, said that this proof of feasibility will be followed by additional research aimed at addressing biologically important questions. “At this moment, we are using the diffusion data to learn about diffusion properties of differently sized proteins in different cell compartments,” he said. As for commercial prospects, the system is based on a modified Zeiss platform. Thus, a product using the technique is possible. In another example, software could boost the amount of data produced by high-content screening, doing so by a process of machine learning. Researchers at the European Molecular Biology Laboratory (EMBL) in Heidelberg, Germany, have developed software that they named Micropilot. Software is rendering high-content imaging easier and more useful. Here, automated selection of an interphase (top left) or prophase (early, middle left, and late, bottom left) cell is done with a trained classifier. Time lapse images after fluorophore labeling follow (second left to right). Courtesy of Christian Conrad, European Molecular Biology Laboratory. Reprinted from Nature Methods. The software includes a classifier that sorts objects into groups, said Christian Conrad, senior scientific officer at the EMBL’s Advanced Light Microscopy Facility. It does so using a training set of roughly 50 to 100 objects per class and uses techniques that make it relatively insensitive to noninformative object features. “The design of Micropilot is such that it does not matter which images are fed into the classification. The features suit mostly cell-based applications; however, it requires that the cells be segmented as objects on a cellular or subcellular level,” Conrad said. The software has been used in various applications, including quantifying microtubule dynamics, as described by Sironi et al in the March 2011 issue of Cytoskeleton. Micropilot, or software based on it, could soon be showing up in commercial systems. Several companies are collaborating with EMBL staff to facilitate this, Conrad reported. Cytoskeleton of HeLa cells acquired on the Thermo Scientific CellInsight personal image cytometer. ©Thermo Fisher Scientific. Finally, high-content imaging is about to take on a new dimension. Cell cultures today are largely 2-D affairs, with cells and imaging confined to a plane. In natural settings, however, cells live and grow in 3-D structures, with differences in cell shape and function dependent upon location in X, Y and Z. To truly represent a larger organ or animal, a cellular assay and the associated high-content imaging has to take the third dimension into account. The first steps in this direction are under way. For example, Thierry Dorval, cellular differentiation team leader at Institut Pasteur Korea in Seongnam, leads a group developing generalized software and a hardware platform for the task. The work is a joint effort between computer scientists and biologists. High-content imaging in 3-D starts with data acquisition, here of an embryonic body (top), with green indicating cell nuclei and red stemness. Then image processing software allows extraction of cellular phenotype at the level of the cell population (bottom). Courtesy of Thierry Dorval, Institut Pasteur Korea. To be useful in a high-content setting, the software algorithms must be robust and efficient. That’s the only way to handle artifacts, reduce errors and still deal with the hundreds of thousands of images generated, Dorval said. Currently, software to make the process of extracting and analyzing 3-D high-content data easy and effective doesn’t exist. In a July 2010 Journal of Biomolecular Screening paper, Dorval and others published results from what he characterized as 2.5-D software, a partial solution to the problem. The group quantified the shape, texture and fluorescence intensity of multiple stained subcellular structures, including the nucleus, the Golgi apparatus and the centrioles. One goal of the software project is to recognize observable characteristics at the cellular level. Another is to do such phenotype extraction one level up, at the level of the structure itself. In justifying this 3-D high-content imaging objective, Dorval said, “This is not only one cell. This is the cell that is surrounded by others. So how do the others influence the cell?”