High-content screening (HCS) has emerged as a unique approach that combines the power of automated image analysis with fluorescence microscopy in detecting various cellular processes and interactions.
This approach has been supported by almost a decade of efforts in improving methods for the quantification of signals and management of image data files.
At CHI’s recent “High-Content Analysis” conference, several scientists and computer software developers presented novel approaches to high-content screening in different scenarios and platforms.
High-content screening experiments by their nature generate a tremendous amount of information in images and numerical results, according to Oliver Leven, Ph.D., head of professional services of Genedata Screener®.
“Each well in a microtiter plate contains hundreds or thousands of cells, and the goal of high-content screening is to capture these cells and to quantify their response to the changed experimental conditions as expressed by their phenotypes. Typical experiments range from four 96-well plates to hundreds of 384-well plates. Combining the results from multiple wells enables researchers to study the potency of new chemical substances or to quantify the damage to cells after drug administration.”
To support such experiments, Genedata has thus developed a data analysis workflow system that consolidates image-data gathered from multiple instruments, allowing researchers to import data into a single platform.
“To look at one screen for the analysis of dose-response curves and then go to another computer—or just change software to compare the underlying images is a time-consuming and painful activity for a scientist,” explained Dr. Leven. “The Genedata Screener image workflow system has been designed as a scalable platform that can handle up to 400 cellular features generated from different instruments and analyze them side by side in a single computer screen with easy access to images.”
An in-depth analysis of HCS experiments with thousands of images is often prevented due to the difficulties in accessing the enormity of available information.
“One common stumbling block for phenotypic screening assays involves knowing where your cells are after running a microplate-based experiment. Every researcher wants to interpret files from yesterday’s experiment, see the raw data, and compare this to today’s experiment. However, once you scale-up your experiments to process 50 96-well plates, data workflow becomes a problem,” Dr. Leven further explained.
“Often the scientist spends more time on obtaining the information than on analysis itself.” He said that Genedata Screener supports reliable throughput information and full access to all information enabling the processing of the data that gives value to each well analyzed.
High-content screening has also been employed in toxicity assessment of new drugs. According to Evan Cromwell, Ph.D., director of research at Molecular Devices, phenotypic screening assays are useful in identifying drugs that could damage certain types of cells.
“Toxicology is generally difficult to predict, even in animal models and humans, and thus high-content screening assays using stem cell-derived cardiomyocytes, hepatocytes, and neurons can facilitate the determination of complex responses and phenotypes, for example, viability and mitochondrial damage, due to exposure to specific drugs,” Dr. Cromwell said.
In addition, high-content screening also allows scientists to multiplex information from other assays for specific cellular activities, including caspase activation, DNA damage, and changes in nuclear shape. Cell morphology, adhesion, and spreading are indicators of toxicity and they need to be accurately characterized by software analysis.
Together with the efforts of other scientists at Molecular Devices, the success of phenotypic screens is based on the development of instrumentation that allows them to capture images that could be subjected to statistical image data analysis.
“These algorithms for analysis of cell-based assays allow us to identify which drugs are safe or toxic, and potentially predict mechanisms of action,” explained Oksana Sirenko, Ph.D., research scientist at the firm. “We have done proof of concept for cardiotoxicity and hepatotoxicity that shows such assays can be predictive, yet also face certain challenges.”