Über die Autorin bzw. den Autor

STEVEN A. HANEY, PHD, is a Principal Scientist in the Department of Biological Technologies at Wyeth Research, where he has developed programs for oncology drug development, built a HCS program for use in target validation and drug discovery, and prepared gene family-based target validation strategies. Dr. Haney has authored many peer-reviewed articles and has spoken at numerous conferences on HCS.

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The authoritative reference on HCS in biological and pharmaceutical research

High Content Screening (HCS) has been a leading methodology in toxicology studies for years. Recent advances have broadened the range of applications to encompass new areas. High Content Screening: Science, Techniques, and Applications provides comprehensive coverage of HCS in four sections:

• The basics of HCS, from the definition to detailed discussions of component technologies
• Examples of HCS used in biological applications and early drug discovery, with an emphasis on applications in oncology and neuroscience
• The use of HCS across the drug development pipeline
• Data management, data analysis, and systems biology, with guidelines for using the large datasets generated by HCS in systems-level studies

With chapters contributed by leading authorities from academia and industry, this guide covers:

• A wide range of topics, including assay development, cell culture, image processing, robotics, database architecture and management, model systems for analysis, and more
• Focused discussions on imaging in 3D, imaging of tissues for pharmacodynamic studies, and screening of both small molecule and RNAi libraries by HCS
• The roles of bench researchers and IT personnel in implementing and maintaining HCS platforms
• The challenges and advantages of using HCS today, and a look at future directions

With eighty-seven detailed figures readers can refer to in full color on the accompanying Website, this is the premier, hands-on reference on HCS for researchers in academia, biotechnology, and pharmaceutical companies. It's also an excellent resource for lab managers and graduate students in biochemistry, cell biology, toxicology, and related fields.

Aus dem Klappentext

The authoritative reference on HCS in biological and pharmaceutical research

High Content Screening (HCS) has been a leading methodology in toxicology studies for years. Recent advances have broadened the range of applications to encompass new areas. High Content Screening: Science, Techniques, and Applications provides comprehensive coverage of HCS in four sections:

• The basics of HCS, from the definition to detailed discussions of component technologies
• Examples of HCS used in biological applications and early drug discovery, with an emphasis on applications in oncology and neuroscience
• The use of HCS across the drug development pipeline
• Data management, data analysis, and systems biology, with guidelines for using the large datasets generated by HCS in systems-level studies

With chapters contributed by leading authorities from academia and industry, this guide covers:

• A wide range of topics, including assay development, cell culture, image processing, robotics, database architecture and management, model systems for analysis, and more
• Focused discussions on imaging in 3D, imaging of tissues for pharmacodynamic studies, and screening of both small molecule and RNAi libraries by HCS
• The roles of bench researchers and IT personnel in implementing and maintaining HCS platforms
• The challenges and advantages of using HCS today, and a look at future directions

With eighty-seven detailed figures readers can refer to in full color on the accompanying Website, this is the premier, hands-on reference on HCS for researchers in academia, biotechnology, and pharmaceutical companies. It's also an excellent resource for lab managers and graduate students in biochemistry, cell biology, toxicology, and related fields.

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High Content Screening

John Wiley & Sons

Chapter One

SCOTT KEEFER and JOSEPH ZOCK

1.1 INTRODUCTION

The topic of this book is the study of cells. What is in them, on them, around them, and between them. How they eat, sleep, grow, react to stimuli, and die. How they complete tasks and work as a team by signaling, influencing, stimulating, inhibiting, and sometimes destroying each other. High content screening (HCS) is an imaging approach to cell-based assays that has had an impact in the fields of neurobiology, oncology, cell signaling, target identification and validation and in vitro toxicology. If you have opened this book, you have most likely heard of high throughput screening (HTS), understand the premise, and have probably seen it utilized somewhere in the workflow of your organization. You have probably also heard of HCS and, hopefully, want to learn more about how it works and where it should be implemented. You might be a drug discovery scientist trying to transition targets from biochemical to cell-based assays. Or you could be an academic cell biologist who wants to generate a larger amount of statistically relevant data in a shorter time frame. Either way, our guess is that you are not viewing HCS as an all-encompassing career move, but rather as a new set of tools to get your job done. This chapter attempts to provide a frame of reference to fit HCS into your mindset by comparing the similarities and differences with several current assay methods. Some of the advantages of cellular imaging will then be discussed as we cover key process steps. Finally, we will leave you with some advice in the form of six points to remember to get the most out of your HCS data. The goal is to open your eyes to the possibilities this new tool has for rapidly expanding the breadth of cell biology that can be quantified, leading to new discoveries in both basic and applied scientific research.

1.2 WHAT IS HCS AND WHY SHOULD I CARE?

High content screening can be defined as an automated imaging approach to understanding compound activities in cellular assays where, in each well of a microplate, you can measure spatial distribution of targets in cells, individual cell and organelle morphology, and complex phenotypes. It provides the flexibility to measure cell subpopulations and to combine multiple measurements per cell, while simultaneously eliminating unwanted cells and artifacts. Recently, the term high content analysis (HCA) has also emerged to describe the broader view of multiparametric interrogation of cellular processes in any format.

The "content" is a set of output feature numbers, derived from an algorithmic extraction of fluorescence intensities per pixel within the digitized image of a cell. The number of measurements made for each cell can climb into the hundreds, depending on the number of fluorescent probes used. The raw data that are generated can then be combined to define a staggering number of biological states and phenotypes. These measurements, when applied to the screening of potentially bioactive entities, can describe a compound's cellular bioavailability, potency, specificity, and toxicity. Remarkably, this can often be achieved with one HCS assay by multiplexing assays with probes spread across the visible spectra.

The "C" in HCS also stands for "context." All HCS assays are performed with intact, living cells and, therefore, preserve the state of cell physiology created by the assay environment. In the early years of the pharmaceutical industry, before the development of the mainstream tools of modern molecular biology and biochemistry, context was one of the only ways scientists had of understanding a potential drug candidate's pharmacology. The process was essentially to make a test animal sick, "treat" it with a compound or extract, and observe it for indications that the animal was getting better or getting worse. Often, odd behaviors were noted that, although attributed to the treatment, could not be readily explained. Hence the moniker "black box" science. Then, over time, the application of advances in protein chemistry combined with genetic engineering allowed the isolation or creation of active proteins outside of the cell and the biochemical assays were born. This format could be completed in small volumes, and technologies to take advantage of this drove the number of assays carried out to over 10,000 a day (HTS) and eventually over 100,000 a day (uHTS). The problem was that the context of the "box" was lost in the process.

Why is context so important? We, as a scientific community, collect an enormous amount of biochemical assay data from HTS and try to use it to understand both general cell biology and compound effects, and yet some of the most important questions remain unanswered due to a lack of context. An analogy might help here. You are a brain surgeon trying to remove a tumor without destroying function. Your patient is on the table and you are stimulating different parts of the brain around the tumor to see the response. Stimulation in one spot causes the right index finger to move. Stimulation in another spot causes the right wrist to move. In an effort to not hit the wrong spot you ask the patient to watch a screen and recite aloud either the text or a description of a picture flashed before him. These pieces of behavioral information need to be collected and pieced together to get an idea of what the tumor might be doing. Additionally, this process needs to have an intact patient to do it. It is not really about the index finger, or the wrist, and even if you have very specific and sensitive ways to identify and measure them, without the context of the whole patient you will not have the right information to be successful.

So it is with cell biology. HCS effectively shines a light into the black box, allowing for context of cell physiology and behavior while collecting multiple pieces of information simultaneously. Context allows for the determination of function. From quantifying the activation of multiple transcription factors in a cell signaling model, through identifying differentiated cell states in a stem cell assay, to assessing true target function in a genome wide RNAi knockdown study, HCS is the detection method of choice.

Finally, the "C" in HCS also stands for "correlation." Trying to interpret correlated results from multiple biochemical assays is often difficult because of compounding variability (lot to lot, pipetting, environmental, and so on). Additionally, each cell in the well has the potential to be in a different physiological state (i.e., cell cycle), often causing a blunting of activity readouts after population averaging. Systemic noise can be great enough to mask the interesting revelations you are trying to uncover. The best way to overcome these issues is to be able to make multiple measurements in each cell (biological variability) in the same well (environmental variability). High content screening not only collects data in this way, but it allows the results to be analyzed collectively from each cell to create highly correlated insights into how various targets react as a network.

1.3 HOW DOES HCS COMPARE WITH CURRENT ASSAY METHODS?

Useful assays that can be validated for screening have a common set of important characteristics, including selectivity, sensitivity, scalability, and robustness to automation....