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.

High Content Screening

Techniques and Applications

John Wiley & Sons

Copyright © 2008 John Wiley & Sons, Inc.
All right reserved.
ISBN: 978-0-470-03999-1

Chapter One

Approaching High Content Screening and Analysis: Practical Advice for Users

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. In this way HCS is no different than other current screening methods. The requirements for accurate pipetting, incubation, reagent control, plate washing, and proper assay development are very much the same. As the throughput requirements increase, automation of the assay process steps becomes necessary and is straightforward with commercially available instrumentation and robotics. So what are the advantages of HCS compared to current assay methods?

An enzyme-linked immunosorbent assay (ELISA) is designed to capture and quantify the amount of specific proteins or peptides by their epitopes using high affinity anti-bodies to create a target “sandwich.” Recently, bead-based ELISA formats like Luminex[R] (Luminex Corporation, Austin, Texas, USA) have expanded the number of targets that can be simultaneously measured from a single sample. Typically, the target proteins are either already purified or come from an extract of cells in a particular biological state, resulting in the loss of spatial context. Therefore, it is impossible to readily identify which cells had a protein and where it was inside them. Many HCS assays also use antibodies as immunocytochemical affinity tags to label various cellular proteins, but retain the advantage of individual cell measures and subcellular location.

In one example, Gasparri et al. developed a multiparameter high content assay for proliferation of human dermal fibroblasts with fluorescent indicators for brdU incorporation, histone H3 phosphorylation, pRb phosphorylation, and KI-67 expression (22). Cross-validation by ELISA and flow cytometry uncovered comparatively fewer false-positive (fluorescent artifacts) and false-negative (cell loss) rates with the HCS assay, leading to the assertion that HCS data were inherently of higher quality. In summary, the authors cited higher accuracy of data, both single-cell and population readouts, and the ability to report morphological features as important advantages of the HCS approach.

Secondary signal assays like luciferase measure transcription activation indirectly and also require the cells to be disrupted into an extract before the luciferase reaction creates the chemiluminescent signal, thus losing the resolution of individual cell responses. This type of assay requires genetic engineering of a target promoter/luciferase gene chimera into the cells that competes with endogenous transcription factors (not measured). High content screening can directly measure endogenous protein levels and their positions over time. The individual cell responses are maintained, allowing the identification of subpopulations of cells with similar responses in each well. The following HCS example would be impossible with a standard second signal assay approach.

Vogt et al. performed a high content screen of a small compound library for inhibitors of ERK dephosphorylation (23). They confirmed the hits by visually inspecting cell images and with standard western blotting techniques. Analysis of the data showed that this group of compounds was enriched for known cdc25 inhibitors. In vitro enzyme assays showed that the ERK inhibitors identified in the high content screen inhibited at least one of the DSPases (MKP-3, cdc25B, cdc25A) in vitro. The authors then performed a multi-parameter high content assay for MKP-3 inhibition by transiently transfecting a c-myc-tagged version of MKP-3 into cells, then assaying for ERK phosphorylation via an intensity increase in the nuclear compartment in the two subpopulations. They reported a significant measurable difference in phospho-ERK accumulation between the MPK-3 overexpressing cells and the untransfected cells in the same wells. Additionally, the group determined that the compound having the best cellular activity was not one identified as potent in the biochemical screen, suggesting that performing this type of cell-based assay earlier in the drug discovery process is useful.

Flow cytometry or automated cell sorting, which has been the gold standard in cell biology for the last 30 years, has likely the most critical advantage when approaching a cell-based assay. The context of the cell is retained. By keeping the cell intact, flow cytometry permits measurements such as intensity, size, and count to be made. In addition, the multiple spectra capability of flow cytometry permits the multiplexing of targets. This is advantageous, because multiplexing tends to scale well and will often provide more insight as to a sequence of events rather than a single target screen.

There are, however, a few limitations to flow cytometry that one must consider. Cell sorting in general does not lend itself to adherent cell lines and there are a very limited number of morphologies that can be measured. Structure-related measurements on a cell are difficult, if not impossible to make due to the flow of the sample. The process is to flow a stream of single cells, passing them through a laser beam to be detected, so it requires the use of large sample volumes (lots of cells) and results in high quantities of potentially hazardous wastes. Other considerations for flow cytometry include its high cost, large size, high maintenance, and extensive training requirements for the instrumentation, and have put it far beyond the reach of many laboratories. Most recently, these concerns have been addressed by vendors, who have built high quality, small application focused, benchtop systems, permitting this type of technology to be delivered even to the most modest of laboratories.

Microscopy, compared to HCS/HCA, has essentially the same technology and biological requirements, but workflow requirements for automation and reproducibility are quite different. Few microscopes have the walk up and run capability to scan multiple plates with multiple fluorochromes. Once scalability is necessary, attempts at in-house solutions can create a whole new set of issues. One might be able to reduce cost by building a system using a microscope and then integrating parts and pieces from various vendors instead of having a tested integrated solution. This takes considerable time and effort, resulting in a system that needs your expertise to maintain. Turnover of resources in this situation will be problematic, and the return on investment will erode as the number of plates and assay types that need to go through the system increases. The conclusion is that HCS platforms are much more than just a “microscope in a box” and provide technology transfer capability within the organization.

The greatest advantage of image-based platforms is the ability to see and record the biology by means of a picture. Truly, a picture is worth not only a thousand words, but with HCS, a thousand data points as well. Table 1.1 shows a variety of cell-based methods and how these map to features that are often significant when looking to implementing a cell-based assay.

Instruments for HCS are the best of many worlds, and as you proceed through this chapter and book, you will undoubtedly see the broad scope of its applications, technologies, and functionality. The unique superset that HCS provides combines the best features from imaging and fluorescence microscopy, microtiter plate readers, and the single-cell analysis of the flow cytometer. Combining these tried and true capabilities provides researchers of all kinds with a powerful and relevant new tool set to investigate and scale cell biology.

1.4 THE BASIC REQUIREMENTS TO IMPLEMENT HCS

All assays can be represented by the simple equation:

Defined biology + change agent + detection = measured biological change:

HCS is no different in this respect, so we will use these parts of the equation as the topics for discussion.

(Continues…)

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