November 15, 2011 (Vol. 31, No. 20)

Kathy Liszewski

Technology Shedding Its Slow, Cumbersome Reputation and Entering High-Throughput Realm

Although established more than 30 years ago, the field of flow cytometry continues to grow and advance. It remains an indispensable tool for clinicians and researchers. The “Northwest Regional Cytometry” meeting held earlier this year focused on cytometry data: mining, modeling, and management.

A number of presentations focused on the two big players, flow and image cytometry that analyze the composition of several components in individual cells utilizing labeled antibodies. New technologies include using acoustics to more accurately align cells, robotics for higher throughput, and supercomputing for rapid desktop analysis. A newer kid on the block is chemical cytometry that employs a suite of analytical tools to characterize a large number of cellular components.

Traditionally, flow cytometry has not been considered a major player for high-throughput drug screening. But there is a paradigm shift afoot, according to J. Paul Robinson, Ph.D., professor of cytomics and professor of biomedical engineering, School of Veterinary Medicine, Purdue University.

Dr. Robinson spoke about flow cytometry and high-throughput screening. “Two outdated ideas in the field of drug discovery are that high-throughput screening is all about imaging and that flow cytometry is a slow, cumbersome technology. This is no longer valid.”

Dr. Robinson and colleagues have developed a new format in which all samples from a complex assay are collected in a single file and deconvolved in a time series for sample separation and analysis.

“The fundamental change is that, using high-throughput flow in which thousands of samples can be run quickly, opens the opportunity to a systems approach to assay design. In the past, sampling was done with single tubes. Now we can use 384-well plates to sample and analyze.”

The key breakthrough is the use of robotics. “These were developed by Larry Sklar to add cells, buffers, and dyes. Automation provides lower cost, better quality control, and faster results. Further, classification tools allow us to enhance the automation process and provide rapid useful results.”

Data analysis is also important. “We are working on this bottleneck and have spawned some solutions to merge high-throughput analysis and high-content screening. Solving this problem will be transformational. A key step is to improve and optimize assay design. We now can utilize 1 microliter volume of cells and from that get many parameters to assess multiple populations of cells, do time courses, dose responses, etc. Instead of doing 25 samples in an hour, with robotics we can do 10,000 samples easily in one afternoon.”

Dr. Robinson said scientists need to think differently. “We should be thinking systems biology and flow cytometry, since we want to simultaneously obtain information on multiple pathways and cell operations. Basically, we want instantaneous gratification of data analysis. The good news is that this is now becoming a reality.

“One of the leaders in the systems approach is Garry Nolan who has developed advanced barcoding tools allowing dozens of complex pathways to be studied simultaneously. Nolan’s developments have been transformational in increasing sample multiplexing and complexity.

“Data analysis is again one of the limiting issues faced by scientists. Analyzing 15 to 50 simultaneous parameters is not something that current approaches can handle. The increase in complexity and sampling rates is defining a new application space for informatic, high-content flow cytometry.”

Garry Nolan, Ph.D., professor, department of microbiology and immunology at Stanford, not surpringly, agreed. “Currently, lab automation is coming along for academics, but it has to be scaled up to become effective. Obviously, we’ve been able to benefit from the huge investment pharma has made over the years and the innovation that has come from supporting the pharma drug screening market. But I would say that for most purposes, it’s still being patched together with duct tape and good programming skills in labs.

“Perhaps the main reason it has not moved out into main use is that a workflow that everyone will use has not been finalized upon as mutually needed by many labs. Thus, the automation still requires maximum flexibility—meaning only a few labs can afford it.

“We think we have identified certain key needs in the staining and preparation of whole blood or cell lines that are amenable to cost-effective, broadly usable automation. We’ll see in the next few months whether that comes to pass.”

Researchers at Purdue University have developed a new sample-processing approach. Shown here are dose-response curves in 3-D mode for a 384-well plate with 32 drugs and 10 point dose-response curves. On the bottom right (col 23, 24) are the positive controls. Rows 2 and 13 are the highest concentration of drugs. This representation is a method for visualizing the entire 384-well plate for any of the collected parameters simultaneously. This data was collected on a CyAn flow cytometer.

Desktop Supercomputing

Supercomputing technology is now being applied to process the large amounts of data generated in flow cytometry. “The field of flow cytometry is challenged by two major problems: massive data generation and real-time analysis of the data,” reported Bob Zigon, senior staff R&D engineer flow cytometry at Beckman Coulter.

“In about a 13-year period, one pharmaceutical company estimated it had 60 billion parameter/events and 250 gigabytes of flow data. This is a real data-mining challenge.”

According to Zigon, the solution is a compact board that Beckman recommends as an option for its Kaluza flow cytometry software. “The NVIDIA Tesla C2050 general purpose processing unit has 3 gigabytes of RAM, 448 processors (as compared to 1–8 on a typical desktop computer), and performs one trillion arithmetic operations per second. This allows Kaluza to process up to 10 million events in real time and offers an analytical speed that is several hundred times faster than other commercially available cytometry software.”

In flow cytometry, a user typically employs complex gating techniques to identify subsets of cells. When these subsets are identified, the user then wants statistics such as the mean, median, and standard deviation computed on those sets. The Tesla-enabled version of Kaluza allows for these sets and statistics to be computed in real time. When the feedback from moving the mouse occurs instantaneously, the user is able to quickly explore complicated what-if scenarios without breaks. The net effect is a faster, more reliable means to generate and understand data in real time.

Beckman Coulter’s Kaluza flow cytometry software can process multiparametric fluorescent data of millions of cells in real time, not only for display in n-dimensions but also to calculate multiple statistics simultaneously.

Quality Assurance Issues

Achieving reproducible and accurate flow cytometry data depends on several critical factors that, if ignored, lead to significant inter-laboratory variation. Maria C. Jaimes, M.D., senior staff scientist at BD Biosciences, a segment of BD, provided a perspective.

“There are a number of ongoing clinical trials being performed simultaneously at several sites to assess the effectiveness of prophylactic HIV vaccines using flow cytometry functional assays, among others. The accuracy of these assays is critical because the data is utilized to make product-advancement decisions. BD was approached by the DAIDS/NIAID/NIH to develop a quality-assurance program to compare the reproducibility of the flow cytometry data being generated across many of these labs.”

According to Dr. Jaimes, these assays require the use of peripheral blood mononuclear cells from vaccines, and are able to discriminate T-cell subsets that respond to a specific antigen by using a technique called intracellular cytokine staining (ICS). The monitoring of cytokines such as interferon-gamma, interleukin-2, and/or tumor necrosis factor-alpha provides a measure of antigen-specific immune responses.

“We performed seven rounds of testing in 16 laboratories worldwide and found that the co-efficient of inter-laboratory variation was 35% on average. We utilized the data to identify key factors that led to variability among laboratories. One source of variability, for example, is how the cells were treated during the procedure: cells left in contact with certain buffers for extended periods of time will lead to a suboptimal result.”

Dr. Jaimes also indicated that the number of collected events is also important, i.e., how many relevant cells are acquired in the flow cytometer. “When few cells are acquired, the accuracy of the data decreased. Another factor was variable gating strategies. Finally, instrument set up and performance differences also accounted for inter-laboratory assay differences.”

The up side of BD’s extensive analysis is to draw attention to sources of variation in flow cytometry cell-based assays, often ignored by the laboratories. “Much of the field lacks rigor and reproducibility. This can be particularly problematic for clinical laboratories that use flow cytometry. Our studies allowed us to determine pass and fail criteria for ICS assays. This could easily be extrapolated for a quality assurance program for other flow cytometry assays.”

Dr. Jaimes said things are changing slowly. “I think we are seeing an increasing sense of awareness of these issues, just as the field of qPCR took some time to begin evolving more stringent guidelines. It’s a step-by-step process but the harmonization and optimization is well worth the effort.”

Acoustic Focusing Cytometer

Traditional flow cytometers manipulate cells with hydrodynamic forces. That is, a suspension of fluorescently labeled cells is withdrawn from the test tube by the instrument, focused with the sheath fluid, and propelled across the path of a laser that excites the fluors to create readouts. This can be a time-consuming process, depending on the number of cells and the volume of sample. One of the new technologies described at the meeting is Life Technologies’  Attune® Acoustic Focusing Cytometer that utilizes sound waves to precisely direct cells into the center of the sample stream.

“This new instrumentation is not your father’s flow cytometer,” remarked Greg Kaduchak, Ph.D., director of engineering. “This technology utilizes acoustic focusing to drive a single coherent stream of cells ultrafast through the instrument. This allows an unprecedented volumetric sample throughput and enables a 10-fold increase in the acquisition speed while at the same time promoting very low variation, especially as compared to current hydrodynamic focusing technology.”

According to Wenlan Hu, senior marketing manager, the Attune has been out for about a year and now the company has released a new blue/red laser configuration that will allow users a wider flexibility for applications.

“If researchers already have a panel set up on other instruments, they won’t have to reconfigure now that the blue/red is available. Also, for some researchers, especially performing marine and bacterial sampling, the ability to very precisely sample large volumes in a small time frame is an important benefit. Overall, this allows a simpler, faster, and more accurate workflow since the instrumentation does not require samples to be lysed or even washed.”

Life Technologies’ Attune® Acoustic Focusing Cytometer uses sound waves to control the movement of cells.

Chemical Cytometry

Cytometry literally means measuring cells. Although many people may be familiar with traditional flow cytometry, an emerging area called chemical and metabolic cytometry examines the composition of single cells. “While flow and image cytometry analyze a few components, chemical and metabolic cytometry can characterize thousands of components such as nucleic acids, proteins, and even metabolites. Indeed, it is now possible to detect down to the yoctomole levels of analytes within cells,” said Norman Dovichi, Ph.D., professor of chemistry and biochemistry, University of Notre Dame.

Chemical and metabolic cytometry can provide new layers of cellular information. “These types of analyses are important because single cells can differ dramatically from their neighbors; classical analytical methods average the composition of cells, but this masks cell-to-cell differences. Traditional flow cytometry measures physical properties such as cell size and the forward and side light-scatter patterns. However, only a few components can be measured utilizing affinity probes against known targets. Thus, the unexpected is invisible to the analysis.”

Chemical cytometry lyses cells, labels proteins and biogenic amines, and employs capillary-based separation of components (e.g., capillary electrophoresis or microbore liquid chromatography). Signals are generated typically from laser-induced fluorescent readings to describe cellular composition.

Another arm of chemical cytometry is metabolic cytometry, which measures with exquisite sensitivity selected metabolic pathways in single cells. It differs from chemical cytometry in that cells are first treated with a fluorescent substrate that is taken up and enzymatically processed within the cell. Then labeled cells are aspirated into a capillary, lysed, and components electrophoretically separated. The metabolites are detected using laser-induced fluorescence.

Dr. Dovichi is applying the technology to better understand the glycolipid metabolism of neurons. “We see that there are dramatic differences among individual neurons that could be important for understanding their functional roles and how this may impact illnesses such as Tay-Sach’s disease.”

For the future, Dr. Dovichi is working on interfacing the power of mass spectrometry with chemical and metabolic cytometry methodologies. “This would be especially powerful because of the great detail afforded by mass spec.”

The workhorse technology of flow cytometry continues to evolve and grow. As technologies advance, generating increasingly complex datasets, quick and accurate analysis will remain a challenge.

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