There's a lot of high-tech equipment in today's clinical laboratory. Microscopes and histology stains are sharing bench-space with flow cytometers and microfluidic processors, DNA sequencers, and microarray readers—and mass spectrometers. Long the province of chemists and proteomicists, mass spectrometry has more recently established itself as a bona fide clinical tool. Now a new application is heading to the clinic, mass spec imaging.
By Jeffrey M. Perkel
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"We human beings are built to gather a tremendous amount of information visually," says Richard Caprioli, the Stanford Moore Chair of Biochemistry and director of the Mass Spectrometry Research Center at Vanderbilt University School of Medicine. "We love patterns, we love pictures, and we get a great deal of information by looking at a simple picture."
That, Caprioli says, explains in part the growing popularity of mass spec imaging (MSI): It plays into the expertise that histologists, in particular, spend years developing. "The fact that it's in molecular dimensions rather than a color dimension, is less important, as long as that molecular dimension is more informative," he says.
MSI is like a high throughput form of immunohistochemistry, but without the antibodies. Instead of proactively staining tissue sections for specific markers, MSI uses the mass spec to pick out and map the spatial arrangement of hundreds or thousands of molecular species at once. The technique can do that without the researcher knowing a priori what molecules might be important, and do so quickly. "We have lasers now on our instruments that can do 5,000 mass spectra per second," Caprioli says—fast enough to scan an entire tissue microarray, containing hundreds of patient biopsies, in under an hour.
On the other hand, MSI also presents significant hurdles. Imaging resolution improves with decreasing spot size, for instance, but at the cost of decreasing ionized material yield. With no prefractionation steps, MSI tends to sample only the most abundant molecules. And on the computational side, figuring out how to work with those data, and especially making sense of them, is particularly challenging. Nevertheless, researchers are now using MSI to localize drug metabolites in tissue sections with subcellular resolution, pin down disease biomarkers, and identify tumor boundaries, among other applications. They are even taking the technique into the clinic—or at least, just outside of it.
So just what is MSI? Picture a standard digital photograph. The colors in digital images are built by overlaying three color channels, red, green, and blue, with the color of any pixel given by the intensity of those colors in its tiny slice of screen real-estate.
Now imagine an image with thousands of color channels. That's exactly what MSI does, Caprioli says, devoting one channel to each particular molecular species—or mass spectral peak—you wish to represent. By overlaying those different channels, researchers can produce a Technicolor map of a tissue's molecular makeup and spatial distribution, whether of proteins, neuropeptides, metabolites, or increasingly, lipids.
Researchers have devised dozens of strategies for doing MSI, but as described in a 2012 review (J. Proteomics, 75:4883, 2012) three are the most common. Caprioli's approach, matrix-assisted laser desorption ionization (MALDI)-MSI, builds an image by rastering an ultraviolet laser over a matrix-coated tissue slice. Pixel size in this technique is typically on the order of 1 to 10 μm or so, meaning it can achieve subcellular resolution. But because it requires deposition of a MALDI matrix and vacuum conditions, MALDI-MSI is incompatible with living samples. Also, the matrix, which serves to absorb the laser energy and transfer it to the sample itself, can be difficult to apply and produces an abundance of small molecular weight ions, which can obscure the metabolite region of the resulting spectra.
Nick Winograd, the Evan Pugh Professor of Chemistry at Pennsylvania State University, uses a second approach, secondary ion mass spectrometry (SIMS). SIMS induces sample ionization by "sputtering" a sample surface with an ion beam (for instance, charged C60 or argon cluster beams from U.K.-based Ionoptika) rather than a laser, an approach that offers two advantages, Winograd says. The first is resolution: SIMS can produce pixels on the order of 300 nm or so, compared to, at best, 1 mm with MALDI. The other is molecular depth profiling, which allows researchers to use the collision-induced craters to "dig into" a sample and map its molecular composition in three dimensions.
The third strategy is DESI (desorption electrospray ionization), an ambient (that is, non-vacuum) ionization method in which a stream of solvent is sprayed at an untreated tissue surface, where it pools and extracts surface molecules. Additional droplets splash that extracted material into the mass spectrometer, where it is ionized and analyzed. (DESI has been commercialized by Prosolia, a company cofounded by R. Graham Cooks, the Purdue University chemist who developed the technique.)
DESI, MALDI, SIMS, and their variants all operate in what Ron Heeren of the FOM-Institute AMOLFM in Amsterdam calls a "microprobe" mode, in which resolution increases with decreasing pixel size. The challenge there is to maximize sample ionization from the smallest possible spot. But smaller spots mean fewer ions to detect, not to mention longer imaging times (as there are more pixels).
Heeren favors an alternative "microscopy" mode, which actually uses defocused pixels for faster imaging, plus a pixelated detector, like a CCD, to effectively capture 262,144 (512x512) spectra at once.
"It's like a photo camera," Heeren explains. "The only thing is, we make molecular flash photographs."
Key to this "mass microscope," as Heeren calls it, is the Timepix detector, which essentially is a cross between a CCD and a time-of-flight mass analyzer. (Timepix is available from Omics2Image, a company Heeren cofounded.) Most mass spec detectors, he explains, treat the detector as one large pixel, integrating all ion collisions over the surface into a single signal; Timepix splits that signal into 262,000 spatially resolved ones, such that the spatial orientation of molecules on the imaged surface is maintained and recorded as they strike the detector, producing an ultrafast image.
How fast? A MALDI-MSI instrument capable of one-micron resolution and one second per pixel would take 2.7 hours to image the 10,000 pixels in a 100x100 mm area, Heeren says. Using the mass microscope and a Timepix detector, "We get the same information in a second."
The rest of the microscope is a Physical Electronics TRIFT SIMS-TOF system tricked out with a second MALDI source, which Heeren's team recently used to explore the biological changes underlying osteoarthritis. "We could actually show that both on the protein level, the lipid-metabolite level, as well as the mineralization of the cartilage combined lead to the loss of mechanical strength in the cartilage," he says.
Ambient ionization methods like DESI and LAESI, a laser-based alternative commercialized by Protea Biosciences, offer several advantages over MALDI and SIMS. Most significantly, they require no sample preparation, and operate in ambient air rather than a vacuum. Thus, they can be applied to live samples, or even a human patient.
That's a goal Cooks has been working towards for decades. "Doing mass spectrometry without doing sample workup has actually been a personal, lifelong quest," he says.
As a graduate student, Cooks worked to extract and determine the structure of plant alkaloids. After a frustratingly long time, during which he extracted only "a little bit of impure alkaloid and was making no progress on the structure," an encounter with a visiting speaker, Carl Djerassi of Stanford University, opened his eyes. Djerassi, Cooks says, took a sample of his material back to the lab, collected mass spectra, and 10 days later returned its structure. "That convinced me of the power of mass spectrometry,"
Cooks says. "And, at the same time, I was convinced of the limitations of the extraction methodologies."
Since then, he has worked steadily to decouple mass spectrometry from some of its less-biologically friendly technical limitations, developing several ambient ionization methods, most notably DESI. In 2011, a team led jointly by Cooks and Nathalie Agar at Harvard Medical School applied DESI-MS to banked brain cancer tissues, using the resulting lipid profiles to teach a computer how to differentiate different forms and histopathologic grades of glioma (a brain tumor).
Lipids might seem an odd choice for such an analysis. Indeed, their popularity among MSI practitioners is something of a lemonade-from-lemons situation. In standard cellular analyses, researchers can fractionate cell extracts to remove unwanted components, often including lipids. But in MSI and other in situ applications, researchers must image what's in front of them. And what's in front of them, for the most part, is lipids. Fortunately, lipids are not only abundant—and exceptionally ionizable—they also are highly informative.
"If you are looking at just lipids, the histological specificity is much better than looking at proteins," says Zoltán Takáts, a reader in Medical Mass Spectrometry at Imperial College London.
More recently, Cooks and Agar applied their approach to 32 surgical specimens taken from five brain cancer patients currently under treatment. Pixel-by-pixel, the system reported the tumor subtype, grade, and fraction of cancerous cells. Using those data, the team could resolve specific regions of different histopathologic grade, complementing MRI data in mapping tumor boundaries, Cooks says. And they did all that, he emphasizes, using "bottom-of-the-line" mass spectrometry, a unit-resolution single- (as opposed to tandem-) stage Thermo Fisher LTQ ion trap.
Still, this was a research project; the team could not communicate those findings to the surgeons in real time, Agar notes, as the samples were collected in Boston but imaged in Indiana. Since then, her team has installed a DESI-enabled Bruker amaZon Speed ion trap in Brigham and Women's AMIGO (Advanced Multimodality Image Guided Operating) surgical suite, part of the hospital's National Center for Image Guided Therapy, and tested it on brain tumor cases. Breast cancer testing could come soon, but the team still cannot guide the surgeons actually doing the cutting, Agar says; the methodology first must be validated, "which will eventually need a clinical trial to do so."
Ultimately, if MSI is to reach the clinic it must expand beyond the mass spec cognoscenti and into the hands of those who will be using it. Yet few clinicians are well versed in the nuances of MSI technology, data handling, and informatics, and even fewer likely have the time to become so. The technology cannot spread if it requires "extremely expensive and delicate and Ph.D.-run mass spectrometry," Cooks says. "It's got to all be automated and the instrument's got to be rugged and reliable and relatively simple."
For typical histopathology applications that shouldn't be much of a problem, as the systems can be configured as turnkey boxes programmed to look for specific biomarkers. Non-imaging mass specs already are used routinely in clinical laboratories worldwide, including Bruker's MALDI BioTyper and Sequenom's MassARRAY. Caprioli imagines arming histologists and pathologists with under-the-desk MSIs that behave like microscopes. The lab techs simply need to be taught how to prepare sample and operate the machine, and the software can take care of the rest.
But more complex applications like biomarker identification are another matter. "The datasets in mass spec imaging scale with the number of pixels in the image and the resolution of the mass spectrometer, and in recent years those are the two things that have drastically changed," says Ben Bowen, a research scientist at Lawrence Berkeley National Laboratory, who develops MS imaging data analysis software.
Pixels have been shrinking while resolving power has increased. At the same time, researchers performing discovery mode experiments don't know a priori which molecules are important, so they have to consider all of them, running countless pairwise comparisons on thousands upon thousands of color channels.
All those pixels add up. Bowen's colleague Trent Northen, who uses MS imaging in his own work, has collected terabytes of data over the years, he says. For newbies, just opening the datafiles can be challenging, making them reliant on more knowledgeable experts. "You can understand why this leaves a bad taste in a scientist's mouth," Bowen says.
To mitigate that problem, Northen and Bowen worked with Berkeley Lab data visualization scientist, Oliver Ruebel to develop a cloud-based platform called OpenMSI, which allows users to view and manipulate cloud-based MS imaging data directly in a web browser. The system derives its power from the U.S. Department of Energy-funded National Energy Research Scientific Computing Center (NERSC) supercomputer, Bowen says, reducing processing time from days to minutes.
Bowen says one of his and Northen's collaborators was able to use OpenMSI to traverse a 50 gigabyte dataset she had collected a year-and-a-half earlier but had never been able to access. "Now she's doing it in [Google] Chrome," he says—viewing the RGB images, examining the underlying spectra, and sharing the data with colleagues. "All the things that you would expect in the 21st century that the Internet offers, we've made that possible for MS imaging through OpenMSI."
But for the ultimate in clinical translatability, researchers may have to shed the imaging part of MSI altogether. That's what Imperial College London's Takáts has done.
Takáts, Cooks' former postdoc and lead author on the paper first describing DESI, developed and is testing a new ambient ionization technique called rapid evaporative ionization mass spectrometry (REIMS) and a device called the 'Intelligent Knife' (iKnife), which enables surgeons to assess the histology and histopathology of tissue in situ, right in the operating room.
"The final device is very simple," Takáts explains, and relies on electrosurgery, a cutting technique that uses electrical current to vaporize tissue. That process releases smoke, a combination of tar, particulates and ionized lipids, which the iKnife continuously samples through an attached Teflon tube coupled to a mass spec inlet.
Over the past several years, Takáts has built up a database of some 200,000 lipid profiles of human cancers and healthy tissue, and using those data, he has identified the lipid biomarkers that can distinguish one from the other. As a result, his system can, using the ionized lipid profiles arising during electrosurgical procedures, determine essentially in real time whether the tissue under the iKnife is healthy or cancerous, as well as its histologic state.
To be clear, this isn't imaging. "The answer that comes out is histology-level identification," Takáts says. "The system would say that this is a non-small-cell lung cancer, grade 2—something like that."
The iKnife (being developed by MediMass and Imperial College) has already been tested in the course of more than 500 interventions to date in Hungary, Germany, and the United Kingdom, producing "in most of the cases close to 100% correct classification," Takáts says. That's over a range of tumors including gastrointestinal, liver, lung, breast, and brain. In some cases, the technique revealed that what surgeons thought were tumors actually were something else, such as benign tissue or inflammatory disease. Now Takáts is working on a new system to perform similar assessments of growths for endoscopy.
Ultimately, says Takáts, such applications could make MSI "meaningful" not only as a research tool but as a routine clinical technique. Histopathologists, he notes, will likely be reluctant to adopt such a comparatively slow and expensive technology. But speed and cost will improve, he says. If the technology enables physicians to make a diagnosis in seconds, and in vivo, that previously took at best a half-an-hour after dissection, uptake of the technology could grow.
"It gives an advantage to this bundle which histopathology cannot compete with," he says.
|Brigham and Women's Hospital
FOM Institute AMOLF
Harvard Medical School
Imperial College London
Lawrence Berkeley National Laboratory
Pennsylvania State University
Vanderbilt University Mass Spectrometry Research Center
Thermo Fisher Scientific
Note: Readers can find out more about the companies and organizations listed by accessing their sites on the World Wide Web (WWW). If the listed organization does not have a site on the WWW or if it is under construction, we have substituted its main telephone number. Every effort has been made to ensure the accuracy of this information. Inclusion of companies in his article does not indicate endorsement by either AAAS or Science, nor is it meant to imply that their products or services are superior to those of other companies.
Jeffrey M. Perkel is a freelance science writer based in Pocatello, Idaho.
|This article was published as a special advertising feature in the 31 May 2013 issue of Science magazine.|
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