November 15, 2013 (Vol. 33, No. 20)

Nsikan Akpan, Ph.D.

“Better, faster, stronger” is the mantra that has defined the last 15 years of the omics era.

The rise of next-generation sequencing in microbial genomics is a perfect example. Progressive gains in resolution and accuracy with high-speed sequencers have allowed microbiologists to solve outbreaks in days rather than months. Such was the case when the E. coli O104:H4 strain struck Europe in May 2011. It took less than two weeks to sequence its bacterial genome and develop rapid diagnostics, and by early June, microbe hunters had tracked the source to a beansprout farm in Germany.

Proteomics, whose mainstream popularity has arguably lagged behind genomics, is poised to vault into the scientific zeitgeist. The last two years has featured a slate of discoveries with the field’s signature tool—mass spectrometry (MS)—to unite the speed and power of high-throughput proteomics with simplicity of use.

“There was no other way, as far as we knew, to dynamically measure this drug from biopsies,” said U.S. Army medical resident surgeon Preston Sparks, D.O., who capitalized on the clarity of PerkinElmer’s AxION Direct Sample Analyzer (DSA) to measure the delivery of gemcitabine.

Picking an adequate dose of chemotherapy without excess drug filtering into peripheral organs is a common challenge for oncologists. Proven useful against the advanced stages of multiple types of cancer, gemcitabine is hampered, like many chemo agents, by deleterious side-effects in nontarget organs, such as the kidneys and liver.

Dr. Sparks and his research partner at PerkinElmer, Jesse Hines, relied on the AxION DSA to create a simplified method for analyzing tissue samples from various organs in a pig model of gemcitabine delivery.

Typically prior to running time-of-flight (TOF) MS, lengthy gas and liquid chromatography steps are required to purify compounds before ionization and injection into the mass analyzer. The AxION DSA accomplishes this compound separation in a single step, by vaporizing the sample before shooting it into the mass analyzer. This unique adaptation cuts the time for a single MS run from 25 minutes to 25 seconds.

“We had spent months troubleshooting HPLC methods, but continued to see ion suppression effects with wetform preparations on our quadrupole instrument,” said Hines, who stated that their readouts instantly became stronger with dried, pulverized tissue samples examined by DSA.

Hines continued, “You would expect with tissue extracts that the spectra would be far too complex for DSA analysis because everything in the sample would be ionized at once. However, what actually happened was the lightest, smallest compounds floated off first and DSA-based ionization left interfering compounds behind.”

This extra resolution led to a quick and targeted measurement of gemcitabine levels in pig tissue samples of kidney, lung, liver, blood, and lymph nodes. At one stage, they analyzed 120 samples in triplicate in less than three hours.

Preston Sparks, D.O., a U.S. Army medical resident surgeon, loads samples into the PerkinElmer Direct Sample Analysis (DSA) system to measure the delivery of gemcitabine.

MS Platforms Get a Makeover

Bruker’s CaptiveSpray device is another example of a new approach to a classic ionization technique, electro spray.

This plug-and-play apparatus offers more reliability by “maintaining a constant inner diameter that isn’t susceptible to the clogging or fouling experienced by traditional pulled tips, which have a tapered inner diameter,” according to Bruker’s applications development manager, Shannon Cornett, Ph.D. Attaching the company’s nanoBooster to the manifold permits vapor enrichment in the nebulizer gas, which can manipulate the analyte’s charge-state distributions to higher or lower values.

“Analysis of large biomolecules, for example, benefits from a higher charge state distribution because the mass-to-charge (m/z) values are lower,” Dr. Cornett remarked. “Higher charge states offer more efficient fragmentation by electron transfer dissociation (ETD), which increases the amount of sequence information that can be obtained.”

During an investigation of protein identification rates with a HeLa cell extracts, enhancing the regular nebulizer gas—nitrogen—with acetonitrile vapor increased the base peak intensity and expanded the catalogue of proteins identified by 25%.

“Sensitivity and fragmentation efficiency are key measurement characteristics for bottom-up samples that subsequently affects the ability to identify proteins from fragmentation spectra of the detected peptides. We have found CaptiveSpray nanoBooster improves both of these key metrics,” said Dr. Cornett, who added that the nanoBooster can manipulate the charge state toward a more optimal range for top-down fragmentation efficiency as well.

Efforts to accelerate the acquisition of the mass spectral data must be matched by rapid data interpretation. This is especially true for nontargeted proteomics and metabolomics, where scientists want MS to reveal the most fetching aspects of their biological samples.

The Bruker CaptiveSpray ion source in combination with the nanoBooster dopant-addition option allows the modification and vapor enrichment of gas that flows around the emitter. Charge stripping or enhancement can be achieved during ionization.

Researchers at Shimadzu have developed an automated system for MS data exploration that both identifies and quantifies compounds. This methodology, which can be applied in many areas, including human serum and plants, was explained with a tea leaf example by Kevin Krock, applications scientist.

In the past, a cultivator would need to have a compound in mind before conducting a quantitative MS analysis, according to Krock. Next, it would need to compare the compound’s quantity in “good” versus “bad” tea. However, if no relationship were found, the cultivator would be back to square one.

“Our nontargeted metabolomics technique is useful when you have no idea of what makes your sample better or what compounds are included in the sample…a common scenario in the realm of biology,” said Krock.

Rather than limit the field in the beginning, Shimadzu’s approach narrowed the candidates after the MS data was collected. Samples of tea leaves were ranked by taste and processed by LC/MS. By combining formulae prediction and product ion assignment, the team developed an automatic workflow that could quickly decode MS data, such as retention time and m/z, into biologically relevant information such as chemical name and structure.

Shimadzu’s metabolomics platform is used to collect multiple-stage mass spectrometry (MSn) data, which is used to generate molecular formulae. The formulae are compared with known structures from a number of databases, generating agreement scores on the basis of predicted mass accuracies and matching MSn spectra. Then the scores are used to rank the structures in an automated identification process. It may be used to analyze the hundreds or thousands of compounds found in complex samples.

These chemical signatures were then instantly scored based on their abundance in good tea versus bad tea. Obvious candidates such as like caffeine made the list, but the technique also highlighted a swath of lesser-known organic compounds.

“The beauty is automation of the search. Rather than manually hunting down each compound in a textbook, it just searches based on the data that’s acquired,” said Krock.

The triple quadrupole mass spectrometer has been the staple platform for small molecule analysis for the last 30 years. This allegiance has held firm during the recent rise of targeted proteomics, given the dual mass filter of the triple quadrupole instrument allows for the selection of molecular ions of predetermined masses prior to fragmentation—a so-called mass window.

The MS spectrum contains many peaks, most of which indicate the m/z and intensity of an intact peptide. But to identify the peptide, we need to fragment it in an MS/MS scan. The problem is, there are more peptide peaks in the MS scan than we have time to sample by MS/MS (using data-dependent analysis, or DDA). So the instrument chooses to preferentially target high-abundance peptides for MS/MS, which is one ingrained drawback with DDA.

Next-generation shotgun proteomics aims to eliminate this bias through data-independent analysis (DIA), which systematically collects MS/MS spectra without predefined target selection and through a wider m/z window. Removing bias, however, adds complexity to the MS/MS data, making it harder to search for and identify proteins with traditional methods of parsing MS/MS spectra.

To enhance “searchability” within DIA, a team of researchers from the University of Washington developed a reductionist approach that multiplexes spectra by either randomizing (MSX) or overlapping windows.

MSX is one solution for a more comprehensive DIA. This technique analyzes five randomly selected narrow windows (4 m/z) from a series of 200 windows in each MS/MS scan. Given that each sliver of spectra is indiscriminately chosen, the majority of peptides within the larger 400 m/z will be sampled at least once by MS/MS.

While this process is all-inclusive, the final spectra amount to a quagmire of peptide data. Like a cryptographer cracking a code, Jarrett Egertson, Ph.D., figured out an algorithm to decipher, “demultiplex,” and extract the individual peptide chromatograms from the muddle.

To collect the randomized windows, Dr. Egertson relied on Thermo Scientific’s Q Exactive™ hybrid Orbitrap mass spectrometer.

“The randomized multiplexing technique wouldn’t make much sense on a TOF instrument because the time for ion accumulation compared to the time it takes for mass analysis is important,” Dr. Egertson said. “You need an instrument capable of isolating from multiple noncontiguous windows, and currently the Q Exactive™, to the best of my knowledge, is the only instrument that has this feature built into the software.”

Overlapping Windows

For the overlap method, co-developed by Dario Amodei, Ph.D., a postdoctoral scholar at Stanford University, a user would isolate one 20 m/z window in each MS/MS scan, but shift the isolation window in later scans by 10 m/z. As a result, there is overlapping information in each pair of MS/MS scans.

“The basic gist is that you’re looking for similarities between the two spectra,” said Dr. Egertson, who conceived the multiplexing strategy while earning his doctorate with his professor Michael MacCoss, Ph.D.

“If you compare the fragment ions from the two spectra, the ones in common are highly likely to come from the overlapping range, and the ones that aren’t shared probably came from outer ranges.” What’s left is a much cleaner spectrum overall and improved selectivity because excess fragment ions are excluded during the interpretation of each narrow window.

According to Dr. Egertson, this overlapping windows approach is very simple to implement on any DIA-capable instrument, and the demultiplexing process is already built into Skyline, the freely available software for targeted quantitative proteomics created by the MacCoss lab.

“With a small adjustment in how they acquire their data, they should see a great improvement in sensitivity and in their ability to identify and quantify peptides,” added Dr. Egertson.

AB Sciex is turning to DIA-equipped software like Skyline to support its efforts to develop next-generation proteomics platforms. Leading the charge is its TripleTOF® instrument, a high-resolution machine that can combine qualitative and quantitative workflows.

Drug design operates between these two workflows, with some researchers concerned with how a parent drug metabolizes (qualitative) and others more focused on half-life (quantitative). AB Sciex has partnered with Pfizer to evaluate the system’s ability to measure drug metabolism in hepatocytes.

“Typically there are two questions that you are trying to answer in drug discovery and development,” said Gary Impey, Ph.D., senior product manager. “What’s in my sample and how much do I have?”

“The TripleTOF has the ability to use generic methods for setup, but speed and selectivity aren’t compromised,” noted Dr. Impey.

And with next-generation omics, speed wins.

Kits Introduced for QC of MRM-Based Quantitative Plasma Proteomic Workflows

Agilent Technologies and MRM Proteomics reported the launch of MRM Proteomics’ PeptiQuant™ MRM assay kits for quality control in quantitative plasma proteomic analyses based on multiple-reaction monitoring.

The two new kits are designed for use on Agilent’s latest-generation standard-flow UHPLC-QQQ MS/MS platform, which consists of the 1290 Infinity UHPLC system and the 6490 triple quadrupole mass spectrometer platform.

MRM Proteomics developed the kits to assist in the inter- and intralaboratory quality control of MRM experiments in conjunction with stable isotope-labeled standards.

The PeptiQuant LC-MS platform performance kit tests the effectiveness of the LC-MS platform.

The PeptiQuant workflow performance kit evaluates the performance of an entire LC-MRM-MS analytical workflow in human plasma from denaturation through digestion to detection.

“Mass spectrometry is becoming increasingly important in biomedical and biological research, especially with the launch of large-scale initiatives such as the Human Proteome Project, which depends on mass spectrometry as one of the main technology platforms,” said Andrew Munk, CEO of MRM Proteomics.

“The mass spec and proteomics communities currently lack standardized protocols and reagents to ensure high-quality data that can be reproduced by laboratories across the world using different mass-spec technologies. Our PeptiQuant targeted proteomics platform and workflow performance kits will ensure that high-quality data can be reproduced.”

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