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

Mass Spec Pushes Proteomics’ Tempo

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    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.

    "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.

  • MS Platforms Get a Makeover

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    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.

    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.

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    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.

    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.



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