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PNNL Team Pushing Sensitivity Limits of Targeted Proteomics


NEW YORK (GenomeWeb) – Researchers from Pacific Northwest National Laboratory have developed a targeted proteomics workflow that significantly improves the sensitivity of selected-reaction monitoring mass spectrometry.

Described in a study published last week in Analytical Chemistry, the method, which the researchers have named Deep-Dive SRM, uses extensive fractionation and multiple dimensions of separation to enable quantification of endogenous proteins in non-depleted serum at levels in the range of 10 picograms per mL — a roughly five order of magnitude improvement over conventional SRM assays.

While the extension separations involved limit the approach's throughput, its high sensitivity could make it an attractive tool for measuring protein targets for which good antibodies don't exist, said Tao Liu, a senior scientist at PNNL and senior author on the paper.

He cited as examples of potential targets low abundance proteins like cytokines or rare post-translational modifications or mutations.

The method "does take longer, but you will get the results, and you don't have to rely on any affinity reagents," Liu said.

The approach builds on an SRM workflow previously developed at PNNL that similarly aimed to allow antibody-free quantitation of low-abundance biomarkers. That method, called PRISM, used reverse-phase capillary LC to separate a sample into 96 fractions while also monitoring heavy-isotope-labeled synthetic peptide internal standards spiked into the sample.

By monitoring these standards, the researchers are able to determine in which of the 96 fractions their target peptides will be present. They can then select just these fractions for subsequent nano-LC SRM-MS analysis, allowing them to run a less complex sample, which ups assay sensitivity.

The PRISM assay allowed the PNNL researchers to approach sensitivity levels in the range of 100 picograms per mL. However, Liu said, the approach still required immunodepletion of high abundance proteins.

Depletion steps are commonly required for assays attempting to survey low abundance proteins, Lui said, noting that roughly 95 percent of the proteins' mass in blood comes from the 20 highest abundance proteins. Removing them through depletion steps allows researchers to inject more of their target protein into the mass spectrometer, improving assay sensitivity.

However, depletion methods, which commonly use columns with antibodies to high-abundance proteins to extract these analytes from a sample, are expensive and time consuming. Additionally, Liu noted, the process can also remove proteins not targeted by the depletion column itself, but which are either bound non-specifically by the depletion antibodies or are bound to high-abundance proteins like albumin that are targeted for depletion.

The DD-SRM method represents an improvement over the PRISM approach in that it can reach as low as 10 picograms per mL without requiring upfront depletion steps.

Doing this, Liu said, required devising a separations workflow that allowed the researchers to load much higher volumes of sample. By loading larger sample volumes, Liu and his colleagues could ensure enough of their target protein would be present, even though most of the sample would consist of the high-abundance analytes typically removed during upfront depletion.

To allow for these larger sample volumes, the researchers started the workflow on a standard-flow liquid chromatography system, which has a much higher loading capacity than the nanoflow systems used in the PRISM workflow. After fractionating the initial sample on this system into 96 different portions, they isolated the portion with their peptides of interest and ran this sample on a high-resolution capillary LC system, again splitting it into 96 fractions. Isolating the fraction from this set of 96 that contained their peptides of interest, they then ran that fraction on a nanoflow LC system and quantified the peptides using a Thermo Fisher Scientific TSQ Quantum triple quadrupole instrument.

By moving from high to lower flow LC systems, the researchers were able to maximize their starting sample volumes and achieve high sensitivity without using depletion steps, Liu said. Also key, he noted, was alternating between low and high pH LC conditions.

"In the first dimension [of separation] there's acidic pH, the second is basic, and in the third we get back to acidic again," he said. "By doing this in kind of a staggered fashion, we can get very good orthogonality between the different dimensions of separations. That's how we can guarantee we are removing a lot of [potential interferents], so that in the end you are not only benefiting from higher loading volumes but also from a much cleaner signal in the final dimension of analysis."

In the Analytical Chemistry paper, the researchers demonstrated use of the approach for measuring the proteins IL13, IL8, TNFα, and IL9 in serum from a healthy female subject. They were able to determine the concentrations of these proteins as 20.7 pg/mL (IL8), 39.9 pg/mL (IL9), 46.8 pg/mL (TNFα), and 126.6 pg/mL (IL13), demonstrating the method's ability to measure very low abundance analytes.

They also used the DD-SRM technique to measure the proteins AURKB and FOSB in ovarian tumor tissue, finding that they could detect and quantify them expressed at levels of roughly 40 and 10 copies per cell for AURKB and FOSB, respectively. This, the authors wrote, compares to a sensitivity of around 7,500 copies per cell for a conventional SRM assay.

Liu said that he and his colleagues could currently process around three samples a day using the method, though he added that throughput would increase as they nailed down the optimal conditions for a given target.

Even with improvements in speed, the approach will likely remain too time-consuming and labor-intensive to serve as a broadly applicable method. However, for analytes to which there are not good antibodies, it could prove quite useful, Liu said.

In addition to proteins like cytokines or rare isoforms, he gave as an example the case of genomics-driven biomarker research where scientists are interested in verifying genomic phenomena at the protein level.

"A lot of these markers from genomic studies can be very, very low abundance," he said. "And that is where the DD-SRM method can also come into play."