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New Mass Spec Method Might Enable Single-Cell ID of Cancer-Related Amino Acid Variants


A team led by researchers at the University of Michigan and Wayne State University has developed a mass spec-based approach for measuring single amino acid variants (SAAVs) in small populations of cells.

Described in a paper published last week in the Journal of Proteome Research, the method allows scientists to look at SAAVs linked to diseases like cancer, where it could aid investigations of tumor heterogeneity, said David Lubman, professor of surgical immunology at the University of Michigan and senior author of the study. He added that he and his colleagues are now working to implement the approach for single-cell analyses.

Characterized by a change in a single amino acid in a protein, SAAVs can cause disruptions of protein folding, localization, interaction, and other activity, which can lead to various disease states. With the rise of next-generation sequencing, scientists have identified large numbers of mutations at the genetic level that could give rise to SAAVs, which has led proteomics researchers to explore methods for detecting these alterations at the protein level.

Confidently identifying SAAVs is challenging, however, due to their low abundance and the fact that they are identical to the standard protein form save for a single amino acid change. Additionally, Lubman and his colleagues set out to identify SAAVs in samples as small as nine individual cells, which further heightened these challenges.

To address these issues, the researchers combined a streamlined sample prep process to minimize protein loss with a carrier/reference mass spec strategy implemented by Wayne State University researcher Paul Stemmer, a co-author of the study, in which a large population of cells is used to trigger the mass spectrometer to detect proteins in the smaller target cell population. Additionally, to distinguish between true and false SAAV identifications, they used a software called SAVControl, originally developed by Xinpei Yi, a researcher at the Chinese Academy of Sciences and another co-author of the JPR study.

Using this workflow, Lubman and his colleagues identified 79 SAAVs in a sample of nine Panc-1 pancreatic cancer cells, eight of which had known links to cancer. Additionally, they were able to detect SAAVs at amino acid positions linked to the loss or gain of glycosylation.

The researchers collected Panc-1 samples of different sizes, including a nine-cell sample and a 31-cell sample. They then labeled these samples with TMT isobaric tags and added a 5,000-cell TMT-labeled carrier/reference sample.

The carrier/reference strategy is based on an approach described by a team of Harvard and Northeastern University researcher, initially in a BioRxiv preprint published last year and ultimately in a paper published last month in Genome Biology. The method uses TMT labeling to combine large and small cell populations and uses the more abundant proteins in the larger population to trigger mass spec detection of those proteins in the smaller cell populations.

Isobaric labeling methods like TMT use stable isotope tags attached to peptides that fragment during the second stage of mass spec to produce signals corresponding to the amount of peptide present in a sample. Typically, they have been used to multiplex samples in single mass spec runs as the tags allow researchers to pool samples and still distinguish between the signals generated by each.

The carrier/reference approach puts TMT reagents to a different use. In an LC-MS/MS experiment, peptides are first analyzed in an initial (MS1) scan. Peptides selected for an MS1 scan are then fragmented and scanned again, generating the MS2 spectra that are used to identify the peptides. However, very small cell populations may not generate enough MS2 spectra to allow for peptide identifications. The carrier/reference method addresses this limitation by combining small samples featuring one TMT label with a large (carrier/reference) sample featuring a different TMT label. This provides enough overall peptides to generate the MS2 spectra required for peptide IDs, while enabling researchers to still distinguish the peptides coming from the different samples.

"The problem is that, at very low levels, you can't actually get enough of a signal to trigger MS/MS," Lubman said. "With the carrier/reference [sample], you get a signal that the mass spectrometer can recognize and trigger off of."

In addition to the carrier/reference approach, the researchers used fractionation to improve their analysis, dividing the pooled sample into nine fractions, which reduced the sample's complexity and improved their depth of coverage.

In total, they were able to detect 47,414 unique peptides corresponding to 6,261 proteins from the nine-cell sample, using for each fraction a 90-minute LC gradient upfront of analysis on a Thermo Fisher Scientific Orbitrap Fusion mass spec.

They then analyzed these peptides to look for SAAVs, using the SAVControl software to assign scores to detect SAAVs based on the likelihood that they are true positives. The SAVControl tool first uses the transfer false-discovery rate to filter false positives and then offers alternative possibilities, such as the presence of post-translational modifications.

The researchers ultimately identified 79 SAAVs in the nine-cell sample and 174 in the 5,000-cell carrier/reference sample. Among the SAAVs identified were eight cancer-related variants, including a variant in the KRAS oncoprotein, which is involved in the vast majority of pancreatic cancers.

Lubman and his colleagues were also able to identify nine glycosylation sites that were potentially affected — three involving a possible gain of glycosylation and six involving a potential loss of glycosylation. In two of the gain-of-glycosylation sites, they were able to establish the presence of glycosylation.

Lubman said the researchers now hope to use mathematical modeling to better understand how specific SAAVs might alter a protein's structure or folding in ways that could contribute to cancer development. They are also trying to take the approach down to the single-cell level, which he said could allow them to explore questions around cancer heterogeneity with higher resolution.

He said the team had managed to perform single-cell analyses, but that they were still working to improve their depth of coverage.

He added that the researchers are also beginning to work with patient cancer samples, including circulating tumor cells. This work, Lubman noted, was more challenging than the cell line work presented in the JPR study, but he said their initial results indicate it was feasible.

"It's always trickier in [patient] tissues than in cell lines, because cells lines are nice and clean, whereas tissues are never like that," he said. "We can do it, but it's obviously going to be a lot tougher than just doing cell lines."