Scientists at the University of Wisconsin-Madison have launched an open-access database for collecting and analyzing quantitative information about pluripotent stem cells, including data on mRNA, protein, and post-translational modifications.
Named the Stem Cell -Omics Repository, the resource was launched this week to coincide with a study published in Nature Methods comparing the proteomes and phosphoproteomes of human embryonic and induced pluripotent stem cells.
In addition to providing this protein-level comparison, the paper lays out a proteomic workflow using a relatively large number of samples and biological replicates to draw out subtle but potentially important differences between similar cell types, said study leader Joshua Coon, UW-Madison assistant professor
For the stem-cell work the researchers "combined high accuracy mass spectrometry and isobaric tagging on a large scale" in a way that let them compare proteins and phosphorylation sites across four ESC lines and four iPSC lines in biological triplicate, Coon told ProteoMonitor. This, he noted, enabled them to identify differences between the lines that would otherwise have gone undetected.
For instance, an initial analysis comparing a single replicate of one ESC and one iPSC line suggested that the two lines were essentially indistinguishable at the protein level. The analysis detected twofold or greater differences in less than 1 percent of proteins and phosphorylation sites and roughly the same number of absolute protein differences between the two lines as between two ESC lines.
Studying the four ESC and four iPSC lines in triplicate, however, revealed much more significant differences between ESCs and iPSCs. Using iTRAQ tagging and a Thermo Scientific Orbitrap Velos mass spectrometer, the researchers identified 293 proteins and 292 phosphoisoforms that differed significantly between the two types of cells.
More than 90 percent of these proteins and phosphorylation sites differed by less than twofold, and thus were only detectable due to the use of biological replicates, which increased sample size and thus statistical power.
The team further increased the power of the analysis by using multiple cell lines, which enabled them to distinguish actual differences between cell types from simple variance between cell lines.
To demonstrate that point, the researchers compared an H1 ESC line and a DF4.7 iPSC line in triplicate. They found that 72 percent of the differences detected by analyzing all eight cell lines in triplicate went undetected in the comparison of the single lines.
"The take-home message is that if you want to tell the difference between cells that are very different, you don't really need that [larger number of samples and replicates]," Coon said. "But if you want to be able to tell the difference between cells that are very simple or if you're looking for very subtle differences, then you really need to be able to compare lots of cell lines and do it in lots of replicates. Only then will you be able to pull out the really significant changes."
The researchers ultimately identified nearly 8,000 proteins and 19,000 phosphorylation sites, "so it's proteomics on a very broad scale but with lots of biological replicates, and that affords the statistics to find the really meaningful differences and similarities in these different cell types," he said.
Keys to the workflow were improvements in mass spec and informatics technologies that streamlined isobaric tagging-based protein detection and quantification, Coon noted. In particular, he said, a software suite developed by his group "has as a major component tools for doing exactly the kind of analysis we did here."
The software, based on the Open Mass Spectrometry Search Algorithm, or OMSSA, is called the Coon OMSSA Proteomic Analysis Software Suite, or COMPASS.
While the Nature Methods study focused on stem cells, the workflow behind it could be broadly applicable to other areas of research, particularly biopsy samples or biobanked animal specimens where metabolic labeling techniques aren't typically an option, Coon added.
"With this technology platform you can imagine going to tissues and disease tissues and looking at all sorts of different things where you're going to expect to see not very stark differences, but subtle and pervasive difference that you're going to need statistics to pull out," he said.
In the case of stem cells, the study revealed differences between ESCs and iPSCs indicative of their different origins, Justin Brumbaugh, a graduate student in Coon's lab and an author on the paper, told ProteoMonitor.
"The differences we found look like they largely have to do with the fundamental origins of the cells," he said. "So the ES cells are more predisposed to a neural fate, and it turns out that things that were enriched in the ES cells all reflected that sort of neural predisposition. [By comparison], with the iPS cells the differences more reflected their mesodermal origins."
While noting that more investigations are necessary to determine what implications these differences might hold for stem-cell research, Brumbaugh suggested they could prove important to scientists working with such cells.
"For example, if the iPS cells are more predisposed to becoming a mesoderm type [cell] because they have that type of regulation in their background, then that could change how you have to use them and the ways you try to differentiate them," he said.
The UW-Madison team envisions the Stem Cell -Omics Repository as a resource from which iPSC researchers can access data such as those generated by this study, Brumbaugh said.
"You can look up any gene that you want and get transcript, protein, and post-translational modification information all at the same time." he said.
"We're inviting other researchers when they get data or publish data to deposit it there," Coon said. "Hopefully, it will grow and become one of the main sites when people want to access systems information relevant to pluripotency."
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