NEW YORK (GenomeWeb) – Researchers at Simon Fraser University have developed a model for determining the optimal mass spectrometry settings for single-cell proteomics experiments.
Described in a paper published last week in the Journal of Proteome Research, the model could streamline mass spec workflows aimed at measuring proteins in extremely small sample sizes and improve the performance of such analyses, said Bingyun Sun, an assistant professor of chemistry at Simon Fraser University and first author on the study.
Single-cell analyses are an emerging area in proteomics, but most of the platforms in this space are immunoassay-based. Nanostring, for instance, offers single-cell proteomics using barcoded antibodies, while Zephyrus Biosciences, now a part of Bio-Techne, uses a form of western blotting for its single-cell proteomic assays. Fluidigm's mass cytometry technology uses antibodies combined with TOF mass spec to measure proteins at the single-cell level.
Strictly mass spec-based proteomics have been more difficult, however, due to the sensitivity challenges presented by such analyses. Sun noted that researchers have for some time been able to detect substantial numbers of proteins with single-cell sensitivity at the MS1 level, but that they have not typically been able to collect the MS2 level data needed to identify them.
"If you don't sequence the protein, you actually can see many peaks for hundreds to thousands of features," she said. "But to able to sequence individual peptides [using MS2] and ID unambiguously the [associated] proteins is very challenging. Normally you can identify less than 100 peptides, so maybe 10 or 20 proteins, which is not very good coverage and provides only very limited biological information."
Recently, researchers at Northeastern and Harvard Universities developed a technique using tandem-mass-tagging (TMT) combined with mass spectrometry to quantify on the order of thousands of proteins in single cells.
In their JPR study, Sun and her colleagues focused on increasing single-cell proteome coverage by optimizing the instrument parameters used in such experiments. In particular, they looked at the ideal precursor ion isolation windows and ion injection times for single-cell work. In the case of these parameters, trade-offs are involved between the volume of ions collected for analysis and the level of interferences and speed of duty cycle. Achieving the optimal balance is key to maximizing proteome coverage, Sun noted.
For instance, using a larger isolation window will allow more ions into the analyzer for measurement, but it will also increase the amount of precursor ion interference, which can reduce data quality and, ultimately, the number of proteins an experiment is able to identify and quantify.
Likewise, allowing for a longer ion injection time will increase the volume of ions in an analysis, which could up identifications. But this comes at the cost of a slower duty cycle, meaning a reduction in the number of MS2 scans and, as a result, a reduction in identifications.For mass spec experiments using conventional sample sizes, researchers have arrived at a set of broadly used values for these parameters, but Sun said that "when you are going down to really small amounts of samples, the story is completely different."
In bulk sample experiments "proteins are pulled from many, many different cells," she noted. "So if each cell carries slightly different proteins and there are millions of cells together, then their overall profile will be much more complex than [that of] individual cells. And so the complexity and abundance of the proteins push the [ideal] instrument parameters to be completely different from when you are dealing with very minute amounts of sample."
For example, the ideal isolation window for a single-cell experiment could be wider than it would be for a conventional experiment, Sun said, because the smaller sample size involved in single-cell work potentially makes precursor interference less of an issue.
Determining the ideal parameters experimentally, however, is a time-consuming process, Sun said. "To design a series of experiments to try to narrow down those variables is an incredible effort."
Instead, she and her colleagues developed a mathematical model to identify the ideal conditions for single-cell analysis.
"We can actually mathematically describe how these parameters are related to each other and define the optimal values," she said. "There will be a separate compromise for individual proteins, but overall [the model shows how] to get the highest number of [protein] identifications with accurate detection.
Using the parameters optimized according to their model, Sun and her colleagues identified more than 200 proteins from a sample size of 1 nanogram, representing a more than 300 percent increase in identifications compared to analyses not optimized using the model.
The researchers used a Thermo Fisher Scientific Orbitrap HF for the work, and Sun said that while the fundamental principles underlying the model should apply to any mass spec system, the specific values arrived at will be instrument specific.
"We can't simply apply this set of parameters to other instruments without testing," she said. "The numbers we provided can't be exactly translated to other instruments, but they provide some direction [researchers] can explore."
Beyond single-cell proteomics, the model could help optimize conditions for analyses looking at a variety of low-abundance protein populations, Sun said. "There are many areas where people are interested in really low-abundance signals, and [this could help] them pick them up and accurately sequence them."
"For example, for people looking at post-translationally modified proteins, the modified proteins tend to be a lower abundance than the unmodified," she said.
It could also prove useful for protein-protein interaction studies using methods like protein cross-linking. "We know that cross-linking efficiency is not that high, so the cross-linked peptides are low abundance compared to the regular non-cross-linked peptides," she said.