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IFOM Researchers Combine DDA and DIA Mass Spec to Improve Quantitation


NEW YORK (GenomeWeb) – Researchers at Milan's FIRC Institute of Molecular Oncology (IFOM) have developed a combined data-dependent (DDA) and data-independent analysis mass spec workflow that they say improves quantitation, particularly of low-abundance proteins.

The method, which the researchers have termed missing value monitoring (MvM), addresses the missing value problem of shotgun mass spec by using DIA to fill in data on fragment ions not measured in all of an experiment's mass spec runs.

In a study published this month in the Journal of Proteome Research, a team led by Angela Bachi, a principle investigator at IFOM and senior author on the paper, used the technique to measure cell cycle regulated proteins and found that with MvM they were able to double the number of robustly quantified proteins and confidently quantify proteins present at levels as low as around 50 copies per cell.

In DDA mass spec, the instrument performs an initial scan of precursor ions entering the instrument and selects a sampling of those ions for fragmentation and generation of MS/MS spectra. However, because instruments can't scan quickly enough to acquire all the precursors entering at a given moment, many ions — particularly low-abundance ions — are never selected for MS/MS fragmentation and so are not detected.

The fact that all fragment ions are not measured in every run makes high-quality quantification across samples challenging for DDA methods, especially in the case of low-abundance molecules, which are most likely to be missed in a particular analysis.

One method researchers have developed to address this problem is to infer the missing values for particular peptides based on runs where those peptides were detected.

Although DDA runs select only a sample of all available precursor ions for MS/MS-based identification, the information (such as monoisotopic m/z and retention time) required for identification and quantification is still present at the MS1 level.

Bachi suggested, though, that while such computational approaches are useful, they are not actually measuring the levels of the proteins in a sample and may not reflect the full biological variation of the samples being analyzed.

"It's a way of doing this," she said, "but it's different from seeing the real value of the protein in your sample."

Her lab's MvM approach instead uses DIA measurements to fill in the gaps left by the initial DDA run. In DIA mass spec, the instrument selects broad m/z windows and fragments all precursors in that window, allowing the machine to collect MS/MS spectra on all ions in a sample. Because the method collects data on all ions in a sample, DIA, unlike DDA, offers consistent protein quantitation across runs, allowing for robust comparisons of, for instance, protein expression levels in different samples.

The broad m/z windows used in DIA result in complicated spectra with high levels of precursor interference, which has meant DIA methods typically offer narrower proteome coverage than DDA approaches. By combining the two, Bachi and her colleagues sought to improve DDA quantitation while retaining its depth of coverage.

In the JPR study they applied the method to the study of cell cycle proteins, many of which are low abundance and therefore difficult to quantify consistently using standard DDA approaches. Working with yeast cells, they compared protein expression levels during mitosis and G1, looking at three biological replicates and two technical replicates for each condition, quantifying a total of 3,612 proteins.

Looking at the missing values in their dataset, the researchers found that roughly 20 percent of the proteins they identified had some missing values across runs, with an especially high percentage of missing values among cell cycle and related proteins. Filling in these values using DIA, they were able to recover 97 percent of these missing values, which enabled them to robustly quantify a variety of cell cycle proteins that they'd been unable to confidently quantify from the DDA data alone.

The method's benefit is most noticeable for proteins numbering in the low hundreds of copies per cell, Bachi noted.

"There you really see the difference," she said. "Measuring proteins [of this abundance] by data dependent you detect maybe the protein in only one out of five, six, 10 samples, while with MvM you can follow up your runs [with DIA] so you have actually six quantitative measurements, and these, of course, create a much better measurement."