analytical chemistry

Viewing posts tagged analytical chemistry

A Strategy for Increasing Analytical Throughput in Quantitative Proteomics

Zhong et al (2019) developed a novel strategy aimed towards solving challenges in absolute quantification, and detailed these efforts in a recent issue of Analytical Chemistry.

Absolute quantification is both an effective technique– which allows for robust results in proteomics research– and a challenging one. Problems that absolute quantification presents include low specificity in complex backgrounds, limited analytical throughput and wide dynamic range.

To solve these issues, Zhong et al (2019) developed hybrid offset-triggered multiplex absolute quantification (HOTMAQ), a strategy which increases the analytical throughput (the increase in analysis production rate) of targeted quantitative proteomics by up to 12 times. This technique accomplishes this by using mass-difference and isobaric tags to create an internal standard curve in the MS1 precursor scan, identify peptides at the MS2 level, and mass offset-trigger the quantification of target proteins in synchronous precursor selection at the MS3 level. All of this is accomplished at the same time. 

Because HOTMAQ results in greater quantitative performance, higher flexibility and quicker analysis rate, HOTMAQ is a strategy that can easily be applied to target peptidomics, proteomics, and phosphoproteomics.

Graphical Abstract, demonstrating the technique of hybrid offset-triggered multiplex absolute quantification (HOTMAQ).  "Zhong, X., Q. Yu, F. Ma, D.C. Frost, L. Lu, Z. Chen, H. Zetterberg, C. Carlsson, O. Okonkwo, and L. Li,
Hotmaq: A multiplexed absolute quantification method for targeted proteomics. Analytical Chemistry,
2019. 91(3): p. 2112-2119. PMCID: PMC6379083"

Increasing MS lipidomics power through parameter optimization and In Silico Simulation

Hutchins et al (2019) recently published a paper in Analytical Chemistry presenting an algorithm which identifies parameter sets in a way that is quicker and more accurate than typical methods.

The issue of effectively profiling the diversity and range of biomolecules is an important one to consider in Mass Spectrometry, and relies on well-sought out selection of acquisition parameters. However, acquisition parameters are generally selected in a way that is time-consuming and tends to produce lacking results.

By creating an algorithm which simulates LC-MS/MS lipidomic data acquisition performance in a benchtop quadrupole-Orbitrap Mass Spectrometer system and pairing it with an algorithm that defines constrained parameter optimization, researchers were able to efficiently identify LC-MS/MS method parameter sets for specific sample matrices. Additionally, researchers used a simulation called in silico to demonstrate how developments in mass spectrometer speed and sensitivity will result in even more effective biomolecule identification.

Graphical abstract from Hutchins et al (2019) which details the parameter optimization and in silico simulation methods.
Instrument parameters --> model MS acquisition --> simulate lipid IDs.

An Accurate Mass Defect-based labeling strategy for Quantitative Proteomics

Zhong et al (2019) wrote about their development of an accurate mass-defect based labelling strategy for MS1-centric quantification in a recent Analytical Chemistry paper

Specifically, researchers developed 5-plex mass defect N, N-dimethyl leucine (mdDiLeu) tags. These tags have multiple benefits; they can aid in the quantification of biological samples and have increased multiplexing due to the addition of mass difference isotopologues. Additionally, the synthesis of these cost effective tags is straightforward and only requires one reaction step, which can be done in any lab. Also, this mass defect-based labelling strategy is more accurate than isobaric label-based reporter ion quantification, as the latter is impacted by ratio compression.

In this paper, Zhong et al (2019) demonstrate the efficacy of 5-plex mdDiLeu tags for quantitative proteomics by conducting mass spectrometry experiments with these tags on labelled Saccharomyces cerevisiae lysate digest.

Graphical abstract for Zhong et al (2019) depicting the accurate mass-defect labelling strategy used.

Fixed mass-to-charge ratio scan ranges generates more MS/MS scans than standard approaches

Trujillo et al (2019) published an article on maximizing tandem mass spectrometry acquisition rates for shotgun proteomics in a recent issue of Analytical Chemistry.

While advances in mass spectrometry (MS/MS) have lead to increased performance in shotgun proteomics experiments, ion trap scan duration is highly variable and often depends on the mass of the precursor.

Looking into this variability, the authors compared the performance of various static mass-to-charge ratio scan ranges for ion trap MS/MS acquisition to conventional dynamic mass-to-charge ratio scan ranges. Compared to the standard dynamic approach, the fixed mass-to-charge ratio scan range generated 12% more MS/MS scans and identified more unique peptides.

Graphical abstract for Trujillo et al (2019) depicting a graph titled "Increase # MS/MS collected." We see that as the maximum ion injection time decreases, the number of MS/MS collected increases. Additionally, as the m/z scan range increases, the # of MS/MS increases as well.