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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"

Proteomic and Transcriptomic analyses of Toxoplasma gondii infection

Garfoot et al recently published a paper in BMC Genomics on the proteomic and transcriptiomic analyses of early and late-stage Toxoplasma gondii infection found in mice brains.

Toxoplasma gondii is a protozoan pathogen responsible for the infectious disease “toxoplasmosi,” and this pathogen is of researcher utility as it is capable of infecting a host’s brain, transitioning from fast-growing to latent morphology morphology life stages (from “tachysoite” to “bradyzoite”), and eventually creating neuronal cysts which are largely invisible to the host, as well as resilient against the host’s immune response and modern therapeutics.

Garfoot et al analyzed results from transcriptional and proteomic analyses of fast-growing (bradyzoite) fractions of the infection from mouse brains over a period of 21-150 days and, through deep sequencing of expressed transcripts found that one third of the transcripts were more enriched compared to the slow-growing tachysoites. Furthermore, researchers found that the transcript which grew the most over the course of the infection was the sporoAMA1 transcript.

As a result of this work, researchers have expanded the transcriptional profile of in vivo toxoplasmosis bradyzoites.

Graphical abstract for Garfoot et al's recently published a paper in BMC Genomics on the proteomic and transcriptiomic analyses of early and late-stage Toxoplasma gondii infection found in mice brains.

Metandem, a free and online software for MS-based isobaric labeling metabolomics

Hao et al. (2019) recently published a paper in Analytica Chimica Acta detailing the utility of Metandem, a data analysis software which is aids in isobaric labeling-based metabolomics.

While mass spectrometry-based stable isotope labeling is advantageous compared to other methods of isotope labeling due to its multiplexing and accurate quantification capabilities, its data analysis requires specifically customized bioinformatic tools. However, Metandem, a free, unique and online software, can aid in the analysis of stable isotope labeling-based metabolomics data.

Metandem has a number of different features that assist in MS-based isobaric labeling, such as integrating feature extraction, metabolite quantification and identification, batch processing of multiple data files, online parameter optimization for custom datasets, data normalization and statistical analysis.

Metatandem is available free and online at

Graphical abstract for Metandem paper published by Hao et al in Analtica Chimica Acta demonstrating the software's utility in  isobaric labeling, integrating feature extraction, and metabolite quantification.

Selecting a Labeling Strategy for Quantitative Proteomics of Multiple Samples

Buchberger AR et al (2019) recently published a chapter reviewing various labelling strategies for quantitative proteomic analysis in Mass Spectrometry-Based Chemical Proteomics.

Specifically, the chapter reviews strategies such as label-free quantitation, metabolic labeling, and chemical stable isotope labeling, and also discusses which labeling approach is best for various types of proteomic analyses. The chapter also provides an explanation on how to use N,N‐dimethyl alanine (DiAla) and N,N‐ dimethyl valine (DiVal) isobaric labeling strategies for quantitative analyses in ways which are economic and effective.

The chapter states that quantitative proteomics is crucial for biomarker discovery in studying and understanding various diseases and biological research, as proteins are crucial in all biological processes. Because biomarker studies can be time-consuming, heavily reliant on instruments and vary depending on the strategy used, selecting the appropriate labeling strategy is important in quantitative analysis.

A graphical abstract from Buchberger et al (2019) which depicts labeling strategy options for a type of sample. The sample in the picture is a mouse, the labeling strategies include 5-plex iDiLeu, 12-Plex DiLeu, 4-Plex DiLeu, 2-Plex mDiLeu, and 4-Plex DiAla.

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.

Identification of Alzheimer’s Biomarkers for Early Diagnosis and Treatment

Alzheimer’s disease begins with a long, hard-to-discern and symptom-free phase which may be a key opportunity for early diagnosis and therapeutic intervention. Zhong et al (2019) defined reliable and valid biomarkers that could identify the disease during this period, as published in a recent article in Frontiers in Molecular Neuroscience

Alzhiemer’s disease is a progressive neurodegenerative disease which is characterized by the progressive buildup of senile plaques, neurofibrillary tangles, and loss of synapses and neurons in the brain. Behaviorally, this is presented as a progressive degeneration of overall function, such as difficulty with memory, mood instability and loss of motor function. Currently, there is no cure.

Using discover proteomics analysis of cerebrospinal fluid (CSF), Zhong et al found that in both healthy controls and in preclinical Alzheimer’s Disease patients, 732 proteins in women and 704 men proteins in men had more than one unique peptide. Then, Zhong et al found that 79 (women) and 98 (men) proteins were significantly altered in preclinical alzheimer’s patients who have already demonstrated some symptoms of mild cognitive impairment or dementia.

Using N,N-dimethyl leucine (iDiLeu) tags, researchers verified the Alzheimer’s disease biomarkers called neurosecretory protein VGF and apolipoprotein E. Then, researchers used a four-point internal calibration curve to determine the “absolute amount” of target analytes in cerebrospinal fluid through a single liquid chromatography-mass spectrometry run.

Graphical abstract for Zhong et al (2019) depicting the difference between healthy control and preclinical Alzheimer's Disease biomarkers in label free and labeled quantification and peptide identification.

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.

Quantification of the Human Pancreatic ECM

Ma et al (2019) recently published a paper on the quantification of human pancreatic extracellular matrix proteins in the Journal of Proteome Research.

In this study, researchers characterized the composition of the human pancreatic extracellular matrix (ECM) before and after decellularization. To find the relative quantification of ECM proteins, they used isobaric dimethylated leucine (DiLeu) labeling.

It was important for researchers to look at the ECM of the pancreatic microenvironment as it is essential to pancreatic function– it regulates β cell proliferation, differentiation, and insulin secretion.

As a result of decellularization, and through quantitative proteomic analysis, most cellular proteins were removed while matrisome proteins remained. This process generated a large data set of matrisome proteins from a single tissue type. 

Researchers then quantified the distinct expression of ECM proteins, comparing adult and fetal pancreas ECM. This revealed a correlation between matrix composition and postnatal β cell maturation.

Overall, the results of this study sheds light on the prospect of bioengineering a pancreas. Additionally, the study demonstrates the roles that matrisome proteins have in postnatal β cell maturation.

Graphical abstract for Ma et al (2019), depicting native and decelled pancreatic extracellular matrix proteins, sample preparation technique, and LFQ

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.

Acute-Phase proteins differ in mice with and without prostate inflammation

L. Hao et al conducted a quantitative proteomic analysis on induced prostate inflammation in mice and published a paper in the American Journal of Physiology-Renal Physiology.

Researchers compared the quantitative proteomic analysis of urine from mice with and without prostate-specific inflammation. Prostate inflammation as it is a key symptom of many different prostate conditions, such as infection and cancer, and therefore by doing so one gains a better understanding of disease mechanisms.

Researchers induced prostate-specific inflammation by conditional prostate epithelial IL-1β expression. Next, they ran urine sample tests and quantified urinary proteins. L. Hao et al found that different levels of acute-phase response proteins (proteins which have plasma concentrations that increase or decrease in response to inflammation) were represented between mice with and without prostate inflammation; these were haptoglobin, inter-α-trypsin inhibitor, and α1-antitrypsin 1-1.

Mass-spectrometry-based quantitative urinary proteomics is an important and powerful method for discovering biomarkers and uncovering molecular urological mechanisms.

Graphical abstract for Hao et al, depicting the quantitative proteomic analysis of mice urine.