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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"

3rd Annual North American Mass Spectrometry Summer School Registration Open

Join us for our third annual mass spectrometry summer school, which will be held in Madison, WI from June 15-18. We are proud to have assembled over a dozen world leading experts in mass spectrometry for this four-day course. Our goal is to provide our students, both from academia and industry, an engaging and inspiring program covering the latest in the application of mass spectrometry to omic analyses. Tutorial lectures range from mass analyzers to the basics of data analysis. Also planned are several hands-on workshops – aimed at both scientific and professional development. This program is made possible by generous funding from the National Science Foundation (Integrated Organismal Systems, Plant Genome Research Program, Grant No. 1546742) and the National Institutes of Health National Center for Quantitative Biology of Complex Systems (P41 GM108538). As such, there is no cost to participate.

Registration open through March 1, 2020: https://www.ncqbcs.com/resources/training/summer-school.

Please help us spread the word about this program by sharing the news with anyone who might have possible interest to participate.

See below for a list of expert instructors who will be leading the courses, as well as premium tutorial lectures and hands-on workshops that you can experience.

Thank you,
Josh Coon, Evgenia Shishkova, and Laura Van Toll (organizing committee)


Expert Instructors:

Scott McLuckey | Purdue University

Rachel Loo | University of California-Los Angeles

Joshua Coon | University of Wisconsin-Madison

Donald Hunt (invited) | University of Virginia

Shawnna Buttery | STAR Protocols

Jesper Velgaard Olsen | University of Copenhagen

Lingjun Li | University of Wisconsin-Madison

Jürgen Cox | Max Planck Institute of Biochemistry

Edward Huttlin | Harvard University

Susan Olesik | Ohio State University

Evgenia Shishkova | University of Wisconsin-Madison

Jessica Prenni | Colorado State University

Vicki Wysocki | Ohio State University

John Bowden | University of Florida

Tutorial Lectures:
Mass analyzers

Ionization

Tandem MS

Data acquisition

Quantification  

Experimental design

Separations  

PTMs

Metabolomics  

Top-down/Native MS

Lipidomics


Hands-on Workshops:
Mass analyzers

Spectral interpretation
Publishing and reviewing

Science writing

Science illustrations

Software Highlight: LipiDex

LipiDex is a free and open-source software package offered by NCQBCS. This software package unifies all stages of the LC-MS/MS lipid identification process, and also utilizes intelligent data filtering to reduce manual result curation while increasing identification confidence.

One can use LipiDex to accomplish a variety of functions. For instance, one can create and manage custom in-silico lipid spectral libraries; model complex lipid MS/MS fragmentation using intuitive fragmentation templates; generate high-confidence MS/MS lipid identifications; annotate chromatographic peak tables with lipid identifications; and automatically filter peak tables for adduct peaks, in-source fragments and dimers.

Information on both LipiDex and Library Forge can be found here, and the software download is located here. Additionally, information on other software that the National Center for Quantitative Biology of Complex Systems offers can be found here.

Graphical Abstract depicting the software package lipidex accomplishing a variety of functions, such as the modeling of complex lipid ms/ms fragmentation, generation of high-confidence ms/ms lipid identifications, annotation of chromatographic peak tables, and the creation of in-silico lipid spectral libraries.

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 http://metandem.com/web/

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.

NCQBCS Offers Broad Range of Training Programs for all Levels of Learners

A key goal of the National Center for Quantitative Biology of Complex Systems is to extend its expertise to the broader scientific community. Therefore, NCQBCS offers hands-on-training programs ranging from basic basic proteomic methodology to advanced technological techniques.

NCQBCS, which works to develop next-generation protein measurement technologies for biomedical application, has programs available for a wide range of students. This means that there are introductory training programs available for those interested in learning the basics of mass spectrometry, as well as programs geared for experts on specific technologies.

NCQBCS divides its training topics into four broad categories: Sample Preparation, Instrumentation, Data Analysis, and Protein Quantification. Trainees can build their own syllabus of workshops from a variety of categories and experience levels.

Comprehensively, we offer programs in:
Sample Preparation: Peptide Fractionation, Protein Digestion, Protein extraction.
Mass Spectrometry: MS Methods, Instrument Troubleshooting, Nano-chromatography.
Data Analysis: Data Visualization, Data Interpretation, Data Searching.
Protein Quantification: Label-free, Metabolic labeling, Isobaric chemical labeling.

More information on our training programs are located here, and one can sign up for training here.

Additionally, one can also receive coaching at the 3rd Annual North American Mass Spectrometry Summer School, which will take place June 15-18, 2020. This event, which will be hosted by international experts on Mass Spectrometry, will feature workshops, lectures and networking, among other activities.

One may find more information, as well as sign up for summer school, here.

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.