W Yang Et Al. Proteomic Approaches to the Analysis of Multiprotein
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Standard Flow Multiplexed Proteomics (Sflompro) – an Accessible and Cost-Effective Alternative to Nanolc Workflows
bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964379; this version posted February 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Standard Flow Multiplexed Proteomics (SFloMPro) – An Accessible and Cost-Effective Alternative to NanoLC Workflows Conor Jenkins1 and Ben Orsburn2* 1Hood College Department of Biology, Frederick, MD 2University of Virginia Medical School, Charlottesville, VA *To whom correspondence should be addressed; [email protected] Abstract Multiplexed proteomics using isobaric tagging allows for simultaneously comparing the proteomes of multiple samples. In this technique, digested peptides from each sample are labeled with a chemical tag prior to pooling sample for LC-MS/MS with nanoflow chromatography (NanoLC). The isobaric nature of the tag prevents deconvolution of samples until fragmentation liberates the isotopically labeled reporter ions. To ensure efficient peptide labeling, large concentrations of labeling reagents are included in the reagent kits to allow scientists to use high ratios of chemical label per peptide. The increasing speed and sensitivity of mass spectrometers has reduced the peptide concentration required for analysis, leading to most of the label or labeled sample to be discarded. In conjunction, improvements in the speed of sample loading, reliable pump pressure, and stable gradient construction of analytical flow HPLCs has continued to improve the sample delivery process to the mass spectrometer. In this study we describe a method for performing multiplexed proteomics without the use of NanoLC by using offline fractionation of labeled peptides followed by rapid “standard flow” HPLC gradient LC-MS/MS. -
Proteomics in Forensic Analysis: Applications for Human Samples
applied sciences Review Proteomics in Forensic Analysis: Applications for Human Samples Van-An Duong 1,† , Jong-Moon Park 1,† , Hee-Joung Lim 2,* and Hookeun Lee 1,* 1 College of Pharmacy, Gachon University, Incheon 21936, Korea; [email protected] (V.-A.D.); [email protected] (J.-M.P.) 2 Forensic Science Center for Odor Fingerprint Analysis, Police Science Institute, Korean National Police University, Asan 31539, Korea * Correspondence: [email protected] (H.-J.L.); [email protected] (H.L.); Tel.: +82-41-968-2893 (H.-J.L.); +82-32-820-4927 (H.L.) † These authors contributed equally. Abstract: Proteomics, the large-scale study of all proteins of an organism or system, is a powerful tool for studying biological systems. It can provide a holistic view of the physiological and biochemical states of given samples through identification and quantification of large numbers of peptides and proteins. In forensic science, proteomics can be used as a confirmatory and orthogonal technique for well-built genomic analyses. Proteomics is highly valuable in cases where nucleic acids are absent or degraded, such as hair and bone samples. It can be used to identify body fluids, ethnic group, gender, individual, and estimate post-mortem interval using bone, muscle, and decomposition fluid samples. Compared to genomic analysis, proteomics can provide a better global picture of a sample. It has been used in forensic science for a wide range of sample types and applications. In this review, we briefly introduce proteomic methods, including sample preparation techniques, data acquisition using liquid chromatography-tandem mass spectrometry, and data analysis using database search, Citation: Duong, V.-A.; Park, J.-M.; spectral library search, and de novo sequencing. -
Challenges in Clinical Metaproteomics Highlighted by the Analysis of Acute Leukemia Patients with Gut Colonization by Multidrug-Resistant Enterobacteriaceae
proteomes Article Challenges in Clinical Metaproteomics Highlighted by the Analysis of Acute Leukemia Patients with Gut Colonization by Multidrug-Resistant Enterobacteriaceae Julia Rechenberger 1,† , Patroklos Samaras 1,†, Anna Jarzab 1, Juergen Behr 2, Martin Frejno 1 , Ana Djukovic 3, Jaime Sanz 4,5, Eva M. González-Barberá 4, Miguel Salavert 4, Jose Luis López-Hontangas 4, Karina B. Xavier 6 , Laurent Debrauwer 7,8, Jean-Marc Rolain 9, Miguel Sanz 4,5 , Marc Garcia-Garcera 10, Mathias Wilhelm 1,* , Carles Ubeda 3,11,* and Bernhard Kuster 1,2,* 1 Chair of Proteomics and Bioanalytics, Technical University of Munich, 85354 Freising, Germany; [email protected] (J.R.); [email protected] (P.S.); [email protected] (A.J.); [email protected] (M.F.) 2 Bavarian Center for Biomolecular Mass Spectrometry, Technical University of Munich, 85354 Freising, Germany; [email protected] 3 Centro Superior de Investigación en Salud Pública-FISABIO, 46020 Valencia, Spain; [email protected] 4 Hospital Universitari i Politècnic La Fe, 46026 Valencia, Spain; [email protected] (J.S.); [email protected] (E.M.G.-B.); [email protected] (M.S.); [email protected] (J.L.L.-H.); [email protected] (M.S.) 5 CIBERONC, Instituto Carlos III, 28029 Madrid, Spain 6 Instituto Gulbenkian de Ciência, 2780 Oeiras, Portugal; [email protected] 7 Toxalim, Université de Toulouse, INRA, INP-ENVT, INP-EI-Purpan, Université de Toulouse 3 Paul Sabatier, 31027 Toulouse, France; [email protected] 8 Axiom Platform, UMR 1331 Toxalim, MetaToul-MetaboHUB, -
Improving Data-Independent Acquisition Proteomics 12 July 2021
Improving data-independent acquisition proteomics 12 July 2021 'shotgun proteomics'. The mass-to-charge ratio of these peptides is subsequently determined with a mass spectrometer, resulting in MS spectra. From these spectra, information about the identity of the analyzed proteins can be reconstructed. However, the enormous amount and complexity of data make data analysis and interpretation challenging. Two ways to analyze proteins with mass spectrometry Two main methods are used in shotgun proteomics: Data-dependent acquisition (DDA) and MaxQuant 2.0 unites the two branches of shotgun data-independent acquisition (DIA). In DDA, the proteomics—DIA and DDA—into one software most abundant peptides of a sample are environment. Credit: Sonja Taut, MPI of Biochemistry preselected for fragmentation and measurement. This allows to reconstruct the sequences of these few preselected peptides, making analysis simpler and faster. However, this method induces a bias Proteomics produces enormous amounts of data, towards highly abundant peptides. DIA, in contrast, which can be very complex to analyze and is more robust and sensitive. All peptides from a interpret. The free software platform MaxQuant has certain mass range are fragmented and measured proven to be invaluable for data analysis of at once, without preselection by abundance. shotgun proteomics over the past decade. Now, Jürgen Cox, group leader at the Max Planck As a result, this method generates large amounts of Institute of Biochemistry, and his team present the data, and the complexity of the obtained new version 2.0. It provides an improved information increases considerably. Up to now, computational workflow for data-independent identification of the original proteins was only acquisition (DIA) proteomics, called MaxDIA. -
The Power of Three in Cannabis Shotgun Proteomics: Proteases, Databases and Search Engines
proteomes Article The Power of Three in Cannabis Shotgun Proteomics: Proteases, Databases and Search Engines Delphine Vincent *, Keith Savin , Simone Rochfort and German Spangenberg Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; [email protected] (K.S.); [email protected] (S.R.); [email protected] (G.S.) * Correspondence: [email protected]; Tel.: +61-3-90327116 Received: 28 May 2020; Accepted: 12 June 2020; Published: 15 June 2020 Abstract: Cannabis research has taken off since the relaxation of legislation, yet proteomics is still lagging. In 2019, we published three proteomics methods aimed at optimizing protein extraction, protein digestion for bottom-up and middle-down proteomics, as well as the analysis of intact proteins for top-down proteomics. The database of Cannabis sativa proteins used in these studies was retrieved from UniProt, the reference repositories for proteins, which is incomplete and therefore underrepresents the genetic diversity of this non-model species. In this fourth study, we remedy this shortcoming by searching larger databases from various sources. We also compare two search engines, the oldest, SEQUEST, and the most popular, Mascot. This shotgun proteomics experiment also utilizes the power of parallel digestions with orthogonal proteases of increasing selectivity, namely chymotrypsin, trypsin/Lys-C and Asp-N. Our results show that the larger the database the greater the list of accessions identified but the longer the duration of the search. Using orthogonal proteases and different search algorithms increases the total number of proteins identified, most of them common despite differing proteases and algorithms, but many of them unique as well. -
Bioinformatics Mining of the Dark Matter Proteome For
BIOINFORMATICS MINING OF THE DARK MATTER PROTEOME FOR CANCER TARGETS DISCOVERY by Ana Paula Delgado A Thesis Submitted to the Faculty of The Charles E. Schmidt College of Science In Partial Fulfillment of the Requirements for the Degree of Master of Science Florida Atlantic University Boca Raton, Florida May 2015 Copyright 2015 by Ana Paula Delgado ii ACKNOWLEDGEMENTS I would first like to thank Dr. Narayanan for his continuous encouragement, guidance, and support during the past two years of my graduate education. It has truly been an unforgettable experience working in his laboratory. I also want to express gratitude to my external advisor Professor Van de Ven from the University of Leuven, Belgium for his constant involvement and assistance on my project. Moreover, I would like to thank Dr. Binninger and Dr. Dawson-Scully for their advice and for agreeing to serve on my thesis committee. I also thank provost Dr. Perry for his involvement in my project. I thank Jeanine Narayanan for editorial assistance with the publications and with this dissertation. It has been a pleasure working with various undergraduate students some of whom became lab mates including Pamela Brandao, Maria Julia Chapado and Sheilin Hamid. I thank them for their expert help in the projects we were involved in. Lastly, I want to express my profound thanks to my parents and brother for their unconditional love, support and guidance over the last couple of years. They were my rock when I was in doubt and never let me give up. I would also like to thank my boyfriend Spencer Daniel and best friends for being part of an incredible support system. -
Phosphoproteomics for the Masses
REVIEW Phosphoproteomics for the Masses Paul A. Grimsrud†, Danielle L. Swaney†, Craig D. Wenger†, Nicole A. Beauchene‡, and Joshua J. Coon†,‡,* †Departments of Chemistry and ‡Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706 PHOSPHOPROTEOMICS APPLICATIONS Protein phosphorylation is a central mechanism of sig- ABSTRACT Protein phosphorylation serves as a primary mechanism of signal nal transduction across species, with kinases and phos- transduction in the cells of biological organisms. Technical advancements over the phatases accounting for 2Ϫ4% of eukaryotic proteomes last several years in mass spectrometry now allow for the large-scale identifica- (1, 2). Current estimates suggest that one-third of eu- tion and quantitation of in vivo phosphorylation at unprecedented levels. These de- karyotic proteins are phosphorylated (3, 4); determin- velopments have occurred in the areas of sample preparation, instrumentation, ing the sites, abundances, and roles of each of these quantitative methodology, and informatics so that today, 10 000Ϫ20 000 phos- modifications in a biological sample is a critical chal- phorylation sites can be identified and quantified within a few weeks. With the lenge. Traditional biochemical techniques are chiefly rapid development and widespread availability of such data, its translation into limited to testing how phosphoryl modifications at spe- biological insight and knowledge is a current obstacle. Here we present an over- cific sites affect a single protein of interest (5). Identifica- view of how this technology came to be and is currently applied, as well as future tion of unknown in vivo phosphoryl modification sites challenges for the field. on a broad scale, however, is simply not possible with these approaches. -
Leveraging Shotgun Proteomics for Optimised Interpretation of Data- Independent Acquisition Data: Identification of Diagnostic Biomarkers for Paediatric Tuberculosis
Leveraging shotgun proteomics for optimised interpretation of data- independent acquisition data: identification of diagnostic biomarkers for paediatric tuberculosis By Ashley Ehlers Thesis presented in the fulfilment of the requirements for the degree of Master of Science in Medical Sciences in the Faculty of Medicine and Health Sciences at Stellenbosch University. The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF. Supervisor: Professor David Tabb Co-supervisor: Professor Hanno Steen December 2020 Stellenbosch University https://scholar.sun.ac.za Declaration By submitting this thesis electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification. Ashley Ehlers December 2020 Copyright © 2020 Stellenbosch University All rights reserved i Stellenbosch University https://scholar.sun.ac.za Abstract Although diagnostic tests for paediatric tuberculosis (TB) are available, no specific test has been tailored to fit the diagnostic challenges children present as well as cater to limited resource settings. The high mortality rates recorded annually are associated with late diagnosis as well as insufficient household contact management (HCM). Further, urine has been identified as an attractive biofluid for urine protein biomarker discovery. Urine is non- invasive, easily attainable in large quantities and is associated with a low cost of collection. -
2. Introduction to Shot Gun Proteomics
MDC m/z mass spectrometry Introduction to mass spectrometry Gunnar Dittmar What is mass spectrometry? What is the accuracy of a mass spectrometer? MDC m/z mass spectrometry Accuracy: 0.1u = 1,6 * 10-28 kg Examples: Hydrogen atom: 1u Oxygen atom: 16 u Neutron: 1 u Electron: 0.005 u MDC Uses for mass spectrometry m/z mass spectrometry Identification of chemicals quality control biological degradation Water quality control Control of food and beverages check for pesticides, toxins etc Detection of explosives toxic chemicals Doping MDC Mass spectrometry general setup m/z mass spectrometry Samples Mass Ionization Detector analyzer Data analysis MDC General principle of mass spectrometry m/z mass spectrometry + + + + + + + + + + + + electrostatic field + + + + + + + + - - - - - - - - - - - - - - - - ion source mass analyzer detector MDC General principle of mass spectrometry m/z mass spectrometry + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - + ion source mass analyzer detector MDC Mass spectrometry m/z mass spectrometry Sir Joseph John Thomson, Nobel prize in physics for the discovery of the electron, 1906 MDC Mass spectrometry m/z mass spectrometry Sir Francis Aston, first fully functional mass spectrometer in 1919 MDC m/z mass spectrometry Ionization MDC Ionization m/z mass spectrometry ESI soft ionization Proteomics Electro-spray ionization MALDI Matrix assisted laser Proteomics desorption ionization FACS-MS hard ionization Plasma CyTOF MDC MALDI m/z mass spectrometry Image from Ekman et al. Mass spectrometry -
Proteomics in Non-Model Organisms: a New Analytical Frontier
Proteomics in non-model organisms: a new analytical frontier Michelle Heck1,2,3 and Benjamin A. Neely4* 1 Emerging Pests and Pathogens Research Unit, USDA Agricultural Research Service, Ithaca, NY, USA 2 Plant Pathology and Plant Microbe Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA 3 Boyce Thompson Institute, Ithaca, NY, USA 4 Chemical Sciences Division, National Institute of Standards and Technology, Charleston, SC, USA * Corresponding author: BN; [email protected] Abstract For the last century we have relied on model organisms to help understand fundamental biological processes. Now, with advancements in genome sequencing, assembly and annotation, non-model organisms may be studied with the same advanced bioanalytical toolkit as model organisms. Proteomics is one such technique, which classically relies on predicted protein sequences to catalog and measure complex proteomes across tissues and biofluids. Applying proteomics to non-model organisms can advance and accelerate biomimicry studies, biomedical advancements, veterinary medicine, agricultural research, behavioral ecology, and food safety. In this post-model organism era we can study almost any species, meaning that many non-model organisms are in fact important emerging model organisms. Herein we focus specifically on eukaryotic organisms and discuss steps to generating sequence databases, analyzing proteomic data with or without a database, interpreting results, and future research opportunities. Proteomics is more accessible than ever before, and will continue to rapidly advance in the coming years, enabling critical research and discoveries in non-model organisms that were hitherto impossible. Keywords: comparative biology, biomimicry, proteomics, genomics, non-model 1 Introduction Beginning with fruit flies at the turn of the 20th century, model organisms have enabled biological discoveries by bringing testable yet complex biological systems to the lab. -
Human Proteinpedia Enables Sharing of Human Protein Data
Human Proteinpedia enables sharing of human protein data Proteomic technologies, such as yeast twohybrid, mass spectrometry (MS), protein/peptide arrays and fluorescence microscopy, yield multi-dimensional data sets, which are often quite large and either not published or published as supplementary information that is not easily searchable. Without a system in place for standardizing and sharing data, it is not fruitful for the biomedical community to contribute these types of data to centralized repositories. Even more difficult is the annotation and display of pertinent information in the context of the corresponding proteins. Wikipedia, an online encyclopedia that anyone can edit, has already proven quite successful1 and can be used as a model for sharing biological data. However, the need for experimental evidence, data standardization and ownership of data creates scientific obstacles. Here, we describe Human Proteinpedia (http://www.humanproteinpedia.org/) as a portal that overcomes many of these obstacles to provide an integrated view of the human proteome. Human Proteinpedia also allows users to contribute and edit proteomic data with two significant differences from Wikipedia: first, the contributor is expected to provide experimental evidence for the data annotated; and second, only the original contributor can edit their data. Human Proteinpedia’s annotation system provides investigators with multiple options for contributing data including web forms and annotation servers. Although registration is required to contribute data, anyone can freely access the data in the repository. The web forms simplify submission through the use of pull-down menus for certain data fields and pop-up menus for standardized vocabulary terms. Distributed annotation servers using modified protein DAS (distributed annotation system) protocols developed by us (DAS protocols were originally developed for sharing mRNA and DNA data) permit contributing laboratories to maintain protein annotations locally. -
Understanding Physiology of Diseases and Cell Lines Using Omics Based Approaches
UNDERSTANDING PHYSIOLOGY OF DISEASES AND CELL LINES USING OMICS BASED APPROACHES by Amit Kumar A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland May, 2015 © 2015 Amit Kumar All Rights Reserved Abstract This thesis focuses on understanding physiology of diseases and cell lines using OMICS based approaches such as microarrays based gene expression analysis and mass spectrometry based proteins analysis. It includes extensive work on functionally characterizing mass spectrometry based proteomics data for identifying secreted proteins using bioinformatics tools. This dissertation also includes work on using omics based techniques coupled with bioinformatics tools to elucidate pathophysiology of diseases such as Type 2 Diabetes (T2D). Although the well-known characteristic of T2D is hyperglycemia, there are multiple other metabolic abnormalities that occur in T2D, including insulin resistance and dyslipidemia. In order to attain a greater understanding of the alterations in metabolic tissues associated with T2D, microarray analysis of gene expression in metabolic tissues from a mouse model of pre-diabetes and T2D to understand the metabolic abnormalities that may contribute to T2D was performed. This study also uncovered the novel genes and pathways regulated by the insulin sensitizing agent (CL-316,243) to identify key pathways and target genes in metabolic tissues that can reverse the diabetic phenotype. Specifically, he found significant decreases in the expression of mitochondrial and peroxisomal fatty acid oxidation genes in the skeletal muscle and adipose tissue of adult MKR mice, and in the liver of pre-diabetic MKR mice, compared to healthy mice. In addition, this study also explained the lower free fatty acid levels in MKR mice after treatment with CL-316,243 and provided biomarker genes such as ACAA1 and HSD17b4.