Methods in molecular biology vol 1598 neuroproteomics methods and protocols 2nd edition

Methods in
Molecular Biology 1598

Firas H. Kobeissy
Stanley M. Stevens, Jr. Editors

Neuroproteomics
Methods and Protocols
Second Edition


Methods

in

Molecular Biology

Series Editor
John M. Walker
School of Life and Medical Sciences
University of Hertfordshire
Hatfield, Hertfordshire, AL10 9AB, UK

For further volumes:
/>

Neuroproteomics
Methods and Protocols
Second Edition

Edited by

Firas H. Kobeissy
Department of Psychiatry, University of Florida McKnight Brain Institute, Gainesville, FL, USA

Stanley M. Stevens, Jr.
Department of Cell Biology, Microbiology, & Molecular Biology,
University of South Florida, Tampa, FL, USA


Editors
Firas H. Kobeissy
Department of Psychiatry
University of Florida McKnight Brain Institute
Gainesville, FL, USA

Stanley M. Stevens, Jr.
Department of Cell Biology,
  Microbiology, & Molecular Biology
University of South Florida
Tampa, FL, USA

ISSN 1064-3745    ISSN 1940-6029 (electronic)
Methods in Molecular Biology
ISBN 978-1-4939-6950-0    ISBN 978-1-4939-6952-4 (eBook)
DOI 10.1007/978-1-4939-6952-4
Library of Congress Control Number: 2017935482
© Springer Science+Business Media LLC 2017
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is
concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction
on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation,

computer software, or by similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not
imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and
regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to
be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty,
express or implied, with respect to the material contained herein or for any errors or omissions that may have been made.
The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Cover illustration: Painting: “Art of expression”, 2016. Acrylic on canvas 30x40 inches. American University of Beirut,
Located at Dr. Elie El-Chaer Office. By the artist Iman Karout, MSc, Email:
Printed on acid-free paper
This Humana Press imprint is published by Springer Nature
The registered company is Springer Science+Business Media LLC
The registered company address is: 233 Spring Street, New York, NY 10013, U.S.A.


Dedication
To my brother Rabih Moshourab, and to those who have faith in science.
Firas H. Kobeissy
To the memory of my mother, Mary E. Stevens, and sister, Donna Stevens Miraoui.
Stanley M. Stevens, Jr.

v


Foreword I
Major health care challenges remain to be the diagnosis and treatment of stroke and traumatic brain injury (TBI). An improved understanding of the neurotrauma biological attributes of proteins and peptides is expected to enable a better understanding of molecular
changes prompted by brain conditions. Such understanding will substantially improve
patient care. Neuroproteomics is an emerging and dynamic area of research that is deservedly drawing immense attention. This book edited by Stevens and Kobeissy is timely and
provides concise sets of articles that capture recent advancements in neuroproteomics and

clinical application of this dynamic area of research to understand the molecular protein
changes that are directly related to the development and progression of many central nervous system diseases, stroke, and traumatic brain injury. The book is divided into three
parts covering a wide spectrum of neuroproteomics.
The first part of the book is entitled “current reviews of neuroproteomics approaches and
applications.” This part of the book encompasses six chapters that review and highlight
advances in a wide range of research activities pertaining to neuroproteomics, including
exhaustive review of the advantages and challenges associated with neuroproteomics,
potential of imaging mass spectrometry to study TBI and other central nerve system conditions, advances in degradomics and proteomics to study TBI, systems biology approach to
study PTSD, and neuroproteomics in Alzheimer’s disease. An attractive feature of this book
that makes it useful for new and advanced researchers is the breadth of topics covered, not
only in this part but throughout the book.
Another attractive feature of the book is the fact that it included chapters that reviewed
the current state-of-the-art areas of research as well as chapters that discussed and described
experimental methods. The second part of the book is dedicated to discussing and describing experimental methods of neuroproteomics. This part of the book constitutes the heart
of the book and included ten chapters that describe experimental methods related to photoaffinity labeling, quantitative phosphoproteomics of brain tissue, glycoprotein enrichment in CNS, 2-DE proteomics, neuroproteomics CSF profiling by multiplexed affinity
arrays, brain proteomics by IMS of parafilm-assisted microdissection-based LC-MS/MS,
SILAC of primary microglia, and TBI neuroproteomics by 2-DE and Western blotting.
The comprehensiveness of the book is evident by dedicating the third part of the book
to “Bioinformatics and Computational Methods.” This is an important area of research and
the success of all the activities described in Parts I and II hinges on the development and
implementation of bioinformatics and computational tools that are critical for the automated interpretation and quantitation of the “big data” generated by neuroproteomics
analytical approaches. This part includes five chapters. The first chapter in this part describes
an algorithm capable of degradomics prediction. This chapter is aligned with the review of
degradomics (Chapter 4). A systems biology and bioinformatics approach to the effect of
secondhand tobacco smoke on the nitration of brain proteome is the subject of the second
chapter in this part. Advanced “Omic” approach to identify co-regulated clusters and transcription regulation network with AGCT and SHOE methods is discussed in the third
chapter of this part. AutoDock and AutoDock tools for protein-ligand docking and an

vii

viii

Foreword I

integration of decision tree and visual analysis to analyze intracranial pressure are the subjects of the fourth and fifth chapters of this part, respectively.
Stevens and Kobeissy should be commended for the fine job they have done editing
this book. The collection of topics and the quality of the chapters are excellent and a perfect
fit for an edited book in neuroproteomics. The book is timely, and the breadth and depth
of topics are outstanding. This book will be an excellent resource for the new and expert
researcher. Students and researchers will benefit from reading the book and keeping a copy
handy.
A world-renowned expert in biomolecular mass
spectrometry proteomics/glycoproteomics and glycomics,
Lubbock, TX, USA

Yehia Mechref, Ph.D.


Foreword II
Over the past few centuries, a number of technological advances have uncovered new horizons for the scientific study of the nervous system. From uncovering the electrical excitability of neurons and the invention of the microscope to modern imaging techniques
capable of visualizing molecules in a functional brain, we have come a long way in refining
our speculations about brain function. Today, it is possible to correlate the molecular
dynamics of neuronal circuits with the mechanisms of sensorimotor transformations in the
brain and to connect them all with observable behavior.
With every new technique, the excitement for novelty and the promise of discovery had to
be disciplined with a word of caution: a reminder that the brain is different from other
organs and studying it requires vigilance against overindulgence in interpreting results.
When Dr. Firas Kobaissy first mentioned to me that he was about to write the second
edition of this book, I said to myself here’s a much needed revision of Neuroproteomics

waiting to be written! I have known Firas for more than 5 years, through which he has been
focused on the use of proteomics in the study of disease and injury, including brain injury.
His passion for proteomics is rivaled only by his interest in the mechanisms of brain injury.
In the first edition, “Neuroproteomics” presented a number of experimental proteomic
approaches to the study of the central nervous system (CNS) and its dysfunction in trauma
and disease. In four contiguous sections, it covered animal models used in neuroproteomics
research, methods for separating and analyzing subcomponents of the neuroproteome,
wide-ranging approaches for proteome characterization and quantification in the CNS, in
addition to other methods to translate neuroproteomic results clinically. This second edition offers more updated and novel protocols that encompass both brain-wide and targeted
neuroproteomic topics. It includes exploration of advanced methods used for neuroproteomics research including protein quantitation by mass spectrometry, characterization of
post-translational modifications, as well as bioinformatics and computational approaches.
Methodology chapters follow a well-organized presentation of their respective topics, starting with an introduction, followed by a list of materials and reagents, step-by-step reproducible protocols, and instructions on troubleshooting and addressing potential pitfalls. It
is a cookbook for established and new scientists looking for molecular and biochemical
markers of brain function and disease.
I have studied the brain and its mechanisms for nearly three decades using neurophysiology, neuroanatomy, neuropharmacology, molecular, behavioral, and imaging techniques
and I have taught the same over the same period. My work spanned the fields of discovery
and translational sciences, with clinical applications in a couple of instances. If anything, my
neurotrek has taught me one important lesson about the brain: it functions more like a Jeep
than a Ferrari and it constantly adapts to changing circumstances. This makes the outcomes
of reductionist neuroscience techniques—be they physiological, cellular, molecular, or proteomic—too precise and limited to the experimental question at hand, reflecting mere
snapshots of the brain state at a given point in time; fleeting moments that vary with changing conditions.

ix


x

Foreword II

Reconstructing behavioral and cognitive states from these snapshots requires more

integrated conceptual questions that put together the observations of many disciplines, and
push them far beyond what a single technique can offer. Along those lines, an amazing
unification within the biological sciences has taken place over the past few decades and it
has set the stage for addressing this challenge. Genomics and proteomics have unmasked
surprising similarities among proteins, their functions, and their mechanisms of action
throughout the body including the nervous system. This has resulted in a common conceptual framework for all cell biology including the neuron. However, the more daunting challenge remains a unification between the many disciplines of biology to explain the neural
basis of behavior.
This final unification requires an admission, by reductionists, of the impossibility of a
bottom-up reconstruction of biological systems, and an integrationist approach that does
not deny or ignore the validity and results of successful reduction.
This book is a step in the right direction towards unifying cellular and molecular methodologies in the study of neurons. Hopefully, it will be followed by similarly successful steps
towards a general biological unification.
Professor & Chairperson
Department of Anatomy, Cell Biology
and Physiological Sciences
Faculty of Medicine
Professor and Chairman,
Interfaculty Neuroscience Graduate Program
American University of Beirut, Bliss Street,
Beirut, Lebanon

Elie D. Al-Chaer, Ph.D, JD


Preface
The application of proteomics to the study of the central nervous system (CNS) has greatly
enhanced our understanding of fundamental neurobiological processes and has enabled the
identification of proteins and pathways related to the complex molecular mechanisms
underlying various diseases of the CNS. This field, termed neuroproteomics, has facilitated
scientific discovery through major technological and methodological advances in recent

years. As part of the Methods in Molecular Biology series, this new edition will include several
exciting areas of advanced methods used for neuroproteomics research including relative
and absolute protein quantitation by mass spectrometry, characterization of post-­
translational modifications, as well as bioinformatics and computational approaches.
In the introductory part of the book (Current Reviews of Neuroproteomic Approaches
and Applications), we have six timely reviews of various neuroproteomic approaches such
as neuroproteomics genesis, degradomics, proteomic analysis for the identification of biofluid biomarkers, mass spectrometry-based imaging, and computational methods. In addition to methodology, the application of neuroproteomic approaches to understand CNS
disorders such as posttraumatic stress disorder and Alzheimer’s disease is also reviewed.
The second part of the book focuses on experimental methods in neuroproteomics. We
are excited to present updated approaches for the global-scale analysis of post-translational
modification analysis. These post-translational modifications include phosphorylation, glycosylation, as well as proteolytic cleavage. In addition to post-translational modification
analysis, several chapters detail procedures for quantitation of protein expression using both
label-free and also novel stable isotope labeling approaches. In terms of label-free quantitation, both mass spectrometry and multiplexed affinity arrays are described in relation to
protein profiling in cerebrospinal fluid and also microvesicles and exosomes derived from
neuronal cells. In relation to stable isotope labeling methods in neuroproteomics, two
chapters detail stable isotope labeling by amino acids in cell culture (SILAC) approaches for
the analysis of primary or ex vivo microglia. The SILAC chapters are focused on a single
CNS cell type; however, the approach can be potentially applied to other CNS cell types
after appropriate optimization. Moreover, specialized method chapters are presented
including proteomic approaches for identification of allosteric ligand binding sites, matrix-­
assisted laser desorption/ionization-based imaging, and targeted analysis of protein expression in a tissue-specific approach related to neuroendocrine response.
In addition to experimental protocol chapters, we present five chapters in the last part
of the book that are related to bioinformatic and computational approaches in neuroproteomics. These chapters include a novel degradomics prediction algorithm as well as systems biology and bioinformatics approaches to characterize the global-scale effects of
protein nitration and to determine transcriptional regulation networks in the context of the
CNS. Specialized protocols are also presented that describe methods for computational
assessment of protein-ligand interactions as well as a detailed decision tree for the analysis
of intracranial pressure.
Overall, this new edition provides updated and novel protocols of neuroproteomics
methods that encompass both global-scale as well as targeted and specialized topics, which

xi


xii

Preface

are timely additions for the molecular and phenotypic analysis of the CNS and CNS-related
disorders. The new compilation will be of high interest among researchers and clinical scientists involved in the area of biomarker research and protein biochemistry. Moreover, the
topics covered will be of interest to molecular biologists and biochemists who have been
involved in proteomics research already or even for those new to the field.
Finally, we thank all the authors for their significant effort in writing such excellent
methods and review chapters for this new edition. We are also sincerely grateful to each
author for their patience during the compilation and final editing of this book.
Gainesville, FL, USA
Tampa, FL, USA

Firas H. Kobeissy
Stanley M. Stevens Jr.


Acknowledgments
There are many silent workers who deserve to be acknowledged for compiling this book.
Our special thanks go to the authors of the chapters who provided their top quality manuscripts, comments, and expertise.
We would like to take this opportunity to thank our colleagues at the American
University of Beirut, Department of Biochemistry and Molecular Genetics, Faculty of
Medicine at the American University of Beirut, Lebanon, who provided help, time, technical support, and resources for completing this book. We also thank our colleagues at the
Byrd Alzheimer’s Institute and Department of Cell Biology, Microbiology and Molecular
Biology at the University of South Florida. We wish to thank Hawraa Abou Raya for her
editorial support. We thank Mrs. Iman Karout, M.Sc., who contributed to the design of the

cover art, a painting featured in the office of Professor Elie El-Chaer, at the American
University of Beirut, Lebanon.

xiii


Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii

Part I Current Reviews of Neuroproteomics Approaches
and Applications
  1 Neuroproteomics Studies: Challenges and Updates . . . . . . . . . . . . . . . . . . . . . 3
Naify Ramadan, Hussein Ghazale, Mohammad El-Sayyad,
Mohamad El-Haress, and Firas H. Kobeissy
  2 Progress and Potential of Imaging Mass Spectrometry Applied
to Biomarker Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Jusal Quanico, Julien Franck, Maxence Wisztorski, Michel Salzet,
and Isabelle Fournier
  3 Biofluid Proteomics and Biomarkers in Traumatic Brain Injury . . . . . . . . . . . . . 45
Safa Azar, Anwarul Hasan, Richard Younes, Farah Najdi, Lama Baki,
Hussein Ghazale, Firas H. Kobeissy, Kazem Zibara, and Stefania Mondello
  4 Degradomics in Neurotrauma: Profiling Traumatic Brain Injury . . . . . . . . . . . . 65
Hadi Abou-El-Hassan, Fares Sukhon, Edwyn Jeremy Assaf, Hisham Bahmad,
Hussein Abou-Abbass, Hussam Jourdi, and Firas H. Kobeissy
  5 Evolving Relevance of Neuroproteomics in Alzheimer’s Disease . . . . . . . . . . . . 101
Simone Lista, Henrik Zetterberg, Sid E. O’Bryant, Kaj Blennow,
and Harald Hampel
  6 Genome to Phenome: A Systems Biology Approach to PTSD
Using an Animal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Nabarun Chakraborty, James Meyerhoff, Marti Jett,
and Rasha Hammamieh

Part II Experimental Methods
  7 Photoaffinity Labeling of Pentameric Ligand-Gated Ion
Channels: A Proteomic Approach to Identify Allosteric
Modulator Binding Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
Selwyn S. Jayakar, Gordon Ang, David C. Chiara,
and Ayman K. Hamouda
  8 Quantitative Phosphoproteomic Analysis of Brain Tissues . . . . . . . . . . . . . . . . . 199
Bing Bai, Haiyan Tan, and Junmin Peng
  9 Glycoproteins Enrichment and LC-MS/MS Glycoproteomics
in Central Nervous System Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Rui Zhu, Ehwang Song, Ahmed Hussein, Firas H. Kobeissy,
and Yehia Mechref

xv


xvi

Contents

10 A Novel 2-DE-Based Proteomic Analysis to Identify Multiple Substrates
for Specific Protease in Neuronal Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chiho Kim and Young J. Oh
11 Neuroproteomic Profiling of Cerebrospinal Fluid (CSF)
by Multiplexed Affinity Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Anna Häggmark-Månberg, Peter Nilsson, and Jochen M. Schwenk
12 Isolation and Proteomic Analysis of Microvesicles and Exosomes

from HT22 Cells and Primary Neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Richard Witas, Dale Chaput, Hirah Khan, Stanley M. Stevens Jr.,
and David Kang
13 Combined MALDI Mass Spectrometry Imaging and Parafilm-Assisted
Microdissection-Based LC-MS/MS Workflows in the Study of the Brain . . . . .
Jusal Quanico, Julien Franck, Maxence Wisztorski, Michel Salzet,
and Isabelle Fournier
14 De Novo and Uninterrupted SILAC Labeling of Primary Microglia . . . . . . . . .
Ping Zhang, Ashley Culver-Cochran, Stanley M. Stevens Jr., and Bin Liu
15 Spike-In SILAC Approach for Proteomic Analysis of Ex Vivo Microglia . . . . . .
Joao Paulo Costa Pinho, Harris Bell-Temin, Bin Liu,
and Stanley M. Stevens Jr.
16 A Proteomic Evaluation of Sympathetic Activity Biomarkers
of the Hypothalamus-Pituitary-Adrenal Axis by Western Blotting
Technique Following Experimental Traumatic Brain Injury . . . . . . . . . . . . . . .
Hale Zerrin Toklu, Yasemin Sakarya, and Nihal Tümer

229

247

255

269

285
295

313

Part III  Bioinformatic and Computational Methods
17 Efficient and Accurate Algorithm for Cleaved Fragments Prediction
(CFPA) in Protein Sequences Dataset Based on Consensus
and Its Variants: A Novel Degradomics Prediction Application . . . . . . . . . . . . .
Atlal El-Assaad, Zaher Dawy, Georges Nemer, Hazem Hajj,
and Firas H. Kobeissy
18 Effect of Second-Hand Tobacco Smoke on the Nitration
of Brain Proteins: A Systems Biology and Bioinformatics Approach . . . . . . . . .
Firas H. Kobeissy, Joy Guingab-Cagmat, Adriaan W. Bruijnzeel,
Mark S. Gold, and Kevin Wang
19 An Advanced Omic Approach to Identify Co-Regulated Clusters
and Transcription Regulation Network with AGCT and SHOE Methods . . . . .
Natalia Polouliakh and Richard Nock
20 AutoDock and AutoDockTools for Protein-Ligand Docking:
Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1(BACE1)
as a Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nehme El-Hachem, Benjamin Haibe-Kains, Athar Khalil,
Firas H. Kobeissy, and Georges Nemer
21 An Integration of Decision Tree and Visual Analysis
to Analyze Intracranial Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Soo-Yeon Ji, Kayvan Najarian, Toan Huynh, and Dong Hyun Jeong

329

353

373

391

405

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421


Contributors
Hussein Abou-Abbass  •  Faculty of Medicine, Beirut Arab University, Beirut, Lebanon;
Faculty of Medicine, Department of Biochemistry and Molecular Genetics, American
University of Beirut, Beirut, Lebanon
Hadi Abou-El-Hassan  •  Faculty of Medicine, American University of Beirut Medical
Center, Beirut, Lebanon
Gordon Ang  •  Department of Pharmaceutical Sciences, College of Pharmacy, Texas A&M
Health Sciences Center, Kingsville, TX, USA
Edwyn Jeremy Assaf  •  Faculty of Medicine, American University of Beirut Medical Center,
Beirut, Lebanon
Safa Azar  •  Department of Biochemistry and Molecular Genetics, American University
of Beirut, Beirut, Lebanon
Hisham Bahmad  •  Faculty of Medicine, Beirut Arab University, Beirut, Lebanon; Faculty
of Medicine, Department of Anatomy, Cell Biology and Physiological Sciences, American
University of Beirut, Beirut, Lebanon
Bing Bai  •  Department of Structural Biology, St. Jude Children’s Research Hospital,
Memphis, TN, USA; Department of Developmental Neurobiology, St. Jude Children’s
Research Hospital, Memphis, TN, USA
Lama Baki  •  Department of Biochemistry and Molecular Genetics, American University of
Beirut, Beirut, Lebanon
Harris Bell-Temin  •  Department of Cell Biology, University of Pittsburgh School
of Medicine, Pittsburgh, PA, USA
Kaj Blennow  •  Clinical Neurochemistry Laboratory, Department of Psychiatry
and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy
at the University of Gothenburg, Mölndal, Sweden; The Torsten Söderberg Professorship

in Medicine at the Royal Swedish Academy of Sciences, Stockholm, Sweden
Adriaan W. Bruijnzeel  •  Department of Psychiatry and Neuroscience, McKnight Brain
Institute, University of Florida, Gainesville, FL, USA
Nabarun Chakraborty  •  Integrative Systems Biology, Geneva Foundation, USACEHR,
Fredrick, MD, USA
Dale Chaput  •  Department of Cell Biology, Microbiology and Molecular Biology,
University of South Florida, Tampa, FL, USA
David C. Chiara  •  Department of Neurobiology, Harvard Medical School, Boston, MA, USA
Ashley Culver-Cochran  •  Department of Cell Biology, Microbiology, and Molecular
Biology, University of South Florida, Tampa, FL, USA
Zaher Dawy  •  Faculty of Engineering and Architecture, Department of Electrical and
Computer Engineering, American University of Beirut, Riad El Solh, Beirut, Lebanon
Atlal El-Assaad  •  Faculty of Engineering and Architecture, Department of Electrical and
Computer Engineering, American University of Beirut, Beirut, Lebanon
Nehme El-Hachem  •  Integrative Computational Systems Biology, Institut de Recherches
Cliniques de Montreal, Montreal, QC, Canada
Mohamad El-Haress  •  Department of Biochemistry and Molecular Genetics, Faculty of
Medicine, American University of Beirut, Beirut, Lebanon; Faculty of Medicine, Beirut
Arab University, Beirut, Lebanon

xvii


xviii

Contributors

Mohammad El-Sayyad  •  Department of Family Medicine, University of Toledo, Toledo,
OH, USA
Isabelle Fournier  •  Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de

Masse-PRISM, INSERM, U1192, Univ. Lille, Lille, France
Julien Franck  •  Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de
Masse-PRISM, INSERM, U1192, Univ. Lille, Lille, France
Hussein Ghazale  •  Faculty of Medicine, Department of Biochemistry and Molecular
Genetics, American University of Beirut, Beirut, Lebanon
Mark S. Gold  •  Department of Psychiatry and Neuroscience, McKnight Brain Institute,
University of Florida, Gainesville, FL, USA; Department of Psychiatry, School
of Medicine, Washington University, St. Louis, MO, USA
Joy Guingab-Cagmat  •  Southeast Center for Integrated Metabolomics, Clinical
and Translational Science Institute, University of Florida, Gainesville, FL, USA
Anna Häggmark-Månberg  •  Affinity Proteomics, Science for Life Laboratory, School of
Biotechnology, KTH - Royal Institute of Technology, Solna, Sweden
Benjamin Haibe-Kains  •  Department of Medical Biophysics, University of Toronto, Toronto,
ON, Canada
Hazem Hajj  •  Faculty of Engineering and Architecture, Department of Electrical and
Computer Engineering, American University of Beirut, Beirut, Lebanon
Rasha Hammamieh  •  Integrative Systems Biology, US Army Center for Environmental
Health Research, Frederick, MD, USA
Ayman K. Hamouda  •  Department of Pharmaceutical Sciences, College of Pharmacy, Texas
A&M Health Sciences Center, Kingsville, TX, USA; Department of Neuroscience and
Experimental Therapeutics, College of Medicine Texas A&M Health Science Center,
Bryan, TX, USA; Department of Neuroscience and Experimental Therapeutics,
College of Medicine, Texas A&M Health Science Center, Kingsville, TX, USA
Harald Hampel  •  AXA Research Fund & UPMC Chair, Paris, France; Sorbonne
Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la
Maladie d’Alzheimer (IM2A) & Institut du Cerveau et de la Moelle Épinière (ICM),
Paris, France; Département de Neurologie, Hôpital de la Pitié-Salpêtrière, Paris, France
Anwarul Hasan  •  Department of Mechanical and Industrial Engineering, Qatar
University, Doha, Qatar; Biomedical Engineering and Department of Mechanical
Engineering, American University of Beirut, Beirut, Lebanon; Center for Biomedical

Engineering, Department of Medicine, Brigham and Women’s Hospital, Harvard
Medical School, Cambridge, MA, USA; Harvard-MIT Division of Health Sciences
and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
Ahmed Hussein  •  Department of Biotechnology, Institute of Graduate Studies
and Research, University of Alexandria, Alexandria, Egypt
Toan Huynh  •  Division of Trauma, Surgical Critical Care, Carolinas Medical Center,
Charlotte, NC, USA
Selwyn S. Jayakar  •  Department of Neurobiology, Harvard Medical School, Boston, MA, USA
Dong Hyun Jeong  •  Department of Computer Science and Information Technology,
University of the District of Columbia, Washington, DC, USA
Marti Jett  •  Integrative Systems Biology, US Army Center for Environmental Health
Research, Frederick, MD, USA
Soo-Yeon Ji  •  Department of Computer Science, Bowie State University, Bowie, MD, USA
Hussam Jourdi  •  Faculty of Science, Department of Biology, University of Balamand, Aley,
Lebanon


Contributors

xix

David Kang  •  Department of Molecular Medicine, Byrd Alzheimer’s Institute, College
of Medicine, University of South Florida, Tampa, FL, USA
Athar Khalil  •  Department of Biochemistry and Molecular Genetics, American University
of Beirut, Beirut, Lebanon
Hirah Khan  •  Department of Molecular Medicine, Byrd Alzheimer’s Institute, College
of Medicine, University of South Florida, Tampa, FL, USA
Chiho Kim  •  Department of Systems Biology, Yonsei University College of Life Science
and Biotechnology, Seoul, Korea
Firas H. Kobeissy  •  Department of Biochemistry and Molecular Genetics,

Faculty of Medicine, American University of Beirut, Beirut, Lebanon; Department of
Psychiatry, Center for Neuroproteomics and Biomarkers Research, University of Florida,
Gainesville, FL, USA
Simone Lista  •  AXA Research Fund & UPMC Chair, Paris, France; Sbonne Universités,
Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du cerveau
et de la moelle (ICM), Département de Neurologie, Institut de la Mémoire et de la
Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, Paris,
France
Bin Liu  •  Department of Pharmacodynamics, College of Pharmacy, University of Florida,
Gainesville, FL, USA
Yehia Mechref  •  Department of Chemistry and Biochemistry, Texas Tech University,
Lubbock, TX, USA
James Meyerhoff  •  Integrative Systems Biology, Geneva Foundation, USACEHR,
Fredrick, MD, USA
Stefania Mondello  •  Department of Biomedical, Odontoiatric and Morphological and
Functional Imaging Sciences, University of Messina, Messina, Italy
Kayvan Najarian  •  Department of Computational Medicine and Bioinformatics, University of
Michigan, Ann Arbor, MI, USA
Farah Najdi  •  Department of Biochemistry and Molecular Genetics, American University
of Beirut, Beirut, Lebanon
Georges Nemer  •  Faculty of Medicine, Department of Biochemistry and Molecular
Genetics, American University of Beirut, Beirut, Lebanon
Peter Nilsson  •  Affinity Proteomics, Science for Life Laboratory, School of Biotechnology,
KTH - Royal Institute of Technology, Solna, Sweden
Richard Nock  •  Data61 & The Australian National University, Alexandria, NSW,
Australia
Sid E. O’Bryant  •  Institute for Healthy Aging, University of North Texas Health Science
Center, Fort Worth, TX, USA
Young J. Oh  •  Department of Systems Biology, Yonsei University College of Life Science
and Biotechnology, Seoul, Korea

Junmin Peng  •  Department of Structural Biology, St. Jude Proteomics Facility, St. Jude
Children’s Research Hospital, Memphis, TN, USA; Department of Developmental
Neurobiology, St. Jude Proteomics Facility, St. Jude Children’s Research Hospital,
Memphis, TN, USA
Joao Paulo Costa Pinho  •  Department of Cell Biology, Microbiology and Molecular
Biology, University of South Florida, Tampa, FL, USA
Natalia Polouliakh  •  Sony Computer Science Laboratories, Inc., Tokyo, Japan;
Department of Ophthalmology and Visual Sciences, Yokohama City University Graduate
School of Medicine, Yokohama, Japan


xx

Contributors

Jusal Quanico  •  Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de
Masse (PRISM), INSERM, U1192, Université de Lille, Lille, France
Naify Ramadan  •  Department of Biochemistry and Molecular Genetics, American
University of Beirut, Beirut, Lebanon
Yasemin Sakarya  •  Department of Pharmacology and Therapeutics, University of Florida
College of Medicine, Gainesville, FL, USA
Michel Salzet  •  Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de
Masse-PRISM, INSERM, U1192, Univ. Lille, Lille, France
Jochen M. Schwenk  •  Affinity Proteomics, Science for Life Laboratory, School of
Biotechnology, KTH - Royal Institute of Technology, Solna, Sweden
Ehwang Song  •  Department of Chemistry and Biochemistry, Texas Tech University,
Lubbock, TX, USA
Stanley M. Stevens Jr.  •  Department of Cell Biology, Microbiology and Molecular Biology,
University of South Florida, Tampa, FL, USA
Fares Sukhon  •  Faculty of Medicine, Department of Internal Medicine, American

University of Beirut Medical Center, Beirut, Lebanon
Haiyan Tan  •  St. Jude Proteomics Facility, St. Jude Children’s Research Hospital, Memphis,
TN, USA
Hale Zerrin Toklu  •  Department of Pharmacology and Therapeutics, University
of Florida College of Medicine, Gainesville, FL, USA; Geriatric Research Education &
Clinical Center, Malcolm Randall Veterans Affairs Medical Center, Gainesville, FL,
USA; North Florida Regional Medical Center, Department of Graduate Medical
Education, FL, USA
Nihal Tümer  •  Department of Pharmacology and Therapeutics, University of Florida
College of Medicine, Gainesville, FL, USA; Geriatric Research Education & Clinical
Center, Malcolm Randall Veterans Affairs Medical Center, Gainesville, FL, USA
Kevin Wang  •  Department of Psychiatry and Neuroscience, McKnight Brain Institute,
University of Florida, Gainesville, FL, USA; Department of Psychiatry, Center for
Neuroproteomics and Biomarkers Research, Gainesville, FL, USA
Maxence Wisztorski  •  Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie
de Masse-PRISM, INSERM, U1192, Univ. Lille, Lille, France
Richard Witas  •  Department of Molecular Medicine, Byrd Alzheimer’s Institute, College
of Medicine, University of South Florida, Tampa, FL, USA
Richard Younes  •  Department of Biochemistry and Molecular Genetics, American
University of Beirut, Beirut, Lebanon
Henrik Zetterberg  •  Clinical Neurochemistry Laboratory, Department of Psychiatry and
Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at
the University of Gothenburg, Mölndal, Sweden; Department of Molecular Neuroscience,
UCL Institute of Neurology, London, UK
Ping Zhang  •  Department of Pharmacodynamics, College of Pharmacy, University of
Florida, Gainesville, FL, USA
Rui Zhu  •  Department of Chemistry and Biochemistry, Texas Tech University, Lubbock,
TX, USA
Kazem Zibara  •  Department of Biochemistry and Molecular Genetics, American University
of Beirut, Beirut, Lebanon

Part I
Current Reviews of Neuroproteomics Approaches
and Applications


Chapter 1
Neuroproteomics Studies: Challenges and Updates
Naify Ramadan, Hussein Ghazale, Mohammad El-Sayyad,
Mohamad El-Haress, and Firas H. Kobeissy
Abstract
The Human Genome Project in 2003 has resulted in the complete sequence of ~99% of the human
genome paving the road for the Human Proteome Project (HPP) assessing the full characterization of the
translated protein map of the 20,300 protein-coding genes. Consequently, the emerging of the proteomics
field has successfully been adopted as the method of choice for the proteome characterization. Proteomics
is a term that is used to encompass multidisciplinary approaches combining different technologies that aim
to study the entire spectrum of protein changes at a specific physiological condition. Proteomics research
has shown excellent outcomes in different fields, among which is neuroscience; however, the complexity
of the nervous systems necessitated the genesis of a new subdiscipline of proteomics termed as “neuroproteomics.” Neuroproteomics studies involve assessing the quantitative and qualitative aspects of nervous
system components encompassing global dynamic events underlying various brain-related disorders ranging from neuropsychiatric disorders, degenerative disorders, mental illness, and most importantly brain-­
specific neurotrauma-related injuries. In this introductory chapter, we will provide a brief historical
perspective on the field of neuroproteomics. In doing so, we will highlight on the recent applications of
neuroproteomics in the areas of neurotrauma, an area that has benefitted from neuroproteomics in terms
of biomarker research, spatiotemporal injury mechanism, and its use to translate its findings from experimental settings to human translational applications. Importantly, this chapter will include some recommendation to the general studies in the area of neuroproteomics and the need to move from this field from
being a descriptive, hypothesis-free approach to being an independent mature scientific discipline.
Key words Neuroproteomics, High-throughput immunoblotting, IMS, Imaging mass spectrometry
(MS), Proteomics, Human Genome Project, Human Proteome Project (HPP)

1  Introduction: Proteomics and Neuroproteomics Genesis

The completion of the Human Genome Project in 2003 has
resulted in the complete sequence of ~99% of the human genome,
which paved the way for the Human Proteome Project (HPP) [1, 2].
The global translated protein map of ~20,300 protein-coding genes
is expected to be finalized, illustrating the functional and biological
characteristics of the human proteome, which will facilitate deciphering the different role(s) of gene-coded proteins in ­disease and
Firas H. Kobeissy and Stanley M. Stevens, Jr. (eds.), Neuroproteomics: Methods and Protocols, Methods in Molecular Biology,
vol. 1598, DOI 10.1007/978-1-4939-6952-4_1, © Springer Science+Business Media LLC 2017

3


4

Naify Ramadan et al.

under pathological conditions. This is accomplished with the aid of
proteomics and systems biology bioinformatics studies.
In the early draft of the human genome completion, around
30,000–36,000 genes were reported [3] which raises the question
of complexity observed in humans compared to less-developed
organisms with a relatively matched gene number (Arabidopsis
thaliana has 25,500 genes) [4]. This emphasized the complexity
of the proteome systems. It is noted that a single gene can translate
into different protein isoforms, and on average approximately ten
protein isoforms/genes are translated in humans with an estimate
of one quarter of a million of different proteins and isoforms existing in the human proteome [5–7]. This is in part due to the
advanced transcriptional process allowing fine and higher regulation in gene expression, where it is estimated that around 3000
transcription factors exist in humans [6, 7]. This is coupled to
alternative splicing machinery driven via the genetic rearrangement

observed clearly in the immune system [8]. Additionally, this is
exacerbated by the presence of the different static and dynamic
posttranslational modifications (PTMs) existing on different proteins and their isoforms [9, 10].
In 1994–1995, the word proteome was first coined by Marc
Wilkins to denote the expression of the entire protein produced
similar to the entire genes derived from the genome. The proteome is derived from the words: PROTEin expressed by a
genOME [11]. From the year 1995, there has been extensive
research in the area of proteomics where there are almost
(52,525 + 10,316) articles containing the word genomics either in
the abstract or in the title compared to (51,675 + 10,572) articles
containing the word proteomics in the abstract or in the title
(search conducted using Endnote version 17 with the PubMed
database). These numbers reflect the paste at which proteomics
research is progressing compared to genomics highlighting its versatile applications in different fields.
A major characteristic of the proteome is its dynamic versatility
responding to different internal and external stimuli, while the
genome is a relatively static entity of the translated proteome [12, 13].
The dynamic features of the proteome are modulated at different
regulation stages of DNA transcription to mRNA, translation to
polypeptides followed by the correct folding, and the insertion of
the proper PTMs (glycosylation and phosphorylation). The rise in
the area of proteomics was observed with the introduction of different high-resolution approaches as advanced separation techniques, high-resolution mass spectrometry (MS), and versatile
labeling techniques, in addition to other proteomics methods
involving antibody-based approaches.
Proteomics is a term that is used to encompass multidisciplinary
approaches combining different technologies that aim to study the


Recent Updates on Neuroproteomics Studies

5

entire spectrum of protein changes (abundance, structure, interaction,
expression, and modification) at a specific physiological condition.
Proteomics research have shown excellent outcomes in different fields
among which is neuroscience; however, the complexity of the nervous
systems necessitated the genesis of a new subdiscipline of proteomics
termed as “neuroproteomics.” Neuroproteomics studies involve
assessing the quantitative and qualitative aspects of nervous system
components encompassing global dynamic events underlying various
brain-related disorders ranging from neuropsychiatric disorders
(PTSD, anxiety, depression, etc.), degenerative disorders (Alzheimer’s
disease, Parkinson disease, etc.), mental illness, and most importantly
brain-specific neurotrauma-related injuries (traumatic brain injury
(TBI), spinal cord injury (SCI), stroke, etc.) [14–16]. Of interest, the
term neuroproteomics was coined for the first time in 2004 by Kim
et al.; interestingly, the authors never used “neuroproteomics” in the
text which was substituted with the term “neuromics” all throughout
the manuscript [17].
Neuroproteomics in conjunction with systems biology has
led to revolutionize how we interpret our views on the global
regulation of brain-related disorders via understanding dynamics
of protein changes (neural proteome expression, function, or
modification) [18, 19]. Several hundreds of neuroproteomics
studies have been published discussing different aspects of brain-­
related disorders involving degenerative and neuropsychiatric disorders (a subdiscipline of psychoproteomics has been proposed
[20, 21]). For illustrating the role of neuroproteomics application on brain disorders, we will focus on brain neurotrauma as
one prominent disorder that has benefited highly from the application of neuroproteomics application especially in the area of
biomarker research.

2  Neuroproteomics: The Study of Brain Proteome
The brain is considered among the major complex organs in the
human body with a noticeable capability to perform a spectrum
of metabolic, physiological, and behavioral processes that require
the intervention of several components of the nervous system at
the cellular and molecular and protein levels [22, 23]. Any
abnormalities pertaining to this complex neural system would
result in a number of brain-related disorders ranging from
degenerative, neuropsychiatric, and altered mental health-related
symptoms. Of interest, due to the complexity of the nervous
system, several advanced approaches have been developed and
applied to decipher the causalities of these altered events, which
focused on a number of culprits including changes in the gene/
protein function/expression and interaction.


6

Naify Ramadan et al.

On the level of the brain proteome, it is estimated that there
exist around 20,000 brain proteins that are differentially expressed
in various brain regions [24]. Thus, it would be extremely challenging to dissect the neuroproteome and its dynamic functions
without the use of advanced separation and high-resolution protein identification instrumentation. Traditionally, when referring
to classical proteomics, the use of mass spectrometry coupled with
advanced separation systems (online or offline with or without gel
use) would be the method of choice to assess the entire spectrum
of protein characteristics. This include abundance, structure, interaction, expression levels, and modification at a certain physiological condition [please refer to Ottens et al. for detailed discussion
on proteomics methods [25, 26]].

3  Methods in Neuroproteomics Studies
Several choices of MS-based proteomics applications have been
developed; these involve the bottom-up technique vs. top-down
analysis [27]. The bottom-up technique involves protein enzymatic
digestion followed by MS analysis to identify peptide fragments
within the complex sample mixture as applied by shotgun proteomic
methods that can be applied on different biospecimens (tissue, CSF,
and serum) involving nanoflow liquid chromatography (nanoLC)
followed by MS analysis [28]. On the other hand, “top-down” proteomics involves the complete, intact protein analysis without the
need for enzymatic digestion [29, 30]; however, it requires special
expertise and is used for special purposes; nevertheless, the “topdown” proteomic approach has been used to identify candidate of
TBI protein biomarkers, such as GFAP and UCH-L1 [31]. The
above techniques can be coupled with chemical tryptic tagging with
different kinds of isotopic labels [32] such as the isobaric tags for
relative and absolute quantitation (iTRAQ), stable isotope labeling
with amino acids in cell culture (SILAC), or the use of super SILAC
as discussed elegantly in Chapters 14 and 15. These techniques have
been utilized to study neuroproteome changes as well as to assess
PTM expression such as phosphorylation-­dependent activitivation
[33, 34]. This kind of dynamic modifications is reversible in nature
where different approaches have been developed for tryptic peptide
enrichments to quantify these phosphorylated peptides using TiO2
or via the use of IMAC columns followed by LC-MSMS [35].

4  Antibody-Based Neuroproteomics Approaches
Other proteomic techniques have been introduced to depict global
changes that involve antibody-based techniques which are MS-free
and involve a targeted detection of biomarker proteins representing antigens against an antibody panel or array platforms that will

Recent Updates on Neuroproteomics Studies

7

allow proteins to bind to them (Zyomyx protein biochips, BD
PowerBlot, and BD Clontech antibody microarrays 500) [36–41].
This approach allows a global protein discovery and has several
advantages including specificity and sensitivity of the probes that
can target proteins in complex high protein content milieu (CSF
and serum) coupled with the rapid confirmation of the identified
hits. On the other hand, this technology lacks the ability to identify
novel protein biomarker; in addition, it is biased toward upregulated protein hits. Furthermore, this method lacks the ability to
bind to all protein isoforms that may have different binding affinity
to the antibody arrays. Finally, this technique that suffers from the
probed antibodies may include low fidelity antibodies that may be
of low sensitivity to the biomarker proteins. Two forms of antibody-­
based proteomics exist.
4.1  High-Throughput
Immunoblot Screening

High-throughput immunoblotting (HTPI) technology (Power­
Blot™, BD Biosciences) is one novel proteomic method based on
manifold immunoblotting system with usable channels that allows
non-labeled samples to be PAGE resolved, and probed with multiple monoclonal antibodies is HTPI which is a Western blot-based
technology [36, 37]. It provides protein detection with good separation data (molecular mass difference), with the advantage or
requiring no bioinformatics analysis compared to regular MS data
[42]. In addition, this method requires less complicated instrumentation compared to the MS-based techniques, and its results
are easily validated since the antibody in question is already available. Again, the major shortcoming of this technique is the lack of
exhaustiveness due to the lack of the ~30,000 different proteins
and isoforms. In addition, different antibody source may exhibit

different affinity to define proteins as well as different species reactivity. In our laboratory, HTPI method was used to identify a comprehensive set of calpain and caspase-3 degradome and was
compared to experimental TBI [43]. Data showed 92 altered proteins (54 were substrates to calpain-2) (38 sensitive to caspase-­3)
(48 protein were downregulated), while nine proteins were upregulated post-TBI. Several of the identified proteins were validated
against human samples and were translated into clinical studies
(βII-spectrin) [44].

4.2  Antibody Panel/
Microarray Approach

Alternative to the HTPI, antibody microarray technology is
designed based on DNA microarrays such as the Zymox protein
biochips or antibody ELISA arrays c [38–41, 45]. The method is
based on the concept of capturing the protein of interest using
antibody-based platform. By pre-labeling the protein samples from
control and experimental samples using differential fluorescent
Cy-3/Cy-5/Cy-2 dyes, these are probed against an antibody platform (standard size glass slide) leading to a differential expression
profile that can be quantified [46] mimicking gene chip mRNA
quantification method [47]. Furthermore, quantification can also


8

Naify Ramadan et al.

be achieved using additional antibodies specific to the same protein
antigen, similar to the sandwich ELISA method (antibody-­antigen-­
antibody). This method has been used to identify multiple proteins
biomarkers for clinical pathologies such as cancer [48]. This
method represents another dimension to assess known targeted
protein in a high-content protein complex (CSF and serum); however, similar to HTPI global approach is lacking as well.

5  Neuroproteomics: Challenges
Although the brain constitutes 2% of the body mass, however, it
consumes 20% of the oxygen with an abundance of 60% fat mostly
localized in the myelin representing 25% of the total amount of
cholesterol in the body [49–51]. The brain consists of several
structures or substructures architecture with the existence of several neural cell types including glia, astrocytes, and neurons with an
approximation of 100 billion neurons and 10× more glial cells
[52]. In addition, the neuronal structure analysis of axons, dendrites, and forming synapses and initiating new connections represent another level of complexity [53]. Furthermore, different brain
regions are small in size and are hard to obtain in sufficient amounts
for analysis with the major central nervous system (CNS); proteins
are either transmembrane or membrane associated (G proteins, ion
channels, etc.) [54–56]. These proteins, hence, are expressed differentially in small quantities which hamper their proteomic identification due to the low copy numbers of proteins and their
conjugated neuropeptides [55] which necessitate the need for subcellular fractionation.
One major characteristic of the proteome is its dynamic features where it reflects both temporal and spatial dynamicity depending on the physiological condition as compared to the static status
of the genome. As discussed previously, there is a nonlinear relation between the genome and the proteome where it is challenging
to draw a direct correlation and association between mRNA expression and protein translation (number of proteins from a single
gene) [47, 57–59]. This is attributable to different factors ­including
alternative splicing, which is highly common in brain tissue, generating thousands of copies of highly related splices from a single
gene (cadherin, e.g., has 18 different isoforms linked to morphogenic and functional roles in the developing nervous system) [57,
60–63]. Similarly, there are 20 different isoforms of the glial-specific protein glial fibrillary acidic protein (GFAP) reflecting the fact
that the most complexity rises from the proteome level [64].
It is estimated that there exist 100% folds of complexity in the
proteome compared to the genome with an average of ~10 protein
isoforms that can be generated per single gene [17, 65]. This is
more complicated by the presence of several dynamic PTMs