Dedicated to my parents, Angela & Rino

160 Pages·2013·10.99 MB·English

Dedicated to my parents, Angela & Rino
Mamma, Papá, senza il vostro supporto, amore, dedizione e stima, la mia
ambizione non avrebbe mai raggiunto livelli tali da volare in cieli così alti
e sondare terre tanto lontane. Voi rappresentate per me quel modello
esemplare di vita che sempre mi accompagnerà nel futuro, ovunque saró.
The research in this thesis was performed in the Biomolecular Mass Spectrometry
and Proteomics Group, Utrecht University, Utrecht, the Netherlands.
Financial support from the J.E. Jurriaanse Stichting for printing this thesis is kindly
acknowledged.
Cover photo: superb view of the Olympic Mountains from Vancouver Island, as a
good memory of the sensational time I spent there during my PhD.
Printed by: Proefschriftmaken.nl || Uitgeverij BOXPress
ISBN 978-90-8891-558-1
Optimizing hydrophilic
interaction liquid chromatography
for ultrasensitive proteome analysis
Optimaliseren van hydrofiele interactie
vloeistofchromatografie voor
ultragevoelige proteoomanalyse
(met een samenvatting in het Nederlands)
Proefschrift
ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag
van de rector magnificus, prof.dr. G.J. van der Zwaan, ingevolge het besluit
van het college voor promoties in het openbaar te verdedigen op
vrijdag 8 februari 2013 des middags te 2.30 uur
door
Serena Di Palma
geboren op 1 februari 1983, te Campobasso, Italiё
Promotor: Prof. dr. A.J.R. Heck
Co-promotor: Dr. S. Mohammed
TABLE OF CONTENTS
CHAPTER 1.............................................................................................. 7
Introduction
CHAPTER 2.............................................................................................. 33
A review: recent advances in peptide separation by multidimensional liquid
chromatography for proteome analysis
CHAPTER 3.............................................................................................. 69
Zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC and
ZIC-cHILIC) provide high resolution separation and increase sensitivity in
proteome analysis
CHAPTER 4.............................................................................................. 83
Highly sensitive proteome analysis of FACS-sorted adult colon stem cells
CHAPTER 5.............................................................................................. 93
Evaluation of the deuterium isotope effect in zwitterionic hydrophilic interac-
tion liquid chromatography separations for implementation in a quantitative
proteomic approach
CHAPTER 6.............................................................................................. 105
Towards a comprehensive characterization of a human cancer cell phospho-
proteome
CHAPTER 7.............................................................................................. 121
A protocol: ZIC-cHILIC as a fractionation method for sensitive and powerful
shotgun proteomics
CHAPTER 8.............................................................................................. 145
Summary
Nederlandse Samenvattig
Curriculum Vitae
List of publications

Chapter 1
1
Introduction
7
TABLE OF CONTENTS
1 1. PROTEOMICS
1.1 Generic shotgun proteomics workflow
1.2 Proteomics strategies
2. MASS SPECTROMETRY
2.1 Ionization
2.2 Mass analyzers
2.2.1 Quadrupoles
2.2.2 Quadrupole ion trap and linear ion trap
2.2.3 FT-ICR
2.2.4 Orbitrap
2.2.5 Hybrid instruments
2.3 Peptide fragmentation
3. PEPTIDE AND PROTEIN IDENTIFICATION
4. PHOSPHOPROTEOMICS
4.1 Enrichment strategies
4.2 Fractionation strategies
4.3 Phosphotyrosine enrichment
4.4 Phosphopeptide sequencing
5. QUANTITATIVE PROTEOMICS
5.1 Absolute quantification
5.2 Relative quantification
5.2.1 Metabolic labeling
5.2.2 Chemical labeling
6. OUTLINES OF THE THESIS
7. REFERENCES
8
1 PROTEOMICS
Analytical protein chemistry, or “proteomics”, refers to the branch of analytical science focus-
ing on proteins.[1] The term proteome appeared in the literature in 1997 as a linguistic equiva- 1
lent to the concept of genome and is used to describe the complete set of proteins expressed
by the entire genome of a cell.[2] Moreover, proteomics expresses the ambition to obtain a
global view at the protein level, in analogy to what is possible to obtain at the DNA and RNA
levels, bridging the gap between our understanding of the genome sequence and the cellular
response.[3, 4]
From a ‘systems biology’ point of view, proteomics delivers mainly three types of information.
First, it embraces the study of interactions of proteins with other proteins, modified peptides,
small molecules, and specific RNA and DNA sequences, a branch that is also defined as “inter-
actomics”.[5] A second type of information is the posttranslational modification (PTM) state
of a protein.[6] PTMs can affect structure and stability of a protein, having potential effects
on its biological function, as well as switching the protein into an active or deactivate state.
Third, “expression proteomics” determines the relative and absolute amount of proteins in a
sample.[7] This is analogous to “transcriptomics”, which measures mRNAs by microarrays or
deep sequencing methods. However, the mRNA levels have been found to not fully correlate
with eventual protein expression due to differences in the extent of re-use of mRNA.[8] Thus,
the main advantages of focusing on proteins is that it takes into account the regulation at the
posttranscriptional and posttranslational level.[9] These three fields can be woven together and
applied in a myriad of different formats to study biological and medical questions.
The technological basis of most current proteomics studies is biological mass spectrometry
(MS). This field was first catapulted to mainstream prominence with the ‘development of soft
desorption ionization methods for mass spectrometric analyses of biological macromolecules’,
for which in 2002 John B. Fenn and Koichi Tanaka jointly received a Nobel Prize in Chemistry.
Then, a number of decisive breakthroughs followed, notably optimized protocols to handle
biological sample for MS analysis, powerful separation methods for the analysis of complex
protein mixtures, automated peptide identification software and bioinformatics tools for data
analysis, establishment of quantitative techniques, and improvements in mass spectrometric
instrumentation.
During the past decade, most proteomics studies relied on tandem mass spectrometry (MS/
MS) as the core technology, specifically on a method referred to as ‘bottom-up’ proteomics.[10]
The key concept is fundamentally explained by the controlled decomposition of a proteome
into peptides and their analysis by mass spectrometry. One idea behind this is that an ensemble
of peptides has a narrower distribution of physico-chemical properties compared to a mixture
of proteins, and peptides are analytically and preparatively less challenging than proteins. The
other idea is that the fragmentation of peptides by tandem MS is well understood and occurs
in a more or less predictable way, yielding fragment spectra that can be used for peptide iden-
tification.
1.1 Generic shotgun proteomics workflow
A ‘standard’ proteomics workflow does not exist and always depends on the specific research
question and available resources. However, bottom-up proteomics can be summarized as fol-
lowing. Proteins are extracted from cells or tissues and digested into peptides. Peptides in the
sample are separated, typically by liquid chromatography, and then ionized by electrospray ioni-
zation (ESI) or matrix-assisted laser desorption/ionization (MALDI). Ionization is required
to transfer analytes into the mass spectrometer as gaseous ions, where they are subjected to
fragmentation and their spectra are recorded. Fragment ion spectra are the currency of infor-
9
mation, as they can be assigned to peptide sequences
n
1 ple atio fFrroamgm wenhtic iho nth sep ceoctrrrae saplsoon dcionngt apinro pteriencsi oaurse iinnffoerrmreda-.
ampar tion to detect modified amino acid residues and to
Sre identify and locate modifications within the peptide
p
1. sequence. Moreover, MS signals can be used to esti-

mate the quantity of a peptide or protein.[11, 12] For

every step of the process, including sample prepara-
n tion and fractionation, MS data acquisition, quantifi-
no
eiti  cation, and data analysis, multiple methods and tools
ts
oe
rg have been developed.[13-15] This also applies to the
Pdi
MS instrumentation, which continuously enjoys in-
2.
creases in performance in regard to speed, mass ac-

curacy, sensitivity and analytical robustness.[16, 17]

n In Figure 1, I outline the general steps that would
o
e ti apply to most proteomics strategies and to the work
da
ption described in this thesis. The sample preparation step
eti largely depends on the type of sample and on the ulti-
Pc
a
r mate goal of the research, e.g. a comprehensive global
f 3.
 proteome analysis, or a more targeted study on specif-
ic cell types, subcellular components or certain PTMs.

t A sample can include cells from laboratory culturing,
n
me isolated primary cells, tissue, biological fluid, plant ma-
h  terial, etc. In general, the sample is lysed employing
c
nri denaturing agents such as the surfactants sodium do-
E
4. decyl sulfate (SDS) or sodium deoxycholate (SDC), or
 such as the chaotropic reagents urea or thiourea. The
use of these reagents enhances cell and tissue solubi-
noLC-MS Relative Abundance lvaitez nalottiiwonng c taohnnedcir e pnprtroreatcetiiiponin tuast,n iocfoannl.d Hipnrogew calenuvdde erd, eesnnuazrftyaumcrtaaattniioctn sd,, eipgvereesn--
a tion and dominate mass spectra due to their favora-
n
20 40 T6im0e (min)80 1005. ble ionizability and their great abundance compared

to individual peptides. Therefore, depletion of deter-
n gents is a prerequisite for efficient mass-spectrometric
o
ptideentati 1570050%%%S+28R V ET EE NQ SGy5ALy6EEb8y7LAGSy9QNy10EE TEy12V RS+28 aTnhaely fsoisl lionw pinrogt esotempi c(sS. tep 2) is the protein digestion
Peragm 205%%0 250yb23 yb34500y4b5b6b77m50/z by9810b0100 b1112yb151120 b13y1135006. wficitihty teon zgyemneesra tpe opsseepstsidinegs tshpaetc iafirec isnu bthsetr amteo lespcuelcair-
f weight window suitable for MS analysis. The choice

of enzyme is undoubtedly important as each enzyme
s generates a unique pool of peptides with differing se-
si
Data analy Relative abundance 7. qaifisnuc dtieern ynccpthelsy ai nrcagh,t e aw.tr hahTeci chtCeher - itcsmelteriocmassv,tie nslcae olnp msgroitdmhtee o idnonisf s tpslryrpiboseiutcnetieoifio lcaynant,li dlcys oaearlnnugzbdiny imlieintfeey-
m/z
residues.[18] These basic residues are adequately dis-
Figure 1: General proteomics workflow. tributed over a protein sequence resulting in the gen-
10