1

Bachelor Thesis Scheikunde

Optimization of Analytical Procedure for Protein Analysis

in Fresh Frozen Laser Microdissectioned Human Tissue

door

Naomi Uwugiaren

juni 2016

Studentnummer

10362053

Verantwoordelijk docent

Prof. dr. Garry Corthals

Begeleider

Dr. Irena Dapic

Onderzoeksinstituut

Van 't Hoff Institute for Molecular Sciences

Onderzoeksgroep

Biomolecular Systems Analytics

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Abstract

Sample preparation in tissue proteomics represents a challenge due to the limited availability of the

specimen, the low reproducibility of the current methods and possible sample loss during preparation

steps. Extraction of proteins is easily influenced by the components of the extraction buffer and protein

digestion might be incomplete.

Here we tested and optimized several protocols for protein extraction and digestion from fresh

frozen laser capture microdissectioned human uterine tissue. The protocols varied in composition of

extraction buffers by means of detergents (SDS or SDC), chaotropes (urea), organic solvents (ACN),

and in the proteases used for digestion (trypsin or trypsin/Lys-C). The results were evaluated by means

of the number of identified protein groups, number of identified peptides, and number of acquired

spectra and by the physiochemical properties of the identified proteins.

The results showed that SDS and SDC were not completely removed from the samples. Samples

extracted with SDS gave a lower number of identified protein groups compared to the extraction buffer

containing urea. Samples extracted with SDC could not be analyzed due to precipitation of SDC. The

extraction buffer containing 8 M urea and 60% ACN resulted in the highest number of protein group

identifications. In addition, supplementing the trypsin digestion with Lys-C showed less protein group

identifications than a trypsin digestion, whereas an addition of trypsin two consecutive times increased

the protein group identifications by 17% compared to the digestion with the single addition of trypsin.

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Populair wetenschappelijke samenvatting

De vraag naar het analyseren van eiwitten in weefsels is groot geworden, aangezien zij veel informatie

kunnen bevatten over de processen die plaatsvinden in organismen op een moleculair niveau. De

resultaten hiervan kunnen gebruikt worden voor het vinden van biomarkers en het ontwikkelen van

medicijnen. Weefsels hebben echter een beperkte beschikbaarheid en zijn daardoor in kleine

hoeveelheden beschikbaar. Een ander probleem dat kan optreden is het verlies van sample. Het doel van

dit onderzoek was daarom om een analytische methode te optimaliseren voor het analyseren van de

aanwezige eiwitten in kleine hoeveelheden menselijk baarmoederweefsel met behulp van een

combinatie van vloeistofchromatografie en massaspectrometrie (MS).

Eerst zijn de eiwitten uit de samples gehaald door gebruik te maken van een extractiebuffer.

Eiwitten zijn normaal gesproken gevouwen, maar ze moeten worden ontvouwen zodat ze goed

toegankelijk zijn. Dit wordt gedaan door het verbreken van de ionbindingen, van der Waalsbindingen

en waterstofbruggen tussen de eiwitten. Hiervoor kunnen detergentia en chaotropen worden gebruikt,

aangezien zij die bindingen breken. Voorbeelden van detergentia zijn natriumdodecylsulfaat (SDS) en

natriumdeoxycholaat (SDC) en een voorbeeld van een chaotroop is ureum. Het nadeel van SDS is dat

deze moeilijk te verwijderen is uit de sample, wat kan leiden tot vervuilingen. Verder kan het toevoegen

van een organisch oplosmiddel, zoals acetonitril, helpen in het ontvouwen van eiwitten. Daarna worden

de eiwitten afgebroken tot peptiden met behulp van enzymen, ook wel proteasen genoemd. Dit wordt

uitgevoerd omdat intacte eiwitten moeilijk te analyseren zijn met MS vanwege hun grootte. Trypsine

wordt hiervoor veel gebruikt vanwege zijn hoge specificiteit en efficiëntie, maar deze knipt niet op alle

mogelijke plaatsen. Daarom kan trypsine met andere proteasen worden gecombineerd, zoals Lys-C,

zodat zij op de overige plaatsen knippen. Hierna worden de peptiden gescheiden met

vloeistofchromatografie op basis van hun polariteit en geanalyseerd met MS, zodat hun sequenties

bepaald kunnen worden. Door gebruik te maken van een database kan uiteindelijk worden bepaald welke

eiwitten oorspronkelijk aanwezig waren in de sample. Deze stappen zijn geïllustreerd in Figuur 1.

Figuur 1. De uitgevoerde procedure voor het analyseren van eiwitten.1

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In dit experiment zijn verschillende extractiebuffers getest die SDC, SDS of ureum bevatten.

SDS en SDS konden niet volledig worden verwijderd, waardoor sommige samples niet konden worden

geanalyseerd en andere samples een laag aantal eiwitten gaven. De extractiebuffer met ureum gaf de

meeste aantal eiwitten en is daarom gekozen om verder te optimaliseren. Het toevoegen van acetonitril

zorgde voor een verhoging in het aantal eiwitten. Het combineren van trypsine en Lys-C hielp niet in

het verhogen van het aantal eiwitten, maar wat wel hielp was het twee keer toevoegen van trypsine.

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List of abbreviations

ACN Acetonitrile

Arg Arginine

Asp Aspartic acid

BPC Base peak chromatogram

C18 Octadecyl hydrocarbon chain

CHAPS 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate

CV Coefficient of variation

Cys Cysteine

DMSO Dimethylsulfoxide

DTE Dithioerythritol

DTT Dithiothreitol

ESI Electrospray ionization

FA Formic acid

FASP Filter-aided sample preparation

FF Fresh frozen

FFPE Formalin fixed paraffin embedded

Glu Glutamic acid

h Hour

HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid

His Histidine

IAA Iodoacetamide

ICAT Isotope-coded affinity tag

LC Liquid chromatography

LCM Laser capture microdissection

LC-MS/MS Liquid chromatography tandem mass spectrometry

LFQ Label-free quantification

LOD Limit of detection

Lys Lysine

M Molar

MALDI Matrix-assisted laser desorption/ionization

MeOH Methanol

min Minute

m-NBA meta-nitrobenzyl alcohol

MS Mass spectrometry

MW Molecular weight

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NP-40 Nonidet P-40

NSAF Normalized spectral abundancy factor

OCT Optimal cutting temperature

PEG Polyethylene glycol

Pro Proline

PVA Polyvinyl alcohol

QTOF Quadrupole time-of-flight

RIPA Radioimmunoprecipitation assay

ROI Region of interest

RP Reversed-phase

RT Room temperature

SDC Sodium deoxycholate

SDS Sodium dodecyl sulfate

SDS page Sodium dodecyl sulfate polyacrylamide gel electrophoresis

Ser Serine

SILAC Stable isotope labeling by amino acids in cell culture

SOP Standard operating procedure

SP3 Single-pot solid-phase-enhanced sample preparation

SPE Solid-phase extraction

TCA Trichloroacetic acid

TCEP Tris(2-carboxyethyl)phosphine

TFA Trifluoroacetic acid

TIC Total ion chromatogram

TOF Time-of-flight

Tris Tris(hydroxymethyl)aminomethane

XIC Extracted ion chromatogram

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Table of contents

Abstract ................................................................................................................................................................... 2

Populair wetenschappelijke samenvatting............................................................................................................... 3

List of abbreviations ................................................................................................................................................ 5

1. Introduction ......................................................................................................................................................... 9

1.1. Tissue proteomics ........................................................................................................................................ 9

1.2. Techniques in protein research .................................................................................................................. 10

1.3. Bottom-up proteomics ............................................................................................................................... 11

1.3.1. Protein extraction ............................................................................................................................... 12

1.3.1.1. Buffers and salts .......................................................................................................................... 12

1.3.1.2. Detergents ................................................................................................................................... 13

1.3.1.3. Chaotropes .................................................................................................................................. 15

1.3.1.4. Solvent-assisted digestion ........................................................................................................... 15

1.3.2. Reduction and alkylation .................................................................................................................... 16

1.3.3. Protein digestion ................................................................................................................................. 16

1.3.3.1. Multiple enzyme digestion .......................................................................................................... 17

1.3.3.2. Techniques to accelerate the digestion ........................................................................................ 17

1.3.4. Sample purification ............................................................................................................................ 18

1.3.5. LC-MS analysis .................................................................................................................................. 20

1.3.6. Protein identification .......................................................................................................................... 21

1.4. Aim of the research .................................................................................................................................... 21

2. Materials and methods ...................................................................................................................................... 22

2.1. Chemicals. ................................................................................................................................................. 22

2.2. Sample preparation .................................................................................................................................... 22

2.2.1. Uterus tissue samples ......................................................................................................................... 22

2.2.2. Protein extraction and digestion ......................................................................................................... 23

2.2.3. Protocols ............................................................................................................................................. 23

2.2.3.1. Protocol 1 .................................................................................................................................... 24

2.2.3.2. Protocol 2 .................................................................................................................................... 24

2.2.3.3 Protocol 3.1 .................................................................................................................................. 24

2.2.3.4. Protocol 3.2 ................................................................................................................................. 24

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2.2.3.5. Protocol 4.1, 4.3, 4.4, and 4.6 ..................................................................................................... 25

2.2.3.6. Protocol 4.2 ................................................................................................................................. 25

2.2.3.7. Protocol 4.5 ................................................................................................................................. 25

2.2.3.8. Protocol 5.1.1 .............................................................................................................................. 25

2.2.3.9. Protocol 5.1.2 .............................................................................................................................. 26

2.2.3.10. Protocol 5.2.1 ............................................................................................................................ 26

2.2.3.11. Protocol 5.2.2 ............................................................................................................................ 26

2.2.3.12. Protocol 5.3 ............................................................................................................................... 26

2.2.3.13. Protocol 6 and 7 ........................................................................................................................ 27

2.2.4. Sample purification ............................................................................................................................ 27

2.3. Instrumental analysis ................................................................................................................................. 27

2.4. Software used............................................................................................................................................. 29

2.4.1. Determination of the tissue areas ........................................................................................................ 29

2.4.2. Database search .................................................................................................................................. 29

3. Results and discussion....................................................................................................................................... 30

3.1. Optimization of the MS and MS/MS parameters ....................................................................................... 30

3.2. Evaluation of chromatographic conditions ................................................................................................ 31

3.2.1. Influence of packing material of the analytical columns on protein identifications ........................... 31

3.2.2. Influence of the chromatographic run time on protein identifications................................................ 31

3.2.3. Influence of trap columns on protein identifications .......................................................................... 32

3.2.4. Influence of the injected amount on protein identifications ............................................................... 33

3.3. Comparison and optimizing extraction buffers .......................................................................................... 33

3.3.1. Evaluation of different extraction buffers ........................................................................................... 33

3.3.2. Optimizing the extraction buffer ........................................................................................................ 39

3.3. Distribution of identified proteins according to their molecular weight .................................................... 41

3.4. Distribution of proteins according to their cellular location ...................................................................... 43

3.5. Correlation of protein and peptide abundance among different protocols ................................................. 44

4. Conclusion ........................................................................................................................................................ 46

Supplementary information ................................................................................................................................... 47

References ............................................................................................................................................................. 49

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1. Introduction

Proteins as parts of cells, tissues, and organisms are important in metabolic pathways and biochemical

processes. Since they are the products of genes, their study can play a role in the better understanding

of the functions of genes and biological processes at molecular level. Proteomics which is the large scale

study of proteins has the potential to lead to new tools in medical intervention. Retrieved information

might be used for the development of personal responses in medical treatment and in biomedical

research, such as drug screening and biomarker discovery.2

Tissues are a valuable source of information on processes involved in organisms at a molecular

level. However, tissue samples are precious and unique and therefore their use is limited, leading to a

need for analyzing small sample amounts. In addition, solving difficulties emerging from minimizing

amount of starting material and sample loss during sample preparation remains a challenge.3 Therefore,

the aim of this study is to optimize the analytical procedure of the LC-MS method for the analysis of

small tissue amounts from fresh frozen (FF) laser capture microdissectioned (LCM) human uterine

tissue.

1.1. Tissue proteomics

In recent years, there has been an increasing interest in analyzing the protein content of tissue samples.

Biobanks are used for the storage and collection of biological samples, including tissue samples.

Advantages of biobanks are that they enable researchers to gain an easier access to samples and that the

samples are stored according to standard operating procedures (SOPs).4 SOPs assure that the samples

are stored in a reproducible way with a high quality. Furthermore, research groups are able to compare

samples, since their samples are preserved in the same way.5

Tissue samples are precious and therefore several methods for the preservation of tissues for

later studies exist. First, tissues can be placed in formalin and then embedded with paraffin, which are

defined as formalin fixed paraffin embedded (FFPE) tissues. This preservation method has been used

routinely for a long time and an advantage of this method is that it is possible to store the samples at

room temperature. However, it can lead to chemical modifications of the proteins due to crosslinking,

because formaldehyde is able to react with the side chains of lysine (Lys), arginine (Arg), histidine (His),

serine (Ser), and aspartic acid (Asp) residues.6 Alternatively, tissues can be freshly frozen using optimal

cutting temperature (OCT) compound or using liquid nitrogen or dry ice.5 The samples are then stored

at -80 °C, which can be disadvantageous to the costs when the samples are stored for a long time.7

Advantages of these methods are that the samples can be stored long-term and that the structures of the

tissues are not changed as in FFPE tissues.8

OCT compound, which is used for the embedding of tissues, contains among other components

polyvinyl alcohol (PVA) and polyethylene glycol (PEG), which can cause ion suppression and peaks in

the spectrum that might cover the peaks of the peptides. Removing OCT compound would therefore

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improve the MS analysis. Several studies investigating the removal of OCT compound have been

conducted. Weston et al. carried out the removal of OCT compound with diethyl ether-MeOH

precipitation, filter-aided sample preparation (FASP), and sodium dodecyl sulfate polyacrylamide gel

electrophoresis (SDS-PAGE). Inspection of the spectra after the removal confirmed that OCT compound

was not present and that the peptide peaks were more evident after OCT removal, which resulted in an

improved number of identified protein groups.8 Another study by Enthaler et al. reported that washing

tissue samples multiple times with EtOH and H2O was successful in the removal of OCT compound,

since it dissolves in polar solvents, but protein loss occurred.9

Small amounts of tissue samples can be obtained by LCM, which is a technique wherein a laser

is operated that cuts tissues into sections.10

1.2. Techniques in protein research

Facing proteome complexity requires the implementation of different technologies for the analysis, with

each of them showing advantages and limitations. A variety of techniques are used in proteomics for the

profiling and characterization of single proteins or protein mixtures, the dynamic study of proteins, and

the investigation of post-translational modifications.11 Some of them are gel based (SDS-PAGE and 2-

D gel electrophoresis), which separate proteins according to their isoelectric points and/or molecular

weights. However, these techniques have a low reproducibility and an insufficient separation of acidic,

basic, and hydrophobic proteins.11 Others of them are chemical isotope labeling methods (ICAT and

SILAC), which allow the quantification of proteins.11

Proteomics using MS has enabled the detection of the protein content of tissues, due to the high

sensitivity and efficiency of MS.10 Proteins can be studied in their intact form or as a mixture of peptides

obtained after digestion. While there is no single approach that provides all the necessary information,

there are several strategies used in protein studies: top-down proteomics, middle-down proteomics, and

bottom-up proteomics (Figure 1).

Figure 1. An overview of the three sub-groups of proteomics: top-down (a), middle-down (b), and

bottom-up proteomics (c).12

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In top-down proteomics, intact proteins are analyzed, though the performance is limited as it is difficult

to ionize and fragment entire proteins in MS due to their high molecular weight (MW). On the contrary,

in bottom-up (shotgun proteomics) and middle-down proteomics proteins are digested into peptides.12

Since peptides are more easily ionized and fragmented than proteins, this approach faces less difficulties.

After the digestion, the peptides are analyzed by MS and identified using bioinformatics tools.

Subsequently, the proteins are identified using database searches.13 The difference between middle-

down and bottom-up proteomics is that middle-down proteomics uses peptide fragments with a size of

2000-20000 Da, while bottom-up proteomics uses fragments of 500-3000 Da.12

1.3. Bottom-up proteomics

The past decade has seen the rapid development of bottom-up proteomics, mostly with the goal of

discovering protein biomarkers. Identification of peptides from MS/MS spectra requires high resolution

and highly accurate tandem mass spectra, since peptide identification is achieved by the comparison of

experimental spectra of peptide fragments with theoretical ones. However, the main disadvantages of

bottom-up proteomics, such as a low reproducibility and sensitivity, still need to be overcome. As a

result, not all proteins which are present in the sample can be identified, as shown in Figure 2.

Figure 2. Overview of correlation between the number of proteins in a sample, protein identified, and

proteins quantified in MS-based proteomics.14

Bottom-up protein analysis is a multistep process and a consistent and reproducible procedure still

represents an obstacle. Sample preparation is widely accepted as one of the critical steps, since small

differences in the processing of specimen can largely influence results. In the next Section, an outline

of the sample preparation and LC-MS analysis of bottom-up proteomics will be discussed as well as its

difficulties.

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Figure 3 provides the workflow of bottom-up proteomics. It consists of the following steps:

extraction of the proteins, reduction of the disulfide bonds formed between cysteine (Cys) residues,

alkylation of the Cys residues, digestion of the proteins into peptides, sample purification, and analysis

with LC-MS.

Figure 3. An overview of the standard workflow in bottom-up proteomics. First, the proteins are

extracted, followed by reduction of the disulfide bonds and alkylation of the Cys residues. Then, the

proteins are digested into peptides and sample purification takes place. The peptides are analyzed with

LC-MS.

1.3.1. Protein extraction

Protein extraction is usually one of the first steps in sample preparation in bottom-up proteomics. The

cells are lysed and the proteins are extracted from the cells using an extraction buffer. The solubility of

the proteins needs to be promoted, because they are frequently insoluble in their native form. The

solubility might be facilitated by the unfolding of proteins which is achieved by disrupting the

hydrophobic interactions, hydrogen bonds, disulfide bonds, and ionic interactions between amino acid

residues and proteins.15 The solubilization and denaturation of proteins might be promoted by the

addition of different chaotropes, detergents, organic solvents, reducing agents, and salts to extraction

buffers.

1.3.1.1. Buffers and salts

Buffers are added to extraction buffers, since they maintain an alkaline pH which favors protein

stability.16 Commonly used buffers for protein extraction are ammonium bicarbonate, 4-(2-

hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), radioimmunoprecipitation assay (RIPA), and

trisaminomethane hydrochloride (Tris HCl).17 The often used RIPA buffer contains 150 mM NaCl, 0.5%

sodium deoxycholate (SDC), 0.1% sodium dodecyl sulfate (SDS), and 50 mM Tris HCl and is highly

efficient in the solubilization of proteins due to the combination of the detergents SDC and SDS.18

Protein extraction

Reduction

Alkylation

Digestion

Sample purification

LC-MS analysis

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Salts are used to control the pH and ionic strength of the extraction buffer.19 Furthermore, they

can either increase or decrease the solubility of proteins due to the ionic strength of the buffer. At a high

ionic strength, water molecules form interactions with the salt ions instead of the proteins. As a result,

proteins form hydrophobic interactions with each other, followed by their precipitation (salting-out).17

At a low ionic strength however, the solubility of the proteins is increased. The ions surround the charged

amino acid residues and as a result, disruption of the ionic interactions between the residues will take

place and ionic interactions will form between the residues and ions. Consequently, the stabilization and

solubilization of the proteins is increased (salting-in).17 Salts can be divided into salting-in ions, neutral

ions, and salting-out ions. Salting-in ions weaken hydrophobic interactions and therefore increase the

solubility of proteins. Salting-out ions strengthen hydrophobic interactions between proteins and are

therefore used to precipitate proteins, whereas salting-in or neutral ions are used for the solubilization

of proteins. Examples of salting-in salts used in extraction buffers are ammonium bicarbonate, KCl,

NaCl, and Tris HCl.7,17,20,21

1.3.1.2. Detergents

Detergents are used to disrupt hydrophobic interactions, since they form micelles in aqueous media.

Hydrophobic molecules dissolve in the micelles, leading to an increased solubility of the proteins.16

Detergents are classified into three types: ionic, non-ionic, and zwitterionic. The latter type of detergents

means that the compound contains both positive and negative charged groups. Ionic detergents are more

effective in the solubilization of proteins than non-ionic and zwitterionic detergents, because their

charged head is able to interact with charged amino acid residues and is therefore able to break ionic

interactions.22 In addition, non-ionic and zwitterionic detergents form adducts with proteins and give

signal suppression making them less preferred than ionic detergents.23 SDS is a commonly used ionic

detergent and is very efficient in solubilizing hydrophobic and amphipathic membrane proteins15;

though a major drawback is that it is incompatible with LC-MS analysis and that it can give significant

interferences during the analysis. In addition, the concentration of SDS must be reduced prior to

digestion, since the activity of the commonly used protease is decreased in the presence of

concentrations higher than 0.1% SDS.24

The removal of SDS often leads to sample loss and thus, there has been an increasing interest

in the use of other detergents. Proc and co-workers have reported the ionic detergent SDC as a good

alternative for SDS with a similar efficiency, but with an easier removal.25 This is in agreement with the

results of Leon et al. They carried out the digestion with extraction buffers containing 5% SDC, and 8

M urea, and found that SDC resulted in more identified proteins compared to urea.26 The structures of

SDS and SDC are illustrated in Figure 4.

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Figure 4. Structures of the ionic detergents SDS and SDC.

Other alternatives for SDS are the zwitterionic detergent CHAPS and the non-ionic detergents

Nonidet P-40 (NP-40) and Triton X-100 (Figure 5), but they have a lower efficiency in the solubilization

of proteins compared to SDS. Studies have shown that they can however be added to the buffer in order

to complement the protein extraction.7,27

Figure 5. Structures of the zwitterionic detergent CHAPS and the non-ionic detergents NP-40 and Triton

X-100.

Also, several MS-compatible detergents have been developed, such as RapiGest SF28,

MaSDeS22 and ProteaseMAX29 (Figure 6). These detergents are acid-labile, meaning that they hydrolyze

under acidic conditions. The products do not interfere with MS analysis and do not show ion

suppression.29 Several studies have reported a similar or higher protein extraction efficiency of the MS-

compatible detergents compared to SDS, but their main disadvantage is their cost.30,31

Figure 6. Structures of the MS-compatible detergents RapiGest SF, MaSDeS, and ProteaseMax.

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1.3.1.3. Chaotropes

Chaotropes disrupt intermolecular hydrogen bonds and hydrophilic interactions and are therefore used

to promote the denaturation of proteins. Examples of chaotropes include urea, thiourea, and guanidine

HCl. However, the activity of trypsin is decreased in the presence of urea with a concentration higher

than 4 M or guanidine HCl with a concentration higher than 0.1 M.32,33 Therefore, the concentration of

chaotropes needs be reduced before the addition of trypsin.

Furthermore, incubation of samples containing urea at a high temperature should be avoided,

since proteins are more prone to carbamylation. Urea is in equilibrium with ammonium cyanate and

high temperatures will shift the equilibrium to the side of ammonium cyanate. The cyanate anion can be

protonated to form isocyanic acid, leading to the carbamylation of the amino and sulfhydryl groups of

amino acids present in proteins (Scheme 1).34

Scheme 1. Reaction scheme of the carbamylation of an amino group with isocyanic acid. R: side chain.

Consequently, the digestion of carbamylated proteins is hindered, which affects the digestion efficiency.

In addition, the retention times and intensity of the peaks and masses of the ions are changed, resulting

in a more complicated analysis of the peptides. In order to prevent this, cyanate scavengers are added to

the extraction buffer, such as ammonium bicarbonate, methylamine, and Tris HCl.35

Urea has been successfully used in extraction buffers for the protein extraction of FF and FFPE

tissues.36–38 A combination of SDS and urea can be used to supplement each other, since they extract

different proteins.39–41

1.3.1.4. Solvent-assisted digestion

The addition of an organic solvent to the extraction buffer has the benefit that the denaturation of proteins

is promoted, leading to an increased solubility of the proteins. Another advantage is that the removal of

organic solvents is easy to accomplish through lyophilization. Furthermore, the addition of an organic

solvent can help in the extracting of membrane proteins, because they have an increased solubility in

organic-aqueous buffers compared to aqueous buffers.42

Russell et al. investigated the tryptic digestion of multiple proteins using various concentrations

of MeOH, ACN, 2-propanol, and acetone as organic solvents. They found that the sequence coverage

of the proteins was increased using the organic buffers compared to an aqueous buffer.43 Another study

by Zhang et al. used two extraction buffers containing 60% MeOH or 1% SDS for the tryptic digestion

of E. coli. They reported that using the extraction buffer containing 60% MeOH resulted in a higher

16

number of protein identifications and a higher number of unique identified proteins compared to the

extraction buffer containing 1% SDS.44

It is still unclear what the optimal concentrations of organic solvents are, since several studies

have shown different results. A study comparing the tryptic digestion efficiency of extraction buffers

containing 6 M guanidine HCl, 80% ACN, or 0.1% RapiGest was carried out by Hervey and co-workers.

They found that the number of identified peptides was the highest when using 80% ACN.45 In contrast,

Wall et al. demonstrated that the enzyme activity of trypsin was decreased in the presence of 80% ACN.

They concluded that this was the result of autolysis and denaturation of trypsin, resulting in deactivation

of the enzyme.46

1.3.2. Reduction and alkylation

Reduction of disulfide bonds between Cys residues to thiol groups might be performed using reducing

agents, such as dithiothreitol (DTT), tris(2-carboxyethyl)phosphine (TCEP), β-mercaptoethanol, or

dithioerythritol (DTE). Breaking of disulfide bonds leads to the denaturation of proteins, which in turn

improves their solubilization. DTT is one of the mostly used reducing agents, because it is a strong

reducing agent and it prevents the reformation of disulfide bonds.47

Subsequently, the thiol groups of the Cys residues are alkylated to prevent the reformation of

disulfide bonds. This is generally achieved by the addition of iodoacetamide (IAA) or iodoacetic

acid.16,47 The reaction of the reduction of disulfide bonds and the alkylation of Cys residues is shown in

Scheme 2.

Scheme 2. Reaction scheme of the reduction of a disulfide bond between two Cys residues, followed by

the alkylation of the thiol groups with an alkylating agent to prevent the reformation of disulfide bonds.47

1.3.3. Protein digestion

The cleavage of proteins into peptide fragments can be achieved with proteases or through chemical

cleavage and this Section will only focus on proteolytic cleavage. Additionally, several methods that

have been developed to accelerate the digestion time will be explored.

Proteases are used to cleave proteins into peptides. Trypsin is the most widely used protease for

protein digestion due to its high specificity and efficiency, which is essential for protein identification.

Furthermore, peptides produced by trypsin are in favorable length for MS fragmentation. The optimal

pH for trypsin activity is 7.5-8.5. Trypsin cleaves at the carboxyl side of Lys and Arg residues, but not

when proline (Pro) is linked on the carboxyl side of Lys and Arg or when Asp is N-linked to Lys and

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Arg.13 Lys and Arg are one of the most abundant amino acids in the human body12 and are well

distributed in a protein,33 creating peptides with a favorable length for MS fragmentation.48

The susceptibility of trypsin to autolysis has been reduced using laboratory modified trypsin that

is highly resistant to autocatalytic reactions.49 Moreover, the autolysis of trypsin can be decreased by

the addition of calcium ions when their natural concentration in samples in low.47

However, a key problem is that the efficiency of trypsin might be easily affected by the other

reagents, such as urea, and that it sometimes misses cleavage sites, resulting in miscleaved peptide

fragments that are not reproducible and difficult to predict. This can lead to the miscalculation of the

actual occurrence of the peptides since it is possible that the miscleaved fragments are too long to detect

in the MS.49

Several attempts have been carried out to improve the digestion efficiency. Fang and co-workers

tried to reduce the number of nonspecific trypsin cleavages by investigating the effect of the protein to

trypsin ratio. They found that a higher ratio resulted in a higher number of nonspecific trypsin cleavages.

This may be explained by the fact that the chance of a trypsin molecule encountering another trypsin

molecule is higher at an increased concentration of trypsin, resulting in a higher autolytic rate.50 In

addition, several studies have investigated the effects of the digestion time on the efficiency of trypsin,

as autodigestion of trypsin can occur with a long digestion time. Klammer et al. and Proc et al. suggested

a digestion time shorter than the usual overnight digestion, since it resulted in the same number of protein

identifications. 25,32

1.3.3.1. Multiple enzyme digestion

However, sometimes trypsin digestion leaves cleavage sites uncleaved and therefore, two mostly used

proteases to supplement the trypsin digestion are chymotrypsin and Lys-C.48 Furthermore, trypsin has a

lower cleavage efficiency towards Lys compared to Arg and to prevent this, trypsin might be combined

with Lys-C, which cleaves at the carboxyl end of Lys residues.48Another useful characteristic of Lys-C

is that it has a similar pH range of activity as trypsin and that it maintains its activity in harsh conditions

as 8 M urea.32 As a consequence, the digestion conditions do not need to be changed and the same

extraction buffer can be used. Multiple studies have shown that a combination of trypsin and Lys-C

results in a higher digestion efficiency and more identified proteins compared to a trypsin only

digestion.20,51–53 Additionally, the number of miscleaved peptides decreased when using trypsin/Lys-C

instead of trypsin only.20,53

1.3.3.2. Techniques to accelerate the digestion

Several studies have been carried out on accelerating the tryptic digestion in order to shorten the overall

preparation time, since the digestion is usually conducted overnight. In this Section, several techniques

to accelerate and improve the protein extraction and digestion will be described.

18

It has been shown that microwave-assisted digestion decreases the tryptic digestion time to less

than 30 min and that it increases the digestion efficiency.54–56 Microwave irradiation accelerates the

digestion in the first minutes of the reaction, followed by the denaturation and inactivation of trypsin

after 30 min. The mechanism remains unclear, but it is thought that microwave irradiation helps in the

unfolding of proteins which provides better accessibility to the cleavage sites.57

In addition, it has been demonstrated that ultrasonic energy speeds up the digestion by increasing

the diffusion rate.58 An ultrasound bath is not powerful and efficient enough to accelerate the enzymatic

digestion59, but developed alternatives are ultrasonic probes and sonoreactors.58,60 They are able to

reduce the digestion time to less than five minutes.

Yang et al. carried out a pressure-assisted tryptic digestion using a syringe with a pressure of 6

atm that completed the digestion in 30 min.61 An advantage of this method is that the same sample

preparation steps were used as without the syringe. An explanation for the decreased digestion time is

that an elevated pressure increases the number of collisions between proteins and trypsin molecules,

speeding up the digestion.

1.3.4. Sample purification

Following the digestion, the samples need to be purified from compounds as buffers, salts, and

detergents, since they can interfere with LC-MS. Presence of these contaminants might cause ion

suppression, which negatively influences the sensitivity, accuracy, and precision of the analysis. Sample

purification might be achieved by solid-phase extraction (SPE) or C18 ZipTips. ZipTips are pipette tips

consisting of C18 material, which allows purification of the sample. Disadvantages are that sample loss

might occur and that a limited amount of sample can be loaded.47

As mentioned before, the removal of SDS from protein samples remains a challenge. SDS can

be removed using precipitation methods or dialysis, however protein recoveries are often low. Therefore,

several new methods to remove SDS have been developed. For example, Zhou et al. reported that no

sample losses occurred when using KCl to precipitate SDS in the form of KDS,62 and Wisniewski and

co-workers developed filter-aided sample preparation (FASP) which implements a filtration device to

remove SDS.63 Puchades et al. compared three methods to remove SDS: protein precipitation with

acetone, SDS precipitation with chloroform/MeOH/water, and SPE. They found that the method using

SPE was not able to sufficiently remove SDS and gave a protein recovery of 50%. The acetone and

chloroform/MeOH/water methods were able to reduce SDS to a concentration that allowed MS analysis,

giving protein recoveries of 80% and 50%, respectively. Their conclusion was to use acetone since it

showed the largest recovery. Additionally, in a study carried out by Kachuk et al., seven methods for

SDS removal were compared: SDS page, protein precipitation with acetone, protein precipitation with

TCA (trichloroacetic acid), detergent precipitation with KCl, strong cation exchange, SPE, and FASP.64

They found that KCl, TCA, and SPE were the most unsuccessful in the removal of SDS and had the

19

lowest protein recoveries (< 40%). FASP removed SDS completely, but the protein recovery was shown

to be 24-40%. SDS page and strong cation exchange removed SDS to a large extent, but the protein

recoveries were lower than using acetone, which had a SDS removal efficiency similar to SDS page and

strong cation exchange. Therefore, they suggested protein precipitation with acetone, which is consistent

with the results of Puchades et al. However, another disadvantage of acetone precipitation besides

sample loss is that the resulting pellet has a low solubility making it hard to dissolve the precipitated

proteins.65

A method was developed to prevent sample loss in which the entire preparation is carried out in

a single tube, called single-pot solid-phase-enhanced sample preparation (SP3).66 The tube is filled with

paramagnetic beads, which are coated with hydrophilic carboxylate material. The carboxylate surface

has a neutral charge in acidic conditions and a negative charge in basic conditions, which will allow the

binding of charged proteins. Furthermore, when an organic solvent is added to the aqueous buffer, a

hydrophilic layer will form on the magnetic beads, since the aqueous buffer and beads are attracted to

each other. The analytes will distribute between the hydrophilic layer and the organic buffer. Proteins

and peptides bind to the hydrophilic layer through hydrophilic interactions and ionic interactions with

the beads. Then, contaminants, such as detergents and chaotropes, are removed by changing the

composition of the organic buffer. Proteins and peptides are eluted by decreasing the organic component

of the buffer and the pH. The principle of SP3 is shown in Figure 7.

Figure 7. Principle of single-pot solid-phase enhanced sample preparation (SP3).67

20

1.3.5. LC-MS analysis

After the sample preparation, the instrumental analysis is an important part. Different instruments have

been used for the separation and detection of peptides in bottom-up proteomics. In this thesis, nano LC-

ESI-MS has been used for all the sample analysis and therefore, only this technique will be discussed in

more details.

To decrease sample complexity, peptides might be separated with reversed phase (RP) nano

liquid chromatography (LC) due to their different chromatographic behavior arising from their amino

acid sequences.68 In addition, the small flow rates ensure a better ionization of the peptides, because the

created droplets have a higher surface-to-volume ratio.69

The LC-system is hyphenated with MS where ionization and fragmentation of the peptides

occur. Peptides are ionized using electrospray ionization (ESI), since it is a soft ionization technique

causing little fragmentation of the ions due to their low internal energy.70 ESI might be used for the

analysis of complex samples, such as peptides.71 Several studies have suggested that the sensitivity of

ESI might be increased by the addition of the supercharging reagents dimethylsulfoxide (DMSO)72,73

and meta-nitrobenzyl alcohol (m-NBA)72 to the LC buffers. Supercharging reagents enhance charging

on the peptides and thereby the fragmentation.72 Meyer and Komives digested five proteins using pepsin,

elastase, and trypsin and carried out the LC-MS analysis in the presence of 5% DMSO and found that

the number of identified peptides increased.72 DMSO causes charge state reduction and coalescence in

one charge state, resulting in an increase of the signal of that state, which leads to a simpler precursor

spectrum.72,73 ESI is mostly coupled to hybrid tandem MS systems as quadrupole time-of-flight (QTOF)

systems, which show a high resolution and mass accuracy. In this work, TripleTOF 5600+ MS was used,

a hybrid quadrupole time-of-flight system that shows a high resolution, fast acquisition rates, and a high

sensitivity.74 An initial scan of the peptide fragments (MS1) is acquired in Q1 and then these fragments

are further fragmented in Q2 and scanned (MS/MS).74 Subsequently, the MS/MS spectra are used by

bioinformatics tools for structure elucidation and amino acid sequence determination.

The peptides can be fragmented on their amino terminus or carboxyl terminus. The

fragmentation of peptides can take place at three different cleavage sites (Figure 8). The bond between

the alpha carbon and the carbonyl carbon can be broken, which creates a-ions and x-ions. Next, the bond

between the carbonyl carbon and amide nitrogen can be broken creating b-ions and y-ions. Peptides are

usually fragmented in this manner. Finally, the bond between the amide nitrogen and alpha-carbon can

be broken, which creates c-ions and x-ions.75

Figure 8. An illustration of the possible cleavage sites for peptide fragmentation.75

21

1.3.6. Protein identification

Acquired data is further processed using bioinformatics tools. Obtained MS/MS spectra are compared

with theoretical fragment ion MS/MS spectra using a search engine to identify peptides. Several search

engines exist, such as Protein Pilot (AB Sciex), Mascot76, and SEQUEST77. These programs have

different algorithms, and therefore the identifications might differ. After the peptides have been

identified, their sequences are compared to the sequences of known proteins to determine which proteins

were present in the sample.78

Label-free quantification (LFQ) is one of the mostly used approaches in quantitative proteomics.

LFQ can be divided into two strategies. In the first strategy, the peaks of the extracted ion

chromatograms (XICs) are integrated over the retention time to calculate the peak area, which indicates

the abundancy of the peptides.14 The second strategy is known as spectral counting. The number of

MS/MS spectra are counted which are a measurement of the abundancy of the peptides. However, the

spectral count of a large peptide will be higher than the spectral count of a small peptide and therefore

must be corrected. This is accomplished by using the normalized spectral abundancy factor (NSAF),

which divides the spectral counts by the length of the protein and then divides it by the sum of all spectral

counts divided by the length of all proteins.79 An example of software packages to calculate LFQ values

is MaxQuant.80

1.4. Aim of the research

The aim of this study was to optimize an analytical method for the determination of proteins from small

amounts of laser microdissectioned OCT embedded human uterine tissue. Sample preparation will be

optimized by testing several extraction buffers, consisting of different detergents, chaotropes, and a

varied amount of organic solvent for the protein extraction. Furthermore, the impact of different

proteases on the protein digestion will be examined. After sample preparation, samples will be analyzed

on nano LC-ESI-MS using optimized instrumental parameters. Finally, the different protocols will be

compared in terms of protein identifications, physiochemical properties, and acquired LFQ values.

22

2. Materials and methods

2.1. Chemicals

ACN (LC-MS grade) and formic acid (FA, 99%) were obtained from Biosolve and NH4HCO3 was

delivered by Fluka Analytical. Lys-C (Mass Seq Grade) was bought from Promega. KCl was purchased

from Merck and Tris HCl was delivered by Roche. Acetone (≥ 99.9%), CaCl2, DTT (≥ 99.0%), IAA (≥

99%), NaCl, SDC (≥ 98.0%), SDS (≥ 98.5%), thiourea (≥ 99.5%), trifluoracetic acid (TFA, 99%),

trypsin (European Pharmacopoeia Reference Standard), and urea were obtained from Sigma Aldrich.

2.2. Sample preparation

2.2.1. Uterus tissue samples

Cryosectioned fresh frozen tissues of smooth muscle from uterus with a thickness of 16 µm were used

for the optimization of the analytical procedure. Using a scalpel (Martor KG, Germany), small regions

of interest (ROIs) were cut from the tissue samples (Figure 9). Variations between the ROIs selected for

the optimization of the analytical procedure were kept to a minimum in order to ensure that they were

homogeneous.

Figure 9. Example of a glass slide of 16 µm cryosectioned uterine tissue. Each spot was cut into 6

sections and assigned from 1 to 6.

23

2.2.2. Protein extraction and digestion

Proteins from uterine tissue samples were extracted using the buffers shown in Table 1 and the

experimental protocols are in details explained in Section 2.2.3. Information on the area of the samples,

the protocols used for sample preparation, sample purification methods, amount of sample analyzed in

LC-MS, and chromatographic conditions is summarized in Supplementary table 1.

Table 1. An overview of the extraction buffers that were tested with their composition.

Protocol Composition of the extraction buffer

1 1. 5% SDC

2. 7.5 mM DTT

2 1. 50 mM Tris HCl

2. 150 mM NaCl

3. 0.25% SDC

3 1. 4% SDS

2. 0.1 M Tris HCl

4 1. 1.44 g urea

2. 10 µL 1 M NH4HCO3

3. 2.8 µL 700 mM DTT

4. 30% ACN in 100 mM NH4HCO3 to a total volume of 2 mL (4.1)

5. 40% ACN in 100 mM NH4HCO3 to a total volume of 2 mL (4.3)

6. 60% ACN in 100 mM NH4HCO3 to a total volume of 2 mL (4.4)

5 1. 1.44 mg urea

2. 200 µL of 1 M NH4HCO3

3. 56 µL of 700 mM DTT

4. 30% ACN in 100 mM NH4HCO3 to a total volume of 2 mL

6 1. 80% ACN in 50 mM NH4HCO3

7 1. 60% MeOH in 50 mM NH4HCO3

2.2.3. Protocols

Protocols tested for sample processing are explained in detail here. Centrifugation was carried out with

a centrifuge (Centrifuge 5804 R, Eppendorf), incubation was performed with an incubator

(Thermomixer Compact, Eppendorf), sonicating was performed with in an ultrasonic bath (Transsonic

Digital S, Elma Schmidbauer GmbH), and vortexing was carried out with a shaker (Vortex 4 basic,

IKA).

24

2.2.3.1. Protocol 1

100 µL of Buffer 1 was added to the sample, followed by sonication for 30 min at RT and incubation

for 10 min at 85 °C. Then, 50 µL of 45 mM DTT was added and incubated for 20 min at 60 °C, followed

by the addition of 50 µL of 100 mM IAA and incubation in the dark for 30 min at RT. Subsequently,

the sample was diluted with H2O up to a final volume of 1 mL. 0.7 µL of 0.1 µg/µL of trypsin solution

was added. The digestion was carried out for 17h at 37 °C. Next, 5 µL of 10% TFA was added to the

sample. The sample was centrifuged for 45 min at 13000 rpm and the supernatant was collected.

2.2.3.2. Protocol 2

100 µL of Buffer 2 was added to the tissue sample, followed by sonication for 30 min at RT and

incubation at 4 °C (Protocol 2.1) or 85 °C (Protocol 2.2). Then, the sample was centrifuged at 13000

rpm for 30 min. The proteins were precipitated with 20% TFA for 30 min at 4 °C, followed by

centrifugation at 13000 rpm for 10 min and the decantation of the supernatant. 50 µL of 45 mM DTT

and 50 µL of H2O were added to the precipitated proteins. The sample was incubated for 30 min at 37

°C, followed by the addition of 50 µL of 12 mM IAA and incubation for 30 min at 37 °C in the dark.

Subsequently, 100 µL of a digestion buffer was added (100 mM Tris HCl, 6 M urea, 2 M thiourea, and

0.5% SDS) and the sample was diluted to a final volume of 1 mL with a dilution buffer (100 mM Tris

HCl, 10 mM CaCl2). 0.7 µL of 0.1 µg/µL trypsin was added and the digestion was carried out for 17h

at 37 °C. 5 µL of 10% TFA was added to the sample. The sample was centrifuged for 45 min at 13000

rpm and the supernatant was collected.

2.2.3.3 Protocol 3.1

The sample was mixed with 100 µL of Buffer 3 and sonicated for 30 min. Subsequently, the sample was

incubated for 10 min at 85 °C. 50 µL of 10 mM DTT was added and the incubation was carried out for

60 min at 37 °C. Next, 50 µL of 20 mM IAA was added and the sample was incubated for 30 min in the

dark at RT. 1.2 mL of cold acetone was added, followed by incubation for 20 min at 4 °C and

centrifugation for 10 min at 13000 rpm. 100 µL of a denaturing buffer (8 M urea and 50 mM NH4HCO3)

was added to the pellet and the sample was diluted with 50 mM NH4HCO3 to a final volume of 1 mL.

0.7 µL of 0.1 µg/µL trypsin was added and the digestion was carried out for 17h at 37 °C. Next, 5 µL

of 10% TFA was added to the sample. The sample was centrifuged for 45 min at 13000 rpm and the

supernatant was collected.

2.2.3.4. Protocol 3.2

The sample was mixed with 100 µL of Buffer 3, followed by sonication and incubation for 10 min at 85

°C. Subsequently, 50 µL of 10 mM DTT was added and the incubation was carried out for 60 min at 37

°C. Next, 50 µL of 20 mM IAA was added and the sample was incubated for 30 min in the dark at RT.

200 µL of 0.5 M KCl was added and the sample was incubated for 5 min at RT and centrifuged for 10

min at 13000 rpm. 100 µL of a denaturing buffer (8 M urea and 50 mM NH4HCO3) was added to the

25

supernatant. Subsequently, the sample was diluted with 50 mM NH4HCO3 to a final volume of 1 mL.

0.7 µ of 0.1 µg/µL trypsin was added and the digestion was carried out for 17h at 37 °C. 5 µL of 10%

TFA was added. The sample was centrifuged for 45 min at 13000 rpm and the supernatant was collected.

2.2.3.5. Protocol 4.1, 4.3, 4.4, and 4.6

100 µL of Buffer 5 was added to the tissue sample, followed by sonication for 30 min at RT and

incubation for 30 min at 37 °C. Next, 9.2 µL of 700 mM IAA was added and the incubation was carried

out for 30 min at 37 °C in the dark. The sample was diluted with 120 µL of 1 M NH4HCO3 and 880 µL

H2O. 7 µL of 0.01 µg/µL trypsin was added and the digestion took place for 17h at 37 °C. Next, the

sample was centrifuged for 45 min at 13000 rpm and 48 µL of 5% TFA was added to the supernatant.

2.2.3.6. Protocol 4.2

The tissue sample was mixed with 100 µL of Buffer 5 and the sample was sonicated for 30 min and

incubated for 30 min at 37 °C. Next, 9.2 µL of 700 mM IAA was added and the sample was incubated

for 30 min at 37 °C in the dark. The sample was diluted with 12 µL of 1 M NH4HCO3 and 88 µL H2O.

10 µL of 0.1 µg/µL Lys-C was added and the digestion took place for 3h at 37 °C. Next, the sample was

further diluted with 108 µL of 1 M NH4HCO3 and 792 µL H2O. 7 µL of 0.01 µg/µL trypsin was added

and the digestion took place for 17h at 37 °C. The sample was centrifuged for 45 min at 13000 rpm and

48 µL of 5% TFA was added to the supernatant.

2.2.3.7. Protocol 4.5

100 µL of Buffer 5 was added to the tissue sample, followed by sonication for 30 min at RT and

incubation for 30 min at 37 °C. 9.2 µL of 700 mM IAA was added and the incubation was carried out

for 30 min at 37 °C in the dark. 120 µL of 1 M NH4HCO3 and 880 µL H2O were added to the sample. 7

µL of 0.01 µg/µL trypsin was added and the digestion took place for 17h at 37 °C. Subsequently, another

7 µL of 0.01 µg/µL trypsin was added and the digestion took place for 6h at 37 °C. Subsequently, the

sample was centrifuged for 45 min at 13000 rpm. 48 µL of 5% TFA was added to the supernatant.

2.2.3.8. Protocol 5.1.1

The tissue sample was mixed with 100 µL of Buffer 5 and the sample was sonicated for 30 min at RT

and incubated for 30 min at 37 °C. Next, 9.2 µL of 700 mM IAA was added and the incubation was

carried out for 30 min at 37 °C in the dark. 120 µL of 1 M NH4HCO3 and 880 µL H2O were added to

the sample. 7 µL of 1 µg/µL trypsin was added and the digestion took place for 17h at 37 °C.

Subsequently, the sample was centrifuged for 45 min at 13000 rpm and 48 µL of 5% TFA was added to

the supernatant.

26

2.2.3.9. Protocol 5.1.2

100 µL of Buffer 5 was added to the tissue sample, followed by sonication for 30 min at RT and

incubation for 30 min at 37 °C. Next, 9.2 µL of 700 mM IAA was added and the incubation was carried

out for 30 min at 37 °C in the dark. The sample was diluted with 120 µL of 1 M NH4HCO3 and 880 µL

H2O. 0.7 µL of 0.1 µg/µL trypsin was added and the digestion took place for 18.5h. After the digestion

had completed, 1 (sample 5c-2) or 2 µL (sample 5c-3) of 0.1 µg/µL Lys-C was added. The digestion

took place for 4h at 37 °C. Subsequently, the sample was centrifuged for 45 min at 13000 rpm and 48

µL of 5% TFA was added to the supernatant.

2.2.3.10. Protocol 5.2.1

100 µL of Buffer 3 was added to the tissue sample, followed by sonication for 30 min at RT and

incubation for 10 min at 85 °C. 100 µL of 0.5 M KCl was added and the incubation was carried out for

5 min at RT. Next, the sample was centrifuged at 13000 rpm for 10 min and the supernatant was

collected. 100 µL of buffer 5 was added to the sample. Subsequently, the sample was sonicated for 30

min at RT and incubated for 30 min at 37 °C. Next, 9.2 µL of 700 mM IAA was added and the sample

was incubated for 30 min at 37 °C in the dark. 120 µL of 1 M NH4HCO3 and 880 µL H2O were added,

followed by the addition of 0.7 µL of 0.1 µg/µL trypsin and the digestion took place for 17h at 37 °C.

The sample was centrifuged for 45 min at 13000 rpm and 48 µL of 5% TFA was added to the

supernatant.

2.2.3.11. Protocol 5.2.2

100 µL of Buffer 3 was added to the tissue sample, followed by sonication for 30 min at RT and

incubation for 10 min at 85 °C. 100 µL of 0.5 M KCl was added and the incubation was carried out for

5 min at RT. The sample was centrifuged at 13000 rpm for 10 min and then, 100 µL of Buffer 5 was

added to the supernatant. Subsequently, the sample was sonicated for 30 min at RT and incubated for

30 min at 37 °C. Next, 9.2 µL of 700 mM IAA was added and incubated for 30 min at 37 °C in the dark.

120 µL of 1 M NH4HCO3 and 880 µL H2O were added, followed by the addition of 0.7 µL of 0.1 µg/µL

trypsin and the digestion took place for 17h at 37 °C. 1 µL of 0.1 µg/µL Lys-C was added and the Lys-

C digestion took place for 4h at 37 °C. The sample was centrifuged for 45 min at 13000 rpm. 48 µL of

5% TFA was added to the supernatant.

2.2.3.12. Protocol 5.3

100 µL of Buffer 5 was added to the tissue sample, followed by sonication for 30 min at RT and

incubation for 10 min at 37 °C. Subsequently, 50 µL of the sample was taken out and mixed with 100

µL of Buffer 3, followed by sonication for 30 min at RT and incubation for 10 min at 85 °C. 150 µL of

0.5 M KCl was added and incubated for 5 min at RT. The sample was centrifuged for 10 min at 13000

rpm and the supernatant was added to the first 50 µL of the sample. Next, 9.2 µL of 700 mM IAA was

added and incubated for 30 min at 37 °C in the dark. 120 µL of 1 M NH4HCO3 and 880 µL H2O were

27

added, followed by the addition of 0.7 µL of 0.01 µg/µL trypsin and the digestion took place for 17h at

37 °C. Subsequently, the sample was centrifuged for 45 min at 13000 rpm and 48 µL of 5% TFA was

added to the supernatant.

2.2.3.13. Protocol 6 and 7

100 µL of Buffer 6 (Protocol 6) or Buffer 7 (Protocol 7) was added and the sample was sonicated for

30 min, followed by incubation for 20 min at 37 °C. 10 µL of 200 mM DTT was added and incubation

was carried out for 1h at 37 °C. Next, 16 µL of 100 mM IAA was added and the sample was incubated

for 15 min at 37 °C in the dark. 7 µL of 0.01 µg/µL trypsin was added and the digestion took place for

17h at 37 °C. Subsequently, the sample was centrifuged for 45 min at 13000 rpm and 48 µL of 5% TFA

was added to the supernatant.

2.2.4. Sample purification

Subsequently, SPE or lyophilization proceeded with the samples, as described in Supplementary table

1. Samples were subjected to SPE using Empore 1 mL columns, following the protocol in Table 2.

Eluent was collected and the samples were evaporated with SpeedVac (miVac DNA concentrator

GeneVac, SP Scientific).

Table 2. Procedure used for SPE.

Step Solution Volume

Conditioning 50% ACN/H2O

0.1% TFA

2 x 300 µL

2 x 300 µL

Sample loading - -

Washing 0.1% TFA 2 x 300 µL

Sample elution 0.1% HCOOH in 60% ACN 2 x 150 µL

The samples that were not subjected to SPE were lyophilized overnight using a freeze dryer

(ThermoHetoPowerDry LL1500 Freeze Dryer, Thermo Fisher Scientific).

Dry residues were stored at -20 ⁰C until the analysis. Prior to LC-MS analysis, the samples were

reconstituted in 25% ACN in 1% FA or 3% ACN in 1% FA (Supplementary table 1) to a final

concentration of 0.2 mm2/µL.

2.3. Instrumental analysis

LC-MS/MS analysis was performed with Eksigent ekspert nanoLC 425 system coupled to a TripleTOF

5600+ System. Three trap columns were used for the concentrating and desalting of the samples: an AB

Sciex 0.5 mm long, 350 µm inner diameter trap column (Chrom XP C18 resin, 120 Å pore size, 3 µm),

an in-house packed 1.5 cm long, 75 µm inner diameter trap column (Magic C18 resin, 100 Å pore size,

5 µm), and an in-house packed 5.0 cm long, 200 µm inner diameter trap column (Magic C18 resin, 100

28

Å pore size, 5 µm). An in-house packed 10 cm long, 75 µm inner diameter column (Magic C18 resin,

100 Å pore size, 5 µm) was used for peptide separation. Peptides were eluted using a 60 or 95 min long

gradient composed of solvent A (0.1% FA in H2O) and solvent B (0.1% FA in ACN). The 60 min

gradient consisted of 5% to 40% B over 45 min, followed by 40% B to 100% B over 5 min and was

constant at 100% B for 9 min. Then, the gradient was changed from 100% B to 5% B in 1 min. The 95

min gradient consisted of 5% to 30% B over 65 min and was then changed from 30% to 80% B in 1

min. It was constant at 80% B for 8 min, and subsequently changed from 80% to 5% B in 1 min and

was constant at 5% B for 20 min.

Peptides were detected in positive ionization mode. The MS and MS/MS parameters for the

detection of the peptides were optimized to yield the highest number of protein identifications, resulting

in the parameters shown in Table 3.

Table 3. MS optimized parameters used for the detection of the peptides.

MS Start mass and end mass (Da) 400-1250

Accumulation time (ms) 500

From charge state to charge state 2 to 4

With intensity greater than (cds) 100

Mass tolerance (mDa) 40

Switch after 30 spectra

Exclude for (s) 30

Exclude after occurrences 1

MS/MS Start mass and end mass (Da) 200-1800

Accumulation time (ms) 100

From charge state to charge state 2 to 4

With intensity greater than (cds) 100

Mass tolerance (mDa) 50

Switch after 30 spectra

Exclude for (s) 30

Exclude after occurrences 1

29

2.4. Software used

2.4.1. Determination of the tissue areas

Areas of the tissue samples were determined using ImageJ software v 1.50i (National Institutes of

Health, USA).

2.4.2. Database search

Proteins were identified with Protein Pilot Software v 5.0 (AB Sciex, Singapore) with the parameters

given in Table 4.

Table 4. Parameters used for the database search with Protein Pilot.

Sample type Identification

Cysteine alkylation Iodoacetamide

Digestion Trypsin / trypsin + Lys-C

Species Homo sapiens

Database Uniprot human thorough

Search effort Thorough ID

Detected protein threshold 0.05 (10%)

Proteins were quantified with MaxQuant v 1.5.4.1 (Max-Planck Institute for Biochemistry, Germany)

and the results were viewed with Perseus (Max-Planck Institute for Biochemistry, Germany) v 1.5.4.1.

The parameters used for data processing with MaxQuant that were changed from their initial value are

shown in Table 5.

Table 5. Parameters used for the database search with MaxQuant that were changed from their initial

value.

Group-specific parameters

Digestion mode Enzyme Trypsin

Modifications Variable modifications Carbamidomethyl (C)

Label-free quantification Label-free quantification LFW

Instrument Instrument type AB Sciex Q-TOF

Max. charge 4

Intensity threshold 100

Global parameters

Sequences Fixed modifications Carbamidomethyl (C)

Max. peptide mass 5500

Label free quantification Separate LFQ in parameter groups Checked

iBAQ Checked

30

3. Results and discussion

Sample preparation in bottom-up proteomics remains an obstacle. This is caused by the low

reproducibility and sensitivity of the current methods together with the limited availability of the tissue

samples. While many methods exist for the analysis of large amounts of proteins, the analysis of small

amounts of tissue sample still represents a challenge. Here several sample preparation protocols for the

analysis of small FF uterine tissue amounts have been examined and evaluated on the outcome on

peptide and protein level. Several different protocols have been tested and due to time limitations, many

of them have only been carried out once. For the total method validation, the protocol that showed the

best performances has to be repeated and examined on analytical parameters as reproducibility,

repeatability, and limit of detection (LOD). An overview of the number of identified protein groups,

identified peptides, and acquired spectra for all performed experiments is given in Supplementary table

1.

3.1. Optimization of the MS and MS/MS parameters

The MS and MS/MS parameters for the detection of peptides need to be optimized to maximize the

number of peptides selected for MS detection. Instrumental parameters as the accumulation time,

scanned mass range, charge state, intensity threshold, number of top ions, and exclusion time were

chosen for optimization. Hence, the MS and MS/MS parameters were first optimized to obtain the

highest number of protein groups (Figure 10).

1 2 3 4 MS Accumulation time (ms) 50 250 250 500 Mass range (Da) 400-1250 400-1250 400-1250 400-1250 Charge range 2 to 4 2 to 4 2 to 4 2 to 4 Intensity threshold (cds) 100 100 100 100 Top ions 30 30 30 30 Exclusion time (s) 30s/1occ 30s/1occ 30s/1occ 30s/1occ MS/MS Accumulation time (ms) 100 100 250 100 Mass range (Da) 200-1800 200-1800 200-1800 200-1800

Figure 10. The optimization of the MS and MS/MS parameters for the analysis of peptides using

samples prepared by Protocol 5.1.1 (n=1).

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

0

50

100

150

200

250

300

350

400

450

Number of spectra and identified peptides

Number of identified

protein groups

Protein groups Spectra Peptides

31

The initial MS analysis parameters were as follows: an accumulation of 50 ms, a mass range of 400-

1250 Da, a charge range of 2+ to 4+, an intensity threshold of 100 cds, an exclusion time of 30 s per 1

occurrence, and a collection of the 30 top ions. The MS/MS parameters consisted of an accumulation

time of 100 ms and a mass range of 200-1800 Da (1). After changing the accumulation time of the MS

analysis from 50 to 250 ms (2), the number of protein groups was almost doubled. This may be explained

by the fact that a higher amount of ions is accumulated when the accumulation time is broadened, which

improves the signal intensities and therefore the number of spectra and peptides. Based on this, the

accumulation time of the MS/MS analysis was changed from 100 to 250 ms (3). However, this did not

result in an increase of the number of identified protein groups which may be the effect of overloading.

Increasing the accumulation time of the MS analysis to 500 ms increased the number of identified

protein groups and was therefore further used in the experiments (4). As a result, the parameters that

were used for the remaining experiments are those of 4 and they are further detailed in Table 3.

3.2. Evaluation of chromatographic conditions

To evaluate the influence of the chromatographic conditions on the outcome of the analysis, analytical

columns with 3 and 5 µm particle sizes, different chromatographic run times, various amounts of

analyzed tissue, and several trap columns were tested.

3.2.1. Influence of packing material of the analytical columns on protein identifications

Columns with a lower particle size have a higher efficiency compared to columns with larger particles,

which could potentially lead to an increase in the number of protein identifications. For example, two

columns packed with 3 and 1.9 µm material were compared on the digestion of bovine protein mixture

and the 1.9 µm column had a higher peak capacity and resolution.81 Therefore, another column with a

particle size of 3 µm was tested besides the column packed with 5 µm. The number of identified protein

groups, identified peptides, and acquired spectra of the 5 and 3 µm column were 296, 2724, 14500 and

227, 1921, 10268, respectively. Thus, the number of identified protein groups, spectra, and peptides was

not improved using smaller particles of 3 µm. Therefore, all further analysis was carried out using

columns with 5 µm particles.

3.2.2. Influence of the chromatographic run time on protein identifications

The chromatographic run time was changed from 60 to 95 min in order to determine its effect, since a

longer run time might improve the separation of the peptides and an elution of the peptides which were

originally retained on the column. As can been seen from Figure 11, the number of spectra increased by

66%, which is in agreement with the study of Hsieh et al., who found that increasing the run time from

60 min to 90 min doubled the number of spectra.82 However, an increase in the number of identified

peptides and protein groups was not observed, suggesting that extra information on unique peptides and

proteins was not retrieved with the additional spectra. These results are consisted from those of Richards

32

and co-workers who reported that the number of uniquely identified peptides decreases with an

increasing chromatographic run time.83

Figure 11. Number of identified protein groups, spectra, and identified peptides obtained using a 60

min gradient and a 95 min gradient time using samples prepared by Protocol 4.1. The injected amount

of tissue was 1 mm2, the chromatographic conditions were as described in Section 2.3, and the 75 µm

trap column was used (n=1).

3.2.3. Influence of trap columns on protein identifications

Three trap columns were compared on their loading capacity using Protocol 4.6: an AB Sciex trap

column, a 200 µm trap column, and a 75 µm trap column. Figure 12 shows the number of identified

protein groups, spectra, and identified peptides of the 3 trap columns. The AB Sciex and 75 µm trap

column showed similar results, but the 200 µm trap column had approximately 13% protein group

identifications less compared to the other two trap columns. During the analysis, the pressure with the

200 µm trap column was lower than when other trap columns were used, which might have affected the

results.

Figure 12. Number of identified protein groups, spectra, and identified peptides obtained using an AB

Sciex trap column, a 200 µm trap column, and a 75 µm trap column using samples prepared by Protocol

4.6. The injected amount was 2 mm2, the chromatographic run time was 95 min, and the

chromatographic conditions were as described in Section 2.3 (n=1).

0

5000

10000

15000

20000

25000

0

50

100

150

200

250

300

350

60 min 95 min

Number of spectra and identified

peptides

Number of identified protein

groups

Protein groups Spectra Peptides

0

5000

10000

15000

20000

25000

30000

0

50

100

150

200

250

300

350

400

AB Sciex 200 µm 75 µm

Number of spectra and identified

peptides

Number of identified protein

groups

Protein groups Spectra Peptides

33

3.2.4. Influence of the injected amount on protein identifications

In addition, samples with an area of 1 and 2 mm2 were injected in order to determine the effect of the

injected amount on the protein identifications. It is hypothesized that a higher amount of sample would

result in more protein identifications, since their concentration is higher, resulting in more peptides

which might be analyzed. This was proven to be correct as shown in Figure 13, since the number of

protein group identifications increased. The increase of protein identifications when injected a higher

amount was in agreement with previous research, but the number of spectra decreased, which is in

contrast with similar studies.84,85 However, previous research injected various amounts of FFPE tissue

in range of 0.1 and 1.0 µg and it was shown that the difference in spectra between 0.5 and 1.0 µg was

smaller compared to 0.1 and 0.25 µg, which might indicate that a limit of spectra is reached around 1.0

µg.84

Figure 13. Number of identified protein groups, spectra, and identified peptides obtained with an

injected amount of 1 and 2 mm2 using samples prepared by Protocol 4.6. The chromatographic run time

was 95 min, the chromatographic conditions were as described in Section 2.3, and the AB Sciex trap

column was used (n=1).

3.3. Comparison and optimizing extraction buffers

3.3.1. Evaluation of different extraction buffers

Next, different extraction buffers containing various concentrations of SDC, SDS, ACN, and urea were

tested: 0.25% SDC (Protocol 1), 5% SDC (Protocol 2), 4% SDS (Protocol 3.1), 80% ACN (Protocol 6),

8 M urea with 30% ACN (Protocol 5.1.1) followed by sample purification with lyophilization and SPE.

However, precipitation occurred in the samples containing SDC after sample purification with SPE

indicating that the removal of SDC was not successful. As a result, the samples containing SDC were

not analyzed.

The number of identified protein groups, spectra, and peptides using the described extraction

buffers containing 4% SDS, 80% ACN, and 8 M urea with 30% ACN are shown in Figure 14.

0

5000

10000

15000

20000

25000

30000

0

50

100

150

200

250

300

350

400

1 mm^2 2 mm^2

Number of spectra and identified

peptides

Number of identified protein

groups

Protein groups Spectra Peptides

34

Figure 14. The number of spectra, identified peptides, and identified protein groups obtained using

extraction buffers containing 4% SDS (Protocol 3.1), 80% ACN (Protocol 6), 8 M urea with 30% ACN

combined with lyophilization (Protocol 5.1.1), and 8 M urea with 30% ACN combined with SPE

(Protocol 5.1.1). The injected amount of tissue was 1 mm2, the chromatographic run time was 60 min,

the chromatographic conditions were as described in Section 2.3, and the AB Sciex trap column was

used (n=1).

The extraction buffer containing 4% SDS gave less spectra, identified peptides, and identified protein

groups than 8 M urea with 30% ACN. As can been from Figure 15, the intensities of the peaks using the

extraction buffer containing 4% SDS in the Total Ion Chromatogram (TIC) and Base Peak

Chromatogram (BPC) were lower than those using 8 M urea with 30% ACN combined with SPE, which

was probably the result of the insufficient removal of SDS from the sample resulting in ion suppression.

0

5000

10000

15000

20000

25000

0

50

100

150

200

250

300

350

400

450

4% SDS 80% ACN 8 M urea with30% ACN

(lyophilization)

8 M urea with30% ACN (SPE)

Number of spectra and identified

peptides

Number of identified

protein groups

Protein groups Spectra Peptides

35

Figure 15. TIC (a) and BPC (b) using the extraction buffer containing 8 M urea with 30% ACN

combined with SPE (Protocol 5.1.1) (red), 8 M urea with 30% ACN combined with lyophilization

(Protocol 5.1.1.) (pink), and 4% SDS (Protocol 3.1) (blue). The injected amount of tissue was 1 mm2,

the chromatographic run time was 60 min, the chromatographic conditions were as described in Section

2.3, and the AB Sciex trap column was used (n=1).

Moreover, the peptides extracted using the extraction buffer that contained 4% SDS had lower intensities

than the buffer containing 8 M urea with 30% ACN as shown in their heat maps (Figure 16). This may

be caused by the high concentration of SDS (4%), whereas other studies usually use a concentration of

< 2%.7,27,86

36

A

B

Figure 16. Heat map of the extraction buffer containing 4% SDS (Protocol 3.1) (a) and of the extraction

buffer containing 8 M urea with 30% ACN combined with SPE (Protocol 5.1.1) (b). The injected amount

of tissue was 1 mm2, the chromatographic run time was 60 min, the chromatographic conditions were

as described in Section 2.3, and the AB Sciex trap column was used (n=1).

Additionally, the extraction buffer containing 80% ACN gave less spectra, identified peptides,

and identified protein groups compared to 8 M urea with 30% ACN. These results are not in agreement

with the results of Hervey et al., who compared extraction buffers containing 80% ACN, 6 M guanidine

HCl, or 0.1% RapiGest SF and found that 80% ACN resulted in the highest number of identified

proteins.45 They are however consistent with the results from Wall et al., who showed that the activity

of trypsin is reduced in the presence of ACN concentrations higher than 40%.46

37

The effect of the purification procedure was investigated by comparing the purification of

samples prepared according to Protocol 5.1.1 (extraction buffer containing 8 M urea with 30% ACN)

by lyophilization or SPE. Not visible large amounts of salts after lyophilization indicated that

ammonium bicarbonate was removed from the sample due to its decomposition. The analysis however

resulted in less spectra, identified peptides, and identified protein groups than SPE, which might suggest

the presence of other coeluting compounds which are usually removed with SPE. In addition, the signals

of the peptide peaks were lower compared to SPE, which might be the result of the ion supression

(Figure 15).

The chosen instrumentation has a large influence on the outcome of the results. Other similar

studies using Q-TOF MS obtained around 500 protein identifications using FFPE tissue and similar

results (426 protein group identifications) were obtained in this thesis using the extraction buffer

containing 8 M urea.21,87,88 In contrast, similar studies identificated more than 1000 protein groups using

a Q-Orbitrap MS that combines as a high scan speed, sensitivity, resolution, and mass accuracy13.89,90

To determine similarities in extracted proteins using different protocols, the overlap between

the identified protein groups and peptides using buffers containing 4% SDS and 8 M urea was examined.

Results are shown using Venn diagrams (http://www.cmbi.ru.nl/cdd/biovenn/) in Figure 17. It can be

seen from Figure 17 that 8 M urea had the highest amount of uniquely identified protein groups and

peptides compared to 4% SDS. These results together with the ones shown in Figure 14 indicated the

use of 8 M urea as an extraction buffer and SPE as a sample purification method for further method

optimization.

Figure 17. Venn diagrams depicting the overlap of the identified protein groups (a) and peptides (b)

between extraction buffers containing 4% SDS (Protocol 3.1) and 8 M urea with 30% ACN (Protocol

5.1.1). The injected amount of tissue was 1 mm2, the chromatographic run time was 60 min, the

chromatographic conditions were as described in Section 2.3, and the AB Sciex trap column was used

(n=1).

Additionally, overlap between the identified protein groups and peptides for different tissue

spots using the same digestion method with an area of 11.5, 11.7, and 14.9 mm2 was investigated. Venn

38

diagrams showing the overlap of the identified protein groups and peptides between three replicates of

8 M urea with 30% ACN are shown in Figure 18. The results showed that three replicates have a high

number of overlapping identified protein groups and peptides, suggesting that the digestions were

reproducible. The number of identified protein groups and peptides between the different samples

showed a coefficient of variation (CV) of 12.7% and 15.8%, respectively.

Figure 18. Venn diagram depicting the identified protein groups (a) and peptides (b) overlap of three

replicates of the extraction buffer containing 8 M urea with 30% ACN (Protocol 4.1). The injected

amount of tissue was 1 mm2, the chromatographic run time was 60 min, the chromatographic conditions

were as described in Section 2.3, and the 75 µm trap column was used (n=1).

The instrumental reproducibility was examined by injecting a sample prepared according to Protocol

4.1 three consecutive times. The results are shown in a Venn diagram that illustrates the overlap between

the number of identified protein groups and peptides for three consecutive sample injections (Figure

19). The replicates have a high number of overlapping protein groups and peptides with a CV of 5.5%

and 1.8% respectively. The results indicate that there was minimal instrumental variation during LC-

MS analysis.

Figure 19. Venn diagram depicting the overlap between the identified protein groups (a) and peptides

(b) overlap of three instrumental replicates of the extraction buffer containing 8 M urea with 30% ACN

(Protocol 4.1). The injected amount of tissue was 1 mm2, the chromatographic run time was 60 min, the

chromatographic conditions were as described in Section 2.3, and the 75 µm trap column was used

(n=3).

39

3.3.2. Optimizing the extraction buffer

After the evaluation of the discussed protocols, the extraction protocol consisting of 8 M urea was further

optimized to investigate the impact of the ACN concentration and protease used for digestion. The

concentration of ACN was increased from 30% (Protocol 4.1) to 40% (Protocol number 4.3) and 60%

(Protocol number 4.4), since it was reported that the addition of an organic solvent promotes the

unfolding of proteins.42 Furthermore, the digestion was carried out with Lys-C followed by trypsin

(Protocol number 4.2), and with a double trypsin addition (Protocol number 4.5). Finally, one ROI was

further cut into six sections for a separated digestion but a combined analysis (Protocol number 4.6).

Figure 20 shows the number of identified protein groups, spectra, and identified peptides of these

extraction buffers. Protocol 4.1 was carried out in triplicates and the calculated CV of the identified

protein groups was 12.7%.

Figure 20. Number of identified protein groups, spectra, and identified peptides obtained using

extraction buffers existing of 8 M urea with 30% ACN (Protocol 4.1), 8 M urea with 40% ACN (Protocol

4.), 8 M urea with 60% ACN (Protocol 4.4), 8 M urea with trypsin and Lys-C (Protocol 4.2), 8 M urea

with digestion using double trypsin addition (Protocol 4.5), and 8 M urea using a sample spot which was

further cut into six sections (Protocol 4.6). The injected amount of tissue was 1 mm2, the

chromatographic run time was 60 min, the chromatographic conditions were as described in Section 2.3,

and the 75 µm trap column was used (n=1).

First, the influence of the concentration of ACN on the protein identifications was determined by

increasing the original ACN concentration from 30% to 40% and 60% (Figure 20). Increasing the ACN

concentration from 30% to 40% had no effect on the number of identified protein groups, spectra, and

identified peptides, indicating that the difference in concentrations was not sufficiently high to improve

0

5000

10000

15000

20000

25000

30000

0

50

100

150

200

250

300

350

400

8 M ureawith 30%ACN (SPE)

8 M ureawith 40%ACN (SPE)

8 M ureawith 60%ACN (SPE)

8 M ureawith

trypsinand Lys-C

(SPE)

8 M ureawith

doubletrypsin

addition(SPE)

8 M urea,6 sections

(SPE)

Number of spectra and identified

peeptides

Number of identified protein

groups

Proteins Spectra Peptides

40

the unfolding of proteins. However, increasing the concentration to 60% increased the number of

identified protein groups in about 27%, suggesting that it improved the unfolding of proteins in

agreement with previous research.43,46

Next, the effect of different combinations of proteases on the protein digestion efficiency was

examined (Figure 20). The digestion was carried out with a single trypsin addition, a double trypsin

addition, and Lys-C followed by trypsin. The results indicate that the trypsin/Lys-C digestion did not

improve the digestion, since the number of identified protein groups, spectra, and peptides are lower

than that using trypsin digestion. These results differ from other studies, who have shown that a

trypsin/Lys-C digestion increased the number of identifications.20,51–53 In contrast, the number of

identified protein groups increased when carrying out the digestion with double trypsin addition by

approximately 17%.

Furthermore, Venn diagrams of the overlap between the identified protein groups and peptides

of the three methods were constructed as shown in Figure 21. These results show that the digestion when

trypsin was added two consecutive times had the highest amount of unique identified protein groups and

an amount of unique identified peptides which was similar to the amount of peptides which were present

in all three samples.

Figure 21. Venn diagram depicting the identified protein groups (a) and peptides (b) overlap of the

digestion using trypsin (Protocol 4.1), double trypsin addition (Protocol 4.5), and trypsin/Lys-C

(Protocol 4.1) on an extraction buffer containing 8 M urea. The injected amount of tissue was 1 mm2,

the chromatographic run time was 60 min, the chromatographic conditions were as described in Section

2.3, and the 75 µm trap column was used (n=1).

One ROI was cut into six sections for a separated digestion and combined analysis, because it

was thought that the sections might extract different proteins, which would be combined during the

analysis. The number of spectra showed an increase of about 53% (Figure 20), but it did not largely

affect the number of identified protein groups and peptides indicating that the additional spectra that

were obtained did not add in identifying more unique peptides.

41

3.3. Distribution of identified proteins according to their molecular weight

The results were further evaluated at protein level to obtain insights in the difference among tested

methods.

MW distribution of the identified proteins of the sample that was prepared using the extraction

buffer containing 8 urea with 30% ACN with a trypsin digestion (Protocol 4.1) was determined (Figure

22). Figure 22 shows that the proteins with a MW in a range of 10-20 kDa had the highest LFQ values

and that proteins with other MWs showed lower LFQ values. This might indicate better extraction of

smaller proteins using this extraction buffer or a preferred digestion by trypsin due to the possible easier

access to the cleavage sites compared to larger proteins. Tanca et al. and Leon et al. used extraction

buffers containing 0.2% SDS and 0.1% RapiGest SF respectively and also observed a higher abundancy

of proteins with a MW in range of 10-20 kDa compared to other MWs.26,86

Furthermore, the MW distribution of the proteins was calculated by the percentage of proteins

with a particular MW in total number of proteins, which is shown in Figure 22 as the size of the bubbles.

The results show that proteins with a MW lower than 60 kDa were more represented.

Figure 22. Protein distribution according to MW using an 8 M urea extraction buffer and a single trypsin

digestion (Protocol 4.1). The size of the bubbles indicates the percentage of proteins with that MW.

Next, relation of the LFQ values to the MW distribution for Protocols 4.2, 4.4, 4.5, and 4.6, were

compared with Protocol 4.1 (Figure 23). Proteins with a MW between 10 and 20 kDa had a higher

intensity using the 8 M urea with single trypsin digestion protocol than when double digestion of the

samples was performed (trypsin/Lys-C or a double addition of trypsin). Furthermore, the LFQ of

proteins with a MW of 30-50, 80-90, 100-150, and higher than 200 kDa was lower with single trypsin

digestion than the digestions with trypsin/Lys-C and a double addition of trypsin, which might indicate

a better cleavage of the proteins when carrying out a double digestion (Figure 23A and B). Additionally,

the results show that when a higher concentration of ACN is used in the extraction the LFQ values of

proteins with a MW in range of 40-60 and higher than 200 kDa (Figure 23C) were increased. In addition,

proteins with a MW higher than 20 kDa showed higher LFQ values when smaller sample amounts were

-100

10203040506070

LFQ (%)

Molecular weight (kDa)

42

digested separately and analyzed together (Figure 23D). A possible reason might be that cleavage sites

are more easily accessible when smaller sample concentrations are used.

0

10

20

30

40

50

60

70

LFQ (%)

Molecular weight (kDa)

a

Extraction buffer containing 8 Murea and 30% ACN, followed bysingle trypsin digestion

Extraction buffer containing 8 Murea and 30% ACN, followed bydouble trypsin digestion

0

10

20

30

40

50

60

70

LFQ (%)

Molecular weight (kDa)

b

Extraction buffer containing 8 Murea and 30% ACN, followed bysingle trypsin digestion

Extraction buffer containing 8 Murea and 30% ACN, followed bysingle trypsin/Lys-C digestion

0

10

20

30

40

50

60

70

LFQ (%)

Molecular weight (kDa)

c

Extraction buffer containing 8 Murea and 30% ACN, followed bysingle trypsin digestion

Extraction buffer containing 8 Murea and 60% ACN, followed bysingle trypsin digestion

43

Figure 23. Protein distribution according to their molecular weights and LFQ intensities. The extraction

protocols containing 8 M urea with digestion with double trypsin addition (Protocol 4.5) (a), 8 M urea

with trypsin/Lys-C digestion (Protocol 4.2) (b), 8 M urea with 60% ACN (Protocol 4.4) (c), and 8 M

urea wherein the ROI was further cut into 6 sections (Protocol 4.6) (d) were compared with the

extraction protocol containing 8 M urea with single trypsin digestion (Protocol 4.1).

3.4. Distribution of proteins according to their cellular location

In order to determine the difference of the extraction buffers on the extraction of proteins based on their

cellular location, the results were analyzed by Gene Ontology (http://pantherdb.org). The cellular

locations that the proteins are being classified into are extracellular region, organelle, cell part,

extracellular matrix, macromolecular complex, membrane, and cell junction. The extracellular region is

the space external to the structure of a cell. The extracellular matrix is a structure lying outside the cells

giving them support. An organelle is defined as an organized structure with a specific function and

includes nuclei, mitochondria, plastids, vacuoles, vesicles, ribosomes, and cytoskeletons whereas cell

parts can refer to any constituent part of the cell. An organization of two or more macromolecules is

defined as a macromolecular complex. Furthermore, cells are enclosed by membranes and are connected

by cell junctions.1

The results of the classification of proteins among different protocols are shown in Figure 24.

The highest representation of proteins in the extracellular region was extracted using 8 M urea with 30%

ACN followed by trypsin digestion and using 8 M urea followed by digestion with double trypsin

addition (Protocol 4.1). Furthermore, the extraction buffer containing 80% ACN (Protocol 6) extracted

a higher amount of organelles compared to the other methods and the extraction buffer containing 4%

SDS (Protocol 3.1) extracted the highest amount of macromolecular complexes. The extraction buffer

containing 4% SDS (Protocol 3.1) resulted in the lowest amount of membrane proteins. No difference

1 http://www.pantherdb.org/panther/prowler.jsp

0

10

20

30

40

50

60

70

LFQ (%)

Molecular weight (kDa)

d

Extraction buffer containing 8 Murea and 30% ACN, followed bysingle trypsin digestion

Extraction buffer containing 8 Murea and 30% ACN, followed bysingle trypsin digestion after spotwas cut into 6 sections

44

in the distribution between the protocols were observed in the extraction of cell parts, extracellular

matrixes, and cell junctions.

The cellular locations that had the highest representation were organelles and cell parts and

membranes and cell junctions had the lowest representations, which is in agreement with other studies

who identified the highest amount of proteins originating from the intracellular region and the lowest

amount of proteins originating from extracellular regions and membranes using FFPE and FF tissue.86,91

Figure 24. Comparison of the cellular locations of the proteins extracted using different extraction

buffers containing 4% SDS (Protocol 3.1), 80% ACN (Protocol 6), 8 M urea with 30% ACN (Protocol

4.1), 8 M urea with 40% ACN (Protocol 4.3), 8 M urea with 60% ACN (Protocol 4.4), 8 M urea with

30% ACN followed by digestion with double trypsin addition (Protocol 4.5), and 8 M urea with 30%

ACN followed by trypsin/Lys-C digestion (Protocol 4.6) (n=1).

3.5. Correlation of protein and peptide abundance among different protocols

Correlation of protein abundance between the different extracted protocols were determined by means

of the Pearson correlation of the logarithms of the LFQ values (Figure 25 a). The highest correlations

could be observed between the digestion with double trypsin addition (Protocol 4.5) and a concentration

of 30% ACN (Protocol 4.1), digestion with double trypsin addition (Protocol 4.5) and a trypsin/Lys-C

(Protocol 4.2) digestion, and a concentration of 40% ACN (Protocol 4.3) with the section that was

divided into 6 parts (Protocol 4.6). These results were confirmed by the correlation of peptide abundancy

between the different protocols (Figure 25 b). The same combinations of protocols showed the highest

correlations, although they were lower than the correlations of the protein abundancy.

0 10 20 30 40

Cell junction

Membrane

Macromolecular complex

Extracellular matrix

Cell part

Organelle

Extracellular region

% genes of total genes

4% SDS followed by trypsindigestion

80% ACN followed by trypsindigestion

8 M urea with 30% ACNfollowedby trypsin digestion

8 M urea with 40% ACNfollowed by trypsin digestion

8 M urea with 60% ACNfollowed by trypsin digestion

8 M urea with 30% ACNfollowed by double trypsindigestion

8 M urea with 30% ACNfollowed by trypsin + Lys-Cdigestion

45

Figure 25. Correlation of protein (a) and peptide (b) abundance between 8 M urea with 30% ACN (Protocol 4.1), 8 M urea with 40% ACN (Protocol 4.3), 8 M

urea with 60% ACN (Protocol 4.4), 8 M urea with a trypsin/Lys-C digestion (Protocol 4.2), 8 M urea with the digestion using double trypsin addition (Protocol

4.5), and 8 urea wherein the spot was cut into 6 sections (Protocol 4.6). Protein abundance is expressed as the log LFQ value and peptide abundancy as the log

of their intensities. The correlation is shown as Pearson coefficient (n=1).

46

4. Conclusion

The largest number of identified proteins was obtained with the extraction buffer containing 8 M urea

and 60% ACN and with the digestion procedure using double addition of trypsin. The removal of SDC

from the samples was not successful and therefore, the samples could not be analyzed. Furthermore,

SDS was not completely removed from the samples, leading to ion suppression. The trypsin/Lys-C

digestion showed a decrease in the number of protein group identifications compared to a trypsin

digestion.

In addition, these results suggest that the extraction buffer containing 8 M urea with 60% ACN

and a digestion using double trypsin addition might be combined in order to extract more proteins. The

concentration of SDS (4%) was high in compared to concentrations usually used in other studies and

further work could assess the effect of a lower concentration of SDS on the protein extraction.

47

Supplementary information

Supplementary table 1. An outline of the samples with their areas in mm2, their assigned protocol, clean-up method, solvent in which they were reconstituted

prior to LC-MS analysis, final concentration in mm2/µL, injected volume in µL, injected amount in mm2, chromatographic run time, trap column and column

used for the LC-MS analysis, and number of identified protein groups, peptides, and spectra.

Sample Area

(mm2)

Protocol Clean-up

method

Dissolution Concentration

(mm2/µL)

Injection

volume

(µL)

Amount

injected

(mm2)

Run

time

(min)

Trap

column

Particle

size column

(µm)

Number of

protein

groups

Number of

peptides

Number

of spectra

5a-4 14.60 2.2 SPE 25% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 1 1 2322

5a-5 7.58 3.1 SPE 25% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 108 735 4720

5b-2 14.92 3.2 SPE 25% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 19 99 4219

5b-3 10.83 5.1.1 Freezedrying 25% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 185 1907 9149

5b-4 13.45 5.2.1 SPE 25% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 5 19 3430

5c-2 14.78 5.1.2 Freezedrying 25% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 161 1643 6815

5c-3 18.96 5.1.2 Freezedrying 25% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 176 1818 7333

5c-4 12.73 5.2.2 SPE 25% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 5 17 3671

5c-5 11.91 5.3 SPE 25% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 76 373 6117

5c-6 20.49 5.1.1 SPE 25% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 426 4672 19514

25% ACN in 1% FA 0.62 10 6 60 75** 5 397 4016 20806

7a-2 12.68 4.5 SPE 3% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 325 3010 14814

5 1 60 200*** 5 236 2429 13190

5 1 60 200*** 5 139 1285 13762

10 2 95 200*** 5 303 3754 29431

7a-3 11.45 4.1 SPE 3% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 262 2316 11529

5 1 60 75** 5 297 2821 15441

5 1 60 75** 5 269 2787 14557

5 1 60 75** 5 296 2724 14500

5 1 60 75** 3 227 1921 10268

48

5 1 95 75** 5 263 2621 21915

7a-4 11.65 4.1 SPE 3% ACN in 1% FA 0.20 5 1 60 75** 5 299 3043 16014

7a-5 9.05 4.2 SPE 3% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 219 1936 9310

7a-6 14.74 4.5 SPE 3% ACN in 1% FA 0.20 5 1 60 75** 5 297 2988 13128

7b-1 9.3 5.1.1 SPE 3% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 366 3615 12965

5 1 60 AB Sciex* 5 381 3257 12936

7b-2 14.89 4.1 SPE 3% ACN in 1% FA 0.20 5 1 60 75** 5 237 2221 13564

7b-5 7.84 7 SPE 3% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 32 114 1894

7c-1 12.55 4.3 SPE 3% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 268 2938 11103

7c-2 15.65 6 SPE 3% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 79 496 3330

7c-5 13.99 4.4 SPE 3% ACN in 1% FA 0.20 5 1 60 AB Sciex* 5 353 2641 16171

7c-6 20.61 4.6 SPE 3% ACN in 1% FA 0.20 10 2 95 300*** 5 312 3321 27633

10 2 60 75** 5 319 3204 18327

5 1 95 AB Sciex* 5 278 2037 28273

10 2 95 AB Sciex* 5 360 3416 22959

10 2 95 75*** 5 357 4253 27633

* = AB Sciex 0.5 mm long, 350 µm inner diameter trap column (Chrom XP C18 resin, 120 Å pore size, 3 µm)

** = in-house packed 1.5 cm long, 75 µm inner diameter trap column (Magic C18 resin, 100 Å pore size, 5 µm)

*** = an in-house packed 5.0 cm long, 200 µm inner diameter trap column (Magic C18 resin, 100 Å pore size, 5 µm)

49

References

(1) Richards, A. L.; Merrill, A. E.; Coon, J. J. Proteome Sequencing Goes Deep. Curr. Opin. Chem.

Biol. 2015, 24, 11–17.

(2) Marko-Varga, G.; Fehniger, T. E. Proteomics and Diseases - the Challenges for Technology and

Discovery. J. Proteome Res. 2004, 3, 167–178.

(3) Feist, P.; Hummon, A. B. Proteomic Challenges: Sample Preparation Techniques for Microgram-

Quantity Protein Analysis from Biological Samples. Int. J. Mol. Sci. 2015, 16 (2), 3537–3563.

(4) Vora, T.; Thacker, N. Impacts of a Biobank: Bridging the Gap in Translational Cancer Medicine.

Indian J.Med.Paediatr.Oncol. 2015, 36 (1), 17–23.

(5) Mager, S. R.; Oomen, M. H. A.; Morente, M. M.; Ratcliffe, C.; Knox, K.; Kerr, D. J.; Pezzella,

F.; Riegman, P. H. J. Standard Operating Procedure for the Collection of Fresh Frozen Tissue

Samples. Eur. J. Cancer 2007, 43 (5), 828–834.

(6) Scicchitano, M. S.; Dalmas, D. A.; Boyce, R. W.; Thomas, H. C.; Frazier, K. S. Protein

Extraction of Formalin-Fixed, Paraffin-Embedded Tissue Enables Robust Proteomic Profiles by

Mass Spectrometry. J. Histochem. Cytochem. 2009, 57 (9), 849–860.

(7) Broeckx, V.; Boonen, K.; Pringels, L.; Sagaert, X.; Prenen, H.; Landuyt, B.; Schoofs, L.; Maes,

E. Comparison of Multiple Protein Extraction Buffers for GeLC-MS/MS Proteomic Analysis of

Liver and Colon Formalin-Fixed, Paraffin-Embedded Tissues. Mol. Biosyst. 2015, 12, 553–565.

(8) Weston, L. a; Hummon, A. B. Comparative LC-MS/MS Analysis of Optimal Cutting

Temperature (OCT) Compound Removal for the Study of Mammalian Proteomes. Analyst 2013,

138 (21), 6380–6384.

(9) Enthaler, B.; Bussmann, T.; Pruns, J. K.; Rapp, C.; Fischer, M.; Vietzke, J. P. Influence of

Various on-Tissue Washing Procedures on the Entire Protein Quantity and the Quality of Matrix-

Assisted Laser Desorption/ionization Spectra. Rapid Commun. Mass Spectrom. 2013, 27 (8),

878–884.

(10) Longuespée, R.; Fléron, M.; Pottier, C.; Quesada-Calvo, F.; Meuwis, M.-A.; Baiwir, D.;

Smargiasso, N.; Mazzucchelli, G.; De Pauw-Gillet, M.-C.; Delvenne, P.; et al. Tissue Proteomics

for the next Decade? Towards a Molecular Dimension in Histology. OMICS 2014, 18 (9), 539–

552.

(11) Chandramouli, K.; Qian, P. Proteomics: Challenges, Techniques and Possibilities to Overcome

Biological Sample Complexity. Hum. Genomics Proteomics 2009, 2009, 22.

(12) Switzar, L.; Giera, M.; Niessen, W. M. a. An Overview of the Available Techniques and Recent

Developments. J. Proteome Res. 2013, 12 (3), 1067–1077.

(13) Zhang, Y.; Fonslow, B. R.; Shan, B.; Baek, M. C.; Yates, J. R. Protein Analysis by

Shotgun/bottom-up Proteomics. Chem. Rev. 2013, 113 (4), 2343–2394.

(14) Bantscheff, M.; Schirle, M.; Sweetman, G.; Rick, J.; Kuster, B. Quantitative Mass Spectrometry

in Proteomics: A Critical Review. Anal. Bioanal. Chem. 2007, 389 (4), 1017–1031.

(15) Bodzon-Kulakowska, A.; Bierczynska-Krzysik, A.; Dylag, T.; Drabik, A.; Suder, P.; Noga, M.;

Jarzebinska, J.; Silberring, J. Methods for Samples Preparation in Proteomic Research. J.

Chromatogr. B Anal. Technol. Biomed. Life Sci. 2007, 849 (1-2), 1–31.

(16) Rabilloud, T. Solubilization of Proteins for Electrophoretic Analyses. Electrophoresis 1996, 17

(5), 813–829.

(17) Haneskog, L. Protein Sample Preparation Handbook; GE Healthcare Life Sciences: Uppsala,

50

2008.

(18) http://www.sigmaaldrich.com/catalog/product/sigma/r0278?lang=en®ion=NL, accessed on

2016-06-10.

(19) Zhang, J. Protein-Protein Interactions in Salt Solutions. Protein-Protein Interact. - Comput. Exp.

Tools 2012, No. 6, 359–376.

(20) Glatter, T.; Ludwig, C.; Ahrné, E.; Aebersold, R.; Heck, A. J. R.; Schmidt, A. Large-Scale

Quantitative Assessment of Different in-Solution Protein Digestion Protocols Reveals Superior

Cleavage Efficiency of Tandem Lys-C/trypsin Proteolysis over Trypsin Digestion. J. Proteome

Res. 2012, 11 (11), 5145–5156.

(21) Luebker, S. A.; Wojtkiewicz, M.; Koepsell, S. A. Two Methods for Proteomic Analysis of

Formalin-Fixed, Paraffin Embedded Tissue Result in Differential Protein Identification, Data

Quality, and Cost. Proteomics 2015, 15 (21), 3744–3753.

(22) Chang, Y. H.; Gregorich, Z. R.; Chen, A. J.; Hwang, L.; Guner, H.; Yu, D.; Zhang, J.; Ge, Y.

New Mass-Spectrometry-Compatible Degradable Surfactant for Tissue Proteomics. J. Proteome

Res. 2015, 14 (3), 1587–1599.

(23) Loo, R. R.; Dales, N.; Andrews, P. C. Surfactant Effects on Protein Structure Examined by

Electrospray Ionization Mass Spectrometry. Protein Sci. 1994, 3 (11), 1975–1983.

(24) Arribas, J.; Castano, J. G. Kinetic Studies of the Differential Effect of Detergents on the

Peptidase Activities of the Multicatalytic Proteinase from Rat Liver. J. Biol. Chem. 1990, 265

(23), 13969–13973.

(25) Proc, J. L.; Kuzyk, M. a; Hardie, D. B.; Yang, J.; Smith, D. S.; Jackson, A. M.; Parker, C. E.;

Borchers, C. H. A Quantitative Study of the Effects of Chaotropic Agents, Surfactants, and

Solvents on the Digestion Efficiency of Human Plasma Proteins by Trypsin. J Proteome Res.

2010, 9 (10), 5422–5437.

(26) León, I. R.; Schwämmle, V.; Jensen, O. N.; Sprenger, R. R. Quantitative Assessment of in-

Solution Digestion Efficiency Identifies Optimal Protocols for Unbiased Protein Analysis. Mol.

Cell. Proteomics 2013, 12 (10), 2992–3005.

(27) Shen, K.; Sun, J.; Cao, X.; Zhou, D.; Li, J. Comparison of Different Buffers for Protein

Extraction from Formalin-Fixed and Paraffin-Embedded Tissue Specimens. PLoS One 2015, 10

(11), e0142650.

(28) Nomura, E.; Katsuta, K.; Ueda, T.; Toriyama, M.; Mori, T.; Inagaki, N. Acid-Labile Surfactant

Improves in-Sodium Dodecyl Sulfate Polyacrylamide Gel Protein Digestion for Matrix-Assisted

Laser Desorption/ionization Mass Spectrometric Peptide Mapping. J. Mass Spectrom. 2004, 39

(2), 202–207.

(29) Saveliev, S. V.; Woodroofe, C. C.; Sabat, G.; Adams, C. M.; Klaubert, D.; Wood, K.; Urh, M.

Mass Spectrometry Compatible Surfactant for Optimized in-Gel Protein Digestion. Anal. Chem.

2013, 85 (2), 907–914.

(30) Chen, E. I.; Cociorva, D.; Norris, J. L.; Yates, J. R. Optimization of Mass Spectrometry-

Compatible Surfactants for Shotgun Proteomics. J. Proteome Res. 2007, 6 (7), 2529–2538.

(31) Waas, M.; Bhattacharya, S.; Chuppa, S.; Wu, X.; Jensen, D. R.; Omasits, U.; Wollscheid, B.;

Volkman, B. F.; Noon, K. R.; Gundry, R. L. Combine and Conquer: Surfactants, Solvents, and

Chaotropes for Robust Mass Spectrometry Based Analyses of Membrane Proteins. Anal. Chem.

2014, 86 (3), 1551–1559.

(32) Klammer, A. A.; MacCoss, M. J. Effects of Modified Digestion Schemes on the Identification

of Proteins from Complex Mixtures. J Proteome Res. 2006, 5 (3), 695–700.

51

(33) Brownridge, P.; Beynon, R. J. The Importance of the Digest: Proteolysis and Absolute

Quantification in Proteomics. Methods 2011, 54 (4), 351–360.

(34) Stark, G. R.; Stein, W. H.; Moore, S. Reactions of the Cyanate Present in Aqueous Urea with

Amino Acids and Proteins. J. Biol. Chem. 1960, 235 (11), 3177–3181.

(35) Sun, S.; Zhou, J. Y.; Yang, W.; Zhang, H. Inhibition of Protein Carbamylation in Urea Solution

Using Ammonium-Containing Buffers. Anal. Biochem. 2014, 446 (1), 76–81.

(36) Guo, T.; Wang, W.; Rudnick, P. A.; Song, T.; Li, J.; Zhuang, Z.; Weil, R. J.; Devoe, D. L.; Lee,

C. S.; Balgley, B. M. Proteome Analysis of Microdissected Formalin-Fixed and Paraffin-

Embedded Tissue Specimens The Journal of Histochemistry & Cytochemistry. J. Histochem.

Cytochem. 2007, 55 (7), 763–772.

(37) Bell, L. N.; Saxena, R.; Mattar, S. G.; You, J.; Wang, M. Utility of Formalin-Fixed , Paraffin-

Embedded Liver Biopsy Specimens for Global Proteomic Analysis in Nonalcoholic

Steatohepatitis. Proteomics - Clin. Appl. 2011, 5, 397–404.

(38) Ronci, M.; Bonanno, E.; Colantoni, A.; Pieroni, L.; Ilio, C. Di; Spagnoli, L. G.; Federici, G.;

Urbani, A. Protein Unlocking Procedures of Formalin-Fixed Paraffin-Embedded Tissues :

Application to MALDI-TOF. Proteomics 2008, 8, 3702–3714.

(39) Xu, H.; Yang, L.; Wang, W.; Shi, S.; Liu, C.; Liu, Y.; Fang, X.; Taylor, C. R.; Lee, C. S.; Balgley,

B. M. Antigen Retrieval for Proteomic Characterization of Formalin-Fixed and Paraffin-

Embedded Tissues Research Articles. J. Proteome Res. 2008, 7, 1098–1108.

(40) Balgley, B. M.; Guo, T.; Zhao, K.; Fang, X.; Tavassoli, F. A.; Lee, C. S. Evaluation of Archival

Time on Shotgun Proteomics of Formalin-Fixed and Paraffin-Embedded Tissues Research

Articles Experimental Section. J. Proteome Res. 2009, 8, 917–925.

(41) Ngoka, L. C. Sample Prep for Proteomics of Breast Cancer: Proteomics and Gene Ontology

Reveal Dramatic Differences in Protein Solubilization Preferences of Radioimmunoprecipitation

Assay and Urea Lysis Buffers. Proteome Sci. 2008, 6, 30.

(42) Blonder, J.; Goshe, M. B.; Moore, R. J.; Pasa-Tolic, L.; Masselon, C. D.; Lipton, M. S.; Smith,

R. Enrichment of Integral Membrane Proteins for Proteomic Analysis Using Liquid

Chromatography-Tandem Mass Spectrometry. J. Proteome Res. 2002, 1 (4), 351–360.

(43) Russell, W. K.; Park, Z.-Y.; Russell, D. H. Proteolysis in Mixed Organic−Aqueous Solvent

Systems: Applications for Peptide Mass Mapping Using Mass Spectrometry. Anal. Chem. 2001,

73 (11), 2682–2685.

(44) Zhang, N.; Chen, R.; Young, N.; Wishart, D.; Winter, P.; Weiner, J. H.; Li, L. Comparison of

SDS- and Methanol-Assisted Protein Solubilization and Digestion Methods for Escherichia Coli

Membrane Proteome Analysis by 2-D LC-MS/MS. Proteomics 2007, 7 (4), 484–493.

(45) Hervey, W. J.; Strader, M. B.; Hurst, G. B. Comparison of Digestion Protocols for Microgram

Quantities of Enriched Protein Samples. J. Proteome Res. 2008, 6, 3054–3061.

(46) Wall, M. J.; Crowell, A. M. J.; Simms, G. A.; Liu, F.; Doucette, A. A. Implications of Partial

Tryptic Digestion in Organic-Aqueous Solvent Systems for Bottom-up Proteome Analysis. Anal.

Chim. Acta 2011, 703 (2), 194–203.

(47) Hustoft, H. K.; Malerod, H.; Wilson, S. R.; Reubsaet, L.; Lundanes, E.; Greibrokk, T. A Critical

Review of Trypsin Digestion for LC-MS Based Proteomics. Integr. Proteomics 2010, 73–92.

(48) Tsiatsiani, L.; Heck, A. J. R. Proteomics beyond Trypsin. FEBS J. 2015, 282 (14), 2612–2626.

(49) Vandermarliere, E.; Mueller, M.; Martens, L. Getting Intimate with Trypsin, the Leading

Protease in Proteomics. Mass Spectrom. Rev. 2013, 32, 453–465.

52

(50) Fang, P.; Liu, M.; Xue, Y.; Yao, J.; Zhang, Y.; Shen, H.; Yang, P. Controlling Nonspecific

Trypsin Cleavages in LC-MS/MS-Based Shotgun Proteomics Using Optimized Experimental

Conditions. Analyst 2015, 140 (22), 7613–7621.

(51) Choudhary, G.; Wu, S. L.; Shieh, P.; Hancock, W. S. Multiple Enzymatic Digestion for

Enhanced Sequence Coverage of Proteins in Complex Proteomic Mixtures Using Capillary LC

with Ion Trap MS/MS. J. Proteome Res. 2003, 2 (1), 59–67.

(52) Guo, X.; Trudgian, D. C.; Lemoff, A.; Yadavalli, S.; Mirzaei, H. Confetti: A Multiprotease Map

of the HeLa Proteome for Comprehensive Proteomics. Mol. Cell. Proteomics 2014, 13 (6), 1573–

1584.

(53) Chiva, C.; Ortega, M.; Sabido, E. Influence of the Digestion Technique, Protease, and Missed

Cleavage Peptides in Protein Quantitation. J. Proteome Res. 2014, 13 (9), 3979–3986.

(54) Sun, W. Microwave-Assisted Protein Preparation and Enzymatic Digestion in Proteomics. Mol.

Cell. Proteomics 2005, 5 (4), 769–776.

(55) Pramanik, B. N.; Mirza, U. a; Ing, Y. H.; Liu, Y.-H.; Bartner, P. L.; Weber, P. C.; Bose, A. K.

Microwave-Enhanced Enzyme Reaction for Protein Mapping by Mass Spectrometry: A New

Approach to Protein Digestion in Minutes. Protein Sci. 2009, 11 (11), 2676–2687.

(56) Vesper, H. W.; Mi, L.; Enada, A.; Myers, G. L. Assessment of Microwave-Assisted Enzymatic

Digestion by Measuring Glycated Hemoglobin A1c by Mass Spectrometry. Rapid Commun.

Mass Spectrom. 2005, 19 (19), 2865–2870.

(57) Lill, J. R.; Ingle, E. S.; Liu, P. S.; Pham, V.; Sandoval, W. N. Microwave-Assisted Proteomics.

Mass Spectrom. Rev. 2007, 26, 657–671.

(58) Lopez-Ferrer, D.; Capelo, J. L.; Vazquez, J. Ultra Fast Trypsin Digestion of Proteins by High

Intensity Focused Ultrasound. J Proteome Res 2005, 4, 1569–1574.

(59) Capelo, J. L.; Carreira, R.; Diniz, M.; Fernandes, L.; Galesio, M.; Lodeiro, C.; Santos, H. M.;

Vale, G. Overview on Modern Approaches to Speed up Protein Identification Workflows

Relying on Enzymatic Cleavage and Mass Spectrometry-Based Techniques. Anal. Chim. Acta

2009, 650 (2), 151–159.

(60) Rial-Otero, R.; Carreira, R. J.; Cordeiro, F. M.; Moro, A. J.; Fernandes, L.; Moura, I.; Capelo, J.

L. Sonoreactor-Based Technology for Fast High-Throughput Proteolytic Digestion of Proteins.

J. Proteome Res. 2007, 6 (2), 909–912.

(61) Yang, H, Y.; Hong, J.; Lee, S.; Shin, S.; Kim, J.; Kim, J. Pressure-Assisted Tryptic Digestion

Using a Syringe. Rapid Commun. Mass Spectrom. 2010, 24, 901–908.

(62) Zhou, J. Y.; Dann, G. P.; Shi, T.; Wang, L.; Gao, X.; Su, D.; Nicora, C. D.; Shukla, A. K.; Moore,

R. J.; Liu, T.; et al. Simple Sodium Dodecyl Sulfate-Assisted Sample Preparation Method for

LC-MS-Based Proteomics Applications. Anal. Chem. 2012, 84 (6), 2862–2867.

(63) Wísniewski, J. R.; Zougman, A.; Nagaraj, N.; Mann, M. Universal Sample Preparation Method

for Proteome Analysis. Nat. Methods 2009, 6 (5), 377–362.

(64) Kachuk, C.; Stephen, K.; Doucette, A. Comparison of Sodium Dodecyl Sulfate Depletion

Techniques for Proteome Analysis by Mass Spectrometry. J. Chromatogr. A 2015, 1418, 158–

166.

(65) Duan, X.; Young, R.; Straubinger, R. M.; Page, B.; Cao, J.; Wang, H.; Yu, H.; Canty, J. M.; Qu,

J. A Straightforward and Highly Efficient Precipitation/on-Pellet Digestion Procedure Coupled

with a Long Gradient Nano-LC Separation and Orbitrap Mass Spectrometry for Label-Free

Expression Profiling of the Swine Heart Mitochondrial Proteome. J. Proteome Res. 2009, 8 (6),

2838–2850.

53

(66) Hughes, C. S.; Foehr, S.; Garfield, D. A.; Furlong, E. E.; Steinmetz, L. M.; Krijgsveld, J.

Ultrasensitive Proteome Analysis Using Paramagnetic Bead Technology. Mol. Syst. Biol. 2014,

10, 757.

(67) Kanshin, E.; Thibault, P. Efficient Sample Processing for Proteomics Applications — Are We

There Yet? Mol Syst Biol 2014, 758 (10), 1–3.

(68) Barton, C.; Kay, R.; Scientists, S.; Services, D. D. Protein Analysis Using Proteotypic Peptides

and LC-MS : Choosing the Chromatographic Separation for Optimal Coverage or Throughput.

Chromatogr. Today 2008, 11–14.

(69) Nováková, L.; Vaast, A.; Stassen, C.; Broeckhoven, K.; De Pra, M.; Swart, R.; Desmet, G.;

Eeltink, S. High-Resolution Peptide Separations Using Nano-LC at Ultra-High Pressure. J. Sep.

Sci. 2013, 36 (7), 1192–1199.

(70) Banerjee, S.; Mazumdar, S. Electrospray Ionization Mass Spectrometry: A Technique to Access

the Information beyond the Molecular Weight of the Analyte. Int. J. Anal. Chem. 2012, 2012, 1–

40.

(71) Yates, J. R.; Ruse, C. I.; Nakorchevsky, A. Proteomics by Mass Spectrometry: Approaches,

Advances, and Applications. Annu. Rev. Biomed. Eng. 2009, 11, 49–79.

(72) Meyer, J. G.; Komives, E. A. Charge State Coalescence during Electrospray Ionization Improves

Peptide Identification by Tandem Mass Spectrometry. J. Am. Soc. Mass Spectrom. 2012, 23 (8),

1390–1399.

(73) Hahne, H.; Pachl, F.; Ruprecht, B.; Maier, S. K.; Klaeger, S.; Helm, D.; Médard, G.; Wilm, M.;

Lemeer, S.; Kuster, B. DMSO Enhances Electrospray Response, Boosting Sensitivity of

Proteomic Experiments. Nat. Methods 2013, 10 (10), 989–991.

(74) Andrews, G. L.; Simons, B. L.; Young, J. B.; Hawkridge, A. M.; Muddiman, D. C. Performance

Characteristics of a New Hybrid Triple Quadrupole Time-of-Flight Tandem Mass Spectrometry.

Anal. Chem. 2011, 83 (13), 5442–5446.

(75) Marcotte, E. M. How Do Shotgun Proteomics Algorithms Identify Proteins? Nat. Biotechnol.

2007, 25 (7), 755–757.

(76) Perkins, D. N.; Pappin, D. J. C.; Creasy, D. M.; Cottrell, J. S. Probability-Based Protein

Identification by Searching Sequence Databases Using Mass Spectrometry Data. Electrophoresis

1999, 20 (18), 3551–3567.

(77) Eng, J. K.; McCormack, A. L.; Yates, J. R. An Approach to Correlate Tandem Mass Spectral

Data of Peptides with Amino Acid Sequences in a Protein Database. J. Am. Soc. Mass Spectrom.

1994, 5 (11), 976–989.

(78) Domon, B.; Aebersold, R. Challenges and Opportunities in Proteomics Data Analysis. Mol. Cell.

Proteomics 2006, 5 (10), 1921–1926.

(79) Kannaste, O.; Suomi, T.; Salmi, J.; Uusipaikka, E.; Nevalainen, O.; Corthals, G. L. Cross-

Correlation of Spectral Count Ranking to Validate Quantitative Proteome Measurements. J.

Proteome Res. 2014, 13 (4), 1957–1968.

(80) Cox, J.; Mann, M. MaxQuant Enables High Peptide Identification Rates, Individualized P.p.b.-

Range Mass Accuracies and Proteome-Wide Protein Quantification. Nat. Biotechnol. 2008, 26

(12), 1367–1372.

(81) Burgess, M. W.; Keshishian, H.; Mani, D. R.; Gillette, M. a; Carr, S. a. Simplified and Efficient

Quantification of Low-Abundance Proteins at Very High Multiplex via Targeted Mass

Spectrometry. Mol. Cell. Proteomics 2014, 13 (4), 1137–1149.

(82) Hsieh, E. J.; Bereman, M. S.; Durand, S.; Valaskovic, G. A.; MacCoss, M. J. Effects of Column

54

and Gradient Lengths on Peak Capacity and Peptide Identification in Nanoflow LC-MS/MS of

Complex Proteomic Samples. J. Am. Soc. Mass Spectrom. 2013, 24 (1), 148–153.

(83) Richards, A. L.; Hebert, A. S.; Ulbrich, A.; Bailey, D. J.; Coughlin, E. E.; Westphall, M. S.;

Coon, J. J. One-Hour Proteome Analysis in Yeast. Nat. Protoc. 2015, 10 (5), 701–714.

(84) Alkhas, A.; Hood, B. L.; Oliver, K.; Teng, P. N.; Oliver, J.; Mitchell, D.; Hamilton, C. A.;

Maxwell, G. L.; Conrads, T. P. Standardization of a Sample Preparation and Analytical

Workflow for Proteomics of Archival Endometrial Cancer Tissue. J. Proteome Res. 2011, 10

(11), 5264–5271.

(85) Zhou, R.; Li, L. Effects of Sample Injection Amount and Time-of-Flight Mass Spectrometric

Detection Dynamic Range on Metabolome Analysis by High-Performance Chemical Isotope

Labeling LC-MS. J. Proteomics 2015, 118, 130–139.

(86) Tanca, A.; Abbondio, M.; Pisanu, S.; Pagnozzi, D.; Uzzau, S.; Addis, M. Critical Comparison of

Sample Preparation Strategies for Shotgun Proteomic Analysis of Formalin-Fixed, Paraffin-

Embedded Samples: Insights from Liver Tissue. Clin. Proteomics 2014, 11 (1), 28.

(87) Tanca, A.; Pagnozzi, D.; Burrai, G. P.; Polinas, M.; Uzzau, S.; Antuofermo, E.; Addis, M. F.

Comparability of Differential Proteomics Data Generated from Paired Archival Fresh-Frozen

and Formalin-Fixed Samples by GeLC-MS/MS and Spectral Counting. J. Proteomics 2012, 77,

561–576.

(88) Xiao, Z.; Li, G.; Chen, Y.; Li, M.; Peng, F.; Li, C.; Li, F.; Yu, Y.; Ouyang, Y.; Xiao, Z.; et al.

Quantitative Proteomic Analysis of Formalin-Fixed and Paraffin-Embedded Nasopharyngeal

Carcinoma Using iTRAQ Labeling, Two-Dimensional Liquid Chromatography, and Tandem

Mass Spectrometry. J. Histochem. Cytochem. 2010, 58 (6), 517–527.

(89) Craven, R. A.; Cairns, D. A.; Zougman, A.; Harnden, P.; Selby, P. J.; Banks, R. E. Proteomic

Analysis of Formalin-Fixed Paraffin-Embedded Renal Tissue Samples by Label-Free MS:

Assessment of Overall Technical Variability and the Impact of Block Age. Proteomics - Clin.

Appl. 2013, 7 (3-4), 273–282.

(90) Gámez-Pozo, A.; Ferrer, N. I.; Ciruelos, E.; López-Vacas, R.; Martínez, F. G.; Espinosa, E.;

Vara, J. Á. F. Shotgun Proteomics of Archival Triple-Negative Breast Cancer Samples.

Proteomics - Clin. Appl. 2013, 7 (3-4), 283–291.

(91) Jiang, X.; Jiang, X.; Feng, S.; Tian, R.; Ye, M.; Zou, H. Development of Efficient Protein

Extraction Methods for Shotgun Proteome Analysis of Formalin-Fixed Tissues. J. Proteome Res.

2007, 6 (3), 1038–1047.