Age determination of body fluids by
mass spectrometry
Katarzyna Szykuła
Master Thesis
2015 – 2016
2
MSc Chemistry
Analytical Sciences
Master Thesis
Age determination of body fluids by mass spectrometry
by
Katarzyna Szykuła
June 2016
Supervisor:
Second examiner:
Prof. dr. Garry Corthals Dr. Michelle Camenzuli
Analytical Chemistry Group
3
Acknowledgements
I would like to express my gratitude to professor Garry Corthals for his supervision and guidance
during this Master’s project. I have enjoyed my time carrying out this research and I would like to
thank everyone from the Analytical Chemistry group for their help and kindness. Additionally, I would
like to thank Dr. Michelle Camenzuli for reviewing this thesis.
Moreover, I would like to thank professor Maurice Aalders and his group for their help and the
opportunity to do experiments at Academic Medical Center.
4
Abstract
In forensic science, biological traces have a significant importance for forensic investigators. They can
provide crucial information about the donor or about a crime itself, for example what happened on a
crime scene or when the criminal incident was committed. In order to estimate the latter one, aging
of body fluids found on a crime scene is studied. This research investigated aging process of four
body fluids: blood, saliva, semen and fingermarks. As all of them are protein-containing fluids, this
type of compounds was examined by using mass spectrometry. Samples of aforementioned body
fluids were analyzed at different age, and subsequently, the numbers of proteins identified as well as
their quantification were compared. For blood, saliva and semen, the number of unique proteins was
changing with the age of a sample. Additionally, some proteins showed variations in quantity when
sample was getting older. These findings can indicate that protein levels are changing over time in
biological traces, and these biomolecules can be potential biomarkers for age estimation of specific
body fluid found on a crime scene.
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Table of Contents
Acknowledgements ... 3
Abstract ... 4
1. Introduction ... 7
1.1 Forensic biological traces ... 7
1.1.1 Blood ... 7
1.1.2 Saliva ... 8
1.1.3 Semen ... 8
1.1.4 Fingermarks ... 8
1.2 Mass spectrometry ... 9
1.2.1 Proteomics ... 9
1.2.2 Quantitation in proteomics ... 11
1.2.3 Proteomics in forensic science ... 13
2. Materials and methods ... 15
2.1 Materials ... 15
2.2 Methods ... 15
2.2.1 Blood ... 15
2.2.2 Saliva ... 17
2.2.3 Semen ... 17
2.2.4 Fingermarks ... 17
3. Results and Discussion ... 19
3.1 Blood... 19
3.1.1 Method optimization ... 19
3.1.2 Examining aging of blood ... 20
3.2 Saliva ... 25
3.3 Semen ... 30
3.4 Fingermarks ... 34
4. Conclusions and future research ... 35
5. References ... 36
Appendix 1. ... 39
Appendix 2. ... 44
Appendix 3. ... 51
Appendix 4. ... 57
Appendix 5. ... 61
6
Appendix 6. ... 65
Appendix 7. ... 70
Appendix 8. ... 76
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1. Introduction
orensic science is an enormously developing field nowadays. As an interdisciplinary study, it
utilizes the knowledge and technology from several fields, such as chemistry, physics, medicine
and mathematics to solve crimes. On a crime scene, investigators can find many forensically
important traces. Traces like: pieces of glass, hairs, fibers or paints are defined as physical traces. On
the other hand, body fluids, which can be commonly found on a crime scene as well, belong to the
group of biological traces. All traces found on a crime scene can play a crucial role during
investigations. They can provide information about the suspect/victim or what happened during the
criminal incident. However, this thesis will focus only on body fluids, therefore, only biological
evidence will be further described in details.
Examining biological evidence has a high importance for investigators as they can provide
intelligence about the donor (by DNA profiling) or about the crime of investigation. The latter one is
often studied by analyzing the bloodstain pattern, which can give the information about events that
occurred on the crime scene [1].
Moreover,
by studying the aging of body fluids, it can be estimated
when a crime took place, which can narrow down the time intervals for detectives.
To date, several
attempts have been performed to study body fluids aging by using mainly spectroscopic [2, 3] or
chromatographic methods [4, 5]. However, these studies did not provide with sufficient information
about the aging process. Therefore, the aim of this research was to use mass spectrometry
technique, suitable for trace analysis, to study the aging of body fluids.
1.1 Forensic biological traces
1.1.1 Blood
This body fluid is composed of blood cells (red cells, white cells and platelets) suspended in blood
plasma. The red color of blood is due to an iron-containing protein – hemoglobin, which its main
function is to transport the oxygen from lungs to tissues[6]. As blood contains proteins, lipids,
electrolytes, metabolites, hormones and more [6], this fluid is the most extensively studied, not only
in medical fields. In forensics, studying blood traces has various purposes. Besides obtaining DNA
profile, or studying blood stain pattern [1], also aging of this body fluid has been examined.
The idea to study blood aging is not new. Many attempts were made to investigate aging process by
studying different blood components with various methods, for example RNA with reverse
transcription polymerase chain reaction (RT-PCR) [7], erythrocytes with electron paramagnetic
resonance (EPR) [3] or globulins with immunoelectrophoresis [8]. More recent, three hemoglobin
derivatives: oxy-hemoglobin (oxy-Hb), methemoglobin (met-Hb) and hemichrome (HC) were studied
[2, 9, 10]. As the fractions of these derivatives are changing in time (fraction of oxy-Hb decreases and
met-Hb and HC increase), Bremmer et al.[2] applied reflectance spectroscopy to record spectra of
blood stains and calculated these fractions. They presented the potential of this method, as they
were able to discriminate age of blood stains with larger time intervals (e.g. 20 days). Edelman et al.
applied hyperspectral imaging to record the spectra of the described hemoglobin derivatives and
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observed a decreased error compared to reflectance spectroscopy [10]. However, Bremmer et al. [9]
showed that conversion of oxy-Hb to met-Hb and HC is dependent on temperature and humidity.
Moreover, both methods were successful only when spectra were recorded against a white
background.
1.1.2 Saliva
This very essential body fluid has many functions, but the most important are: food solubilization,
lubrication, antibacterial, buffering and food digestion. Saliva is produced by salivary glands and it
contains water, electrolytes, proteins and other organic molecules [11]. Its chemical composition has
been studied widely, mostly for cancer [12], autoimmune diseases [13], hereditary diseases [14], or
viral diseases [15]. Saliva can also be used for drug analysis [16].
On a crime scene, this oral body fluid can be found for instance on food or cigarettes. In forensic
science, it is mainly studied for DNA profiling and aging studies have never been reported before.
Nevertheless, some study has shown that when saliva is in ex situ conditions [17], proteins can
degrade over time. This information is of importance and can indicate that saliva proteins have
potential to be used as a biomarker for age determination of saliva.
1.1.3 Semen
The main composition of semen is seminal fluid which may contain spermatozoa [18]. This fluid
contains high concentration of sugars (fructose and glucose) and proteins [19]. As study showed,
in a
single individual seminal fluid, over 900 proteins can be identified [20] which can suggest that human
seminal fluid proteome can be used for biomarker studies, for example in cancer [21] or infertility
research[22].
In forensic science, this biological evidence can be found on a crime scene in a case of
sexual assaults. Semen has a high evidential importance as from this fluid donor DNA profile can be
obtained.
For identification purposes, forensic investigators utilize the proteins’ natural fluorescence
from semen. However, there is yet no literature published to use this biomolecules in age
determination of seminal fluid.
1.1.4 Fingermarks
This type of traces is often found a crime scene. The chemical composition of fingermarks is diverse.
They contain elements (e.g. sodium, potassium, chloride), amino acids, proteins, fatty acids and
other lipids [23]. Forensic scientists study fingermarks to obtain as much information as possible
about the donors. For instance, from analysis of fingermarks information about the sex [24] or drug
abused [25] can be obtained. Similar to the other body fluids, the knowledge of the age of
fingermarks can be of significant importance for investigators. Wolstenholme et al. [26] attempted to
determine the age of fingermarks by investigating the degradation of olenaic acid. The other
research group, van Dam et al. [27], showed that the fluorescence signal is changing with the age of
the sample. However, the estimation error of aged samples was relatively high - only 55% of aged
male fingermarks estimated within the error of 1.9 days.
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1.2 Mass spectrometry
Mass spectrometry (MS) is a widely used method in analytical chemistry. Its very intense
development can be proved by five Nobel Prizes, starting from 1906 with J.J.Thomson for his work on
the electrical conductivity of gases. Subsequently, other prizes were for isotope discovery by mass
spectrograph, for cyclotron and for the development of the ion trap technique. The last Nobel Prize
in mass spectrometry, until now, was awarded to J. Bennet and K. Tanaka in 2002. The first one for
developing soft desorption ionization methods, and the second one for developing a new method for
mass spectrometric analyses of biological macromolecules; both extremely important in protein
analysis. Mass spectrometry, as a versatile technique, has found its application in various disciplines,
such as toxicology, forensic science, clinical or environmental chemistry. Its main advantage is high
sensitivity [28], which can be critical, especially, in forensic science for analyzing biological traces,
which often are found on the crime scene in scarce or mixed.
1.2.1 Proteomics
Proteomics is the field of science which studies the biological processes in organisms by protein
analysis. A proteome is the entire protein complement expressed by a genome. However, in contrast
to genes, the nature of proteins is not static but dynamic. Proteins can undergo modifications
(phosphorylation, oxidation), their abundance can change or they can form clusters. Those variations
are dependent on the physiological state of the organism. Therefore, the proteome reflects the state
of a cell or a tissue, and it is crucial to not only identify all the proteins but also to measure their
changes as well as pathways of change[29].
Protein analysis is a challenging task, as proteome is not only very complex because of all interactions
and modifications described before, but also, compared to genes, proteins cannot undergo
amplifications, which is the limiting factor when a sample amount is scarce. Hence, only appropriate
tools (i.e. high sensitivity) can be applied in proteomics, which makes mass spectrometry a suitable
method.
In proteomics, there are two ionization methods in widespread use: matrix-assisted laser
desorption/ionization (MALDI) and electrospray ionization (ESI). Both of them can generate ions from
large nonvolatile biomolecules. MALDI and ESI are knows as soft ionization methods, which means
that there is a minimal or no fragmentation of the analytes. The first one is mostly used for peptide
mass fingerprinting or tissue imaging. In MALDI, sample is mixed with a matrix, which molecules have
a strong absorption at the applied laser wavelength. The absorbed energy is transferred to the
analyte, which is desorbed and ionized together with matrix molecules. This solid-phase technique
mostly generates singly charged ions. Electrospray ionization is a liquid technique, which is easy to
couple to such separation methods as liquid chromatography or electrophoresis. Here, a high voltage
is applied, which generates highly charged droplets of the analyte. In contrast to MALDI, ESI
generates multiply charged ions [28].
The typical workflow in proteomics for LC-MS/MS analysis is presented on the Fig. 1. Briefly, proteins
in a sample undergo digestion into peptides, mostly by using enzyme digestion, trypsin for example.
Those peptides prior to MS/MS analysis are separated with reverse-phase liquid chromatography. In
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MS/MS analysis, the precursor ions as well as product ions are scanned, which provides crucial
information about the structure of a sample. After the analysis, by using appropriate software,
protein and peptide identification can be obtained.
Figure 1. Typical workflow in proteomics for LC-MS/MS analysis. Adapted from reference [30].
The instrument used in this project was TripleTOF mass spectrometer. The scheme below presents
the features in this instrument. Briefly, QJet and Q0 are used for ion focusing, Q1 selects the
precursor ion and Q2 is the collision cell, where the ions collide with neutral gas molecules
(nitrogen). That formed product ions leave the collision cell and are re-accelerated and focused by
ions optics into a parallel beam which constantly enters TOF analyzer. There, a pulsed electric field is
pushing ions orthogonally to their previous direction, and then they acquire their final energy [31].
The accelerated ions travel through the tube, where at the opposite site is an electrostatic ion mirror
which reverses the ions direction to the detector. Ions gain the same kinetic energy, however, their
velocities are different, due to the mass differences. And consequently, ions reach the detector at
different time.
MS1
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Figure 2. The scheme of triple TOF MS features. (a) The major platform features in details. (b) an image of this platform[32].
1.2.2 Quantitation in proteomics
Nowadays, only protein identification is no longer sufficient in proteomics studies. In order to gain
more knowledge about living organisms, tissues or cells, it is important to quantify the proteins. Their
abundances can change over a time or under specific conditions, such as disease for example. In
proteomics, there are two quantitative approaches, one which is based on labeling the sample and
the second one is label-free. Labeling techniques can be divided into three categories such as:
metabolic labeling, chemical labeling and addition of standard peptides. In those approaches, labeled
peptides have the same chemical properties as unlabeled ones, with the only difference in their
masses. In metabolic labeling, such as stable isotope labeling by amino acids in cell culture (SILAC) for
example, samples of “light” and “heavy” amino acids are mixed at early stage. In chemical labeling,
the label can be introduced at protein or peptide levels. One of commonly used chemical labeling is
the isotope-coded affinity tag (ICAT). This label is bound to sulfhydryl groups of cysteine residues and
is useful to study oxidation or reduction level of proteins. However, as cysteine belongs to low
abundant amino acids in proteins, it reduces the number of peptides that can be examined. The
other type of chemical labeling is isobaric mass tags. There are two tags commercially available:
tandem mass tags (TMT) and isobaric tags for relative and absolute quantitation (iTRAQ)[33]. The
third technique of labeling approach in quantitative proteomics is by using synthetic isotope-labeled
peptides as a standard for absolute quantification (AQUA). These reference peptides allow to precise
measure the level of proteins and posttranslationally modified proteins. For AQUA, selected reaction
monitoring (SRM) analysis is performed [34].
In label-free approach, two quantitation techniques can be distinguished. First one is based on under
the curve (AUC) or signal intensity measurements, both based on precursor ion spectra. With these
methods chromatographic alignment is required. The second technique, spectral counting, is based
on counting the number of peptides assigned to a protein in MS/MS analysis. In spectral counting
methods, more abundant peptides are selected for fragmentation, and they produce a higher
abundance of MS/MS spectra. This process is proportional to protein amount in data-dependent
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acquisition. In order to assess protein abundance in the analyzed sample, protein abundance index
(PAI) is used. This index is defined as the number of identified peptides divided by the number of
observable generated peptides for every protein within a preferred mass range [35]. In order to
obtain absolute quantitation, PAI can be converted into exponentially modified emPAI, which is
equally to exponential form of PAI minus one, and this is proportional to protein content in analyzed
samples [36]. As in spectral counting, longer proteins generate more peptides than smaller ones, this
also can be used for quantification. In this case, normalized spectral abundance factor (NSAF) is used
which takes into account the length of the protein. In order to calculate NSAF, the number of spectral
counts for protein is divided by its length and this is defined as spectral abundance factor (SAF),
which has to be normalized by division by the sum of all SAFs for all proteins in a mixture [37].
Before applying a specific quantitation method, it is essential to consider which parameters are
crucial in the performed analysis.
For example, it has to be determined what proteome coverage is
desired in the analyzed sample. This and other parameters for quantitative methods are overviewed
in the table below.
Table 1. Overview of quantitation methods applied in proteomics [33].
Method Proteome coverage Quantitative accuracy Quantitation type
Metabolic labeling
SILAC Medium Precise Relative
Chemical labeling
ICAT Poor Precise Relative
iTRAQ, TMT Medium Medium Relative
Standard peptide Poor Precise Relative/Absolute
Label-free
Ion intensities (PCP) Good Medium Relative
Spectrum count Good Poor Relative
Derived indices (APEX, emPAI)
Good Poor Relative/Absolute
Figure 3 shows that from all proteins in a sample, only a certain amount can be identified.
Furthermore, from this amount, even a smaller fraction can be successfully quantified.
It emphasizes
how challenging protein analysis is and how important it is to perform appropriate sample
preparation with suitable enrichment and purification steps. Additionally, it also indicates how much
of the proteome is still unknown.
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Figure 3. Graphical representation of the amount of proteins that can be identified relative to all proteins in a sample [33].
1.2.3 Proteomics in forensic science
In forensic science it is very important to identify as many traces as possible which can be found on a
crime scene. The information of the trace’s origin can facilitate to reconstruct what has happened on
a crime scene. Especially, very crucial is the analysis of biological matrices as they can contain DNA,
which can have a huge value for investigators. However, as mostly those matrices are the mixtures of
several body fluids, such analysis is a complicated process. To identify such mixtures, an advanced
and sensitive method, like mass spectrometry, is needed. With this method, such body fluids as
blood, saliva, semen, vaginal fluids, urine and nasal secretions can be identified by a protein-specific
biomarker, which is unique for every body fluid [38, 39]. Moreover, analyzing biological traces with
MS is not destructive for DNA, which is an another advantage [40]. Hence, identification of the
source of a biological trace can be performed prior to DNA analysis.
The other approach of applying proteomics in forensic field is investigating organ-specific proteome.
This studies are especially useful in a case of ballistic reconstruction of the event [41] as the
information about which organ was damaged with the bullet can help to reconstruct the bullet’s
projectile.
Table 2. Organ-specific proteins [41].
Organ Protein
Heart
myosin binding protein C,cardiac-type glycogen phosphorylase, brain form troponin I, cardiac muscle
myosin light chain 3
pyruvate dehydrogenase E1 component subunit beta, mitochondrial
calpastatin
NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 4
cytochrome c oxidase subunit 4 isoform 1, mitochondrial
Kidney
calbindin
Na(+)/H(+) exchange regulatory cofactor NHE-RF3 low-density lipoprotein receptor-related protein 2 villin 1
retinyl ester hydrolase type 1
membrane metalloendopeptidase variant 2 phosphotriesteraserelated protein plastin-1
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Liverhydroxymethylglutaryl- CoA synthase, mitochondrial carbamoyl-phosphate synthase [ammonia], mitochondrial phenylalanine-4- hydroxylase 3-oxo-5-beta-steroid 4-dehydrogenase catechol Omethyltransferase acetyl-CoA acetyltransferase cytochrome P450 2E1 dimethylaniline monooxygenase Lung plastin-2 cathelicidin-4 tubulin beta chain
cysteine and glycine-rich protein 1 prostaglandin F synthase 2 myosin light polypeptide 6 calpain-2 catalytic subunit alpha-actinin-1
Muscle
bridging integrator 1
myosin-binding protein C, slow-type fructose-1,6- bisphosphatase isozyme 2 myosin-binding protein C, fast-type troponin C, skeletal muscle (fast type)
myosin regulatory light chain 2, skeletal muscle isoform nebulin
PDZ and LIM domain protein 3
As it can be seen, protein studies by mass spectrometry are common used in forensic science. It can
provide with very substantial information about the donor of a sample, or about the circumstances
that the crime happened. However, one of the very important information obtained from body fluids
has not been yet investigated using mass spectrometry – age determination of body fluids by protein
analysis. This thesis presents a novel approach of studying the aging process with LC-MS/MS method.
15
2. Materials and methods
2.1 Materials
All samples were taken from healthy donors and kept in the dark at room temperature.
Chemicals:
Acetonitrile, methanol, water, formic acid (Biosolve)
Ammonium bicarbonate, DTT, iodoacetamide, Tris(2-carboxyethyl)phosphine hydrochloride, sodium
deoxycholate, Brilliant Blue G (Sigma Aldrich)
Sequencing Grade Modified Trypsin (Promega)
4-20% Tris-glycine precast gel, Tricine-Glycine SDS Running Buffer (Novex)
4-15% Tris-HCl precast gel (Biorad)
Apparatus:
Samples were collected on: Human ID Bloodstain Card; glass microfibers filter (Whatman) or into 2.0
ml Eppendorf vials.
Solid-phase extraction was performed with C18 solid-phase extraction cartridges, 4mm/1mL
(Empore).
LC system consist of nanoLC 425; separation column C18, 3µm, 120Å, 0.075x150mm, and trap
column C18, 3µm, 120Å, 350µmx0.5mm (Eksigent). Flow rate: 300nL/minute, injection volume 1-5
µL.
MS/MS analysis was performed with Triple TOF 5600+ (ABSciex) in positive mode. TOF m/z range:
400-1250, product ion m/z range 100-2000.
2.2 Methods
2.2.1 Blood
Dried blood spots were prepared by spotting 15µl of blood on Whatman filter paper. Samples
preparation for LC-MS/MS analysis was performed according to the protocol described by Chambers,
A.G., et al. [42]. Briefly, dried blood spots were excised from the filter paper and transferred to 1.5 ml
Eppendorf tubes. Fresh sample (15µl) was pipetted directly to the vial. In order to dissolve dried
blood spots and dilute fresh blood, 800µl of 25mM ammonium bicarbonate was added to the vials
and vortexed. Subsequently, 100 µl of 10% sodium deoxycholate and 10 µl of 0.5 M
Tris(2-carboxyethyl)phosphine hydrochloride were added and samples were incubated for an hour at 60°C.
Then, samples were alkylated for one hour in the dark (covered with aluminum foil) in room
temperature after addition of 52.63µl 200mM iodoacetamide. In order to consume remain
iodoacetamide, 55.4µl of 200mM dithiotreitol (DTT) was added and samples were incubated for 30
minutes at 37°C. Next, 10 µl of trypsin solution was added and protein digestion was performed
16
overnight at 37°C. Protein concentration in samples, before digestion, was estimated with Bradford
assay (Biorad). After digestion, sodium deoxycholate was precipitated by adding 40 µl of 2% formic
acid and the samples were centrifuged at 4000 g for 20 minutes. The supernatant was collected into
the new vials. Prior to LC-MS/MS analysis, samples were desalted by using solid-phase extraction.
Cartridges were first pre-washed twice with 50% ACN/water and 0.1% TFA. Afterwards, samples
were loaded and washed with 0.1% TFA. Analytes were eluted with 0.1% formic acid in 60% ACN.
The collected samples were, subsequently, evaporated to dryness and peptides were reconstituted
with water to obtain a final protein concentration of 0.2µg/µl.
In order to obtain the highest number of protein identification, three different LC methods were first
tested with fresh blood samples. For all methods, the same chemicals were used; mobile phase A:
0.1% formic acid, mobile phase B: 100% acetonitrile.
Table 3. Gradient profile for method 1 [43].
Time [min] Mobile phase A% Mobile phase B%
0 97.3 2.7 1.5 93.7 6.3 16 86.5 13.5 18 86.2 13.8 33 77.5 22.5 38 59.5 40.5 39 19 81 42.9 19 81 43 97.3 2.7
Table 4. Gradient profile for method 2.
Time [min] Mobile phase A% Mobile phase B%
0 100 0
10 95 5
40 40 60
45 20 80
60 100 0
Table 5. Gradient profile for method 3 [42].
Time [min] Mobile phase A% Mobile phase B%
0 95 5
90 55 45
92 10 90
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2.2.2 Saliva
Prior to LC-MS/MS analysis, proteins in saliva samples were first separated with SDS-PAGE using
precast 4-15% Tris-HCl gel. 10 µl of saliva was mixed with 10 µl of reducing sample buffer and
incubated for 5 minutes in 95°C. Then samples were applied to the gel lanes (20 µl per lane). Samples
were run at a voltage of 100V for 75 minutes. Afterwards, proteins were stained with Coomassie blue
and the gel was destained with distilled water.
Selected bands were excised from the gel and in-gel digestion was performed. Firstly, Coomassie
blue staining was removed from gel bands by incubation with 200µl of 0.04M NH
4HCO
3/50% ACN.
Next, proteins in gel bands were reduced with 100µl of 20mM DTT for 30 minutes in 37°C.
Afterwards, 100µl of 55mM iodoacetamide was added to every sample for alkylation in room
temperature for 20 minutes. Afterwards, proteins were digested with 10µl of 0.02µg/µl of trypsin
solution for 20 minutes on ice and subsequently 20µl of 40mM ammonium bicarbonate/10% ACN
was added and digestion was performed overnight in 37°C. Next day, digested proteins were
extracted from the gel bands with first 60µl ACN for 15 minutes in 37°C and then with 150µl of
50%ACN/5%HCOOH also for 15 minutes at the same temperature. Finally, peptides were dried and
dissolved in 10 µl of water prior to LC-MS/MS analysis and 94 minutes method was used, the same as
for blood samples.
2.2.3 Semen
Semen samples were analyzed similarly to saliva samples described in section 2.2.2. Firstly, proteins
were separated with SDS-PAGE (however, in this case, samples were first diluted 21 times with
phosphate-buffered saline) and later in-gel digestion was performed according to the same protocol.
LC-MS/MS analysis of semen peptides was performed with 20 minutes method (see Table 6).
Table 6. Gradient profile for semen samples.
Time [min] Mobile phase A% Mobile phase B%
0 95 5 15 70 30 16 5 95 18 5 95 20 95 5
2.2.4 Fingermarks
For fingermark samples collection, donors (males and females) were asked to put their finger onto a
glass surface, without previous washing their hands, in order to obtain “realistic” conditions.
Deposited fingermarks were later swabbed with cotton swabs. Swabs were subsequently transferred
into Eppendorf vials, 900 µl of 25mM ammonium bicarbonate was added and samples were
incubated at 37°C for 30 minutes. Afterwards, swabs were removed and samples were centrifuged
for 15 minutes at 3000g. The supernatants were collected and in-solution digestion was performed
18
according to the same protocol as for blood samples. For LC-MS/MS analysis, 66 minutes method
was used (see Table 7).
Table 7. Gradient profile for fingermark samples.
Time [min] Mobile phase A% Mobile phase B%
0 90 10
60 0 100
65 0 100
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3. Results and Discussion
Due to problems with the instrumentation, which had occurred during this research project, not all
samples were analyzed.
This chapter presents the results obtained from analysis of aging body fluids by mass spectrometry.
3.1 Blood
3.1.1 Method optimization
Prior to blood samples analysis, LC-MS/MS conditions were initially optimized with fresh blood
samples. Three different gradients were tested and the number of identified proteins and peptides
was compared. The graphs below present the obtained results.
Figure 4. Number of proteins identified in fresh blood with three different methods.
Figure 5. Number of peptides identified in fresh blood with three different methods. 0 50 100 150 200
Proteins identified
43 minutes method 60 minutes method 94 minutes method
0 200 400 600 800 1000 1200
Peptides identified
20
From the foregoing graphs can be derived that the highest number of identification was obtained by
using 94 minutes method. With this gradient, 179 proteins and 953 peptides were identified. The 60
minutes method provided a significant lower number of proteins and peptide, 117 and 748
respectively. Finally, application of the shortest gradient resulted in 167 identified proteins and 748
identified peptides.
3.1.2 Examining aging of blood
Blood samples were analyzed at three different ages: fresh blood sample (liquid), 1 day old (stain)
and 7 days old blood (stain) with LC-MS/MS. Subsequently, the number of proteins and peptides
from every sample were compared (see Table 8). All measurements were performed in triplicates
and data was generated using human thorough protein database with Protein Pilot
TMsoftware using
false discovery rate (FDR) 1%.
Table 8. Number of proteins and peptides identified in aged blood samples, in triplicates.
Sample Proteins #1 Proteins #2 Proteins #3 Peptides #1 Peptides #2 Peptides #3 Fresh blood 115 131 139 1280 1712 1646 1 day old 119 130 128 1552 1522 1512 7 days old 129 110 111 1458 1369 1058
Coefficient of variation (CV) in every sample for proteins and peptides, respectively, is as follow: fresh
sample 9.5%, 15%; one day old sample 4.7%, 1.4%; and seven days old sample 9.1%, 16.2%.
In the next step, data was generated using other software. First, Search Gui 2.7.1, and subsequently,
Peptide Shaker 1.8.1. Two combined search engines were used: Open Mass Spectrometry Search
Algorithm (OMSSA) and MS-GF+. The used database for identification was Target-Decoy version
Homo sapiens protein database + common contaminants. Quality control filtering was performed
with 1% FDR. Data from three technical replicates were pooled together.
The number of identified proteins from all three samples was plotted and presented in the Figure 4.
The complete lists of identified proteins in blood samples (using Search Gui and Peptide Shaker) can
be found in Appendices 1-3.
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Figure 6. Comparison of proteins in all three blood samples.
78 proteins were detected in every blood sample. Nonetheless, the number of unique proteins in
every sample is different. These number decreased from 14 proteins in fresh blood to 12 protein in
one day old sample and finally to 4 proteins in 7 days old blood.
Protein quantification was performed by using normalized spectrum abundance factor (NSAF).
10 proteins were found with alterations in NSAF values. 9 proteins have decreasing NSAF value (Fig.
7-15) and for one it is increasing (Fig. 16).
Figure 7. NSAF values for alpha-1 acid glycoprotein-2. 0 0,02 0,04 0,06 0,08 0,1
fresh 1 day old 7 days old
NS
A
F
22
Figure 8. NSAF values for alpha-2-macroglobulin.
Figure 9. NSAF values for ankyrin-1.
Figure 10. NSAF values for apolipoprotein B-100. 0 0,005 0,01 0,015 0,02 0,025 0,03 0,035
fresh 1 day old 7 days old
NS A F
Alpha-2-macroglobulin
0 0,001 0,002 0,003 0,004 0,005 0,006 0,007fresh 1 day old 7 days old
NS A F
Ankyrin-1
0 0,0005 0,001 0,0015 0,002 0,0025 0,003 0,0035fresh 1 day old 7 days old
NS
A
F
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Figure 11. NSAF values for band 3 anion transport protein.
Figure 12. NSAF values for Ig gamma-4 chain region.
Figure 13. NSAF values for haptoglobin. 0 0,01 0,02 0,03 0,04 0,05 0,06 0,07
fresh 1 day old 7 days old
NS
A
F
Band 3 anion transport protein
0 0,01 0,02 0,03 0,04 0,05 0,06
fresh 1 day old 7 days old
NS
A
F
Ig gamma-4 chain C region
0 0,002 0,004 0,006 0,008 0,01
fresh 1 day old 7 days old
NS
A
F
24
Figure 14. NSAF values for hemopexin.
Figure 15. NSAF values for putative uncharacterized protein.
Figure 16. NSAF values for putative bisphosphoglycerate mutase.
From all aforementioned proteins, two characterize with significant changes in NSAF values:
apolipoprotein B-100 and haptoglobin. The first one is responsible for lipids transporting and
proteins and phospholipids binding (See Table 9). The latter one is accountable for capturing and
0 0,01 0,02 0,03 0,04 0,05 0,06
fresh 1 day old 7 days old
NS A F
Hemopexin
0 0,02 0,04 0,06 0,08 0,1fresh 1 day old 7 days old
NS
A
F
Putative uncharacterized protein
0,019 0,02 0,021 0,022 0,023 0,024 0,025 0,026
fresh 1 day old 7 days old
NS
A
F
25
combining free plasma hemoglobin and it allows for hepatic cyclin of heme iron which prevents the
damage of kidneys.
For two proteins, alpha-1 acid glycoprotein-2 and putative uncharacterized proteins, the NSAF value
is decreasing notably during first 24 hours, however, posteriorly, this decline is less noticeably.
Alpha-1 acid glycoprotein-2 is transporting protein, however, the function of putative
uncharacterized proteins in humans is still unknown. In other mammals, mouse for example, it has a
regulation function of proteins activity.
Only bisphosphoglycerate mutase protein demonstrates an increasing trend in NSAF values over
examined time period. It can be explained by the function of this protein, to regulate hemoglobin
oxygen affinity, which is stronger with the increasing age of the sample.
Table 9. Functions of proteins with changing NSAF values. Information took from http://www.uniprot.org/.
Protein Function
Alpha-1 acid glycoprotein-2 Transporting protein; binding hydrophobic ligands and synthetic drugs
Alpha-2-macroglobulin Inhibiting proteinases by unique 'trapping' mechanism
Ankyrin-1 Attaching integral membrane proteins to cytoskeletal
elements
Apolipoprotein B-100 Lipid transporter activity, binding proteins and phospholipids,
Band 3 anion transport protein Transporting protein, mediator in electroneutral anion exchange across the cell membrane; structural protein
Bisphosphoglycerate mutase regulating hemoglobin oxygen affinity, exhibiting mutase and phosphatase activities
Ig gamma-4 chain C region Antigen binding
Haptoglobin Capturing and combining with free plasma
hemoglobin (preventing kidney damage)
Hemopexin Binding heme and transporting it to the liver for
breakdown and iron recovery Putative uncharacterized protein Unknown function in homo sapiens
3.2 Saliva
Saliva samples at ages of one day old, one week old and three weeks old were analyzed.
Firstly, proteins were separated with SDS-PAGE (Fig. 17). The arrows indicate from which part of the
gel bands were excised. Per sample, three bands were excised and each band was analyzed
separately. Afterwards, results from three bands were pooled together.
26
Figure 17. SDS-PAGE of saliva samples.
Data were generated according to the second step with blood analysis, by using First Search Gui 2.7.1
and Peptide Shaker 1.8.1 software. The number of all identified proteins in all three samples is
presented in the Venn diagram below. The all lists of proteins identified in saliva samples can be
found in Appendices 4-6.
Figure 18. Comparison of proteins in all three saliva samples.
39 proteins were present in every saliva sample, nonetheless, the number of unique proteins
detected is increasing with the age of saliva samples, from 9 unique proteins in one day old sample to
22 in three weeks old.
27
Similarly to blood samples, saliva samples also underwent quantification with NSAF. For eight
proteins, an alteration of NSAF was observed (See Fig. 19-26).
Figure 19. NSAF values for keratin, type I cytoskeletal 13.
Figure 20. NSAF values for keratin, type I cytoskeletal 17.
Figure 21. NSAF values for keratin, type II cytoskeletal 2 epidermal. 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16
one day one week 3 weeks
NS
A
F
Keratin, type I cytoskeletal 13
0 0,005 0,01 0,015 0,02 0,025
one day one week 3 weeks
NS
A
F
Keratin, type I cytoskeletal 17
0 0,005 0,01 0,015 0,02 0,025
one day one week 3 weeks
NS
A
F
Keratin, type II cytoskeletal 2
epidermal
28
Figure 22. NSAF values for keratin, type II cytoskeletal 3.
Figure 23. NSAF values for keratin, type II cytoskeletal 6A.
Figure 24. NSAF values for lactotransferrin. 0 0,01 0,02 0,03 0,04 0,05
one day one week 3 weeks
NS
A
F
Keratin, type II cytoskeletal 3
0 0,005 0,01 0,015 0,02 0,025 0,03 0,035 0,04
one day one week 3 weeks
NS
A
F
Keratin, type II cytoskeletal 6A
0 0,002 0,004 0,006 0,008 0,01 0,012
one day one week 3 weeks
NSA
F
29
Figure 25. NSAF values for leukocyte elastase inhibitor.
Figure 26. NSAF values for tubulin alpha-4A chain.
Interestingly, in this case, there are more proteins in which NSAF values were increasing with the age
of the sample. Moreover, most of proteins with variation in NSAF are keratin type proteins. The most
significant changes occur for keratin, type I cytoskeletal 13 with upward trend and for keratin, type II
3 with the downward trend. Furthermore, those two proteins have similar function: structural
molecule activity. The other two proteins with considerable alterations are leukocyte elastase
inhibitor and tubulin alpha-4A chain. The first one is responsible for the activity of neutrophil
proteases elastase, and the latter one for GTPase activity and GTP binding.
Table 10. Functions of proteins with changing NSAF values. Information took from http://www.uniprot.org/.
Protein Function
Keratin, type I cytoskeletal 13 Structural molecule activity; cellular response to retinoic acid; tongue morphogenesis
Keratin, type I cytoskeletal 17 Structural molecule activity
Keratin, type II cytoskeletal 2 epidermal Associated with keratinocyte activation; proliferation and keratinization
Keratin, type II cytoskeletal 3 Structural molecule activity; intermediate filament cytoskeleton organization 0 0,01 0,02 0,03 0,04 0,05
one day one week 3 weeks
NS
A
F
Leukocyte elastase inhibitor
0 0,005 0,01 0,015 0,02 0,025 0,03 0,035
one day one week 3 weeks
NS
A
F
30
Keratin, type II cytoskeletal 6A Involved in the activation of follicular keratinocytes after wounding
Lactotransferrin Iron ion binding
Leukocyte elastase inhibitor Regulating the activity of the neutrophil proteases elastase
Tubulin alpha-4A chain GTPase activity, GTP binding
3.3 Semen
Semen samples (fresh, one week old and two weeks old) were analyzed according to the same
protocol as used for saliva samples. Proteins were first separated with SDS-PAGE, then 7 bands per
lane were excised and analyzed separately. At the end, results were pooled together for every
sample.
31
The Venn diagram below depicts the identified proteins in all semen samples. The lists of all
identified proteins in semen samples can be found in Appendices 7-9.
Figure 28. Comparison of proteins in all three semen samples.
In the case of this body fluid, 41 common proteins for all aging samples were found, however, the
number of the unique proteins is variable. In fresh samples 36 unique proteins were identified. In 7
days old samples this number increased to 37. Nevertheless, this number decreased to only 11
unique proteins in two weeks old samples.
Proteins in all semen samples were quantified with NSAF and 6 proteins showed variations in NSAF
values (Fig. 29-34).
Figure 29. NSAF values for clusterin. 0 0,005 0,01 0,015 0,02 0,025
fresh one week two weeks
NS
A
F
32
Figure 30. NSAF values for dermcidin.
Figure 31. NSAF values for keratin, type I cytoskeletal 10.
Figure 32. NSAF values for keratin, type II cytoskeletal 6A. 0
0,05 0,1 0,15 0,2
fresh one week two weeks
NS A F
Dermcidin
0 0,01 0,02 0,03 0,04 0,05 0,06fresh one week two weeks
NS
A
F
Keratin, type I cytoskeletal 10
0 0,002 0,004 0,006 0,008 0,01 0,012
fresh one week two weeks
NS
A
F
33
Figure 33. NSAF values for semenogelin-2.
Figure 34. NSAF values for serum albumin.
Keratin proteins are also present in semen samples with the downward or upward trend for type I
and type II, respectively. However, there are two proteins which NSAF is changing significantly. First
one, dermcidin, with upward trend, has antimicrobial properties. The second one, with reducing
NSAF, is semenogelin-2, which is involved in gel matrix formation. This result can indicate why the
older semen is more liquid then the fresh one.
Table 11. Functions of proteins with changing NSAF values. Information took from http://www.uniprot.org/.
Protein Function
Clusterin
Maintains partially unfolded proteins in a state appropriate for subsequent refolding by other chaperones
Dermcidin Displays antimicrobial activity; exhibits proteolytic activity
Keratin, type I cytoskeletal 10 Cellular response to calcium ion
Keratin, type II cytoskeletal 6A Involved in the activation of follicular keratinocytes after wounding
Semenogelin-2 Participates in the formation of a gel matrix 0 0,005 0,01 0,015 0,02 0,025
fresh one week two weeks
NS A F
Semenogelin-2
0 0,005 0,01 0,015 0,02 0,025 0,03 0,035
fresh one week two weeks
NS
A
F
34
Serum albumin Transporting
3.4 Fingermarks
7 female (2 donors) and 6 male (one donor) fingermarks samples were analyzed at different age:
fresh (only female), one day old, 10 days old and 17 days old. Results were generated with Protein
Pilot software. Table 12 presents proteins identified in each sample.
Table 12. Proteins identified in every fingermark sample.
Sample Proteins
Fresh female fingermark keratin, type I cytoskeletal (9, 10, 14, 16); keratin, type II cytoskeletal (1, 1b, 2 epidermal, 5, 6A, 78); dermcidin;
keratinocyte proline-rich protein; filaggrin-2;
skin-specific protein 32; loricrin;
junction plakoglobin
One day old female fingermark keratin, type II cytoskeletal 1
One day old male fingermark keratin, type I cytoskeletal (9, 10, 13, 14, 15, 16, 17); keratin, type II cytoskeletal (1, 2 epidermal); charged multivesicular body protein 2a; serum albumin;
phosphofurin acidic cluster sorting protein 1 10 days old female fingermark Keratin, type I cytoskeletal 9;
keratin, type II cytoskeletal (1, 2 epidermal, 5, 6A, 73); apoptosis-enhancing nuclease;
10 days old male fingermark Keratin, type I cytoskeletal 9;
keratin, type II cytoskeletal (1, 2 epidermal); sodium/potassium-transporting ATPase subunit alpha-4;
dermcidin
17 days old female fingermark Keratin, type I cytoskeletal (9, 10, 14, 16, 17); keratin, type II cytoskeletal (1, 2 epidermal, 5); dermcidin
17 days old male fingermark Keratin, type I cytoskeletal 10; prolactin-inducible protein
All samples contain keratin proteins, which is a protein found in skin cells. Interestingly, some
samples contain proteins which were not found in other fingermarks. This can suggest that those
proteins originate from contamination, as the donors deposited their fingermarks directly on the
glass slides, without previous finger preparation.
The results from fingermark samples could not be quantified as the signal to noise ratio in the
spectra was too low.
35
4. Conclusions and future research
For the first time mass spectrometry method was applied to study the aging of blood, saliva, semen
and fingerprints. In this research, protein changes were investigated, which occurs when the body
fluid is aging. It was shown that for some proteins, their concentration is increasing with time, for
example biphosphoglycerate mutase in blood, or keratins, type II in saliva. There were also several
proteins with decreasing concentration, such as apolipoprotein B-100 or semengelin-2. In the present
study, it was observed that for saliva, more proteins increased in concentration in comparison to
blood and semen. With regard to fingermarks samples, in this case, different extraction method is
required to obtain enough protein concentration that can be measured. This work presented the
potential of mass spectrometry technique for age determination of four body fluids commonly found
on a crime scene, although more research is needed in this area.
For future research, it is recommended to broaden the range of analyzed samples. In the case of
investigating proper biomarkers, it is important to expand studies to several donors from both sexes
with a wider range of age.
Additionally, in forensic science it is significant to investigate fluids on
different substrates. Also, environmental conditions are crucial, for example how proteins undergo
degradation at different temperatures and humidity levels. Furthermore, label-free quantification
methods used in this study, can also suggest that more accurate methods should be applied.
36
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39
The data presented in appendixes were generated by Search Gui 2.7.1 and Peptide Shaker 1.8.1.
Note that some proteins are identified more than one time per sample. emPAI - exponentially
modified protein abundance index; NSAF - normalized spectrum abundance factor; MW – molecular
weight.
Appendix 1.
All validated proteins in fresh blood sample.
Accession
Protein Inference
Class Protein name
Sequence Coverage (%) #Validate d Peptides
emPAI NSAF MW (kDa)
P01024 Unrelated Proteins Complement C3 40.8899579 0739627 44 0.714125253 2913134 0.01152003971 2574301 187.0298753 1966258 F6KPG5 Single Protein Albumin (Fragment) 76.7521367 5213675 56 4.115178099 293143 0.06854929340 53836 66.48798147 102801 P01023 Single Protein Alpha-2-macroglobulin 47.3541383 98914515 44 1.249054605 8357808 0.02915375633 5619455 163.1878914 6924135 P11277 Related and Unrelated Proteins Spectrin beta chain, erythrocytic 17.5011698 64295743 26 0.249264143 7899512 0.00369787553 21113903 246.3157334 321971 A0A087W ZE4 Related Proteins Spectrin alpha chain, erythrocytic 1 15.7201646 09053498 23 0.177572498 21932586 0.00339278233 15763428 280.9387841 720546 A0A087W TM7 Related and Unrelated Proteins Apolipoprotei n B-100 6.88305709 0239411 18 0.091578487 87739664 0.00315395313 4692732 489.5273949 3234196 C8C504 Single Protein Beta-globin 95.9183673 4693877 23 33.14548873 833603 1.08852418504 30756 15.98734254 8192922 P02730 Related and Unrelated Proteins Band 3 anion transport protein 24.2590559 82436884 15 0.674482438 0115348 0.05793226863 3375625 101.7274121 8512044 P02042 Related Proteins Hemoglobin subunit delta 93.1972789 1156462 18 14.84893192 4611133 0.45956003636 3566 16.04528986 0997062 A0A0C4D GB6 Related and Unrelated Proteins Serum albumin 35.2649006 62251655 21 0.790655529 6923772 0.15593423608 704202 69.18136901 532891 A0A0K0K 1H8 Related and Unrelated Proteins Epididymis secretory sperm binding protein Li 71p 33.3810888 252149 19 0.739828052 9303642 0.04192341630 458897 77.02963611 452142 P02787 Related and Unrelated Proteins Serotransferri n 32.5214899 713467 19 0.739828052 9303642 0.02566325370 2962904 77.01362996 651181 P02787 Related and Unrelated Proteins Serotransferri n 32.9512893 9828081 19 0.739828052 9303642 0.02241122118 2637716 77.01362996 651181 P02671 Protein Single alpha chain Fibrinogen 28.521939953810627 17 0.5524271734347945 0.021934645114181275 94.91441497589778
P04040 Related Proteins Catalase 35.8633776 09108156 13 0.690714103 4735806 0.03718063723 218359 59.71875531 355211 P16157 Single Protein Ankyrin-1 13.1313131 31313131 15 0.207722655 13446264 0.00599593074 032388 206.1369244 395263 P02675 Unrelated Proteins Fibrinogen beta chain 50.3054989 8167006 16 0.887391822 1350972 0.04582730151 411469 55.89226078 115152
