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DOI 10.1007/s00018-013-1360-y Cellular and Molecular Life Sciences
Cell. Mol. Life Sci. (2013) 70:3709–3722
RESEARCH ARTICLE
Paleoproteomic study of the Iceman’s brain tissue
Frank Maixner · Thorsten Overath · Dennis Linke · Marek Janko · Gea Guerriero ·
Bart H. J. van den Berg · Bjoern Stade · Petra Leidinger · Christina Backes · Marta Jaremek ·
Benny Kneissl · Benjamin Meder · Andre Franke · Eduard Egarter‑Vigl · Eckart Meese ·
Andreas Schwarz · Andreas Tholey · Albert Zink · Andreas Keller
Received: 30 October 2012 / Revised: 27 March 2013 / Accepted: 30 April 2013 / Published online: 6 June 2013
© Springer Basel 2013
multiple-databases and -search algorithms-driven data-
analysis approach. Thereby, we identified a total of 502
different proteins. Of these, 41 proteins are known to be
highly abundant in brain tissue and 9 are even specifically
expressed in the brain. Furthermore, we found 10 proteins
related to blood and coagulation. An enrichment analysis
revealed a significant accumulation of proteins related to
stress response and wound healing. Together with atomic
force microscope scans, indicating clustered blood cells,
our data reopens former discussions about a possible injury
of the Iceman’s head near the site where the tissue samples
have been extracted.
Abstract The Tyrolean Iceman, a Copper-age ice
mummy, is one of the best-studied human individuals.
While the genome of the Iceman has largely been decoded,
tissue-specific proteomes have not yet been investigated.
We studied the proteome of two distinct brain samples
using gel-based and liquid chromatography–mass spec-
trometry-based proteomics technologies together with a
F. Maixner, T. Overath, and D. Linke contributed equally as first
authors.
A. Tholey, A. Zink and A. Keller contributed equally as senior
authors.
Electronic supplementary material The online version of this
article (doi:10.1007/s00018-013-1360-y) contains supplementary
material, which is available to authorized users.
F. Maixner · A. Zink
Institute for Mummies and the Iceman, EURAC research,
39100 Bolzano, Italy
T. Overath · D. Linke · B. H. J. van den Berg · A. Tholey
Division for Systematic Proteome Research,
Institute for Experimental Medicine,
Christian-Albrechts-Universität Kiel,
24105 Kiel, Germany
M. Janko
Center of Smart Interfaces, TU Darmstadt,
64287 Darmstadt, Germany
G. Guerriero
Department Environment and Agro-biotechnologies (EVA),
Centre de Recherche Public-Gabriel
Lippmann, 41, rue du Brill,
4422 Belvaux, Luxembourg
B. Stade · A. Franke
Institute of Clinical Molecular Biology, Christian-Albrechts-
Universität Kiel, 24105 Kiel, Germany
P. Leidinger · C. Backes · E. Meese · A. Keller (*)
Department of Human Genetics, Saarland University,
66421, Saar, Homburg, Germany
e-mail: ack@bioinf.uni-sb.de
M. Jaremek · A. Keller
Siemens Healthcare, 91052 Erlangen, Germany
B. Kneissl
Software Engineering and Bioinformatics,
Johannes Gutenberg-University of Mainz,
55128 Mainz, Germany
B. Meder
Department of Internal Medicine III, University of Heidelberg,
69120 Heidelberg, Germany
E. Egarter-Vigl
Department of Pathological Anatomy and Histology,
General Hospital Bolzano, 39100 Bolzano, Italy
A. Schwarz
Department of Neurosurgery, General Hospital Bolzano,
39100 Bolzano, Italy
3710 Andreas Keller et al.
1 3
Keywords Tyrolean Iceman · Mummy ·
Neolithic · Paleoproteomics · Mass spectrometry ·
Brain proteome · Ancient proteins
Introduction
The Tyrolean Iceman, Ötzi, is one of the oldest human
mummies discovered. His body was preserved for more
than 5,300 years in an Italian Alpine glacier before he was
discovered by two German mountaineers at an altitude of
3,210 m above sea level in September 1991. Since this dis-
covery, not only his historical age and his precious prehis-
toric belongings make him extremely valuable for scien-
tists, but also the way he was preserved over time [1, 2].
The Iceman is a so-called “wet mummy”, i.e. humidity was
retained in his cells while he was naturally mummified by
freeze-drying [3]. The body tissues are therefore still well
preserved, and this feature makes them suitable for modern
scientific investigations.
Next generation sequencing techniques enabled the
reconstruction of the genome of the Tyrolean Iceman,
which provided precious novel insights into the Ice-
man’s phenotype and origin [4]. However, comple-
mentary molecular platforms allow additional in-depth
analysis of biomolecules other than DNA, such as lipids
and proteins. It is believed that these molecules can be
preserved in mummified samples even longer than DNA
[5]. Considering that proteins perform the majority of
functional events encoded in the genome, it is interest-
ing how little is known about the post-mortem fate of
the proteome in ancient human remains. Identification
of these biomolecules can thus additionally complement
and further extend the molecular knowledge of the Ice-
man’s biology. Most bio-anthropological studies on pro-
teins so far have been linked to the stable isotope anal-
ysis of carbon and nitrogen in order to reconstruct the
dietary history of individuals [6, 7]. Only a few reports
dealing with the presence and preservation of proteins
in mummified tissue are available [8, 9]. The gel-based
methodologies applied therein already indicated that
fibrous proteins (mainly collagen), as well as globular
proteins, become highly degraded to smaller peptides
during and after the mummification process. Post-mor-
tem protein deradation is initiated via the hydrolytic
action of intracellular and microbial enzymes [10, 11].
In addition, rapid pH decline and the post-mortem for-
mation of reactive oxygen species (ROS) can cause rapid
protein denaturation and degradation [12]. The overall
rate of protein degradation is, however, highly temper-
ature-dependent, and rapid desiccation supports protein
preservation [13].
Osteocalcin, a small extracellular bone matrix protein,
was the subject of the first study using mass spectrometry
(MS)-based peptide identification in Bison priscus perma-
frost fossils [14]. Several follow-up studies focused on the
identification of collagen in ancient biological remains, the
best preserved protein complex after tissue taphonomic
processes. Even in dinosaur fossils, it seems to be still
detectable [15, 16]. Specific diagnostic collagen peptide
patterns have been used for the species identification of
ancient animal remains [17–19]. Further MS-based protein
studies could identify peptidic remains other than colla-
gen in a Neanderthal bone, in archaeological potsherds and
ancient grape seeds [20–22]. Quite recently, Cappellini and
colleagues [23] could elegantly demonstrate that an ancient
mammoth bone sample (43,000 years old) contains signifi-
cantly more proteins than have been reported in all previ-
ous paleoproteomic studies. These studies indicated the
potential of the MS-based analysis of ancient proteins and
started the emerging scientific field of paleoproteomics.
In the present study, the proteomes of two tissue biopsies
of the Iceman’s brain (Fig. 1) have been analysed in detail.
This is the first in-depth paleoproteomic study performed
on an ancient human soft tissue sample. By using different
proteomics approaches, encompassing multidimensional
separation of proteins and peptides followed by nano-elec-
trospray mass spectrometry (LC–MS) and the combination
of different database search engines, a total of 502 different
proteins were reliably identified.
Fig. 1 Transverse CT section through the skull shows an irregular
area (actual sampling site) of increased radiographic transparency in
the posterior cerebral regions (asterisk). The meninges have become
detached from the skull vault and surround the shrunken, inhomog-
enously disintegrated brain
3711
Paleoproteomic study of the Iceman’s brain tissue
1 3
Materials and methods
Iceman brain biopsy sampling
Two tissue biopsies (1024, 1025) of the “dark area” of the
occipital lobe (Fig. 1) were extracted for further proteomic
and microscopic analysis during an endoscopic investiga-
tion of the Iceman’s brain. Samples were withdrawn using
a 3-D endoscope (Visionsense, USA) linked to an optical
neuronavigation system. Already existing CT scans of the
head of the mummy served as input data for the electro-
magnetic navigation system of Medtronic (StealthStation
AxiEM surgical navigation system, USA). The sampling
took place under sterile conditions at a temperature of
4 °C in the Iceman’s conservation cell at the Archaeologi-
cal Museum of Bolzano, Italy. The samples were immedi-
ately stored at −20 °C in the ancient DNA laboratory of the
EURAC Institute for Mummies and the Iceman.
Atomic force microscopy
Iceman’s brain tissue cryo-sections were examined using
a combined inverted optical microscope (Axiovert 135,
Zeiss, Oberkochen, Germany) and Atomic Force Micro-
scope (AFM; NanoWizard-II; JPK Instruments, Berlin,
Germany) setup. The optical microscope was used to define
appropriate sample areas for AFM imaging. AFM measure-
ments were subsequently performed to obtain high-reso-
lution images of the samples. AFM images were taken in
the intermittent contact mode under ambient conditions.
Silicon cantilevers (BS Tap 300; Budget Sensors, Redding,
USA) with typical spring constants of 40 N/m and nominal
resonance frequencies of 300 kHz were used. The nominal
tip radius was smaller than 10 nm. The images were ana-
lysed using SPIP (SPIP 4.5.2; Image Metrology, Hørsholm,
Denmark).
Proteome isolation and separation
For both samples 1024 and 1025, a sample workup and sep-
aration approach consisting of two sequentially performed
strategies was used. Strategy A: first proteome extraction
with gel LC–MS-based separation/analysis; followed by
strategy B: second proteome extraction and bottom-up LC–
MS analysis. The applied protocol is shown schematically
in Supplementary Figure S1 and is outlined in detail in the
Supplementary Materials and methods.
In-silico analysis of mass spectra
In order to recalibrate spectra and slice out peptide free
regions, each raw MS file was searched in a first step
against a protein database using the SEQUEST algorithm
in the Proteome Discoverer 1.2 software (Thermo Fisher
Scientific, Bremen, Germany). The human canonical pro-
tein database containing all reviewed protein entries for
organism Homo sapiens (taxonomy ID 9606) without pro-
tein isoforms (SwissProt, http://www.uniprot.org, build
date: 01 November 2011, 20,229 entries) was used. The
precursor and fragment ion mass tolerances were set to
10 ppm and 0.5 Da, respectively. Two missed cleavages
for trypsin were allowed, carbamidomethylation was set
as fixed and methionine oxidation as variable modifica-
tion. The peptide identification false discovery rate (FDR)
was determined by searching the raw MS/MS data against
a decoy database. The retention times of the first and last
peptide identified within the specified limits (p ≤ 0.1, i.e.
FDR ≤ 10 %) were used to slice the raw files into smaller
reprocessed raw files. Afterwards, for each raw fileh the
FT-MS-spectra were recalibrated using the measured and
theoretical accurate mass of the known polysiloxane con-
tamination peak at m/z 445.12003.
For the final data analysis, performed with the sliced
and recalibrated spectra, a combined approach utilis-
ing four search engines was performed with: SEQUEST
(implemented in Proteome Discoverer v.1.2), Mascot
(Matrix Science, v.2.2.04), OMSSA (Open Mass Spec-
trometry Search Algorithm, v.2.1.9) and X!Tandem (v.
CYCLONE 2010.12.01). A detailed description of the
employed four search-engine approach will be presented
elsewhere (in preparation). Prior to database search, the
recorded raw files were converted into mgf-format using
the built-in exporter node in Proteome Discoverer 1.2. To
avoid potential differences in database search, the setting
of the spectrum selector node (default setting) were applied
and added as a node in the export process. SEQUEST- and
Mascot-searches were performed with the Proteome Dis-
coverer 1.2 software, OMSSA and X!Tandem searches
with the SearchGUI (v.1.7.3) [39] in combination with
Peptide Shaker (v.0.16.2; code.google.com/p/peptide-
shaker). For the in-gel and in-solution digested samples,
the precursor and fragment ion mass tolerances were set
to 5 ppm and 0.5 Da, respectively. For the size exclusion
flow-through samples, these mass tolerances were set to
3 ppm and 0.5 Da, respectively. Two missed cleavages for
trypsin were allowed, carbamidomethylation was set as
fixed and methionine oxidation as variable modification.
The FDR was calculated by the software being used: Pro-
teome Discoverer 1.2 for SEQUEST and Mascot and Pep-
tideShaker in Combination to SearchGui for OMSSA and
X!Tandem. Peptides were classified as high confident with
an FDR ≤ 1 % (i.e. p ≤ 0.01) and classified as medium
confident with an FDR ≤ 5 % (p ≤ 0.05).
A protein identification within one search engine result
file was accepted as confirmed when this protein was iden-
tified by at least two high confidence or three medium
3712 Andreas Keller et al.
1 3
confidence peptides. Additionally, database searches with-
out specification of a protease were performed in order to
identify non-tryptic peptides potentially formed by protein
degradation.
The MS raw data as well as database search results
(Filename: “Protein_Results.xlsx”) are accessible at:
“ftp.interop.uni-kiel.de” under the login: “sukmb289b” and
the password: “eiwai9ce”.
Matching peptides to the Iceman’s specific protein
database
To identify peptides that match single nucleotide variants
(SNVs) in the Iceman’s genome, the following approach was
applied. First, the SNVs detected in the Iceman’s genome
were annotated based on their location in genomic func-
tional elements. In order to filter the subset of potentially
important SNVs from the whole dataset, we developed a
software called snpActs (http://snpacts.ikmb.uni-kiel.de).
snpActs performs genomic region-based annotations
employing a local mirror of the UCSC Genome Browser
database [40, 41]. The gene annotation systems CCDS [42]
and RefSeq Genes [43] are utilised for region-based anno-
tation of SNVs. snpActs scans the gene annotation systems
and identifies among other exonic SNVs that are either
classified as synonymous, missense, nonsense, cancelled-
start, or readthrough SNVs. If an SNV of the Iceman’s
genome could not be annotated in CCDS the gene table
of RefSeq Genes was used instead. For all missense and
nonsense SNVs the mutated amino-acid was determined,
and substituted in the corresponding protein sequence. The
employed databases for protein sequences were CCDS and
refSeq Proteins [42].
Next, the search against this database and identified
peptides was performed with SEQUEST search algorithm
as described above. Afterwards, the peptides not match-
ing the human reference sequence, but corresponding to an
amino acid exchange as represented in the Iceman’s DNA
sequence, were filtered. Finally, the identified peptides
were BLASTed against all other human proteins to check
whether any other protein corresponds 100 % to the par-
ticular peptide. All remaining fragments were reported as
Iceman-specific.
Statistical enrichment analysis
The above described experiments yielded a total of 502
identified proteins in the two Iceman samples. To find
enrichment of proteins in certain biological groups, we
applied an over-representation analysis. For more than
10,000 functional categories represented by PFAM domains
[44], pathways from the KEGG database [45] or Gene
Ontology annotations from GO [46], the number of human
proteins represented in each category were computed.
Next, the expected number of proteins in each group given
a random sampling of 502 proteins has been accessed. In
addition, the actual number of proteins detected in the Ice-
man sample has been determined for each category, and the
likelihood that more than the detected number of proteins
could have been detected by chance using the Hypergeo-
metric distribution. Finally, the computed significance val-
ues were adjusted for multiple testing using the Benjamini–
Hochberg approach [29]. All calculations have been carried
out using the freely available gene set analysis tool Gen-
eTrail [27]. Visualization of pathways has been carried out
using the KEGG representation and 3D representation of
single proteins has been done using BallView [47].
Results
Protein extraction from Iceman’s brain biopsies
In this study, two buffy, granulose brain biopsies, hereaf-
ter denoted as samples 1024 and 1025, obtained during an
endoscopic investigation of the Iceman’s brain, were sub-
jected to proteomic and microscopic analyses. In order to
reach a high protein yield during the extraction process, a
sequence of two extraction strategies (strategies A and B;
Supplementary Figure S1 and Supplementary Materials
and Methods) was used.
Strategy A applied a rehydration step using physiologi-
cal saline solution followed by cell disruption using trif-
luoroethanol (TFE), which has been previously shown to
be suitable to disrupt cells [24], in particular from brain-
derived tissues [25]. In order to study the suitability of this
extraction procedure for freeze-dried samples, as those of
the Iceman’s brain tissue, the rehydration and extraction
steps were first tested on fresh, freeze-dried brain biopsies.
This was followed by analysis of the weight gain during
rehydration and by control measurements on the recovery
of proteins by means of protein concentration determina-
tion and SDS-PAGE. The protein extraction procedure
displayed no significant differences in terms of protein
amounts (data not shown) and the protein distribution as
visible from gel-based separation for both freeze-dried
and non-freeze-dried control samples (Supplementary
Figure S2). The results demonstrate that freeze drying has
no negative effect on the extraction efficiency; hence, the
rehydration/TFE protocol originally developed for fresh
tissue samples can be applied for freeze-dried ancient tis-
sue samples.
Two tissue samples from the Iceman’s brain, 6.5 mg for
sample 1024 and 3.4 mg for sample 1025, were used for
protein extraction with strategy A. In a first step, the two
tissue samples were rehydrated using physiological saline,
3713
Paleoproteomic study of the Iceman’s brain tissue
1 3
according to the procedure applied for the fresh tissue. For
sample 1024, the mass increased to 20.5 mg, and for 1025
to 11.6 mg, corresponding to 2.15- and 2.4-fold weight
gains, respectively. These values are significantly lower
than those observed for freeze-dried fresh tissues (mean
over 2 samples: 7.5-fold). The reason maybe either that the
ancient tissues still contained significant amount of water
(which is not likely according to the shape of the buffy,
granulose samples) or that cellular disintegration prevented
significant uptake of solvent.
After TFE extraction, for sample 1024-A, a total pro-
tein amount of 22 μg (0.11 wt% compared to the weight
of the rehydrated tissue), and for sample 1025-A, of 11 μg
(0.09 wt%), could be extracted using strategy A. These
amounts were significantly lower compared to the amounts
of protein extracted from fresh, freeze-dried and rehydrated
tissues (2.3 wt% of protein weight compared to the weight
of the rehydrated tissue).
In order to recover the proteins, which were not extracted
by strategy A, e.g. still present in the cell debris, we addi-
tionally performed a second extraction strategy (strategy
B, Supplementary Figure S1), denoted as samples 1024-B
and 1025-B. Here, protein extraction was performed using
the same rehydration and extraction steps as in strategy A,
but using harsher conditions, e.g. longer rehydration times
and prolonged sonication cycles. In addition, the superna-
tant from sample work-up in strategy A was added to the
extracts obtained by strategy B. The samples were desalted
and concentrated by size exclusion filtration through 3-kDa
cut-off filters to remove interfering salts from rehydration
solution. The total remaining protein amounts after filtra-
tion were 10 and 5 μg for the samples 1024-B and 1025-B,
respectively. These samples were digested in-solution using
trypsin prior to LC–MS analysis.
Analysis of Iceman’s brain proteome
To identify the proteins extracted by strategy A, a gel LC–
MS-based analytical strategy was chosen. Proteins were
separated by means of SDS-PAGE (Supplementary Figure
S3), followed by trypsin digestion. The resulting peptides
were separated and analysed by LC–MS. Proteins extracted
by strategy B were analysed using a shotgun proteomics
approach.
Two different multi-database searches were performed
to identify peptides and proteins from the raw MS/MS data.
The first search was performed using trypsin as specified
protease, the second without the definition of a specific
protease. The latter search was performed to identify poten-
tial protein fragments formed in the tissues by other pro-
teolytic processes or by unspecific hydrolysis during the
course of the Iceman’s preservation. Both searches were
performed allowing for methionine oxidation as variable
modification. As deamidation has been described to be a
major modification occurring during protein aging [26], we
performed a third analysis of the MS/MS data where we
used deamidated asparagine and glutamine as additional
variable modifications.
Only a few distinct bands were visible after colloi-
dal Coomassie staining of the SDS-gel separated proteins
extracted by strategy A (Supplementary Figure S3). The
most intense stained areas were observed in the high and
low mass range of the gel. As is typical for complex pro-
teome samples and enforced by the progressed protein
degradation, poor separation with characteristic smearing
over the entire separation space was observed. SDS-PAGE
was performed as a first dimension separation in order to
reduce sample complexity prior to protein digest, second
dimension LC-separation and mass spectrometric analysis.
In order to prevent possible negative effects of staining on
later protein identification, we did not restain the gels by
more sensitive, e.g., fluorescent stains. For further analysis,
both gel lanes were cut into 22 pieces and proteins were
digested in-gel with trypsin prior to LC–MS analysis.
From the in-gel, the in-solution digestion and the flow-
through experiments, 488 proteins in sample 1024 and 222
proteins in sample 1025 could be unambiguously identi-
fied. Of those, 208 proteins were detected in both samples.
Overall, 502 different proteins were identified, indicating
that, even after 5,300 years, a high number of proteins can
still be identified using proteomics approaches.
As shown in the area-proportional Venn diagram in Fig.
S4, the proteins identified in sample 1025 represent almost
a subset of sample 1024. Of the 222 proteins, 208 (94 %)
were likewise detected in sample 1024, whereas only 14
additional proteins were detected in sample 1025. The
lower number of proteins identified in sample 1025 is most
likely caused by the lower amount of material available
and, consequently, the reduced number of precursors being
selected for MS/MS fragmentation by CID in the ion trap.
In order to achieve a high coverage of the Iceman’s brain
proteomes, both a shotgun and a gel LC-based proteomics
strategy were applied. The protein identification was per-
formed using a multi-database search strategy, applying 4
different search engines (SEQUEST, Mascot, OMSSA and
X!Tandem), which apply different algorithms and statistics
to match fragment spectra to predicted peptide sequences
from a protein database [27, 28]. The measured spectra
were searched independently with each of the four search
engines. In all searches, peptides identified with 1 % (high)
or 5 % (medium) FDR were used for protein identification.
A protein was denoted as unambiguously identified by the
particular search engine when at least 2 high or 3 medium
peptides were identified. The eight resulting lists (4 search
engines, each with and without enzyme specificity) con-
taining the identified proteins of each search engine were
3714 Andreas Keller et al.
1 3
finally merged to the final protein identification list (Sup-
plementary Material, Dataset S1). From the 502 proteins
identified, more than 87 % were identified with at least two
search engines (62 % with four, 12 % with three and 13 %
with two engines); 13 % were only identified with a single
search engine.
In total, 48 % of the identified peptides showed non-
tryptic cleavage sites when matched back to the human
protein database in samples 1024 and 1025. These peptides
can at least partially originate from protein C-termini but
are most likely products of protein degradation, either by
cellular or environmental proteases (e.g. from microorgan-
isms) or were formed by chemical degradation. As known
from other proteomics studies encompassing a similar
sample workup strategy [25], the formation of such a high
number of unspecific cleavage peptides induced by experi-
mental artefacts is not likely.
In the SDS-PAGE, several proteins were smeared over
a wider range of the gel (Supplementary Figure S3; Sup-
plementary Dataset S3). For example, proteotypic peptides
of cytochrome c subunit 2 (P00403; Supplementary Table
S2A) were identified in bands 15–22 in sample 1024-A.
This 25.5-kDa protein is known not to be extensively post-
translational modified (e.g. glycosylated), ruling out the
possibility that the observed smearing over eight bands
is caused by isoform formation. This is further supported
by finding the protein in gel bands of the lower molecular
weight range. Over 90 % of the peptides identified in bands
15–21 had tryptic cleavages sites, whereas in band 22, 50 %
of the 10 identified peptides were non-tryptic, clearly indi-
cating protein degradation. A second example for the occur-
rence of potentially truncated proteins distributed over a
wide range in the gel is the neural cell-specific myelin pro-
teolipid protein (P60201; Supplementary Table S2B).
Deamidation of glutamine and asparagine residues is a
known process in protein aging but may also be induced
during sample workup and analysis. In order to study the
degree of this modification in the Iceman’s brain proteome
samples, we performed a third database analysis which
allowed for deamidation as an additional variable modifi-
cation, whereas in the two first searches (with or without
trypsin as defined protease) only methionine oxidation was
allowed for as a variable modification. For this third data-
base analysis of the gel-separated samples (from strategy
A), we applied an additional data-merging step, which is
common in shotgun analysis (as done for 1024-B and
1025-B). In this step, the peptides identified in all bands
of the gel lanes of samples 1024-A and 1025-A were
merged to a single peptide dataset. At the peptide level,
we observed about 20 % of deamidated species in samples
1024-A, 16 % in 1025-A, and about 28 % in both 1024-B
and 1025-B. The number of protein identifications did not
increase when deamidation was taken into account.
Functional annotation of the identified proteins
Of the 502 proteins identified, 41 proteins are known to
be involved in neuronal- and brain-specific processes
(Table 1), e.g. synaptosomal-associated protein 25, neu-
rofascin and neuroplastin, including 16 proteins that are
highly expressed in brain cells, 9 brain-specific proteins
and 16 proteins specific for neural cells.
Since the original samples contained a dark spot that
might be coagulated blood from an injury to the brain or
head, we also searched for blood-related proteins. As
shown in Table 2, we identified 10 such proteins, includ-
ing the beta subunit of haemoglobin, annexin A5 and serum
albumin. These proteins were identified with high sequence
coverage. For example, 44 distinct peptides cover 420 of
585 amino acid residues of serum albumin.
It has to be noted that, amongst the 502 proteins identi-
fied in this study, a high number of keratins and other cell
structure forming proteins were identified. It cannot be
ruled out that, despite the usual efforts to minimize con-
tamination of the samples by operator’s keratins in prot-
eomic analyses, such contaminations contribute to these
identifications. On the other hand, the amounts of proteins
found also reflected by a high number of peptide spectral
matches, clearly indicating that the majority of these pro-
teins were derived from the Iceman itself.
In order to obtain a summary of the functional roles of
the identified proteins by the four database search engines
approach, functional modelling was done using an over-
enrichment analysis with GeneTrail [27]. To this end, we
investigated the 502 proteins and asked, for over 10,000
functional biological categories represented in KEGG path-
ways and gene ontologies from the GO database, whether
the 502 proteins contain significantly more members of
each category than one would expect by chance. The cal-
culated significant scores were adjusted for multiple testing
using the Benjamini–Hochberg approach [29]. After adjust-
ment, 1,019 categories remained significant, including 72
PFAM and 10 CATH domains, 907 GO categories and 30
KEGG pathways. The full list of significant pathways is
provided in the Supplementary Dataset S2.
Figure 2 presents the most relevant PFAM domains
together with representative examples from the PFAM
database. Most significantly, we found intermediate fila-
ment proteins (p = 1.5 × 10−56) and core histone HSA/
H2B/H3/H4 (p = 5 × 10−25). Besides these, we detected
tubulins building up the microtubule and we found a
strong enrichment for the Immunoglobulin C1-set domain
(p value = 1.5 × 10−6; including IGHG2 IGKC AZGP1
IGHG1 IGHG3 IGHA1 SIRPB1 IGHG4 IGHA2 B2M).
Many of the detected KEGG pathways provide evi-
dence for an enrichment of neurological disorders, namely
Alzheimer’s (p = 8 × 10−8), Huntington’s (p = 10−8) or
3715
Paleoproteomic study of the Iceman’s brain tissue
1 3
Table 1 Brain-related proteins
Protein Specificity UniProt accession Unique peptide hits
1024 In-gel/in-solution 1025 In-gel/in-solution
Synaptosomal-associated
protein 25
Brain tissue-specific P60880 13/16 3/3
Protein kinase C gamma type Brain tissue-specific P05129 12/6 2/0
Brevican core protein Brain tissue-specific Q96GW7 3/2a0/0
Versican core protein Brain tissue-specific P13611 5/6 5/0
Tubulin beta-4 chain Brain tissue-specific P04350 5/4 0/0
Neuroplastin Brain tissue-specific Q9Y639 1a/5 0/2
Microtubule-associated
protein 1B
Brain tissue-specific P46821 1a/2 0/0
Dipeptidylaminopeptidase-like
protein 6
Brain tissue-specific P42658 1a/5 0/0
Ras-related protein Rab-3A Brain tissue-specific P20336 1a/3 0/0
Neurochondrin High expression in brain tissue Q9UBB6 2/0 0/0
Synaptojanin-1 High expression in brain tissue O43426 2/3 0/0
Glial fibrillary acidic protein High expression in brain tissue P14136 11/6 2a/0
Palmitoyl-protein thioesterase 1 High expression in brain tissue P50897 5/1a0/0
Tubulin alpha-1A chain High expression in brain tissue Q71U36 18/20 2*/0
Tubulin beta-2A chain High expression in brain tissue Q13885 4/4 0/0
Sortilin High expression in brain tissue Q99523 2/3 0/0
Tubulin beta chain High expression in brain tissue P07437 5/3 0/0
Excitatory amino acid
transporter 1
High expression in brain tissue P43003 2/2 0/0
Leukocyte surface antigen
CD47
High expression in brain tissue Q08722 2*/2 0/0
Contactin-associated protein 1 High expression in brain tissue P78357 1a/5 0/0
Solute carrier family 2 High expression in brain tissue P11169 3/1a0/0
Pleckstrin homology
domain-containing family B
member 1
High expression in brain tissue Q9UF11 1a/4 0/0
Tyrosine-protein phosphatase
non-receptor type substrate 1
High expression in brain tissue P78324 7/4 4/0
Tubulin beta-2B chain High expression in brain tissue Q9BVA1 4/3 0/0
Tubulin beta-3 chain High expression in brain tissue Q13509 3/3 0/0
Myelin proteolipid protein Neural cell-specific P60201 38/34 67/28
Myelin-oligodendrocyte
glycoprotein
Neural cell-specific Q16653 13/9 12/7
Neurofascin Neural cell-specific O94856 9/7 7/0
Visinin-like protein 1 Neural cell-specific P62760 5/7 0/0
Neurofilament heavy
polypeptide
Neural cell-specific P12036 1a/3 2/3
Neurofilament light
polypeptide
Neural cell-specific P07196 0/3 0/0
Syntaxin-binding protein 1 Neural cell-specific P61764 1a/3 0/0
Myelin-associated
oligodendrocyte basic protein
Neural cell-specific Q13875 3/4 4/0
Myelin P2 protein Neural cell-specific P02689 4/4 2/2
Neural cell adhesion
molecule 1
Neural cell-specific P13591 1a/4 0/0
Syntaxin-1B Neural cell-specific P61266 3/1a0/0
3716 Andreas Keller et al.
1 3
Parkinson’s diseases (p = 7 × 10−10), as detailed in the
Supplementary Figures S5–S7. While these findings do
not point at an acute disease, they rather provide further
evidence for enrichment of proteins related to neurologi-
cal function in general. The overall distribution of pro-
teins among the most significant pathways (p < 0.001) is
presented in Supplementary Figure S8. Interpreting the
most significant GO categories (p < 10−15; Fig. S9), we
found many proteins related to the structural molecule
activity (p = 1.5 × 10−55), the intermediate filament
(p = 2.3 × 10−41) and the intermediate filament cytoskel-
eton (p = 3.3 × 10−40). Besides these, proteins belonging
to the cytoplasm or the mitochondrion, as well as the cel-
lular matrix and membrane proteins, were also found. Our
String protein network analysis [30] supports the presence
of these common cellular proteins, which are still highly
linked in a protein–protein interaction network (Supple-
mentary Figure S10). Inspecting the relevant GO categories
in detail, especially focusing on the biological function, a
set of 84 proteins that are related to stress response were
Intermediate
filament protein
35%
Core histone
H2A/H2B/H3/H4
21%
Tubulin C-
terminal
domain
10%
Tubulin/FtsZ family,
GTPase domain
10%
Immunoglobulin
C1-set domain
8%
Actin
8%
C2 domain
8%
Fig. 2 Most relevant PFAM domains. The figure presents the most
relevant PFAM domains, as discovered by our statistical enrichment
analysis
Table 2 Blood-related proteins
Protein Specificity/Function UniProt accession Unique peptide hits
1024 In-gel/in-soltion 1025 In-gel/in-solution
Hemoglobin subunit beta Red blood cells P68871 8/6 7/4
Hemoglobin subunit delta Red blood cells P02042 5/3 3/2
Hemoglobin subunit alpha Red blood cells P69905 4/4 6/0
Serum albumin Blood plasma P02768 20/35 19/12
Plasma protease C1 inhibitor Blood plasma P05155 2/0 0/0
Annexin A5 Blood coagulation P08758 13/8 12/3
Alpha-1-antitrypsin Blood coagulation P01009 8/6 2/0
Antithrombin-III Blood coagulation P01008 2a/3 0/0
Prothrombin Blood coagulation P00734 3/0 0/0
Fibrinogen alpha chain Blood coagulation P02671 10/0 0/0
All unique peptides for samples 1024 and 1025 from either in-gel or in-solution digests (separated by a “/”)were identified with a FDR ≤ 5 %
a Unique peptides not leading to a positive identification of the protein
All unique peptides for samples 1024 and 1025 from either in-gel or in-solution digests (separated by a “/”)were identified with a FDR ≤ 5 %
a Unique peptides not leading to a positive identification of the protein
Protein Specificity UniProt accession Unique peptide hits
1024 In-gel/in-solution 1025 In-gel/in-solution
Calcium/calmodulin-dependent
protein kinase type II subunit
alpha
Neural cell-specific Q9UQM7 1a/3 0/0
Myelin basic protein Neural cell-specific P02686 10/11 14/8
Synaptic vesicle
glycoprotein 2A
Neural cell-specific Q7L0J3 2a/2 0/0
Synaptophysin Neural cell-specific P08247 6/3 0/0
Major prion protein Neural cell-specific P04156 0/2 0/2
Table 1 continued
3717
Paleoproteomic study of the Iceman’s brain tissue
1 3
found. Of these, 39 are directly linked to the response to
wounding and 15 to wound healing. Besides two mem-
bers of the annexin family (annexin A2 and annexin A5, a
coagulation factor; see also Table 2), these 15 contain the
coagulation proteins alpha-1-antitrypsin (encoded by ser-
pina1), antithrombin-III (encoded by serpinc1), prothrom-
bin (encoded by the gene F2) and fibrinogen alpha chain
(encoded by FGA). The three GO categories together with
the respective p values and members of each category are
shown in Fig. 3.
CT and AFM measurements
Radiological and CT scan-based investigations of the Ice-
man’s skull provided evidence for pre- or perimortem frac-
tures at the right side of the neuro- and viscerocranium [1].
Together with the irregular areas of increased radiological
transparency in the posterior cerebral region and the soft
tissue swelling at the right facial side, they indicate a skull
injury shortly before death. Besides applying a paleoprot-
eomic approach, we also investigated the biopsies micro-
scopically. Both samples used in this study were taken from
the patchy brain area of increased radiological transparency.
Our imaging approach revealed surprisingly well-preserved
neuronal structures with networks of dendritic fibers (Sup-
plementary Figure S11). Further AFM measurement seems
coherent with the identification of blood and wound heal-
ing-related proteins. Optical microscope images revealed
round particles with the approximate size and shape of red
blood cells (RBCs) within the histological sample. Several
particles located close to one another and sometimes over-
lapping were disclosed (see Fig. 4a). Higher magnification
AFM images verify the characteristic RBC structures. Cir-
cular, concave, disc-like particles with an average diameter
of 6.4 ± 0.7 μm were found (Fig. 4b). The particles are
smaller than in vivo RBCs with their diameter of about
7.2 μm when traversing the circulatory system, or air-dried
RBCs as examined in another AFM study [31]. Their size,
however, matches those of RBCs found in other Iceman
samples processed to histologic sections [32], indicating
that the sample preparation influenced their dimension. The
sample preparation may have also influenced the stability
of the RBC membrane. Some of the particles feature holes
in the regions of the RBC membrane depression. All parti-
cles found are clustered (see Fig. 4c), resembling structures
typical for a blood clot. No other blood clot residues such
as fibrin, the fibrillar protein that stabilizes the clot by the
formation of a reinforcing meshwork, which polymerizes
during the final steps of the haemostatic plug formation
[33], were detected. This may suggests that the blood clot
decomposed, or that it was formed shortly before the man
passed away.
Matching to genomic Iceman mutations
To test whether we detect protein fragments that match
mutations present in the Iceman genome [4], we first trans-
lated the genomic sequence to an Iceman-specific protein
Fig. 3 Gene ontology enrich-
ment, showing a part of the GO
tree which contains the three
significantly enriched nodes
response to stress, response to
wounding, and wound heal-
ing. Below each blue node are
detailed the proteins identified
in each category
3718 Andreas Keller et al.
1 3
database. This database contained 7,787 different poly-
peptides with an average length of 772 amino acids. This
database was then used to identify fragments from the mass
spectrometry experiments matching the Iceman’s sequence.
Thereby, we identified a total of 2,216 polypeptides with
an FDR ≤ 5 % and an average length of 15 amino acids. In
a third step, we searched for fragments that contain a dif-
ferent amino acid than the reference sequence. Finally, we
BLASTed the respective set of fragments to ensure that no
other known protein sequence, e.g. belonging to a homolo-
gous protein, matches the short polypeptide.
In total, four peptide sequences were identified with
SEQUEST, which matched to the Iceman’s specific
mutations (Table 3; Supplementary Figure S12). Two of
these, namely peptides TSMQKDTPQEMDQTR and
AHM(ox)ETMAKLEKM(ox), were classified as high con-
fident. This classification is based on the fact that they were
identified in both samples (1024 and 1025) and with an
FDR < 1 %. Additionally, we checked the retention time
and the corresponding % of eluent B during LC-separation
to ensure a comparable (normalized) elution time. For both
peptides, the mass accuracy was better than 1.5 ppm and
the XCorr scores were above 2.5.
The other two peptides, DLEEELHPGEVLVM-
LMGNK and SASPNFNTSGGASAGGSDEGSSSSLDR,
were reported as potential mutation candidates (Table 3),
as, in contrast to the high confident peptides, both were
detected with an FDR < 5 %. Peptide DLEEELHPGEV-
LVMLMGNK was identified in both samples (1024-B and
1025-B), and the corresponding normalised elution times
(expressed by the %content of eluent B) were compara-
ble. In both cases, a mass accuracy better than 1.5 ppm
could be achieved. Peptide SASPNFNTSGGASAGGS-
DEGSSSSLDR was only detected in sample 1024-A with
a mass accuracy of about 4 ppm. The quality of the MS/
MS spectra of these two candidates was poor, in the sense
that low S/N signals were obtained and that, in particular
for the two potential mutated peptides, a number of strong
Fig. 4 Optical and atomic force
microscope images of RBCs in
the Iceman’s brain tissue. a A
×400 magnified optical image
of clustered round particles. The
inset displays the optical image
of the sample at ×100 magni-
fication. Red squares indicate
the region of interest imaged
by AFM. b Particles revealed
by AFM are circular, concave,
and disc-like, resembling the
structure typical of normal
RBC. The 3D representation (c)
of the AFM data illustrates the
randomly agglomerated parti-
cles that overlap one another in
some regions
3719
Paleoproteomic study of the Iceman’s brain tissue
1 3
Table 3 Iceman-specific amino acid substitutions detected in 4 proteins
UniProt
accession
Protein name No. of IDs (FDR 1 %/5 %) AAS Alignment Iceman vs. best BLAST hit
Peptide sequence FDR RT (min) %B PPM XCorr Probability Ions matched In-solution In-gel Sample
Q13617 Cullin 2 4/2 E666D TSMQKDTPQEMDQTR
–TSMQKDTPQEMEQTR–
TSMQKDTPQEMDQTR p ≤ 0.01 91 31 0.19 3.45 12.88 9/28 x – 1024
TSMQKDTPQEMDQTR p ≤ 0.01 92 31 0.98 3.33 19.57 9/28 x – 1025
TSMQKDTPQEMDQTR p ≤ 0.01 85 29 −0.47 3.28 7.03 8/28 x – 1025
TSMQKDTPQEMDQTR p ≤ 0.05 85 29 −0.06 3.00 27.49 10/28 x – 1025
TSMQKDTPQEMDQTR p ≤ 0.01 74 36 1.23 2.96 20.66 10/28 – x 1024
TSMQKDTPQEMDQTR p ≤ 0.05 73 35 0.21 2.51 10.49 8/28 – x 1025
Q3B820 Protein FAM161A isoform 2 2/0 I107 M AHMETMAKLEKM
–AHIETMAKLEKM–
AHM(ox)ETMAKLEKM(ox) p ≤ 0.01 67 23 −1.51 2.80 28.28 9/22 x – 1024
AHM(ox)ETMAKLEKM(ox) p ≤ 0.01 67 24 −0.11 2.66 25.90 8/22 x – 1025
Q9H0T7 Ras-related protein Rab-17 0/2 V130 M DLEEELHPGEVLVMLMGNK
–DLEEELHPGEVLVMLVGNK–
DLEEELHPGEVLVMLMGNK p ≤ 0.05 52 19 −1.36 2.69 12.78 14/72 x – 1024
DLEEELHPGEVLVMLMGNK p ≤ 0.05 59 21 0.14 2.61 19.28 16/72 x – 1025
O15018 PDZ domain-containing protein 2 0/1 G715D SASPNFNTSGGASAGGSDEGSSSSLDR
–SASPNFNTSGGASAGGSDEGSSSSLGR–
SASPNFNTSGGASAGGSDEGSSSSLDR p ≤ 0.05 46 23 −3.99 2.82 18.26 12/104 – x 1024
The upper two peptides were classified as higher confidence identifications based on the FDR and the number of PSM matches, the lower two were classified as candidates. The table contains
the number of peptide identifications based on the FDR approach [No. of IDs (1 %/5 %)]; the retention time (RT) in minutes; the composition of the LC solution at the specific retention time
(%B); the mass accuracy; the scores (XCorr and Probability); the number of matched versus theoretical expected ions in spectra; and their source of origin (In-gel or In-solution) and the sample
itself (1024 or 1025). The corresponding MS/MS spectra are shown in the Supplementary Figure S12
3720 Andreas Keller et al.
1 3
signals in the MS/MS spectra also could not be assigned to
the sequences; the length of these peptides is like a cause
for this.
Discussion
The occurrence of cerebral tissues from archaeological
human remains are exceptional rare findings that deserve
the utmost scientific attention, since post-mortem decom-
positions (autolysis, bacterial/fungal attacks) and tapho-
nomics conditions hamper the preservation of brain soft
tissues [34]. Moreover brain tissues were often removed
prior to the embalming process of anthropogenic mummifi-
cations [35]. Therefore only few cases of brain tissues from
human remains have been reported in the literature (Sup-
plementary Table S1) and very few studies have undertaken
a multidisciplinary approach to analyse the preserved cer-
ebral tissues [34].
To our knowledge, this is the first report selectively ana-
lysing the cerebral tissue from a natural mummy, by using
proteomics coupled to an imaging approach. The proteome
analysis of two brain biopsies of the Iceman identified a
total of 502 different proteins. The numerous proteins iden-
tified represent not only the common, most abundant pro-
teins (e.g. cellular structural proteins) but also structurally
well-preserved brain tissue-specific proteins of which 41
are known to be highly abundant in brain tissue and fur-
ther 9 are even specifically expressed in the brain, provid-
ing evidence for the authenticity of the presented ancient
proteome. The detection of Iceman-specific amino acid
substitutions in addition underlines the true nature of the
sample. This first in-depth paleoproteomic study performed
on an ancient human soft tissue sample describes the so-
far richest ancient proteome in terms of number of confi-
dently identified proteins. The high number of identified
proteins can be attributed to the excellent preservation of
the 5,300-year-old glacier mummy. The extensive freeze-
drying process in the glacial ice environment stopped the
post-mortem taphonomic changes of the soft tissue quite
rapidly [36]. Interestingly, besides an enriched set of pro-
teins that are related to stress response, we were able to
detect 10 proteins related to blood and coagulation. These
proteins could support the theory of an injury of the head
near the site where the samples have been extracted, which
is further corroborated by computed tomography analysis
and atomic force microscope images displaying possible
clotted blood. In the presented ancient proteome dataset,
evidence for the injury of the head is, however, limited to a
few indicative proteins, and alternative explanations for the
presence of these proteins should be considered. Especially,
the enriched set of proteins related to stress response could
also display the remnants of the regular, site-unspecific cell
response to intracellular stress stimuli induced by the Ice-
man’s death [37]. Moreover, the post-mortem alteration
of the original brain anatomy including the disruption of
blood vessels could result in the presence of blood-related
proteins in the brain tissue [36]. However, soft tissue tapho-
nomic processes alone cannot fully explain the ultimate
cause of the two irregular areas of increased radiological
transparency in the Iceman’s brain.
The choice of the analytical strategy determines the out-
come and success of any proteomics study and is in par-
ticular critical when only limited and partially degraded
biological material is available, as in this study. We applied
a double-track proteomic strategy, both for the extraction
of the proteomes form the tissue as well as for the follow-
ing separation and mass spectrometric analysis. The two-
fold extraction procedure resulted in an increased available
protein amount, which seems to be an important factor for
degraded cells as obtained from Iceman’s brain. For the
identification of the proteins, two established proteomics
approaches were applied. In the first place, the gel LC-
based approach (strategy A) maybe not well suited for the
detection of smaller degradation products. However, we
decided to use this approach as it simultaneously allowed
a precleaning of the sample, which would have been a
necessary step for LC-based approaches. Such steps are
generally accompanied with severe sample loss. The iden-
tification of a number of truncated proteins in form of non-
tryptic peptides identified in the low molecular weight
region of the gel shows that the choice of this strategy was
successful. Strategy B encompassed a shotgun strategy in
which we were able to identify a number of further cleav-
age products of degraded proteins. Clearly, it will be a
trade-off in further studies to decide whether this strategy
or the use of LC-bases separation together with preclean-
ing steps will be more suitable. A potential way to circum-
vent this problem will be the application of semi-top–down
approaches, encompassing chromatographic separation at
the protein level in first and peptide separation in second
dimension as shown previously [38].
The quality of MS and MS/MS spectra observed in this
study was in many cases very poor in terms of precursor
ion intensities, which in consequence frequently led to poor
ion series in MS/MS experiments. We therefore decided
to use the Orbitrap for full scan analysis and the ion trap
for CID measurements. This again is a trade-off between
high mass accuracies achievable for MS/MS (Orbitrap),
which are clearly beneficial for protein identification, and
the achievable sensitivities (ion trap). Further, the veloci-
ties of MS/MS acquisition are higher in the latter, which
allowed us to acquire more MS/MS data in an online LC–
MS experiment. In order to minimize the rate of false posi-
tive identifications, we decided to apply conservative rules
for the acceptance of proteins; this encompasses the use of
3721
Paleoproteomic study of the Iceman’s brain tissue
1 3
minimum 2 high (FDR ≤ 1 %) or 3 medium (FDR ≤ 5 %)
ranked peptides.
Overall, for the increase of proteome coverage in ancient
tissues, the parallel performance of alternative proteom-
ics approaches was demonstrated to be beneficial. Further,
the involvement of database searches without definition of
specific proteases is recommended for the identification of
degraded proteins. The application of four search engines
increased the number of identifications, even if the major-
ity of proteins were identified with more than one search
algorithm. While in ancient genomics studies bacterial
DNA fragments are often detected, we were not able to
identify any non-human proteins from the two brain biop-
sies. Ancient human proteins, even though highly degraded
to small polypeptides, were nevertheless still traceable and
harbour precious information.
The proteomics approach optimised here constitutes an
important contribution to the emerging field of paleoprot-
eomics by opening new multidisciplinary research avenues
for the molecular in-depth study of human archaeological
specimens dating as far back as the Neolithic. Especially,
the applied comparative approach between the obtained
proteome data and the Iceman genome [4] supports, at dif-
ferent molecular levels, the authenticity of both datasets,
the Iceman genome and proteome. Therefore, we highly
recommend the integration in future studies of ancient
biomolecules the comparison of genomic and proteomic
datasets of one individual or sample, since it further corrob-
orates the authenticity of the data and holds a true poten-
tial to detect functional important mutations on different
molecular levels.
Acknowledgments The work was supported in part by the Südtirol-
erSparkasse. F.M., G.G. and A.Z. were supported by the law 14 grant
of the province Bolzano, South Tyrol, Italy. A.T., T.O. and D.L. were
supported by the Cluster of Excellence “Inflammation at Interfaces”.
B.V.D.B. was supported by the SFB877-project Z2.
Conflict of interest A.K. and M.J. are affiliates of Siemens Health-
care, Erlangen, Germany.
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