Toward a Confocal Subcellular Atlas of the Human Proteome

Department of Biotechnology, AlbaNova University Center, Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
Molecular & Cellular Proteomics (Impact Factor: 6.56). 04/2008; 7(3):499-508. DOI: 10.1074/mcp.M700325-MCP200
Source: PubMed


Information on protein localization on the subcellular level is important to map and characterize the proteome and to better understand cellular functions of proteins. Here we report on a pilot study of 466 proteins in three human cell lines aimed to allow large scale confocal microscopy analysis using protein-specific antibodies. Approximately 3000 high resolution images were generated, and more than 80% of the analyzed proteins could be classified in one or multiple subcellular compartment(s). The localizations of the proteins showed, in many cases, good agreement with the Gene Ontology localization prediction model. This is the first large scale antibody-based study to localize proteins into subcellular compartments using antibodies and confocal microscopy. The results suggest that this approach might be a valuable tool in conjunction with predictive models for protein localization.


Available from: Hjalmar Brismar
Toward a Confocal Subcellular Atlas of the
Human Proteome*
Laurent Barbe‡, Emma Lundberg‡, Per Oksvold‡, Anna Stenius‡, Erland Lewin§,
Erik Bjo¨ rling‡, Anna Asplund¶, Fredrik Ponte´ n¶, Hjalmar Brismar§, Mathias Uhle´n,
and Helene Andersson-Svahn‡
Information on protein localization on the subcellular level
is important to map and characterize the proteome and to
better understand cellular functions of proteins. Here we
report on a pilot study of 466 proteins in three human cell
lines aimed to allow large scale confocal microscopy
analysis using protein-specific antibodies. Approximately
3000 high resolution images were generated, and more
than 80% of the analyzed proteins could be classified in
one or multiple subcellular compartment(s). The localiza-
tions of the proteins showed, in many cases, good agree-
ment with the Gene Ontology localization prediction
model. This is the first large scale antibody-based study
to localize proteins into subcellular compartments using
antibodies and confocal microscopy. The results sug-
gest that this approach might be a valuable tool in con-
junction with predictive models for protein localization.
Molecular & Cellular Proteomics 7:499 –508, 2008.
The identification of the subcellular localization of all pro-
teins can provide key insights into the cellular function of the
individual proteins as well as their probable interacting part-
ners (1). So far systematic studies of subcellular localization of
proteins have been performed either with cellular fractionation
(2) or fluorescence microscopy (3, 4). The most commonly
used approach in the latter case is based on transfection of
cells with cDNA clones fused to a fluorescent reporter protein,
like green fluorescent protein. The fluorescent reporter protein
is cloned to either the N or C terminus of the target protein.
Although numerous experiments have shown the usefulness
of this approach (3, 4), recent studies have indicated that
green fluorescent protein can induce translocation to the nu-
cleus causing artifactual localization results (5).
An alternative approach for localization studies is to use
antibodies or other affinity reagents to specifically visualize
the subcellular localization of proteins. The major advantage
of using the antibody-based method is that no genetic con-
structs are needed and that the possible artifactual effects of
protein fusions are avoided. One drawback is that this strat-
egy requires fixed and permeabilized cells, limiting the exper-
iments to dynamically fixed end points. The antibody-based
and fluorescent tag methods are therefore complementary for
large scale studies of protein localization in cellular systems.
The reason that no one, so far, has attempted to use the
antibody approach in large scale is most likely the lack of
protein binders to an adequate number of proteins (6). The
Swedish Human Protein Atlas (HPA)
program has recently
been set up to allow for systematic exploration of the human
proteome using antibody-based proteomics (7). This is ac-
complished by combining high throughput generation of af-
finity-purified (monospecific) antibodies with protein profiling
in a multitude of tissues/cell types assembled in tissue mi-
croarrays. Due to the difficulty to acquire spatial information
on the fine cellular and subcellular level using immunohisto-
chemistry, we have therefore decided to expand the analysis
with confocal microscopy using fluorescently labeled antibod-
ies. Here we report on a pilot study of 466 proteins in three
human cell lines as a first attempt to generate an atlas of
human proteins using confocal microscopy. This pilot study
has generated 3000 high resolution images all available on
the Human Protein Atlas portal.
Cell Culture—Three different human cell lines were used in this
study: U-251MG, a glioblastoma cell line, provided by Prof. Bengt
Westermark, Uppsala University (Uppsala, Sweden); A-431, an epi-
dermoid carcinoma cell line, obtained from the Deutsche Sammlung
von Mikroorganismen und Zelkultwien (DSMZ) cell line bank (Braun-
schweig, Germany); U-2OS, an osteosarcoma cell line, obtained from
ATCC-LGC Promochem (Boras, Sweden). All cell lines were grown at
37 °C in a 5% CO
environment in media suggested by the provider.
All growth media were supplemented with 10% fetal bovine serum
and an antibiotic/antimycotic solution (all from Invitrogen).
Immunofluorescent Sample Preparation—Immunofluorescent
stainings were prepared in 96-well glass bottom plates (Whatman).
After coating each well with human 12.5
g/ml fibronectin, (Sigma-
Aldrich) for 1 h followed by washes with PBS, 15,000 cells (10,000 for
A-431 cells) were seeded per well and grown for 3 h. All the subse-
quent steps were performed in 96-well format using a pipetting work
From the Departments of ‡Biotechnology and §Cell Physics, Alba-
Nova University Center, Royal Institute of Technology, SE-106 91
Stockholm, Sweden and ¶Department of Genetics and Pathology,
Rudbeck Laboratory, Uppsala University, SE-75185 Uppsala,
Received, July 19, 2007, and in revised form, September 27, 2007
Published, MCP Papers in Press, November 19, 2007, DOI
The abbreviations used are: HPA, Human Protein Atlas; GO,
Gene Ontology; PFA, paraformaldehyde; DAPI, 4,6-diamidino-2-
© 2008 by The American Society for Biochemistry and Molecular Biology, Inc. Molecular & Cellular Proteomics 7.3 499
This paper is available on line at
Page 1
station (Matrix 2 2, Matrix Technologies Corp., Hudson, NH). After
washes with PBS the cells were fixed with ice-cold 4% paraformal-
dehyde (PFA) (Sigma) in growth media for 15 min and permeabilized
using freshly prepared 0.1% Triton X-100 in PBS (Sigma) for 3 5
min. Rabbit monospecific HPA antibodies were dissolved to 2
in blocking buffer (PBS 4% fetal bovine serum) containing 1
mouse anti-tubulin (Abcam, Cambridge, UK) and 1
g/ml chicken
anti-calreticulin (Abcam). Incubation of primary antibodies was per-
formed overnight at 4 °C. After four washes with PBS, secondary
antibodies, goat anti-rabbit Alexa488, rabbit anti-mouse Alexa555,
and goat anti-chicken Alexa647 (Molecular Probes, Invitrogen), di-
luted to 1
g/ml in blocking buffer were incubated for1hatroom
temperature. The cells were counterstained with the nuclear probe
DAPI, diluted to 300 n
M, for 4 min. After extensive washes with PBS
the wells were filled up with PBS containing 78% glycerol and then
sealed. On each plate a negative control well was included where the
primary antibodies had been left out. This was used as a reference to
distinguish positive from negative staining.
Image Acquisition—Image acquisition was performed with a LSM
510 Meta confocal laser scanning microscope equipped with a mo-
torized table and a 63/1.4 numerical aperture oil immersion objec-
tive (all equipment from Carl Zeiss GmbH, Jena, Germany). Images
were acquired in four sequential steps (one for each dye) to minimize
the spectral bleed-through. In each well two lateral images (selected
from the center of the cells) and a single z axis slice were recorded.
The confocal settings were as follows: 12-bit acquisition; line aver-
aging, 2; pixel time, 1.84
s; and a pixel size of 80 80 nm. The
detector gain and offset were adjusted for each sample to use the full
dynamic range of the detector and to obtain as high signal to noise
ratios as possible. The operator ensured that the acquired images
were representative for the well.
Image Annotation—Image annotation was performed manually us-
ing a Web-based annotation document (Supplemental Fig. 4). This
enabled linking each image to an antibody identification number and
existing data available in our Laboratory Information Management
System (7). For each image pair, sample identification tags and mi-
croscope parameters were entered, and the image was further visu-
ally inspected before the antibody-specific subcellular localization
and staining characteristics were described. Based on the image and
the detector gain settings, the staining was classified as negative,
weak, moderate, or strong. Ten different subcellular localizations
were listed in the annotation document: cytoplasm, nucleus, nuclear
membrane, nucleolus, mitochondrion, endoplasmic reticulum, Golgi
apparatus, cytoskeleton, extracellular matrix, and vesicles. This last
category groups organelles as lysosomes, peroxisomes, endosomes,
and all types of protein transport bodies. The description of the
subcellular compartments was further combined with parameters
describing the staining characteristics; smooth, granular, speckled,
dotty, fibrous, or clustered. For an indiscernible pattern, the image
was annotated as unspecific (Supplemental Fig. 5). For a pattern that
did not fall into the 10 subcellular categories, the image was anno-
tated as “other” combined with a comment.
Data Handling—After scanning of the microplate, images were
collected on our servers and compressed into different resolution
JPEG images for display purposes. All raw (TIFF) images were kept
and stored in the campus facility. Each well of a plate corresponded
to a specific antibody and was described by an identification number.
This allowed linking the new subcellular information to the existing
database with immunohistochemistry data and antibody information
(see Fig. 6).
Data Analysis—Figs. 2 and 3 and Supplemental Figs. 1, 2, and 5
were prepared using ImageJ software (Wayne Rasband, National Insti-
tutes of Health) and Adobe Photoshop (Adobe Systems, Kista, Swe-
den). Fig. 5 was made using Matlab R2007a (Mathworks, Natick, MA).
Work Flow—Fig. 1 shows the work flow used in this study
from high throughput production of monospecific polyclonal
antibodies to protein profiling and subcellular localization (7–
9). The first step (step 1) is the design of protein fragments
(100 –150 amino acids) using bioinformatics tools and the
Ensembl database. The next step (step 2) yields monospecific
antibodies by means of cloning, protein expression, immuni-
zation, and affinity purification (8). A key step is the standard-
ized quality controls of the antibodies (step 3 in Fig. 1), and
here we analyzed the antibody specificity and selectivity by
protein arrays and Western blots to validate protein size and
expression pattern (9). All antibodies are further used for
immunohistochemical staining of microarrays comprising a
variety of normal and cancer tissue as well as cell samples
FIG.1. Schematic work flow of the Human Protein Atlas. The
first step consists of amino acid sequence design from a gene using
bioinformatics tools (1) followed by cloning, expression in Escherichia
coli, immunization of rabbits, and affinity purification (2). Protein mi-
croarrays and cell-tissue Western blots validate the antibody (3).
Immunohistochemistry of tissue and cell microarrays enable protein
profiling (4). Immunofluorescence of cell lines makes possible sub-
cellular localization (5). The output is a Web-based human protein
atlas integrating the previous steps (6).
Toward a Confocal Subcellular Atlas of the Human Proteome
500 Molecular & Cellular Proteomics 7.3
Page 2
and cell lines (7, 10) (step 4). Each image from the tissue
microarrays is annotated by certified pathologists to score
protein expression levels in the different organs. For cell mi-
croarrays, automated annotation is achieved with a tailored
image analysis application (10). The confocal atlas (step 5),
described in this study, adds subcellular information to the
previously described protein profiling tools. The data pre-
sented here are embedded in a publicly available Website
(step 6) with 2.8 million immunohistochemistry images cor-
responding to 3015 antibodies (version 3.0).
Sample Preparation and Image Acquisition—Three cell
lines, U-2OS, A-431, and U-251MG, originating from different
human tissues were chosen to be included in the atlas. To
enhance the probability for a large number of proteins to be
expressed, we selected cell lines from different lineages, i.e.
tumor cell lines from mesenchymal, epithelial, and glial tu-
mors. The selection was furthermore based on morphological
characteristics, widespread use, and multitude of publications
using these cell lines. The U-2OS cell line was derived from an
osteosarcoma from a 150-year-old patient and exhibits an
epithelial adherent morphology despite that it originates from
a malignant cell type displaying a mesenchymal phenotype.
The A-431 cell line, derived from a squamous cell carcinoma
from an 85-year-old female patient, originates from an epider-
moid carcinoma and represents an epithelial cell lineage. This
cell line has been extensively used as a model for epithelial
cancer. The third cell line, U-251MG, has also been exten-
sively used in glioma research. This cell line is derived from a
high grade malignant glioma (glioblastoma multiforme) and is
expected to express several brain-specific proteins, which
may not be present in cells derived from epithelial or sarcom-
atous tumors.
Several different cellular probes were tested as reference
for identification of organelles and other cellular compart-
ments. Due to limitation of the excitation/emission character-
istics of the fluorescent probes used (i.e. to avoid problems
associated with spectrally overlapping fluorophores) and the
need to maintain a high throughput operation, we decided to
limit the pilot study to three cellular probes in addition to the
probe for the analyte. The following three probes were se-
lected: (i) DNA stain DAPI for the nucleus, (ii) tubulin as internal
control for fixation quality and homogeneity, and (iii) calreti-
culin for the endoplasmic reticulum.
When preparing samples for immunofluorescence labeling,
an important factor is the choice of fixative and detergent to
permeabilize the cells. There are many different options avail-
able, all with different advantages and disadvantages (11, 12).
Several different alternatives to suit a high throughput ap-
proach were investigated,
and examples of PFA/Triton X-100
and PFA/saponin are shown in Supplemental Fig. 1. Although
saponin yielded excellent images for proteins localized in the
cytoplasm, our results suggest that stronger detergents, such
as Triton X-100, are needed to stain proteins localized to
mitochondria or nuclei (data not shown) (11). The Triton per-
meabilization was therefore chosen as the standard procedure
for the high throughput effort. In Supplemental Fig. 2, an exam-
ple of the split view of the four channels is presented showing
staining of a putative mitochondrial protein (A) with probes for
nucleus (B), tubulin (C), and endoplasmic reticulum (D).
Annotation of Subcellular Localization—The analysis of
three cell lines using four probes was performed for 466
antibodies selected at random from the validated list of anti-
bodies generated within the framework of the protein atlas
program. The set of antibodies covers proteins originating
from all chromosomes with slightly higher representation on
chromosomes 1, 3, 11, 14, 19, 22, and X (see Supplemental
Fig. 3). Fig. 2 shows an example of the nine different subcel-
lular localizations annotated in this work, namely cytoplasm,
microtubules, nucleus, mitochondria, vesicles, microfila-
ments, endoplasmic reticulum, Golgi apparatus, and extracel-
lular matrix. The microscopic parameters were set to reach a
lateral resolution close to the diffraction limit, i.e. about 200 nm.
All images in the atlas were manually annotated using a Web-
based annotation tool (Supplemental Fig. 4). The subcellular
localization, staining pattern characteristics (e.g. smooth,
speckled, etc.), and intensity were manually annotated.
The diversity of staining patterns is illustrated by examples
of nuclear staining in Fig. 3. Eight different proteins are shown,
all having different distributions within the nucleus (nucleus,
nucleoli, and nuclear membrane) as well as varying charac-
teristics of the staining (smooth, granular, speckled, dotty,
and clusters of spots). More detailed annotation could provide
more information about the function of individual proteins, but
the large spectrum of staining patterns makes a more detailed
annotation risky and could lead to false positives (13). There-
fore the annotations are focused on the main organelles, and
to ensure that no information is lost, both the images and
annotation comments are accessible for the viewers.
Quality of Antibodies—All HPA antibodies were diluted to a
final concentration of 2
g/ml. To analyze whether the spec-
ificity of the staining could be related to the initial antibody
concentration, a diagram showing the relation between the
antibody stock concentrations and staining specificity is dis-
played in Supplemental Fig. 6. It is noteworthy that even
though the antibody concentration was normalized there were
a higher number of unspecific and negative stainings for low
stock concentration compared with higher concentration sug-
gesting that high concentrations of specific antibodies after
immunization is an indicator of good antibody quality. How-
ever, it is worth pointing out that unspecificity of the staining
can also be associated with the fixation/permeabilization pro-
tocol used as shown previously (11, 12).
Evaluation of Localization—In Fig. 4, the distribution of the
different subcellular localization is shown for the three cell
lines. A, B, and C show protein localization distributed in three
major compartments: nucleus, cytoplasm, and nucleus with
L. Barbe and H. Andersson-Svahn, unpublished data.
Toward a Confocal Subcellular Atlas of the Human Proteome
Molecular & Cellular Proteomics 7.3 501
Page 3
cytoplasm. A fourth category groups unspecific and negative
staining. Antibodies were annotated as unspecific when there
was a homogeneous staining all over the cell, as shown in
Supplemental Fig. 5A, in contrast to the compartment “nu-
cleus cytoplasm” (Supplemental Fig. 5B) where the anno-
tator was able to distinguish different staining intensities
within the two localizations. Negative staining was identified
when laser gain was above a defined threshold about which
the noise level is too high compared with signal intensity. Data
are similar between U-2OS (Fig. 4A) and A-431 (Fig. 4C) with
about 30% of all antibodies located in the cytoplasmic com-
partment, 17% located in the nuclear compartment, and 30%
located in both. The U-251MG cell line (Fig. 4B) had more
proteins with an apparent nuclear localization (43%), and this
finding needs to be further investigated. For the three cell
lines, an average of 18% of antibodies was considered as
unspecific or negative.
A comparison of the concordance of subcellular localization
in the three cell lines is shown in Fig. 4D. Because it was
expected that many of the protein targets would only be
expressed in one or two of the cell lines, a complete match
between the cell lines was not expected for many of the
antibodies. The comparison of proteins with an exact match
of all subcellular localizations (stringent) demonstrated that
68% of the antibodies show identical localization of the target
protein in three (23%) or two (45%) cell lines. A comparison in
which at least one subcellular localization was overlapping
(non-stringent) showed that 82% of the protein targets have
identical subcellular localization in at least two cell lines and
that almost half of the antibodies (49%) have the same local-
ization in all three cell lines.
Fig. 4E gives a more detailed overview of subcellular local-
izations. For this figure, only antibodies specific to one or-
ganelle were taken into account. Differences among the three
FIG.2.Selection of subcellular organelles. Selected subcellular organelles found in U-2OS cell lines were counterstained with nuclear
probe DAPI (blue) and immunolabeled with HPA antibodies (green) generated from the different genes. A, T-complex protein 1 subunit
(cytoplasm); B, androgen receptor coactivator ARA55 isoform 2 (microtubules); C, splicing factor arginine/serine-rich 11 (nucleus); D,
elongation factor Ts, mitochondrial precursor (mitochondrion); E, eukaryotic translation initiation factor 4E transporter (vesicles); F,
(microfilaments); G, protein-disulfide isomerase A3 precursor (endoplasmic reticulum); H, Golgin subfamily A member 5 (Golgi apparatus); I,
vasodilator-stimulated phosphoprotein (extracellular matrix).
Toward a Confocal Subcellular Atlas of the Human Proteome
502 Molecular & Cellular Proteomics 7.3
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cell lines can be noticed. For example, more antibodies are
annotated to the mitochondria compartment in U-2OS cell
line (26 antibodies) compared with the other cell lines (21 and
four antibodies for U-251MG and A-431, respectively). About
200 antibodies show a strict localization, 200 show localiza-
tions in multiple cellular compartments, and about 70 are
considered unspecific (or negative). The latter figure can be
explained through multiple parameters, independent or inter-
related, like sample preparation, antibody specificity in a par-
ticular cell line, cell morphology, or annotation.
Comparisons with Gene Ontology Annotations—In Fig. 5,
localization annotations from this study are compared with
the Gene Ontology (GO) annotations in a “score map.” To
be able to link the two annotation ensembles, the first
database was populated to connect each HPA antibody
with its corresponding cellular component GO terms (data
are mined from the antibody information pages available on
our servers). A second database links our annotation terms
(i.e. nuclear, cytoplasm, mitochondrion, etc.) with GO terms
from the cellular component category (available at the Gene
Ontology Website) based on term similarity. Finally a color score
was generated for our 10 subcellular compartments to evaluate
a match or mismatch between our annotations and Gene On-
tology (Fig. 5).
Fig. 5 enables a direct comparison between our subcellular
annotations of protein localization and GO terminology of the
gene products. For instance, the antibody cluster denoted by
a in Fig. 5A highlights a good agreement for localization into
nuclear and cytoplasmic compartments. b illustrates a group
of about 20 proteins annotated to the nucleus and cytoplas-
mic compartments but without any GO annotations for cyto-
plasmic localization. However, this localization is supported
by other GO information based on molecular functions or
biological processes. c indicates a group of proteins for which
annotations are in good agreement between our study and
GO, and c shows a mismatch between the two databases
highlighting antibodies directed against cytoskeleton proteins
as predicted by GO. For some of these antibodies, the sample
preparation (especially the fixation step of the immunofluores-
cence protocol) might have masked antigens or modified
protein conformations and ultimately hindered annotations. d
represents a group of proteins predicted by the Gene Ontol-
ogy to belong to the vesicle subcellular compartment. Vesi-
cles (e.g. endosomes or clathrins) are by definition small
subcellular organelles and thus are more difficult to annotate
as described by Glory and Murphy (14). e shows a set of 20
proteins for which there is no existing GO terms and where
our results add new localization information.
Overall the agreement between GO and our annotations is
larger than 80%, calculated as the percentage of proteins in
which the GO and the experimental data predict the presence
(shown in green in Fig. 5) or absence (light blue in Fig. 5) of a
protein in a defined subcellular compartment. It is obvious
that comparisons of the nuclear and cytoplasmic compart-
ments are more complex than comparisons for the other
compartments (Fig. 5). These are the most often annotated
compartments, and here the agreement with GO data was
lower (45 and 55%, respectively). One explanation for this
discrepancy is the limitations due to using only one method
for sample preparation. For example the clusters indicated by
c and d in Fig. 5A (cytoskeletal and vesicular proteins, re-
spectively) had in the experimental evaluation been annotated
as nuclear or cytoplasmic. This might be expected because
some types of proteins will not easily be resolved in their
intact subcellular compartment and hence fall into a less
resolved compartment, i.e. cytoplasm or nucleus. Another
reason for this discrepancy is that annotations often take
multiple localizations into account. The nuclear and cytoplas-
FIG.3.Examples of different nuclear and subnuclear patterns in U-2OS cell lines for the proteins indicated. A, methyl-CpG-binding
protein 2; B, RNA-binding motif protein 14; C, zinc finger and SCAN (SRE-ZBP, CTfin51, AW-1[ZNF174], and 18 cDNA or ZnF20) domain-
containing protein 1; D, emerin; E, Bcl-2-associated transcription factor 1; F, probable RNA-binding protein 25; G, bromodomain-containing
protein 1; H, mucin-4 precursor.
Toward a Confocal Subcellular Atlas of the Human Proteome
Molecular & Cellular Proteomics 7.3 503
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mic compartments could in this context be considered as
metacompartments including cytoplasmic and nuclear or-
ganelles. This will sometimes lead to false mismatches when
comparing with predictive data. Interestingly when compiling
data from all three cell lines (Fig. 5, A, B, and C), there is a
60% agreement between the GO prediction and the experi-
mental localization determined here (data not shown). The
presented data therefore add valuable experimental informa-
tion to the Gene Ontology terminology by evaluating subcel-
lular localization in cells with different phenotypes.
Integration into the Human Protein Atlas—The images and
subcellular localization data resulting from this project has
been released as a new feature of the currently available Human
Protein Atlas. In Fig. 6, a screen shot of the layout of the
FIG. 4. Distribution of localization into broad compartments (nucleus, cytoplasm, nucleus cytoplasm, and unspecific negative) for U-2OS
cell lines (A), U-251MG (B), and A-431 (C). D, localization agreement for the three cell lines. Overall statistics are separated in two categories.
Non-stringent condition represents a group of antibodies with at least one match in a subcellular compartment. Stringent condition only
includes antibodies with the exact same localization pattern. The two categories have three different criteria to calculate the concordance
among the three cell lines: agreement in all three cell lines, agreement in two cell lines; agreement in any cell line. *, this calculation includes
the case where three cell lines are in concord.) E, subcellular localization distribution for the three cell lines. ER, endoplasmic reticulum; ECM,
extracellular matrix.
Toward a Confocal Subcellular Atlas of the Human Proteome
504 Molecular & Cellular Proteomics 7.3
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subcellular atlas is shown. The objective is to include this anal-
ysis for every antibody/protein included in the atlas in the future.
Here we describe a pilot study for the systematic localiza-
tion of human proteins with confocal microscopy. A single
fixation/permeabilization protocol was used, and a manual
annotation was conducted with the aid of three organelle
probes, representing nucleus, cytoskeleton, and endoplas-
mic reticulum. Over the past decade, automated image
systems for cell-based assays have been developed, often
referred to as high content screening or high throughput
microscopy systems, and these are increasingly being used
for basic research on biological pathways (15–17). However,
systems with ultrahigh throughput capacities are often lack-
ing the single cell or subcellular resolution. Therefore, we
used a conventional confocal microscope and manual im-
age capturing to generate a subcellular atlas with high spatial
In the past decade, fluorescence microscopy imaging has
gone through a dramatic change due to development of new
types of fluorescent probes, new advancement in imaging
instrumentation (18, 19), and new applications (3, 20). All
proteomics studies using cells and imaging rely on three
major steps: sample preparation, image acquisition, and data
analysis. For the first step, two different techniques are avail-
able: live cell studies (21) or immunostaining of fixed cells (16).
An advantage of live cell studies compared with fixed cells is
the possibility to study temporal patterns of protein expres-
sion, which is a parameter of importance in cell behavior. The
cell lines used in this study do not have their cell cycles
synchronized, meaning that at a fixed time point the cell
population exhibits cells randomly distributed in G
and M phases. Manual image acquisition alleviates this prob-
lem by choosing a representative region of interest. An attrac-
tive expansion of the work described here could therefore be
to include specific cell cycle markers (e.g. DNA probe, bromo-
2-deoxyuridine incorporation, and cyclin expression) to bring
a pseudodynamic input for protein localization (22). New im-
aging techniques, such as imaging-bleaching cycles could
also be use on selected sets of proteins to study protein
networks and interactions (23).
Here we performed manual annotation on each image to
classify antibody staining patterns into different subcellular
compartments. As shown in a recent review, human and
computer accuracy scores are comparable for large or-
FIG.5.Score map comparing localization information from our
study and Gene Ontology annotations. The number of antibodies,
on the y axis, represents the number of antibodies with a defined
localization. The cell lines represented are U-2OS in A, U-251MG in B,
and A-431 in C. The scores for the different subcellular compartments
are shown in the following order on the x axis: cytoplasm (cyto),
nucleus, nucleolus (nucleol), nuclear membrane (n.m), mitochondrion
(mito), endoplasmic reticulum (ER), Golgi apparatus, cytoskeleton
(cytosk), vesicles, and extracellular matrix (ecm). Dark blue indicates
no available GO cellular component information but experimental
data from our study. Light blue indicates no protein localization for a
specific compartment in both databases, and green indicates when
both databases agree on the localization. Dark red indicates conflict-
ing subcellular localization, and orange indicates a localization in a
particular compartment not predicted by GO. Thus, supporting data
are shown in light blue and green, whereas conflicting data are shown
in orange and dark red. Dark blue indicates that there is no available
information from Gene Ontology, but experimental data exist from our
study. The clustering on the y axis is merely performed to enable the
identification of groups of proteins localized in the same compartment
and may thus be different for the three cell lines.
Toward a Confocal Subcellular Atlas of the Human Proteome
Molecular & Cellular Proteomics 7.3 505
Page 7
ganelles like mitochondria, endoplasmic reticulum, or cy-
toskeleton (14). However, human annotation is labor-intensive
and is susceptible to annotator subjectivity, and automated
image analysis is therefore attractive. Such analysis can be
performed using two different principles: (i) co-localization or
(ii) pattern recognition. For co-localization analysis, or-
ganelle-specific markers are needed for every subcellular
compartment to be analyzed, and three-dimensional image
acquisitions are desired, making such studies difficult and
demanding. A pilot study has been conducted on a reduced
set of organelle markers
demonstrating the feasibility of such
an approach, although more technical development is needed
to implement such a strategy in a streamlined, high through-
put scheme. The pattern recognition strategy has shown fea-
sibility and accuracy in large scale studies (24 –26). It can be
divided into two groups, supervised and unsupervised clas-
sifications. Supervised learning requires an initial training
phase before the subcellular location class of the protein can
be determined. Unsupervised learning methods are able to
define classes based on the distance of objects in a feature
space (27). These different approaches are currently being
investigated to enable automated image annotation.
Here our manual annotations of 466 antibodies led to more
than 80% classified in one or multiple subcellular compart-
FIG.6.Preview of the confocal Web-
based atlas to be added to the existing
protein atlas. The first level shows all in-
formation available for an antibody. Click-
ing in the last section “cell lines immu-
nofluorescence” a new page (second
level) appears and gives detailed localiza-
tion information for the chosen cell line in
addition to the fluorescence images. By
selecting one image, it opens (third level)
another window displaying a high resolu-
tion picture where the different channels
can be switched on and off. IHC, immu-
nochemistry; IF, immunofluorescence.
Toward a Confocal Subcellular Atlas of the Human Proteome
506 Molecular & Cellular Proteomics 7.3
Page 8
ment(s) and about 20% left with undefined localization. Sev-
eral factors can be responsible for these non-localized anti-
bodies such as (i) the lack of the protein target in the analyzed
cell lines, (ii) the failure to permeabilize the compartment in
which the protein target is present, or (iii) the failure to recog-
nize the epitope of the protein target due to denaturation of
the corresponding protein by the fixation (detergent). More
work is needed to distinguish between these alternatives in-
cluding the use of an alternative antibody recognizing the
same target but recognizing another epitope.
As reviewed recently (14), there is a need for additional
experimental determination of protein localization, and this
project attempts to contribute to this effort. The annotated
images in this atlas classify proteins into subcellular compart-
ments and can be compared with existing databases, such as
the Gene Ontology consortium, Proteome Analyst WoLF
PSORT, or hum-PLOC (see Nucleic Acids Research for an
extensive list). The data in this pilot study were evaluated on
the degree of agreement in protein localization using the GO
database. The results show the usefulness of complementing
theoretical prediction methods with experimental procedures,
such as the antibody-based method presented or with a
fluorescent tagging method as demonstrated earlier (15).
The scope of proteomics is broad from identification and
quantification of proteins in cells and tissues (in normal and
cancer states) to deeper understanding of molecular pro-
cesses within cells. The recent addition of cytomics to the
“omics” data sets brings knowledge of cell systems and mo-
lecular architecture thanks to imaging techniques and molec-
ular biology. The presented subcellular atlas aims to provide a
publicly available atlas for subcellular localization of all human
proteins and to facilitate studies comparing normal and dis-
ease protein profiles with the ultimate objective to discover
new biomarkers and targets for the design of diagnostic tools
and drugs.
* This work was supported by The Royal Academy of Sciences. The
Human Proteome Resource program is funded by the Knut and Alice
Wallenberg Foundation. The costs of publication of this article were
defrayed in part by the payment of page charges. This article must
therefore be hereby marked advertisement in accordance with 18
U.S.C. Section 1734 solely to indicate this fact.
S The on-line version of this article (available at http://www. contains supplemental material.
To whom correspondence should be addressed. E-mail: mathias@
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  • Source
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