The urologic epithelial stem cell database (UESC)—A web tool for cell type-specific gene expression and immunohistochemistry images of the prostate and bladder

Abstract

Public databases are crucial for analysis of high-dimensional gene and protein expression data. The Urologic Epithelial Stem Cells (UESC) database http://scgap.systemsbiology.net/ is a public database that contains gene and protein information for the major cell types of the prostate, prostate cancer cell lines, and a cancer cell type isolated from a primary tumor. Similarly, such information is available for urinary bladder cell types.
Two major data types were archived in the database, protein abundance localization data from immunohistochemistry images, and transcript abundance data principally from DNA microarray analysis. Data results were organized in modules that were made to operate independently but built upon a core functionality. Gene array data and immunostaining images for human and mouse prostate and bladder were made available for interrogation. Data analysis capabilities include: (1) CD (cluster designation) cell surface protein data. For each cluster designation molecule, a data summary allows easy retrieval of images (at multiple magnifications). (2) Microarray data. Single gene or batch search can be initiated with Affymetrix Probeset ID, Gene Name, or Accession Number together with options of coalescing probesets and/or replicates.
Databases are invaluable for biomedical research, and their utility depends on data quality and user friendliness. UESC provides for database queries and tools to examine cell type-specific gene expression (normal vs. cancer), whereas most other databases contain only whole tissue expression datasets. The UESC database provides a valuable tool in the analysis of differential gene expression in prostate cancer genes in cancer progression.

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BMC Urology
Open Access
Database
The urologic epithelial stem cell database (UESC) – a web tool for
cell type-specific gene expression and immunohistochemistry
images of the prostate and bladder
Laura E Pascal*
1,2
, Eric W Deutsch
2
, David S Campbell
2
, Martin Korb
2
,
Lawrence D True
3
and Alvin Y Liu
1,2
Address:
1
Department of Urology, and the Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle WA 98195, USA,
2
Institute for Systems Biology, Seattle WA 98103, USA and
3
Department of Pathology, University of Washington, Seattle WA 98195, USA
Email: Laura E Pascal* - lpascal@systemsbiology.org; Eric W Deutsch - edeutsch@systemsbiology.org;
David S Campbell - dcampbell@systemsbiology.org; Martin Korb - mkorb@systemsbiology.org; Lawrence D True - ltrue@u.washington.edu;
Alvin Y Liu - aliu@u.washington.edu
* Corresponding author
Abstract
Background: Public databases are crucial for analysis of high-dimensional gene and protein
expression data. The Urologic Epithelial Stem Cells (UESC) database http://
scgap.systemsbiology.net/ is a public database that contains gene and protein information for the
major cell types of the prostate, prostate cancer cell lines, and a cancer cell type isolated from a
primary tumor. Similarly, such information is available for urinary bladder cell types.
Description: Two major data types were archived in the database, protein abundance localization
data from immunohistochemistry images, and transcript abundance data principally from DNA
microarray analysis. Data results were organized in modules that were made to operate
independently but built upon a core functionality. Gene array data and immunostaining images for
human and mouse prostate and bladder were made available for interrogation. Data analysis
capabilities include: (1) CD (cluster designation) cell surface protein data. For each cluster
designation molecule, a data summary allows easy retrieval of images (at multiple magnifications).
(2) Microarray data. Single gene or batch search can be initiated with Affymetrix Probeset ID, Gene
Name, or Accession Number together with options of coalescing probesets and/or replicates.
Conclusion: Databases are invaluable for biomedical research, and their utility depends on data
quality and user friendliness. UESC provides for database queries and tools to examine cell type-
specific gene expression (normal vs. cancer), whereas most other databases contain only whole
tissue expression datasets. The UESC database provides a valuable tool in the analysis of differential
gene expression in prostate cancer genes in cancer progression.
Background
Public databases for the storage and retrieval of genomic
and proteomic data have become an integral component
of biomedical research. These databases can aid in the
identification of genes and proteins responsible for dis-
ease and health and defining their function by enabling
Published: 11 December 2007
BMC Urology 2007, 7:19 doi:10.1186/1471-2490-7-19
Received: 22 June 2007
Accepted: 11 December 2007
This article is available from: http://www.biomedcentral.com/1471-2490/7/19
© 2007 Pascal et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0
),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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investigators in diverse research areas and interests with a
range of computer expertise to have ready access to the
stored information through one user interface. Previously,
the Prostate Expression Database (PEDB) established a
centralized archive of gene expression information for
human prostate [1]. This database contains a large cDNA
library of gene sequences obtained for normal/benign,
benign prostatic hyperplasia (BPH), prostatic intraepithe-
lial neoplasia (PIN) and malignant prostate disease states.
The Prostate Gene Database (PGDB) is another prostate
database that stores factual data about genes related to the
human prostate and prostatic diseases supported by liter-
ature references [2]. These genes are grouped under
molecular events of amplification, mutation, gross dele-
tion, methylation, polymorphism, overexpression and
linkage. These two databases provide valuable informa-
tion obtained from whole prostate tissue. The characteri-
zation of tissues based on cell-surface protein expression
[3] allows the possibility of separating cells of interest
from that tissue for gene array analysis and determination
of cell-type specific transcriptomes [4]. Public availability
of cell-type specific data will be an important additional
tool in future studies.
The Stem Cell Genome Anatomy Project (SCGAP) initi-
ated by the National Institute of Diabetes and Digestive
and Kidney Diseases (NIDDK) included seven organ-spe-
cific groups that were funded to form a research consor-
tium. The aims of this consortium were to collectively
develop necessary biological procedures and reagents for
characterization of tissue specific progenitor cells and to
characterize gene expression patterns in these cells using
advanced technologies and bioinformatic techniques. The
official web portal for SCGAP [5] was designed to deliver
an overview of the progress of the consortium's research
efforts and to function as a gateway to the websites of the
consortium participants. As such, the detailed data, proto-
cols and descriptions are accessible from the respective
website of the participating SCGAP projects.
Our group, the urologic epithelial stem cells project,
investigated the molecular basis of the differentiation of
epithelial cells of the human prostate and bladder. We are
interested in studying development and the cancer proc-
ess in the context of interaction between individual cell
types. Expression levels of CD cell surface antigens was
first used to distinguish the constituent cell types of the
prostate, as well as cancer cells from their normal counter-
part [3,6]. The cell CD phenotyping data acquisition
involved immunohistochemistry with ~200 commer-
cially available CD monoclonal antibodies (BD-PharMin-
gen). Magnetic cell sorting (MACS) based on the cell type-
specific CD expression was then used to isolate the follow-
ing prostatic cell types: CD31
+
endothelial cells, CD26
+
luminal secretory and CD104
+
basal cells of the epithe-
lium, and CD49a
+
fibromuscular cells of the interglandu-
lar stroma for transcriptome profiling [4]. In addition, a
CDw338+ (ABCG2) stem cell population was profiled
[7]. These microarray datasets were also deposited in the
UESC database [8]. Here, we will illustrate the utility of
our UESC database, and a future consortium report will
describe in detail the central SCGAP site and its federated
search and data analysis tools.
Construction and Content
The UESC database was based on the Systems Biology
Experiment Analysis Management System (SBEAMS) [9],
a software and database framework for collecting, storing,
and accessing different types of experimental data.
SBEAMS combined a relational database management
system (RDBMS) back-end, a collection of tools to store,
manage, and query experimental information and results,
a web front-end for querying the database and providing
integrated access to remote data sources, and an interface
to other data processing and analysis programs. Since all
data from each part of any experiment were organized in
a modular schema using similar designs, quality control,
analysis, and data integration tasks were greatly simpli-
fied. In SBEAMS, each module was made to operate inde-
pendently but was built upon a core functionality, which
included user authentication and auditing, web interface
tools, result set management, Gene Ontology integration,
centralized BioSequenceSet linking, RDBMS-independ-
ence layer, and others. Support for microarrays, proteom-
ics, molecular interactions, macroarrays, gene expression
localization, protein functional predictions, and
expressed sequence tag (EST) clustering was provided in
the current major modules. The SBEAMS module queries
were automatically piped to Cytoscape [10] for network
visualization and further exploration. Data includes cell
type specific information for human and mouse prostate
and bladder from immunostaining and microarray. Feed-
back options and data availability questions are made
accessible through contact information listed on the web-
site or through a feedback form.
Populating the database
The methods of tissue collection, immunostaining and
expression data used in this database have been published
previously [3,4,11]. Briefly, tissue samples consisted of
both cancer-enriched and cancer-free samples obtained
from over 50 radical prostatectomies or cystectomies
under approval by the University of Washington Institu-
tional Review Board following a standard protocol.
Immunohistochemistry data
The immunohistochemistry data for human and mouse
prostate and bladder were annotated and uploaded in the
database following the data standard, Minimum Informa-
tion Specification For In Situ Hybridization and Immuno-

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histochemistry Experiments (MISFISHIE) [11]. The files
are systematically named based on the antibody used in
staining, the organism, tissue type, tissue block, and mag-
nification. As an example, the file 'CD44 98-395F HP ba
100.jpg' was stained with anti-CD44, was derived from
human prostate tissue block 98-395F, is human prostate
tissue, and an image of microscopic field of view b within
field a was captured at 100× magnification. Annotation
describes the tissue, distribution of reaction product in the
tissue, distribution of reaction product in the tissue, local-
ization patterns within histologic cell types, and provides
an assessment of the level of expression of the protein for
the immunostaining data. Data is available for ~200 CD
antibodies for human prostate and bladder and ~20 CD
antibodies for mouse prostate and bladder.
Microarray data
Affymetrix array analysis of prostate cancer cell lines
The prostate cell transcriptomes of CD26
+
luminal epithe-
lial, CD104
+
basal epithelial, CD49a
+
stromal fibromus-
cular, plus CD31
+
endothelial, CDw338
+
stem, and side
population (SP) are all available in UESC [4,7]. To date,
prostate cancer transcriptomes include those of lineage-
related cancer cell lines LNCaP, C4-2, and CL1, those of
PC-3 and DU145, plus that of a CD26
+
cancer cell type
sorted from a primary tumor of Gleason 3+3. The bladder
transcriptome data includes 1 replicate each of CD13
+
stromal and CD13
-
stromal cells representing two sub-
domains of the bladder lamina propria [12]. In the data
module, the CEL files are the raw data from the array scan,
the RPT files hold the statistical analysis of chip signals,
the XML files are MAGE-ML descriptions of the experi-
ments, and the Image files are synthetic JPEG images of
the Affymetrix HG-U133_Plus_2 GeneChips.
MPSS analysis of prostate cancer cell lines
Results from MPSS (Massively Parallel Signature Sequenc-
ing) experiments on the LNCaP and C4-2 cell lines are
also available in the UESC database [13]. Data may be
downloaded as an Excel spreadsheet for each cell type,
containing the accession number and experimentally
detected TPM (transcripts per million) for each analyzed
sequence. This data was used to compare MPSS with
Affymetrix arrays in their coverage overlap [13].
Searching the database
CD immunohistochemistry images
The CD immunohistochemistry images may be viewed or
downloaded as ZIP archives. Additionally, a summary of
staining for various species and cell types can be down-
loaded directly as tab-delimited text files. Fig. 1 shows the
data summary for CD138 (syndecan-1). Cell type-specific
expression is scored by staining intensity, and the
uploaded images (at multiple magnifications) of different
tissue sections can be opened for examination. Fig. 2
shows CD138 staining at 200× magnification for human
bladder and prostate.
Transcriptome data
The available array datasets (usually after accepted for
publication) are listed and can be chosen for interroga-
tion.
(1) Single gene search – in which one can enter Affymetrix
Probe Set ID, Gene Name, or Accession Number together
with the options of coalescing probesets and replicates. In
the Affymetrix HG-U133 arrays often times genes would
be represented by multiple probesets, of which not all
would give meaningful results. The hybridization signals
for all probesets of one gene can be combined if the COA-
LESCE PROBESETS tick box is clicked. The tick box COA-
LESCE REPLICATES averages the signal for each of the
biological replicates that make up a sort. The greyscale gra-
dient indicates RMA normalized Affymetrix signal inten-
sity. Signals of 10 or less are represented as white and
signals greater than or equal to 10,000 are represented as
black. Higher Affymetrix signal (more black) indicates
higher levels of gene expression. Fig. 3 shows the analysis
output for CD138 (SDC1). Fig. 3A shows the signal inten-
sities scored by the three probesets for SDC1 of all repli-
cates (n = 5) of four prostate cell types (CD104
+
basal,
CD26
+
luminal, CD31
+
endothelial, CD49a
+
stromal) and
one replicate each of two bladder cell types (CD13
-
stro-
mal and CD13
+
stromal) queried. CD138 expression is
detectable in prostate basal cells, and lowered or undetec-
table in luminal, endothelial and stromal cells. As an illus-
tration of probe variability, the 239256 probeset scored
no expression or Absent Call in basal cells (5/5 replicates),
and other cell types (5/5 replicates). Fig. 3B shows the
analysis summary of CD138 cell-type expression coa-
lesced by replicate, 3C coalesced by probeset, and 3D coa-
lesced by replicate and probeset. This expression data is in
accordance with the pattern of prostatic CD138 expres-
sion scored by immunohistochemistry (Fig. 1). Included
in this summary is the level of CD138 expression in basal
urothelial cells.
(2) Multiple gene (batch) search – in which searches can
be initiated by using "%" as a wildcard character (e.g.,
CD% to list all official gene names with the CD designa-
tion) ["_" is a single character wildcard such that, e.g., A_
brings up AR; A__ brings up A2M, ABO to AXL, A___
brings up A1BG to ASB8, etc.]. Fig. 4 shows the query out-
put for gene names with SOX% (sex determining region Y
box). Other batch search examples would be IL (inter-
leukin), ITG (integrin), TNF (tumor necrosis factor),
ADAM (a disintegrin and metalloprotease domain) genes.
This query feature is not widely available in many other
databases.

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CD immunohistochemistryFigure 1
CD immunohistochemistry. Shown is the image data summary for CD138 (SDC1). The top table provides annotation data
including the tissue type, distribution of reaction product in the tissue, localization pattern within histologic cell types and an
assessment of the level of protein expression for the immunostaining data. The bottom table provides links to the available
images for each annotated sample. Available immunostaining images and additional data can be retrieved by clicking on the
links.
Antibody Summary
CD138
Alternate Names: heparan sulfate proteoglycan; syndecan-1
Locus Link:
6382
Genome Coordinates:
chr2:20385070-20409385-
Total Assays: 15
Tissue type Cell type
%
Intense
% Equivocal % None # Assays
Urinary Bladder
Cap Cells
40 58 3 4
Intermediate Cells
40 54 6 5
Basal Epithelial Cells
40 54 6 5
Lamina propria - superficial
0 0 100 6
Lamina propria - deep
0 0 100 6
Submucosa
1 1 97 7
Muscularis propria
0 8 92 6
Transitional Cell Carcinoma
010901
Prostate
Atrophic glands
80 20 0 1
Hyperplastic glands
010901
Normal glands
010901
Basal Epithelial Cells
60 22 18 3
Stromal Endothelial Cells
0 0 100 1
Stromal Fibromuscular Cells
5 0 95 1
Stromal Nerve Sheath Cells
90 5 5 2
Stromal Perineural Cells
0 0 100 1
Gleason Pattern 3
010901
Assay Name
Channel Name
Characterizations
Available Images
CD138 02-034A 5 CD138 02-034A 5 - chan 1
8
40x 100x 200x 200x 200x 400x
CD138 02 -047A
1
CD138 02 -047A 1 - chan 1
CD138 99-010E 2 CD138 99-010E 2 - chan 1
10
CD138 03-035A1 CD138 03-035A1 - chan 1
7
40x 100x 200x 200x
CD138 02-054A CD138 02-054A - chan 1 40x 100x 200x 200x 200x 200x
CD138 03-041B2 CD138 03-041B2 - chan 1
2
40x 100x 200x
CD138 03-035A2 CD138 03-035A2 - chan 1
7
100x 200x 40x 100x 200x 200x 200x
CD138 03-043B1 CD138 03-043B1 - chan 1
7
100x 10x 40x 100x 200x 200x
CD138 02-047A 1 CD138 02-047A 1 - chan 1 40x 100x 200x 200x 200x 200x 200x
CD138 03-024A1 CD138 03-024A1 - chan 1
6
40x 100x 200x 200x 400x 200x

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Utility and Discussion
UESC data types were organized in separate modules to
afford a good balance between flexibility and consistency.
The management system was designed to allow efficient
data access to all levels of users, with both easy web and
scriptable, sophisticated interfaces, and to be reusable so
that a new project may be built on a previous one (e.g.,
kidney and bladder cancer data to prostate cancer data).
CD138 immunostaining imagesFigure 2
CD138 immunostaining images. CD138 (SDC1) immunoreactivity of normal human prostate and bladder (immunoreac-
tion product red-brown; pale blue hematoxylin nuclear counterstain). (A) CD138 staining of human bladder urothelium, assay
name CD138 03-035A1. (B) CD138 staining of human prostate atrophic glands, basal epithelial cells and nerve sheath cells,
assay name CD138 02-007C 5. Original magnification is 200×.
A. B.
Single gene searchFigure 3
Single gene search. The expression of CD138 (SDC1) among the four sorted prostate cell populations (CD104
+
basal epi-
thelial, CD26
+
luminal epithelial, CD31
+
endothelial and CD31
+
stromal fibromuscular) in addition to sorted bladder cell popu-
lations (CD13
-
and CD13
+
bladder lamina propria) is illustrated. The Affymetrix signal intensity levels are represented by the
grey scale. The data can be displayed in full (A), coalesced with respect to biological replicates (B), probesets (C), or both rep-
licate and probeset (D). Present call boxes have no border, Absent calls have a red border, Marginal call is blue (not shown in
this example).
A.
C.
B.
D.

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The database will therefore continuously expand as more
cell type-specific information becomes available. The
UESC database will be a valuable tool in the analysis of
differential gene expression in prostate cancer genes in
cancer progression.
Conclusion
Strategies for the analysis of the interface between gene
expression and protein information involve a variety of
computational methods that require the storage and
retrieval of large datasets. These databases become per-
force an integral component of biomedical research. The
UESC database is a unique, web-accessible, searchable
compilation of published data concerning the identifica-
Multiple gene searchFigure 4
Multiple gene search. Shown is the query output for gene names containing SOX (sex determining region Y box) among the
four sorted prostate cell populations (CD104
+
basal epithelial, CD26
+
luminal epithelial, CD31
+
endothelial and CD31
+
stromal
fibromuscular) in addition to sorted bladder cell populations (CD13
-
and CD13
+
bladder lamina propria).

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tion and characterization of genes and proteins in specific
cell types of the urologic organs where cancer is a major
disease. These cell populations retain to a high degree
their CD phenotype as determined by immunostaining in
intact tissue; concordance between gene expression meas-
ured by DNA array and immunohistochemistry was good
and will be published separately. These cell type transcrip-
tomes allow us to pursue many studies which are not pos-
sible with whole tissue transcriptomes.
Availability and requirements
The UESC database is freely accessible at http://scgap.sys
temsbiology.net/. It has been tested to work with Mozilla
Firefox and Internet Explorer.
List of abbreviations used
BPH: Benign prostatic hyperplasia
CD: Cluster designation
EST: Expressed sequence tag
MACS: Magnetic cell sorting
MISFISHIE: Minimum Information Specification For In
Situ Hybridization and Immunohistochemistry Experi-
ments
MPSS: Massively Parallel Signature Sequencing
NIDDK: National Institute of Diabetes and Digestive and
Kidney Diseases
PEDB: Prostate Expression Database
PGDB: Prostate Gene Database
PIN: Prostatic intraepithelial neoplasia
RDBMS: Relational database management system
RMA: Robust multi-array average
SBEAMS: Systems Biology Experiment Analysis Manage-
ment System
SCGAP: Stem Cell Genome Anatomy Project
TPM: Transcripts per million
UESC: Urologic Epithelial Stem Cell
Competing interests
The author(s) declare that they have no competing inter-
ests.
Authors' contributions
LEP drafted the manuscript with input from DSC, EWD
and AYL. Database design and programming was per-
formed by EWD, DSC and MK with input from AYL, LEP
and LDT. LDT provided immunohistochemistry data
annotation and staining summaries. All authors have read
and approved the final manuscript.
Acknowledgements
This work was supported by grant 1U01 DK63630 from NIDDK. Addi-
tional funding came from grants CA85859, CA98699 and CA111244 from
NCI. We thank Tracy Sherertz for her contribution to the database con-
struction.
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