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IMMUNOBIOLOGY
Gene expression profile in human leukocytes
Shin-ichi Hashimoto, Shigenori Nagai, Jun Sese, Takuji Suzuki, Aya Obata, Taku Sato, Nobuaki Toyoda, Hong-Yan Dong, Makoto Kurachi,
Tomoyuki Nagahata, Ken-ichi Shizuno, Shinichi Morishita, and Kouji Matsushima
Leukocytes are classified as myelocytic
or lymphocytic, and each class of leukocytes consists of several types of cells
that have different phenotypes and different roles. To define the gene expression
in these cells, we have performed serial
analysis of gene expression (SAGE) using human leukocytes and have provided
the gene database for these cells not only
at the resting stage but also at the activated stage. A total of 709 990 tags from
17 libraries were analyzed for the manifestation of gene expression profiles in various types of human leukocytes. Types of
leukocytes analyzed were as follows: pe-
ripheral blood monocytes, colony-stimulating factor–induced macrophages,
monocyte-derived immature dendritic
cells, mature/activated dendritic cells,
granulocytes, natural killer (NK) cells, resting B cells, activated B cells, naive T
cells, CCR4ⴚ memory T cells (resting TH1
cells), CCR4ⴙ memory T cells (resting TH2
cells), activated TH1 cells, and activated
TH2 cells. Among 38 961 distinct tags that
appeared more than once in the combined total libraries, 27 323 tags were
found to represent unique genes in certain type(s) of leukocytes. Using probability (P) and hierarchical clustering analy-
sis, we identified the genes selectively
expressed in each type of leukocytes.
Identification of the genes specifically
expressed in different types of leukocytes provides not only a novel molecular
signature to define different subsets of
resting and activated cells but also contributes to further understanding of the
biologic function of leukocytes in the
host defense system. (Blood. 2003;101:
3509-3513)
© 2003 by The American Society of Hematology
Introduction
Leukocytes can be divided into various types such as monocytes/
macrophages, dendritic cells (DCs), T cells, B cells, and natural
killer (NK) cells. These cells communicate with each other through
various surface molecules such as CD markers and through
secreted factors such as cytokines.
In the past several years, the accumulation of a gene database
including cDNA and genome has accelerated the identification of
the molecules involved in cell–cell interaction, cell activation, and
cell differentiation. In addition, the functional genomic technology
such as DNA microarray1 and serial analysis of gene expression
(SAGE)2 allow the expression of thousands of genes to be
analyzed. These analyses are useful to know the function of each
cell type because the characterization of each cell type depends on
the genes selectively expressed at various stages. Among gene
expression analyses, the SAGE method is quantitative and can
cover the expressed genes that are unequaled by any mammalian
DNA microarray systems available.3
We have recently reported the results of SAGE in human
monocytes, macrophages,4,5 DCs,6 helper T (TH1 and TH2) cells,7
and NK cells.8 In this study, we analyzed gene expression by means
of SAGE in leukocytes, including phagocytes, T cells, and B cells,
at various differentiation and activation stages, and we constructed
a gene database for these. In addition, we analyzed the set of genes
restricted to myeloid or lymphoid cells.
From the Department of Molecular Preventive Medicine and the Department of
Complexity Science and Engineering, Graduate School of Frontier Science,
University of Tokyo, Japan.
Submitted June 24, 2002; accepted December 7, 2002. Prepublished online as
Blood First Edition Paper, January 9, 2003; DOI 10.1182/blood-2002-06-1866.
Materials and methods
Cell preparation
Monocytes, macrophage–colony-stimulating factor (M-CSF)– and granulocyte macrophage–colony-stimulating factor (GM-CSF)–induced macrophages, immature and mature DCs, and lipopolysaccharide (LPS)–
stimulated monocytes were prepared as described before.4-6 Langerhanslike cells were obtained by culture of purified monocytes for 7 days at 37°C
in RPMI 1640 medium containing 7.5% heat-inactivated fetal calf serum
(FCS; Gibco/Life Technologies, Tokyo, Japan), recombinant human GMCSF (rhGM-CSF; 500 U/mL), interleukin-4 (IL-4; 100 U/mL), and
transforming growth factor-␤ (TGF-␤; 10 ng/mL) (R&D Systems, Minneapolis, MN). Granulocytes were prepared from fresh peripheral blood after
the depletion of red blood cells by sedimentation in 0.25% dextran in
phosphate-buffered saline (PBS) for 40 minutes at room temperature, and
the remaining cells were subjected to Ficoll-Hypaque gradient centrifugation to obtain the buffy coat. Red blood cells in the buffy coat were depleted
by hypotonic lysis, and the remaining cells were used as granulocytes.
Peripheral blood mononuclear cells (PBMCs) were isolated from
venous blood drawn from healthy volunteers by centrifugation on a
Ficoll-Metrizoate density gradient (d ⫽ 1.077; Lymphoprep; Nycomed,
Oslo, Norway). CD4⫹CD45RA⫹ naive T cells were purified using CD4
multisort. CCR4⫹ resting TH1 and CCR4-resting TH2 cells were obtained
from PBMCs by incubation with anti-CCR4 monoclonal antibody (mAb)
followed by sorting with an EPICS XL (Beckman Coulter). NK, CD8⫹ T,
and CD8⫹ B cells were separated from PBMCs by incubation with
The online version of the article contains a data supplement.
Reprints: Kouji Matsushima, Department of Molecular Preventive Medicine,
School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo
113-0033, Japan; e-mail: [email protected].
Supported by CREST/SORST and by a Grant-in-Aid for Scientific Research on
Priority Areas (C) “Medical Genome Science” from the Ministry of Education,
Culture, Sports, Science and Technology of Japan.
The publication costs of this article were defrayed in part by page charge
payment. Therefore, and solely to indicate this fact, this article is hereby
marked ‘‘advertisement’’ in accordance with 18 U.S.C. section 1734.
S.N. and J.S. contributed equally to this work.
© 2003 by The American Society of Hematology
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HASHIMOTO et al
anti-CD56 mAb-coated, anti-CD8 mAb-coated, and anti-CD19 mAbcoated microbeads, respectively. Activated B cells were obtained from
purified B cells by stimulation with a membrane containing CD40 ligand
for 3 hours at 37°C. Activated TH1 and activated TH2 cells were generated
as described before.7
Generation of SAGE and cDNA libraries
A modification of the original SAGE and micro-SAGE methods was used to
generate all SAGE libraries. In brief, mRNAs of monocytes and macrophages were purified from a mixture of total RNA of at least more than 5
donors. Monocytes were incubated for 7 days at 37°C with M-CSF (100
ng/mL) or GM-CSF (500 U/mL) in RPMI 1640 containing 7.5% FCS. Total
RNA from these cells was isolated by direct lysis in RNAzol B. Poly(A)⫹
RNA was isolated using a FastTrac (Invitrogen, Carlsbad, CA) mRNA
purification kit according to the manufacturer’s instructions.
SAGE libraries were generated using 1.5 ␮g poly(A)⫹ RNA and were
converted to double-stranded cDNA with the use of a BRL synthesis kit,
including biotin-5⬘-T18-3⬘ primer as described in the manufacturer’s
protocol. cDNA was cleaved with the restriction enzyme NlaIII, and the
3⬘-terminal cDNA fragments were bound to streptavidin-coated magnetic
beads (Dynal). After ligation with oligonucleotides containing recognition
sites for BsmF1, the linked cDNAs were released from the beads by
digestion with BsmF1. Released tags were ligated to one another, ligated to
concatemers, and cloned into the SphI site of pZero 1.0 (Invitrogen).
Colonies were screened with polymerase chain reaction (PCR) using M13
forward and M13 reverse primers. PCR products containing inserts of more
than 600 bp were sequenced with the Big Dye terminator version 2 kit and
were analyzed using a 377 ABI automated sequencer (Applied Biosystems,
Tokyo, Japan).
SAGE software (version 1) was used to quantify the abundance of each
tag. Correction for tags containing linker sequences and other potential
artifacts was made as described.9 Gene identification and UniGene cluster
assignment of each SAGE tag were performed by using the SAGE Tag to
UniGene Maps from http://www.ncbi.nlm.nih.gov/SAGE/?SAGEtag.cgi
and the table (updated March 2001; UniGene cluster from http://
www.sagenet.org/SAGEDatabases/unigene.htm). The cluster and treeview
programs (http://rana.lbl.gov/EisenSoftware.htm) were used for clustering
SAGE data. Briefly, the frequency tables of the 17 libraries were first
normalized to expression levels per 55 000 tags. SAGE tags appearing at
least 5 times in all 17 libraries were subjected to clustering analysis. Data
were adjusted through centering on the median and mean by sample.
Noncentered Pearson correlation coefficient was used for distance calculations, and the weighted-complete linkage was used for clustering as
described.10
sense, 5⬘-AAATATGCCCTCCCCGTAATG-3⬘, antisense, 5⬘-AGCTTGCTTTTGGGACTATTGC-3⬘; granzyme B: sense, 5⬘-TCCCCCATCCAGCCTATAA-3⬘, antisense, 5⬘-TGAGACATAACCCCAGCCA-3⬘;
EST (Hs. 98785): sense, 5⬘-TCTTCCCCAGAGTGCGTTTTT-3⬘, antisense, 5⬘-CATGGAACACCAAGTTGGTGAT-3⬘; FLJ20706: sense,
5⬘-CTGAAAGGCATGGTCACAAAGA-3⬘, antisense, 5⬘-TCCACCATTGTCCCT GGTAAG-3⬘; EST (Hs. 290825): sense, 5⬘-CATGAATGTGTTCGTAGGGCC-3⬘, antisense, 5⬘-TCTTCCAGGAAACCACAGGCT3⬘; MHC class IB: sense, 5⬘-TCTACC CTGCGGAGATCACACT-3⬘,
antisense, 5⬘-TTC AGG TGC CTT TGC AGA AA-3⬘. Reaction mixtures
were incubated in a Perkin-Elmer DNA Thermal Cycler (denaturation at
60 seconds, 94°C; annealing at 60 seconds, 58°C; extension at 120
seconds, 72°C; 29-31 cycles).
Statistics
Statistical significance (P) between samples was calculated as described
previously.11 Gene ranking, a framework for finding the genes expressed in
specific samples, was used for statistical evaluation of differential expression of SAGE tags between samples. Statistical significance (P) in the
evaluation is the expansion of the Audic and Claverie method12 to manage
more than 2 samples.
Results
SAGE libraries in leukocytes
Seventeen independent SAGE libraries were summarized in Table
1 (generated from human peripheral blood monocytes, CSFderived macrophages, monocyte-derived immature DCs, mature/
activated dendritic cells, granulocytes, NK cells, resting B cells,
activated B cells, naive T cells, CCR4-negative memory T cells
(resting TH1 cells), CCR4-positive memory T cells (resting TH2
cells), activated TH1 cells, and activated TH2 cells. CCR4-negative
and -positive memory T cells are called resting TH1 and resting TH2
cells because, when these cells were stimulated with PMA (phorbol
Table 1. SAGE in human leukocytes and transcript tags
No. tags
Unique
transcripts
Monocytes*
58 700
10 391
8 141
Reverse transcription–polymerase chain reaction
LPS-stimulated monocytes*
35 991
8 172
6 710
M-CSF-induced macrophages*
54 047
10 629
8 295
Total RNA (200 ng) was prepared by the use of RNAzol B. RNA was
reverse-transcribed in 50 ␮L of 10 mM Tris-HCl (pH 8.3), 1.5 mM MgCl2,
50 mM KCl, 10 mM dithiothreitol, 1 mM each dNTP, 2 ␮M random
hexamer, and 2.4 U/␮L Moloney murine leukemia virus reverse transcriptase for 1 hour at 42°C. Complementary DNA (cDNA) was obtained by
treating total RNA corresponding to 40 ng in boiled water for 3 minutes and
quenching on ice before amplification by PCR. Conditions for PCR were as
follows: in a 50-␮L reaction, 0.15 ␮M each primer, 1.25 ␮M each dGTP,
dATP, dCTP, and dTTP (Toyobo), 50 mM KCl, 10 mM Tris-HCl, pH 8.3,
0.15 mM MgCl2, and AmpliTaq (Perkin-Elmer, Branchburg, NJ).
PCR cycle numbers for genes and primers used were as follows—
hemoglobin ␣ 1: sense, 5⬘-TCTGGTCCCCACAGACTCAGA-3⬘, antisense, 5⬘-TTAACCTGGGCA GAGCCG T-3⬘; EST (Hs. 103296): sense,
5⬘-CTGGGCAGGAAATTGAAGGA-3⬘, antisense, 5⬘-TTTGAGATGGAGTCTCGCTCTG-3⬘; GRO2 oncogene: sense, 5⬘-TCCAACTGACCAGAAGGAAGGA-3⬘, antisense, 5⬘-CGTCACATTGATCTTACTGGCC-3⬘; EST (Hs.192427): sense, 5⬘-ATC CTCATCTCCTTGATGGGC3⬘, antisense, 5⬘-TGAAAACACCCATGCTTG CA-3⬘; CCL18: sense,
5⬘-CATCATGAAGGGCCTTGCA-3⬘, antisense 5⬘-CGAAGAGTTGAAGGGAAAGGG-3⬘; p21SNFT: sense, 5⬘-AGAGCCCTGAGG ATGATGACA-3⬘, antisense, 5⬘-TCCATGCTGGATCTGCACAA-3⬘; CD6:
GM-CSF-induced macrophages*
57 525
10 722
8 434
Immature dendritic cells*
58 700
12 577
9 844
Mature dendritic cells*
31 862
8 017
6 583
Langerhans-like cells
57 717
13 630
10 874
Granulocytes
31 466
8 007
6 821
CD4 T cells (naive)
50 433
11 290
9 124
CD4 T cells (memory, CCR4-negative)
31 919
7 572
6 220
CD4 T cells (memory, CCR4-positive)
30 700
7 820
6 521
Activated T cells (TH1)*
32 219
8 111
6 676
Activated T cells (TH2)*
32 288
9 047
7 382
CD8 T cells*
51 017
11 789
9 380
NK cells*
34 831
8 187
6 569
B cells
53 236
10 903
8 499
Cells
Unique
genes
Myeloid
Lymphoid
Activated B cells
Total
7 339
2 798
2 439
709 990
38 961
27 323
Number of unique libraries is 17. Each cell was purified as described in “Materials
and methods.” No. unique tags refers to number of tags observed in each cell. Unique
transcripts representing the tags appeared more than once in all 17 libraries. Unique
genes were counted using the UniGene database.
*Published data.
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SAGE DATABASE IN HUMAN LEUKOCYTES
12-myristate-13-acetate) and ionomycin, the cells typically showed
TH1 and TH2 phenotypes as described in Imai et al.13
We identified 709 990 SAGE tags that represent 112 555
distinct transcripts. Among them, to provide an accurate estimation
of unique genes and to avoid sequencing errors, we omitted the tags
that appeared only once in the data set. In 38 961 distinct tags that
appeared more than once in the total libraries, 27 323 tags were
represented as unique genes. Of the 27 323 genes, 14 557 tags had
at least one match to a UniGene cluster, whereas 12 766 tags had no
match. A list of all tags found is available at our Web site
(http://bloodsage.gi.k.u-tokyo.ac.jp/). Among the 38 961 identified
unique transcripts, 0.2% of these had more than 501 copies, 1.6%
had between 500 and 51 copies, 16.8% had between 50 and 6
copies, and 81.4% had fewer than 5 copies (Table 2). Most of the
unique transcripts were expressed at low levels; however, the mass
of mRNAs with more than 5 copies per cell accounted for more
than 80%. These categorized copies per cell in leukocytes were
similar to those of other tissues from 3 496 829 tags, as described
before. The gene abundance at moderate to high copies (more than
50 copies/cell) in activating cells such as LPS-stimulated monocytes, activated TH1 and TH2 cells, and mature DCs is higher than
that of resting/nonstimulated cells (data not shown).
3511
Figure 1. Comparisons of cell types. (A) Dendrogram of clustered libraries. (B)
Scatter plot for comparisons between arbitrarily selected cell types. Frequency tables
of the 17 libraries were first normalized to expression levels per 55 000 tags. Cluster
and tree view programs were used for clustering of SAGE data. Briefly, the frequency
tables of the 17 libraries were first normalized to expression levels per 55 000 tags.
SAGE tags appearing at least 5 times in all 17 libraries were subjected to the
clustering analysis described in “Materials and methods.” Acti indicates activated;
M␾, macrophages.
as the similarity between the gene expression patterns. These data
suggest that the genes are differentially expressed in each leukocyte
population, depending on their differentiation stages.
Genes selectively expressed in each type of leukocytes
RT-PCR of genes selected in the SAGE analysis
At each stage of differentiation—from pluripotent hematopoietic
stem cells to monocytes/macrophages, DCs, granulocytes, T cells,
B cells, and NK cells—a different combination of restricted and
ubiquitous regulatory factors may be important in inducing a
distinct pattern of gene expression. Therefore, we examined the
gene expression in each cell type. To assess a rigorous statistical
significance of the observed differences, multiple statistical tests
were conducted. We detected transcripts that were selectively
expressed in each population of granulocytes, monocytes, macrophages, DCs, T cells, NK cells, and B cells (Supplemental Table S1
on Blood website; see the Supplemental Data Set link at the top of
the online article). These data included well-known genes related to
each cell type, such as the T-cell receptor gene for T cells, the
perforin 1 gene for NK cells, and immunoglobulin-related genes
for B cells.
Next, to identify the overall similarities of the libraries derived
from each leukocyte population, we used hierarchical cluster
analysis. Resultant dendrograms were divided into myeloid and
lymphoid cells (Figure 1A). Furthermore, to evaluate the resultant
dendrograms, scatter plots for comparisons between arbitrarily
selected cell types were also shown in Figure 1B.
Gene expression patterns of resting TH1 and TH2 cells were
close, as shown in the mean of the tree view and scatter plots. In
contrast, gene expression patterns in granulocytes and activated
TH1 cells were entirely unrelated. Therefore, the tree view is shown
To validate our SAGE data for leukocytes, we arbitrarily selected
12 differently expressed genes and evaluated them in cells obtained
from more than 5 donor-derived samples by RT-PCR (Figure 2),
and the expression of each transcript was compared with SAGE
data. MHC class IB was almost equally expressed in all cell types;
and hemoglobin ␣ 1 and EST (Hs. 103296) (granulocyte), Gro2
oncogene (monocyte), EST (Hs. 192427) (macrophage), CCL18
and p21SNFT (DCs), CD6 (T cell), granzyme B and EST (Hs.
98785) (NK cells), and FLJ20706 and EST (Hs. 290825) (B cells)
were highly expressed in each group.
Table 2. Summary and classification of sequenced tags in the combined
leukocyte libraries
Frequency
More than 500
51-500
6-50
Unique
transcripts
%
Mass fraction
mRNA (%)
Unique
genes
75
0.2
27.5
73
609
1.6
26.2
573
6 545
16.8
28.2
5 441
5 or fewer
31 732
81.4
18.1
21 236
Total
38 961
100.0
100.0
27 323
Frequency denotes the category of expression level analyzed in transcript copies
per cell in the combined libraries. Unique genes represent a total number of unique
genes matched to the Unigene cluster and to no reliable genes.
Figure 2. RT-PCR analysis of the genes differently expressed in leukocytes.
RT-PCR was performed on total RNA isolated from each cell type, as described in
“Materials and methods.” The number next to each tag sequence is the number of
tags per cell.
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HASHIMOTO et al
Comparison of expression patterns between myeloid and
lymphoid cells
We next analyzed the genes differentially expressed in myeloid and
lymphoid cells. Ubiquitously and highly expressed genes in
myeloid and lymphoid cells are shown in Supplemental Table S2.
The total number of genes significantly expressed in myeloid cells
was 147 (P ⫽ .0 to 1 ⫻ 10⫺20). The total number of genes
significantly expressed in lymphoid cells was 88 (P ⫽ .0 to 1 ⫻
10⫺20). Functional breakdowns of genes selectively expressed in
myeloid and lymphoid cells are depicted in Figure 3. Genes related
to metabolism, signaling, cytoskeleton, proteolysis, membrane
channels, and transporters were predominantly expressed in myeloid cells or at no or low levels in lymphoid cells. On the other
hand, the transcripts for ribosomal proteins were highly expressed
in lymphoid cells.
Gene expression in antigen-presenting cells
Genes commonly expressed in antigen-presenting cells (APCS),
such as monocytes, macrophages, DCs, and B cells, are shown in
Supplemental Table S3. As expected, genes related to major
histocompatibility complex (MHC) class II were specifically
expressed in APC libraries. APC-specific genes were composed of
genes encoding proteins related to cytoskeleton, metabolism,
proteolysis, capping (gelsolin-like protein), acid phosphatase 5, D
component of complement, N-acetylglucosamine kinase, and solute carrier family 16.
Selectively and commonly expressed genes in leukocytes
To find out the leukocyte-specific genes, SAGE tags in leukocytes were compared with 4 280 231 tags in other tissues from
the SAGE database (http://www.ncbi.nlm.nih.gov/SAGE/). Lplastin 1 (P ⫽ 7.8 ⫻ 10–636; UniGene no. 76506), proteoglycan
1 (P ⫽ 1.3 ⫻ 10–491; UniGene no. 1908), and dual-specificity
phosphatase 2 (P ⫽ 3.1 ⫻ 10–387; UniGene no. 1883) were
found to be more selectively and commonly expressed in
leukocytes than in other tissues.
Figure 3. Functional classification of myeloid and lymphoid cells. Putative
functional breakdown of myeloid and lymphoid cell genes. The number next to each
category indicates the total number of genes in that class. The total number of genes
significantly and highly expressed in myeloid cells is 147. The total number of genes
significantly and highly expressed in lymphoid cells is 88.
Discussion
Leukocytes play pivotal roles in inflammation and immunity. To
molecularly define the type and function of human leukocytes, we
performed SAGE in human leukocytes. Among 38 961 distinct tags
that appeared more than once in all libraries combined, 27 323 tags
were represented as unique genes. The number of genes assessed in
leukocytes is reasonable for the current estimate of 30 000 to
40 000 genes predicted in the human genome.14,15
Hierarchical cluster analysis of expressed genes in leukocytes
revealed 2 main branches as myeloid and lymphoid cells. In the
lymphocyte branch, resting/circulating T cells were more alike as a
group. Functionally, NK cells and CD8⫹ T cells played pivotal
roles as cytotoxic lymphocytes in host defense; however, NK cells
did not resemble CD8 T cells but did resemble B cells. In fact,
several reports showed that NK and B cells were generated from
the same precursor cells.16,17 Therefore, gene expression profiles in
NK cells and CD8 T cells depended on origin rather than function.
Genes differentially expressed in each leukocyte type depended not only on the differentiation pathways but also on their
functions. Metabolism, signaling, cytoskeleton, proteolysis,
membrane channels, and transporter-related genes were expressed in myeloid cells but only at low or no levels in lymphoid
cells. Myeloid cells responded to environmental stimuli as a
major player in the primary host defense. Because myeloid cells
infiltrated various tissues or made contact with other types of
cells through immunologic synapses or adhesion proteins to
process and present antigens, they may need specified gene
expression to maintain these functions and higher amounts of
energy than lymphoid cells. On the other hand, the transcripts of
ribosomal proteins were highly expressed in lymphoid cells.
Ribosomal proteins are known to play an important role in
translational regulation, and they have been implicated in the
control of differentiation, cellular transformation, tumor growth,
and metastasis. However, the interrelation between individual
cells and the massive transcription of ribosomal protein is still
unclear. Transcription of a set of ribosomal proteins may be
involved in enhancing the transcription of specific genes.
Through a comparison of leukocytes and other tissue, L-plastin
1, proteoglycan 1, and dual-specificity phosphatase 2 were observed as the specifically and commonly expressed genes in
leukocytes. One of the specific genes, L-plastin, is capable of
bundling actin filaments through its actin-binding domains and is
related to cancer progression (eg, metastasis).18,19 L-plastin may
regulate cell invasion. The function of proteoglycan 1, which is
constitutively secreted by leukocytes, is less clear. Dual-specificity
phosphatase 2 acts as a dual-specific protein phosphatase with
stringent substrate specificity for MAP kinase.20 The genes may be
important for the maintenance of the leukocyte function and can be
good common markers for leukocytes.
This SAGE analysis showed the significant number of
sequences that do not give reliable matches. However, at
present, we cannot explain whether “no reliable matches” are
real tags or not. Several possibilities may account for the
appearance of these tags—sequences derived from hnRNA,
PCR amplification error, unknown splicing variant of mRNA,
random binding of oligo-dT to mRNA except for the poly A tail
during construction of the cDNA library, and rear mRNA (highly
specific expression in rear population). Recently, Saha et al21
developed the Long SAGE methods that generate 21-bp tags
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SAGE DATABASE IN HUMAN LEUKOCYTES
derived from the 3⬘ ends of transcripts that can be rapidly
analyzed and precisely matched to genomic sequence data. We
will attempt the Long SAGE method to identify “not reliable
matches” or “multiple matches” in leukocyte SAGE libraries,
and we plan to improve this database.
3513
In conclusion, identification of the genes specifically expressed
in different types of leukocytes not only provides novel insight into
the ontogeny and function of leukocytes, but also provides the
diagnostic basis for blood and immune disorders such as rheumatism, diabetes, and atopic dermatitis.
References
1. Alizadeh AA, Eisen MB, Davis RE, et al. Distinct
types of diffuse large B-cell lymphoma identified
by gene expression profiling. Nature. 2000;403:
503-511.
2. Velculescu VE, Zhang L, Vogelstein B, Kinzler
KW. Serial analysis of gene expression. Science.
1995;270:484-448.
3. Velculescu VE, Madden SL, Zhang L, et al. Analysis of human transcriptomes. Nat Genet. 1999;
23:387-388.
4. Hashimoto S-I, Suzuki T, Dong H-Y, Yamazaki N,
Matsushima K. Serial analysis of gene expression in human monocytes and macrophages.
Blood. 1999;94:837-844.
5. Suzuki T, Hashimoto S-I, Toyoda N, et al. Comprehensive gene expression profile of LPS-stimulated human monocytes by SAGE. Blood. 2000;
96:2584-2591.
6. Hashimoto S-I, Suzuki T, Nagai S, Yamashita T,
Toyoda N, Matsushima K. Identification of genes
specifically expressed in human activated and
mature dendritic cells through serial analysis of
gene expression. Blood. 2000;96:2206-2214.
7. Nagai S, Hashimoto S-I, Yamashita T, et al. Comprehensive gene expression profile of human activated Th1- and Th2-polarized cells. Int Immunol.
2001;13:367-376.
8. Obata-Onai A, Hashimoto S-I, Onai N, et al. Com-
prehensive gene expression analysis of human
natural killer cells and CD8⫹ T lymphocytes. Int
Immunol. 2002;14:1085-1098.
9. Velculescu VE, Zhang L, Zhou W, et al. Characterization of the yeast transcriptome. Cell. 1997;
88:243-251.
10. Eisen MB, Spellman PT, Brown PO, Botstein D.
Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A.
1998;95:14863-14868.
11. Sese J, Morishita S, Okubo K. Ordering genes by
significance of occurrences. Proceedings of the
fourth annual International Conference on Computational Molecular Biology (RECOMB 2000),
Tokyo, Japan. 2000:14-15. 2000:14-15.
12. Audic S, Claverie JM. The significance of digital
gene expression profiles. Genome Res. 1997;7:
986-995.
13. Imai T, Nagira M, Takagi S, et al. Selective recruitment of CCR4-bearing Th2 cells toward antigenpresenting cells by the CC chemokines thymus
and activation-regulated chemokine and macrophage-derived chemokine. Int Immunol. 1999;11:
81-88.
14. Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science. 2001;
291:1304-1351.
15. Lander ES, Linton LM, Birren B, et al. Initial se-
quencing and analysis of the human genome.
Nature. 2001;409:860-921.
16. Giarratana MC, Verge V, Schmitt C, et al. Presence of primitive lymphoid progenitors with NK or
B potential in ex vivo expanded bone marrow cell
cultures. Exp Hematol. 2000;28:46-54.
17. Puel A, Leonard WJ. Mutations in the gene for the
IL-7 receptor result in T(⫺)B(⫹)NK(⫹) severe
combined immunodeficiency disease. Curr Opin
Immunol. 2000;12:468-473.
18. Otsuka M, Kato M, Yoshikawa T, et al. Differential
expression of the L-plastin gene in human colorectal cancer progression and metastasis. Biochem Biophys Res Commun. 2001;289:876-881.
19. Jones SL, Wang J, Turck CW, Brown EJ. A role
for the actin-bundling protein L-plastin in the regulation of leukocyte integrin function. Proc Natl
Acad Sci U S A. 1998;95:9331-9336.
20. Yi H, Morton CC, Weremowicz S, McBride OW,
Kelly K. Genomic organization and chromosomal
localization of the DUSP2 gene, encoding a MAP
kinase phosphatase, to human 2p11.2-q11.
Genomics. 1995;28:92-96.
21. Saha S, Sparks AB, Rago C, et al. Using transcriptome to annotate the genome. Nat Biotech.
2002;19:508-512