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A gene atlas of the mouse and human
protein-encoding transcriptomes
Andrew I. Su*
†
, Tim Wiltshire*
†
, Serge Batalov*
†
, Hilmar Lapp*, Keith A. Ching*, David Block*, Jie Zhang*,
Richard Soden*, Mimi Hayakawa*, Gabriel Kreiman*
‡
, Michael P. Cooke*, John R. Walker*, and John B. Hogenesch*
§¶
*The Genomics Institute of the Novartis Research Foundation, 10675 John J. Hopkins Drive, San Diego, CA 92121; and
§
Department of Neuropharmacology,
The Scripps Research Institute, 10550 North Torrey Pines Road, San Diego, CA 92037
Edited by Peter K. Vogt, The Scripps Research Institute, La Jolla, CA, and approved March 2, 2004 (received for review February 3, 2004)
The tissue-specific pattern of mRNA expression can indicate im-
portant clues about gene function. High-density oligonucleotide
arrays offer the opportunity to examine patterns of gene expres-
sion on a genome scale. Toward this end, we have designed custom
arrays that interrogate the expression of the vast majority of
protein-encoding human and mouse genes and have used them to
profile a panel of 79 human and 61 mouse tissues. The resulting
data set provides the expression patterns for thousands of pre-
dicted genes, as well as known and poorly characterized genes,
from mice and humans. We have explored this data set for global
trends in gene expression, evaluated commonly used lines of
evidence in gene prediction methodologies, and investigated pat-
terns indicative of chromosomal organization of transcription. We
describe hundreds of regions of correlated transcription and show
that some are subject to both tissue and parental allele-specific
expression, suggesting a link between spatial expression and
imprinting.
T
he completion of the human and mouse genome sequences
opened an historic era in mammalian biology. One common
conclusion from these projects was the determination that
mammals have only ⬇30,000 protein-encoding genes (1, 2). Yet,
despite the apparent tractability of this figure (earlier estimates
were much higher), to date all existing research has determined
the function of only a fraction of these genes. Currently, only
⬇15,000 human and ⬇10,000 mouse genes are described in the
literature (Medline, www.ncbi.nih.gov兾Pubmed). The challenge
and opportunity for genomics strategies and techniques are to
accelerate the functional annotation of novel genes from the
uncharted genome.
High-throughput technologies for biological annotation have
the capacity to partially address the discrepancy between the
identification of genes and the understanding of their function.
For example, proteins have well defined molecular roles encoded
in their primary amino acid sequence as domains. Using se-
quence informatics, these domains can be used as a tool to search
the entire genome to find protein family members that likely
function in an analogous manner. Gene expression arrays have
also been a useful tool for genome-wide studies where changes
in gene expression can be associated with physiological or
pathophysiological states (3). Recently, other high-throughput
techniques such as RNA interference (4) and cDNA overex-
pression (5) have been developed, further accelerating func-
tional genome annotation. The integration of these diverse
strategies is critical to annotation efforts and remains a signif-
icant challenge.
Previously, we generated a preliminary description of the
human and mouse transcriptome using oligonucleotide arrays
that interrogate the expression of ⬇10,000 human and ⬇7,000
mouse target genes (6). We explored this data set for insights
into gene function, transcriptional regulation, disease etiology,
and comparative genomics. However, this data set was based on
commercially available gene expression arrays and therefore was
biased toward previously characterized genes. In this report, we
significantly extend this earlier work by determining the expres-
sion patterns of previously uncharacterized protein-encoding
genes and de novo gene predictions from the mouse and human
genome projects. Using custom-designed whole-genome gene
expression arrays that target 44,775 human and 36,182 mouse
transcripts, we have built a more extensive gene atlas using a
panel of RNAs derived from 79 human and 61 mouse tissues.
This data set constitutes one of the largest quantitative evalua-
tions of gene expression of the protein-encoding transcriptome
to date.
Building on our previous analyses, these expression patterns
were examined for global trends in gene expression. We also
provide experimental validation of thousands of gene predic-
tions and use these data to determine which of the commonly
used types of evidence for gene prediction most accurately
correlates with expressed genes. In addition, we used this data set
to search for chromosomal regions of correlated transcription
(RCTs), which may indicate higher-order mechanisms of tran-
scriptional regulation. Furthermore, we show that some of these
tissue-specific coregulated genes are subject to another form of
regulation, parental imprinting, and thus that several of these
regions are under the control of both tissue- and parental
allele-specific expression. Finally, we have made these data
publicly available for searching and visualization by keyword,
accession number, sequence, expression pattern, and coregula-
tion at our web site (http:兾兾symatlas.gnf.org).
Materials and Methods
Microarray Chip Design. We identified a nonredundant set of
target sequences for the human and mouse using the following
sources: RefSeq (15,491 human and 12,029 mouse sequences)
(7); Celera (49,859 human and 29,331 mouse sequences) (8);
Ensembl (33,698 human sequences); and RIKEN (46,299 mouse
sequences) (9). First, all sequences were screened with
REPEAT-
MASKER (www.repeatmasker.org) to remove repetitive elements.
Next, sequence identity between individual sequences was es-
tablished by using pairwise
BLAT (10) or BLAST (11) and SIM4
(12). The results from single-linkage clustering were further
triaged to produce a final target set of 44,775 human and 36,182
mouse targets with the highest degree of confidence of compu-
tational prediction [biasing toward sequences containing Inter-
pro domains (13) and away from noncoding RNAs]. Finally, the
human sequence set was pruned of all targets already repre-
sented on the Affymetrix (Santa Clara, CA) commercially
available HG-U133A array, leaving 22,645 target sequences for
our custom array. One hundred target sequences from the
This paper was submitted directly (Track II) to the PNAS office.
Abbreviations: RCT, regions of correlated transcription; AUC, area under the curve; LCR,
locus control regions.
†
A.I.S., T.W., and S.B. contributed equally to this work.
‡
Present address: Center for Biological and Computational Learning, Massachusetts Insti-
tute of Technology, MIT E25-201, Cambridge, MA 02142.
¶
To whom correspondence should be addressed. E-mail: hogenesch@gnf.org.
© 2004 by The National Academy of Sciences of the USA
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HG-U133A chip were also included in the GNF1H design for the
normalization procedure (see below). The final human and
mouse target sets were submitted to the Affymetrix chip design
pipeline for fabrication of the GNF1H and GNF1M arrays,
respectively.
Tissue Preparation. Human tissue samples were obtained from
several sources: Clinomics Biosciences (Pittsfield, MA), Clon-
tech, AllCells (Berkeley, CA), Clonetics兾BioWhittaker (Walk-
ersville, MD), AMS Biotechnology (Abingdon, Oxfordshire,
U.K.), and the University of California at San Diego. When
samples from four or more subjects were available, equal num-
bers of male and female subjects were used to make two
independent pools; when fewer than four samples were available,
RNA samples were pooled, and duplicate amplifications were
performed for each pool (detailed annotation for human sam-
ples is on our web site and in Table 1, which is published as
supporting information on the PNAS web site). Adult (10- to
12-week-old) mouse tissue samples were independently gener-
ated from two groups of four male and three female C57BL兾6
mice by dissection, and tissues were subsequently quickly frozen
on dry ice. Tissues were pulverized while frozen, and total RNA
was extracted with Trizol (Invitrogen, Carlsbad) by using ⬇100
mg of tissue, then further processed by using the RNeasy
miniprep kit according to manufacturer’s protocols (Qiagen,
Chatsworth, CA). The quality of all samples was determined with
an Agilent Bioanalyzer (Palo Alto, CA).
Microarray Procedure. Microarray analysis was performed essen-
tially as described (14). Briefly, 5
g of total RNA was used to
synthesize cDNA that was then used as a template to generate
biotinylated cRNA. cRNA was fragmented and hybridized to
Affymetrix custom or commercially available gene expression
arrays. The arrays were then washed and scanned with a laser
scanner, and images were analyzed by using the MAS5 algorithm
(15). Arrays were normalized by using global median scaling.
The human HG-U133A and GNF1H chips, which were hybrid-
ized to the same biological sample, were first paired and
prenormalized by using the common targets. The condensed
data files are available from our web site (http:兾兾symatlas.
gnf.org) and Gene Expression Omnibus (www.ncbi.nih.gov兾
geo) (16). Raw CEL files will be provided upon request
(http:兾兾symatlas.gnf.org).
Identification of RCTs. All target genes were mapped to their
corresponding genome assembly (human to National Center for
Biotechnology Information Hs34 assembly, mouse to February
2003 Mm30 assembly) by using
BLAT (10). To account for
multiple probes interrogating a single gene, target sequences
were also compared to UniGene (www.ncbi.nih.gov兾UniGene)
by using
BLAST. Target sequences that map within 25 kb of each
other and to a common UniGene cluster were pooled, and their
expression values were averaged and treated as a single target in
the RCT analysis. Next, each chromosome was scanned in
window sizes of 3–10 adjacent genes. Windows where ⬎50% of
all pairwise comparisons of expression pattern showed a Pearson
correlation coefficient ⬎0.6 were identified as RCTs. Random-
ization studies of gene order confirmed the significance of both
the overall number of RCTs and the average pairwise correlation
of each individual RCT (P ⬍ 0.005, correcting for multiple
testing). Pairwise sequence similarity within each RCT was
assessed by using
TBLASTX (11), where a similarity value is the
product of the alignment similarity and the percentage of total
sequence length aligned. Synteny between the human and mouse
genome assemblies was derived from a published analysis of
syntenic anchors (17). For the analysis of evolutionarily con-
served RCTs, only the 32 tissues profiled in common between
the mouse and human data sets were used. All analyses and
visualizations were performed by using
R (www.r-project.org).
Imprinting Analysis. Allele-specific probe expression analysis was
used to identify genes with an imprinted expression pattern. Two
distinct mouse strains, C57BL兾6J (B6) and Mus musculus cas-
taneus (CAST兾Ei), were bred to produce four independent
mouse crosses (male::female): B6::B6, B6::CAST兾Ei,
CAST兾Ei::B6, and CAST兾Ei::CAST兾Ei. Each litter of embryonic
day 14–16 embryos was pooled, and RNA from four to five
separate litters was labeled and hybridized to GNF1M arrays. A
probe-level analysis was performed to detect naturally occurring
polymorphisms between the two strains. Individual probes (but
not entire probe sets) showing a significantly different signal
between the two homozygous groups were identified as putative
polymorphisms in the target gene. Next, the hybridization signal
from the two reciprocal crosses was examined for statistically
significant differences in signal based on the paternal or mater-
nal allele, as assessed by t test (P ⬍ 0.001), indicating a pattern
of male or female imprinting.
Results and Discussion
The tissue-specific RNA expression pattern of a gene can
indicate important clues to its physiological function. To build an
extensive atlas of tissue-specific gene expression, we created
custom arrays that interrogate the expression of known and
predicted protein-encoding genes from the mouse and human
genomes. The design process used a nonredundant set of known
genes and gene predictions compiled from Refseq, Celera,
Ensembl (for human), and RIKEN (for mouse). For our GNF1H
custom human array, we further removed gene targets that were
already represented on the commercially available HG-U133A
array from Affymetrix. Finally, we biased the final selection
toward gene predictions with likely protein-coding regions. In
total, the U133A兾GNF1H chipset interrogates 44,775 probe sets,
and our custom-designed GNF1M mouse array interrogates
36,182 probe sets. As of the most current annotation in January
2004, these correspond to 33,698 and 33,825 unique human and
mouse genes, respectively, after accounting for multiple probe
sets interrogating single genes and split transcripts.
Using these whole-genome gene expression arrays, we mea-
sured the expression of an extensive set of transcripts and
transcript predictions on a single technology platform across a
diverse panel of 79 human and 61 mouse tissues. This gene atlas
represents the normal transcriptome and allowed us to examine
global trends in gene expression. Classical reassociation kinetics
(Rot) has been used to assess global trends in gene expression at
a population level (18). The analysis of our data set expands this
knowledge by examining transcript expression across a large
number of tissues at the individual transcript level. We find that
52% (16,454) and 59% (17,924) of target genes are detected in
at least one tissue in the human and mouse, respectively (Fig. 4A,
which is published as supporting information on the PNAS web
site). The average number of transcripts expressed in a single
tissue was ⬇8,200 (mouse). These observations generally concur
with previous findings derived from Rot analyses, which indicate
that ⬇10,000–15,000 mRNAs are expressed in a given tissue at
⬇1–10 copies per cell, and that 90% of these are common
between two tissues (19). However, although Rot analysis sug-
gests that the majority of transcripts are present in many or all
tissues, our data show that ⬍1% of human target sequences are
ubiquitously expressed. Approximately 3% of mouse target
sequences are detected in all samples profiled, although this
number will certainly decline as the number of samples increases.
Not surprisingly, the expression of these ubiquitously expressed
housekeeping genes is ⬇30-fold higher than for all genes in the
data set (Fig. 4B).
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Another valuable use of this data set is characterization of
novel predicted genes derived from the mouse and human
genome projects (1, 2). Many of these exist solely as in silico
predictions, and therefore evidence of their expression can serve
as validation of these predictions. Furthermore, determining the
expression pattern of an uncharacterized gene can indicate the
appropriate tissue(s) from which the transcript can be cloned, as
well as provide a base layer of physiological annotation. Gene
prediction is an inexact art, where distinct methods and research-
ers often produce largely nonoverlapping sets of gene predic-
tions (20). For the human data, we subdivided the transcripts
into four classes based on annotation information at the time of
design: known genes found in Refseq, genes predicted indepen-
dently by two groups (Celera and Ensembl), singleton predic-
tions found by the Ensembl group only, and singleton predictions
found by the Celera group only. As expected, the set of known
genes (Refseq) has the highest rate of detection in our data set,
because 79% have detectable expression in at least one sample
(Fig. 1). Because all Refseq genes are known to be expressed, this
suggests that our methodologies and current tissue libraries have
a minimum false-negative rate of ⬇21% in detection of expres-
sion. This can certainly be improved with the profiling of
additional tissues and cell types. Consensus gene predictions
have a higher rate of detectable expression (53%) than either
singleton gene predictions offered by Ensembl or Celera only
(30% and 24%, respectively) (Fig. 1). Although the Ensembl-
only group had a slightly higher rate of detection, a greater total
number of Celera-only predictions was detected (2,918 Celera vs.
618 Ensembl predictions). Analogous results are seen in the
mouse data set, in which Refseq genes had a higher rate of
detection than gene predictions by Celera (79% vs. 46%). The
differences among these three classes are also reflected in the
quantitative measures of gene expression. On average, human
Refseq genes are expressed at a level 2-fold higher than con-
sensus predictions, which in turn are 66% higher than singleton
predictions (P⬍⬍0.001; data not shown). This observation likely
reflects a historical bias in the biology of studying highly
abundant proteins. In total, we find evidence of expression for
5,641 (31.2%) human and 2,629 (46.2%) mouse gene predictions
through detection of their transcribed mRNA product in at least
one tissue. In addition, we describe the expression pattern for
9,708 mouse RIKEN-derived genes, many of which lack signif-
icant expression annotation. It is important to note that the gene
predictions for which we do not observe detectable expression
are not necessarily incorrect, because the appropriate tissue(s)
for a given gene may have not been profiled, the gene may be
present in a small number of copies (e.g., in a small subset of cells
within a tissue), or the probe set may not properly interrogate the
expression of the gene (e.g., UTRs, split transcripts, or missing
or mistaken terminal exons). Despite these caveats, this data set
provides the expression pattern of thousands of gene predictions
and poorly characterized transcripts from the mouse and human
genome projects, offering the opportunity to study the function
of these genes in their most relevant tissues.
Given the differing methods and subsequent results from gene
prediction efforts, we next investigated which characteristics of
a predicted transcript were better indicators of its detectable
expression. In the methodology used by Celera, the following
lines of evidence were considered in their gene prediction
algorithm: ‘‘conservation between mouse and human genomic
DNA, similarity to human [and] rodent transcripts (ESTs and
cDNAs), and similarity of the translation of human genomic
DNA to known proteins’’ (1). Using the detectable expression of
a gene product as validation of the prediction, we created
receiver operating characteristic curves for each line of evidence
that plot the true positive rate as a function of the false positive
rate. The area under the curve (AUC) measures the strength of
the predictor; a perfect predictor would have AUC ⫽ 1, and a
random factor would have AUC ⫽ 0.5. When comparing the
predictor strength among the three lines of evidence above in the
human data set, we find that although no single line of evidence
is universally predictive of expression, EST evidence has the
most predictive value (AUC ⫽ 0.77) (Fig. 5, which is published
as supporting information on the PNAS web site), an observa-
tion likely linked to the fact that highly expressed genes are more
likely to be represented in EST databases. Protein homology
support and sequence similarity between human and mouse
genomic sequences both had a lesser impact on the validation of
gene predictions (AUC of 0.66 and 0.65, respectively). The
availability of additional mammalian genome sequences should
increase the power of sequence conservation in gene prediction.
Somewhat surprisingly, simply the length of the transcript pre-
diction was also a reasonable predictor of detection in our data
set (AUC ⫽ 0.68), suggesting that incomplete transcript predic-
tions were significant factors in the nonobservation of many gene
targets.
We and others have used gene expression information, ge-
nome sequence, and de novo motif discovery tools to search for
enhancer elements that direct tissue-specific gene expression
(21, 22). In contrast to enhancers that generally direct the
expression of a single gene, locus control regions (LCR) are
characterized by their ability to promote the expression of
multiple genes at a single locus. To date, only a handful of LCRs
have been reported (23). Recently, Spellman and Rubin (24)
used Drosophila gene expression arrays to identify ⬇200 clusters
of adjacent and similarly expressed genes and suggest that these
patterns are most consistent with regulation of chromatin struc-
ture. Others (25–27) have also performed similar analyses in
humans, Caenorhabditis elegans, and yeast on more limited sets
of experimental conditions.
To identify potential loci in our data set, the expression of
which may be controlled in a locus-dependent manner, we
mapped the transcripts represented on our gene expression
arrays to genome assemblies and scanned each chromosome for
windows of genes with correlated expression patterns. We called
these sites RCTs as a general term encompassing LCRs and
correlated expression achieved through gene duplication. It is
important to note that detection of these RCTs is heavily
influenced by comparison algorithms, normalization proce-
dures, and underlying data. In particular, the inclusion of several
purified immune cell populations in our human sample set
Fig. 1. Validation of gene predictions in humans. Gene targets on the GNF1H
array were divided into four categories: contained in Refseq, predicted by
both gene prediction efforts considered (‘‘Consensus’’), and predicted by only
one group (‘‘Ensembl-only’’ and ‘‘Celera-only’’). On the left axis (solid bars),
rates of validation are shown, where detectable expression in at least one
tissue is taken as evidence of the validity of a gene prediction. The right axis
(blue line) indicates the total number of validated genes per group.
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skewed the normalization procedure and led to an increase in
RCTs whose expression is enriched in these samples. In total, we
identified 156 and 108 RCTs in human and mouse, respectively
(descriptions of all RCTs are available for download from
http:兾兾symatlas.gnf.org). Tissues with very specific clusters of
genes such as those in the immune system, liver, testis, and
placenta had more RCTs than other tissues in both the mouse
and human data sets. Mechanistically, expression of these RCTs
is likely mediated through either common promoter elements
(resulting from gene duplication) or through higher-order gene
regulation such as site-specific chromatin remodeling. To sepa-
rate these two possibilities, we identified likely paralogs using
TBLASTX, a local six-frame translated nucleotide-to-nucleotide
alignment algorithm (11). RCTs whose genes share significant
sequence similarity in their coding sequences are likely to be
products of gene duplication, whereas dissimilar genes may result
from an LCR or other higher-order transcriptional regulation.
As expected, we found RCTs with both related and unrelated
genes. Fig. 2A illustrates an example of an RCT driven by gene
duplication. This cluster of genes on mouse chromosome 9
represents a family of 11 uncharacterized F-box and WD40
repeat containing proteins that are specifically expressed in
fertilized eggs and oocytes. Because of their high degree of
sequence similarity, we hypothesize that their correlated expres-
sion pattern is a result of duplicated regulatory elements present
in their structural genes, and that these genes may play an
important role in the specialized protein expression of oocytes.
In contrast, we also note a cluster of three genes with no
apparent sequence similarity on human chromosome 13 that are
highly enriched in samples derived from brain tissues, particu-
larly the fetal brain sample (Fig. 2B). The genes in this cluster
are neurobeachin, an uncharacterized mRNA, and doublecortin-
and calmodulin kinase-like 1 protein (DCAMKL1). It is appeal-
ing to hypothesize that the correlated expression patterns of
these genes and their colocalization at a chromosomal locus
indicate a common role in a neurological process or network.
Because these genes do not share sequence similarity, this region
may also contain a previously unrecognized LCR or strong
regional enhancer. Overall, 97 (62%) and 78 (72%) of the human
and mouse RCTs identified have an average pairwise sequence
similarity of ⬍20% and do not encode related genes.
We next examined both the mouse and human data for RCTs
that were identified in both data sets and are likely evolutionarily
conserved. The majority of the RCTs were not found in both
human and mouse, in many cases because the orthologs or
syntenic regions have not yet been defined or the patterns were
not conserved. However, in some cases, the apparent lack of
conservation likely reflects physiological differences between
the two organisms. For example, we observed RCTs with
expression enriched in the olfactory bulb present in the mouse
Fig. 2. RCT. (A) An RCT was identified on mouse chromosome 9, consisting of 11 genes that share a highly conserved expression pattern. (Upper) The y axis
is average normalized expression value, the x axis contains the 61 different tissues, and red bars are fertilized egg and oocytes. The correlation plot (Lower Left)
visualizes the pairwise correlation coefficients. Each row represents a gene, ordered vertically according to their position on the chromosome. The center yellow
vertical strip represents autocorrelation (R ⫽ 1); positions to the right of center represent correlation of the gene to its downstream neighbors, whereas positions
to the left represent correlation to the upstream neighbors. Yellow indicates high correlation; blue indicates low correlation (scale at bottom). The sequence
similarity plot (using
TBLASTX, Lower Right) has the same structure as the correlation plot, except pairwise sequence similarity is shown. In this RCT with high
expression levels in fertilized eggs and oocytes, the genes share a high degree of sequence similarity, likely indicating they are all members of a single gene family
and the result of one or more gene duplication events. (B) An example RCT is identified on human chromosome 13, which contains three genes with highly
correlated expression (red bars are brain regions, green bar is fetal brain). In contrast to the first example, these genes share very little pairwise sequence
similarity. (C) An evolutionarily conserved RCT is shown from human chromosome 2 (Left) and the syntenic region on mouse chromosome 6 (Right). These RCTs
share a pancreas-enriched expression pattern (red bar), as well as significant sequence similarity.
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but not the human data set. Nevertheless, several RCTs were
conserved, including a cluster of pancreas-specific genes map-
ping to human chromosome 2 and its syntenic region on mouse
chromosome 6 (Fig. 2C). The human cluster is comprised of five
genes, including pancreatitis-associated proteins (PAP), three
regenerating islet-derived proteins (REG1A, REG1B, and
REGL), and one protein of unknown function (LPPM429). The
mouse cluster contains the ortholog to PAP, four isoforms of
regenerating islet-derived proteins, and islet neogenesis-
associated protein-related protein. The conservation of this
RCT in human and mouse suggests that these genes perform
analogous and important roles in both of these mammals.
After mapping all target genes to their respective genome
assemblies, we noted a region of mouse chromosome 7 (130 Mb)
that contained several genes previously shown to be imprinted
(28–30), three of which (H19, Igf2, and Cdkn1c) shared a pattern
of enriched expression in placenta, umbilical cord, and embry-
onic tissues. We also noted another pair of adjacent genes (Zim1
and Peg3) elsewhere on chromosome 7 (6 Mb) that shared this
tissue-specific expression pattern, and whose expression has
been shown to be imprinted (31). Prompted by these observa-
tions, we examined our set of RCTs for other imprinted genes
that were clustered in a single locus. On mouse chromosome 12
(103 Mb), we observed an RCT that consists of six adjacent
genes, all with enriched expression in brain regions and umbilical
cord (Fig. 3 A and B). Recently, several groups showed that two
genes in this locus, Dlk1 and Gtl2, are imprinted (reviewed in ref.
32). Later, it was also shown that another gene at this locus, Rian,
and several adjacent tandemly repeated C兾D small nucleolar
RNA genes are also imprinted (33, 34). Furthermore, although
we do not have a probe set on our array that reliably detects its
expression, Dio3 is located proximal to this locus and has also
Fig. 3. Six genes on mouse chromosome 12 share a distinctive pattern of expression. (A) A genomic view of this region (not to scale). Locations of the genes
on the mouse genome assembly: Dlk (103.508 Mb), Gtl2 (103.593 Mb), 1110006E14Rik (103.646 Mb), Rian (103.696 Mb), 5330411G14Rik (103.788 Mb),
C130007E11Rik (103.798 Mb), and Dio3 (104.328 Mb). (B) These genes share enriched expression in brain regions (green bars) and umbilical cord (red bar). The
y axes indicate normalized expression values, whereas each bar along the x axis indicates a sample profiled in our data set. (C) Three of these genes (Dlk1, Gtl2,
and Rian) have been previously reported to be imprinted. Using our allele-specific probe expression analysis approach (see text), we confirmed the imprinted
regulation of Gtl2 and Rian and report two previously undescribed imprinted transcripts at this locus (5330411G14Rik and C130007E11Rik). The y axes indicate
the normalized signal intensity for individual probes on the array, and each bar represents a pooled sample from a cross indicated by color (see key).
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shown to exhibit genomic imprinting (35). The imprinting status
of the three remaining RIKEN clones at this locus
(1110006E14Rik, 5330411G14Rik, and C130007E11Rik)isnot
known, although they share the brain- and umbilical cord-
enriched expression characteristic of all of the genes in the RCT.
To investigate whether these three genes were also imprinted,
we used two distinct mouse strains, C57BL兾6J (B6) and M. m.
castaneus (CAST兾Ei), to set up four independent mouse crosses
(male::female): B6::B6, B6::CAST兾Ei, CAST兾Ei::B6, and
CAST兾Ei::CAST兾Ei. Four independent litters of pooled embry-
onic day 14–16 embryos were dissected, and RNA expression
was analyzed by allele-specific probe expression analysis, which
allows us to determine whether the transcript is expressed
exclusively or preferentially from either the paternal or maternal
allele. This analysis reconfirmed the imprinted expression of
Gtl2 and Rian (Fig. 3C). Because no probes could distinguish
between the B6 and CAST兾Ei forms of Dlk1, we were unable
to reconfirm its imprinted expression. Two of the uncharacter-
ized RIKEN genes at this locus, 5330411G14Rik and
C130007E11Rik, showed expression from the maternal allele
only, further expanding the number of known imprinted genes at
this locus (Fig. 3C). Because these cDNAs are within 10 kb of one
another, it is possible they are derived from the same structural
gene. The third gene (1110006E14Rik), like Dlk1, did not contain
a probe capable of ascertaining its imprinting status. During the
preparation of this manuscript, another gene in this locus sharing
the 3⬘-end of C130007E11Rik was also shown to be imprinted
(36). In sum, the allele-specific probe expression analysis method
has identified another two imprinted transcripts at this locus.
Furthermore, based on the observation that well-characterized
imprinted loci on mouse chromosomes 7 and 12 share a common
pattern of gene expression in our data, we speculate that the
LCR machinery that regulates the parental expression of these
genes may also influence their tissue-specific expression pattern.
Conclusion
Here we report an extensive compendium of gene expression of the
protein-encoding transcriptomes of the mouse and humans. Fur-
ther augmentation by additional samples, including region-specific
dissections using laser capture microdissection or even cell type-
specific gene expression, will undoubtedly increase the utility of
these resources. We have investigated this data set for global
signatures in tissue-specific gene regulation, expression character-
istics of de novo predicted transcripts, and chromosomal RCTs. The
identification of several known imprinted loci in our tissue-specific
RCT list suggests that these regulatory mechanisms that direct
tissue- or parental allele-specific expression may be intertwined.
Consistent with this observation, we were able to identify two
previously undescribed transcripts that were imprinted on mouse
chromosome 12 based on the observation that they share a tissue-
specific expression pattern with their neighbors.
With the sequencing phase of the human and mouse genome
projects nearly complete, and with the rapid progress in the
sequencing of other mammalian genomes, we are now poised to
develop and exploit a variety of methods to ascertain the
function of the thousands of recently described genes. In this
regard, the genome-scale RNA expression data described herein
provide a framework for the functional annotation process. By
making the underlying data available on our web site (http:兾兾
symatlas.gnf.org) and through the Gene Expression Omnibus
(www.ncbi.nih.gov兾geo), we anticipate that this study will aid
researchers throughout the global research community to reap
the harvests of the human and mouse genome projects.
We thank the following individuals for providing human RNA samples:
Gino Van Heeke, Novartis (bronchial epithelial cells); Graeme Bilbe,
Novartis (fetal thyroid); Clifford Shults, University of California at San
Diego (whole blood); Bill Sugden, University of Wisconsin, Madison
(721 B-lymphoblasts); Joseph D Buxbaum, Mt. Sinai School of Medicine,
New York (prefrontal cortex). We also thank Ines Hoffmann and Satchin
Panda for preparation of mouse embryonic samples and Peter Dimitrov,
Christian Zmasek, and Michael Heuer for technical expertise. This work
was supported by the Novartis Research Foundation.
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Su et al. PNAS
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