Quantitative Expression Profile of Distinct Functional
Regions in the Adult Mouse Brain
Takeya Kasukawa1., Koh-hei Masumoto2,3., Itoshi Nikaido1,2., Mamoru Nagano3, Kenichiro D. Uno1,
Kaori Tsujino2,4, Carina Hanashima5, Yasufumi Shigeyoshi3*, Hiroki R. Ueda1,2,4,6,7*
1 Functional Genomics Unit, RIKEN Center for Developmental Biology, Kobe, Hyogo, Japan, 2 Laboratory for Systems Biology, RIKEN Center for Developmental Biology,
Kobe, Hyogo, Japan, 3 Department of Anatomy and Neurobiology, Kinki University School of Medicine, Osaka, Japan, 4 Graduate School of Science, Osaka University,
Osaka, Japan, 5 Laboratory for Neocortical Development, RIKEN Center for Developmental Biology, Hyogo, Japan, 6 Department of Mathematics, Graduate School of
Science, Kyoto University, Kyoto, Japan, 7 Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center, Kobe, Hyogo, Japan

Abstract
The adult mammalian brain is composed of distinct regions with specialized roles including regulation of circadian clocks,
feeding, sleep/awake, and seasonal rhythms. To find quantitative differences of expression among such various brain
regions, we conducted the BrainStars (B*) project, in which we profiled the genome-wide expression of ,50 small brain
regions, including sensory centers, and centers for motion, time, memory, fear, and feeding. To avoid confounds from
temporal differences in gene expression, we sampled each region every 4 hours for 24 hours, and pooled the samples for
DNA-microarray assays. Therefore, we focused on spatial differences in gene expression. We used informatics to identify
candidate genes with expression changes showing high or low expression in specific regions. We also identified candidate
genes with stable expression across brain regions that can be used as new internal control genes, and ligand-receptor
interactions of neurohormones and neurotransmitters. Through these analyses, we found 8,159 multi-state genes, 2,212
regional marker gene candidates for 44 small brain regions, 915 internal control gene candidates, and 23,864 inferred
ligand-receptor interactions. We also found that these sets include well-known genes as well as novel candidate genes thatmight be related to specific functions in brain regions. We used our findings to develop an integrated database (http://
brainstars.org/) for exploring genome-wide expression in the adult mouse brain, and have made this database openly
accessible. These new resources will help accelerate the functional analysis of the mammalian brain and the elucidation of
its regulatory network systems.
Citation: Kasukawa T, Masumoto K-h, Nikaido I, Nagano M, Uno KD, et al. (2011) Quantitative Expression Profile of Distinct Functional Regions in the Adult Mouse
Brain. PLoS ONE 6(8): e23228. doi:10.1371/journal.pone.0023228
Editor: Raja Jothi, National Institutes of Health, United States of America
Received March 25, 2011; Accepted July 12, 2011; Published August 12, 2011
Copyright: ß 2011 Kasukawa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was done as a part of the Genome Network Project of the Ministry of Education, Culture, Sports, Science and Technology of Japan (H.R.U.).
This research was supported by an intramural Grant-in-Aid from the RIKEN Center for Developmental Biology (H.R.U.), and a Grant-in-Aid for Scientific Research (C)from the Ministry of Education, Culture, Sports, Science and Technology of Japan (Y.S.). The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: uedah-tky@umin.ac.jp (HRU); shigey@med.kindai.ac.jp (YS). These authors contributed equally to this work.

hybridization (ISH) methods have been widely used, and recently,
genome-wide collections of ISH data have been created [5,6,7,18],
including the EMAGE (Edinburgh Mouse Atlas Gene Expression
Database) [19], GenePaint [6], BGEM (St. Jude Brain Gene
Expression Map) [8], BrainMaps.org [20], and Allen Brain Atlas[9]. Although the expression data obtained by ISH can provide
good, cellular-level resolution in sliced surfaces, its signals have anarrow dynamic range [21], which can hinder relative comparisons of expression levels between brain regions. DNA-microarray
technology is an alternative way to obtain quantitative genomewide expression data in tissues and cell culture [22,23]. This
technology is widely used in biological research, including inneuroscience, and several groups have published resources
showing transcript expression profiles in areas of the mammalianbrain [10,11,12,13,14,15,16,17]. Although these resources provide
quantitative expression data, the size of each sampled region isrelatively large to ensure that adequate volumes of RNA samples
are collected, and therefore, multiple functional nuclei, loci,
ganglia, or substantia are merged into a single sampled region.

Introduction
The adult mammalian brain is one of the most sophisticatedand complex organs devised by nature. The distinct functional
regions that comprise it are responsible for processing internal andexternal information into the panoply of mammalian experience.
The different locations in the adult brain have specialized
functions, and various kinds of brain ‘‘maps’’ (or atlases), including
anatomical and functional maps [1,2,3,4], have been developed toillustrate them. Recently, ‘‘expression’’ brain maps, showing thegene transcription profiles of different brain regions, have been
constructed. Since the distinct anatomical structures of the brain
and their functions develop from and are regulated by transcription, at least in part, expression maps should, to some extent,
delineate the same brain regions. In fact, this idea is supported by
the results of published brain transcription profiles[5,6,7,8,9,10,11,12,13,14,15,16,17], which show concordance
between gene transcription and anatomical and functional brain
regions. To obtain expression maps of various brain regions, in situ
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Quantitative Expression in the Mouse Brain Regions

Therefore, no single approach can satisfy quantitativeness andspatial resolution simultaneously even at the nucleus-level.
To achieve a good balance between quantitativeness andspatial resolution for an expression profile of distinct functional
regions in the adult mouse brain, we attempted a two-step
approach. We first obtained the expression data of nucleus-level
resolution in the adult mouse brain as a primary data resource, byusing DNA-microarray technology. Although nucleus-level resolution adopted in this study is larger than cellular-level resolution,
the nucleus-level expression profile can still provide useful
information to identify the genes whose expression are changed
in a target brain region related to a specific function (e.g. foodintake or circadian and photoperiodic behavior), or to identify thebrain regions where a gene of interest has differential expression.
The information of identified genes or brain regions can be used
to plan, for example, construction of knock-out or knock-in
mouse or further inspection of cellular-level ISH datasets.
Therefore, as a second step, we integrated the primary expression
data obtained with the various existing mouse brain expression
maps including ISH expression data of cellular-level resolution.
We call this entire project (nucleus-level, quantitative expression
profiling as well as construction of integrated web interface) as theBrainStars (B*) project. We then used informatics to analyze thespatial and quantitative genome-wide expression patterns in
distinct functional brain regions. The entire BrainStars dataset ispublicly available through the integrated database (http://
brainstars.org/).

Global clustering of CNS regions

Results‘‘Multi-state’’ expression patterns in CNS regions

For the first expression data analysis, we performed global
clustering of these 51 CNS regions for all the DNA microarrayprobe sets (45,037) except for the controls, using different distancemetrics (Figure 1C and Figure S1C). The clustering results
indicated that most of the 51 regions were grouped according totheir developmental/evolutional/anatomical classifications independently of the distance metrics; for example, into thetelencephalon, thalamus, hypothalamus, or mesencephalon. Interestingly, some regions did not cluster according to their classicaldevelopmental/evolutional/anatomical classifications, and these
exceptional clusters seemed to represent more recent evolutionarydevelopmental processes supporting their sophisticated functional
linkage. For example, the globus pallidus (GP) and substantia nigra(SN), which are classified into different classical developmental/
evolutional/anatomical divisions, the telencephalon and mesencephalon, respectively, but are functionally and anatomicallylinked, exhibit a tight and robust clustering of their genome-wide
expressions in correlation and Euclidean distance (Figure 1C and
Figure S1C–E; see also Text S1). We also noted that three CNS
regions (the retina, pituitary, and pineal) were significantly
separated from the other 48 in the global clustering analysis(Figure 1C and Figure S1C–E), possibly owing to theiranatomical differences or different proportions of multiple cell
types. Therefore, we used only the other 48 regions for our further
analysis in the following sections, to focus on subtler differences ingene expression.

‘‘Multi-state’’ expression patterns, which are represented by amulti-modal distribution of gene expression, are thought tocontribute to the spatial and temporal specificity of various
biological functions [25,26,27]. To identify genes with such spatial
expression patterns in the adult mouse CNS (‘‘multi-state genes’’),we used the BrainStars data from the 48 regions, and identified
8,159 genes (12,514 probe sets, ,39% of all the genes in the DNA
microarray) that showed multi-state expression patterns across theregions sampled (Figure 2A; see also Text S1 and Figure S2).
This multi-state expression analysis provides information about the‘‘high’’ and ‘‘low’’ states of two-state genes, as well as the
additional ‘‘middle’’ state(s) of genes that have three or morestates. We noted that multiple states (‘‘high,’’ ‘‘middle,’’ or ‘‘low’’states) observed in the expression pattern could reflect either 1)
different regulation of a gene’s expression level in individual cells,or 2) regional heterogeneity, with different proportions of multiple
cell types that express a given gene. Even in the latter case,classification as a multi-state gene indicates the existence ofmultiple states of a given gene in the CNS (because this is a
preposition of regional heterogeneity). Thus, as the multi-stategene analysis may provide useful information for screening anddetection of interesting genes with multi-state expression patterns,
we decided to further analyze the states of multi-state genes.
Since different regions of the adult mammalian brain havespecialized functions, the expression patterns of multi-state genes
could reflect the roles played by the products (proteins) of thesegenes in carrying out such specific regional functions. For
example, cerebellin 1 precursor protein (Cbln1), a two-state gene
(Figure 2C), contributes to the control of synaptic structure andplasticity in the cerebellum [28], and Solute carrier family 17 member 6(Slc17a6), a three-state gene (Figure 2C), is a vesicular glutamate
transporter in the thalamus [29]. We found that Slc17a6 washighest in the thalamus and lowest in the cerebellum, and that in
the hippocampus, which is telencephalic, it was expressed in the
middle range (Figure 2C). Calcium/calmodulin-dependent protein kinase

Quantitative expression profile of the brain
We sampled 51 regions with distinct functions in the central
nervous system (CNS) of the adult mouse: 49 brain regions and 2
spinal-cord regions (Figure 1A–B and Table S1), including
centers of vision, hearing, taste, olfaction, touch, motion, clocks,
calendar, memory, fear, and feeding. We used cylindrical punchsamples, 0.5-mm thick and 0.5 mm in diameter, from 51 distinct
CNS regions. To ensure the accuracy of our spatial expression
dataset, we took samples of the CNS regions every 4 hours,
starting at ZT0 (Zeitgaber time 0; the time of lights on), for
24 hours (6 time-point samples for each region) to avoid potential
artifacts caused by the circadian regulation of gene expression. We
sampled 5–25 mice for each CNS region at each time point (every
4 hours during one day), resulting in samples from 30–150 mice
being collected for each replicate of a single CNS region. This
entire procedure was performed twice (n = 2) to obtain experimental replicates for every CNS region. The sample quality wasconfirmed before carrying out the DNA-microarray experiments
by the quantitative PCR (q-PCR) analysis of known region-specificgenes. After the DNA-microarray experiments, we also confirmed
the sample quality by the visual inspection of quality-check graphs,
such as global sample clustering (Figure S1A–B) and degradation
plots [24]. This sampling strategy and quality check of samples
helped to reduce the expression variability between experimental
replicates of the same brain region, as evidenced by the highcorrelation (0.994) between experimental replicates (Figure S1F–
G). We also noted that most experimental replicates were clustered
together (Figure S1A–B). Even for mis-clustered replicates of
CNS regions (e.g. the cerebral cortex motor and cerebral cortex
cingulate), when we used an appropriate set of regional marker
genes (e.g. Myl4) retrieved from other in situ databases, theirexpression patterns in our data resource could correctly distinguishthem. These results support the reproducibility and consistency ofthe data in the BrainStars project.
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Figure 1. Sampled adult mouse CNS regions. (A) Map of 44 of the 51 sampled CNS regions. The other seven regions are listed. (B) Abbreviations
and full names of CNS regions. (C) Hierarchical clusters of the expression profiles of the 51 CNS regions. Background colors indicate the classicaldevelopmental/evolutional/anatomical classification of each region.
doi:10.1371/journal.pone.0023228.g001

II alpha (Camk2a), a four-state gene (Figure 2C), is involved inlong-term potentiation in the hippocampus [30]. We found that its
highest expression level (‘‘high’’ state) was in fact in regions of the
hippocampus (cornu ammonis 1 [CA1], CA2/CA3, and dentate
gyrus, DG). Tyrosine hydroxylase (Th), a rate-limiting enzyme for
dopamine synthesis, was classified as a five-state gene, and its
highest expression level (‘‘high’’ state) was in the dopaminergic
nuclei, ventral tegmental area (VTA), and SN.
We also found that some well-known GPCRs (G proteincoupled receptors) exhibited a two-state expression pattern in the
BrainStars dataset (Figure 2D). For example, 5-hydroxytryptamine(serotonin) receptor 1A (Htr1a) exhibited a two-state expression
pattern with highest expression in the CA1 [31], Somatostatin receptor
2 (Sstr2) in the habenular nucleus (Hb) [32], Duffy blood group,
chemokine receptor (Darc) in the cerebellar cortex vermis (Cb vermis)
and cerebellar cortex lobe (Cb lobe) [33], and Chemokine (C-X3-C)
receptor 1 (Cx3cr1) in the GP, supraoptic nucleus (SO) and SN [34].
In addition, we found further interesting examples; G protein-coupled
receptor 151 (Gpr151) exhibited a two-state expression pattern withthe highest expression in the Hb (Figure 2D), which is consistent
with the previous report that the expression of Gpr151 is localized
in the Hb of adult mouse brain [35]. Gpr151 shows 25–26%
identity and 41–43% similarity at the amino-acid level with thegalanin-receptor subfamily, and is inferred to respond to Galanin.
Since Galanin is related to pain [36] and Hb is also a nucleus
related to pain, Gpr151 might have a pain-related function.
Interestingly, opioid receptor, mu 1 (Oprm1), the opioid receptor
related to analgesic effects of morphine, also exhibited a two-state
expression pattern with the highest expression in the Hb(Figure 2D). Another example is the G protein-coupled receptor 126(Gpr126) showing a two-state expression pattern with highexpression in the median eminence (ME) and suprachiasmatic
nucleus (SCN) (Figure 2D). ME and SCN are centers for thephotoperiodic calendar and circadian clock, respectively. Although
Gpr126 was recently reported to drive the differentiation ofpromyelinating Schwann cells in the peripheral nervous system(PNS) probably through elevating cAMP levels [37], the functionof Gpr126 in the CNS has not yet been clarified. One possiblefunction of Gpr126 in the CNS might be related to thephotoperiodic calendar or circadian clock through the elevationof cAMP levels. The G protein-coupled receptor 34 (Gpr34) was also
highly expressed in the GP and SN. Although the function of
Gpr34 in the brain has not been clarified yet, it is known that SN
and GP have a shared function in working as the output nuclei ofthe basal ganglia [38], and therefore, Gpr34 might have aphysiological function related to this process. We also foundmulti-state expression patterns of GPCRs that have not been yet
examined so much in the adult mouse brain. For example, we
noted that G protein-coupled receptor 81 (Gpr81) was highly expressedin the SO, G protein-coupled receptor 158 (Gpr158) in the caudate
putamen lateral (CPu lateral), G protein-coupled receptor 174 (Gpr174)
in the GP, neuropeptide Y receptor Y6 (Npy6r) in the SCN, andsphingosine-1-phosphate receptor 3 (S1pr3) and cysteinyl leukotriene receptor
1 (Cysltr1) in the ME (Figure 2D). We also found Leucine rich repeat
containing 16A (Lrrc16a) expressed at high levels in the olfactory bulb
anterior (OB anterior) and posterior (OB posterior), G proteincoupled receptor 4 (Gpr4) in the mediodorsal thalamic nucleus (MD),medial geniculate nucleus (MG) and Hb, and G protein-coupled
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receptor 124 (Gpr124) in the OB anterior, OB posterior and SO(Figure 2D).
Region-specific functions of multi-state genes were also evident
at the level of individual gene categories (Figure 2B, and see also
lists in BrainStars database, http://brainstars.org/). For example,
33 out of the 48 nuclear receptor genes in the DNA microarray
were significantly overrepresented among the multi-state genes(Fisher’s exact test, P = 0.013), supporting previous findings thatthese genes are differentially expressed in regions of the adultmouse brain [39,40]. In addition, 136 of 239 homeobox genes,
which control developmental processes in the embryo [41], werealso significantly overrepresented among the multi-state genes(Fisher’s exact test, P = 5.061024), supporting previous and recent
findings that suggest they play a role in the adult body [42,43] andbrain [11]. Finally, members of the cell adhesion and extracellularmatrix gene categories were also overrepresented among themulti-state genes (Fisher’s exact test, P = 5.061026, andP = 4.161025, respectively).

Regional marker genes
The data of multi-state expression patterns among CNS regionsin the BrainStars dataset can be used to identify candidate genes
whose expression levels can ‘‘mark’’ a specific CNS region. To findsuch candidate genes, we defined ‘‘marker’’ genes as the subclassof multi-state genes whose highest (or lowest) state of expression
occurs in only a single CNS region. We found 2,573 (high) and
381 (low) probe sets (1,889 and 323 genes, respectively) for suchmarker genes (Figure 3A and see also lists in BrainStars database,
http://brainstars.org/). For example, the highest expression of amulti-state gene Gpr151 can ‘‘mark’’ Hb in the adult mouse brain(Figure 3B). We performed ISH with several of these marker
gene candidates, and the results validated their regional specificity(Figure 3B). We noted that the marker gene candidates included
genes related to a specialized function of each region. For
example, choline acetyltransferase (Chat), a rate-limiting enzyme foracetylcholine synthesis, was a marker gene candidate for the Hb, acholinergic basal forebrain complex. We also noted that amongmarker gene candidates expressed by a certain region, we
sometimes identified gene pairs that constituted a transcriptional
regulator and its target gene. For example, nuclear receptor subfamily
0, group B, member 1 (Nr0b1) and nuclear receptor subfamily 5, group A,
member 1 (Nr5a1), marker gene candidates for the subparaventricular zone dorsal (SPa dorsal) region, are known to be coexpressed [44], and Nr0b1 regulates Nr5a1 [45].

Internal control gene candidates in CNS
In addition to expression differences among the CNS regions
discussed above, we also focused on a final class of genes which
encompassed those that did not exhibit multi-state expression
patterns in CNS regions. We called these ‘‘one-state’’ genes,
because they were expressed unimodally across the CNS regions,and roughly followed a log-normal distribution (Figure 2A and
2C). Some one-state genes exhibited stable expression patterns,
characterized by a log-normal distribution with small variance,
whereas others exhibited a more variable expression, showing a
log-normal distribution with larger variance (Figure S3A). By
using a ‘‘variability score’’ determined from the expression data
(Materials and Methods and Figure S3B–D), we identified
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Figure 2. Multi-state genes. (A) Histogram giving the number of different states observed for the 45,037 non-control probe sets. (B) CNS regionsthat tended to be selected repeatedly as having ‘‘up’’ states of multi-state genes in various gene categories. Rows represent gene categories, andcolumns represent CNS regions. Asterisks indicate that the number of genes was significantly enriched in the designated CNS region (Bonferroni
corrected P-value ,0.05). (C) Examples of one-state, two-state, three-state, and four-state genes. Upper graphs show the expression values in 48 CNS
regions, and lower graphs are histograms of the expression values and fitted Gaussian mixture models. The order of CNS regions in the expression
graphs is the same as shown in Figure 2B. States are distinguished by color. (D) Examples of GPCR genes with multi-state spatial expression
patterns. The order of CNS regions in the expression graph is the same as shown in Figure 2B.
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1,055 ‘‘stable’’ one-state genes (variability score less than 21.0)
and 2,362 ‘‘variable’’ one-state genes (variability score more than1.0) in the adult mouse CNS (Figure 4A, and see also lists in
BrainStars database, http://brainstars.org/). We also confirmed
the expression patterns of some of the stable and variable one-state
genes by q-PCR (Figure S3E–L).
We observed that the stable and variable expression patterns of
the one-state genes seemed to correlate with the subcellular
localizations, molecular functions, and biological processes of theirproducts (Text S1 and Figure S3M–T). Although eachfunctional category contained some members with stable orvariable expression tendencies, remarkably stable one-state genes
were found in most of the categories, including the metabolic
process proteins, structural proteins, and transcription factors(Figure 4B). These genes could be novel candidates for internal
controls in experiments using various methodologies, including qPCR and ISH. They may prove to be more appropriate controls
than the commonly used glyceraldehyde-3-phosphate dehydrogenase(Gapdh), actin, beta (Actb), or TATA box binding protein (Tbp), for someexperiments (Figure 4B).

coming into the SCN, the circadian-clock center [47]. This implies
that the SCN receives a wide variety of environmental or internal
information from distinct CNS regions, allowing it to keep proper
circadian timing. This finding is consistent with a previous reportshowing that many NH/NT pathways are active in the SCN [48].
We next analyzed the NH/NT ligand-receptor expressions
within given CNS regions to infer their intrinsic connections. To
identify such intraconnections, we retrieved pairs of ligand andreceptor genes that had at least one common ‘‘high’’- or ‘‘up’’-stateregion (see lists in BrainStars database, http://brainstars.org/), andranked them according to the total number of ligand-receptor
matched states. One possible signaling pathway of intrinsic ligandreceptor interactions was the vasoactive intestinal peptide (VIP)signaling pathway (Figure 5C; the ligand gene was Vip and thereceptor gene was vasoactive intestinal peptide receptor 2 [Vipr2]). These
two genes were highly expressed in the SCN, supporting previous
findings that Vip and Vipr2 contribute to the synchronization of clock
cells within the SCN [49,50,51]. A second example of an intrinsic
pathway was the thyroid-stimulating hormone (TSH) signaling
pathway (Figure 5D; ligand genes were glycoprotein hormones, alphasubunit [Cga] and thyroid stimulating hormone, beta subunit [Tshb], and thereceptor gene was thyroid stimulating hormone receptor [Tshr]). These
three genes were highly expressed in the ME, a possible center forthe photoperiodic calendar, supporting previous findings that the
TSH signaling pathway is involved in photoperiodism [52,53] andthat that Tshr itself is required to maintain the high expression of
Tshb in the ME [53]. A third example was the acetylcholine
signaling pathway (ligand gene was Chat and receptor genes werecholinergic receptor, nicotinic, alpha polypeptide 3 [Chrna3] and cholinergic
receptor, nicotinic, beta polypeptide 4 [Chrnb4]); these three genes werehighly expressed in the Hb, one of the cholinergic nuclei(Figure 5E). A fourth example was the hepatocyte growth factor(HGF) signaling pathway (Figure 5F; ligand gene was Hgf, receptor
gene was met proto-oncogene [Met]). These two genes wereexpressed in the amygdala posterior (A posterior). Hgf and Met are
expressed in the brain [54], and their signaling mediates multiple
neurodevelopmental and neurophysiological processes. However,
little is known about the function of HGF in the amygdala. HGF
infusion into the cerebral lateral ventricles influences anxiety in rats[55]. Because the amygdala has an important role in fear andanxiety [56], the HGF signaling pathway in the amygdala may be
important for emotion. We also analyzed NH/NT ligand-receptor
expressions among different CNS regions to infer extrinsic
connections. To identify such interconnections, we retrieved pairsof ligand and receptor genes that had ‘‘up’’ states in different CNS
regions. One example of a possible extrinsic ligand-receptor
interaction was the Gastrin signaling pathway (Figure 5G; theligand gene was gastrin [Gast] and the receptor gene was cholecystokinin
B receptor [Cckbr]). Gast was expressed in the medial preoptic area
(MPA), and Cckbr was mainly expressed in the retrosplenial cortex(RS), Cx motor, Cx cingulate, piriform cortex (Pir), and A posterior.
The amygdala plays a key role in fear and anxiety [56], asmentioned above, and Cckbr knock-out mice are less anxious thannormal mice [57], implying a possible role of Gastrin signaling
between the MPA and the amygdala.

Inferred connections among CNS regions
As one application of the multi-state expression analysis among
CNS regions, we focused on genes related to ligands and receptorsof neurohormones (NHs) and neurotransmitters (NTs). In the
CNS, various NHs and NTs are secreted from neurons to convey
information among distinct regions [46]. Therefore, expression
data (especially multi-state expression patterns) for NH and NT(NH/NT) genes may be useful for investigating interconnections
among CNS regions and intraconnections within the same CNS
region.
To analyze the expression patterns of multi-state NH/NT
genes, we first made a list that included the multi-state genes forthe ligands themselves and those for enzymes that were ratelimiting in the biosynthesis of these ligands. Here we termed bothof these categories as ‘‘ligand’’ genes. We also included the genes
for NH/NT receptor proteins (i.e., ‘‘receptor’’ genes). Beginningwith the multi-state NH/NT genes, we analyzed the ligandreceptor expressions in distinct CNS regions and found 68
neurohormone (NH) and neurotransmitter (NT) signaling pathways out of a total of 23,864 ligand-receptor interactions(including 519 intrinsic ligand-receptor interactions within thesame CNS region) (see lists in BrainStars database, http://
brainstars.org/). We counted the number of NH/NT ligandreceptor expressions for each CNS region pair, and drew a density
plot of these counts (Figure 5A). In this density plot, we foundstrong connections between the hypothalamic and olfactory bulb
regions. We have represented these findings in a network graph,
which illustrates the presence of more than 17 ligand-receptor
pairs (the 0.05 quantile of the distribution of all combinations ofregions, and also with P,0.01 in a binomial test with Bonferroni
correction) with arrows drawn between the CNS regions
expressing ligand genes and those expressing their cognate
receptors (Figure 5B). This analysis confirmed the findings fromthe density plot. Note that the network graph shows many arrows
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Figure 3. Regional marker gene candidates. (A) Heat maps of the expressions of marker gene candidates. Genes expressed at higher (left-lower)
or lower (right-lower) levels in single regions are shown. The columns of the heat maps represent CNS regions whose order is shown at the left top of
this panel. (B) GeneChip expression profiles (upper) of six marker gene candidates: Dsp (highly expressed in the DG), Il1r1 (DG), Gpr151 (Hb), Chrnb4(Hb), Hcrt (DM), and Npvf (DM) were also confirmed to be expressed in the corresponding regions by in situ hybridization (lower). Inferred states ofmarker gene candidates are distinguished by color in the upper charts. The order of CNS regions in the expression graphs is the same as in Figure 2B.
doi:10.1371/journal.pone.0023228.g003

datasets, we first evaluated the global correlation between the
BrainStars expression dataset and the other resources (BioGPS,
Teragenomics, and ABA). The Pearson’s correlation coefficient
between the BrainStars dataset and the BioGPS dataset was 0.88
(Figure 6A top-left), and the correlation between the BrainStars
dataset and the Teragenomics dataset was 0.77 (Figure 6A top-

Comparison of the BrainStars with other resources
Although the BrainStars dataset is intended to be a valuable
resource in itself, it is probably most useful when compared andcombined with other available datasets that show gene expression
in the adult mouse brain, such as BioGPS [58], Teragenomics[11], and the Allen Brain Atlas (ABA) [9]. To compare these
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Figure 4. Internal control genes. (A) One-state genes with the 50 most stable (left panel) and variable (right panel) expression patterns. Each box
shows the 0.25- to 0.75-quantiles of expression levels in 48 CNS regions for each probe set, and the error bars show the range of expression for all 48
regions. (B) Expression graphs for nine stable one-state genes identified in this study and three well-known internal control genes (Gapdh, Actb, andTbp, right). The order of CNS regions in the expression graphs is the same as in Figure 2B. Top row, genes for metabolic process proteins. Middle
row, genes for structural proteins. Bottom row, genes for transcription factors, expressed at high (left), intermediate (middle), and low (right) levels.
doi:10.1371/journal.pone.0023228.g004

right). The correlation between the BrainStars dataset and the
ABA dataset (‘‘expression energy,’’ see Lau et al [59] for its
definition) was 0.45 (Table S2; Figure 6A bottom-left). These
results were similar to a published comparison among the GNF
SymAtlas (BioGPS), Teragnomics, and ABA datasets [21], inwhich the Pearson’s correlations between the GNF SymAtlas(BioGPS) and Teragnomics datasets were 0.71–0.73, and between
ABA and each of the other datasets were 0.39–0.52. These results
suggest that the correlation level between the BrainStars dataset
and the other datasets is acceptable, even though our sampling
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areas were much smaller than those used for the GNF SymAtlas(BioGPS) and Teragnomics datasets. We also found highvariability in the correlations between our expression dataset
and the ABA dataset (0.32–0.56) among the sampled regions(Table S2). The variability in the correlations between the
BrainStars and ABA datasets depended to some extent on thecomplexity of the sampled region: the hippocampus and cortex,
which are large and homogeneous, showed higher correlations,and the hypothalamic regions, which are smaller and more
complex, showed lower ones.
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Quantitative Expression in the Mouse Brain Regions

Figure 5. Inferred connections among CNS regions. (A) Pairs of CNS regions that tended to express the ligand gene for a neurohormone (NH)or neurotransmitter (NT) in one region and its cognate receptor gene in the other. The color of each tile represents the number of ligand-receptor
pairs that had ‘‘up’’ states in the corresponding pair of CNS regions. (B) Graphical representation of the putative connections among CNS regions.
Arrows originate in the ligand-expressing region and point to the region expressing the cognate NH/NT receptors, when .17 (0.05 quantile of the
distribution of all combinations of regions) ligand–receptor pairs were expressed in the two regions. (C–F) Examples of inferred intrinsic ligandreceptor connections. (G) Examples of inferred extrinsic connections. The order of CNS regions in the expression graphs is as in Figure 2B.
doi:10.1371/journal.pone.0023228.g005

As the first set of analyses in the BrainStars project, we sampled
51 regions with distinct functions in the CNS of the adult mouse.
We intended to include as many nuclei, loci, ganglia, andsubstantia as possible in the telencephalon, thalamus, hypothalamus, mesencephalon, and metencephalon. However, in this first
analysis, we did not include some prominent brain regions, such as
the SVZ (sub-ventricular zone; neurogenesis), LC (locus coeruleus;
noradrenergic), raphe nuclei (serotonergic), and TMN (tuberomammillary nucleus, which is histaminergic), which will besampled, analyzed, and reported in a future paper.
In collecting RNA of CNS regions, we used cylindrical punchsamples, 0.5-mm thick and 0.5 mm in diameter, from 51 distinct
CNS regions. This is a natural extension of our previous study on
DNA-microarray-based expression profiling of SCN [60] to aquantitative and spatial genome-wide expression study of distinct
functional regions of the adult mouse brain. To ensure theaccuracy of our spatial expression dataset, we avoided potential
artifacts caused by the circadian regulation of gene expression,
which affects 2–10% of all genes [61], by taking samples of thesesmall brain regions every 4 hours, starting at ZT0 (Zeitgaber time
0; the time of lights on), for 24 hours (6 time-point samples foreach region). This strategy allowed us to avoid artifacts caused bytemporal differences in gene expression [60], and therefore, tofocus on spatial differences. We chose this strategy because we
wanted to concentrate on spatial differences among the expression
profiles of small brain regions, and this facilitates the identificationof candidate genes. Once candidate genes are selected, we cancollect time-course samples for these genes; this is one of our future
works.
In our analysis of the expression data, we identified ‘‘multistate’’ expression patterns in CNS regions. In the multi-state genes,
we can find well-known CNS-active genes (e.g. Camk2a, Th) and
GPCRs (e.g. Htr1a, Sstr2), and this result shows feasibility of ouranalysis method with using variational Bayesian inference.
Furthermore, we identified many examples of genes that have
not yet been examined so much in the adult mouse brain (e.g.
Gpr81, Gpr158). These genes might have some physiological
functions in the corresponding CNS regions, and further studies ofsuch genes could be expected. We also performed a statisticalanalysis on region-specific functions of multi-state genes at thelevel of individual gene categories, and showed that several gene
categories (e.g. nuclear receptor and cell adhesion) were overrepresented. Although we need to be careful about theobservations in which mRNA levels do not necessarily correlatewith protein expression levels [62,63], this statistical analysis
indicate that digitalized expression patterns of multi-state genes
imply some functional insights into the mouse adult CNS regions.
In our analysis of ‘‘multi-state’’ genes, we simply fit each
expression profile to Gaussian mixture models with 1 to 6 normal
distributions and chose the one with the best fit based on thevariational Bayesian inference. There were several points wenoticed. First, the analysis result contains genes with many states (5states of Tyrosine hydroxylase, for example), and a few only show
small changes among the states. However, such small changes
among states are sometimes difficult to capture with current
microarray technologies and the limited number of samples thatWe next evaluated our marker gene candidates with the ABA
dataset at the level of individual genes (Table S3; Figure 6B and
6C). Of the 120 marker gene candidates determined in the
BrainStar dataset (see Materials and Methods), 95 could be
associated with corresponding entries in the ABA dataset (FigureS4). Of the 95 marker gene candidates associated with the ABA
dataset, 31 were confirmed in the same regions (‘‘matched’’), 24
were discrepant (‘‘mismatched’’), and 40 could not be confirmed
due to a lack of good-quality expression data in the ABA dataset(‘‘unconfirmable’’). Examples of marker gene candidates that werematched (forkhead box B1 [Foxb1]) and mismatched (paternallyexpressed 10 [Peg10], and transcription factor AP-2, delta [Tcfap2d])
are shown in Figure 6D. Peg10, which was expressed only in the
DM in the BrainStars dataset, was also expressed in the PVA(paraventricular thalamic nucleus, anterior part) and the Gi(gigantocellular reticular nucleus) in the ABA dataset. Tcfap2d
could not be detected only in the ABA dataset, because of nonspecific signals in the ISH images.
Among the 24 ‘‘mismatched’’ genes in the comparison with the
ABA dataset, we also found several that were expressed inadditional regions in the ABA dataset (e.g., our Hb marker genecandidate Gpr151 was also expressed in the PVA, and our MD
marker gene candidate gastrulation brain homeobox 2 [Gbx2] was also
expressed in the PVA). Furthermore, the expressions of theremaining genes were detected in different regions from ourdataset (e.g., our A posterior marker gene candidate Met was
expressed in the lateral septal nucleus [LS] in the ABA dataset).
Among the genes that were ‘‘unconfirmable’’ by comparison with
the ABA dataset, we found that our ME marker gene candidates
were not detected in the ABA dataset, which might have beencaused by a disproportionate loss of the ME during slice dissection.
We also found cases in which signals that were significant in the
BrainStar dataset were not detected in the ABA dataset, owing to
non-specific signals (e.g. for our LS marker gene candidate PR
domain containing 16 [Prdm16], and inferior colliculus [IC] marker
gene candidate Tcfap2d) or to the narrow dynamic range of
expression signals detected in the ABA dataset (e.g., for our SCN
marker gene candidate myocilin [Myoc] and ventromedial hypothalamic nucleus [VMH] marker gene candidate G protein-coupled
receptor 103 [Gpr103]; see also Text S1 and Figure S5).

Discussion
In this study, we constructed a quantitative expression profile(expression map) of the adult mouse brain at the nucleus-level
resolution. Our resource is especially useful for functional analyses
focusing on specific functional brain regions. Marker genecandidates can be used to highlight CNS regions of interest.
Multi-state genes can provide information for screening genes
whose expressions in targeted regions are different from ones inother regions. One-state stable genes are novel internal control
gene candidates for studies on the mouse brains. Although thesekinds of data might be obtained with ISH resources (e.g. ABA),
such data would be indirect and would require post-processing of
ISH images, possibly introducing artifacts by quantification, imagealignment, etc.
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Figure 6. Comparison of BrainStars dataset with other resources. (A) Scatter plots comparing the BrainStars expression datasets with the
BioGPS (top-left), Teragenomics (top-right), and Allen Brain Atlas (ABA) expression level (bottom-left) datasets. All expression values were log2transformed. (B) Summary of the comparison of our marker gene candidates with ABA. Our 120 marker gene candidates with corresponding entries
in the target dataset were classified as ‘‘matched’’, ‘‘mismatched’’, ‘‘unconfirmable’’, or ‘‘N/A (not available)’’. (C) Heatmaps of 120 marker genecandidates in the BrainStars, and ABA datasets. (D) The BrainStars, and ABA expression data for three marker gene candidates: Foxb1, Peg10, andTcfap2d, show, respectively, agreement in datasets, or a lack of agreement between the datasets. For each gene, the BrainStars expression values
were mapped onto slice images and are also represented as bar graphs, and the ABA in situ images are shown. The order of CNS regions in theexpression graphs is as in Figure 6C. PVA: paraventricular thalamic nucleus, anterior part. Gi: gigantocellular reticular nucleus.
doi:10.1371/journal.pone.0023228.g006

we used. Therefore, we classified the multiple states identified
through variational Bayesian inference into ‘‘on/off’’ or ‘‘up/
down’’ states, and used these re-classified states in our further
analyses. Second, there were ‘‘one-state’’ genes that look like‘‘multi-state’’ genes, and there were ‘‘multi-state’’ genes that look
like ‘‘one-state’’ genes. In some cases, there is not an obvious
difference between some of the one-state and multi-state genes.
These are because the fitness of Gaussian mixture models with one
and multiple normal distributions were similar. We used thevariational Bayesian inference because this method can select amore appropriate Gaussian mixture model than other methods
such as EM (Expectation Maximization) algorithm [64]. We thus
believe that the misclassification between ‘‘one-state’’ and ‘‘multistate’’ genes were lower than other methods.
For ‘‘one-state’’ genes, we should be careful when interpreting
their data because these ‘‘one-state’’ expressions may be caused bytechnical factors, such as the dynamic range of the microarrayprobe, alternative splicing not detectable by the probe, outdated
probe design, etc. Moreover, since our sample set does not cover
all CNS regions, developmental stages, and conditions such asenvironmental stress and diet, we cannot observe the change in aparticular set of circumstances. However, we believe that we canuse the ‘‘one-state’’ gene set in various studies, such as for
identifying candidate positive control genes.
We also identified marker gene candidates for various CNS
regions from the multi-state genes, and validated some of them byin situ. These genes can be used to highlight specific regions in theadult mouse brain for a range of further studies. Furthermore, we
found that the candidate marker gene set included transcriptional
regulators and its target gene pairs. Thus, it is possible that certain
information about transcriptional regulation can also be retrieved
from the marker gene candidates. We noted that a set of marker
gene candidates in our genome-wide and nucleus-level expression
data include region-specific genes that were not detected in similar
resources. For example, the Allen Brain Atlas has a genome-wide
ISH data and provides ‘‘fine structure’’ dataset (equivalent to‘‘marker gene’’ candidates in this study) on their web site (http://
mouse.brain-map.org/). In the ‘‘fine structure’’ dataset, Gpr151(marker gene candidate in Hb) and Vip (marker gene candidate inSCN) cannot be detected, although their regional specificities
seemed to be found in the corresponding ABA ISH images. Thismay be caused by difficulties in quantitative analysis with ISH data
or by their data quality for positioning brain regions, implying theadvantage of BrainStars dataset in the quantitativeness.
As one application of the multi-state expression analysis among
CNS regions, we searched for genes related to ligands and receptorsof neurohormones (NHs) and neurotransmitters (NTs). We found
68 neurohormone (NH) and neurotransmitter (NT) signaling
pathways in a total of 23,864 ligand-receptor interactions, and wedrew an inferred ligand-receptor interaction map of the CNS
regions. Although these inferred interactions represent a ‘‘possibility
map’’ (i.e. there is a possibility of connection between two regions
because they selectively express the appropriate ligands andreceptors), these ligand-receptor interaction candidates will provide
interesting hypotheses for future studies in neuroscience. We expect
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various neuroscience studies would be advanced by the close
investigation of such candidate sets by experts in the field.
The results of the comparison among our BrainStars dataset
and other resources indicate the advantages of our strategy forconstructing the BrainStars database, which improved the
dynamic range of detection (compared with the ABA). Althoughsome discrepancies in the ‘‘mismatched’’ results between our
database and the ABA may be caused by the limited number andsize of the samples used for the BrainStars dataset, this limitation
can be compensated for by comparing and combining datasets,
preventing this from becoming a critical deficit of the BrainStars
dataset. These comparisons indicate that no single method devised
to date can provide complete genome-wide expression data for the
adult mouse brain that has 1) a large dynamic range, 2) highspatial resolution, and 3) coverage of the whole brain. Because of
the limitations of each method, we believe that the complementaryand cooperative usage of these genome-wide expression datasets isthe most useful platform for further investigation of the structureand function of the adult mouse brain. Therefore, to make the bestuse of these datasets, we constructed an integrated database andviewer for them (BrainStars viewer). The BrainStars viewer ispublicly available at http://brainstars.org/.
The elucidation of the regulatory mechanisms of the mammalian brain is still a challenging goal that requires a variety ofresources, including CNS expression maps. Our new resource
should help accelerate the functional analysis of the mammalianbrain and the elucidation of its regulatory network systems.

Materials and Methods
Ethics Statement
This study was approved by the Animal Care and Use
Committee, Kinki University School of Medicine, and carefully
followed the Guide for the Care and Use of Laboratory Animals,
Kinki University School of Medicine (approved without IDs). Mice
were also carefully kept and handled according to the RIKEN
Regulations for Animal Experiments (AH18-02-18).

Nucleus-level sampling of CNS regions
Balb/c mice (all mice were male) purchased 5 weeks
postpartum, were adapted under a standard 12-h light/dark cycle(LD) for 2 weeks, before samples were obtained under LD orconstant darkness (DD) conditions, every 4 h over 1 day, startingat ZT0. Slices (0.5-mm thick) of mouse brain were cut on a Mouse
Brain Matrix (Neuroscience, Tokyo), frozen, and the specificregions were punched out bilaterally with a microdissecting needle(gauge 0.5 mm) under a stereomicroscope. We sampled 5–25 micefor each CNS region at each time point, and, as a result, samples
from 30–150 mice were collected for each replicate of a single
CNS region. This whole procedure was repeated twice (n = 2) toobtain experimental replicates for every CNS region.

Microarray Analysis
The total RNA was prepared from the pooled samples for eachregion taken at all time points using Trizol reagent (Gibco BRL).
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GO:0015267 [channel activity], GO:0004930 [G-protein coupled
receptor activity], GO:0007155 [cell adhesion], GO:0005198[structural molecule activity], GO:0031012 [extracellular matrix],
GO:0022008 [neurogenesis], respectively), which were found in
the annotation file (Mouse 430 2.0, na27) provided at theAffymetrix website. Genes assigned to GO:0003735 [structural
constituent of ribosome] were excluded from the structuralproteins. We chose 293 homeobox genes and 49 nuclear receptors,
respectively, using the following references [40,69]. NH/NT
ligand/receptor genes, SLC transporter genes, and forkhead genes
were manually retrieved. For the NH/NTs, the ligand-coding
genes or the rate-determining enzymes for their biosynthesis wereselected as ‘‘ligand genes,’’ and their receptors were selected as‘‘receptor genes’’ for ,140 neurohormones. SLC transporter
genes and forkhead genes were also manually retrieved from the
NCBI Entrez Gene database. All the gene category lists can befound in the BrainStars database (http://brainstars.org/).

The cDNA synthesis and cRNA labeling reactions were performed
as previously described [65]. Affymetrix high-density oligonucleotide arrays for Mus musculus (GeneChip Mouse Genome 430 2.0)
were hybridized, stained, and washed according to the Expression
Analysis Technical Manual (Affymetrix). The expression values
were summarized by the RMA method [66]. The resulting
expression values were used in all the subsequent analyses. All data
is MIAME compliant and the GEO accession number for the
microarray data deposited and reported in this paper is
GSE16496.

Quantitative PCR
Quantitative PCR was performed with the ABI Prism 7900 and
SYBR Green Reagents (Applied Biosystems). The cDNAs weresynthesized from 0.25 mg of total RNA using Superscript II reverse
transcriptase (Invitrogen). Samples contained 16 SYBR Green
Master Mix, 0.8 mM primers, and 1/40 synthesized cDNA in a
10 ml volume. The PCR conditions were as follows: 10 min at
95uC, then 45 cycles of 15 s at 94uC, 1 m at 59uC. The absolute
cDNA abundance was calculated using a standard curve obtained
from murine genomic DNAs. We used Tbp as the internal control.

Identification of multi-state genes
Genes with multi-state expression patterns were identified witha variational Bayesian inference to fit a Gaussian mixture model[64]. We used the Gaussian mixture model with six components,
in which each component has three parameters (mean, variance,
mixture probability) with five hyper-parameters, and we assumed
that components of Gaussian mixture have different variances.
The detailed procedure for determining prior hyper-parametersand fitting Gaussian mixture models is described in the Text S1and Figure S2. We used log2-transformed expression values forthe 45,037 non-control probe sets in the 48 CNS regions that did
not include the retina, pituitary, or pineal. After the fitting
procedure, the CNS regions were grouped according to predictedstates. For example, CNS regions were classified into three groups
for three-state genes because there were three states (‘‘high,’’‘‘low,’’ and ‘‘middle’’).

In situ hybridization (ISH)
Mice were deeply anesthetized with ether and intracardiallyperfused with 10 ml saline and 20 ml of a fixative containing 4%paraformaldehyde in 0.1 M phosphate buffer (PB), pH 7.4. Mousebrain samples were postfixed in the same fixative for 24 h at 4uC,
soaked in PB containing 20% sucrose for 48 h, and finally stored
frozen at 270uC. The ISH method was described in detailpreviously [67]. Serial coronal and sagittal sections (40-mm thick)of the mouse brain were made using a cryostat. Fragments ofcDNA were obtained by PCR, and the products were thensubcloned into the PGEM-T easy vector (Promega). Radiolabeledprobes were generated using 35S-UTP (PerkinElmer) via astandard protocol for cRNA synthesis. The primers used in
the ISH were ctcacagtgatgctgctaagc (Gpr151, forward),
ccctctgtctcttggccttc (Gpr151, reverse), ctacccagcgtgttatgggg (Chrnb4,
forward), catgggagtagatctctgcc (Chrnb4, reverse), cgaggtctggagactactac (Dsp, forward), agcagaaccctcaacctctc (Dsp, reverse), gggagaaatgtcgctggat (Il1r1, forward), cataagggcacacaagacttcc (Il1r1,
reverse), ctgagaggaatcccaaaagg (Rfrp [Npvf], forward), gctttccaccaggactctga (Rfrp [Npvf], reverse), ctgctgctgctgctactgct (Hcrt,
forward), and gacgattctctgttggtgtgac (Hcrt, reverse).

Statistical analysis on multi-state genes

For the hierarchical clustering of the 51 CNS regions and 102
samples, the correlation dissimilarity, i.e., 1-(Pearson’s correlation
coefficient) and Euclidean distance were used as distancefunctions, and a complete linkage method was used to build theclusters. For the statistical analysis of the separation of 51 CNS
regions, pvclust clustering [68] was also performed with the four
distance metrics indicated above. For every hierarchical clustering
analysis, the natural expression values of 45,037 non-control probe
sets in the 51 CNS regions or 102 samples were used.

To test whether a multi-state gene with an ‘‘up’’ state wassignificantly enriched in a particular CNS region, we performed
one-sided binomial tests to calculate its P-value based on theprobability where a multi-state gene has an ‘‘up’’ state at a single
CNS region: the total number of regions with ‘‘up’’ states of allunique multi-state genes/(the number of unique multi-state
genes6the number of CNS regions), i.e., 117,097/(8,159648).
After the P-values were calculated, they were subjected to theBonferroni correction.
To determine what kinds of genes were enriched among those
repeatedly selected as having ‘‘up’’ states in pairs of regions, we
performed the hyperGTest in the Bioconductor packages [24],which assigns P-values to show that a gene category (geneontology, or GO term) is enriched in those genes repeatedly
selected as having an ‘‘up’’ state in pairs of regions against all
multi-state genes. After the P-values were assigned, false discovery
rates (FDRs) were calculated within the GO molecular function,
biological process, and cellular component classes for each pair ofregions, and the GO terms whose FDR was less than or equal to0.01 were retrieved.

Gene categories

Identification of regional marker gene candidates

Genes for transcription factors (TF), channels, GPCRs, celladhesion proteins, structural proteins, extracellular matrix proteins, and neurogenesis-associated proteins were retrieved using
the corresponding gene ontology term assignments (GO:0003700[transcription factor activity]/GO:0016563 [transcription activator activity]/GO:0016564 [transcription repressor activity],

Multi-state genes whose expression levels were higher or lower
in a single CNS region (marker gene candidates) than in the others
were chosen as marker gene candidates. A multi-state gene wasselected as a marker gene candidate of a designated CNS region if
its probe set had only the single CNS region for the highest (orlowest) state of its expression.

Hierarchical clustering of CNS regions with theirexpression profiles

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Quantitative Expression in the Mouse Brain Regions{CA1, CA2/CA3, DG}, {OB anterior, OB posterior}, {spinal
cord anterior, spinal cord posterior}, {M, ME, SCN, MPA, SO,
Pa, SPa ventral, SPa dorsal, DM, VMH, Arc, LH}, {Cb vermis,
Cb lobe, Cb nucleus}, and {CPu lateral, CPu medial} in the
BrainStars dataset were compared with the cerebral cortex,
amygdala, hippocampus, olfactory bulb, spinal cord, hypothalamus, cerebellum, and dorsal striatum in the BioGPS dataset,
respectively. To compare globally the BrainStars and Teragenomics datasets, the means of {A anterior, A posterior}, {CA1},{CA2/CA3}, {Cb vermis, Cb lobe, Cb nucleus}, {Cx motor, Cx
cingulate}, {DG}, {CA1, CA2 CA3, DG, ventral S}, {M, ME,
SCN, MPA, SO, Pa, SPa ventral, SPa dorsal, DM, VMH, Arc,
LH}, {IC}, {PAG, SC, IC, VTA, SN, Tg, MD, VA/VL, VPM/
VPL, LG, MG, Hb}, {Cx motor}, {OB anterior, OB posterior},{PAG}, {Pituitary}, {Pn, MVe}, {Retina}, {spinal cord anterior,spinal cord posterior}, {CPu lateral, CPu medial}, {SC} in the
BrainStars dataset were compared with the amygdala, CA1, CA3,
cerebellum, cerebral cortex, dentate gyrus, hippocampal formation, hypothalamus, inferior colliculus, ‘‘midbrain and diencephalon, no hypothalamus’’, motor cortex, olfactory bulb, periaqueductal gray, pituitary, pons, retina, spinal cord, striatum, superior
colliculus in the Teragenomics dataset, respectively.
To perform a more detailed comparison of the BrainStars
dataset with the ABA dataset, we first selected up to three probe
sets of BrainStars marker gene candidates that showed the largest
expression changes within the CNS for each of 46 brain regions(without spinal cord anterior, spinal cord posterior, retina,
pituitary and pineal), choosing 120 unique marker gene candidates(Table S3). We then manually compared these marker genecandidates against the ABA dataset, and classified them as‘‘matched’’ when their regional expression was in the same
location in the Allen Brain Atlas dataset, ‘‘mismatched,’’ when thegenes were confirmed as not being marker genes in thecorresponding brain region, or ‘‘unconfirmable’’ when the genes
could not be confirmed due to a lack of good comparative data.

Expression variability analysis
We used 32,523 one-state probe sets. To assign variability scores tothe chosen probe sets, we first filtered out probe sets that were not‘‘present’’ in any CNS region. We regarded a probe set as ‘‘present’’ ina CNS region when both samples for the region were called ‘‘present’’by the Affymetrix MAS 5.0 detection algorithm (Statistical Algorithms
Description Document; http://www.affymetrix.com/support/techni
cal/whitepapers/sadd_whitepaper.pdf). Of the 32,523 probe sets,
13,619 were filtered out. Next, we made a scatter plot of the mean
(X-axis) and standard deviation (Y-axis) of the log2-transformed
expression values for each of the ‘‘present’’ probe sets, and drew acurve showing the running median of the standard deviation (Figure
S3C). Variability scores were calculated by dividing the standard
deviation by its running median, and then applying log2-transformation (Figure S3D).

Inferred connections among CNS regions
To analyze the expression patterns of the NH/NT genes, wefirst made a list that included the genes for the ligands themselves
and those for enzymes that were rate-limiting in the biosynthesis ofthese ligands (‘‘ligand’’ genes). We also included the genes for NH/
NT receptor proteins (i.e., ‘‘receptor’’ genes). The list of NH/NTrelated genes contained 176 that encoded ligands and 270 that
encoded receptors, which were components of 118 NH and NT
pathways: 6 for monoamines and acetylcholine, 4 for amino acids,
95 for peptides, 2 for gases, and 11 for other types of pathways.
Multi-state genes comprised 253 of these NH and NT genes. For
every CNS region pair, we then counted the number of NH/NTs
whose ligand gene had an ‘‘up’’ state in one of the CNS region
pair, and whose receptor gene was ‘‘up’’ state in the other region.
In this analysis, we ignored the strength of expression, i.e.,
differences between the ‘‘middle’’ and ‘‘high’’ (highest-level) states.
We illustrated the presence of more than 17 ligand-receptor
pairs that correspond to the 0.05 quantile of the distribution of all
combinations of regions in Figure 5B. To test the significance of
this cutoff number (i.e. 17) of NH/NTs used for illustrating theinferred network of CNS regions, we performed the bionomial test
to calculate its P-value as follows. The number of unique multistate genes was 8,159, and the total number of regions with ‘‘up’’states of all the unique multi-state genes was 117,097. Thus, theprobability that a CNS region pair was randomly chosen as aligand-receptor pair region was (117,097/(8159648))2. The
number of NT/NHs whose ligand and receptor genes were
multi-state genes was 68. We performed binomial tests with theseparameters to estimate the P-values of the null hypothesis that the
number of NT/NHs (for each of 0–68) was random, and adjusted
the P-values with the Bonferroni correction.

Supporting Information
Figure S1 Sampled adult mouse CNS regions. (A–E)
Hierarchical clustering of brain regions and samples with various
distance metrics. Brain samples were clustered by (A) correlation
dissimilarity, and (B) Euclidean distance. Brain regions wereclustered by (C) Euclidean distance. Brain regions were also
statistically clustered by (D) correlation dissimilarity and (E)Euclidean distance with significance scores (red and green scores).
(F) Scatter plot comparing the experimental replicates of all CNS
regions. X- and Y-axes show the log2-transformed expression
value of each experimental replicates. (G) Correlation coefficients
indicating the reproducibility of the experimental replicates ofeach CNS region.
(PDF)

Comparison of datasets
To evaluate and compare the BrainStars dataset with the Allen
Brain Atlas (ABA) datasets, we retrieved the expression values
from the ABA dataset for brain regions that were sampled for theBrainStars database. We mapped our brain regions to the ABA
dataset, and retrieved the mean expression values (defined as‘‘expression energies’’ [59]), which are available from http://www.
brain-map.org/. If no expression energy for a gene was found in a
specific brain region, that region was excluded from the analysis.
To compare globally the BrainStars dataset with the ABA dataset,
we used our brain regions which excluded the retina, pituitary,pineal, and spinal cord, drew a scatter plot, and calculated
Pearson’s correlation coefficients. For each gene, the single probe
sets (BrainStars) with the largest mean expression values werechosen. To compare globally the BrainStars and BioGPS datasets,
the means of {Cx motor, Cx cingulate}, {A anterior, A posterior},
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Figure S2 Multi-state genes. (A–C) Determination of priordistributions by variational Bayesian inference of Gaussian
mixture. (A) Distribution of the inverse of the median error
variance. The median error variance was calculated as the medianvariance of the duplicated expression values (n = 2) in 48 brain
regions for each probe set. (B) Plot of false-positive rates and falsenegative rates generated by changing the a0 prior hyperparameter. The X- and Y-axes show the false-negative rate ( = 1– sensitivity) and 1 – the false-positive rate ( = specificity),
respectively. Each curve represents a different false discovery rate(FDR) cut-off for the marker gene candidates, which was regarded
as the true set for parameter evaluation. Gray lines showy = x+(constant), which represent equal sums of the false-positive
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Quantitative Expression in the Mouse Brain Regions

fold change, was from 24 to 214 (,103.0-fold) for the BrainStars
project and 22 to 25 (,100.9-fold) for the ABA project. (E–F)
Correlation coefficient showing the reproducibility of experimental
replicates of each CNS region in the BrainStars (E) and Allen
Brain Atlas (F) projects. (G) Myl4 expression at 51 CNS regions inour data. Error bars show standard errors. In the Cx motor andCx cingulate, the Myl4 expressions were greatly changed withsmall standard errors. (H) Number of redundant probes for thesame transcript in each database. (I–J) Maximum correlation
coefficient between redundant probes for the same transcript. (I)Distribution of the maximum correlation coefficients for theBrainStars oligo-probes. (J) Distribution of the maximum correlation coefficients in the Allen Brain Atlas.
(PDF)

and false-negative rates. (C) An example in which two mixture
components overlapped and one state was nested into anotherstate. A histogram of its expression values is shown.
(PDF)
Figure S3 One-state genes. (A) Distribution of the standard

deviation of one-state (blue) and multi-state (red) genes. (B)Distribution of variability scores. The higher and lower variability
scores of genes indicated that their expression levels were variable
and stable, respectively. (C) Scatter plot of the standard deviations
against the means of the log2-transformed expression values. Blue
dots represent single probe sets, and the red curve shows theirrunning median. (D) Scatter plot of the mean of the log2transformed expression values and the variability scores. (E–L)
Confirmation of several stable and variable one-state genes by qPCR. The expression values relative to the Tbp expression are
shown. Stable one-state genes Sgta (F) and Egln2 (H) were alsostable by q-PCR (E and G, respectively), whereas the variable onestate genes Kcnab1 (J) and Nrip3 (L) were also variable by q-PCR (Iand K, respectively). (M–T) Correlation of the variability scorewith the subcellular localization (M–P), molecular function (Q, R),and biological process (S, T) of the gene products. Each graphrepresents the ratios of genes associated with the Gene Ontology
term for 25 subsets of one-state genes, sorted by the rank of theirvariability scores. The false discovery rate (FDR) for theenrichment of gene functions in stable or variable genes are also
shown.
(PDF)

Table S1 Sample information for all 51 central nervous
system (CNS) regions.
(DOC)
Table S2 Pearson’s correlation coefficients of theBrainStars dataset with the Allen Brain Atlas (ABA)
dataset.
(DOC)
Table S3 Comparison of the marker gene candidates in
the BrainStars dataset with the Allen Brain Atlas (ABA)
dataset.
(DOC)
Text S1 Supporting materials and methods, and re-

Figure S4 Comparison of BrainStars dataset with othersults.
(DOC)

resources. Candidates for 120 marker genes from the BrainStars
dataset are shown along with results from the Allen Brain Atlas(ABA) dataset. For each gene, the BrainStars expression values
were mapped onto images of brain slices (upper-left) andrepresented in a bar chart (upper-right), and the ABA expression
values (‘‘expression energies’’) from a coronal cross-section at theexpressing CNS region are shown (lower-left).
(PDF)

Acknowledgments
We thank Yoshiki Sasai, Yoichi Minami, Tetsuya J. Kobayashi, Cantas
Alev, and Yohei Koyama for helpful discussion. We also thank Yuko
Okamura-Oho, Masaomi Nishimura, Satoko Takemoto, and Hideo
Yokota for providing the 3D brain image models. Programs to access the
Allen Brain Atlas datasets were provided by the Allen Institute for Brain
Science. The calculations were partly performed by the RIKEN Super
Combined Cluster (RSCC).

Figure S5 Comparison of reproducibility and quantita-

tiveness between BrainStars and ABA datasets. (A–B)
Scatter plot showing the reproducibility of the experimental
replicates of all the CNS regions in the BrainStars (A) and Allen
Brain Atlas (B) projects. Blue lines indicate 2-fold changes. (C–D)
The proportion of replicated data points showing a difference
within 2-fold in the BrainStars (C) and Allen Brain Atlas (D)projects. The dynamic range, which we defined as the range ofmore than 50% of replicated data points that showed a less-than 2-

Author Contributions
Conceived and designed the experiments: HRU YS. Performed theexperiments: KM MN YS KDU KT. Analyzed the data: TK IN. Wrotethe paper: TK KM IN HRU. Annotated and evaluated the results: KM
CH YS.

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