Spatial patterns of gene expression in the
David M. Lin*†‡, Yee Hwa Yang‡§, Jonathan A. Scolnick*, Lisa J. Brunet*, Heather Marsh†, Vivian Peng*,
Yasushi Okazaki¶, Yoshihide Hayashizaki¶, Terence P. Speed§?, and John Ngai*,**
*Department of Molecular and Cell Biology, Functional Genomics Laboratory, Helen Wills Neuroscience Institute, and§Department of Statistics, University
of California, Berkeley, CA 94720;¶RIKEN Genomic Sciences Center, Genome Exploration Research Group, Yokohama, Kanagawa 230-0045, Japan;?Division
of Genetics and Bioinformatics, The Walter and Eliza Hall Institute, Melbourne, Victoria 3050, Australia; and†Department of Biomedical Sciences, Cornell
University, Ithaca, NY 14853
Communicated by Richard Axel, Columbia University, New York, NY, July 9, 2004 (received for review November 15, 2003)
How olfactory sensory neurons converge on spatially invariant
glomeruli in the olfactory bulb is largely unknown. In one model,
olfactory sensory neurons interact with spatially restricted guid-
ance cues in the bulb that orient and guide them to their target.
Identifying differentially expressed molecules in the olfactory bulb
has been extremely difficult, however, hindering a molecular
analysis of convergence. Here, we describe several such genes that
have been identified in a screen that compiled microarray data to
create a three-dimensional model of gene expression within the
mouse olfactory bulb. The expression patterns of these identified
genes form the basis of a nascent spatial map of differential gene
expression in the bulb.
neurons (OSNs) to specific targets in the olfactory bulb. OSNs
expressing a given odorant receptor are distributed throughout
large zones in the olfactory epithelium but ultimately converge
on common targets in the bulb, which are called glomeruli.
Remarkably, these glomerular positions are spatially invariant
from animal to animal, consistent with the presence of a
hard-wired map of connectivity in the peripheral olfactory
How are OSNs able to identify their glomerular targets? The
stereotypic nature of this innervation suggests that regions of the
bulb, perhaps the glomeruli, are molecularly distinct. These
spatially distributed cues would then function to guide OSNs
expressing complementary receptors. In this model, the collec-
tive expression patterns of these guidance cues would form a
spatial map within the bulb, molecularly distinguishing potential
paths and substrates encountered by OSNs. However, although
it has become increasingly clear that a number of molecules have
been identified that are differentially expressed by OSNs [for
e.g., along the path that OSNs traverse to the bulb (2–4) and by
ensheathing cells (5–7)], efforts to identify spatially restricted
molecules within the bulb itself have been generally unsuccessful
(8, 9), greatly impeding our understanding of how olfactory
axons select their partner glomeruli. Indeed, the most plausible
models of olfactory development assume that spatially restricted
cues exist within the olfactory bulb to guide the sensory axons to
their targets (1, 10). Yet, despite this widely held assumption, it
has been remarkably difficult to identify such guidance and
Does a spatial map of differential gene expression exist within
the bulb? We decided to address this issue directly by employing
a molecular screen using microarrays to reconstruct, in three
dimensions, patterns of gene expression within the mouse ol-
factory bulb. This statistical representation of the bulb was then
scanned for genes with differential expression patterns, which
were validated by RNA in situ hybridization. A number of genes
known to be involved in neural development and pattern for-
mation were shown to be expressed in restricted patterns,
consistent with their possible roles in imparting spatial informa-
tion within the developing olfactory bulb.
n the olfactory system, a major challenge has been to identify
the mechanisms responsible for guiding olfactory sensory
Materials and Methods
RNA Purification and Amplification. Olfactory bulbs were dissected
from postnatal day 0 CD-1 mice, and slices were obtained by
manual dissection, which was done approximately in thirds along
each principal axis. For each aspect [anterior (A), dorsal (D),
lateral (L), medial (M), posterior (P), and ventral (V)], samples
from ?10 mice were pooled and total RNA was isolated by using
the TRIzol reagent. We amplified 3 ?g of RNA once by using
an optimized T7 RNA polymerase amplification protocol
(D.M.L., P. Luu, T. Serafini, and J.N., unpublished data),
incorporating an oligo(dT)-T7 primer (5?-ATCGATTCGA-
ATAGGGAGACCACAT21-3?) to generate cDNA. Amplified
RNA was then obtained in an in vitro transcription reaction (1?
transcription buffer?2 mM each NTP?1 unit of RNAsin?100
units of T7 RNA polymerase), and 5 ?g of amplified RNA was
used per labeling reaction.
Microarrays. We generated microarrays by using the RIKEN 19K
full-length mouse cDNA set (11). Microarrays were hybridized
with Cy3- and Cy5-labeled cDNAs by using protocols essentially
as described by the Microarrays.org public protocols distributor
Normalization and MA Plot. Raw data from each scanned slide
were processed by SPOT (12), with foreground seeds set at a 5 ?
5 pixels square. Within- and between-slide normalization were
performed on all data (13). An MA plot (14) was used to
represent the data, where M ? log2(red intensity?green inten-
sity) and A ? log2?R?G.
Estimation of Contrasts. For each gene, a linear model was used to
estimate each of the 15 contrasts [AL, DM, VP, etc.; for details,
published as supporting information on the PNAS web site]. For
visualization purposes, we estimated the contrast of a single
effect vs. an average of all six [e.g., a ˜ ? a ?1⁄6(a ? p ? d ? v
? m ? l)], in effect, recreating the pooled bulb reference (a ?
p ? d ? v ? m ? l) in silico. Note that this estimate does not
represent the absolute expression ‘‘profile’’ but rather the rela-
tive expression between a portion of the bulb to the pooled
Two-Stage Cluster Analysis. The top 100 genes from each contrast
were taken (a total of 614 unique genes), and hierarchical
clustering of these genes was performed by building a dendro-
Abbreviations: OSN, olfactory sensory neuron; A, anterior; D, dorsal; L, lateral; M, medial;
P, posterior; V, ventral; ptd2, prostaglandin D2 synthase; IGF-2, insulin-like growth factor
2; pcV?2, procollagen V?2.
‡D.M.L. and Y.H.Y. contributed equally to this work.
**To whom correspondence should be addressed. E-mail: email@example.com.
© 2004 by The National Academy of Sciences of the USA
August 24, 2004 ?
vol. 101 ?
Note that in all of our validated cases, expected average differ-
ences in any one aspect of the bulb as compared with the pooled
reference are ?2-fold (Fig. 3), underscoring the ability of our
approach to identify subtle changes in gene expression within the
bulb and distinguish them from background noise. By clustering
genes with similar patterns together, other, weaker patterns that
may potentially be buried within the noise could be identified
and considered for further analysis.
Several of the genes that were uncovered in this screen (jagged,
IGF-2, cadherin-11, NT3, pcV?2, and vitronectin) have clearly
been implicated in synapse formation and connectivity (18, 19,
22, 31–33). These genes, as well as other members of the families
that they belong to, are excellent candidates for a more detailed
analysis of how they might affect development and target
selection within the bulb. For example, mutants in cadherin-11
have been obtained by employing transgenic techniques (19). In
these mice, the olfactory bulbs are reduced significantly in size
in convergence patterns. Thus, the genes uncovered in this work
could possibly impact many aspects of olfactory bulb develop-
ment and function.
The identification of differentially expressed genes in the
olfactory system is a fundamental and necessary first step toward
defining the mechanisms underlying the patterning of sensory
input in the olfactory bulb. We anticipate that iterations of this
nascent spatial map will incorporate progressively smaller olfac-
tory bulb fragments, allowing finer resolution of gene expression
patterns within the three-dimensional space of this structure.
Ultimately, this information should provide a rational basis for
multiple genetic alterations and interpretation of the subsequent
We thank K. van Fossen for technical assistance; J. Winer for use of the
for help in generation of the microarrays. This work was supported by
grants from the National Institutes of Health (to J.N. and T.P.S.) and by
funds from the Department of Molecular Cell Biology and the Helen
Wills Neuroscience Institute. D.M.L. was supported by the Whitehall
Foundation, the National Science Foundation, the Sloan Foundation,
and the Beckman Young Investigator Program. The 19K RIKEN cDNA
set was supported in part by the Special Coordination Funds for
Promoting Science and Technology and a research grant from the
Science and Technology Agency of the Japanese government (to Y.H.).
1. Key, B. & St. John, J. (2002) Chem. Senses 27, 245–260.
2. Cutforth, T., Moring, L., Mendelsohn, M., Nemes, A., Shah, N. M., Kim, M. M.,
Frisen, J. & Axel, R. (2003) Cell 114, 311–322.
3. Norlin, E. M., Alenius, M., Gussing, F., Hagglund, M., Vedin, V. & Bohm, S.
(2001) Mol. Cell. Neurosci. 18, 283–295.
4. Yoshihara, Y., Kawasaki, M., Tamada, A., Fujita, H., Hayashi, H., Kagamiyama,
H. & Mori, K. (1997) J. Neurosci. 17, 5830–5842.
5. Gong, Q. & Shipley, M. T. (1996) J. Comp. Neurol. 366, 1–14.
6. Crandall, J. E., Dibble, C., Butler, D., Pays, L., Ahmad, N., Kostek, C., Puschel,
A. W. & Schwarting, G. A. (2000) J. Neurobiol. 45, 195–206.
7. Schwarting, G. A., Kostek, C., Ahmad, N., Dibble, C., Pays, L. & Puschel, A. W.
(2000) J. Neurosci. 20, 7691–7697.
8. Marillat, V., Cases, O., Nguyen-Ba-Charvet, K. T., Tessier-Lavigne, M., Sotelo,
C. & Chedotal, A. (2002) J. Comp. Neurol. 442, 130–155.
9. Otaki, J. M. & Firestein, S. (1999) NeuroReport 10, 2677–2680.
10. Gogos, J. A., Osborne, J., Nemes, A., Mendelsohn, M. & Axel, R. (2000) Cell
11. Miki, R., Kadota, K., Bono, H., Mizuno, Y., Tomaru, Y., Carninci, P., Itoh, M.,
Shibata, K., Kawai, J., Konno, H., et al. (2001) Proc. Natl. Acad. Sci. USA 98,
12. Yang, Y. H., Buckley, M. J., Dudoit, S. & Speed, T. P. (2002) J. Comput. Graph.
Stat. 11, 108–136.
13. Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng, V., Ngai, J. & Speed, T. P.
(2002) Nucleic Acids Res. 30, e15.
14. Dudoit, S., Yang, Y. H., Callow, M. J. & Speed, T. P. (2002) Statistica Sinica
15. Speca, D. J., Lin, D. M., Sorensen, P. W., Isacoff, E. Y., Ngai, J. & Dittman,
A. H. (1999) Neuron 23, 487–498.
16. Sandberg, R., Yasuda, R., Pankratz, D. G., Carter, T. A., Del Rio, J. A.,
Wodicka, L., Mayford, M., Lockhart, D. J. & Barlow, C. (2000) Proc. Natl.
Acad. Sci. USA 97, 11038–11043.
17. Zirlinger, M., Kreiman, G. & Anderson, D. J. (2001) Proc. Natl. Acad. Sci. USA
18. Lindsell, C. E., Shawber, C. J., Boulter, J. & Weinmaster, G. (1995) Cell 80,
19. Manabe, T., Togashi, H., Uchida, N., Suzuki, S. C., Hayakawa, Y., Yamamoto,
M., Yoda, H., Miyakawa, T., Takeichi, M. & Chisaka, O. (2000) Mol. Cell.
Neurosci. 15, 534–546.
20. Yagi, T. & Takeichi, M. (2000) Genes Dev. 14, 1169–1180.
22. Nef, S., Lush, M. E., Shipman, T. E. & Parada, L. F. (2001) Dev. Biol. 234, 80–92.
23. Guthrie, K. M. & Gall, C. M. (1991) J. Comp. Neurol. 313, 95–102.
24. St Johnston, D. & Nusslein-Volhard, C. (1992) Cell 68, 201–219.
25. Wilson, S. W. & Rubenstein, J. L. (2000) Neuron 28, 641–651.
26. Price, S. R., De Marco Garcia, N. V., Ranscht, B. & Jessell, T. M. (2002) Cell
27. Sperry, R. W. (1963) Proc. Natl. Acad. Sci. USA 50, 703–710.
28. Winberg, M. L., Mitchell, K. J. & Goodman, C. S. (1998) Cell 93, 581–591.
29. Stein, E. & Tessier-Lavigne, M. (2001) Science 291, 1928–1938.
30. Bailey, M. S., Puche, A. C. & Shipley, M. T. (1999) J. Comp. Neurol. 415,
31. Chernousov, M. A. & Carey, D. J. (2000) Histol. Histopathol. 15, 593–601.
32. Holland, S. J., Peles, E., Pawson, T. & Schlessinger, J. (1998) Curr. Opin.
Neurobiol. 8, 117–127.
33. Schvartz, I., Seger, D. & Shaltiel, S. (1999) Int. J. Biochem. Cell Biol. 31,
Lin et al.PNAS ?
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