Download Comparative molecular neuroanatomy of mammalian neocortex

Comparative molecular neuroanatomy of mammalian neocortex: what can gene
expression tell us about areas and layers?
Akiya Watakabe
Division of Brain Biology, National Institute for Basic Biology
[email protected]
Tel: +81-564-55-7616
Abstract
It is over 100 years since Brodmann proposed the homology of layer and area structure
of the cerebral cortex across species.
His proposal was based on the extensive
comparative analyses of various mammalian brains. Although such homology is now
well accepted, the recent data in our laboratory showed striking variations of gene
expression patterns across areas and species. Are cortical layers and areas really
homologous? If they are, to what extent and how are they similar or different? We
are trying to answer these questions by identifying the homologous neuronal types
common to various areas and species. Toward this goal, we started to classify the
cortical pyramidal neurons by expression of particular sets of genes. By using
fluorescent double in situ hybridization combined with retrograde tracers, we are
characterizing the gene expression phenotypes and projection specificity of cortical
excitatory neuron types. In this review, I discuss the recent findings in our laboratory
in light of the past and present knowledge about cortical cell types, which provides
insight to the homology (and lack thereof) of the mammalian neocortical organization.
Introduction
Korbinian Brodmannn published his influential book “Vergleichende Lokalisationslehre
der Grobhirnrinde (Localization in the Cerebral Cortex)” in 1909 (Brodmann, 1999).
In this book, he proposed that the cerebral cortex consists of multiple modules (areas)
with distinct functions.
His proposal was based on his extensive analyses of
mammalian brains, in which he examined the Nissl-stained cortex of 55 species.
Quite
naturally, he observed a diversity of neuronal arrays across areas and species.
However, he envisaged the similarity of layer and area structure over this diversity,
which suggests that these are the fundamental units of cortical organization. After 100
years, his idea of six-layered structure as a prototypical mammalian neocortex has been
widely accepted (but see Marin-Padilla 1998). Of particular note in the context of this
review is the connectional studies using neuronal tracers.
By use of anterograde and
retrograde tracers to label the destinations and the origins of neuronal connectivity, it
has become clear that the cortical layers are tightly associated with connectional
specificity.
For example, thalamic projection neurons mostly reside in layer 6, while
subcortical projection neurons localize in layer 5.
Layer 4 receives thalamic and
cortical inputs and layers 2/3 project to other cortical areas. Although this is a very
simplified view with many exceptions, there appears to exist a canonical cortical circuit
that can adapt to a range of information processing (Douglas and Martin 2004;
Bannister 2005). The current evidence indicates that different types of extrinsic and
intrinsic neurons constitute each lamina (Lund et al. 1994; Molyneaux et al. 2007;
Thomson and Lamy 2007; Leone et al. 2008). The homology of layer and area as
suggested from gross morphology and connectivity is thus attributed to homology of
cell types. An important question yet to be solved is what types of cells constitute the
canonical neocortical circuit and how they work in concert to function as a unit of
computing (Nelson 2002; Watts and Thomson 2005).
The efforts to classify neuronal cell types date back to the age of Cajal and
other neuroscientists in the late 19th to early 20th centuries. Various cell types were
identified morphologically by Golgi impregnation techniques. Combined with the
ultrastructural analysis by the electron microscopy, two fundamental types of cortical
neurons are identified (LeVay 1973). One is the pyramidal neurons with dendrites
decorated with numerous spines, and the other is the local circuit neurons with smooth
aspiny dendrites, which correspond to the excitatory and inhibitory neurons,
respectively (Ribak 1978). The excitatory neurons are the major types of neurons and
use the glutamate as the neurotransmitter to excite the target neurons. The inhibitory
neurons occupy only 10-20 % of the cortical neurons, but greatly influence the
excitability of pyramidal neurons by GABA-mediated inhibition of neuronal firing.
Interestingly, immunolabeling for various neurotranmitters and calcium-binding
proteins showed that this minor subpopulation consists of diverse subtypes (DeFelipe
1993; Kawaguchi and Kubota 1997; Markram et al. 2004; Ascoli et al. 2008).
Importantly, the morphology and electrophysiological properties were also different
from each other, suggesting that these cell types are physiologically relevant subunits of
neocortical circuits.
Compared with inhibitory neurons, the cell types of excitatory neurons had
been much less clear. This was not because the excitatory neurons are homogeneous.
To the contrary, the phenotypes of excitatory neurons are so diverse that it had been
difficult to unambiguously define particular cell types. In this regard, one of the most
reliable criteria to distinguish pyramidal neurons has been their projection specificities.
For example, the corticotectal neurons (projecting to the superior colliculus) and the
callossal neurons (projecting to the contralateral hemisphere) in layer 5 of the rat visual
cortex can be easily identified by retrograde labeling.
It was shown that these two
types (called type I and type II, respectively) have different morphology and intrinsic
firing properties (Kasper et al. 1994). Thus, the excitatory neurons also appear to
consist of distinct cell types that are optimized for particular functions.
However,
classification by projection cannot necessarily answer the question of whether the same
types of cells exist across areas. This is due to the area-specific nature of the
corticofugal projections.
In the case cited above, the corticotectal neurons are
abundant in the occipital areas but scarce in the frontal areas in adult rats (O'Leary and
Stanfield 1985). Instead, layer 5 of frontal areas is populated with corticospinal,
corticopontine and other projection neurons (Stanfield et al. 1982; Miller 1987; Legg et
al. 1989; Gabbott et al. 2005) that should be considered as distinct cell types in terms of
projections.
Nevertheless, all these cells show morphology and firing property similar
to those of type I but not type II cells (Molnár and Cheung 2006). Should these
corticofugal cells be regarded as the same type or different? This example underscores
the importance of definitive criteria to classify excitatory neurons other than projections.
Unlike inhibitory neurons, there had been few good antibodies to classify the
excitatory neurons, which resulted in the comment that “chemical diversity of neurons
in the neocortex is mainly a feature of the population of nonpyramidal cells.” (DeFelipe
1993). Such situation is rapidly changing with the advent of molecular biological
techniques. Many genes are now known to be expressed in area- and/or layer-specific
manners during development or in adulthood (Rubenstein et al. 1999; Hevner et al.
2003; Rash and Grove 2006; Yamamori and Rockland 2006; Molyneaux et al. 2007).
Microarray analyses combined with retrograde tracing (Arlotta et al. 2005) or transgenic
labeling (Sugino et al. 2006) have found that particular types of projection neurons
exhibit unique gene expression profiles. Importantly, gene disruption experiments
have identified several genes to play critical roles in cell fate determination, as revealed
by changes in gene expression and projection profiles in particular types of pyramidal
cells (Arlotta et al. 2005; Alcamo et al. 2008; Britanova et al. 2008). Of particular note
is the mutually exclusive roles of Ctip2 (Arlotta et al. 2005) and Satb2 (Alcamo et al.
2008; Britanova et al. 2008) in specifying “subcortically projecting” and
“corticocortical” neurons, respectively.
Based on these findings, the genetic basis for
the differentiation of cortical neurons is beginning to be understood (reviewed in
Molyneaux et al. 2007; Leone et al. 2008).
Most of such works are done using rodents (especially mice) as a model with
the contention that the basic cortical mechanism should be conserved across species.
But is this really so?
Our laboratory has been addressing this question, using various
molecular markers for comparative studies (Yamamori and Rockland 2006; Watakabe et
al. 2007). We have shown, for example, that a gene that is highly enriched in the
visual cortex in monkeys does not show such an expression pattern in non-primate
mammals (Takahata et al. 2006). Whatever it means, it clearly demonstrates the
species-specific difference of the cortical areas. Then, what about the lamina
structure? The six-layer model implies that homologous cell types are present in each
layer across areas and species. Therefore, layer-specific genes are good candidates as
markers for such cell types. Based on this rational, we started to screen layer-specific
genes for markers of excitatory cell types.
Technique
Before going through our works, I would like to explain the methodology of
our study.
In our works, we use non-radioactive in situ hybridization (ISH)
histochemistry to examine gene expression. There were many practical and conceptual
reasons why ISH was better than antibody staining for our purpose.
First of all,
making ISH probes is by far faster and easier than making good antibodies. To screen
for many different genes, this was critical. Second, due to flourish of molecular
biological works, there were already good candidates for screening (see references in
McConnell 1995; Miller and Mitra 2002; Molyneaux et al. 2007), and the list of the
candidates was rapidly increasing.
Third, since we wanted to compare gene
expression across species, we wanted to be sure that we are detecting the same gene
products in different species. Although many good antibodies cross-react with the
homologous gene products across species, there is always a problem of cross-reaction to
other proteins, especially, the family genes. In this regard, we can easily prepare
species-specific ISH probes for maximal detection in the species of interest.
Finally,
while antibody staining does not necessarily label the cell body, ISH generally labels
only cell bodies where most mRNAs localize. This was a big advantage for us because
we wanted to identify cell bodies, rather than determine the protein localization.
To make full use of these advantages, we paid attention to the method of ISH.
First, we used non-radioisotopic ISH, because we needed to have cellular resolution.
Non-radioactive ISH is sometimes considered to be less sensitive than radioactive ISH
(see Schaeren-Wiemers and Gerfin-Moser 1993). But in the protocol used in our study
(Liang et al. 2000), we could achieve highly sensitive detection of various mRNAs.
Second, we developed an efficient method of double-label ISH, which was made
possible by utilizing tyramide signal amplification system (Speel et al. 1999). Without
this technique, it was impossible to distinguish different types of cells based on gene
expression. Third, we wanted to combine retrograde tracing with ISH, which was
possible by selecting appropriate tracers.
Having clarified these technical problems,
we were now ready to correlate gene expression to cortical cell types.
Comparative expression analyses of layer-specific genes
To use as markers for the cell type identification, I looked for the layerspecific genes that fulfill the following criteria. (1) There should be a clear-cut border
between the positive and negative cells so that we can definitively identify a particular
cell type. With the same reason, the mRNA signals should be strong enough to be
efficiently detected. (2) The specificity of expression should be observed in the mature
cortex. Because we were interested in the components of the primate cortical circuit, I
performed the ISH of various candidates using the mature brains of monkeys for
screening. In many cases, the candidate genes showed more or less lamina-specific
patterns across species (e.g., see Fig. 1). Nevertheless, the mRNA expression was
often not specific enough to be used as a marker at the single cell level, and I was left
with only a handful of genes for further studies. I expected that these select genes
serve as good markers to classify the cortical pyramidal neurons.
Below, I will
describe the detailed account of each marker expression.
Layer 5 markers (ER81 and others)
Cell type specific expression of ER81 was first discovered in the spinal cord
(Lin et al. 1998; Arber et al. 2000) and then in the cerebral cortex (Weimann et al. 1999;
Hevner et al. 2003; Yoneshima et al. 2006). In the rat and mouse cortex, ER81 mRNA
is highly specific to layer 5.
It is expressed widely across areas, suggesting that its
expression is associated with some general feature of layer 5, rather than with specific
projections.
Consistently, Yoneshima et al. found the lack of strict projection
specificity of ER81-protein positive neurons in the rat (Yoneshima et al. 2006). They
found that almost all of the corticospinal and corticotectal neurons express ER81 protein,
while one third of the callosal-projecting neurons express it.
In the monkey cortex, ER81 mRNA is also most enriched in layer 5, although
it is expressed in layer 6 at a lower level (Watakabe et al. 2007). In our estimate,
ER81-mRNA-positive neurons constituted 60-70 % of the excitatory neurons in layer 5
irrespective of areas in monkeys, while ER81-mRNA positive neurons constituted as
high as over 80 % in the mouse cortex (e.g., see Fig. S2 of Watakabe et al. 2007).
Thus, a majority of layer 5 neurons express ER81 gene, which is also consistent with
the notion that it is a general marker of layer 5. When we examined the strength of
ER81 mRNA expression by densitometry, however, we observed a big difference across
areas. Generally speaking, ER81 mRNA level is high in the association areas, and low
in the primary sensory areas. This tendency was most conspicuous at the primary-
secondary visual area (V1-V2) border, where an abrupt change in ER81 mRNA
expression was observed (Watakabe et al. 2007). Recently, we performed a
quantitative analysis of ER81 mRNA expression in the rat cortex, and found that similar
area difference exists in rats as well (Hirokawa et al. 2008). These observations
suggest general difference of layer 5, which may be more developed in the association
areas than in the sensory areas.
In addition to ER81, there are other genes that mark more restricted
subpopulations of layer 5 neurons (for a review, see Molnár and Cheung 2006). The
transcription factors, SCIP (He et al. 1989) and otx1 (Frantz et al. 1994) have been
known to label a subpopulation of layer 5 neurons, although they are also expressed in
other layers at low level.
Macklis and coworkers found that different sets of genes are
expressed by callosal and corticospinal projecting neurons by microarray analyses of the
retrogradely labeled cells (Arlotta et al. 2005). In the monkey cortex, we found that 5HT (serotonin) 2C receptor mRNA is specifically expressed by a subpopulation (1422 %) of ER81-mRNA positive neurons (Watakabe et al. 2007). We also found that
Sema3E is expressed in layer 5b but not in layer 5a in the mouse and rat cortex
(Watakabe et al. 2006). Thus, layer 5 neurons appear to consist of heterogeneous
subtypes.
However, the further details of these gene expressions remain to be
determined.
Layer 6 markers (Nurr1, latexin and CTGF)
In their pioneering works, Arimatsu and coworkers found two interesting
markers for subtypes of cortical excitatory neurons, latexin and Nurr1, which are both
expressed in layer 6 of the rat cortex. They first identified latexin to be specifically
expressed by a restricted population of neurons that reside only in the lateral region of
the rat cortex (Arimatsu et al. 1992; Arimatsu et al. 1994). By using latexin as a
marker, they provided one of the first evidence for the importance of intrinsic program
in cortical area differentiation (O'Leary et al. 1994; Sur and Rubenstein 2005).
Interestingly, Nurr1 shows an expression pattern strikingly similar to that of latexin
(Xing et al. 1997). One difference between latexin and Nurr1 expression is that the
latter is also expressed in layer 6b throughout areas. Arimatsu and coworkers found
that latexin- and Nurr1 -positive neurons mostly overlap in the lateral region but not in
layer 6b (Arimatsu et al. 2003; see Fig. 2A for a typical pattern). We found by double
ISH that the Nurr1 mRNA-positive neurons in layer 6b coexpress CTGF mRNA (Fig.
2B, Watakabe et al. 2007), a marker for layer 6b cells (Friedrichsen et al. 2003). Thus,
the Nurr1-positive neurons in layers 6a and 6b can be distinguished by co-expression of
latexin or CTGF mRNAs. Importantly, Arimatsu and coworkers found that the latexinand Nurr1-positive neurons show corticocortical but not corticothalamic projections
(Arimatsu et al. 2003; Bai et al. 2004). The correlation between gene expression
phenotype and projection specificity strongly suggests that the Nurr1-positive neurons
represent a functionally relevant subtype.
With this information in mind, we examined the gene expression patterns of
latexin, Nurr1 and CTGF mRNAs in the monkey cortex (Watakabe et al. 2007).
Among the three genes, we could not detect the expression of latexin mRNA in the
neocortical areas (unpublished observation). However, Nurr1 and CTGF mRNAs
were detected in layer 6 throughout the cortical areas of monkeys (Fig. 1 and 2).
Nurr1-mRNA positive neurons were a minority (20-31 % in six different areas) of the
layer 6 excitatory neurons. About half of them (37-63 %) co-expressed CTGF mRNA
and occupied the lower part of layer 6. The segregation of the two Nurr1 populations
was most conspicuous in areas V1 and V2, where the two types formed discrete bands
of layers (Fig. 2F).
In other areas, the two populations intermingled and the sublayers
were not very evident, but still the double positive cells occupied the lower part of layer
6 (Fig. 2G). We also examined the projection specificity of the Nurr1-positive cells by
combining retrograde tracing with ISH and found that the Nurr1-positive cells show
corticocortical but not corticothalamic connectivity (Watakabe et al. 2007).
So far, the cell types defined by Nurr1 and CTGF gene expressions appear to
show consistent properties between monkeys and mice, except latexin expression.
However, there is a prominent species difference in terms of area distribution patterns
(Fig. 2). As mentioned earlier, the Nurr1 (+)/CTGF (-) (latexin-positive) cells show
strict localization in the lateral regions in rodents; in the medial areas, such as primary
somatosensory (S1), V1 and primary motor (M1) areas, there are few numbers of cells
of this type (Fig. 2A). In contrast, we found by double ISH that the Nurr1 (+)/CTGF
(-) cells are present throughout areas in the monkey cortex. For example, this cell type
accounts for 16, 13 and 8 % of the layer 6 excitatory neurons in S1, V1 and M1,
respectively, against 9 % in the temporal area TE. What does this species difference
mean?
One possibility is that it reflects differential area connectivity between rodents
and monkeys. Arimatsu and coworkers found in the rat cortex that Nurr1-positive
cells provide only intrahemispheric cortical projections (Arimatsu et al. 2003). They
suggest that the latexin-positive neurons provide the “feedback” connections to the
primary sensory areas. In layer 6a of S2, for example, latexin-positive neurons
provide as high as 90 % of the S1-projection (Bai et al. 2004). This correlation partly
explains the lack of latexin/Nurr1 in the medial regions, where S1 and V1 locate.
However, the known pattern of cortical connectivity in monkeys is not necessarily
consistent with the hypothesis that Nurr1-positive cells provide “feedback” projections.
In the monkey visual pathways, the “feedforward” projections from the hierarchically
lower to higher areas originate from layer 3, whereas the “feedback” projections from
higher to lower areas originate from layers 2 and 6 (Rockland and Pandya 1979;
Felleman and Van Essen 1991). In this scheme, the “feedback” neurons in V1 project
to thalamus and cannot explain the presence of Nurr1-positive cells in V1.
Besides,
the tracer injection into V1 showed that only about 25 % of the V1-projecting
“feedback” neurons in layer 6 express Nurr1 mRNA in V2 (Watakabe et al. 2007).
The relationship between Nurr1 expression and projection specificity, thus, appears to
be more complex than simply cortical-projecting. We definitely need to understand
more about the property of this cell type.
In the midbrain, Nurr1 is specifically expressed by dopaminergic cells and is
involved in their fate determination (Zetterstrom et al. 1997). I won’t be surprised if
Nurr1 is playing an important role in fate determination of the cortical neurons and
affect the projection profiles of the cells that express it.
Semaphorin 3E and Plexin D1
In the monkey cortex, Sema3E expression is highly specific to layer 6
(Watakabe et al. 2006). It is most intense in V1, but present throughout the neocortical
areas. The ratios of the Sema3E-positive cells among the layer 6 neurons range from
34 % in the motor cortex to 63 % in area TE. Thus, the layer 6 neurons can be roughly
divided into two types based on Sema3E mRNA expression. The expression pattern of
Sema3E mRNA in mice had been already reported and showed deep layer preference
(Miyazaki et al. 1999). When we reexamined its pattern in more detail, we noticed
two important differences between rodents (mice and rats) and monkeys. One
difference was that Sema3E is expressed by scattered neurons in layers 1-4 in rats and
mice, while there is no such expression in the monkeys. We found by double ISH that
this is due to GABAergic expression. Another difference was that Sema3E mRNA is
expressed in both layers 5b and 6 in mice and rats, but only layer 6 in monkeys. The
difference of lamina expression was confirmed by co-expression of ER81 mRNA in the
rat and mouse cortex but not in layer 5 of the monkey cortex (unpublished data).
Semaphorin is a well-known family of axon guidance molecules and plays an
important role in neuronal wiring (Pasterkamp and Kolodkin 2003). When we were
characterizing the layer-specific expression of Sema3E mRNA, it was already known
that neuropilin and plexin family members form the specific co-receptors for
semaphorins (He and Tessier-Lavigne 1997; Kolodkin et al. 1997; Tamagnone et al.
1999; Fujisawa 2002). To gain insight to the role of Sema3E, we performed the ISH of
neuropilin and plexin family members, and noticed that plexin D1 mRNA shows the
patterns complementary to those of Sema3E mRNA.
In the mouse cortex, plexin D1
mRNA showed a highly specific expression in the upper layers, which we later
identified to correspond to layers 2-5a. We were able to draw a clear border between
layers 5a and 5b by expression of Plexin D1 and Sema3E mRNAs. In the monkey
cortex, plexin D1 mRNA was observed widely across layers 2-6, but was generally poor
in layer 6. Especially in V1, we observed very low expression of plexin D1 mRNA in
layer 6.
Based on this finding, we performed the in vitro binding analysis and found
that plexin D1 can specifically bind Sema3E without the presence of neuropilins. Gu
et al. discovered the same thing independently and further found the in vivo role of
Sema3E-plexin D1 interactions in blood vessel formation (Gu et al. 2005).
Considering the general function of this family member, it is intriguing that
the complementary lamina expression is conserved between rodents (mice and rats) and
monkeys, despite differential lamina distribution patterns.
If these molecules are
involved in the formation and/or maintenance of layer-specific connectivity, the
conservation of the complementarity may mean conserved relationships between the
upper and lower layers, although not necessarily in a strict way.
In this regard, the
expression of Sema3E in layer 5b of mice but not in monkeys implies a differential role
for the neurons in this sublayer in the cortical circuit.
Questions arising: what is the identity of the pyramidal cell types?
I have so far described the comparative expression analyses of several layerspecific genes, which may serve as candidates for cortical subtype classification
(summarized in Table 1). Overall, the data are consistent with the notion that unique
gene expression profiles are associated with the types of cortical neurons.
One of the
best examples would be the expression of Nurr1 and ER81 mRNAs at the layer 5/6
border.
Both in rodents and monkeys, these two mRNAs are not co-expressed within
the same cells, even when the positive cells cross the lamina border and intermingle.
Thus, these two genes are specific to some kinds of cells rather than to layers.
Importantly, Nurr1 expression is highly correlated with the projection patterns of the
positive neurons, which strongly supports the legitimacy of classifying cortical neurons
by gene expression. On the other hand, we have also witnessed many species
differences, which make the interpretation of gene expression data more difficult. We
are also aware of the inconsistency between gene expression and projections.
It would
be useful at this point to review these problems.
Strict lamina specificity may not be conserved across species
We often found cases where the strict lamina specificity is not conserved
across species.
ER81 mRNA, for example, is expressed by approximately 20-30 % of
the layer 6 neurons in the monkey cortex, in addition to its conserved layer 5 expression.
Sema3E mRNA is expressed by layer 5b neurons in the rodent cortex in addition to
layer 6.
Related to this subject is the fact that expression of a single gene is often
insufficient to identify a particular subtype. In the case just cited above, we do not
regard the ER81-positive neurons in layer 6 to belong to the same type as those in layer
5, because they mostly co-express Nurr1 and CTGF mRNAs in layer 6 (Watakabe et al.
2007). With the same reason, the two subpopulations of the Nurr1-positive cells
should be regarded as separate types. We also do not regard the Sema3E-positive cells
in layer 5 of rodents to be the same type as those in layer 6, because (1) they are
expected to show differential projection patterns and (2) they differentially co-express
ER81 (unpublished observation). What these examples imply is that the difference
between layers 5 and 6 may be less definitive than we had expected. In this regard, it
is interesting that a transcription factor Fezl, a key factor for differentiation of the
projection neurons in layer 5, is expressed both in layers 5 and 6 during mouse cortical
development (Chen et al. 2005; Molyneaux et al. 2005). The species differences in
lamina specificity may originate from small changes in the downstream effects of such
genes.
In two cases, we observed complete loss of expression across species.
Despite highly characteristic expression, we did not detect latexin mRNA in the monkey
neocortex. This is not due to technical failure, because we could clearly detect latexin
mRNA in the parahipocammpal region (unpublished data). Similarly, 5HT2C receptor
mRNA was not detected strongly in the neocortical areas of the mouse and rat brains,
although relatively strong expression is detected in the cingular and rhinal cortex
(Wright et al. 1995).
In summary, our data demonstrate that specific expression of a particular gene
in layer 5 or 6 (or their sublayers) in one species does not mean that it is associated with
the layer-specific function in other species as well.
Nevertheless, we can still
distinguish layer 5 and layer 6 neurons by combination of gene expression in each
species of interest. And we are able to deduce the homology of each type across
species by the overall similarity of gene expression profiles. Thus, I believe that the
homology of pyramidal neuron types exist across species (see below for more
discussion about homology).
Inconsistency between layer-specific gene expression and projection types
We have already discussed the potential inconsistency of cortical projection
and Nurr1 expression in the monkey cortex. This is actually a common problem for
layer-specific genes, because the extrinsic projections are generally area-specific.
In
layer 5, for example, there are many types of projection neurons, such as, corticospinal,
corticopontine, corticotectal, corticostriatal, and corticocortical neurons among others.
These different types of neurons co-exist in different proportions across various areas
(Killackey et al. 1989; Gabbott et al. 2005). As mentioned above, some genes (e.g., 5-
HT2C receptor gene) are selectively expressed by a subset of layer 5 neurons. Yet,
their distribution patterns do not show the high area-specificity expected for a particular
projection type. The neuronal type specified by ER81 also appears to be only partially
related to projection specificity (Yoneshima et al. 2006). Therefore, the precise
projection seems to be a secondary determinant of the cell’s nature for layer 5 neurons
in the mature cortex.
The relationship between the extrinsic projection and cell type is also
enigmatic in layer 6 of monkey V1. According to the previous studies, the major
projection from this region appears to be the corticothalamic projection to the lateral
geniculate nucleus. However, the corticogeniculate neurons account for only 13 % of
the layer 6 neurons, and show a bilaminar pattern within layer 6 (Fitzpatrick et al. 1994).
Although V1 neurons send massive projections to V2, such feedforward projections
originate from the supragranular layers and only few neurons in the upper part of layer 6
project to V2 (Kennedy and Bullier 1985). The corticoclaustral cells, which are also
present in layer 6, are few or absent in monkey V1 (Sherk 1986, see also Crick and
Koch 2005). Consistent with these tracer studies, Callaway and coworkers showed in
their morphological analysis that only 16 of the 58 neurons examined in layer 6 of
monkey V1 had the axons projecting toward the white matter (Wiser and Callaway
1996). These reports strongly suggest that most of the excitatory neurons in layer 6 of
monkey V1 do not project out of V1. Therefore, the extrinsic projection specificity
cannot be a useful criterion to classify these neurons.
Interestingly, Callaway and
coworkers suggest that they can distinguish two types of layer 6 neurons based on the
lamina distribution of dendritic and axonal arbors, which correlates with functional
connectivity (Wiser and Callaway 1996; Briggs and Callaway 2001). There is thus a
possibility that gene expression phenotype may correlate with intrinsic connection
rather than with projection types in monkey V1.
A consideration on homology, gene expression and projection phenotype
The above observations illustrate the difficulty of deducing homology from
adult gene expression. In this regard, the gene expression pattern in the embryonic
brain has been used successfully to deduce homology across vertebrates.
Puelles and
Rubenstein, for example, revived the Prosomeric model for the forebrain structure based
on similarity of combinatorial expression patterns of transcription factors and signaling
molecules (Puelles and Rubenstein 1993; Rubenstein et al. 1994). One of the main
differences between the adults and embryos would be the importance of positional
information.
In the embryonic brain, the positional information is a primary
determinant of cell types and neuronal wiring. There are gradients of various secreted
signaling molecules, according to which the forebrain patterning occurs (Sur and
Rubenstein 2005; Rash and Grove 2006). In contrast, the basic architecture of the
adult brain is already established.
It is usually not necessary to construct connectional
networks de novo. Thus, the correlation between extrinsic projection and gene
expression, even if it exists, should be secondary in the mature brain. It also needs to
be remembered that gene expression can change dynamically in response to the
environmental inputs (Herdegen and Leah 1998; Tochitani et al. 2001; Sur and
Rubenstein 2005).
After all, gene expression is just one phenotype that may adaptively change in
the course of evolution. There is always a possibility that the common pattern of gene
expression across species is analogy rather than homology.
Similarly, there is also a
possibility that the common pattern of connectivity is analogy. We have to be careful
not to confuse them (see Striedter 2002; Jarvis et al. 2005).
Future directions: what will we need to know about pyramidal cell diversity?
Our knowledge on molecular make-ups of the nervous system is growing in
an explosive pace. Click on Allen Brain Atlas’s search button, and voila, you get
dozens of layer 5-enriched genes, most of which are yet to be characterized (Lein et al.
2007). Obviously, we are only beginning to understand the true diversity of excitatory
neuron types. In concluding this review, I would like to propose five tasks to be
addressed to understand the significance of this diversity.
First, we will need to clarify the combinatorial gene expression profiles for
various types of cortical neurons.
By combining two or three genes, we were able to
classify cortical neurons to some extent. Whether this classification is reasonable or
insufficient depends on how robust it can be applied to explain the expression of many
other genes. This approach is also useful to deduce the homologous neuronal types
across species.
Second, we need to determine the developmental origin of each cell type in
different species, so that we can follow the cell’s identity that is being held throughout
development.
Use of transgenic mouse lines with cell type-specific expression will be
helpful for this purpose (Gong et al. 2003; Livet et al. 2007). The inconsistency
between gene expression and projection phenotypes may be partly resolved if we can
track the process of axonal projections from initial trajectory to pruning.
Third, it is critical that we can correlate the classification by gene expression
to those by other disciplines, such as morphological, electrophysiological,
pharmacological and connectional classification (Monyer and Markram 2004; Nelson et
al. 2006). Many features of mature neurons are formed epigenetically with
interactions of environment. Even in such cases, the types of neurons that are predetermined during development should affect the eventual phenotypes of each cell type.
Fourth, we will need to understand the function of the gene products within
the cell. There may be transcription factors that play a master role in maintaining the
integrity of gene expressions required in that cell.
Diffusible factors and receptors
(e.g., Sema3E and plexin D1) can potentially control the probability of synaptic
connections and/or axonal/dendritic morphology in response to cell’s atmosphere.
Expression of channels, neurotransmitter receptors and transporters would directly
affect the cell’s electrophysiological and pharmacological properties. The ensemble of
gene products present in the cell should determine various properties of cortical
neurons.
Finally, we will have to understand the role of the gene products in the
cortical circuits. There must be a reason for why particular gene products are required
in specific cortical subpopulations and why such specific expressions are conserved
(and/or different) across species. The answer must lie in the role of each cell type
within the cortical circuit. Recently, the electrophysiological recordings and/or
imaging studies are beginning to unravel the microcircuit structure of the neocortex
(Briggs and Callaway 2001; Nelson 2002; Yoshimura et al. 2005; Le Be and Markram
2006; Krieger et al. 2007; Luczak et al. 2007; Frick et al. 2008; Weiler et al. 2008).
Although the precise excitatory cell types involved in such microstructure is not yet
well described, more studies are now paying attention to the projection types for
particular roles in cortical circuits (Watts and Thomson 2005; Morishima and
Kawaguchi 2006; Le Be et al. 2007; Kumar and Ohana 2008). I do not think that the
projection type is enough to resolve this issue. To understand the cortical circuit better,
the classification of cortical neurons by gene expression should become important in the
future studies.
Acknowledgement
Supported by the grant from the JSPS (KAKENHI19500304). This work was
performed in Dr. Tetsuo Yamamori’s laboratory.
I thank him and the lab members, in
particular, Dr. Yusuke Komatsu and Ms. Sonoko Ohsawa, for various discussion and
technical assistance. I also thank Drs. Kathleen S. Rockland and Noritaka Ichinohe in
Brain Research Institute, RIKEN, for their encouragement and valuable advices
throughout the course of our collaboration.
Table 1
<Potentially homologous neuron types>
Layer
Mouse
Monkey
Layer 5
ER81(+)
ER81(+)/Nurr1(-)
Layer 6a
Nurr1(+)/CTGF(-)
Nurr1(+)/CTGF(-)
Layer 6b
Nurr1(+)/CTGF(+)
Nurr1(+)/CTGF(+)
<Species-specific differences in gene expression>
Gene
Mouse
Monkey
ER81
Specific to Layer 5
Expressed both in layers 5 and 6
Very low expression in neocortex
Layer 5b throughout areas
(expressed in layer 5 of the cingular area)
(Layer 6 in V1)
(Mostly) restricted to the lateral regions
Present across areas
Restricted to the lateral regions
Not expressed in the neocortex
5-HT2C
Nurr1
Latexin
(expressed in the parahippocampal area)
Sema3E
Plexin D1
Layers 5b and 6
Subpopulation of layer 6
Layers 2-5a
Widely expressed in layers 2-6 with low
abundance in layer 6
Figure legends
Fig. 1
Layer specific expression is conserved between mice and monkeys.
ISH patterns of RORbeta, ER81, Nurr1 and CTGF genes in mouse and monkey cortices.
In this figure, the patterns in area S2 of the mouse (upper panels) and in area TE of the
monkey cortex (lower panels) are shown. In the rightmost panels, the single color ISH
images for each gene were converted to pseudocolor images (red for RORbeta, blue for
ER81, green for Nurr1 and purple for CTGF mRNAs) for overlay representation. The
lamina expression patterns of these genes are overall conserved, although the stringency
of expression may vary.
For example, ER81 mRNA is expressed in layer 6 in addition
to layer 5 in the monkey cortex. The relative abundance and/or the extent of lamina
specificity for each gene is considerably different across areas.
For details of such area
differences, see Watakabe et al. (2007) and Hirokawa et al (2008).
Fig. 2
Expression patterns of Nurr1 mRNA in the mouse and monkey cortices
Area-specific expression profiles of the Nurr1 mRNA are shown for the mouse (panels
A and B) and monkey (panels C-G) coronal sections (hemisphere). In the mouse
cortex, strong expression of Nurr1 mRNA is restricted to the lateral regions (S2 and Cl
in panel A), whereas relatively weak expression in layer 6b is present throughout the
mouse areas. Double ISH of Nurr1 and CTGF mRNAs (panel B) shows that the
Nurr1-positive neurons in layers 6a and 6b differentially coexpress CTGF mRNA.
The left side of panel B shows the pattern of Nurr1 mRNA only, and the right side
shows the merged image of Nurr1 (green) and CTGF (purple) mRNAs. In the monkey
cortex, a single layer of Nurr1-positive signals covers the entire cortical areas (see
panels C, D and E), except in V1 and V2, where two segregated bands of cells are
observed in the upper and lower parts of layer 6 (panel F).
Double ISH of Nurr1
(green) and CTGF (purple) mRNAs in area V2 (panel G) shows that only the lower
subpopulations of the Nurr1-positive cells coexpress the two mRNAs (white cells in the
right panel). Also note intense staining in claustrum (Cl) in both species.
Scale bars,
(A) 1 mm; (B) 200 µm; (C and D) 1 cm; (E and F) 250 µm; (G) 50 µm. Cl, claustrum;
S1, primary somatosensory area; S2, secondary somatosensory area; TE, area TE; V1,
primary visual area. A part of the data shown here was reproduced from our previous
study (Watakabe et al. 2007).
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Fig. 1
Layer specific expression is conserved between mice and monkeys.
Fig. 2
Expression patterns of Nurr1 mRNA in the mouse and monkey cortices