Identification of Molecular Properties Coding Areas in Rat’s

Olfactory Bulb by Rank Products

Raquel Santano-Martínez

, Raquel Leiva-González

, Milad Avazbeigi

1,2,3

Agustín Gutiérrez-Gálvez

1,2

and Santiago Marco

1,2

Institut for Bioengineering of Catalonia, Baldiri i Rexach 4-8, 08028-Barcelona, Spain

Departament d’Electrònica, Universitat de Barcelona, Martí i Franqués 1, 08028-Barcelona, Spain

European Center for Soft Computing, Gonzalo Gutiérrez Quirós s/n, 33600-Mieres, Asturias, Spain

Keywords: Olfaction, Odour Coding, Feature Selection, Olfactory Bulb, Chemotopy, 2-Deoxyglucose Uptake.

Abstract: Neural coding of chemical information is still under strong debate. It is clear that, in vertebrates, neural

representation in the olfactory bulb is a key for understanding a putative odour code. To explore this code,

in this work we have studied a public dataset of radio images of 2-Deoxyglucose uptake (2-DG) in the

olfactory bulb of rats in response to diverse odorants using univariate pixel selection algorithms: rank-

products and Mann-Whitney U (MWU) test. Initial results indicate that some chemical properties of

odorants preferentially activate certain areas of the rat olfactory bulb. While non-parametric test (MWU) has

difficulties to detect these regions, rank-product provides a higher power of detection.

1 INTRODUCTION

Olfaction is the main chemical sense and it is key for

basic animal survival since it determines food

intake, sexual mating among others basic functions.

It is known that humans are able to differentiate

thousands of low molecular mass, typically organic

compounds. This sense is the least studied and, it is

not known how olfaction encodes chemical

information about the odorants yet. While due to the

advances in genomics, we know today the family of

G-coupled protein receptors in sequenced species,

the affinity those receptors have for the huge number

of putative ligands is only barely known (Hallem

and Carlson, 2006); (Mori et al., 2006). However, it

is well known that chemical information is coded in

a combinatorial way and that OR are only partially

selective. That is, a single odorant may excite many

diverse OR and an OR responds to a set of ligands

with diverse affinity levels (Malnic et al., 1999).

The current dogma of olfaction is that olfactory

sensory neurons (OSN) express a single type of

olfactory receptor (OR) and that sensory neurons

expressing the same receptor converge to the same

glomerolous. The system is characterized by a large

degree of convergence where millions of OSN

converge to few thousands of glomeruli. In the rat

about 1200 OR are supposed to be active for a total

number of glomeruli of about 2400 (Johnson and

Leon, 2007); (Meister and Bonhoeffer, 2001).

Since each glomerulus only receives inputs from

OSN featuring the same OR receptor, it has been

argued that glomerular activity maps could be a

convenient way to explore how chemical

information is encoded in the olfactory bulb. In this

sense, Leon and Johnson (LJ), in a long and

persistent effort, have acquired 2-DG uptake radio

images for a large number of diverse odorants (Leon

and Johnson, 1999). In this work, we attempt to

determine if certain molecular properties excite

particular areas or modules within the olfactory

bulb. This hipothesis has been formulated by Leon

and Johnson, but without backing statistical analysis

of the available data. This Leon and Johnson dataset

has been previously analyzed by the authors for

clustering (Falasconi et al., 2012), coding capacity

(Fonollosa et al., 2012) and properties coding

(Auffarth et al., 2011). In this work, we go deeper in

this last point using a non-parametric hypothesis

testing and newer state of the art feature selection

methods originally proposed for Microarray data

analysis.

383

Santano-Martínez R., Leiva-González R., Avazbeigi M., González-Gutiérrez A. and Marco S..

Identiﬁcation of Molecular Properties Coding Areas in Rat’s Olfactory Bulb by Rank Products.

DOI: 10.5220/0004327403830387

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2013), pages 383-387

ISBN: 978-989-8565-36-5

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

2 MATERIALS AND METHODS

2.1 Activity Maps and Molecular

Features Datasets

In order to carry out this study we employed the

olfactory bulb (OB) activity dataset obtained by the

group of Leon & Johnson at the University of

California in Irvine. They captured OB activity in

response to a large set of odorants with diverse

chemical structures. This activity was measured

crossways the complete glomerular layer of the rat

OB and mapped using uptake of [14C]-radio labeled

2-deoxyglucose (2DG) (Leon and Johnson, 2003). A

remarkable advantage of this technique is that it

allows observation of the complete olfactory bulb,

but the main drawback is that one can examine the

image for just one odor at one concentration per

experimental animal (Johnson and Leon, 2007).

Examples of glomerular activity maps are shown

in figure 1 for two selected odorants. It is important

to realize that these glomerular maps are not the

result of direct imaging of the olfactory bulb, but

synthetic images built from a series of

autoradiographies of sections of the OB. The exact

imaging procedure is described in detail by Johnson.

(Johnson et al., 1999). For the sake of completeness

here we present a short summary of the process to

build these images. For more details, please refer to

the original publication.

The OB was cut perpendicularly to the long axis

and every sixth 20µm section was used to

autoradiography. The original autoradiographies

were digitized at 108 pixels/mm achieving

glomerular resolution.

A main objective of the image formation process

is to align the images in order to standardize the

anatomical differences from animal to animal. On

the one hand, three anatomical landmarks were used

to standardize rostral–caudal distances between

bulbs: the first cresyl violet–stained section that

possessed an external plexiform layer, the first

section that contained an accessory olfactory bulb

and the last section that contained a mitral cell layer

on its medial aspect. Using a total of 78 animals they

found that the average bulb measures 3.0 mm (25

sections) from the first external plexiform layer to

the first accessory bulb and 2.28 mm (19 sections)

from here to the last mitral cell section. These 44

sections correspond to the columns of the image. On

the other hand, they created a standard grid for y

axis of the image with 80 pixels by row. The image

has 80 active pixels in the section with the largest

glomerular layer. For the other sections, the number

of active pixels is reduced in such a way that each

pixel corresponds to the mean activity in circular

areas of about 120µm of diameter. Due to these

image formation procedures, the resulting maps are

aligned to anatomical landmarks and do not require

further alignment.

The data across five rats exposed to the same

stimulus was averaged to obtain the two-

dimensional (80x44 pixels) activity maps reducing

biological variability. Possibly, if authors only had

used one image per rat, the resolution of the images

could be glomerular (the magnification is 108

pixels/mm).

The complete dataset has 472 group-averaged

maps in response to 339 diverse odorants, some of

them at different concentrations. Furthermore, as a

result of the experimental process, most of the

activity maps include missing values, generally

dispersed in the ventro-caudal and dorsal parts and

on the border of the activity map (Falasconi et al.,

2012). We restricted our analysis to the 1778 pixels

that were represented in all the maps; these pixels

cover almost the entire OB, except its borders.

Figure 1: The activity maps of 4-tert-butylpyridine (left),

and 2-acetylpyridine (right).

Besides the activity dataset, we have a molecular

descriptor dataset for some of odorant stimuli. It

contains a list of odorants, identified with their CAS

number, and with 67 molecular properties that

describes them (Leon and Johnson, 2003). From the

initial 339 individual odorants, for this work we

performed the analysis with 155 odorants and a set

of binary molecular properties. Table 1 lists the

selected binary properties that refer to different

functional groups and different cyclization

structures.

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2.2 Data Analysis

For each molecular descriptor, we statistically tested

whether a pixel was differentially activated in the

target group vs. the control group. For this analysis

we have used, as baseline technique, a non-

parametric hypothesis test: the Mann-Whitney U

test. However, in the last decade permutation tests

have been proposed to improve test power. Among

the different options for permutation tests, we have

chosen rank-products.

2.2.1 Rank Products

Rank Products is a rather new technique mostly used

to identify differentially expressed genes in

microarray experiments. This procedure is derived

from biological analysis and provides us a simple

way to establish the significance level of each

element analyzed calculating rank products (RP)

from replicate experiments (Breitling et al., 2004).

In our case we will use it in order to detect

differentially activated pixels in a neuroimage. This

test can be used in other application domains: for the

analysis of diverse –omics data and general feature

selection (Smit et al., 2007). As far as we know, it

has not been previously used for the analysis of

brain activity.

The underlying assumptions for this technique

are fairly weak thanks to its non-parametric nature

and, additionally, the results also are consistent in

highly noisy data or when a small amount of

replicates are obtainable, and it is very robust against

outliers. For this analysis we used the RankProd

Package for R. The used function permits to control

the estimated percentage of false predictions (pfp)

(Hong et al., 2006). We performed 1000

permutations and we selected the pixels that have a

pfp < 10

-5

2.2.2 Mann-Whitney U Test

The Wilcoxon rank-sum test, also called Mann-

Whitney U test (MWU), is a non-parametric

statistical hypothesis test that determines if one

distribution is stochastically greater than the other.

When we use conventional hypothesis testing in

this context, we must take into account the multiple

comparison problems. The p-values have to be

adjusted to control the probability of any pixel

hypothesis. This is formally known as family-wise

Type I error rate. To carry out this test we use

multtest package of R (Pollard et al., 2005). To cope

with the multiple comparison problems we have

used the Benjamini and Yekutieli correction

(Yekutieli and Benjamini, 1999).We selected the

pixels with a corrected p-value < 0.05.

3 RESULTS

In order to obtain the significant pixels of each

molecular feature analyzed, we compute both

statistics. For the MWU, more demanding p-values

resulted in few selected pixels. Even at this

significance level (p<0.05) there are some molecular

properties that do not show significant pixels. Table

1 shows the number of significant pixels obtained

for the selected molecular features, by Rank

Products and by MWU Test. It can be observed that

even at very demanding pfp rank-products selects

more pixels than MWU.

Figure 2: Active regions in olfactory bulb for aromatic

descriptor obtained by RP (left), and MWU Test (right).

We can locate the significant pixels obtained in

the olfactory bulb to visualize the active areas. In

figure 2 we can observe the results of both tests for

the aromatic feature. As we can observe both tests

show approximately the same active regions, but the

Rank-products seems less noisy.

At figure 3 we can notice that there are different

actives areas for each molecular feature. For some

properties (aromatic, alcohol or ketone) the

technique identifies clear regions that show the well-

known image symmetry of the activation in the OB.

Instead, other properties show scattered pixels and

the interpretation is not so clear.

Table 1: Number of significant pixels for each method.

Molecular feature Rank Products

(pfp < 10

-5

)

Wilcoxon Test

(p-value < 0.05)

Aromatic 796 530

Alcohol 512 39

Alicyclic 127 0

Heterocyclic 126 0

Ester 311 0

Ketone 303 62

IdentificationofMolecularPropertiesCodingAreasinRat'sOlfactoryBulbbyRankProducts

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Figure 3: From left to right alcohol, alicyclic, ester, heterocyclic and ketone significant regions obtained by Rank Products

with pfp=10

-5

4 DISCUSSION

A first outcome of this analysis is the clear

difference in hypothesis test power from rank-

products to the MWU test. This was somewhat

expected, but the results clearly show that MWU

fails completely to identify differentially active areas

for some properties.

Concerning the biological interpretation of the

present results, we have to consider that the

existence of a chemotopical organization at the level

of the OB is controversial. While few groups

consider this proven (Johnson and Leon, 2007);

(Mori et al., 2006), recent results seem to prove the

contrary (Soucy et al., 2009).

The chemotopic organization in the OB has been

supported observing that odorants containing similar

functional groups have similar responses of the

activation in the olfactory bulb (Mori et al., 2006);

(Takahashi et al., 2004). Eventually, this could allow

the prediction of the neural activation pattern from

an odorant and vice versa. This is not proved yet.

The identification of coding areas for different

chemical properties is hindered by the high

dimensionality of chemical information, that is, one

odorant can be described by hundreds (or thousands)

of chemical properties. When the target group shares

one property, all the rest may change introducing a

high level of noise.

While for some properties our results are very

clear (aromatic or ketones), many recent studies

claim that the real code is spatio-temporal, and that

2-DG images fully neglect the temporal dimension

of the code. While this could be true, these results

seem to indicate that the spatial part of the code can

convey important information at least for selected

properties.

The authors are aware of the limitations of the LJ

dataset, and consider the present analysis and

discussion limited to the analyzed data. Further

elaboration on this topic will come from the analysis

of newer imaging techniques with glomerular

resolution that additionally are able to see the

activation dynamics in response to the odorant pulse.

5 CONCLUSIONS

Reported results indicate rank-products as a

convenient pixel selection technique to locate

differentially active areas in response to particular

stimulus (in this case odorants sharing certain

molecular descriptors). This technique succeeds for

properties where conventional non-parametric

testing catastrophically fails.

Previously discussions on the existence of

chemotopy in the OB lacked a supporting statistical

analysis of the available data. This results support

the argument that at least for selected chemical

properties, there are differentially active areas that

are topologically connected (Takahashi et al., 2004).

This goes against recent studies that report that

particular chemical properties are encoded in

scattered glomeruli in the OB (Meister and

Bonhoeffer, 2001); (Ma et al., 2012).

ACKNOWLEDGEMENTS

The authors are members of the SGR2009-0753

consolidated research group (Generalitat de

Catalunya, Spain). The authors thank the group

around Michael Leon and Brett Johnson at the

University of California at Irvine for collecting and

providing the data.

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