A 3D transcriptomics atlas of the mouse nose sheds light on the anatomical logic of smell

SUMMARY

The sense of smell helps us navigate the environment, but its molecular architecture and underlying logic remain understudied. The spatial location of odorant receptor genes (Olfrs) in the nose is thought to be independent of the structural diversity of the odorants they detect. Using spatial transcriptomics, we create a genome-wide 3D atlas of the mouse olfactory mucosa (OM). Topographic maps of genes differentially expressed in space reveal that both Olfrs and non-Olfrs are distributed in a continuous and overlapping fashion over at least five broad zones in the OM. The spatial locations of Olfrs correlate with the mucus solubility of the odorants they recognize, providing direct evidence for the chromatographic theory of olfaction. This resource resolves the molecular architecture of the mouse OM and will inform future studies on mechanisms underlying Olfr gene choice, axonal pathfinding, patterning of the nervous system, and basic logic for the peripheral representation of smell.

Graphical Abstract

In brief

Ruiz Tejada Segura et al. employ a spatial transcriptomics approach to create a 3D map of gene expression of the mouse nose and combine it with single-cell RNA-seq, machine learning, and chemoinformatics to resolve its molecular architecture and shed light into the anatomical logic of smell.

INTRODUCTION

The functional logic underlying the topographic organization of primary receptor neurons and their receptive fields is well known for all sensory systems but olfaction (Kandel et al., 2013). The mammalian nose is constantly flooded with odorant cocktails. Powered by a sniff, air enters the nasal cavity until it reaches the olfactory mucosa (OM). There, myriad odorants activate odorant receptors (Olfrs) present in the cilia of olfactory sensory neurons (OSNs), triggering a cascade of events that culminate in the brain and result in odor perception (Buck and Axel, 1991; Kandel et al., 2013). Most mouse mature OSNs express a single allele of one out of ~1,100 Olfr genes (Olfrs) (Chess et al., 1994; Hanchate et al., 2015; Malnic et al., 1999; Saraiva et al., 2015b). Olfrs employ a combinatorial strategy to detect odorants, which maximizes their detection capacity (Malnic et al., 1999; Nara et al., 2011). OSNs expressing the same Olfr share similar odorant response profiles (Malnic et al., 1999; Nara et al., 2011) and drive their axons to the same glomeruli in the olfactory bulb (Mombaerts et al., 1996; Ressler et al., 1994; Vassar et al., 1994). Thus, Olfrs define functional units in the olfactory system and function as genetic markers to discriminate between different mature OSN subtypes (Ibarra-Soria et al., 2017; Saraiva et al., 2015b).

Another remarkable feature of the OSN subtypes is their spatial distribution in the OM. Early studies postulated that OSNs expressing different Olfrs are spatially segregated into four broad areas within the OM, called “zones,” and which define hemicylindrical rings with different radii (Ressler et al., 1993; Vassar et al., 1993). Subsequent studies identified Olfrs expressed across multiple zones, making clear that a division in four discrete zones might not accurately reflect the system, and a continuous numerical index representing the pattern of expression of each Olfr along the zones was implemented (Miyamichi et al., 2005; Strotmann et al., 1992). More recently, a study reconstructed Olfr expression patterns in three dimensions (3D) and qualitatively classified the expression areas of 68 Olfrs in nine overlapping zones (Zapiec and Mombaerts, 2020). However, all these studies sampled a fraction (~10%) of the total intact olfactory receptor gene repertoire and, most importantly, lack a quantitative and unbiased definition of zones or indices. We do not currently understand the full complexity of the OM and lack an unbiased and quantitative definition of zones. In effect, the exact number of zones, their anatomical boundaries, molecular identity, and functional relevance are yet to be determined.

One hypothesis is that the topographic distribution of Olfr and OSN subtypes evolved because it plays a key role in the process of Olfr choice in mature OSNs and/or in OSN axon guidance (Bashkirova et al., 2020; Coppola et al., 2013). An alternative hypothesis is that the spatial organization of Olfr/OSN subtypes is tuned to maximize the detection and discrimination of odorants in the peripheral olfactory system (Ressler et al., 1993). Interestingly, the receptive fields of mouse OSNs vary with their spatial location (Ma and Shepherd, 2000), which in some cases correlates with the patterns of odorant sorption in the mouse OM—this association was proposed as the “chromatographic hypothesis” decades before the discovery of the Olfrs (Mozell, 1966) and later rebranded as the “sorption hypothesis” in olfaction (Schoenfeld and Cleland, 2006; Scott et al., 2014). While some studies lend support to these hypotheses (reviewed in Secundo et al. 2014), others question their validity (Abaffy and Defazio, 2011; Coppola et al., 2019). Thus, the logic underlying the representation of smell in the peripheral olfactory system still remains unknown, and it is subject of great controversy (Kurian et al., 2021; Secundo et al., 2014).

Spatial transcriptomics, which combines spatial information with high-throughput gene expression profiling, expanded our knowledge of complex tissues, organs, or even entire organisms (Achim et al., 2015; Asp et al., 2019, 2020; Junker et al., 2014; Peng et al., 2016). In this study, we employed a spatial transcriptomics approach to create a 3D map of gene expression of the mouse nose, and we combined it with single-cell RNA sequencing (RNA-seq), machine learning, and chemoinformatics to resolve its molecular architecture and shed light onto the anatomical logic of smell.

RESULTS

A high-resolution spatial transcriptomic map of the mouse olfactory mucosa

We adapted the RNA-seq tomography (Tomo-seq) method (Junker et al., 2014) to create a spatially resolved genome-wide transcriptional atlas of the mouse nose. We obtained cryosections (35 μm) collected along the dorsal-ventral (DV), anterior-posterior (AP), and lateral-medial-lateral (LML) axes (n = 3 per axis) of the OM (Figure 1A) and performed RNA-seq on individual cryosections (see STAR Methods). After quality control (Figures S1A–S1D; Table S1; STAR Methods), we computationally refined the alignment of the cryosection along each axis, and we observed a high correlation between biological replicates (Figure 1B). Hence, we combined the three replicates into a single series of spatial data, including 54, 60, and 56 positions along the DV, AP, and LML axis, respectively (Figure 1C; STAR Methods). On average, we detected >18,000 genes per axis, representing a total of 19,249 genes for all axes combined (Figure 1D). Molecular markers for all canonical cell types known to populate the mouse OM were detected in all axes (Figure 1E) and expressed at the expected levels (Saraiva et al., 2015b).

Figure 1. Application of TOMO-seq to mouse OM.

(A) Experimental design. TOMO-seq was performed on nine tissue samples, from which three were sliced along the dorsal-ventral axis (DV), three along the anterior-posterior axis (AP), and three along the lateral-medial-lateral axis (LML).

(B) Boxplots showing the distributions of Spearman’s correlation coefficients (rho) between replicates in each axis.

(C) Heatmaps showing Spearman’s correlation between gene expression patterns at different positions along the three axes.

(D) Number of detected genes along each axis separately or across the whole dataset. Genes were considered as detected when they had at least one normalized count in at least 10% of the samples from one axis.

(E) Heatmaps of log₁₀ normalized expression (after combining the three replicates per axis) of OM canonical markers along the three axes (GBCs, globose basal cells; HBCs, horizontal basal cells; iOSNs, immature olfactory sensory neurons; mOSNs, mature olfactory sensory neurons; RESs, respiratory epithelium cells; RPM, reads per million; SUSs, sustentacular cells).

(F) Normalized expression of canonical OM spatial marker genes along the three axes. Red line shows fits with local polynomial models.

Next, we verified the presence of a spatial signal with the Moran’s I (Schmal et al., 2017; Figure S1E), whose value is significantly higher than 0 for the data along all axes (p < 2 × 10⁻¹⁶ for all axes), indicating that nearby sections have more similar patterns of gene expression than expected by chance. Given the left/right symmetry along the LML axis (Figure 1C), the data were centered and averaged on the two sides—henceforth, the LML axis will be presented and referred to as the lateral-medial (LM) axis (see STAR Methods). We could reproduce the expression patterns for known OM spatial markers, including the dorsomedial markers Acsm4 and Nqo1 (Gussing and Bohm, 2004; Oka et al., 2003) and the ventrolateral markers Ncam2 and Reg3g (Alenius and Bohm, 1997; Yu et al., 2005; Figures 1F and S1F).

Together, these results show that RNA tomography is a sensitive and reliable method to examine gene expression patterns in the mouse OM.

Spatial differential gene expression analysis identifies cell-type-specific expression patterns and functional hotspots in the OM

In the last 3 decades, multiple genes with spatially segregated expression patterns across the OM have been identified. Most of these genes are expressed in mature OSNs and encode chemosensory receptors, transcription factors, adhesion molecules, and many molecules involved in the downstream signaling cascade of receptor activation (Cho et al., 2007; Cloutier et al., 2002; Fulle et al., 1995; Greer et al., 2016; Gussing and Bohm, 2004; Juilfs et al., 1997; Liberles and Buck, 2006; Miyamichi et al., 2005; Norlin et al., 2001; Oka et al., 2003; Pacifico et al., 2012; Saraiva et al., 2015b; Tietjen et al., 2003, 2005; Vassar et al., 1993; Wang et al., 2004; Yoshihara et al., 1997; Yu et al., 2005; Zapiec and Mombaerts, 2020). A smaller number of zonally expressed genes (e.g., metabolizing enzymes, chemokines, and transcription factors) were found to be expressed in sustentacular cells, globose basal cells, olfactory ensheathing cells, Bowman’s gland cells, and respiratory epithelial cells (Cloutier et al., 2002; Duggan et al., 2008; Heron et al., 2013; Juilfs et al., 1997; Miyawaki et al., 1996; Norlin et al., 2001; Peluso et al., 2012; Whitby-Logan et al., 2004; Yu et al., 2005). Despite this progress, our knowledge on what genes display true zonal expression patterns and what cell types they are primarily expressed in is still very limited.

To identify axis-specific differentially expressed genes (DEGs) (hereafter referred to as spatial DEGs), we first filtered out lowly expressed genes, then binarized the expression levels at each position according to whether they were higher or lower than their median expression, and applied the Ljung-Box test to the autocorrelation function calculated on the binarized expression values (Figure S2A; STAR Methods). After correcting for multiple testing, we obtained a total of 12,303 spatial DEGs for the three axes combined (false discovery rate [FDR] < 0.01; Figure 2A)—the AP axis showed the highest number of spatial DEGs (10,855), followed by the DV axis (3,658) and the LM (1,318).

Figure 2. Genes with non-random spatial patterns across different cell types in the OM.

(A) Venn diagram showing the numbers of spatial differentially expressed genes (DEGs) along each axis.

(B) Bar plot showing the log₁₀ number of spatial DEGs that are mOSN specific (“mOSNs”) or that are detected only in cell types other than mOSNs (“other”).

(C) Heatmap of log₁₀ mean expression per cell type of genes that are not expressed in mOSNs but only in other OM cell types (INPs, immediate neuronal precursors; iSCs, immature sustentacular cells; mSCs, mature sustentacular cells; MVCs, microvillous cells; mSCs, mature sustentacular cells).

(D) UMAP plots of spatial DEGs along the three axes (n = 3 per axis). Each gene is colored according to the cluster it belongs to.

(E) Normalized average expression patterns of spatial DEGs clusters along the three axes.

(F) Heatmap showing the log₂ enrichment over the random case for the intersection between lists of genes belonging to different clusters (indicated by colored circles) across pairs of axes.

Next, we added cell-type resolution to the spatial axes by combining our data with a single-cell RNA-seq (scRNA-seq) dataset from 13 cell types present in the mouse OM (Fletcher et al., 2017). We cataloged spatial DEGs based on their expression in mature OSNs (mOSNs) versus the 12 other cell types (non-mOSNs; Figures 2B and 2C; Table S2). This led to the identification of 456 spatial DEGs expressed exclusively in non-mOSNs, which are associated with gene ontology (GO) terms, such as transcription factors, norepinephrine metabolism, toxin metabolism, bone development, regulation of cell migration, T cell activation, and others (Table S2). Some genes are expressed across many cell types, but others are specific to one cell type (Figure 2C; Table S2). As expected, some of these genes are cell-specific markers with known spatial expression patterns, such as the sustentacular cells and Bowman’s glands markers Cyp2g1 and Gstm2 (Yu et al., 2005), the neural progenitor cell markers Eya2 and Hes6 (Tietjen et al., 2003), and the basal lamina and olfactory ensheathing cell markers Aldh1a7 and Aldh3a1 (Norlin et al., 2001; Table S2). We also identified spatial DEGs along a single axis or multiple axes and specific to one or few cell types (Figures S2B and S2C). For example, the ribosomal protein Rps21 plays a key role in ribosome biogenesis, cell growth, and death (Wang et al., 2020) and is primarily expressed in horizontal basal cells (HBCs), consistent with their role in the maintenance and regeneration of the OM (Leung et al., 2007). Another example is the extracellular proteinase inhibitor Wfdc18, which induces the immune system and apoptosis (Jung et al., 2004) and is expressed in microvillous cells type 1 (MVC1s), consistent with their role in immune responses to viral infection (Baxter et al., 2020). Two more examples are the fibroblast growth factor Fgf20 in immature sustentacular cells (iSCs) and the adapter protein Dab2 in mature sustentacular cells (mSCs) (Figures S2B and S2C). Fgf20 is expressed in several cell types, regulates the horizontal growth of the olfactory turbinates, and is preferentially expressed in the lateral OM (Yang et al., 2018), consistent with our data. Dab2 regulates mechanisms of tissue formation, modulates immune responses, and participates in the absorption of proteins (Finkielstein and Capelluto, 2016; Park et al., 2019), consistent with the known maintenance and support roles of mSCs in the OM (Brann et al., 2020).

A GO enrichment analysis on the axis-specific DEGs for non-mOSNs genes revealed a very wide variety of biological processes and molecular functions. Some of the notable terms identified were water and fluid transport (e.g., Ctfr and Aqp3), transcription factors (e.g., Hes1 and Dlx5), oxidation-reduction processes (e.g., Scd2 and Cyp2f2), microtubule cytoskeleton organization involved in mitosis (e.g., Stil and Aurkb), cell cycle (e.g., Mcm3 and Mcm4), cell division (e.g., Kif11 and Cdca3), negative regulation of apoptosis (e.g., Dab2 and Scg2), sensory perception of chemical stimulus (e.g., Olfr870 and Gnas), and cellular processes (e.g., Mal and Pthlh), among many others (Table S2).

The identification of thousands of spatial DEGs prompted us to examine their distribution patterns along each axis and the putative functions associated with such spatial clusters of gene expression. We started by using uniform manifold approximation and projection (UMAP) (Becht et al., 2018) and hierarchical clustering to visualize and cluster all spatial DEGs along the three axes. This analysis uncovered nine patterns of expression in the DV and AP axes each and five patterns in the LM axis (Figures 2D and 2E). These patterns include variations of four major shapes: monotonically increasing (/), monotonically decreasing (\), U-shape (W), and inverted U-shape (X) (Figure 2E). The latter two patterns present clear maximum and minimum at different positions along the axis—for example, the brown, green, pink, magenta, and black AP clusters show a similar inverted U-shape pattern, but their maximum moves along the axis (Figure 2E). As expected, the dorsomedial markers Acsm4 and Nqo1 belong to the turquoise clusters in both the DV and LM axes, while the ventrolateral marker Reg3g belongs to the blue cluster from the DV axis (Figure 1F; Table S3), mimicking their respective expression pattern in the mouse OM.

The total number of genes per cluster had a median value of 236 but varied greatly between clusters—ranging from 57 in the green LM cluster to 8,551 in the turquoise AP cluster (Figure 2D; Table S3). GO enrichment analyses on the spatial DEGs yielded enriched terms for 14 of the 23 spatial clusters (Table S3). For example, the turquoise AP cluster displaying a monotonically increasing pattern (Figure 2E) yielded GO terms associated with the molecular machinery of mOSNs—such as axonal transportation, RNA processing, ribosomal regulation, and regulation of histone deacetylation (Table S3). Interestingly, the brown DV cluster, which displays a monotonically decreasing expression pattern (Figure 2E), had similar GO term enrichment (Table S3). In agreement with these results, we found that most known OSN activity-dependent markers (Wang et al., 2017) are spatial DEGs belonging to the AP turquoise and brown clusters, which contain genes with expression peaks in the posterior region (Figure S2E; Table S3). We also observed a similar trend in the DV axis, with many of these markers being more highly expressed in the dorsal region (Figure S2E).

The results above show that OSN activity is enriched in the dorsoposterior region of the OM, which could be due to an enrichment of OSNs in that region. To test this hypothesis, we estimated the abundance and spatial variability of OSNs and five additional major cell types (HBCs, globose basal cells [GBCs], SCs, MVCs, and immediate neuronal precursors [INPs]) in each section through a cell deconvolution analysis (see STAR Methods). We observed statistically significant changes in the abundance of OSNs, which is predicted to be higher in the dorsoposterior region of the OM, as previously suggested (Nickell et al., 2012; Vedin et al., 2009). Conversely, other cell types like HBCs are predicted to have an opposite pattern, as they tend to be more abundant in the anteroventral region (Figure S2F; Table S2).

Next, we extended our GO analysis to the remaining spatial clusters and found additional terms enriched or shared between several clusters among the three axes. For example, GO terms enriched in the dorsomedial region (turquoise DV, pink AP, and LM green clusters) include detoxification of several metabolites and multiple metabolic and catabolic processes, suggesting that this region is involved in the OM detoxification (Table S3). Another example is the enrichment in terms related to the immune system—such as defense response and humoral immune response—in the anteromedial section along the AP axis (yellow, black, and magenta AP and turquoise LM clusters), which strongly hints at a role of this area in defending OM from pathogenic invaders (Table S3). The anteroventral and posteroventral regions (blue DV and blue AP clusters) are enriched in terms related to the cellular and anatomical organization (e.g., extracellular matrix organization and regulation of cell communication) and bone and cartilage development (e.g., ossification and biomineral tissue development), suggesting these locations are hotspots for the development and regulation of the OM structure. Finally, the ventral portion of the DV (red DV cluster) is associated with terms related to cilia movement and function (e.g., regulation of cilium movement and microtubule-based movement), consistent with both the location and functions of the respiratory epithelium (Yu et al., 2005).

Next, we further explored the relationships between the genes populating each cluster. We found that ventral genes (blue DV cluster) tend to reach a peak in expression in the anterior area of the OM (yellow AP cluster) more often than expected by chance (Figure 2F). We also observed that medial genes (turquoise LM cluster) are more highly expressed in the dorsal (magenta DV cluster) and anterior regions (black, yellow, and magenta AP cluster), while genes peaking in the lateral region (brown LM cluster) tend to be ventral (red DV cluster; Figure 2F). These conclusions hold, even when we exclude Olfrs from the analysis (Figure S2D).

These associations between the clusters of spatial DEGs along different axes suggest that the presence of complex 3D expression patterns in OM is not restricted to either Olfrs or OSNs. Moreover, our results show that our experimental approach can uncover spatially restricted functional hotspots within the OM.

A 3D transcriptomic atlas of the mouse OM

Since the OR discovery 3 decades ago (Buck and Axel, 1991), in situ hybridization (ISH) has been the method of choice to study spatial gene expression patterns across the OM. This method is technically challenging and inherently a very low-throughput experimental approach.

As we showed above, our Tomo-seq data enable a systematic and quantitative estimation of gene expression levels along the three axes of the OM. Here, we take this analysis one step further and generate a fully browsable tridimensional (3D) gene expression atlas of the mouse OM. First, we reconstructed the 3D shape of OM based on publicly available images of OM sections (STAR Methods). We then fed the shape information combined with the gene expression data along the three axes into the iterative proportional fitting (IPF) algorithm (Fienberg, 1970; Junker et al., 2014; Figure 3A). The 3D atlas of the OM faithfully reproduced the known 3D pattern of the dorsomedial marker Acsm4 (Oka et al., 2003; Figure 3B). To further validate our 3D gene expression atlas of the OM, we compared the 3D reconstructed patterns with conventional ISH patterns for five spatial DEGs identified in this study. The first gene validated was Cytl1, which we confirmed to be expressed along the septum throughout the OM (Figures 3C and 3D), consistent with the role Cytl1 plays in osteogenesis, chondrogenesis, and bone and cartilage homeostasis (Shin et al., 2019; Zhu et al., 2019). The four additional genes (Olfr309, Olfr618, Olfr727, and Moxd2) validated via ISH are presented elsewhere in this manuscript (Figures 4, 5, and S4).

Figure 3. The 3D reconstruction of the OM.

(A) Schematic of 3D shape reconstruction strategy. Images of 2D slices along the AP axis of the OM were piled together to build an in silico 3D model of OM, which can also be used to visualize in silico sections. This 3D model, together with the gene expression data along each axis, was the input of the iterative proportional fitting algorithm, which allowed us to estimate a 3D expression pattern for any gene.

(B and C) Reconstruction of the 3D expression patterns of the Acsm4 (B) and Cytl1 (C) in the OM, visualized in 3D and in OM coronal sections taken along the AP axis.

(D) ISH experiment validating Cytl1 spatial expression pattern reconstructed in (C); note that Cytl1 is expressed in the septal region all along the OM. Purple arrowheads indicate the location of labeled cells. The dotted outline marks the borders of the OM dissected and used in the RNA-seq experiments and for the construction of the 3D model.

Figure 4. Zonal organization of Olfrs in the OM.

(A) Number of Olfrs detected in our data and in an OM bulk RNA-seq data (Saraiva et al., 2015b).

(B) Venn diagram of spatially differentially expressed Olfrs per axis (n = 3 per axis).

(C) Visualization of the five zones across the OM (coronal sections) estimated with a latent Dirichlet allocation algorithm. The colors indicate the probability (scaled by its maximum value) that a position belongs to a given zone.

(D) Diffusion map of Olfrs. Genes are colored based on the zone they fit in the most. DC, diffusion component.

(E) Same as (D), with Olfrs colored by their 3D index.

(F) We fitted a random forest algorithm to the 3D indices of 681 spatial Olfrs using nine genomic features as predictors. After training, the random forest was used to predict the 3D indices of 697 Olfrs that have too low levels in our data.

(G) 3D indices versus the indices of 80 Olfrs estimated in Miyamichi et al. (2005) from ISH data. Black circles indicate Olfrs detected in our dataset; green circles are Olfrs whose indices were predicted with random forest. The correlation coefficients computed separately on these two sets of Olfrs are, respectively, rho = 0.92 (p < 2 × 10⁻¹⁶) and rho = 0.69 (p = 0.009).

(H–P) Predicted expression patterns (H, K, and N), degrees of belonging (I, L, and O), and ISH (J, M, and P) for Olfr309, Olfr727, and Olfr618, respectively. Purple arrowheads indicate the location of labeled cells. The dotted outline marks the borders of the OM dissected and used in the RNA-seq experiments and for the construction of the 3D model.

Figure 5. Zonal organization of non-Olfr genes in the OM.

(A) Heatmap of degrees of belonging of most zone-specific non-Olfr genes.

(B) 3D gene expression pattern (coronal sections) of most topic-specific non-Olfrs for each topic along the AP axis.

(C) Reconstruction of the 3D expression pattern of the gene Moxd2 in the OM.

(D) ISH experiment validating Moxd2 spatial expression pattern reconstructed in (B) and (C). Purple arrowheads indicate the location of ISH-labeled cells. The dotted outline marks the borders of the OM dissected and used in the RNA-seq experiments and for the construction of the 3D model.

To make this 3D gene expression atlas of the mouse OM available to the scientific community, we created a web portal (available at http://atlas3dnose.helmholtz-muenchen.de:3838/atlas3Dnose) providing access to the spatial transcriptomic data described here in a browsable and user-friendly format. This portal contains search functionalities allowing the users to perform pattern search by gene, which returns (1) the normalized counts along each of the three axes, (2) the predicted expression pattern in 3D with a zoom function, (3) visualization of the expression patterns in virtual cryosections along the OM by selecting any possible pairwise intersection between two given axes (i.e., DVxAP, DVxLM, and APxLM), (4) the degrees of belonging for each “zone” (see results section below), and (5) single-cell expression data across 14 different cell types.

In sum, here, we generated and made publicly available a highly detailed and fully browsable 3D gene expression atlas of the mouse OM, which allows the exploration of the expression patterns for ~20,000 genes.

Topographical expression patterns of Olfrs

In our combined dataset, we detected a total of 959 Olfrs (Figure 4A), of which we confidently reconstructed the spatial expression patterns for 689 differentially expressed in space (FDR < 0.01; Figure 4B)—a number six times larger than the combined 112 Olfrs characterized by previous ISH studies (Miyamichi et al., 2005; Ressler et al., 1993; Vassar et al., 1994; Zapiec and Mombaerts, 2020). To define Olfr expression in 3D space in a rigorous, unbiased, and quantitative way, we ran a latent Dirichlet allocation (LDA) algorithm (STAR Methods; Liu et al., 2016) on the 689 spatially differentially expressed Olfrs. LDA is a generative statistical model that can infer the topics of a collection of documents based on the variability and frequency of specific words. In the context of this study, if the spatial expression data of Olfrs are considered equivalent to “documents,” the inferred topics correspond to “zones” (STAR Methods). We ran LDA for different numbers of zones, and the trend of the log likelihood function suggested that the minimal number of topics required to represent the diversity of patterns is five (Figure S3A; STAR Methods). Next, we visualized the spatial distribution of these five zones in our 3D OM model, with colors representing the probability that a given spatial position belongs to each zone. These five zones extend from the dorsomedial-posterior to the lateroventral-anterior region (Figure 4C), consistent with the previously described zones (Miyamichi et al., 2005; Ressler et al., 1993; Vassar et al., 1993).

The majority of Olfrs with known spatial patterns are restricted to a single zone, but a small number of Olfrs are expressed across multiple zones in a continuous or non-continuous fashion (Miyamichi et al., 2005; Strotmann et al., 1992; Zapiec and Mombaerts, 2020). Under this logic, each Olfr has a different probability of belonging to the five topics and zones we identified. To test this assumption, we used the same mathematical framework as above to compute the probabilities that the expression pattern of each Olfr belongs to a given zone, i.e., the “degree of belonging” (DOB) (Table S4). The DOBs represent a decomposition of the expression patterns in terms of the five zones (Figure 4C) and quantitatively describe the changes in patterns of genes with overlapping areas of expression (e.g., see Figure S3B). The width of the distribution of DOBs across the five zones, which can be measured with entropy, can distinguish genes whose patterns mostly fit in a single zone from those spanning multiple zones (Figure S3C; STAR Methods).

To visualize the global distribution of the 689 Olfrs, we applied the diffusion map algorithm (Haghverdi et al., 2015) to their DOBs. This showed that the genes are approximately distributed along a continuous line spanning the five zones and without clear borders between zones (Figure 4D), consistent with previous studies (Miyamichi et al., 2005; Strotmann et al., 1992; Zapiec and Mombaerts, 2020). With the diffusion pseudo-time algorithm (Haghverdi et al., 2016), we calculated an index (hereafter referred to as “3D index”) that tracks the position of each Olfr gene along the 1D curve in the diffusion map and represents its expression pattern (Figure 4E).

While our approach yielded an index for the 689 spatially differentially expressed Olfr genes used to build the diffusion map, there were 697 Olfrs that could not be analyzed, either because they were too lowly expressed or not detected at all in our dataset (Figure 4A). Since the spatial expression patterns for some Olfrs are partly associated with their chromosomal and genomic coordinates (Sullivan et al., 1996; Tan and Xie, 2018; Zhang et al., 2004), we hypothesized that we could use a machine-learning algorithm to predict the 3D indices for the 697 Olfrs missing from our dataset. Thus, we trained a random forest algorithm on the 3D indices of the spatially differentially expressed Olfrs in our dataset using nine genomic features as predictors, such as the chromosomal position, number of Olfrs in cluster, and distance to nearest known enhancer (Figure 4F; STAR Methods). The algorithm performance was confirmed by over 100 cross-validation iterations, which revealed a low root-mean-square error (≲10%) on the mean 3D index (Figure S4A; STAR Methods). The five most important predictors were features associated with chromosomal position, distance to the closest Olfr enhancer (Monahan et al., 2017), length of the Olfr cluster, position in the Olfr cluster, and phylogenetic Olfr class (Figure 4F). Using this machine-learning algorithm, we predicted the 3D indices for the 697 Olfrs missing reliable expression estimates in our dataset (Table S4).

Overall, through multiple unsupervised and supervised computational methods, we have quantitatively defined five spatial expression domains in the OM (called zones) and have provided accurate 3D spatial indices for 1,386 Olfrs, which represents ~98% of the annotated Olfrs.

Importantly, we found strong correlations between the “Miyamichi indices” inferred using ISH in Miyamichi et al. (2005) and our 3D indices (rho = 0.88; p < 2 × 10⁻¹⁶; Figure 4G). This correlation remains significant when we separately analyze the 3D indices computed by diffusion pseudo-time (rho = 0.92; p < 2 × 10⁻¹⁶) or predicted by random forest (rho = 0.69; p = 0.009). In addition, our indices also correlated with the “Zolfr indices” (Zapiec and Mombaerts, 2020; rho = 0.88; p < 2 × 10⁻¹⁶; Figure S4B), and with the “Tan indices” (Tan and Xie, 2018; rho = 0.89; p < 2 × 10⁻¹⁶; Figure S4C), inferred by ISH and RNA-seq, respectively.

To confirm our predictions, we performed ISH for three Olfrs that have not been characterized before—two detected in our dataset and for which the 3D index was calculated via diffusion pseudo-time (DPT) (Olfr309 and Olfr727) and one not detected in our dataset and for which the 3D index was predicted with the random forest algorithm (Olfr 618). Notably, all three Olfrs were expressed primarily within the zones they were predicted to be expressed in: zone 2 for Olfr309 (3D index = 30.76), zones 4 and 5 for Olfr727 (3D index = 75.14), and zone 1 for Olfr618 (3D index = 7.42; Figures 4H–4P and S4D–S4F; Table S4).

Topographical expression patterns for non-Olfr genes

A recent study performed RNA-seq in 12 randomly dissected OM pieces along the DV axis and identified ~700 non-Olfr genes with putatively spatial patterns (Tan and Xie, 2018), including many genes with zonal expression patterns identified previously (Duggan et al., 2008; Gussing and Bohm, 2004; Liberles and Buck, 2006; Ling et al., 2004; Norlin et al., 2001; Oka et al., 2003; Tietjen et al., 2003; Whitby-Logan et al., 2004; Yoshihara et al., 1997). By identifying 11,538 non-Olfr spatial DEGs (Figures 2 and 3; Table S5), we increased the list of non-Olfr genes with spatial zonation in the OM by 16-fold.

Using the mathematical framework based on topic modeling described above, we decomposed the expression patterns of non-Olfr genes onto the five zones we identified. This allowed us to identify genes showing zone specificity by calculating the entropy of the DOBs distributions. Interestingly, we found 28 genes highly specific for each of the five zones (i.e., with entropy <1; STAR Methods; Figure 5A; Table S5). For example, S100a8 (zone 1) codes for a calcium-binding protein involved in calcium signaling and inflammation (Yoshikawa et al., 2018), Moxd2 (zone 2) is a mono-oxygenase dopamine hydroxylase-like protein possibly involved in olfaction (Kim et al., 2014), Lcn4 (zone 3) is a lipocalin involved in transporting odorants and pheromones (Charkoftaki et al., 2019; Miyawaki et al., 1994), Gucy1b2 (zone 4) is a soluble guanylyl cyclase oxygen and nitric oxide (Bleymehl et al., 2016; Koglin et al., 2001), and Odam (zone 5) is a secretory calcium-binding phosphoprotein involved in cellular differentiation and matrix protein production and with antimicrobial functions of the junctional epithelium (Lee et al., 2012; Springer et al., 2019; Figure 5B). The high zone specificity of the expression pattern of these genes gives clues into possible biological processes taking place in the zones. Indeed, Gucy1b2 is a known genetic marker for a small OSN subpopulation localized in cul-de-sac regions in the lateral OM, consistent with our reconstruction (Figure 5B), and it regulates the sensing of environmental oxygen levels through the nose (Omura and Mombaerts, 2015; Saraiva et al., 2015b). In addition, our ISH experiments revealed that Moxd2 is expressed in a small ventrolateral patch of the OM (Figure 5D), validating its predicted 3D spatial pattern (Figures 5B and 5C) and highlighting a potential highly localized role of this protein in neurotransmitter metabolism (Goh et al., 2016) in the mouse OM.

A recent study showed that the transcription factors Nfia, Nfib, and Nfix regulate the zonal expression of Olfrs (Bashkirova et al., 2020). To get some insights into the signaling pathways involved in this process, we mined our dataset for genes encoding ligands and receptors (Efremova et al., 2020) correlated with the expression patterns of the Nfis (STAR Methods). This analysis returned 476 genes involved in biological processes associated with the regulation of neurogenesis, regulation of cell development, anatomical structure development, cellular component organization or biogenesis, and regulation of neuron differentiation (Table S5). As expected, some of these genes have known functions in the OM, such as segregating different cell lineages for Notch1–3 (Carson et al., 2006), genes associated with the development of the nervous system (e.g., Erbb2 and Lrp2; Britsch et al., 1998; Spuch et al., 2012), and many others associated with the semaphorin-plexin, ephrin-Eph, and Slit-Robo signaling complexes—which regulate OSN axon guidance and spatial patterning of the OM (Cloutier et al., 2002; Cutforth et al., 2003; Huber et al., 2003; Kania and Klein, 2016). Excitingly, the majority of these 476 genes still have unknown functions in the OM, thus highlighting the potential of our approach to discover genes and pathways involved in the regulation of zonal expression in the OM.

The anatomical logic of smell

For most sensory systems, the functional logic underlying the topographic organization of primary receptor neurons and their receptive fields is well known (Kandel et al., 2013). In contrast, the anatomic logic of smell still remains unknown, and it is subject of great controversy and debate (Kurian et al., 2021; Secundo et al., 2014).

To explore the underlying logic linked to the zonal distribution of Olfrs, we investigated possible biases between their expression patterns and the physicochemical properties of their cognate ligands. First, we compiled a list of known 738 Olfr-ligand pairs, representing 153 Olfrs and 221 odorants (Figure 6A; Table S6). Interestingly, Olfr pairs sharing at least one common ligand have more similar expression patterns (i.e., more similar 3D indices) than Olfrs detecting different sets of odorants (Wilcoxon rank-sum test; p < 2 × 10⁻¹⁶; Figure 6B). This observation is consistent with the hypothesis that the Olfr zonal distribution depends, at least partially, on the properties of the odorants they bind to.

Figure 6. Physiological role of the zones.

(A) Circular network illustrating the pairs of Olfrs and ligands that we found in the literature.

(B) Boxplots showing the distributions of the absolute value of 3D index differences between pairs of Olfrs sharing at least one ligand versus pairs of Olfrs without cognate ligands in common. The difference between the two distributions is statistically significant (p < 2 × 10⁻¹⁶; Wilcoxon rank-sum test).

(C) Scatterplot showing the Spearman correlation coefficients between the ligands’ mean 3D indices and molecular descriptors and the corresponding −log₁₀(adjusted p value). Turquoise circles indicate the descriptors having a significant correlation only when both class I and II Olfrs are considered; red circles mark the descriptors with a significant correlation also when class I Olfrs are removed.

(D) Scatterplot illustrating the correlation between air/mucus partition coefficients of the odorants and the average 3D indices of their cognate Olfrs. Only odorants for which we know at least two cognate Olfrs (110) were used here. Odorants are colored according to the zone they belong to (defined as the zone with the highest average degree of belonging computed over all cognate receptors). The five odorants highlighted in the plot by larger circles are indicated on the right-hand side, along with their molecular structure and common name.

(E) Average expression pattern of the cognate Olfrs recognizing each of the five odorants highlighted in (D), including their respective CAS numbers.

Next, we considered a set of 1,210 physicochemical descriptors, including the molecular weight, the number of atoms, aromaticity index, lipophilicity, and the air/mucus partition coefficient (K_am), which quantifies the mucus solubility of each ligand (Rygg et al., 2017; Scott et al., 2014; STAR Methods). We then computed the Spearman’s correlation of each of these descriptors of the ligands with the average 3D indices of the Olfrs detecting them. We found a statistically significant correlation for 744 descriptors (FDR < 0.05; Figure 6C; Table S6). The top five highest correlations were with the air/mucus partition coefficient K_am (rho = 0.55; adjusted p = 1 × 10⁻⁷), ATSC2S (rho = −0.56; adjusted p = 2 × 10⁻⁷), SPmax2_Bh.s (rho = −0.52; adjusted p = 2 × 10⁻⁶), SPmax1_Bh.s (rho = −0.51; adjusted p = 3 × 10⁻⁶), and ATSC6e (rho = −0.51; adjusted p = 3 × 10⁻⁶; Figure 6C; Table S6). Interestingly, ATSC2S, SPmax1_Bh.s, and SPmax2_Bh.s are also related to solubility (Consonni and Todeschini, 2008; Devillers and Domine, 1997; Hollas, 2003). Notably, the association between K_am and the mean 3D indices does not depend on the number of zones defined with LDA (STAR Methods). Furthermore, it remains robust to changes in the set of ligands and/or Olfrs used for the analysis, namely, when we excluded Olfrs for which the 3D indices were predicted with the random forest model (rho = 0.48; p = 2 × 10⁻⁶; Figure S5B) or when only 3D indices from class II Olfrs were included in the analysis (rho = 0.5; p = 1 × 10⁻⁷; Figure S5C).

In particular, the positive correlation of the 3D indices with K_am (Figure 6D) indicates that the most soluble odorants (lower) preferentially activate Olfrs located in the most antero-dorsomedial region (zone 1) of the OM, while the least soluble odorants (higher K_am) activate Olfrs in the postero-ventrolateral region (zones 4 to 5). In other words, gradients of odorants sorption (as defined by their K_am) correlate with the gradients of Olfr expression from zone 1 to zone 5, consistent with the chromatographic/sorption hypothesis in olfaction (Mozell, 1966; Scott et al., 2014). This is exemplified by the plots in Figure 6E, illustrating the predicted average expression levels across OM sections of the Olfrs binding to five odorants with different values of K_am. These results show a direct association between Olfr spatial patterns and the calculated sorption patterns of their cognate ligands in the OM, providing a potential explanation for the physiological function of the zones in the OM.

DISCUSSION

Past studies yielded inconclusive and sometimes contradictory views on the basic logic underlying the peripheral representation of smell, partly because the topographic distribution of OSN subtypes and their receptive fields still remained vastly uncharted, data on Olfr-ligand pairs were scarce, and there were pitfalls associated with electro-olfactogram recordings used to study spatial patterns of odor recognition in the nose (Kurian et al., 2021; Scott and Scott-Johnson, 2002; Secundo et al., 2015). Here, we combined RNA-seq and computational approaches that utilize unsupervised and supervised machine learning methods to discover and quantitatively characterize spatial expression patterns in the OM. We created a 3D transcriptional map of the mouse OM, which allowed us to spatially characterize 17,628 genes, including ~98% of the annotated Olfrs. We identified and validated by ISH several spatial marker genes, and a clustering analysis pinpointed the OM locations where specific functions related to, e.g., the immune response might be carried out. We also mathematically defined Olfr expression zones in the OM with an unsupervised machine-learning method based on topic modeling. We estimated that the OM includes at least five zones, which can be used to decompose the expression patterns of all genes. However, our analysis showed that there is a continuous distribution of Olfrs patterns in the OM. Thus, while a discrete number of zones might be convenient to provide a first classification of Olfrs, these might obscure the complexity of the OM spatial patterns. To account for this, we adopted a mathematical framework that can rigorously define zones while capturing finer structures in the data, via the degrees of belonging and the 3D index, which are more suitable to describe Olfrs patterns crossing multiple zones.

The global transcriptomic landscape of the vertebrate OM is similar between individuals and broadly conserved among different vertebrate species, ranging from zebrafish to human (Bear et al., 2016; Saraiva et al., 2015a, 2019). Similarly, the spatial segregation of Olfrs into partially overlapping rings of expression, centered around the midline structure of the OM, is also conserved among vertebrates (Freitag et al., 1995; Horowitz et al., 2014; Marchand et al., 2004; Miyamichi et al., 2005; Octura et al., 2018; Ressler et al., 1993; Strotmann et al., 1992; Vassar et al., 1993; Weth et al., 1996). While the number of Olfr zones in zebrafish, frog, and salamander still remain unknown (Freitag et al., 1995; Marchand et al., 2004; Weth et al., 1996), ISH studies suggested that the total number of Olfr expression zones can vary between mammals—ranging from two in macaque (Horowitz et al., 2014) to four in rat (Vassar et al., 1993) and goat (Octura et al., 2018), and between four and nine in mouse (Miyamichi et al., 2005; Ressler et al., 1993; Zapiec and Mombaerts, 2020). While the exact number of Olfr expression zones in OM still remains under debate, our results are consistent with both another recent RNA-seq study (Tan and Xie, 2018) and the largest Olfr ISH study in the mouse OM (Miyamichi et al., 2005), thus supporting the existence of at least five overlapping Olfr expression zones in the mouse nose.

Taking into account how conserved the molecular organization of the OM is in vertebrates, the 2-fold reduction in the number of Olfr expression zones in macaque compared with rodents and goat (an ungulate) is puzzling. While we cannot exclude the presence of confounding factors (e.g., limited Olfr sampling and/or inconsistent definitions of “zones”), it is interesting that the 2-fold reduction in number of zones is associated with a 2-fold reduction in the number of annotated intact Olfrs in macaque (and other higher primates, including human) compared with other rodents and ungulates (Horowitz et al., 2014; Niimura et al., 2014; Saraiva et al., 2019). Since the accelerated loss of Olfr genes during primate evolution has been linked to the acquisition of trichromatic acute vision and dietary changes (Niimura et al., 2018), it is plausible that these evolutionary pressures also helped shape the spatial distribution of Olfrs in macaques and other primates, including human.

The quantitative framework we built for this dataset will facilitate the interrogation of gene expression patterns via an online tool we provide and help answer important questions on the physiology of the nose. Our approach could be easily applied to spatial transcriptomic data collected from other tissues to perform comparisons across tissues from different species or the same tissue across multiple developmental stages. Moreover, the results from this study serve as a template to start answering other important questions about olfaction, such as whether Olfr spatial expression maps can encode maps of odor perception. Because the general molecular mechanisms of olfaction, zonal organization of Olfrs, conservation of ligands among Olfr orthologs, and components of olfactory perception are conserved in mammals (Adipietro et al., 2012; Bear et al., 2016; Freitag et al., 1995; Horowitz et al., 2014; Kurian et al., 2021; Manoel et al., 2021; Octura et al., 2018; Saraiva et al., 2019; Weth et al., 1996), the association we uncovered here between Olfr zones and the solubility of odorants they detect can likely be extrapolated to other mammals, including humans. Finally, the functional logic underlying the mammalian topographic organization of primary receptor neurons and their receptive fields in smell is now starting to be exposed.

Limitations of the study

This study enabled us to answer fundamental and long-standing questions about the rationale behind the spatial organization of the peripheral olfactory system. Specifically, we provide evidence to the hypothesis that the spatial zones increase the discriminatory power of the olfactory system by distributing Olfrs in the areas of the OM more likely to be reached by their cognate ligands, based on their solubility in mucus. A caveat of this approach is that the Olfr-ligand list we compiled from the literature includes odorant libraries of different size and composition and tested using different experimental approaches. Moreover, highly abundant Olfrs have a higher probability of being deorphanized than lowly abundant Olfrs, and ecologically relevant odorants are more likely to activate Olfrs when compared with other odorants (Dunkel et al., 2014; Saraiva et al., 2019; Trimmer and Mainland, 2017). Despite having compiled and performed our analysis on the largest set of Olfr-ligand pairs assembled to date and carrying out multiple robustness checks, we cannot rule out that ascertainment bias might contribute to the associations we found between the Olfr spatial location and the properties of their respective ligands. Future studies investigating the activation profiles for all mouse Olfrs and/or mapping the in vivo activation patterns of mouse Olfrs in the olfactory mucosa will be key to stress test the conclusions of our study.

STAR★METHODS

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and data should be directed to and will be fulfilled by the Lead Contact Luis R. Saraiva (saraivalmr@gmail.com).

Materials availability

This study did not generate new unique reagents.

Data and code availability

RNA-seq raw data have been deposited and are publicly available as of the date of publication at ArrayExpress: E-MTAB-10211. All original code and scripts for the 3D nose atlas shiny app has been deposited at Github and can be found at the Github Repository: https://doi.org/10.5281/zenodo.6036047https://zenodo.org/badge/DOI/10.5281/zenodo.6045897.svg. The 3D nose atlas processed data can be browsed and visualized here: http://atlas3dnose.helmholtz-muenchen.de:3838/atlas3Dnose.

Any additional information required to reanalyze the data reported in this paper is available from the lead contacts upon request.

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Animals

The animals used in this study were adult male C57Bl/6J mice (aged 8–14 weeks, The Jackson Laboratory, Stock # 00664) maintained in group-housed conditions on a 12:12 h light:dark schedule (lights on at 0700 hours). Each mouse was randomly assigned for cryosectioning along one of the three cartesian axes.

The use and care of animals used in this study was approved by the Internal Animal Care and Use Committee (IACUC) of Monell Chemical Senses Center, by the IACUC of the University of São Paulo, and by the Wellcome Trust Sanger Institute Animal Welfare and Ethics Review Board in accordance with UK Home Office regulations, the UK Animals (Scientific Procedures) Act of 1986.

METHOD DETAILS

Dissection of the olfactory mucosa, cryosections, and RNA-sequencing

The olfactory mucosa (OM) of 9 mice was carefully dissected, and all the surrounding tissue (including glands and bone) removed – this was necessary to ensure that the transcripts present in the surrounding tissue do not contaminate the RNA isolated from the OM. The OMs were then embedded in OCT (Tissue Tek), immediately frozen in dry-ice and kept at −80°C. Each OM was then cryosectioned along each of the 3 cartesian axes: dorsal-ventral (DV, n = 3), anterior-posterior (AP, n = 3), or lateral-medial-lateral (LML, N = 3). Every second cryosections (35 μm thick) was collected into 1.5 mL eppendorf tubes containing 350 μL RLT Plus Buffer (Qiagen) supplemented with 1% 2-mercaptoethanol, immediately frozen in dry-ice and kept at −80°C until extraction. RNA was extracted using the RNeasy Plus Micro Kit (Qiagen), together with a genomic DNA eliminator column and a 30-minute incubation with DNAse I (Qiagen). Reverse transcription and cDNA pre-amplification were performed using the SMART-Seq v4 Ultra Low Input RNA Kit for Sequencing (Clontech/Takara). cDNA was harvested and quantified with the Bioanalyzer DNA High-Sensitivity kit (Agilent Technologies). Libraries were prepared using the Nextera XT DNA Sample Preparation Kit and the Nextera Index Kit (Illumina). Multiplexed libraries were pooled and paired-end 150-bp sequencing was performed on the Illumina HiSeq 4000 platform at Sidra Medicine, except for one library (DV-I) for which 125-bp paired-end sequencing was performed on the Illumina HiSeq 2500 platform at the Wellcome Sanger Institute. The raw data are available through ArrayExpress under accession number E-MTAB-10211.

RNA-seq data mapping and gene counting

Reads were aligned to the mm10 mouse genome (release 99). The sequences of the genes “Xntrpc” and “Capn5” were removed from the genome files as in Saraiva et al. (2015b). The alignment was performed with the software STAR version 2.7.3a (Dobin et al., 2013). Genome indexes were generated using STAR –runMode genomeGenerate with default parameters. Then, alignment of reads was performed with the following options: –runThreadN 48 –outSAMunmapped Within –outFilterMultimapNmax 1000 –outFilterMismatchNmax 4 –outFilterMatchNmin 100 –alignIntronMax 50000 –alignMatesGapMax 50500 –outSAMstrandField intronMotif –outFilterType BySJout. The resulting SAM files were converted to bam format and sorted using samtools (version 0.1.19–44428cd) (Li et al., 2009). The multi mapping reads were eliminated using the same software (samtools view -q 255). Finally, the reads for each gene were counted using htseq-count (version 0.11.2) with the options -m intersection-nonempty -s no -i gene_name -r pos (Anders et al., 2014).

Quality control

We excluded all the samples that fulfilled any of these criteria: they had less than 50% mapped reads, less than 4,000 detected genes, more than 20% mitochondrial reads, less than 10,000 total number of reads, or did not express any of the 3 canonical OSN markers Omp, Cnga and Gnal. This resulted in ~51 good-quality sections along the DV axis (~84% out of the collected sections), ~76 (~91% of total) along the AP axis and ~59 (~93% of total) along the LML axis, as averaged across the three replicates per axis.

Data normalization

Gene expression counts were normalized by reads-per-million (RPM), then genes detected in only one replicate and genes that were detected in less than 10% of all samples along one axis were eliminated. To check the similarity between replicates, we calculated Spearman correlations between the transcriptional profiles of sections along each axis (using the top 1000 Highly Variable Genes per axis). Close positions had the most similar transcriptional profiles (Figure 1C). Afterward, the 3 replicates for each axis were aligned as follows: the top 3,000 highly variable genes (HVGs) from each replicate were identified using the method implemented in the scran library in R (Lun et al., 2016) and the intersection of these 3 groups was used in the next steps. For the replicates’ alignment, we took as reference the replicate with the smallest number of slices. We used a sliding window approach that identified the range of consecutive positions on each replicate along which the average value of the Spearman’s correlation coefficient computed with the reference replicate over the HVG was maximum (mean Spearman’s Rho = 0.80, p < 0.05). To mitigate batch effects, the level of every gene was scaled in such a way that their average value in each replicate was equal to the average calculated across all replicates. After this scaling transformation, the data was then averaged between replicates. Once the 3 biological replicates were combined, we had 54 sections along the DV axis, 60 along the AP and 56 along the LML. Along the LML axis a symmetric pattern of expression is expected around the central position, where the septal bone is located. To confirm this in our data, first we identified the central position by analyzing the expression pattern of neuronal markers like Cnga2, Omp and Gnal, whose expression is lowest in the area around the septal bone. Indeed, all three marker genes reach a minimum at the same position along the LML axis (slice 28), which we considered to be the center. The expression patterns of ~90% of genes on either side of the central position show a positive correlation, and ~70% reach statistical significance (Spearman’s correlation computed on the highly variable genes having more than 50 normalized counts in at least 3 slices), further supporting the hypothesis of the bilateral symmetry. Hence, after replicates were averaged, LML axis was made symmetric averaging positions 1:28 and 56:29. Moreover, Olfrs were normalized by the geometric mean of neuronal markers Omp, Gnal and Cnga2, as done previously (Ibarra-Soria et al., 2017).

To verify the presence of a spatial signal, we calculated the Moran’s I and the associated p-values for the top 100 Highly Variable genes along each axis using the “Moran.I″ function from the “ape” library in R with default parameters (Paradis et al., 2004). The p-values of the genes along each axis were combined with the Simes’ method (Simes, 1986) using the function combinePValues from the scran R library (Figure S1E).

Identification of differentially expressed genes and gene clustering

Before testing for differential expression along a given axis, we filtered out genes whose expression levels had low variability. To this aim, for each gene we estimated their highest and lowest expression by taking the average of its three highest and three lowest values respectively. Then, we considered for downstream analyses only the genes that meet either of these two criteria: the highest expression value is greater than or equal to 5 normalized counts and the fold-change between the highest and lowest value is greater than 2; or the difference between the highest and the lowest value is greater than or equal to 4 normalized counts. The expression levels of the genes were binarized according to whether their value was higher or lower than their median expression along the axis. Finally, we used the “ts” function in R to transform the binarized expression values into time series objects, and we applied on them the Ljung-Box test (Box.test function in R with lag = (axis length)-10) which identifies genes with statistically significant autocorrelations, i.e., with non-random expression patterns along an axis. The resulting p-values were adjusted using the FDR method and genes with an FDR <0.01 were considered as differentially expressed. For the next steps, the log₁₀ normalized expression of differentially expressed genes along each axis was fitted with a local regression using the locfit function in the R library locfit (Loader, 2007). Smoothing was defined in the local polynomial model term of the locfit model using the function “lp” from the same library with the following parameters: nn = 1 (Nearest neighbor component of the smoothing parameter) and deg = 2 (degree of polynomial). The fitted expression values of these genes along each axis were normalized between 0 and 1. Clustering was performed separately for each axis on the fitted and normalized patterns of the differentially expressed genes. We used the R function “hclust” to perform hierarchical clustering on the gene expression patterns, with a Spearman’s correlation-based distance (defined as $\sqrt{0.5\left(1-\rho \right)}$) and the “average” aggregation method. The number of clusters were defined with the cutreeDynamic function from the dynamicTreeCut R library, with the parameters minClusterSize = 50, method = “hybrid” and deepSplit = 0. To visualize the data in two dimensions, we applied the UMAP dimensionality reduction algorithm (umap function in the R library umap with default options; see Figure 2D) (Becht et al., 2018; McInnes et al., 2018). To analyze the relationship between the expression patterns of genes along different axes, we computed the intersections of the gene clusters between any pair of axes. The expected number of elements in each intersection under the assumption of independent sets is given by:

$${\left|A\cap B\right|}_{\mathit{\text{exp}}}=\frac{\left|A\right|\left|B\right|}{\left|A\cup B\right|}$$

where A and B indicate the sets of genes in two clusters identified along two different axes and |•| indicates the cardinality of a set (i.e., the number of its elements). The ratio between the observed and the expected number of elements in the intersection |A∩B|_obs / |A∩B|_exp quantifies the enrichment/depletion of genes having a given pair of patterns across two axes with respect to the random case. The log₂ values of (1 + |A∩B|_obs / |A∩B|_exp) are shown in Figure 2F.

Combining Tomo-seq with single-cell RNA-seq data

The TPM (transcripts per million)-normalized single cell RNA-seq (scRNA-seq) data collected from mouse olfactory epithelium available from Fletcher et al. (2017) was used to identify cell-type specific genes. To this aim, we computed the average expression level for each cell type in the scRNA-seq dataset for all the differentially expressed genes that we identified in our TOMO-seq data. The genes with an average expression above 100 TPM in mOSNs and below 10 TPM in all other cell types were considered mOSN-specific. Conversely, genes with an average expression above 100 TPM in any of the non-mOSN cell types and below 10 in mOSNs were considered to be specific for non-mOSN cells.

Gene ontology (GO) enrichment analysis

GO Enrichment analyses were performed using the GOrilla online tool (http://cbl-gorilla.cs.technion.ac.il) with the option “Two unranked lists of genes (target and background lists)”. For each axis, we used as background list the list of the genes we tested.

Cell type deconvolution analysis

To perform cell type deconvolution analysis, we used a previously published single-cell RNA-seq (scRNA-seq) data from the mouse OM (Fletcher et al., 2017). First, the cells included in unclassified clusters were removed and the data was rescaled using the function “pp.log1p” from the scanpy library (Wolf et al., 2018). Then, we obtained 2000 highly variable genes using the function “pp.highly_variable_genes” (scanpy library). In the following analysis, we merged clusters of similar cell populations and considered the following 6 cell types: 1-HBC = HBC1+HBC2+HBC3; 2-INP = INP1+INP2+INP3; 3-GBC = GBC, 4-SC = mSC + iSC, 5-OSN = iOSN + mOSN, 6-MVC = MVC1+MVC2.

This scRNA-seq data was used as input for the AutoGeneS algorithm (Aliee and Theis, 2021). The cell type assignment as well as the list of highly variable genes were passed as input to the function “ag.init” from AutogeneS, and then we estimated the optimal subset of genes to perform cell type deconvolution with the function “ag.optimize” (with parameters: “ngen” = 5000, “nfeatures” = 400 and “mode” = “fixed”). Finally, we deconvolved the Tomo-seq data along the three axes with the function “ag.deconvolve” using Nu Support Vector regression models (“model” = “nusvr”). The results were normalized such that the sums of cell type proportions per slice is equal to 1 (Figure S2F). To identify the cell types with non-random spatial distribution along the axes, we applied the Ljung-Box test as explained above (section “identification of differentially expressed genes and gene clustering“); the p values are reported in Table S2.

Identification of ligands and receptors associated with the NfiA, NfiB or NfiX transcription factors

The genes in the CellphoneDB ligands and receptor database (Efremova et al., 2020) that were among our spatially differentially expressed genes were selected and Spearman correlation tests between their 1D expression patterns and the 1D patterns for the Nfi transcription factors were performed. Correlation coefficients from the three axes were averaged and FDRs from the 3 axes were combined with the Simes’ method (Simes, 1986) using the function combinePValues from the scran R library. Combined FDR values <0.01 were taken as significant.

3D spatial reconstruction

The olfactory mucosa shape was obtained from publicly available images of the mouse nasal cavity along the posterior to the anterior axis published in Barrios et al. (2014). The area of the slices corresponding to the OM was manually selected and images of their silhouettes were made. Those images were then transformed into binary matrices having 1’s in the area occupied by the OM and 0’s in the remaining regions. The binary matrices were rescaled to match the spatial resolution in our dataset, which is composed of 54 voxels along the DV axis, 56 along the LML axis and 60 along the AP axis. Finally, matrices were piled in a 3D array in R to obtain an in-silico representation of the 3D shape of the OM, which, in total, was composed of 77,410 voxels. To perform the 3D reconstruction of the expression pattern for a given gene, first we normalized its expression levels by the volume of the slice at each corresponding position along the three axes, which was estimated using our 3D in silico representation of the OM. Then, we rescaled the data in such a way that the sum of the expression levels along each axis was equal to the average expression computed across the whole dataset. This rescaled dataset together with the binary matrix representing the 3D OM shape was used as input of the Iterative Proportional Fitting algorithm, which produced an estimation of the expression level of a gene in each voxel (Junker et al., 2014). Iterations stopped when the differences between the true and the reconstructed 1D values summed across the three axes was smaller than 1.

Definition of zones by topic modelling

In order to identify zones, we fitted a Latent Dirichlet Allocation (LDA) (Blei et al., 2003) algorithm to the 3D gene expression patterns (in log₁₀ scale) of the differentially expressed Olfrs (689 Olfrs × 77,410 voxels).

The LDA algorithm was originally employed for document classification: based on the words included in each document, LDA can identify “topics”, in which the documents can then be classified. Using this linguistic analogy, in our application of LDA, we considered the genes as “documents”, and the spatial locations as “words”, with the matrix of gene expression levels being the analogous of the “bag-of-word” matrix (Liu et al., 2016). In this representation, the zones are the equivalent of “topics”, and they are automatically identified by LDA. We used the LDA implementation included in the R package “Countclust” (Dey et al., 2017), developed based on the “maptpx” library (Taddy, 2012), which performs a maximum a posteriori estimation to for model fitting. LDA was run for all possible numbers of topics K ∈ [2,9]. The following parameters were chosen: convergence tolerance = 0.1; max time optimization step = 180 seconds; n_init = 3. For each number of topics k, three independent runs were performed with different starting points, in order to avoid biases due to the choice of the initial condition. We estimated the number of topics by computing the log likelihood for each value of K ∈ [2,9]. As seen in Figure S3A, while the log-likelihood is a monotonically increasing function of the number of topic (as expected), for a number of topics around ~5 it shows a “knee” and starts to increase more slowly. This suggests that ~5 is the minimal number of topics needed to describe the complexity of the data without overfitting. Hence, we fix a number of topics equal to 5; however, we also verified that all our conclusions remain substantially unaffected if a different number of topics is chosen.

After running LDA with K = 5, we retrieved the model output, which consists of two probability distributions: the first is P(position| k) with k ∈ [1,5], which is the conditional probability distribution defining the topic k; the second probability distribution is P(k | gene), namely the probability distribution that quantifies the degrees of belonging of a given gene to the topics k∈[1,5]. With these probability distributions, we can identify the spatial positions that form each topic and how the different topics can be combined to generate the spatial expression pattern of each gene.

Being a generative model, once trained, LDA can also decompose into topics the spatial expression patterns of genes that were not used during the training procedure. We exploited this feature of LDA to estimate the degrees of belonging of non-olfactory receptor genes. To this aim, we utilized an algorithm based on the python gensim library Lda.Model.inference function (Rehurek and Sojka, 2010), using as input the estimated probability distribution P(position | k) with k ∈ [1,5]. The model fitting was performed using the Open Computing Cluster for Advanced data Manipulation (OCCAM), the High-Performance Computer designed and managed in collaboration between the University of Torino and the Torino division of the Istituto Nazionale di Fisica Nucleare (Aldinucci et al., 2017).

Definition of Olfr 3D indexes via diffusion pseudo-time

As explained in the section above, we can describe the spatial expression pattern of each gene through a set of five numbers, which represent the degrees of belonging to the five topics identified by LDA. We applied a diffusion map (Haghverdi et al., 2015) to the degrees of belonging of the Olfrs to visualize them in two dimensions by using the “DiffusionMap” function from the “destiny” R package (Angerer et al., 2016) (with distance = “rankcor” and default parameters). In this two-dimensional map, the Olfrs are approximately distributed along a curve that joins the most dorsal/medial genes (those in zones 1–2) with those that are more ventral/lateral (zones 3–5). To track the position of the genes along this curve, we computed a diffusion pseudo-time (DPT) coordinate (Haghverdi et al., 2016) with the “DPT” function from the “destiny” R package (taking as starting point the gene with the smallest first diffusion component among the genes suggested by the function find_tips from the same package). In order to make the indexes go from Dorsal to Ventral, as in previous studies (Miyamichi et al., 2005), we reversed the order of the DPT coordinates by substracting the maximum coordinate from all coordinates and multiplying them by (−1). By doing this, we obtained for each Olfr an index, which we called 3D index, representing its spatial expression pattern in the 3D space: more dorsal/medial genes (zones 1–2) have smaller 3D indexes than Olfrs expressed in the ventral/lateral regions (zones 3–5).

Prediction of zone index for undetected Olfrs with Random Forest

We fitted a Random Forest model to the 3D indexes of 681 of the 689 Olfrs we characterized with our dataset (i.e., those that are located in genomic clusters). The following nine features of each Olfr were used as predictors: genomic position (i.e., gene starting position divided by chromosome length); genomic cluster; genomic cluster length; number of Olfrs in the genomic cluster; number of enhancers in the genomic cluster; cluster position (i.e., starting position of the cluster divided by the chromosome length); distance to the closest enhancer; gene position within the cluster (i.e., the distance of the gene starting position from the end of the cluster divided by the cluster length); and phylogenetic class. These features were computed using the mm10 mouse genome in Biomart (Kinsella et al., 2011), while the list of enhancers and the genomic clusters assigned to each Olfr were taken from Monahan et al. (2017). The Random Forest model was fitted with the function “randomForest” (R library “randomForest” (Liaw and Wiener, 2002), with option “na.action = na.omit”). Afterward, we performed a cross-validation test with the function “rf.crossValidation” from the “rfUtilities” package (Rather et al., 2020) with default parameters. Over 100 cross-validation iterations, the root mean square error (RMSE) was ≲10% of the mean 3D index. The feature importance was computed with the “importance” function from the randomForest library with default parameters. Finally, the Random Forest model trained on the 681 Olfrs was used to predict the 3D indexes of 697 Olfrs that were too lowly expressed or were undetected in our dataset. Overall, we were able to compute or predict with Random Forest a 3D index for all the Olfrs annotated in the mouse genome, except for 28 of them that do not have any genomic cluster assigned. To quantify the consistency between our Olfr 3D indexes and indexes defined previously, we calculated the Spearman’s correlation coefficients between our indexes and those defined in three previous studies (Miyamichi et al., 2005; Tan and Xie, 2018; Zapiec and Mombaerts, 2020) (see Figures 4G, S4B, and S4C).

Odorant information and Olfr-ligand pairs

All odorant structures and associated CAS numbers were retrieved from either Sigma-Aldrich (www.sigmaaldrich.com) or PubChem (https://pubchem.ncbi.nlm.nih.gov). A comprehensive catalog of the cognate mouse Olfr-ligand pairs was collected (last update: March 2021) by combining data from the ODORactor database (Liu et al., 2011) and additional literature sources (Abaffy et al., 2006; Araneda et al., 2004; Bozza et al., 2002; Dunkel et al., 2014; Floriano et al., 2000; Gaillard et al., 2002; Godfrey et al., 2004; Grosmaitre et al., 2006, 2009; Jiang et al., 2015; Jones et al., 2019; Kajiya et al., 2001; Malnic et al., 1999, 2004; Nara et al., 2011; Nguyen et al., 2007; Oka et al., 2004, 2006, 2009; Pfister et al., 2020; Repicky and Luetje, 2009; Saito et al., 2004, 2017; Saraiva et al., 2019; Shirasu et al., 2014; Shirokova et al., 2005; von der Weid et al., 2015; Yoshikawa et al., 2013; Yoshikawa and Touhara, 2009; Yu et al., 2015; Zhuang and Matsunami, 2007).

This catalog includes 738 Olfr-ligand interactions for a total of 153 Olfrs and 221 odorants. These 153 Olfrs include 100 spatial Olfrs in our dataset and for which we have 3D indexes, and 49 additional Olfrs with predicted 3D indexes (see above). Next, we checked whether Olfrs pairs sharing at least one cognate ligand have more similar spatial expression patterns than pairs not sharing ligands. To do this, we computed the absolute values of the differences between the 3D indexes (Δ) of 1706 pairs of ORs sharing at least one odorant and 9,922 pairs of ORs that are known to bind to different odorants (Figure 6B). The two sets of Δ values were significantly different (Mann-Whitney U test, p value < 2 × 10⁻¹⁶). This test remained significant when excluding Olfrs for which 3D indexes were estimated by the Random Forests model (p value < 2 × 10⁻¹⁶), and also when excluding class I Olfrs (p value < 2 × 10⁻¹⁶).

Correlation analysis of physico-chemical descriptors with 3D index

Physicochemical descriptors for ligands were obtained from the Dragon 6.0 software (http://www.talete.mi.it/). After removing the descriptors showing 0 variance, a table of 1911 descriptors for 205 ligands was obtained. In addition to these, we estimated the air/mucus partition coefficients (K_am) of the odorants as done previously (Rygg et al., 2017; Scott et al., 2014). Briefly, we calculated the air/water partition coefficients (K_aw) for each odorant from the Henry’s Law constants obtained using the HENRYWIN model in the US EPA Estimation Program Interface (EPI) Suite (version 4.11; www.epa.gov/oppt/exposure/pubs/episuite.htm). Then, we computed the air/mucus partition coefficients (K_am) according to the formula:

$$Log\left(K_{am}\right)=0.524\cdot Log\left(K_{aw}\right)\cdot Log\left(K_{ow}\right)$$

where K_ow indicates the octanol/water partition coefficient, which were obtained using the KOWWIN model in the EPI Suite.

To increase the robustness of our correlation analysis, we removed the descriptors with 20 or more identical values across our set of ligands, and we initially considered only the ligands having 2 or more known cognate receptors; these filters gave us 1,210 descriptors (including K_am) for 101 ligands.

We performed Spearman’s correlation tests between the physicochemical descriptors and mean 3D index of the cognate Olfrs, and we considered as statistically significant those correlations with FDR <0.05 (see Table S6). The descriptors with the largest correlation coefficients were K_am (rho = 0.55, p = 1 × 10⁻⁷) and ATSC2s (Centred Broto-Moreau Autocorrelation of lag 2 weighted by I-state, rho = −0.56, p = 2 × 10⁻⁷). We obtained statistically significant correlations between K_am and the mean 3D indexes also when excluding Olfrs with 3D indexes predicted by Random Forest (Rho = 0.48, p value = 2 × 10⁻⁶, based on 87 ligands; Figure S5B) or excluding class I Olfrs (Rho = 0.5, p value = 1 × 10⁻⁷, based on 101 ligands; Figure S5C).

In-situ hybridization

In-situ hybridization was basically performed as previously described (Ibarra-Soria et al., 2017). Adult 12-week-old male C57BL/6J mice anesthetized, and then perfused with 4% paraformaldehyde. The snouts containing the OM were dissected out, decalcified in RNase-free 0.45M EDTA solution (in 1× PBS) for two weeks – the bone and tissue encapsulating the OM are necessary to preserve the OM tissue integrity during the ISH. Next, the decalcified snouts were cryoprotected in RNase-free 30% sucrose solution (1× PBS), dried, embedded in OCT embedding medium, and frozen at −80°C. Sequential 16 μm sections were prepared with a cryostat and the sections were hybridized to digoxigenin-labeled cRNA probes prepared from the different genes using the following oligonucleotides: Cytl (5′-AAAGACACTACCTCTGTTGCTGCTG-3′ and 5′-GTAAGCAGAGACCAGAAAGAAGAGTG-3′), Moxd2 (5′-TGTACCTTTCTCCCACTCCCTATTGTC-3′ and 5′-CCCATGCAACTGGAGATGTTAATTCTG-3′), Olfr309 (5′-TACAATGGCCTATGACCGCTATGTG-3′ and 5′-TCCTGACTGCATCTCTTTGTTCCTG-3′), Olfr727 (5′-CGCTATGTTGCAATATGCAAGCCTC-3′ and 5′-GCTTTGACATTGCTGCTTTCACCTC-3′), and Olfr618 (5′-CATGAACCAATGTACCTTTTCCTCTC-3′ and 5′-AAACCTGTCTTGAATTTGCTTTGTC-3′). The PCR products were cloned into pGEM-T Easy vector and the probes were obtained by in vitro transcription of the plasmids, using SP6 or T7 RNA Polymerases (Roche) and DIG RNA Labeling mix (Roche).

QUANTIFICATION AND STATISTICAL ANALYSIS

Information on gene expression thresholds for spatial differential expression analysis is described in the method details section. The presence of a spatial signal along the 3 axes was verified via the Moran’s I statistic (see relevant section above). The presence of spatial non-random patterns was tested using the Ljung-Box test and the resulting p values were adjusted using the FDR method (see relevant section above). Consistency between different datasets and replicates, as well as association between independent data were tested using Spearman correlation tests. Mann-Whitney U tests were employed to test the statistical significance of differences between two distributions. Finally, a cross validation test was used to quantify the accuracy of our Random Forests model through the root mean square error (RMSE). Statistical tests were performed using R (version 4.1.2). Statistical details are reported in the Main text, Figures and Figure legends, the STAR Methods section and supplementary tables. N represents the number of biological replicates (animals) we analyzed. Boxplots are centered at the median of the distribution, the bottom and top of the box represent the 1st and 3rd quartiles respectively, and the whiskers extend for an additional 1.5 times the interquartile range.

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Anti-Digoxigenin-AP, Fab fragments	Merck (Roche)	Cat# 11093274910, RRID:AB_514497
Biological samples
Olfactory mucosae from C57Bl/6J mice (adult males)	The Jackson Laboratory	Stock # 00664
Chemicals, peptides, and recombinant proteins
30% Hydrogen Proxyde Solution	Merck (Sigma-Aldrich)	Cat. # H1009
Triethanolamine	Merck (Sigma-Aldrich)	Cat. # T58300
Acetic anhydride	Merck (Sigma-Aldrich)	Cat. #320102
Deoinized formamide	Merck (Sigma-Aldrich)	Cat. # F9037
Yeast tRNA	Merck (Roche)	Cat. # 10109495001
Denhardt’s solution (50×)	Merck (Sigma-Aldrich)	Cat. # D9905
Dextran sulfate solution (50%)	Merck (Chemicon)	Cat. # S4030
20× SSC	Merck (Calbiochem)	Cat. # 8310-OP
Tween-20	Merck (Sigma-Aldrich)	Cat. #822184
TSA Blocking Reagent	Perkin-Elmer	Cat. # FP1020
NBT/BCIP Stock Solution	Merck (Roche)	Cat. # 11681451001
Critical commercial assays
SMART-Seq v4 Ultra Low Input RNA Kit for Sequencing	Clontech (Takara Bio)	Cat. # 634892
Bioanalyzer DNA High-Sensitivity kit	Agilent Technologies	Cat. # 5067–4626
Nextera XT DNA Library Preparation Kit (96 samples)	Illumina	Cat. # FC-131–1096
Nextera XT Index Kit v2 Set A (96 indexes, 384 samples)	Illumina	Cat. # FC-131–2001
pGEM®-T Easy Vector Systems	Promega	Cat. # A1360
DIG RNA Labeling Kit (SP6/T7)	Merck (Roche)	Cat. # 11175025910
ProbeQuant G-50 Micro Columns	Cytiva Biosciences	Cat. # 28903408
Deposited data
TOMO-seq Olfactory Mucosa dataset	This study	https://www.ebi.ac.uk/arrayexpress/E-MTAB-10211
Single cell RNA-seq data from the Olfactory Mucosa	Fletcher et al., 2017	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95601
Dragon database of molecular descriptors	Talete S.R.L.	http://www.talete.mi.it
CellphoneDB ligands and receptors database	Efremova et al., 2020	https://github.com/ventolab/CellphoneDB
Experimental models: Organisms/strains
Adult male C57Bl/6J mice	The Jackson Laboratory	Stock # 00664
Oligonucleotides
See “method details” section for oligonucleotides	This study	N/A
Software and algorithms
samtools version 0.1.19–44428cd	Li et al., 2009	http://samtools.sourceforge.net/
htseq-count version 0.11.2	Anders et al., 2014	https://github.com/htseq/htseq/
R 4.1.2	The R Foundation	https://www.r-project.org/
Python 3.9.6	Python Software Foundation	https://www.python.org/
Scripts for TOMO-seq data analysis	This study	https://doi.org/10.5281/zenodo.6036047 https://zenodo.org/badge/DOI/10.5281/zenodo.6045897.svg

Highlights.

• We generate a browsable 3D transcriptomic atlas of the mouse olfactory mucosa (OM)

• We identify potential functional hotspots in the mouse OM

• Odorant receptor genes (Olfrs) are continuously distributed over at least five zones

• Spatial locations of Olfrs correlate with the mucus solubility of their ligands