Biology subjects

Valle, F.

Publications and source records attributed to Valle, F..

At least 19 recordsLinked to original sources

bioRxiv2026

BRIDGE: A broad-host platform for interkingdom DNA delivery enabling the genetic domestication of phylogenetically diverse yeasts

Efficient DNA delivery remains one of the greatest barriers to engineering non-conventional yeasts, limiting their genetic domestication and exploitation as next-generation microbial cell factories. Here, we present BRIDGE (Bacteria-to-yeast Rapid Interkingdom DNA Gene Exchange), an integrated synthetic biology platform for broad-host interkingdom DNA delivery. BRIDGE is centred on a compact 6-kb Pan/ARS-oriT broad-host BRIDGE vector, enabled by a superconjugative helper plasmid (pSC5). Using BRIDGE, we expanded interkingdom DNA transfer from representatives of four genera accessible using pSC5 alone to ten phylogenetically diverse yeast genera, including Saccharomyces, Maudiozyma, Starmerella, Kazachstania, Yarrowia, Zygosaccharomyces, Lachancea, Kluyveromyces, Pichia and Komagataella. To demonstrate the versatility of BRIDGE, we engineered a 16-kb visual reporter carrying a synthetic five-gene violacein biosynthetic pathway from Chromobacterium violaceum. Individual transcriptional units were first constructed using the YeastFab modular cloning system and subsequently assembled into the complete pathway by single-step in vivo homologous recombination in Saccharomyces cerevisiae, followed by rapid plasmid recovery using the EASY-C platform. The reporter was successfully delivered to all ten yeast genera, demonstrating BRIDGE-mediated transfer of complete multigene synthetic pathways. Robust violacein production was observed across all six Saccharomyces species tested, providing a rapid visual marker for transformant identification, while modest functional pathway expression was also detected in Kazachstania and Kluyveromyces. Successful pathway transfer, albeit without visible pigmentation, was achieved in the remaining genera, indicating that regulatory compatibility, rather than DNA transfer, is the principal determinant of heterologous pathway expression in distantly related yeasts. Collectively, BRIDGE establishes an integrated Design-Build-Recover-Deliver workflow that combines modular in vitro construction of transcriptional units, single-step in vivo assembly of multigene pathways, rapid plasmid recovery using the EASY-C platform and broad-host interkingdom DNA delivery. This versatile framework enables the rapid genetic modification of previously intractable microorganisms, expanding the synthetic biology toolbox for sustainable biomanufacturing and industrial biotechnology.

synthetic biology↗

bioRxiv2026

Cell free-mtDNA release drives pyroptosis and inflammation in cell models carrying the mitochondrial m.3243A>G pathogenic variant

Mitochondrial Encephalopathy, Lactic Acidosis, and Stroke-like episodes (MELAS) syndrome is primarily caused by the heteroplasmic m.3243A>G/MT-TL1 pathogenic variant. Patients exhibit elevated circulating cell-free mtDNA (cf-mtDNA) in plasma, which acts as a damage-associated molecular pattern. Using patient-derived fibroblasts and neuronal progenitors, as well as transmitochondrial cytoplasmic hybrids (cybrids), we show that mutant cells release higher levels of cf-mtDNA than wild-type controls, demonstrating that the m.3243A>G pathogenic variant drives mtDNA release. Mechanistically, increased mitochondrial oxidative stress promotes mtDNA oxidation and fragmentation, leading to Ca2+ overload and subsequent mtDNA extrusion. This, in turn, triggers inflammasome activation and pyroptosis, resulting in the secretion of pro-inflammatory cytokines and the activation of innate immune pathways. Pharmacological inhibition of the Mitochondrial Calcium Uniporter (MCU) or Voltage-Dependent Anion Channel (VDAC) reduced mtDNA release, confirming their involvement. Overall, our findings reveal a previously unrecognized mechanism in MELAS linking mitochondrial dysfunction to innate immune activation, with potential implications for therapeutic intervention.

cell biology↗

bioRxiv2026

Integration of single-cell multi-omic data with graph-based topic modelling

Recent advances in single-cell biology enable the profiling of multiple molecular layers, such as the transcriptome, epigenome, and surface proteins, within a single cell. Tackling the complexity of these data from different perspectives allows researchers to get the most complete insights into the biological properties of cells. Here, we propose a graph-based topic modelling method called bionSBM. Our method leverages well-known community-detection methods for multipartite graphs and the interpretability of topic modelling to cluster and explain high-dimensional, sparse, and noisy single-cell matrices. We applied our algorithm to paired single-cell multi-omics data, such as 10X Multiome, SHARE-seq, and CITE-seq. We showed that it achieves superior performance compared to state-of-the-art methods for ground-truth label retrieval, with high specificity and distinct biological interpretability.

bioinformatics↗

bioRxiv2025

Construction of a reference genome for Starmerella batistae and annotation of Starmerella species reveal a close evolutionary relationship with Schizosaccharomyces pombe and suggest an alternative pathway for sophorolipid production.

The Starmerella clade is known for displaying osmotolerant and acidophilic traits from their association with bees. Several species in this genus can produce sophorolipids, which are commercially produced as biosurfactants. Here, we isolated a yeast contaminant from the laboratory environment, identified as Starmerella batistae, able to thrive under low pH, including high concentrations of lactic acid, and relative high temperatures. We have sequenced and conducted a de novo genome assembly in three chromosomes and a mitochondrial genome for S. batistae (ca 9.3 Mb). Based on this reference genome we functionally annotated 29 different Starmerella species, using the publicly available sequences. Phylogenetic analysis across different yeast clades revealed a close relationship of Starmerella species with Schizosaccharomyces yeasts. Fifteen genes were uniquely shared between Sz. pombe and S. batistae, of which twelve were involved in cell morphology. Interestingly, the shape of S. batistae cells is elongated rather than round, similar to the Sz. pombe. Additionally, we found that all the Starmerella strains capable of producing sophorolipids shared a last common ancestor. Such clustering can help identify other sophorolipid-producing Starmerella yeasts that have not yet been characterised. We did not find the one-to-one orthologs of S. bombicola sophorolipid pathway in any of the Starmerella sp. with the exception of S. kuoi, S. powellii and S. floricola. In S. etchelsii, the antisense and telomeric pair UGTA1/CYP52M1 was found to be structurally conserved although not functionally. These findings support the notion that alternative pathways for the production of sophorolipids have evolved in different Starmerella lineages.

genomics↗

bioRxiv2025

Unveiling the genetic basis of the low pH response in the acidophilic yeast Maudiozyma bulderi as a potential host for biorefinery

Non-conventional yeasts represent a great genetic and phenotypic diversity with potential for industrial strain development in the bio-production of green chemicals. In recent years, mass genome sequencing of non-conventional yeasts has opened avenues to improved understanding of transcriptional networks and phenotypic plasticity and gene function, including the discovery of novel genes. Here, we investigated the expressional and morphological changes at low-pH in three strains of the acidophilic yeast Maudiozyma bulderi (previously Kazachstania bulderi and Saccharomyces bulderi): CBS 8638, CBS 8639 and NRRL Y-27205. The comparison of the transcriptome of cells growing in a bioreactor at pH=5.5 vs pH= 2.5, primarily showed dysregulation of genes involved in cell wall integrity, with NRRL Y-27205 the least acidophilic strain, showing the largest transcriptional response when compared to the other two strains. We identified four uncharacterised genes, unique to M. bulderi, and predicted function as transporters, upregulated at low pH. Microscopy studies showed that M. bulderi cell wall is not damaged in acidic environment, and the membrane lipid composition remains stable at low pH, unlike S. cerevisiae. Overall, our data on transcriptional variability in M. bulderi highlights genes and cellular pathways involved in the acidophilic adaptation of this species and can aid further strain development.

genomics↗

bioRxiv2024

Single-particle analysis of small extracellular vesicles from the follicular fluid of women undergoing fertility treatments reveals distinct PD-L1+ populations

In some cell systems, small extracellular vesicles bearing PD-L1 (PD-L1+ sEVs) have been shown to be able to suppress T-cell immunity. We have herein investigated whether a distinct profile of PD-L1+ sEVs exists in human follicular fluid (FF). Single-particle interferometric reflectance imaging sensing combined with a single-particle antibody capture and immunofluorescence labelling were used to determine the expression and colocalization of CD63, CD81, CD9, and PD-L1 in sEVs derived from FF of women undergoing fertility treatments (n=10). In addition, the size distribution of sEVs was investigated via atomic force microscopy. Our data indicate that the bulk of tetraspanin-expressing EVs in human FF are less than 50 nm in size. Tetraspanins and PD-L1 exhibit distinct expression and colocalization profiles at sEV level across all cohort samples. A total of 42%, 46%, and 50% of all the particles captured by anti-CD63, anti-CD81, and anti-CD9 antibodies, respectively, were positive for CD81. PD-L1 was expressed at the highest level on CD9+ sEVs, with an average value of 5% within the cohort. The presence of distinct PD-L1+ sEV subpopulations suggests that they may play a role in regulating the immune response in the follicular microenvironment. Further research is needed to fully understand the functional significance of PD-L1+ sEVs in this context and their potential as biomarkers for predicting fertility outcomes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/628903v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@77e67dorg.highwire.dtl.DTLVardef@1bd1ce2org.highwire.dtl.DTLVardef@b376d8org.highwire.dtl.DTLVardef@3f6fad_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Exploring the latent space of transcriptomic data withtopic modeling

The availability of high-dimensional transcriptomic datasets is increasing at a tremendous pace, together with the need for suitable computational tools. Clustering and dimensionality reduction methods are popular go-to methods to identify basic structures in these datasets. At the same time, different topic modeling techniques have been developed to organize the deluge of available data of natural language using their latent topical structure. This paper leverages the statistical analogies between text and transcriptomic datasets to compare different topic modeling methods when applied to gene expression data. Specifically, we test their accuracy in the specific task of discovering and reconstructing the tissue structure of the human transcriptome and distinguishing healthy from cancerous tissues. We examine the properties of the latent space recovered by different methods, highlight their differences, and the pros and cons of the methods across different tasks. Finally, we show that the latent topic space can be a useful embedding space, where a basic neural network classifier can annotate transcriptomic profiles with high accuracy.

bioinformatics↗

bioRxiv2024

Topic Modeling analysis of the Allen Human Brain Atlas

The human brain is a complex interconnected structure controlling all elementary and high-level cognitive tasks. It is composed of many regions that exhibit specific distributions of cell types and distinct patterns of functional connections. This complexity is rooted in differential transcription. The constituent cell types of different brain regions express distinctive combinations of genes as they develop and mature, ultimately shaping their functional state in adulthood. How precisely the genetic information of anatomical structures is connected to their underlying biological functions remains an open question in modern neuroscience. A major challenge is the identification of "universal patterns", which do not depend on the particular individual, but are instead basic structural properties shared by all brains. Despite the vast amount of gene expression data available at both the bulk and single-cell levels, this task remains challenging, mainly due to the lack of suitable data mining tools. In this paper, we propose an approach to address this issue based on a hierarchical version of Stochastic Block Modeling. Thanks to its specific choice of priors, the method is particularly effective in identifying these universal features. We use as a laboratory to test our algorithm a dataset obtained from six independent human brains from the Allen Human Brain Atlas. We show that the proposed method is indeed able to identify universal patterns much better than more traditional algorithms such as Latent Dirichlet Allocation or Weighted Correlation Network Analysis. The probabilistic association between genes and samples that we find well represents the known anatomical and functional brain organization. Moreover, leveraging the peculiar "fuzzy" structure of the gene sets obtained with our method, we identify examples of transcriptional and post-transcriptional pathways associated with specific brain regions, highlighting the potential of our approach.

biophysics↗

bioRxiv2024

Platelet as key cells in endometriosis patients: insights from small extracellular vesicles in peritoneal fluid and endometriotic lesions analysis

Endometriosis is a chronic inflammatory condition characterized by the presence of endometrium-like tissue outside the uterus, primarily affecting pelvic organs and tissues. In this study, we explored platelet activation in endometriosis. We utilized the STRING database to analyze the functional interactions among proteins previously identified in small extracellular vesicles (EVs) isolated from the peritoneal fluid of endometriosis patients and controls. The bioinformatic analysis indicated enriched signaling pathways related to platelet activation, hemostasis, and neutrophil degranulation. Double immunohistochemistry analysis for CD61 and MPO revealed a significant presence of neutrophils and platelets infiltrating endometriotic lesions, suggesting potential cell-cell interactions. Subsequently, we isolated small EVs from the peritoneal fluid of women diagnosed with endometriosis and from women without endometriosis who underwent surgery for non-inflammatory benign diseases. We performed single-particle phenotyping analysis based on platelet biomarkers GPIIb/IIIa and PF4 using nanoflow cytometry, as well as single-particle morphological and nanomechanical characterization through atomic force microscopy. The study demonstrated that patients with endometriosis had a notably higher proportion of particles testing positive for platelet biomarkers compared to the total number of EVs. This finding implies a potential role for platelets in the pathogenesis of endometriosis. Further research is necessary to delve into the mechanisms underlying this phenomenon and its implications for disease progression.

cell biology↗

bioRxiv2024

The intrinsic dimension of gene expression during cell differentiation

Waddingtons epigenetic landscape has long served as a conceptual framework for understanding cell fate decisions. The landscapes geometry encodes the molecular mechanisms that guide the gene expression profiles of uncommitted cells toward terminally differentiated cell types. In this study, we demonstrate that applying the concept of intrinsic dimension to single-cell transcriptomic data can effectively capture trends in expression trajectories, supporting this framework. This approach allows us to define a robust cell potency score without relying on prior biological information. By analyzing an extensive collection of datasets from various species, experimental protocols, and differentiation processes, we validate our method and successfully reproduce established hierarchies of cell type potency.

systems biology↗

bioRxiv2024

Biogenic nanoparticles from liquid and solid matrices: biochemical and biophysical properties of Extracellular Vesicles-enriched samples from human plasma and skeletal muscle tissue.

AimStudies on extracellular vesicles (EVs) focused on samples enriched from liquid matrices, such as cell culture media and blood. Recent research highlights the roles of EVs derived from the extracellular matrix of solid tissues, and how investigating these specific EVs offers insights into their microenvironment and potential biological influences on surrounding cells. This study presents a shared method to separate and compare EV enriched from solid (human skeletal muscle biopsy) and liquid (human plasma) matrices, addressing technical challenges and minimizing biases in separation techniques. MethodsPlasma and skeletal muscle-EVs were obtained combining serial centrifugation steps and discontinuous sucrose density gradient. EVs characterization employed advanced analytical techniques such as Western Blot, Colorimetric Nanoplasmonic assay, Atomic Force Miscoscopy, Nanoparticle Tracking Analysis and Dynamic Light Scattering to focus on biomolecular composition, nanomechanical properties, particle yield, size distribution, and colloidal stability. ResultsThe analysis revealed distinct differences between skeletal muscle-EVs and plasma-EVs including: molecular composition, physical and nanomechanical properties, as well as particle size distribution. Skeletal muscle-EVs exhibited unique colloidal behavior compared to their plasma counterparts, suggesting tissue-specific features that may influence their biological activity and stability. ConclusionsThe findings demonstrate that EVs from skeletal muscle tissue possess unique biochemical and biophysical characteristics when compared to those derived from plasma. These differences reflect their diverse biological origins and microenvironments. Understanding these distinctions could advance the development of EV-based diagnostic tools, particularly for muscular disorders, and broaden our knowledge of EV roles across various tissue contexts. HIGHLIGHTSO_LIShared protocol to enrich and compare extracellular vesicles (EVs) from both solid (skeletal muscle biopsy) and liquid (plasma) human samples, reducing methodological bias across matrices. C_LIO_LISkeletal muscle-derived EVs differ significantly from plasma EVs in biochemical and biophysical properties such as molecular composition, size distribution, nanomechanical properties, and colloidal stability. C_LIO_LIEV heterogeneity across microenvironments emerged as a key biological feature, highlighting their potential for specific diagnostic applications. C_LI

cell biology↗

bioRxiv2023

High quality de novo genome assembly of non-conventional yeast Kazachstania bulderi a new potential low pH production host for biorefineries.

Kazachstania bulderi is a yeast species belonging to a ubiquitous group of non-conventional yeasts which has the ability to grow efficiently on glucose and {delta}-gluconolactone at low pH. This unique trait makes K. bulderi an ideal candidate as a new host for low pH fermentation processes for sustainable production of green chemicals such as organic acids. To accelerate strain development with this species, detailed information of its genetics is needed. Here, by employing high accuracy long read sequencing we report a high-quality phased genome assembly for three strains belonging to K. bulderi species, including the type strain. The sequences were assembled into 12 chromosomes with a total length of 14Mb, and the genome was fully annotated at structural and functional levels, including allelic and structural variants, ribosomal array, centromeres and mating type locus. This high-quality reference genome provides an essential resource to advance our fundamental knowledge of biotechno-logically relevant non-conventional yeasts and to support the development of genetic tools for manipulating such strains towards their use as production hosts biotechnological processes.

genomics↗

bioRxiv2022

Comprehensive analysis of long non-coding RNAs in breast cancer using topic modeling.

Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting cancer subtypes with high accuracy and identifying genes, enhancers, and stable cell types simultaneously from sparse single cell epigenomics data. The advantage of using a topic model is that it not only serves as a clustering algorithm, but it can also explain clustering results by providing word probability distributions over topics. Our study proposes a novel topic modeling approach for clustering single cells and detecting topics (gene signatures) in single-cell datasets that measure multiple omics simultaneously. We applied this approach to examine the transcriptional heterogeneity of luminal and triple negative breast cancer cells using patient-derived xenograft models with acquired resistance to chemotherapy and targeted therapy. Through this approach, we identified protein-coding genes and long noncoding RNAs (lncRNAs) that group thousands of cells into biologically similar clusters, accurately distinguishing drug-sensitive and resistant breast cancer types. In comparison to standard state-of-the-art clustering analyses, our approach offers optimal partitioning of genes into topics and cells into clusters simultaneously, producing easily interpretable clustering outcomes. Additionally, we demonstrate that an integrative clustering approach, which combines the information from mRNAs and lncRNAs treated as disjoint omics layers, enhances the accuracy of cell classification.

bioinformatics↗

bioRxiv2022

Compositional profiling of extracellular vesicles/lipoproteins mixtures by AFM nanomechanical imaging

The widely overlapping physicochemical properties of lipoproteins (LPs) and extracellular vesicles (EVs) represents one of the main obstacles for the isolation and characterization of these pervasive biogenic lipid nanoparticles. We herein present the application of an atomic force microscopy (AFM)-based quantitative morphometry assay to the rapid nanomechanical screening of mixed LPs and EVs samples. The method can determine the diameter and the mechanical stiffness of hundreds of individual nanometric objects within few hours. The obtained diameters are in quantitative accord with those measured via cryo-electron microscopy (cryo-EM); the assignment of a specific nanomechanical readout to each object enables the simultaneous discrimination of co-isolated EVs and LPs even if they have overlapping size distributions. EVs and all classes of LPs are shown to be characterized by specific combinations of diameter and stiffness, thus making it possible to estimate their relative abundance in EV/LP mixed samples in terms of stoichiometric ratio, surface area and volume. As a side finding, we show how the mechanical behaviour of specific LP classes is correlated to distinctive structural features revealed by cryo-EM. To the best of our knowledge, these results represent the first systematic single-particle mechanical investigation of lipoproteins. The described approach is label-free, single-step and relatively quick to perform. Importantly, it can be used to analyze samples which prove very challenging to assess with several established techniques due to ensemble-averaging, low sensibility to small particles, or both, thus providing a very useful tool for quickly assessing the purity of EV/LP isolates including plasma- and serum-derived preparations.

biophysics↗

bioRxiv2021

Multi-omics Topic Modeling for Breast Cancer Classification

The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to accomplish this integration task. More specifically, we show how an algorithm based on a hierarchical version of stochastic block modeling can be naturally extended to integrate any combination of omics data. We test this approach on breast cancer samples from the TCGA database, integrating data on messenger RNA, microRNAs and copy number variations. We show that the inclusion of the microRNA layer significantly improves the accuracy of subtype classification. Moreover, some of the hidden structures or "topics" that the algorithm extracts actually correspond to genes and microRNAs involved in breast cancer development, and are associated to the survival probability. Simple SummaryTopic models are algorithms introduced for discovering hidden topics or latent variables in large unstructured text corpora. Leveraging on analogies between texts and gene expression profiles, these algorithms can be used to find structures in expression data. This work presents an application of topic modeling techniques to the identification of breast cancer subtypes. In particular, we extended a specific class of topic models to allow a multi-omics approach. As an illustrative example, considering both messenger RNA and microRNA expression levels, we were able to clearly distinguish healthy from tumor samples, as well as the different breast cancer subtypes. The integration of different layers of information is crucial for the observed classification accuracy. Our approach naturally provides the genes and the microRNAs associated to the specific topics that are used for the sample organization. We show that indeed these topics often contain genes involved in breast cancer development, and are associated to different survival probabilities.

cancer biology↗

bioRxiv2021

Emergent Statistical Laws in Single-Cell Transcriptomic Data

Large scale data on single-cell gene expression have the potential to unravel the specific transcriptional programs of different cell types. The structure of these expression datasets suggests a similarity with several other complex systems that can be analogously described through the statistics of their basic building blocks. Transcriptomes of single cells are collections of messenger RNA abundances transcribed from a common set of genes just as books are different collections of words from a shared vocabulary, genomes of different species are specific compositions of genes belonging to evolutionary families, and ecological niches can be described by their species abundances. Following this analogy, we identify several emergent statistical laws in single-cell transcriptomic data closely similar to regularities found in linguistics, ecology or genomics. A simple mathematical framework can be used to analyze the relations between different laws and the possible mechanisms behind their ubiquity. Importantly, treatable statistical models can be useful tools in transcriptomics to disentangle the actual biological variability from general statistical effects present in most component systems and from the consequences of the sampling process inherent to the experimental technique. Author summaryGene expression profiles represent how different cells use their genetic information. Similarly, books are specific collections of words chosen from a shared vocabulary, and many complex systems can be ultimately described by the statistics of their basic components. Leveraging on this analogy, we identified several emergent statistical laws in single-cell transcriptomic data that are universally found in complex component systems. A simple mathematical description sets these laws in a treatable quantitative framework and represents a useful tool for dissecting the different sources of gene expression variability.

systems biology↗

bioRxiv2021

Stiffness of fluid and gel phase lipid nanovesicles: weighting the contributions of membrane bending modulus and luminal pressurization

The mechanical properties of biogenic membranous compartments are thought to be relevant in numerous biological processes; however, their quantitative measurement remains challenging for most of the already available Force Spectroscopy (FS)-based techniques. In particular, the debate on the mechanics of lipid nanovesicles and on the interpretation of their mechanical response to an applied force is still open. This is mostly due to the current lack of a unified model being able to describe the mechanical response of gel and fluid phase lipid vesicles and to disentangle the contributions of membrane rigidity and luminal pressure. In this framework, we herein propose a simple model in which the contributions of membrane rigidity and luminal pressure to the overall vesicle stiffness are described as a series of springs; this approach allows estimating the two contributions for both gel and fluid phase liposomes. Atomic Force Microscopy-based FS (AFM-FS), performed on both vesicles and Supported Lipid Bilayers (SLBs), is exploited for obtaining all the parameters involved in the model. Moreover, the use of coarse-grained full-scale molecular dynamics simulations allowed for better understanding the differences in the mechanical responses of gel and fluid phase bilayers and supported the experimental findings. Results suggest that the pressure contribution is similar among all the probed vesicle types; however, it plays a dominant role in the mechanical response of lipid nanovesicles presenting a fluid phase membrane, while its contribution becomes comparable to the one of membrane rigidity in nanovesicles with a gel phase lipid membrane. The herein presented results offer a simple way to quantify two of the most important parameters in vesicular nanomechanics, and as such represent a first step towards a currently unavailable, unified model for the mechanical response of gel and fluid phase lipid nanovesicles.

biophysics↗

bioRxiv2021

The anatomical logic of smell

The sense of smell helps us navigate the environment, but its molecular architecture and underlying logic remain unknown. The spatial location of odorant receptor genes (Olfrs) in the nose is widely thought to be independent of the structural diversity of the odorants they detect. Using spatial transcriptomics, we created 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 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 resolved 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.

neuroscience↗