Biology subjects

Hahaut, V.

Publications and source records attributed to Hahaut, V..

7 recordsLinked to original sources

bioRxiv2026

Odor preference maps to cohesive transcriptional domains in the olfactory bulb

Animals classify odors along behaviorally relevant dimensions, yet how the olfactory bulb, the first stage of olfactory processing, is organized and how this organization relates to odor classification remains unclear. Here, we identify cohesive transcriptional domains within the larval zebrafish olfactory bulb whose differential functional recruitment reflects odor preference. Using a flow-based two-choice assay, we characterized behavioral preferences across chemically diverse odors. Mapping odor-evoked neuronal activity patterns revealed that similarly preferred odors converge onto shared spatial activation patterns in the olfactory bulb. Whole-mount multiplexed spatial transcriptomics revealed that projection neuron and interneuron subtypes form cohesive spatial domains. Mapping odor-activated neurons onto these domains identified preference-biased recruitment of specific neuronal subtypes. Targeted ablation of a TH+ interneuron population associated with attraction selectively impaired attraction while sparing aversion. Together, these findings reveal that the olfactory bulb is organized into spatially cohesive, transcriptionally defined domains and that selective recruitment of specific subtypes is linked to odor preference. Key findingsO_LIChemically diverse odorants elicit behaviors reflecting olfactory preference. C_LIO_LITranscriptionally defined neuronal subtypes form cohesive spatial domains in the olfactory bulb. C_LIO_LIOdorants of opposing preference recruit distinct transcriptional domains in the olfactory bulb. C_LIO_LIA TH+ interneuron population is selectively required for odor attraction but not aversion. C_LI

neuroscience↗

bioRxiv2026

Inferring circadian phases and quantifying biological desynchrony across single-cell transcriptomes

1Single-cell RNA sequencing (scRNA-seq) reveals heterogeneity in circadian clock states across individual cells, yet accurately inferring circadian phase and distinguishing biological desynchrony from technical noise remains challenging. Here, we introduce scRitmo, a probabilistic framework that infers single-cell circadian phases from mRNA count data, providing both a point estimate and a posterior uncertainty for each cell. A simulationcalibrated variance decomposition separates the observed phase dispersion into biological and technical components, enabling direct estimation of intercellular desynchrony. We validate scRitmo using deeply sequenced unsynchronized fibroblasts, where inferred transcriptomic phases accurately predict protein-level oscillations of a circadian reporter. Applied to murine scRNA-seq datasets from liver, aorta, and skin, scRitmo outperforms existing methods and reveals cell-type-specific levels of phase coherence. In SABER-FISH time-series data, the method recovers the progressive accumulation of desynchrony following synchronization, and in Drosophila clock neurons it captures cell-type-specific phase shifts and the expected increase in phase dispersion under constant darkness relative to light-dark entrainment. Together, scRitmo provides a principled approach for quantifying circadian (de)synchrony from transcriptomic data, decoupling biological phase variability from measurement noise across tissues, organisms, and experimental conditions.

bioinformatics↗

bioRxiv2026

Striving towards improved full-length single-cell RNA-sequencing

Full-length single-cell RNA-sequencing (scRNA-seq) methods provide superior transcript coverage and isoform resolution compared to their 3-end counterparts, but are typically limited to short-read platforms. Here, we report efforts to improve the FLASH-seq protocol and adapt it for long-read sequencing on the Oxford Nanopore Technologies (ONT) platform (FLASH-seq-ONT). We developed two plate-barcoding strategies enabling higher multiplexing: a custom PCR-ligation approach (PCR-LIG) and ONT native barcoding (NB-ONT). To support data processing, we built FSNanoporeR, a comprehensive bioinformatics pipeline for barcode demultiplexing, chimeric read detection and splitting, UMI extraction, and transcript quantification. Both barcoding strategies produced high-quality transcriptomic data from HEK293T cells, with notable differences in read length distributions. We further demonstrated that monomeric and trimeric UMIs can be reliably detected in >82% of reads, enabling accurate molecular counting at isoform resolution. However, both multiplexing approaches exhibited also critical limitations, including high chimeric read rates and index-swapping artifacts. Our results highlight both the promise and current technical hurdles of full-length single-cell long-read sequencing, and provide a practical framework for researchers considering ONT-based scRNA-seq workflows.

genomics↗

bioRxiv2023

Cell type-focused compound screen in human organoids reveals molecules and pathways controlling cone photoreceptor death

Human organoids that mirror their corresponding organs in cell-type diversity present an opportunity to perform large-scale screens for compounds that protect disease-affected or damage healthy cell types. However, such screens have not yet been performed. Here, we generated 20,000 human retinal organoids with GFP-labeled cone photoreceptors. Since degeneration of cones is a leading cause of blindness, we induced cone death and screened 2,707 compounds with known targets, for those that saved cones or those that further damaged cones. We identified inhibitors of CK1 or MAPK11 that protected cones, HSP90 inhibitors that saved cones in the short term but damaged them in the longer term, and broad HDAC inhibition by many compounds that significantly damaged cones. This work provides a database for cone-damaging compounds and describes compounds that can be starting points to develop neuroprotection for cones in diseases such as macular degeneration.

neuroscience↗

bioRxiv2021

Retrovirus-induced leukemia - hijack of T-cell activation mechanisms revealed by single-cell analysis

Human T-cell leukemia virus type 1 (HTLV-1) mainly infects CD4+ T-cells and induces chronic, persistent infection in infected individuals with some progressing to develop adult T-cell leukemia/lymphoma (ATL). Whilst HTLV-1 alters cellular differentiation, activation and survival, it is unknown whether and how these changes contribute to malignant transformation of infected T-cells. In this study, we used single-cell RNA-Seq and TCR-Seq to investigate T-cell differentiation and HTLV-1-mediated transformation processes. We analyzed 87,742 single cells from peripheral blood of 12 infected and 3 uninfected individuals. Using multiple independent bioinformatic methods, we demonstrated that naive T-cells dynamically change into activated T-cells including infected cells, which seamlessly transitioned into ATL cells characterized by clonally expanded, highly-activated T-cells. Notably, the more activated ATL cells are, the more they acquire Treg signatures. Intriguingly, HLA class II genes were uniquely induced in infected cells, further upregulated in ATL cells and was induced by viral protein Tax. Functional assays revealed that by upregulating HLA class II, HTLV-1-infected cells can act as tolerogenic antigen presenting cells (APCs) to induce anergy of antigen specific T-cells. In conclusion, our study revealed the in vivo mechanisms of HTLV-1-mediated transformation and immune escape at single-cell level. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/458291v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@38d0fdorg.highwire.dtl.DTLVardef@20cca0org.highwire.dtl.DTLVardef@15a55e2org.highwire.dtl.DTLVardef@1bca6ac_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2021

Lightning Fast and Highly Sensitive Full-Length Single-cell sequencing using FLASH-Seq

In the last 10 years, single-cell RNA-sequencing (scRNA-seq) has undergone exponential growth. Emulsion droplets methods1-3, such as those commercialized by 10x Genomics, have allowed researchers to analyze tens of thousands of cells in parallel in a robust and reproducible way. However, in contrast to SMART-based full-length sequencing protocols4,5, these methods interrogate only the outer portion of the transcripts and still lack the required sensitivity for analyzing comprehensively the transcriptome of individual cells. Building upon the existing SMART-seq forerunners protocols4,5, we developed FLASH-Seq (FS), a new scRNA-seq method which displays greater sensitivity while decreasing incubation times and reducing the number of processing steps compared to its predecessors. The entire FS protocol - from lysed cells to pooled cDNA libraries - can be performed in ~4.5 hours, is automation-friendly and can be easily miniaturized to decrease costs.

genomics↗

bioRxiv2019

Pooled CRISPR Inverse PCR sequencing (PCIP-seq): simultaneous sequencing of retroviral insertion points and the associated provirus in thousands of cells with long reads

Retroviral infections create a large population of cells, each defined by a unique proviral insertion site. Methods based on short-read high throughput sequencing can identify thousands of insertion sites, but the proviruses within remain unobserved. We have developed Pooled CRISPR Inverse PCR sequencing (PCIP-seq), a method that leverages long reads on the Oxford Nanopore MinION platform to sequence the insertion site and its associated provirus. We have applied the technique to three exogenous retroviruses, HTLV-1, HIV-1 and BLV, as well as endogenous retroviruses in both cattle and sheep. The long reads of PCIP-seq improved the accuracy of insertion site identification in repetitive regions of the genome. The high efficiency of the method facilitated the identification of tens of thousands of insertion sites in a single sample. We observed thousands of SNPs and dozens of structural variants within proviruses and uncovered evidence of viral hypermutation, recombination and recurrent selection.

genomics↗