Biology subjects

Staut, J.

Publications and source records attributed to Staut, J..

5 recordsLinked to original sources

bioRxiv2026

A single-cell transcriptome atlas of the barley root apical meristem uncovers conserved and divergent roles of HvWOX5

Single-cell approaches have transformed plant developmental biology; however, cell-type-resolved resources for cereals remain limited. Here, we present a single-cell transcriptome atlas of the barley root apical meristem (RAM), which resolves 24 transcriptionally distinct cell populations. We assigned major root cell identities by integrating marker gene validation using Hybridization Chain Reaction (HCR) RNA fluorescence in situ hybridization and spatial transcriptomics with cross-species comparisons of published root atlases. Pseudotime analysis reconstructed developmental trajectories from the quiescent center to differentiating tissues, supporting the spatial and developmental organization of the atlas. We further demonstrated the utility of this resource by identifying HvWOX5 expression in the quiescent center and metaxylem and showing that HvWOX5 loss-of-function mutants displayed reduced root and meristem lengths, altered stem cell niche homeostasis, and disrupted metaxylem organization. Taken together, this atlas provides a framework for dissecting barley root development and identifies HvWOX5 as a key regulator of RAM organization and metaxylem patterning.

Plant Biology↗

bioRxiv2026

Decoding stage-specific symbiotic programs in the Rhizophagus irregularis-tomato interaction using single-nucleus transcriptomics

Arbuscular mycorrhizal fungi (AMF) establish a dynamic and asynchronous symbiosis with a wide range of land plants, involving distinct stages of root colonization and associated cellular responses that co-occur within the same root. Whilst decades of research have significantly advanced our understanding of the plants symbiotic gene repertoire, this spatial and temporal complexity has hindered a detailed dissection of the molecular mechanisms underlying fungal accommodation. Here, we present the first single-nucleus RNA-sequencing (snRNA-seq) dataset of Solanum lycopersicum roots colonized by Rhizophagus irregularis. Unsupervised subclustering of an AM-specific cell population resolves AM-responsive root epidermal cells as well as a developmental gradient of cortical cells across distinct stages of arbuscule formation, unveiling stage-specific transcriptional signatures during AMF colonization. Moreover, using Motif-Informed Network Inference based on single-cell EXpression data (MINI-EX), we put forward candidate transcription factors orchestrating these stage-specific transcriptional programs. Together, our data support novel hypotheses on how diverse plant developmental and physiological processes - including localized cell cycle reactivation and the integration of multiple nutritional cues - are coordinated to facilitate the establishment of a functional symbiosis. As such, this high-resolution dataset serves as a valuable resource for candidate gene prioritization and future reverse genetic studies.

plant biology↗

bioRxiv2024

A map of integrated cis-regulatory elements enhances gene regulatory analysis in maize

Cis-regulatory elements (CREs) are non-coding DNA sequences that modulate gene expression. Their identification is critical to study the transcriptional regulation of genes controlling key traits that govern plant growth and development. They are also crucial components for the delineation of gene regulatory networks, which represent regulatory interactions between transcription factors (TFs) and target genes. In maize, CREs have been profiled using different computational and experimental methods, but the extent to which these methods complement each other in identifying functional CREs is unclear. Here, we report the data-driven integration of different maize CRE profiling methods to optimize the capture of experimentally-confirmed TF binding sites, resulting in maps of integrated CREs (iCREs) showing increased levels of completeness and precision. We combined the iCREs with a wide diversity of gene expression data under drought conditions to perform motif enrichment and infer drought-specific GRNs. Mining these organ-specific GRNs revealed known and novel candidate regulators of maize drought response, and showed these networks significantly overlap with drought eQTL regulatory interactions. Furthermore, by studying the transposable elements (TEs) overlapping with iCREs, we identified few TE superfamilies displaying typical epigenetic features of regulatory DNA that are potentially involved in wiring specific TF-target gene regulatory interactions. Overall, our study showcases the integration of different omics data sources to generate a high-quality collection of CREs, together with their applicability to better characterize gene regulation in the complex maize genome.

bioinformatics↗

bioRxiv2023

MINI-EX version 2: cell-type-specific gene regulatory network inference using an integrative single-cell transcriptomics approach

Understanding and predicting cell-type-specific gene regulatory networks (GRNs) is essential for unraveling the complex interactions between transcription factors (TFs) that modulate the expression of target genes and control diverse biological processes in multicellular organisms. MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data) is an integrative tool tailored for identifying cell-type-specific GRNs in plants. Leveraging single-cell transcriptomics data, MINI-EX constructs expression-based networks and integrates TF motif information to produce GRNs with increased accuracy. Furthermore, it assigns regulatory modules to distinct cell types and prioritizes candidate regulators by employing a strategy that encompasses network centrality measures, functional annotations, and expression specificity. Taken together, MINI-EX offers a powerful approach to identify cell-type-specific transcriptional cascades and enhance our understanding of TF functions in plant biology. Here, we discuss recent advancements in the tools latest version and explain how single-cell GRNs can be identified for non-model species lacking TF motif information. Additionally, we provide a comprehensive guide to use MINI-EX, covering the entire pipeline from preparing input files starting from a single-cell experiment, over configuring parameters, to interpreting output data.

plant biology↗

bioRxiv2022

Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data

The Human Leukocyte Antigen (HLA) genes are a group of highly polymorphic genes that are located in the Major Histocompatibility Complex (MHC) region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. While knowledge of these genotypes is of utmost importance to study differences in immune responses between cancer patients, gold standard, PCR-derived genotypes are rarely available in large Next Generation Sequencing (NGS) datasets. Therefore, a variety of methods for in silico NGS-based HLA genotyping have been developed, bypassing the need to determine these genotypes with separate experiments. However, there is currently no consensus on the best performing tool. Here, we compiled a list of 13 HLA callers and evaluated their accuracy on three different datasets. Based on these results, best-practice guidelines were constructed, and consensus HLA allele predictions were made for DNA and RNA samples from The Cancer Genome Atlas (TCGA).

bioinformatics↗