Biology subjects

Diez, D.

Publications and source records attributed to Diez, D..

7 recordsLinked to original sources

bioRxiv2026

DeepSpaceDB 2.0: an interactive spatial transcriptomics database for large-scale Xenium data exploration

The 10x Genomics Xenium platform enables high-resolution spatial transcriptomics at single-cell and subcellular scales, but effective reuse of public Xenium datasets is hindered by large data sizes and heterogeneous file formats. We previously developed DeepSpaceDB, a spatial transcriptomics database designed for interactive, in-depth analysis of tissues and tissue microenvironments. Here, we present a major expansion of DeepSpaceDB that integrates large-scale single-cell spatial transcriptomics data generated by the Xenium platform. In this update, we systematically collected 1,539 public Xenium datasets from multiple repositories and processed them through a robust, standardized pipeline that validates, repairs, and harmonizes heterogeneous inputs into a unified representation. To support efficient exploration of these data, we introduced a redesigned DeepSpaceDB interface and complementary Zarr-based storage formats optimized for gene-centric visualization and spatially localized queries, enabling sub-second response times for common interactive operations. The updated platform supports real-time visualization of spatial data and analysis of regions of interest directly in the web browser. Together, this expansion establishes DeepSpaceDB as a unified resource for single-cell spatial transcriptomics, substantially lowering the barrier to accessing, exploring, and reusing large-scale public Xenium datasets.

bioinformatics↗

bioRxiv2025

DeepSpaceDB: a spatial transcriptomics atlas for interactive in-depth analysis of tissues and tissue microenvironments

Spatial transcriptomics provides a revolutionary approach to mapping gene expression within tissues, offering critical insights into the spatial organization of cellular and molecular processes. However, generating new spatial transcriptomics data is expensive and technically demanding, and analyzing such data requires advanced bioinformatics expertise. While publicly available datasets are growing rapidly, existing databases offer limited tools for interactive exploration and cross-sample comparisons. Here, we introduce DeepSpaceDB, a next-generation spatial transcriptomics database designed to address these issues. DeepSpaceDB focuses on interactivity and advanced analytical functionality, enabling users to explore spatial transcriptomics data with unprecedented flexibility. DeepSpaceDB allows for interactive selection and comparison of gene expression across regions within a single tissue slice or between slices, such as comparing hippocampal regions of an Alzheimers model mouse and a control. It also includes quality indicators, database-wide trends, and advanced visualizations that provide real-time interactivity, such as zoomable plots and hover-based information display. Moreover, these functions are not restricted to the samples collected in our database but can also be applied to samples uploaded by users. The current version of DeepSpaceDB focuses explicitly on samples of the 10X Genomics Visium platform, ensuring higher-quality analyses and enhanced exploration tools, including comparison between interactively selected regions of tissue sections. This tradeoff enables unique features like similarity-based sample embeddings and database-wide comparisons, setting it apart from other databases prioritizing broad platform coverage over functionality. With its combination of advanced tools and interactive capabilities, DeepSpaceDB represents a transformative resource for spatial transcriptomics research, paving the way for deeper insights into tissue organization and disease biology. Availability: DeepSpaceDB is available at www.deepspacedb.com.

bioinformatics↗

bioRxiv2024

Th17 cells with a pathogenic signature in joints of ZAP-70 mutant arthritic mice harbor unique TCR repertoires that share features with WT Tregs

Mutations in the ZAP-70 gene that cause moderate attenuation of T cell receptor (TCR) signaling in mice can result in autoimmune manifestations. One explanation for this pathology is a shift in the regulatory-conventional (Treg-Tconv) T cell repertoire composition. To test this hypothesis, we characterized the single-cell gene expression profiles and TCR repertoires of Tconv and Treg CD4+ T cells of arthritic (ZAC), poised (SKG) ZAP-70 mutant, and wild-type (WT) mice. We identified a group of Th17 cells which exhibited a pathogenic signature and occurred exclusively in inflamed joints of ZAC mice. Such pathogenic signature was uniquely detected in CD4+ T cells obtained from inflamed joints of RA patients. Overall, the Tconv repertoires of ZAP-70 mutant mice were increasingly similar to the repertoires of WT Tregs, and this effect was most notable in the subset of pathogenic Th17 cells. Our results support a model where, upon moderate ZAP-70-mediated signal weakening, T cells that would normally develop into Tregs, instead develop into self-reactive Tconvs, resulting in a breakdown in self-tolerance and susceptibility to autoimmune arthritis.

immunology↗

bioRxiv2022

A universal differential expression prediction tool for single-cell and spatial genomics data

With the growing complexity of single-cell and spatial genomics data, there is an increasing importance of unbiased and efficient exploratory data analysis tools. One common exploratory data analysis step is the prediction of genes with different levels of activity in a subset of cells or locations inside a tissue. We previously developed singleCellHaystack, a method for predicting differentially expressed genes from single-cell transcriptome data, without relying on clustering of cells. Here we present an update to singleCellHaystack, which is now a universally applicable method for predicting differentially active features: 1) singleCellHaystack now accepts continuous features that can be RNA or protein expression, chromatin accessibility or module scores from single-cell, spatial and even bulk genomics data, and 2) it can handle 1D trajectories, 2-3D spatial coordinates, as well as higher-dimensional latent spaces as input coordinates. Performance has been drastically improved, with up to ten times reduction in computational time and scalability to millions of cells, making singleCellHaystack a suitable tool for exploratory analysis of atlas level datasets. singleCellHaystack is available as an R package and Python module

bioinformatics↗

bioRxiv2022

Intercellular crosstalk in adult dental pulp is mediated by heparin-binding growth factors Pleiotrophin and Midkine

In-depth knowledge of the cellular and molecular composition of dental pulp (DP) and the crosstalk between DP cells that drive tissue homeostasis or regeneration are not well understood. To address these questions, we performed data analysis of publicly available single-cell transcriptomes of DP. This analysis revealed that DP resident fibroblasts have a unique gene expression profile when compared with fibroblasts from 5 other reference tissues: blood, bone marrow, adipose tissue, lung, and skin. Genes coded for heparin-binding growth-factors, pleiotrophin (PTN) and midkine (MDK), possessed the highest differential expression levels in DP fibroblasts. In addition, we identified extensive crosstalk between DP fibroblasts and several other DP cells, including Schwann cells, MSCs and odontoblasts. These findings suggest that fibroblast-derived growth factors regulate DP niches, and thus have a potential role as dental therapeutic targets.

cell biology↗

bioRxiv2021

Unbiased integration of single cell transcriptomes using a linear hybrid method

Single cell transcriptomic approaches are becoming mainstream, with replicate experiments commonly performed with the same single cell technology. Methods that enable integration of these datasets by removing batch effects while preserving biological information are required for unbiased data interpretation. Here we introduce Canek for this purpose. Canek leverages information from mutual nearest neighbor to combine local linear corrections with cell-specific non-linear corrections within a fuzzy logic framework. Using a combination of real and synthetic datasets, we show that Canek corrects batch effects while introducing the least amount of bias compared with competing methods. Canek is computationally efficient and can easily integrate thousands of single-cell transcriptomes from replicated experiments.

bioinformatics↗

bioRxiv2019

singleCellHaystack: Finding surprising genes in 2-dimensional representations of single cell transcriptome data

SummarySingle-cell sequencing data is often visualized in 2-dimensional plots, including t-SNE plots. However, it is not straightforward to extract biological knowledge, such as differentially expressed genes, from these plots. Here we introduce singleCellHaystack, a methodology that addresses this problem. singleCellHaystack uses Kullback-Leibler Divergence to find genes that are expressed in subsets of cells that are non-randomly positioned on a 2D plot. We illustrate the usage of singleCellHaystack through applications on several single-cell datasets. singleCellHaystack is implemented as an R package, and includes additional functions for clustering and visualization of genes with interesting expression patterns. Availability and implementationhttps://github.com/alexisvdb/singleCellHaystack Contactalexisvdb@infront.kyoto-u.ac.jp

bioinformatics↗