Biology subjects

Vandenbon, A.

Publications and source records attributed to Vandenbon, A..

11 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

oCELLoc: Automated Cell Type Assignment in Transcriptomics Data Using Reference Filtering

Interpreting single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data requires accurate cell-type prediction, which strongly depends on the quality of the reference used. However, prediction accuracy is highly dependent on reference quality; missing relevant cell types or including irrelevant ones can substantially impair performance. To address this challenge, we developed oCELLoc, a regression-based method that selects the most appropriate reference cell types from a large atlas and tailors them to each new sample. oCELLoc takes pseudobulk gene expression from ST or scRNA-seq data together with a broad reference matrix and uses regularized regression with cross-validation to identify a limited number of essential cell types. We applied oCELLoc to toy datasets, scRNA-seq data, and 2,144 Visium samples across diverse tissues and conditions, demonstrating that using the filtered cell types leads to more biologically meaningful downstream predictions. oCELLoc is available as an R package on GitHub and CRAN.

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↗

bioRxiv2023

Low albumin status accompanies multi-layered immunosuppressive phenotypes in metastatic breast cancer patients

Low albumin status is prevalent in advanced cancer patients, but the pathophysiology associated with this anomaly remains largely unexplored. To address this, we aim to search correlations of albumin levels with the transcriptome against peripheral blood mononuclear cells and the plasma metabolome within the same patients having metastatic breast cancers. We confirm that metastatic breast cancer patients exhibit low albumin levels in varying degrees without prominent systemic inflammation. Our data demonstrate that low albumin levels correlate with transcriptome signatures indicative of "neutrophil activation and T-cell down-regulation," an immunosuppressive phenotype. We also find that immunoregulatory metabolites, such as arginine, are reduced in plasma in an albumin-correlated manner, further corroborating systemic immunosuppression. These results are verified using a mouse model of breast cancer. We conclude that low albumin status in metastatic breast cancer patients accompanies immunosuppressive phenotypes, which is likely unfavorable for anti-cancer immunotherapy and thus can be a cause of unsuccessful treatment outcomes.

cancer biology↗

bioRxiv2023

Selective elimination of CD169+ macrophages in lymph nodes invaded by breast cancers

Breast cancer cells suppress the host immune system to efficiently invade the lymph nodes; however, the underlying mechanism remains incompletely understood. Here, we report that metastasized breast cancer cells selectively eliminate CD169+ lymph node sinus macrophages, an initiator of anti-cancer immunity, from the lymph nodes. The comparison between paired lymph nodes with and without metastasis from the same patients demonstrated that the number of CD169+ macrophages was reduced in metastatic lymph nodes, whereas the numbers of other major immune cell types were unaltered. We also detected the infiltration of CD169+ macrophages into metastasized cancer tissues depending on sections, suggesting that CD169+ macrophages were gradually eliminated after anti-cancer reactions. Furthermore, CD169+ macrophage elimination was prevalent in major breast cancer subtypes and correlated with breast cancer staging. Collectively, we propose that metastasized breast cancer cells dispel CD169+ macrophages from lymph nodes in a phased manner, disabling a critical step of anti-cancer immunity.

cancer biology↗

bioRxiv2023

Epigenetic characterization of housekeeping core promoters and their importance in tumor suppression

There has been extensive research on describing cell type-specific (CTS) regulatory interactions, especially between enhancers and promoters. However, constitutively active interactions between CREs have been less studied. In this research, we elucidate the presence of around 11,000 housekeeping CREs (HK-CREs) and describe their main characteristics. Most of the HK-CREs are located in promoter regions, but contrary to expectations, they are not only the promoters of housekeeping genes and are involved in a broader role beyond housekeeping gene regulation. HK-CREs are conserved regions rich in unmethylated CpG sites. Their distribution across chromosomes highly correlates with that of protein-coding genes, and they interact with a large number of target genes in long-distance interactions. In the context of cancer, we observed a reduction in the activity of a subset of HK-CREs, particularly those located at the end of chromosome 19 and associated with zinc finger genes. We investigated the effect of these genes on samples from diverse cancer subtypes, observing a significant reduction in their expression due to aberrant methylation of their core promoters. Finally, an analysis of more than 5,000 patients from 17 cancer subtypes showed an increase in the survival probability of patients with higher expression of these genes, suggesting them as housekeeping tumor suppressor genes. Overall, our work unravels the presence of ubiquitously active CREs indispensable for the maintenance and stability of cells.

genomics↗

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

Murine breast cancers disorganize the liver transcriptome in zonated manners

The spatially organized gene expression program within the liver specifies hepatocyte functions according to their relative distances to the bloodstream (i.e., zonation), contributing to liver homeostasis. Despite the knowledge that solid cancers remotely disrupt liver homeostasis, it remains unexplored whether solid cancers affect liver zonation. Here, using spatial transcriptomics, we thoroughly investigate the abundance and zonation of hepatic genes in cancer-bearing mice. We find that breast cancers affect liver zonation in various distinct manners depending on biological pathways. Aspartate metabolism and triglyceride catabolic processes retain relatively intact zonation patterns, but the zonation of xenobiotic catabolic process genes exhibits a strong disruption. The acute phase response is induced in zonated manners. Furthermore, we demonstrate that breast cancers activate innate immune cells in particular neutrophils in distinct zonated manners, rather than in a uniform fashion within the liver. Collectively, breast cancers disorganize hepatic transcriptomes in zonated manners, thereby disrupting zonated functions of the liver.

cancer biology↗

bioRxiv2021

Guidance for RNA-seq co-expression estimates: the importance of data normalization, batch effects, and correlation measures

MotivationGene co-expression analysis is an attractive tool for leveraging enormous amounts of public RNA-seq datasets for the prediction of gene functions and regulatory mechanisms. However, the optimal data processing steps for the accurate prediction of gene co-expression from such large datasets remain unclear. Especially the importance of batch effect correction is understudied. ResultsWe processed RNA-seq data of 68 human and 76 mouse cell types and tissues using 50 different workflows into 7,200 genome-wide gene co-expression networks. We then conducted a systematic analysis of the factors that result in high-quality co-expression predictions, focusing on normalization, batch effect correction, and measure of correlation. We confirmed the key importance of high sample counts for high-quality predictions. However, choosing a suitable normalization approach and applying batch effect correction can further improve the quality of co-expression estimates, equivalent to a >80% and >40% increase in samples. In larger datasets, batch effect removal was equivalent to a more than doubling of the sample size. Finally, Pearson correlation appears more suitable than Spearman correlation, except for smaller datasets. ConclusionA key point for accurate prediction of gene co-expression is the collection of many samples. However, paying attention to data normalization, batch effects, and the measure of correlation can significantly improve the quality of co-expression estimates.

bioinformatics↗

bioRxiv2019

Codon Optimality Confers GC-Rich-Induced Stability to mRNAs in Humans

Codon bias has been implicated as one of the major factors contributing to mRNA stability in yeast. However, the effects of codon-bias on mRNA stability remain unclear in humans. Here we show that human cells possess a mechanism to modulate RNA stability through a unique codon bias different from that of yeast. Bioinformatics analysis showed that codons could be clustered into two distinct groups - codons with G or C at the third base position (GC3) and codons with either A or T at the third base position (AT3); the former stabilizing while the latter destabilizing mRNA. Quantification of codon bias showed that increased GC3 content entails proportionately higher GC content. Through bioinformatics, ribosome profiling and in vitro analysis, we show that decoupling of the effects of codon bias reveals two modes of mRNA regulation, GC3- and GC-content dependent. Employing an immunoprecipitation-based strategy, we identified ILF2 as an RNA binding protein that differentially regulates global mRNA abundances based on codon bias. Our results demonstrate that codon bias is a two-pronged system that governs mRNA abundance.

molecular biology↗

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↗