Oliver Stegle

This study uses time-resolved single-cell CRISPR screening and a multiome atlas to uncover dynamic gene regulatory networks in human cardiogenesis, identifying stage-specific regulators and CHD-linked gene functions.

The paper shows that aged female CD8 memory T cells exhibit erosion of Xi transcriptional fidelity, linked to immune activation and clonal expansion, with similar findings in humans.

This paper introduces Split-flow, a pipeline that uses concordance between demultiplexing methods to classify cell quality in multiplexed single-cell data, revealing structured quality gradients.

The paper performs a federated cis-eQTL meta-analysis across single-cell datasets to identify cell-type-specific genetic effects and link them to disease mechanisms.

The paper maps variant, enhancer, and gene function in primary human CD4+ T cells by combining Perturb-seq of regulatory elements and genes, identifying CRE-gene pairs and linking variants to disease-related programs.

The paper benchmarks generative models for synthetic bulk RNA-seq data, finding trade-offs between utility, biological preservation, and privacy risks across different methods.

This paper introduces LIVI, a deep learning framework for mapping trans-eQTLs at single-cell resolution, and shows it detects more trans-eQTLs than existing methods and identifies cell types and genes involved in autoimmune disease risk.

This paper introduces scPT-seq, a method to detect CRISPR mutations and transcriptional responses at single-cell resolution in vivo, revealing clonal markers and lineage dynamics in Drosophila intestine.

The paper identifies host genetic effects on microbiome composition in rats and finds evidence of indirect genetic effects contributing to missing heritability.

MOFA-FLEX is a flexible factor analysis framework that integrates multi-omics data with prior knowledge, improving robustness and disentangling variation in scRNA-seq and CITE-seq data.

The paper introduces CHOCOLAT-G2P, a method integrating functional genomics and spatial transcriptomics to study genotype-phenotype relationships in tumors, revealing zonation patterns and epistasis in liver cancer.

BOMS improves cell segmentation in spatial transcriptomics by using mRNA spot locations and gene labels without auxiliary stains, achieving comparable performance to existing methods with faster execution.

The paper identifies a spatiotemporal cancer cell trajectory in glioblastoma that explains tumor heterogeneity across regions and subclones.

This study uses single-cell multiomics to identify regulatory programs and plasticity regulators in glioblastoma, revealing MYT1L as a key repressor of malignant plasticity.

Segger improves cell segmentation in spatial transcriptomics by using a graph neural network, achieving high sensitivity and speed with minimal manual intervention.

The paper shows that DNA methylation ageing is reflected in correlations between genomic loci, with a preference for 500 bp apart sites, and links this to PRC2-dependent H3K27me3 dilution.

The paper presents a genome-scale CRISPRi perturbation map in human pluripotent stem cells using single-cell RNA sequencing to study trans gene regulation across diverse genetic backgrounds.

The paper identifies an X-Y translocation in Four-Core Genotypes mice that increases Tlr7 expression and affects genetic interpretation.

XClone detects allele-specific copy number alterations from scRNA-seq data by smoothing signals on cell and gene coordinate graphs, enabling robust identification of haplotype-aware CNAs and subclones.

This paper introduces SDR-seq, a multiomic scDNA-scRNA-seq method to link genotypes with gene expression, showing variants affect expression in iPSCs and B-cell lymphoma.

This study shows that local regeneration in sea anemones triggers a systemic homeostatic response leading to whole-body tissue remodeling through proteolysis driven by metalloproteases.

SpatialData, a Zarr/OME-NGFF-based format plus Python library, unifies images, labels, points, shapes and tables across spatial omics platforms with coordinate alignment, lazy loading and cross-modal aggregation.

This study shows Xist RNA modulates X-chromosome inactivation beyond early development, reversibly silencing escapee genes and altering topological domain structures in mouse embryos.

This study shows that aged intestinal epithelial cells exhibit a persistent inflammation signature, marked by MHC class II upregulation and increased chromatin accessibility of inflammation-associated loci.

The paper introduces cell2fate, a model-based RNA velocity method that improves cell fate prediction by decomposing velocity solutions into modules and applying it to human brain development data.

This paper introduces DeepRVAT, a deep learning model for rare variant association genetics, showing improved gene discovery and risk prediction using whole-exome sequencing data from UK Biobank.

This paper describes OME-Zarr, a cloud-optimized bioimaging file format developed through community collaboration to improve scalability and data access.

The paper introduces MuAt, a deep learning model that learns representations of somatic mutations to predict tumour types and subtypes with high accuracy.

The paper develops GASPACHO to identify dynamic eQTLs in antiviral responses using single-cell RNA-seq, uncovering thousands of eQTLs and linking them to disease risk loci.

FISHFactor is a probabilistic factor model for spatial transcriptomics data that improves accuracy by modeling single molecule resolution data and sharing information across cells.