A5037274841- Single-cell and spatial transcriptomics
- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
- Gene expression and cancer classification
- Health, Environment, Cognitive Aging
- Cardiac Fibrosis and Remodeling
- Genomics and Chromatin Dynamics
- Cell Image Analysis Techniques
- Heart Failure Treatment and Management
- Machine Learning in Bioinformatics
- Signaling Pathways in Disease
- Immune cells in cancer
- Microbial Metabolic Engineering and Bioproduction
- Reservoir Engineering and Simulation Methods
- Cardiovascular Function and Risk Factors
- Fuel Cells and Related Materials
- Gut microbiota and health
- IL-33, ST2, and ILC Pathways
- Oil and Gas Production Techniques
- Hydraulic Fracturing and Reservoir Analysis
- Lipid metabolism and disorders
- Multiple Sclerosis Research Studies
- Inflammatory Bowel Disease
- Liver physiology and pathology
- Cardiovascular Effects of Exercise
University Hospital Heidelberg
2020-2025
Heidelberg University
2020-2025
European Bioinformatics Institute
2024
German Centre for Cardiovascular Research
2023-2024
Many methods allow us to extract biological activities from omics data using information prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor Python package containing computational these within unified framework. decoupleR allows flexibly run any method with given resource, including that leverage mode of regulation weights interactions, which are not in other frameworks. Moreover, it...
Abstract The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference cell-cell communication. Many computational tools were developed for this purpose. Each them consists a resource intercellular interactions prior knowledge and method to predict potential communication events. Yet impact choice on resulting predictions is largely unknown. To shed light this, we systematically compare 16 resources 7 methods, plus consensus...
Abstract The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any omics data, dozens to thousands measured markers. MISTy builds multiple views focusing on different or functional contexts dissect effects. evaluated in silico breast cancer datasets by imaging mass cytometry transcriptomics. estimated structural...
Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, their target genes, so called TF regulons, can be coupled with computational algorithms to estimate activity TFs. However, interpret these findings accurately, regulons high reliability coverage are needed. In this study, we present evaluate collection created using CollecTRI meta-resource...
Abstract The intestinal barrier is composed of a complex cell network defining highly compartmentalized and specialized structures. Here, we use spatial transcriptomics to define how the transcriptomic landscape spatially organized in steady state healing murine colon. At conditions, demonstrate previously unappreciated molecular regionalization colon, which dramatically changes during mucosal healing. identified spatially-organized transcriptional programs healing, regions with dominant...
The growing availability of single-cell and spatially resolved transcriptomics has led to the development many approaches infer cell-cell communication, each capturing only a partial view complex landscape intercellular signalling. Here we present LIANA+, scalable framework built around rich knowledge base decode coordinated inter- intracellular signalling events from single- multi-condition datasets in both data. By extending unifying established methodologies, LIANA+ provides comprehensive...
Therapeutic promotion of intestinal regeneration holds great promise, but defining the cellular mechanisms that influence tissue remains an unmet challenge. To gain insight into process mucosal healing, we longitudinally examined immune cell composition during damage and regeneration. B cells were dominant type in healing colon, single-cell RNA sequencing (scRNA-seq) revealed expansion IFN-induced subset experimental predominantly located damaged areas associated with colitis severity....
Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type-centric pairwise cross-condition comparisons, disregarding multicellular nature processes. Here, we propose factor for unsupervised samples from and identification programs associated with disease. Our strategy, which repurposes group as implemented in multi-omics analysis, incorporates variation patient across cell-types or other tissue-centric features, such...
Abstract The growing availability of single-cell and spatially-resolved transcriptomics has led to the rapidly popularity methods infer cell-cell communication. Many approaches have emerged, each capturing only a partial view complex landscape Here, we present LIANA+, scalable framework decode coordinated inter- intracellular signalling events from single- multi-condition datasets in both data. Beyond integrating extending established methodologies rich knowledge base, LIANA+ enables novel...
ABSTRACT Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, their target genes, so called TF regulons, can be coupled with computational algorithms to estimate activity TFs. However, interpret these findings accurately, regulons high reliability coverage are needed. In this study, we present evaluate collection created using CollecTRI...
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. Inflammation gradually compartmentalized and restricted to specific tissue niches such as lesion rim. However, precise cell type composition niches, their interactions changes between active inactive stages are incompletely understood. We used single-nucleus spatial transcriptomics from subcortical MS corresponding control tissues map types associated pathways nonlesion areas. identified perivascular...
Mouse models are frequently used to study chronic liver diseases (CLDs). To assess their translational relevance, we quantified the similarity of commonly mouse human CLDs based on transcriptome data. Gene‐expression data from 372 patients were compared with acute and consisting 227 mice, additionally nine published gene sets models. Genes consistently altered in humans mice mapped cell types single‐cell RNA‐sequencing validated by immunostaining. Considering top differentially expressed...
Inflammation, fibrosis and metabolic stress critically promote heart failure with preserved ejection fraction (HFpEF). Exposure to high-fat diet nitric oxide synthase inhibitor N[w]-nitro-l-arginine methyl ester (L-NAME) recapitulate features of HFpEF in mice. To identify disease-specific traits during adverse remodeling, we profiled interstitial cells early murine using single-cell RNAseq (scRNAseq). Diastolic dysfunction perivascular were accompanied by an activation cardiac fibroblast...
Abstract Myocardial infarction is a leading cause of mortality. While advances in the acute treatment have been made, late-stage mortality still high, driven by an incomplete understanding cardiac remodeling processes 1,2 . Here we used single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling different physiological zones timepoints human myocardial control myocardium to generate integrative high-resolution map remodeling. This approach allowed us increase...
Abstract The advancement of technologies to measure highly multiplexed spatial data requires the development scalable methods that can leverage information. We present MISTy, a flexible, and explainable machine learning framework for extracting interactions from any omics data. MISTy builds multiple views focusing on different or functional contexts dissect effects, such as those direct neighbours versus distant cells. be applied spatially resolved with dozens thousands markers, without need...
Abstract The growing availability of single-cell data has sparked an increased interest in the inference cell-cell communication from this data. Many tools have been developed for purpose. Each them consists a resource intercellular interactions prior knowledge and method to predict potential events. Yet impact choice on resulting predictions is largely unknown. To shed light this, we created framework, available at https://github.com/saezlab/ligrec_decoupler , facilitate comparative...
Abstract Multiple sclerosis (MS) is a prototypic chronic-inflammatory disease of the central nervous system. After initial lesion formation during active demyelination, inflammation gradually compartmentalized and restricted to specific tissue areas such as rim in chronic-active lesions. However, cell type-specific spatially drivers chronic damage expansion are not well understood. Here, we investigated properties subcortical white matter lesions by creating spatial map gene expression...
Transcriptomics is widely used to assess the state of biological systems. There are many tools for different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined impact method choices on expression results, little attention has been paid their effects further downstream functional analysis, which typically provides basis interpretation follow-up experiments. To address this, we introduce FLOP, a comprehensive nextflow-based workflow...
Abstract Single-cell atlases across conditions are essential in the characterization of human disease. In these complex experimental designs, patient samples profiled distinct cell-types and clinical to describe disease processes at cellular level. However, most current analysis tools limited pairwise cross-condition comparisons, disregarding multicellular nature effects other biological technical factors variation gene expression. Here we propose a computational framework for an...
Cells regulate their functions through gene expression, driven by a complex interplay of transcription factors and other regulatory mechanisms that together can be modeled as networks (GRNs). The emergence single-cell multi-omics technologies has the development several methods integrate transcriptomics chromatin accessibility data to infer GRNs. While these provide examples utility in discovering new interactions, comprehensive benchmark evaluating mechanistic predictive properties well...