Projects |

Prediction of single cell gene expression

Fri, 21 Aug 2026 00:00:00 +0000

Learning the relationship between gene-regulatory regions from epigenome and transcriptome data measured for single-cell is hard. We have developed an automated Nextflow pipeline that addresses the noise and sparsity of single-cell data by aggregating individual cells into metacells. We show that analysis on metacell level often leads to superior models and we illustrate the use of these models on different datasets.

Linked publications

Laura Rumpf, Fatemeh Behjati Ardakani, Dennis Hecker, Marcel H. Schulz (2026). Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data. Bioinformatics.

DOI

Exploring the regulatory impact of non-coding genetic variants

Fri, 19 Jun 2026 00:00:00 +0000

Overview

SNEEP is a computational method to identify regulatory Single Nucleotide Variants (rSNVs) that alter the binding sites of Transcription Factors (TFs). It can efficiently analyze large collections of genetic variants, such as GWAS datasets. SNEEP combines its statistical approach with cell type-specific regulatory to prioritize functional non-coding variants and connect them to their target genes.

Applications

In a large-scale study of coronary artery disease, SNEEP was used to connect disease-associated variants to candidate target genes based on enhancer-gene interaction prediceted with the gABC-score across 45 cell types. In another cardiovascular disease studies, SNEEP identified thousands of regulatory SNPs and uncovered novel candidate disease genes, including non-coding RNAs with experimentally validated functions in cardiac disease models.

Associated Publications

Linked publications

Dennis Hecker, Xiaoning Song, Nina Baumgarten, Anastasiia Diagel, Nikoletta Katsaouni, Ling Li, Shuangyue Li, Ranjan Kumar Maji, Fatemeh Behjati Ardakani, Lijiang Ma, Zhaolong Li, Aldo Moggio, Daniel Tews, Hendrik Sager, Lars Maegdefessel, Martin Wabitsch, Johan L. M. Björkegren, Heribert Schunkert, Zhifen Chen, Marcel H. Schulz (2026). Cell type-specific epigenetic regulatory circuitry of coronary artery disease loci. Nature Communications.

DOI

Nina Baumgarten, Laura Rumpf, Thorsten Kessler, Marcel H. Schulz (2024). A statistical approach for identifying single nucleotide variants that affect transcription factor binding. iScience.

DOI

Chaonan Zhu, Nina Baumgarten, Meiqian Wu, Yue Wang, Arka Provo Das, Jaskiran Kaur, Fatemeh Behjati Ardakani, Thanh Thuy Duong, Minh Duc Pham, Maria Duda, Stefanie Dimmeler, Ting Yuan, Marcel H. Schulz, Jaya Krishnan (2023). CVD-associated SNPs with regulatory potential reveal novel non-coding disease genes. Human Genomics.

DOI

Quality control for imaging-based spatial transcriptomics

Thu, 18 Jun 2026 00:00:00 +0000

Quality control for imaging-based spatial transcriptomics

Imaging-based spatial transcriptomics platforms (e.g. CosMx, MERSCOPE, Xenium) resolve individual RNA molecules in intact tissue with sub-cellular resolution, but the resulting data carries technical and biological artefacts that are not always apparent from inspection:

Transcript dropout on the cell level
Under- or over-segmented cells
Truncated cells at the edges of imaging tiles (FOVs)
False-positive signal from control probes

Left unaddressed, these artefacts silently interfere with every downstream analysis.

What we’re building toward

We aim to build an R package for quality control (QC) of imaging-based spatial transcriptomics data. It operates directly on standard Seurat and SpatialExperiment objects, computing quality-control metrics at the cell and FOV level. Every check is implemented as a discrete, composable step: compute a metric, flag the cells it identifies, visualise the result, and optionally filter. This ensures that every decision remains visible and reversible. Two entry points are provided: an interactive R API for assembling custom pipelines from individual QC steps, and a command-line mode that runs a fixed pipeline and produces a self-contained QC report for reproducible, code-free runs.Its role is to surface quality problems clearly and consistently, leaving the final keep-or-discard call to the researcher.

Categorization of Spatial Transcriptomics Methods for Cell-Cell Communication Analysis

Thu, 26 Oct 2023 00:00:00 +0000

Cell–cell communication (CCC) plays a central role in regulating cellular behavior within tissues. Spatial transcriptomics provides the spatial context needed for biologically informed CCC analysis, leading to a rapidly growing landscape of computational methods. In this review, we evaluate 31 recent approaches, categorize them according to their analytical objectives, and provide practical guidance for selecting appropriate methods based on specific research questions.

Enhancer-gene interactions and transcription factor affinities

Thu, 26 Oct 2023 00:00:00 +0000

Enhancer–gene interactions are an important factor in epigenomics because they help to explain how regulatory regions control gene expression. Enhancers can act over large distances and do so in a cell type-specific manner. These interactions help connect epigenomic features to functional gene regulation, by finding regulatory programs that define cellular identity or provide interpretation for disease-associated noncoding variants.

We developed STARE, a computational tool for assigning enhancers to their likely target genes and using those to summarize transcription factor affinity on gene-level. The enhancer-gene interactions are predicted with the generalized Activity-by-Contact (gABC)-score, which uses a measurement of enhancer activity and optionally also chromatin contact data. It can be applied to pseudobulked single-cell data as well.

We applied STARE in a variety of different projects, especially the enhancer-gene interactions. Its applications so far include:

Examining the role of an understudied histone modification during cardiogenesis
Investigation of the relation between metabolism and epigenomics at the example of the cysteine pathway
Prioritizing disease genes for coronary artery disease by interpreting the impact of genomic risk variants on gene regulation
The role of STAT3 in pericytes under HFpEF

MISPIL and/or PCMultiome

Thu, 26 Oct 2023 00:00:00 +0000

We use gene-specific models to predict expression from epigenetic activity and perturb candidate regulatory elements in silico to identify small combinations that shift disease-associated expression profiles toward healthy states. By formulating this as a combinatorial optimization problem, we can efficiently prioritize regulatory regions for follow-up experimental perturbation.

Prediction and evaluation of Split open reading frames

Thu, 26 Oct 2023 00:00:00 +0000

The regulation of RNA translation is crucial for maintaining appropriate protein levels within cells. A novel mechanism which controls RNA translation is via the formation of Split open reading frames (Split-ORFs). These occur in transcripts containing at least two open reading frames, each encoding a part of the same full-length protein. These multiple open reading frames arise from alternatively spliced transcript isoforms. We developed the Split-ORF pipeline, a computational tool that predicts Split-ORFs from transcripts’ sequences and identifies regions unique to the predicted Split-ORF products for evaluation with Ribo-seq data.

Prediction of gene expression from epigenome data

Thu, 26 Oct 2023 00:00:00 +0000

Prediction of gene expression from epigenome data is an important task to link epigenome variation to changes in gene expression. Over the years we have designed a number of machine learning methods that present approaches for learning those links. We have made a number of models available for the community in the EpiExpress repository that allows to conduct histone-acetylation based epigenome association studies.

Linked publications

Fatemeh Behjati Ardakani, Shamim Ashrafiyan, Laura Rumpf, Dennis Hecker, Marcel H Schulz (2026). Comparing machine learning methods predicting transcriptome from epigenome with applications to association studies. Genome Biology.

URL DOI

Florian Schmidt, Alexander Marx, Nina Baumgarten, Marie Hebel, Martin Wegner, Manuel Kaulich, Matthias S. Leisegang, Ralf P. Brandes, Jonathan Göke, Jilles Vreeken, Marcel H. Schulz (2021). Integrative analysis of epigenetics data identifies gene-specific regulatory elements. Nucleic Acids Research.

DOI

Florian Schmidt, Fabian Kern, Marcel H. Schulz (2020). Integrative prediction of gene expression with chromatin accessibility and conformation data. Epigenetics & Chromatin.

DOI

Signature reversal based drug ranking from expression data

Thu, 26 Oct 2023 00:00:00 +0000

Linked publications

Nikoletta Katsaouni, Marcel H. Schulz (2026). RANKOR: Direct Drug Prioritization from Bulk and Single-Cell Transcriptomic Signatures. Preprint (BioRxiv).

URL DOI

Nikoletta Katsaouni, Marcel H. Schulz (2026). Hierarchical Representation Learning for Drug Mechanism-of-Action Prediction from Gene Expression Data. Preprint (BioRxiv).

URL DOI

A unified single-cell atlas of mouse tissue damage

Mon, 01 Jan 0001 00:00:00 +0000

Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into cellular responses across tissues and disease states. As part of the collaborative efforts of the SFB 1531 consortium, with contributions from multiple research groups, we constructed an integrated atlas of approximately 390,000 mouse cells spanning multiple tissues (heart, carotid, lung, and brain) and disease models, including heart disease, stroke, and lung injury. To harmonize technical and biological variation across datasets, we applied scVI to integrate raw count data while effectively removing batch effects. Cell types were annotated using curated marker genes from the literature. This comprehensive atlas serves as a unique resource for a wide range of downstream analyses, including the identification of disease-specific and shared transcriptional programs, differential gene expression analysis, gene–disease association studies, dynamic transcription factor network inference, and the integration of spatial transcriptomics data.

Linked publications

Shamim Ashrafiyan, Iaroslav Kosaretskii, Marcel H. Schulz (2026). AtlasLens: Metadata-centric exploration and analysis of single-cell atlases. Preprint (BioRxiv).

URL DOI