Sebastian Preißl

The paper performs single-cell multiomics analysis of the human retina to identify regulatory elements and develop predictors for non-coding risk variants.

The paper maps 3D genome organization and DNA methylation across 86,689 single nuclei from 16 human tissues, revealing cell-type-specific variations and discrepancies between methylation and chromatin structure.

The paper investigates KC plasticity in mycobacterial infections, finding that KClow cells derived from bona fide KCs exhibit unique antimycobacterial activity and adaptability.

The paper uses single-cell epigenomics to study brain aging in mice, finding heterochromatin instability and transcription factor dysfunction across multiple brain regions and cell types.

This study uses single-cell multiomics and spatial transcriptomics to identify gene regulatory programs in pancreatic cell types that drive type 1 diabetes progression.

This study uses multi-omic analysis to identify transcriptional and spatial changes in Mfrp-/- mice, revealing photoreceptor loss and gene regulatory disruptions during retinal degeneration.

The paper uses single-cell multiome profiling to identify gene regulatory changes in pancreatic islets linked to diabetes risk factors like age, sex, and genetic variants.

The paper identifies sex differences in microglial inflammatory responses in glioblastoma, showing that genes on the inactive X chromosome drive these differences.

The paper constructs a single-cell chromatin accessibility atlas of the human brain, identifies cell-type-specific regulatory elements, and develops models to predict regulatory function of disease risk variants.

The paper identifies 14,000 human craniofacial enhancers across developmental stages and shows 56% are conserved in mice, providing cell type- and stage-specific activity profiles.

The paper identifies gene-CRE pairs in mouse brain cells by integrating single-cell transcriptomes and epigenomes, controlling for gene co-expression.

The paper identifies EOMES-induced chromatin accessibility as a key step in initiating mesendoderm lineage commitment during mammalian gastrulation.

The paper integrates single-cell multiomic data with genetics to identify gene regulatory changes in beta cells associated with type 2 diabetes.

The paper identifies T1D risk genes and regulatory elements that influence beta cell survival under proinflammatory cytokine exposure using functional genomics and genetics.

The paper develops a multi-omic single-nucleus sequencing method and framework to analyze regulatory genome diversity in human cortical cells and link it to disease risk variants.

The paper creates a chromatin accessibility atlas across 25 adult human tissues, identifying over 750,000 cis-regulatory elements and linking them to gene regulatory programs and disease-associated variants.

The paper compares cellular diversity in the primary motor cortex across human, marmoset, and mouse using single-nucleus transcriptomics and epigenomics, finding conserved cell types and species-specific adaptations.

The paper identifies 491,818 candidate cis regulatory elements in adult mouse brain sub-types and links them to target genes and transcriptional regulators involved in various pathways.

The paper creates a comprehensive atlas of mouse primary motor cortex cell types using integrated transcriptomic and epigenomic data from over 600,000 samples.

The paper constructs a DNA methylation atlas of the mouse brain at single-cell resolution, revealing epigenetic diversity and spatial organization of neuronal subtypes.

This study defines age-specific brain environmental factors driving microglia phenotypes through transcriptomic and epigenomic analysis of human fetal and postnatal microglia.

This study combines large-scale GWAS and single-cell epigenomics to identify cell type-specific mechanisms of type 1 diabetes risk.

The paper defines cardiac cell type-specific cis-regulatory elements and links disease risk variants to these elements in human hearts.

The paper uses single-cell chromatin accessibility to identify cell type- and state-specific regulatory programs associated with type 2 diabetes risk.

SnapATAC is a software package for analyzing scATAC-seq data, revealing regulatory elements and transcriptional regulators in mouse brain cells.

The paper systematically maps chromatin state landscapes during mouse development by profiling histone modifications across 72 tissue-stages, identifying dynamic enhancers and transcriptional regulators.

The paper uses single nucleus ATAC-seq and RNA-seq to show age-related changes in chromatin accessibility and gene expression linked to SARS-CoV-2 entry genes in human lung cells.

The paper develops an online learning algorithm for integrating large single-cell datasets, enabling efficient processing of millions of cells with reduced memory usage and allowing iterative refinement of cellular identity.

This paper shows that cortical organoids model early human brain network development by exhibiting oscillatory activity that transitions to patterns resembling preterm EEG.