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Richter, M. L.

Publications and source records attributed to Richter, M. L..

2 recordsLinked to original sources

Single-cell metabolic profiling reveals subgroups of primary human hepatocytes showing heterogeneous responses to drug challenge.

Xenobiotics are primarily metabolized by hepatocytes in the liver, and primary human hepatocytes (PHHs) are the gold standard model for the assessment of drug efficacy, safety and toxicity in the early phases of drug development. Recent advances in single-cell genomics have shown liver zonation and ploidy as main drivers of cellular heterogeneity. However, little is known about the impact of hepatocyte specialization on liver function upon metabolic challenge, including hepatic metabolism, detoxification, and protein synthesis. Here, we investigate the metabolic capacity of individual human hepatocytes in vitro, and assess how chronic accumulation of lipids enhances cellular heterogeneity and impairs the metabolisms of drugs. A phenotyping five-probe cocktail was used to identify four functional subgroups of hepatocytes that respond differently to drug challenge and fatty acid accumulation. These four subgroups display differential gene expression profiles upon cocktail treatment and xenobiotic metabolism-related specialization. Notably, intracellular fat accumulation leads to increased transcriptional variability and diminished the drug-related metabolic capacity of hepatocytes. Our results demonstrate that, upon a metabolic challenge such as exposure to drugs or intracellular fat accumulation, hepatocyte subgroups lead to different and heterogeneous transcriptional responses.

cell biology↗

epiScanpy: integrated single-cell epigenomic analysis

Epigenetic single-cell measurements reveal a layer of regulatory information not accessible to single-cell transcriptomics, however single-cell-omics analysis tools mainly focus on gene expression data. To address this issue, we present epiScanpy, a computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data. EpiScanpy makes the many existing RNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities. We introduce and compare multiple feature space constructions for epigenetic data and show the feasibility of common clustering, dimension reduction and trajectory learning techniques. We benchmark epiScanpy by interrogating different single-cell brain mouse atlases of DNA methylation, ATAC-seq and transcriptomics. We find that differentially methylated and differentially open markers between cell clusters enrich transcriptome-based cell type labels by orthogonal epigenetic information.

bioinformatics↗