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81 Publications visible to you, out of a total of 81
Reciprocal Inhibitory Regulation of TGF-β1 Signaling and Cholesterol Metabolism in Hepatocytes

Abstract

Not specified

Authors: Sai Wang, Frederik Link, Mei Han, Roohi Chaudhary, Anastasia Asimakopoulos, Roman Liebe, Ye Yao, Seddik Hammad, Anne Dropmann, Marinela Krizanac, Matthias Ebert, Ralf Weiskirchen, Yoav I. Henis, Marcelo Ehrlich, Steven Dooley

Date Published: 2024

Publication Type: InProceedings

Repeated toxic injuries of murine liver are tolerated through minute steatosis and mild inflammation

Abstract

Not specified

Authors: Seddik Hammad, Christoph Ogris, Amnah Othman, Pia Erdoesi, Wolfgang Schmidt-Heck, Ina Biermayer, Barbara Helm, Yan Gao, Weronika Piorońska, Lorenza D'Alessandro, Fabian J. Theis, Matthias P. Ebert, Ursula Klingmüller, Jan Hengstler, Nikola S. Mueller, Steven Dooley

Date Published: 2023

Publication Type: Journal

Retinoic acid generates a beneficial microenvironment for liver progenitor cell activation in acute liver failure

Abstract

Not specified

Authors: Sai Wang, Frederik Link, Rilu Feng, Stefan Munker, Yujia Li, Roman Liebe, Matthias P. Ebert, Steven Dooley, Huiguo Ding, Shanshan Wang, Honglei Weng

Date Published: 2023

Publication Type: Journal

Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data

Abstract (Expand)

Background Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it … is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. Results To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community. Conclusions Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.

Authors: Christian H. Holland, Jovan Tanevski, Javier Perales-Patón, Jan Gleixner, Manu P. Kumar, Elisabetta Mereu, Brian A. Joughin, Oliver Stegle, Douglas A. Lauffenburger, Holger Heyn, Bence Szalai, Julio Saez-Rodriguez

Date Published: 1st Dec 2020

Publication Type: Journal

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