Single-cell transcriptomics of hepatic stellate cells uncover crucial pathways and key regulators involved in non-alcoholic steatohepatitis

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Abstract

Background

Fibrosis is an important pathological process in the development of non-alcoholic steatohepatitis (NASH), and the activation of hepatic stellate cell (HSC) is a central event in liver fibrosis. However, the transcriptomic change of activated HSCs (aHSCs) and resting HSCs (rHSCs) in NASH patients has not been assessed. This study aimed to identify transcriptomic signature of HSCs during the development of NASH and the underlying key functional pathways.

Methods

NASH-associated transcriptomic change of HSCs was defined by single-cell RNA-sequencing (scRNA-seq) analysis, and those top upregulated genes were identified as NASH-associated transcriptomic signatures. Those functional pathways involved in the NASH-associated transcriptomic change of aHSCs were explored by weighted gene co-expression network analysis (WGCNA) and functional enrichment analyses. Key regulators were explored by upstream regulator analysis and transcription factor enrichment analysis.

Results

scRNA-seq analysis identified numerous differentially expressed genes in both rHSCs and aHSCs between NASH patients and healthy controls. Both scRNA-seq analysis and in-vivo experiments showed the existence of rHSCs (mainly expressing a-SMA) in the normal liver and the increased aHSCs (mainly expressing collagen 1) in the fibrosis liver tissues. NASH-associated transcriptomic signature of rHSC (NASHrHSCsignature) and NASH-associated transcriptomic signature of aHSC (NASHaHSCsignature) were identified. WGCNA revealed the main pathways correlated with the transcriptomic change of aHSCs. Several key upstream regulators and transcription factors for determining the functional change of aHSCs in NASH were identified.

Conclusion

This study developed a useful transcriptomic signature with the potential in assessing fibrosis severity in the development of NASH. This study also identified the main pathways in the activation of HSCs during the development of NASH.

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Most cited references 57

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P Tamayo, V K Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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GSVA: gene set variation analysis for microarray and RNA-Seq data

Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.