Abstract:

The liver is the largest solid organ in the human body and is responsible for a multitude of essential functions for survival. Chronic liver injury affects over 1 billion people worldwide and therapeutic options other than liver transplantation are a critical unmet medical need. Thus, advances in molecular hepatology are essential to facilitate the discovery of new therapeutic targets. Here we describe the aggregation and integration of single cell RNA-sequencing in more than 36,000 cells from 28 human livers reported in five independent studies. Noteworthy, the merged data shows a high degree of overlap, demonstrating the robustness of liver gene expression at single cell level independent of age, gender, liver collection, processing and sequencing methods. Hence, this data allowed us to develop a user-friendly online tool for quick and easy interrogation and comparison of gene expression across different parenchymal and non-parenchymal liver cell populations. Collectively, this study provides the largest human liver transcriptomic single cell atlas accessible for interactive visualization via an open-access web portal to the research community worldwide.

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About Us:

Silvia Vilarinho, MD,PhD
Assistant Professor of Medicine (Digestive Diseases) and Pathology, Yale School of Medicine
silvia.vilarinho@yale.edu

Joseph Brancale
MD/PhD Student, Yale School of Medicine
joseph.brancale@yale.edu

Please feel free to reach out to us with any questions or comments.

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Methods:

Data was accessed at the NCBI GEO using the accession numbers GSE115469, GSE136103, GSE129933, GSE124395, GSE130473. For GSE136103, GSE129933, GSE124395, GSE130473, the data was downloaded from GEO and imported directly into Seurat v.3.2.2 using the barcode, gene expression, and annotation files. For GSE115469, raw sequencing files were downloaded and processed through CellRanger (v. 3.0.2) using default settings before imported into Seurat. Once imported into Seurat, each dataset was individually filtered (transcript present in 3 cells minimum, between 250-2500 transcripts, and mitochondrial sequences < 30%) and normalized. For each dataset 2,000 variable features were identified using the variance stabilizing transformation (VST) method. In order for Seurat integration to be performed a k filter of 94 was applied (maximum acceptable filter) and anchor genes were identified and used for integration with a dimensionality of 30. The data was scaled and PCA and UMAP were run for dimensionality reduction using the same variable of 30. Cluster identification was performed using a shared nearest neighbors-based algorithm with a resolution of 0.5. Marker genes were used to identify cluster cell types.

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Use of Data and Code:

Please acknowledge any use of this data in presentations or publications by citing the following paper

Brancale J, Vilarinho S
A single cell gene expression atlas of 28 human livers Journal of Hepatology. 2021 Jul; 75(1)
https://pubmed.ncbi.nlm.nih.gov/34016468/

Code for analyses can be found at https://github.com/joeb-liver/Single_Cell_Liver_Atlas

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References:

1. MacParland SA, et al. (2018) Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun 9(1):4383.
2. Aizarani N, et al. (2019) A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature 572(7768):199-204.
3. Segal JM, et al. (2019) Single cell analysis of human foetal liver captures the transcriptional profile of hepatobiliary hybrid progenitors. Nat Commun 10(1):3350.
4. Tamburini BAJ, et al. (2019) Chronic Liver Disease in Humans Causes Expansion and Differentiation of Liver Lymphatic Endothelial Cells. Front Immunol 10:1036.
5. Ramachandran P, et al. (2019) Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575(7783):512-518.

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