Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations

Sonya A. MacParland, Jeff C. Liu, Xue-Zhong Ma, Brendan T. Innes, Agata M. Bartczak, Blair K. Gage, Justin Manuel, Nicholas Khuu, Juan Echeverri, Ivan Linares, Rahul Gupta, Michael L. Cheng, Lewis Y. Liu, Damra Camat, Sai W. Chung, Rebecca K. Seliga, Zigong Shao, Elizabeth Lee, Shinichiro Ogawa, Mina Ogawa, Michael D. Wilson, Jason E. Fish, Markus Selzner, Anand Ghanekar, David Grant, Paul Greig, Gonzalo Sapisochin, Nazia Selzner, Neil Winegarden, Oyedele Adeyi, Gordon Keller, Gary D. Bader, and Ian D. McGilvray.
Nature Communications (2018).

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Abstract

The liver is the largest solid organ in the body and is critical for metabolic and immune functions. Surprisingly little is known about the cells that make up the human liver and its immune microenvironment. Here we report a map of the cellular landscape of the human liver using single cell RNA sequencing. We carefully fractionated fragile, fresh hepatic tissue from human livers to obtain viable parenchymal and non-parenchymal cells. Our single cell transcriptomics map reveals 20 discrete cell populations, and includes a description of distinct monocyte/macrophage populations in the human liver. We present a comprehensive view of the human liver at single cell resolution that outlines the characteristics of resident cells in the liver, and in particular provides a map of the human hepatic immune microenvironment.

scClustViz of the Human Liver Atlas

Explore this data interactively in your web browser or download for viewing with scClustViz on your computer.

To explore this data offline in R, download the zip file(s) above, and unzip them to the directory of your choice.
Then install scClustViz using the commands below:

# Installation (takes time, but only run once):
install.packages("devtools","BiocManager")
BiocManager::install("org.Hs.eg.db")
devtools::install_github("BaderLab/scClustViz")

Then to open an interactive scClustViz RShiny session on your computer, run the following commands (replacing path/to/file with the path to the unzipped data file you downloaded above):

# View the data:
library(scClustViz)
runShiny("path/to/file/HumanLiver.RData",
         annotationDB="org.Hs.eg.db",
         rownameKeytype="SYMBOL",
         imageFileType="png")