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Identification of causal genes for nonalcoholic fatty liver disease using
multi-omics based single-cell analysis

1*
Sung Eun Hong , Kyung-Suk Suh , Won Kim , Murim Choi 1
3
2
1. Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
2. Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
3. Department of Internal medicine, Seoul National University College of Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, Republic of Korea
ABSTRACT SAMPLES & METHODS
Background and aims Data generation snRNA-seq analysis
Nonalcoholic fatty liver disease (NAFLD) is an emerging liver disease associated with metabolic
syndrome. Lack of effective treatment drugs urges the discovery of novel therapeutic targets. This study NAFLD (n=25) Cell type 2
utilizes multi-omics-based single-cell analysis to discover biomarkers and therapeutic targets of NAFLD. Control (n=23) Cell type 3
Methods Matched blood & Liver biopsy sample Alignment
Liver biopsy samples obtained from 23 control individuals and 25 NAFLD patients were subjected to QA/QC
Normalization
single nucleus RNA-sequencing (snRNA-seq). DNA samples obtained from the same participants were Batch correction Cell type 1
genotyped by low coverage whole genome sequencing. snRNA-seq profiles of the NAFLD liver were Clustering
analyzed using various bioinformatics tools. Genotype and single-cell gene expression data were
integrated to map single-cell expression quantitative trait loci (sc-eQTL). DNA extraction Single nucleus
dissociation
Results
A total of 250K cells were detected, including hepatocytes and various non-parenchymal cells.
Cell-cell
Differential
Pseudotime analysis recovered the zonation information in hepatocytes, differentiation pattern in Trajectory Gene expression interaction
inference
cholangiocytes, and activation process in stellate cells. Differentially expressed genes revealed cell type- Low-cov WGS 10X Chromium
specific changes in NAFLD. Multiple sc-eQTL signals in each cell type were detected. G eQTL calling
A
<
<
Conclusions ACGTTACCGGGAATTAA • Linear regression with NAFLD Control
We present transcriptomic profile of NAFLD in a single-cell resolution. sc-eQTL analysis identified interaction term
NAFLD-associated genes and their regulatory variants in relevant cell types. The role of putative • Cell type specific & eGene expression
regulatory genes and variants will be subjected to functional validation. Genotype snRNA-seq • Disease specific eQTL
eSNP genotype
RESULTS & DISCUSSION
1. High resolution map of liver cell populations from NAFLD patients

*ctrl = no steatosis, no inflammation
*NAFL = simple steatosis without NASH
*eNASH = NASH, fibrosis stage 0-1
*aNASH = NASH, fibrosis stage 2-4
UMAP of liver cell populations obtained from 23 normal individuals and Dotplot of cell type marker genes in each cell type clusters Cell type proportion in each disease groups. Difference in hepatocyte and
25 NAFLD patients. A total of 249k cells and 25k genes were profilled. immune cell proportion between control and advanced NASH is notable.
2. Hepatocytes: zone specific gene expression changes in NAFLD 3. Hepatic stellate cells(HSC) : dynamic process of activation and
fibrogenesis is observed from single cell transcriptomics
Pseudotime analysis on hepatocytes
Pseudotime analysis on HSC DEG of activated HSC: GO enrichment
Periportal Activated /"'-% $POUSPM
Periportal
Pericentral Lineage 1 Lineage 2
&$. DPOTUJUVFOU
Inactivated (SPXUI GBDUPS CJOEJOH
3FTQPOTF UP 5('C
Pericentral Quiescent Quiescent #MPPE WFTTFM EFWFMPQNFOU
Gene expression
Pseudotime Sample origin of cells
PV Quiescent /"'-% $POUSPM
Pericentral hepatocytes DEG: Gene ontology (GO) enrichment
CV NAFLD > Control NAFLD < Control Fraction of cells $FMM BEIFTJPO
Lipid metabolic process Fibrinogen complex Activated
Liver development Drug metabolic process
Hepatic lobule Collagen containing ECM Cellular response to insulin stimulus
Low pt High pt
=
=
4. Interactions between cell types are altered during NAFLD progression Quiescent Activated
5. sc-NAFLD-eQTL: genetic variants that are associated with cell type
NASH (n=19) – ctrl (n=23) Prioritized Ligands (NASH vs ctrl)
Predicted Targets specific gene expression changes in NAFLD patients
• sc-NAFLD-eQTL= sc-eQTL + NAFLD specific eQTL
• Hepatocytes (figure below), cholangiocytes, stellate cells, endothelial cells and immune cells (data not shown)
Hepatocyte eQTL NAFLD Hepatocyte eQTL
Prioritized Ligands $:1 ' FYQSFTTJPO IFQBUPDZUF $:1 ' FYQSFTTJPO #VML 3/" TFR
TO3/" TFR
(5&Y CVML
Significant eGenes (n = 117, hepatocyte) GO enrichment
0 0.005 •
Cellular response to DNA damage stimulus
• Cell-Cell interaction quantification: CellPhone DB • Ligand prioritization: Nichenet $:1 7 FYQSFTTJPO $:1 7 FYQSFTTJPO • • Intracellular protein transport
• Differences in number of significant interactions between • Ligand-receptor interactions that might drive • Nuclear protein-containing complex
Regulation of cholesterol storage
NASH and control samples gene expression changes between NASH vs • Extrinsic component of mitochondrial inner membrane
control.
• NASH > ctrl
o Endothelial cell – Stellate cell
o Monocyte – Cholangiocyte • Ligand sender: pericentral hepatocyte REFERENCES
o Pericentral hepatocyte – Stellate cell
o DC – T cells, Macrophage, Monocyte • Receiver: activated stellate cell 1. SA MacParland et al., Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations,, Nat
Commun, 2018
• NASH < ctrl 2. N Aizarani et al., A human liver cell atlas reveals heterogeneity and epithelial progenitors, Nature, 2019
o DC – Stellate cell • Prioritized ligands: OCLN, CALM2, ANGPTL3,
o Macrophage – Stellate cell SERPINC1, INHBA, HMGB1 etc 3. P Ramachandran et al., Resolving the fibrotic niche of human liver cirrhosis at single-cell level, Nature, 2019
• Periportal hepatocytes – inactivated Stellate cell 4. K street et al., Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics, BMC Genomics, 2018
5. L Garcia-Alonso et al., Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro, Nat Genet, 2021
6. R Borowaeys et al., Nichenet: modeling intercellular communication by linking ligands to target genes, Nat Methods, 2020