Supplementary Materials Materials and Methods Cell Isolation And

Supplementary material Gut

Supplementary Materials

Materials and Methods

Cell isolation and purification

Blood was collected in Ethylenediaminetetraacetic acid (EDTA) tubes and peripheral

blood mononuclear cells (PBMCs) were isolated by Ficoll density gradient centrifugation

using Ficoll-Paque PLUS (GE Healthcare Life Sciences, UK). CD14+ monocytes and NK

cells were purified from PBMCs by magnetic cell sorting using the CD14+ positive selection

kit and the NK cell negative selection kit, respectively, according to the manufacturer’s

instructions (Miltenyi Biotec, Bergisch-Gladbach, Germany). CD14+ monocyte purity was

>95% and NK cell purity >90%, assessed by flow cytometry.

CD3 and KLRG1+ cells were depleted from PBMCs using the CD3 MicroBead kit

(Miltenyi Biotec) and indirectly with KLRG1-biotin and anti-biotin Microbeads (Miltenyi

Biotec). CD3 and KLRG1+ cell depletion was >90%, as measured by flow cytometry.

Cell separation of PBMCs into KLRG1+ and KLRG1- CD56dim NK cells was

performed using fluorescence-activated cell sorting (FACS). PBMCs were stained with

PECy7 conjugated anti-CD56 (clone NCAM16.2, BD Biosciences), anti-CD3-AlexaFluor700

(clone UCHT1, Biolegend), BV421-conjugated anti-KLRG1 (clone 2F1, Biolegend), and

stained with live/dead Zombie Aqua dye (Biolegend). KLRG1+ and KLRG1- CD56dim NK

cells were sorted to a purity of ≥98% with the BD FACSAria III cell sorter (BD Biosciences).

ELISA

Supernatants from co-culture of purified NK cells with autologous HBsAg-pulsed

moDCs were harvested, spun free of cells, and frozen at -80°C for extracellular Granzyme B

and interleukin-2 (IL-2) measurements by sandwich ELISA kit (Abcam) according to the

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

manufacturer’s instructions. HCMV IgG plasma titres was measured by using HCMV IgG

ELISA kit (K4150, BioVision) according to the manufacturer’s instruction.

Proliferation assays

NK cells (2 x 105 cells/mL) were labelled with carboxyfluorescein succinimidyl ester

(CFSE) (5 µM, Cayman) and incubated at room temperature for 10 minutes. Cells were

washed in complete medium and cultured in the presence or absence of HBsAg-pulsed

moDCs, HBsAg-pulsed moDCs+IL-15 (10ng/mL), HBsAg-pulsed moDCs+IL-7 (10ng/mL)

for 14 days using an NK/DCs ratio of 5:1. IL-15 (1 ng/mL) was added to support NK cell

survival, with 50% of the medium being replenished every 3 days. After culture for 14 days,

the fluorescence of CFSE-labelled CD3-CD56+ NK cells were analysed by flow cytometry.

NK cells were phenotyped using: PECy7 conjugated anti-CD56 (clone NCAM16.2, BD

Biosciences), BUV395-conjugated anti-CD16 (clone 3G8, BD Biosciences), BV421-

conjugated anti-KLRG1 (clone 2F1, Biolegend), and stained with fixable viability stain 700

(BD Biosciences). Anti-CD3-PE (clone UCHT1, Biolegend) was added to exclude T cells.

T-distributed stochastic nearest neighbour embedding (t-SNE)

For high-dimensional analysis of flow cytometry data, we used the Barnes-Hut

implementation of t-distributed stochastic nearest neighbour embedding (t-SNE), a non-linear

dimensionality reduction approach [55]. Cells that share similarity will be located closely

together in the 2D scatter plot of the t-SNE map. For t-SNE analysis, raw flow cytometry data

were imported and compensated in FlowJo. NK cells were defined as CD3-CD56+ after the

following gating strategy; a time and single cells gating to remove the aberrant event and

doublets, respectively; lymphocytes were gated on forward scatter versus side scatter; live

cells were identified by excluding Zombie Aqua positive dye cells. Then, NK cell

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

populations were down-sampled to an equal number of cells per sample using the

‘DownSample’ plugin in FlowJo. These samples were concatenated into a single fcs file and

analysed using the ‘t-Sne’ plugin. The following parameters are used to generate the t-SNE

map; CD107a, CD56, CD57, CD69, KLRG1, under the following t-SNE settings; iteration

1000, perplexity 30, Eta 200, Theta 0.5. To create the coloured t-SNE plots, the samples were

exported from FlowJo as CSV-Channel values therefore the bi-exponential expression can be

reflected on a linear axis and linear colour gradient. The t-SNE plots were performed using R

as previously described [56]. R and the packages of ggplot2, colourRamps, ggthemes, and

scales were used to colour individual cells in the t-SNE plots by the expression intensity of

various markers.

RNA Sequencing Analysis

SeqGeq version 1.1 (Tree Star, Ashland, OR) [57] was used to analyse the data that

consist of flow cytometry parameters from cell sorting and the single RNA expression matrix

simultaneously. R software was used to merge these data before importing data to SeqGeq.

The quality control step was applied in order to remove dead cells, debris, and dimly

expressed genes, and to detect highly dispersed or variable genes across single cells for

downstream analysis (Figure S5B). The DrImpute package in R software (ver. 3.4.1) was

used to estimate the dropout zeros or events in scRNA-seq before further analysis and

visualization [58]. For all downstream analysis, most of which were assessed using Seurat

plugin in SeqGeq version 1.1 or in-house R scripts. The heatmap was generated by using

MORPHEUS (https://software.broadinstitute.org/morpheus). The significant differentially

expressed genes (DEGs) were defined as p-value <0.05 and absolute log2 fold change >1.5

and further were used for enrichment pathway and biological process analysis. Gene set

enrichment analysis was performed using GSEA version 2.0.14 [59]. Kyoto Encyclopedia of

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Genes and Genomes (KEGG) database was used to evaluate the relevant signalling pathways

of the DEGs [60]. Enrichment of the DEGs into biological process (BP) Selected Gene

Ontology (GO) terms was performed using MetaCoreTM (GeneGO, MI, USA). In KEGG and

GO enrichment analysis, the p-value <0.05 was considered statistically significant.

Flow Cytometry Panels

For flow cytometry analysis, the following fluorochrome-labelled antibodies were

used: PE-Cy7 conjugated anti MICA/MICB (clone 6D4), BV711 conjugated anti-CD14

(clone M5E2), BV605 conjugated anti-CD95 (clone DX2), APC conjugated anti-CD85j

(clone GHI/75), BV605 anti-Tim3 (clone F38-2E2), BV711 anti-PD1 (clone EH12.2H7) (all

purchased from Biolegend); BV421 conjugated anti-ULBP2/5/6 (clone 165903) were

purchased from BD Biosciences; APC conjugated anti-ULBP1 (clone 170818) and PE

conjugated anti-ULBP3 (clone 166510) from R&D Systems. Cell viability was determined

using live/dead Zombie Aqua dye from Biolegend. Samples were analysed using the BD

Biosciences LSRII cell analyser and FlowJo software version 10.6.0 (Tree Star, Ashland,

OR).

RNA extraction and quantitative real time PCR

RNA was extracted using the Favorgen total RNA mini kit (Favorgen Biotech). M-

MLV reverse transcriptase (Promega) was used to generate complementary DNA (cDNA)

from 200 ng total RNA (A260/280 ratio of 1.9-2.1) according to the manufacturer’s protocol.

cDNA was amplified with specific primers and EvaGreen Master Mix (Biotium) using the

Rotor-Gene 3000 thermocycler (Corbett Research). Relative mRNA levels were calculated

using GAPDH Ct values as a reference gene. Primer sequences are available in

supplementary table 1.

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Supplementary Table 1. Sequence of PCR primers used for qPCR

Primer Sequence CD2 F: AAT GCC TTG GAA ACC TGG GG R: TGA TCA TCG GTC TTC AGA TGC BECN1 F: GGA AGG GTC TAA GAC GTC CA R: AGT TCC TGG ATG GTG ACA CG ATF4 F: TTC TCC AGC GAC AAG GCT AAG G R: CTC CAA CAT CCA ATC TGT CCC G S100A6 F: AAG CTG CAG GAT GCT GAA AT R: CCC TTG AGG GCT TCA TTG TA ISG20 F: TAG CCG CTC ATG TCC TCT TT R: TGA GGG AGA GAT CAC CGA TT ARL6IP1 F: CAG AGA CTG CAA GTC TGG AAG A R: ATG ATG GCA GGT GGA AAC CA TIMP1 F: AAT TCC GAC CTC GTC ATC AGG R: ATC CCC TAA GGC TTG GAA CC CD40LG F: TGA GCA ACA ACT TGG TAA CCC TGG R: CTG GCT ATA AAT GGA GCT TGA CTC G BNIP3L F: ATG TCG TCC CAC CTA GTC GAG R: CTC CAC CCA GGA ACT GTT GAG CD7 F: GCA GCA GTC TCC CCA CTG R: CCG GAG CCG TAG ACA TTG A EAT-2 F: GGA GGT ACA GCT GCT TAC ACA R: CTC CTG GTA TCG ACT CGC TG GAPDH F: AAG GTG AAG GTC GGA GTC AAC R: GGG GTC ATT GAT GGC AAC AAT A

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Figure S1. The expression of NKG2D ligands on monocyte-derived dendritic cells (moDCs).

moDCs from eight HBV vaccinated subjects were left unstimulated or stimulated in the presence

HBsAg for 24 hours and analysed for the expression of NKG2D ligands by flow cytometry. (A)

Unbiased bulk analysis of eight non-pulsed and pulsed-moDCs with HBsAg using t-SNE algorithm.

moDCs were defined as CD14- cells following the gating of live, single cells. Density plots show the

clustering of cell phenotypes in all samples (left panel) and in moDCs donors stratified based on

absence (middle panel) or presence HBsAg stimulation (right panel). (B) Overlaid histogram plots

showing the expression of NKG2D ligands ULBP1, ULBP2/5/6, ULBP3, MICA/MICB from non-

pulsed moDCs (blue) and pulsed-moDCs with HBsAg (green). Fluorescence minus one (FMO)

controls were performed alongside samples, and values are indicated on histograms. Wilcoxon’s test

was performed for individual comparisons of paired groups, *p<0.05.

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Figure S2. Mature monocyte-derived dendritic cell (moDCs) phenotype after

stimulation with HBsAg, HBcAg, and TNF-α.

(A) After 6 days culture, monocyte-derived dendritic cells (moDCs) were stimulated to

become mature moDCs with antigen (HBsAg and HBcAg) or TNF-α for 24 hours. Flow

cytometry histogram and bar graph showing the maturation state of moDCs HBV vaccinated

individuals, evaluated by the expression of surface markers CD83, CD86, and HLA-DR. (B)

HBsAg-pulsed DCs maturation status in HBV vaccinated and unvaccinated individuals.

Wilcoxon’s test was performed for individual comparisons of paired groups, *p<0.05 for

comparison with unstimulated moDCs. Mann-Whitney test was performed for individual

comparison of two independent groups.

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Figure S3. Immune cells and quality control gating in SeqGeq. (A) Immune cell subsets gating based on flow cytometry parameter (CD56+CD8 T cells,

CD56+CD4 T cells, CD56bright and CD56dim NK cells) and gene expression level of KLRG1

(KLRG1+/- CD56+CD8 T cells and KLRG1+/- CD56dim NK cells). (B) Quality control

procedures in SeqGeq consists of (1) the assessment quality of cells by performing the “knee

calling” plot using “gene expressed per cell” parameter in y axis and “rank of genes

expressed” parameter in x axis, (2) the quality of genes evaluation using “cells expressing”

for each gene parameter in y axis and “total expression/read” for every gene in x axis, and (3)

the detection of highly dispersed genes across single cells.

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Figure S4. Gene set enrichment analysis (GSEA) of KLRG1+CD3+CD56+CD8 T cells.

Gene set transcriptional enrichment analysis of KLRG1+ versus KLRG1-CD3+CD56+CD8 T

cells. Significant gene set enrichment related to KLRG1+CD3+CD56+CD8 T cells included

genes that are upregulated on CD161int CD8 T cell (above) and CD8 stem cell memory

(below), both of which possess an effector memory phenotype in adult circulation.

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Figure S5. Validation of upregulated genes in KLRG1+CD56dim NK cells in single cell

RNA-sequencing data.

The upregulated genes in KLRG1+ compared to KLRG1-CD56dim NK cells from single cell

RNA-sequencing data were validated using qPCR of sorted KLRG1+ and KLRG1- CD56dim

NK cells from 10 subjects. Wilcoxon’s test was performed for individual comparisons of

paired groups, *p<0.05.

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Figure S6. NK cell degranulation stratified by human cytomegalovirus (HCMV)

serology.

The plasma titre of HCMV immunoglobulin G (IgG) was measured among all subjects within

study groups (vaccine (-), vaccine (+), and chronic HBV (CHB) patients). According to IgG

serological status for HCMV, CD107a degranulation after re-stimulation with (A) HBcAg-

pulsed moDCs and (B) HBsAg-pulsed moDCs was compared within individuals in study

groups. Mann-Whitney tests were performed for comparisons of two independent groups

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Figure S7. Cytotoxicity NK cells response of HBV vaccinated subjects in the presence or

absence monocytes (CD14+) during re-stimulation with HBsAg-pulsed moDCs.

NK cells within PBMCs and CD14 depleted PBMCs (∆CD14-PBMCs) from 6 HBV vaccinated

subjects were incubated with HBsAg-pulsed moDCs or K562 cells as a positive control for 18 hours.

(A) Representative flow cytometry contour plot showing the NK cell CD107a response in the

presence or absence monocytes (CD14+) during coculture with HBsAg-pulsed moDCs or K562 cells.

NK cells CD107a response was measured by the percentage of CD107a positive cells and CD107a

staining intensity (geometric mean fluorescence intensity/gMFI). The microarray analysis from two

public datasets (B) GSE2215 and (C) GSE15075 reveals a distinctive expression profile of monocytes

and in vitro generated mature moDCs. Volcano plot showing differentially expressed genes (DEGs)

of mature moDCs versus monocytes with absolute log2 fold change 1.2-fold difference and adjusted p

value <0.05. Blue and red indicate transcript down- and up-regulated, respectively. Heatmap

illustrating the differential expressed genes between monocytes and mature moDCs related to

phenotype and positive regulation of NK and T cells immune response and proliferation according to

biological process (BP) gene ontology (GO) analysis with the DAVID functional annotation tool.

Wilcoxon’s test was performed for individual comparisons of paired groups.

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Figure S8. NK cell receptors expression on CD56bright and CD56dim of HBV vaccinated

subjects and CHB patients.

Flow cytometry was performed on total PBMCs from vaccinated subjects and CHB patients.

NK cells were gated based on CD3-CD56+ expression. The CD56bright and CD56dim NK cell

subsets were gated for evaluating NK cell receptors expression. (A) vaccinated subjects. (B)

CHB patients. Wilcoxon’s tests were performed for comparisons of paired groups, **p<0.01,

***p<0.001.

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Figure S9. CD56dimKLRG1- and CD56dimKLRG1+ NK cell phenotyping.

(A) The expression of NK inhibitory receptors; CD85j/ILT2, TIM-3, PD-1 on

CD56dimKLRG1- and KLRG1+ NK cells were evaluated by flow cytometry. (B) The gene

expression of CD7 and EAT-2 between CD56dimKLRG1- and KLRG1+ NK cells were

measured by qPCR. Wilcoxon’s test was performed for individual comparisons of paired

groups, **p<0.01.

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Figure S10. Correlation of PD-1 and TIM-3 expression with CD56dimKLRG1+NK cells

of HBV vaccinated subjects Correlation of the frequency and gMFI of CD56dimPD-1+ NK

cells and CD56dimTIM-3+ NK cells with CD56dimKLRG1+ NK cells within HBV vaccinated

subjects. Spearman’s rank test was used to evaluate the correlations, *p<0.05.

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Supplementary table 1. Phenotypic comparison of KLRG1+ and KLRG1- NK cells Properties KLRG1+/- Methoda p-valueb (readout system) Adhesion receptors • CD2 ↑ RNA-seq/qPCR RNA-seq (*)/qPCR (*) • CD56 CD56dim FCM (%) FCM (****) • CD57 ↑ FCM (%/gMFI)/ qPCR FCM (*/**)/qPCR (*) • CD7 ↓ qPCR qPCR (**) Inhibitory receptors • panKIR (KIR2DL1/L2/L3) ↔ FCM (%/gMFI) - • KIR2DL1 ↑ RNA-seq RNA-seq (*) • KIR3DL2 ↑ RNA-seq RNA-seq (*) • LIRB1/ILT2/CD85j ↑ FCM (%/gMFI) FCM (**/**) • NKG2A ↓ FCM (%/gMFI)/ qPCR FCM (****/**)/qPCR (**) Activating receptors • CD16 ↑ FCM (%/gMFI)/ qPCR FCM (**/***)/ qPCR (**) • panKIR (KIR2DS1/S2/S4) ↔ FCM (%/gMFI) - • NKG2C ↔ FCM (%/gMFI) - • DNAM-1 ↑ FCM (%/gMFI) FCM (*/**)/ qPCR (**) • NKG2D ↔ FCM (%/gMFI) - • NKp46 ↓ FCM (%/gMFI)/ qPCR FCM (****/*)/qPCR (*) Adaptor molecules • FcεRIγ ↓ qPCR qPCR (**) • SYK ↓ qPCR qPCR (*) • DAB-2 ↓ qPCR qPCR (*) • EAT-2 ↓ qPCR qPCR (**) Cytokines receptors • IFN-α (IFNAR1 and IFNAR2) ↑ qPCR qPCR (*) • IL-18Rα (IL18R1) ↓ qPCR qPCR (*) Death receptors and ligands • CD95L/Fas ligand ↔ FCM (%) - • CD40L ↑ qPCR qPCR (*) • TRAIL ↑ FCM (%) FCM (**) Transcription factor • PLZF (ZBTB16) ↓ qPCR qPCR (*) • IKZF2 (Helios) ↓ qPCR qPCR (*)

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252 Supplementary material Gut

Biological activities

• Cytokine secretion (IFN-γ) ↓ FCM (%)/qPCR FCM (***)/qPCR (*) IL-12/18 stimulation

• Cytotoxicity (CD107a) ↓ FCM (%)/qPCR FCM (**)/qPCR (*) K562 stimulation

• ADCC ↑ FCM/qPCR FCM (*)/qPCR (*) Raji+anti-CD20 stimulation

a The method used to investigate the KLRG1+ and KLRG1- NK cell phenotype and functionality consists of FCM: flowcytometry, RNA-seq: RNA sequencing, qPCR: quantitative PCR. qPCR was carried out on sorted KLRG1+ and KLRG1- NK cells with purity ≥98%, BD FACSAria III cell sorter (BD Biosciences). The flow cytometry result was represented as percentage positive cells (%) or geometric mean fluorescence intensity (gMFI) b The p-value was calculated by Wilcoxon’s test for individual comparisons of paired groups, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. The significant differentially expressed genes (DEGs) in RNA-seq data were defined as p-value <0.05 and absolute log2 fold change >1.5

Wijaya RS, et al. Gut 2020;0:1–13. doi: 10.1136/gutjnl-2019-319252