Single-cell identification of subpopulations with bulk sample phenotype correlation (SCISSOR)

The proportions of each cluster from each sample were determined using the SCISSOR R package (V4.1.2; R Core Team 2021, https://github.com/sunduanchen/Scissor). HCC RNA-seq data were downloaded from the TCGA database. Human HCC single-cell sequence data (GSE151530) were downloaded from the Gene Expression Omnibus (GEO) database, and the three-input data for Scissor were the single-cell expression matrix, batch expression matrix, and target phenotype. The correlation matrix and cell-cell similarity network were then calculated according to the input source, and their phenotypes were further integrated into the network regularization sparse regression model to select the most relevant cell subsets. According to the estimated sign of the regression coefficient, the cells were divided into scissor-positive (Scissor+) and scissor-negative (Scissor−) cells, indicating a positive and negative correlation with the target phenotype, respectively. Subsequently, significance reliability testing was conducted to determine whether the selected data were suitable for phenotypic cell associations. Finally, cells selected using scissors were further characterized by downstream analysis.

Cell culture

Hepa1-6 cells were purchased from the American Type Culture Collection (ATCC) authenticated by short tandem repeat (STR) profiling and tested for Mycoplasma contamination before the experiments. Hepa1-6 cells were cultured in Dulbecco’s Modified Eagle medium supplemented with 10% foetal bovine serum (FBS), 100 IU/mL penicillin, and 100 μg/mL streptomycin at 37 °C under a humidified 5% CO₂ atmosphere.

Animal experiments and ethics statement

Six-to-eight-week-old C57BL/6 mice were purchased from Hangzhou Medical College (Hangzhou, China). All the mice were male. All animal experiments were performed in accordance with the Institutional Animal Care and Use Committee of the Zhejiang Center of Laboratory Animals (No: ZJCLA-IACUC-20020166). A total of 25 μL mixture of PBS and matrigel (Corning, New York, USA) (1:1) containing 5×10⁵ Hepa1-6 cells were injected into the left liver lobe of C57BL/6 mice to establish the HCC orthotopic model. For inhibitors experiments, mice were intraperitoneally injected with CD20 antibody (100 μg/each, BP0356, Bio X Cell, New Hampshire, USA) or clodronate liposomes (CL) (200 μL/each, 40337ES, Yeasen, Shanghai, China) every other day.

Mouse tissue-derived lymphocytes isolation

Liver tissues were excised from HCC orthotopic model mice. Liver-derived lymphocytes were isolated from the HCC tissues using collagenase II/IV (Solarbio, Beijing, China) and Lymphocyte Separation Medium (Dakewe Biotech, Shenzhen, China).

Flow cytometric analysis

For cytometric analysis, nonspecific binding of the cells was blocked using an Fc receptor block (clone 2.4G2, BD Biosciences, New Jersey, USA). The cells were stained with surface antigen-antibodies for 30 min at 4 °C, after incubated with Fc receptor block for 10 min at 4 °C. After washing twice with PBS, the cells were analyzed using a BD FACS Canto II. The data were analyzed using FlowJo X software (Version 10.8; Tree Star, https://www.flowjo.com/solutions/flowjo/downloads). The following antigens were used: CD20 (152108, BioLegend, California, USA), FVS780 (565388, BD), CD45 (147710, BioLegend), PD-L1 (124334, BioLegend), F4/80 (157303, BioLegend), and CD206 (141734, BioLegend).

Human sample and ethics statement

Patient-derived HCC tissues were collected from patients who underwent hepatic surgery between September 2021 and June 2022. Human samples were collected under the supervision of the ethics committee of Hangzhou First People’s Hospital (No. 2024-022) and adhered to the principles of the Declaration of Helsinki. All participants signed an informed consent form.

Western blot

After harvesting, HCC and normal tissues were lysed using a cell lysis buffer (9803, Cell Signaling Technology, Boston, USA). The supernatant was taken after 12,000 r/min centrifugation for 15 min (BECKMAN COULTER Microfuge 20R Centrifuge, USA). After measuring the protein concentration using a BCA kit (P0010, Beyotime, Shanghai, China), 5× loading buffer was added, and the mixture was boiled for 10 min 95 °C, ran through 4%−20% sodium dodecyl sulfate-polyacrylamide gel, and transferred onto polyvinylidene fluoride (PVDF) membranes (Millipore, Massachusetts, USA). The membranes were incubated with indicated primary antibodies at 4 °C overnight, washed three times with TBST buffer (1× TBS, 0.05% V/V TWEEN 20) for a total of 30 min, and incubated with secondary antibodies (1:2,000) for 1 h at room temperature. After washed three times with TBST buffer (1× TBS, 0.05% V/V TWEEN 20) for 30 min, the signal was detected using an ECL kit (Pierce). The primary antibodies used were anti-GAPDH (1:1,000, ab213480; Abcam, Cambridge, UK) and anti-Zinc finger protein 296 (anti-ZNF296) (1:1,000, PA5-69042; Invitrogen, California, USA).

RNA isolation and quantitative real-time polymerase chain reaction (qPCR) analysis

Total RNA from HCC tissues was extracted using TRIzol reagent (Invitrogen). Reverse transcription of RNA and qPCR were performed using Prime ScriptTM Trimester Mix (Takara, Bio, Shiga Prefecture, Japan) and SYBR Premix Ex TaqTM11 (Yeasen, Shanghai, China) with a StepOnePlus qPCR system (Life Technologies, California, USA), according to the manufacturer’s instructions. GAPDH was used as an endogenous control. Primers for qPCR were synthesized by Tsingke Biotech (Beijing, China). The following primer sequences were used: ZNF296 forward primer, ACCCCGAACTATCCCGACC, and ZNF296 reverse primer, AGGCAGTGATGGCCTCCAA. GAPDH forward primer, GGAGCGAGATCCCTCCAAAAT; GAPDH reverse primer, GGCTGTTGTCATACTTCTCATGG.

Multiplex immunofluorescence

The multiplex immunofluorescence assay was performed as follows: after heating, antigen retrieval, and rinsing, the slides were incubated with 3% hydrogen peroxide to remove endogenous peroxidase. After multiplex staining, signal amplification, and fluorescence signal capture, the slides were placed in a microwave oven to remove the antibody complex. A second round of multiplex staining was performed. Finally, DAPI staining and image acquisition were performed using Pannoramic MIDI. The primary antibodies used were anti-CD20 (1:1,000, GB14030, Servicebio, Wuhan, China), anti-CD68 (1:200, GB113150, Servicebio), anti-E cadherin (Ecad, 1:200, GB12083, Servicebio), and 4’,6-diamidino-2-phenylindole (DAPI) (G1012, Servicebio).

Interaction of B cells and macrophages affects HCC progression

To further investigate the correlation between B cells and macrophages in HCC progression, we first constructed a Hepa1-6-Luc orthotopic transplantation mouse HCC model (Luc-HCC). We evaluated the effect of CL, a specific inhibitor for the depletion of macrophages, and the effect of a CD20 antibody, a specific antibody for the depletion of CD20+ B cells. CL treatment did not affect tumor progression or extend the survival of HCC mouse models (Figure 2A,​BB) (13). Unexpectedly, CD20 antibody treatment accelerated tumor progression and shortened the survival of HCC mouse models (P<0.05) (Figure 2C,​DD). Notably, the depletion of CD20+ cells promoted the infiltration of macrophages and the expression of PD-L1 in macrophages under gating in flow cytometry (P<0.05) but did not promote their differentiation into M2 macrophages (Figure 2E−G, Supplementary Figure S1). Overall, these results indicate that B cells suppress the expression of PD-L1 in macrophages and inhibit HCC progression.

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Figure 2

Deletion of B cells increases the expression of PD-L1 in macrophages. (A,B) Luc reporter-carrying Hepa1-6 HCC cell line orthotopic transplantation mouse models were constructed, and CL was injected every 3 d. In vivo imaging was performed on 7 d (A) as well as during the survival analysis (B); (C,D) Luc reporter-carrying Hepa1-6 HCC cell line orthotopic transplantation mouse models were constructed, and CD20 antibody was intraperitoneally injected every 3 d. In vivo imaging was performed on 7 d (C) as well as during the survival analysis (D); (E) Percentages of F4/80+ cells in CD45+ cells were detected by flow cytometry in tumor-infiltrating immune cells; (F,G) Percentages of CD206+ (F) and PD-L1+ (G) cells were detected by flow cytometry in F4/80+ cells isolated from mouse HCC tissues. PD-L1, programmed death ligand 1; HCC, hepatocellular carcinoma; CL, clodronate liposomes. *, P<0.05; **, P<0.01; ns, no significant.

ZNF296 intervenes interaction of B cells and macrophages to promote HCC progression

To explore the different immune response clusters in patients with HCC, they were divided into nine clusters of immune responses via CIBERSORTx analysis (Figure 3A). The OS of patients with HCC in cluster 2 was the worst, while that of patients in cluster 7 was the best (Figure 3B). We investigated the possible reasons for the differences in OS by analyzing the infiltration of different immune cells in HCC. As shown in Supplementary Figure S2A, for cluster 2 and 6, M0 and M2 macrophages were highly infiltrated, which is correlated with HCC progression (14). M1 macrophages were more abundant and M0 macrophages showed lower infiltration in luster 7 than those in lusters 2 and 6 (15). Clusters 2 and 6 were mainly at stages T3/T4 of HCC, and cluster 7 was mainly at an early stage of HCC (Supplementary Figure S2B). Transcription factors (TFs) enrichment analysis showed that ZNF296 was enriched in clusters 2 and 6, but not in cluster 7 (Figure 3C). Compared with other tumors, ZNF296 was highly correlated with immune subtypes in LIHC (Supplementary Figure S3A). Patients with high ZNF296 expression had poor prognosis and were associated with the stage of LIHC (Supplementary Figure S3B,C). We detected the protein ZNF296 in HCC and adjacent normal human tissues by western blot. ZNF296 was more highly expressed in HCC tissues than that in normal tissues (P<0.01) (Figure 3D). We used qPCR to distinguish the expression of different HCC tissues and performed multiplex immunofluorescence between ZNF296 high-expression HCC and ZNF296 low-expression HCC (Figure 3E). Using multiplex immunofluorescence, we found that the interaction between B cells and macrophages was significantly higher in ZNF296 low-expression HCC tissues compared with ZNF296 high-expression HCC tissues (P<0.0001) (Figure 3F,​GG). Correlation analysis revealed that the upregulation of ZNF296 expression was closely related to the upregulation of PAFAH1B3 and H2AFX (Supplementary Figure S3D). PAFAH1B3 is a platelet-activating factor acetyl hydrolase (PAF-AH), which is involved in glycolysis and lipid synthesis signaling pathway of HCC progress (16). In addition, histone H2AFX is highly correlated with immune cell infiltration by tumor-associated macrophages (TAMs), and neutrophils, which may promote HCC (17). Moreover, the downregulation of ZNF296 expression was closely related to the upregulation of MPDZ and ALDH6A1, which might suppress HCC progression (Supplementary Figure S3E) (18,19). In addition, correlation analysis indicated that the expression of lymphocyte-activation gene 3 (LAG3) (r=0.27) and cytotoxic T-lymphocyte associated antigen 4 (CTLA4) (r=0.31) was also related to the expression of ZNF296 (Supplementary Figure S4), which might be a potential target for patients with HCC and high expression of ZNF296 (20). Moreover, the small neutral L- and D-amino acid translocator SLC7A10 was highly expressed in cluster 2, whereas the pH regulator SLC9A11 was mainly enriched in cluster 7, indicating that clusters 2 and 7 may have differential biological activities (Supplementary Figure S2C). All in all, these results suggest that ZNF296 intervenes the interaction of B cells and macrophages to promote HCC progression.

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Figure 3

High expression of ZNF296 promotes the progression of HCC by intervening interaction between macrophages and B cells. (A) t-SNE plot shows 9 immune infiltration subtypes of LIHC; (B) Kaplan-Meier analysis of OS for 9 immune infiltration subtypes of LIHC; (C) Transcription factors heatmap of 9 immune infiltration subtypes of LIHC; (D) ZNF296 and GAPDH protein level were detected using western blot of HCC and adjacent normal tissue. The protein was normalized to GAPDH expression levels (n=3/3); (E) mRNA expression levels of ZNF296 and GAPDH were examined through qPCR analysis of HCC tissues with high and low expression of ZNF296 (n=6/6). The genes were normalized to GAPDH mRNA levels in each sample; (F) Multiplex immunofluorescence of Ecad+ (HCC cells), CD68+ (macrophages) and CD20+ cells (B cells) are shown for HCC tissues with high and low expression of ZNF296 (n=6/6); (G) Interaction counts of macrophages and B cells in ZNF296 high-expression HCC and ZNF296 low-expression HCC were shown. HCC, hepatocellular carcinoma; t-SNE, t-distributed stochastic neighbor embedding; LIHC, liver hepatocellular carcinoma; OS, overall survival; qPCR, quantitative real-time polymerase chain reaction. **, P<0.01; ***, P<0.001; ****, P<0.0001 .

Immune analysis of infiltrated immune cells in HCC by SCISSOR

To mine the prognostic information correlated with infiltrated immune cells in HCC, we performed SCISSOR analysis by combining HCC scRNA-seq and TCGA bulk RNA-seq (21) (Figure 4A). As shown in Figure 4B, 18 cell subsets were observed, including CD8+ T cells, CD4+ T cells, DCs, endothelial cells, neutrophils, liver cells, and myeloid cells (Figure 4B). Furthermore, dead and survival phenotype-associated cells were distributed across cell subsets (Figure 4C). Two types of T cells (T1 and T2), myeloid, and liver cells, were associated with the survival phenotype, whereas two types of DCs (DC1 and DC2) were associated with the dead phenotype (Figure 4D). Differential gene enrichment analysis indicated that GZMK, GZMA, KLRB1, and NKG7 were enriched in T1; HLA-DRA/DRB1/DPB1 and CXCL8 were enriched in T2; cathepsin B (CTSB) was explicitly expressed in DC2 (Figure 4E). Differentially expressed genes (DEGs) analysis suggested that T1 is activated cytotoxic T lymphocytes (CTLs) and T2 is migrated T effector cells (Teff), and these T cell subsets are related to a better prognosis. However, CTSB+ DC2 is associated with worse prognosis, which may contribute to CTSB-promoting cancer metastasis (22).

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Figure 4

T cells are related to survival of patients with HCC. (A) Flow diagram of the analysis process of SCISSOR; (B) UMAP visualization of 10,000 cells in HCC; (C) UMAP visualization of scissor-selected cells. The red and blue dots are dead phenotype cells (bad survival) and survival phenotype cells (good survival); (D) Bar chart shows the cell ratio of 18 cell clusters in LIHC; (E) Heatmap of the top ten gene expression of 18 cell clusters in LIHC. HCC, hepatocellular carcinoma; UMAP, uniform manifold approximation and projection; LIHC, liver hepatocellular carcinoma; T, CD8+ T cell; CD4T, CD4+ T cell; DC, dendritic cell; EC, endothelial cell; Neu, neutrophil; Liver, liver cell; Mye, myeloid cell.

This study was funded by the Key Program of the National Natural Science Foundation of China (No. 81930016), National Natural Science Foundation of China (No. 92159202 and No. 82273177), Key Research and Development Plan of Zhejiang Province (No. 2021C03118 and No. 2022C03108), and Zhejiang Provincial Natural Science Foundation of China (No. LQ20H160029).