ABSTRACT The heterogeneity of the tumour immune microenvironment (TIME), organized by various immune and stromal cells, is a major contributing factor of tumour metastasis, relapse and drug

resistance1,2,3, but how different TIME subtypes are connected to the clinical relevance in liver cancer remains unclear. Here we performed single-cell RNA-sequencing (scRNA-seq) analysis of

189 samples collected from 124 patients and 8 mice with liver cancer. With more than 1 million cells analysed, we stratified patients into five TIME subtypes, including immune activation,

immune suppression mediated by myeloid or stromal cells, immune exclusion and immune residence phenotypes. Different TIME subtypes were spatially organized and associated with chemokine

scRNA-seq analysis of mouse neutrophil subsets revealed that they are largely conserved with those of humans. In vivo neutrophil depletion in mouse models attenuated tumour progression,

confirming the pro-tumour phenotypes of TANs. With this detailed cellular heterogeneity landscape of liver cancer, our study illustrates diverse TIME subtypes, highlights immunosuppressive

functions of TANs and sheds light on potential immunotherapies targeting TANs. Access through your institution Buy or subscribe This is a preview of subscription content, access via your

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subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS SINGLE-CELL PROFILING REVEALS ALTERED IMMUNE LANDSCAPE AND IMPAIRED NK CELL FUNCTION IN

GASTRIC CANCER LIVER METASTASIS Article 26 July 2024 SINGLE-CELL RNA SEQUENCING REVEALED POTENTIAL TARGETS FOR IMMUNOTHERAPY STUDIES IN HEPATOCELLULAR CARCINOMA Article Open access 01

November 2023 THE HETEROGENEOUS IMMUNE LANDSCAPE BETWEEN LUNG ADENOCARCINOMA AND SQUAMOUS CARCINOMA REVEALED BY SINGLE-CELL RNA SEQUENCING Article Open access 26 August 2022 DATA

AVAILABILITY Raw sequencing data reported in this paper have been deposited at the Genome Sequence Archive at the National Genomics Data Center (Beijing, China) under the BioProject ID

PRJCA007744. The data deposited and made public are compliant with the regulations of the Ministry of Science and Technology of China. To facilitate the use of our data by the wider research

community, we developed an interactive web-based tool (http://meta-cancer.cn:3838/scPLC) for analysing and visualizing our single-cell data. Other public data used in this study include

reference genomes for human (https://asia.ensembl.org/, GRCh38.p13) and mouse (https://asia.ensembl.org/, GRCm39) and TCGA datasets (https://portal.gdc.cancer.gov/). Source data are provided

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ACKNOWLEDGEMENTS We thank Y. Guo, C. Shan and J. Ren from National Center for Protein Sciences at Peking University for FACS and CODEX assistance. This work is jointly supported by National

Natural Science Foundation of China (81988101, 82173035, 82030079, 81972656, 81802813, 81902401, 81972735 and 81872508), the National Science and Technology Major Project of China

(2018ZX10723204), Beijing Natural Science Foundation (7212108), Changping Laboratory, the Michigan Medicine and PKU-HSC JI for Translational and Clinical Research (BMU2020JI005) and

Sino-Russian Math Center in PKU. AUTHOR INFORMATION Author notes * These authors contributed equally: Ruidong Xue, Qiming Zhang, Qi Cao, Ruirui Kong, Xiao Xiang * These authors jointly

supervised this work: Jiye Zhu, Zemin Zhang, Ning Zhang AUTHORS AND AFFILIATIONS * Translational Cancer Research Center, Peking University First Hospital, Beijing, China Ruidong Xue, Qi Cao,

 Ruirui Kong, Hengkang Liu, Mei Feng, Fangyanni Wang, Jinghui Cheng & Ning Zhang * BIOPIC, Beijing Advanced Innovation Center for Genomics, School of Life Sciences, Peking University,

Beijing, China Qiming Zhang & Zemin Zhang * Beijing Key Surgical Basic Research Laboratory of Liver Cirrhosis and Liver Cancer, Department of Hepatobiliary Surgery, Peking University

People’s Hospital, Beijing, China Xiao Xiang, Zhao Li & Jiye Zhu * International Cancer Institute, Peking University Health Science Center, Beijing, China Qimin Zhan, Mi Deng & Ning

Zhang * Changping Laboratory, Beijing, China Zemin Zhang * Yunnan Baiyao Group, Kunming, China Ning Zhang Authors * Ruidong Xue View author publications You can also search for this author

inPubMed Google Scholar * Qiming Zhang View author publications You can also search for this author inPubMed Google Scholar * Qi Cao View author publications You can also search for this

author inPubMed Google Scholar * Ruirui Kong View author publications You can also search for this author inPubMed Google Scholar * Xiao Xiang View author publications You can also search

for this author inPubMed Google Scholar * Hengkang Liu View author publications You can also search for this author inPubMed Google Scholar * Mei Feng View author publications You can also

search for this author inPubMed Google Scholar * Fangyanni Wang View author publications You can also search for this author inPubMed Google Scholar * Jinghui Cheng View author publications

You can also search for this author inPubMed Google Scholar * Zhao Li View author publications You can also search for this author inPubMed Google Scholar * Qimin Zhan View author

publications You can also search for this author inPubMed Google Scholar * Mi Deng View author publications You can also search for this author inPubMed Google Scholar * Jiye Zhu View author

publications You can also search for this author inPubMed Google Scholar * Zemin Zhang View author publications You can also search for this author inPubMed Google Scholar * Ning Zhang View

author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS R.X., Z.Z. and N.Z. conceived and designed the project. R.X., X.X., Z.L. and J.Z. collected the

human samples and clinical information. X.X., Z.L. and J.Z. performed pathological examination. R.X. and X.X. performed the scRNA-seq experiments. Q.C., Q. Zhang and R.X. performed

bioinformatic analyses. Q. Zhang, R.X. and Q.C. performed IHC, mIHC and CODEX experiments. R.K., R.X., M.F. and F.W. performed functional experiments of neutrophils. R.K. and R.X.

constructed the mouse models and analysed the in vivo data. R.X., Q. Zhang, Q.C., R.K., X.X., H.L., Q. Zhan, M.D., J.Z., Z.Z. and N.Z. discussed and interpreted the data. Q.C., R.X. and J.C.

built the online website. R.X., Q. Zhang, Q.C. and R.K. wrote the manuscript with help from Z.Z. and N.Z.; Z.Z., J.Z. and N.Z. supervised the project. CORRESPONDING AUTHORS Correspondence

to Jiye Zhu, Zemin Zhang or Ning Zhang. ETHICS DECLARATIONS COMPETING INTERESTS Z.Z. is a founder of Analytical BioSciences and is a consultant for InnoCare Pharma and ArsenalBio. N.Z. is

the CSO of Yunnan Baiyao Group. The other authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature_ thanks Andres Hidalgo, Alexander Swarbrick and the other,

anonymous, reviewer(s) for their contribution to the peer review of this work. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in

published maps and institutional affiliations. EXTENDED DATA FIGURES AND TABLES EXTENDED DATA FIG. 1 PATIENT COHORT AND CLUSTER INFORMATION. A, Pie charts showing the composition of cancer

types in our cohort. HCC, hepatocellular carcinoma; ICC, intrahepatic cholangiocarcinoma; CHC, combined hepatocellular and cholangiocarcinoma; HH, hepatic hemangioma; ASC, adenosquamous

carcinoma; SAR, sarcomatoid carcinoma; SLC, secondary liver cancer. CRC_M, liver metastasis from colorectal cancer, PAN_M, liver metastasis from pancreatic cancer, LYM_M, liver metastasis

from lymphoma, GAS_M, liver metastasis from gastric cancer, BRC_M, liver metastasis from breast cancer. B, UMAP plots showing the distribution of patients, cancer types, viruses and liver

cirrhosis states. Dots represent individual cells. PB, peripheral blood; AL, adjacent liver; HBV, hepatitis B virus, HCV, hepatitis C virus, NBNC, double negative of HBV and HCV. C, UMAP

plots showing expression of canonical marker genes of major cell populations including T cells (_CD3D, CD8A, FOXP3_), NK cells (_NKG7_), B cells (_CD79A_), macrophages (_CD68_), neutrophils

(_CSF3R_), dendritic cells (_CLEC10A_), mast cells (_TPSAB1_), fibroblasts (_COL1A1_), endothelial cells (_VWF_), and epithelial cells (_EPCAM_). D, Stacked barplot showing the distribution

of major cell types in each sample. E, UMAP plots showing the distribution of cell identities for tumour cells and TIME cells. Tumour cells were further coloured by patient, cancer type,

virus, and cirrhosis. F, CNV profiles inferred from scRNA-seq data for each cell and from matched bulk exome data in the sample A014_HCC. G, Boxplots showing hepatic scores and biliary

epithelial scores in tumour (_n_ = 193,877 cells) and TIME cells (_n_ = 898,295 cells). Cells are from 124 patients. H, Boxplots showing hepatic scores and biliary epithelial scores in

tumour cells of different PLC subtypes (HCC, _n_ = 96,211 cells from 79 cases, ICC, _n_ = 52,345 cells from 25 cases, CHC, _n_ = 15,493 cells from 7 cases). Cells are from 111 patients. I,

Pie charts showing the patient number (top) and cell number (bottom) of our study and published single cell studies for PLC. Colours represent different studies. J, Stacked barplot showing

proportions of major cell populations among different studies. Colours represent major cell populations. In G-H, _n_ denotes individual cells. Two-sided Wilcoxon rank-sum test is used. For

boxplots, centre line shows median, box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are

outlying points that are plotted individually. ***, _P_ < 0.001. EXTENDED DATA FIG. 2 GENE EXPRESSION AND TISSUE PREFERENCE OF 89 TIME CELL CLUSTERS. A, UMAP plots showing the expression

of canonical marker genes for clusters in each major cell population. Normalized expression level was abbreviated as Exp. B, Heatmap showing tissue preferences of clusters in each major cell

population revealed by _R_o/e. C, Boxplots showing proportions of several tumour-enriched TIME clusters divided by PLC subtypes. *, _P_ < 0.05; **, _P_ < 0.01; ***, _P_ < 0.001.

(HCC, _n_ = 79 cases, ICC, _n_ = 25 cases, CHC, _n_ = 7 cases). D, Boxplots showing proportions of several cell clusters associated with virus or cirrhosis. (HBV, _n_ = 57 cases, HCV, _n_ = 

6 cases, NBNC, _n_ = 50 cases; cirrhosis, _n_ = 46 cases, non-cirrhosis, _n_ = 67 cases). In C-D, _n_ denotes biologically independent samples. Two-sided Wilcoxon rank-sum test is used. For

boxplots, centre line shows median, box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are

outlying points that are plotted individually. EXTENDED DATA FIG. 3 CLUSTERS, SIGNATURES, AND PROGNOSIS OF FIVE TIMELASER SUBTYPES. A, Heatmap showing frequencies of TIME cell clusters in 5

CMs. B, Forest plot showing the clinical relevance of clusters in each CM revealed by log10(hazard ratio) based on PFS. Cox regression. Log-rank test. C, Dot heatmap showing enriched

pathways across TIMELASER subtypes. Benjamini–Hochberg-adjusted hypergeometric test. D, Boxplots showing the expression of given signatures in different TIMELASER subtypes. Signature scores

of TIMELASER subtypes with overhead asterisk are significantly higher than that of subtypes with corresponding asterisk colour. Wilcoxon rank-sum test, two-sided. (TIME-IA, _n_ = 18 cases,

TIME-ISM, _n_ = 8 cases, TIME-ISS, _n_ = 12 cases, TIME-IE, _n_ = 42 cases, TIME-IR, _n_ = 31 cases, _n_ denotes biologically independent patients). For boxplots, centre line shows median,

box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted

individually. E, Overall survival (OS) with each patient assigned to a single CM. Log-rank test. F, OS of cases stratified by each TIMELASER module. Log-rank test. In B and D, *, _P_ < 

0.05; **, _P_ < 0.01; ***, _P_ < 0.001. EXTENDED DATA FIG. 4 VALIDATION OF FIVE TIMELASER SUBTYPES. A, Boxplots showing the percentage of TIMELASER modules across 3 PLC subtypes. (HCC,

_n_ = 79 cases, ICC, _n_ = 25 cases, CHC, _n_ = 7 cases). B, Heatmap showing the percentage of CM1–5 across tumours in our cohort and three published scRNA-seq cohorts. C, Expression of

signature genes for the five TIMELASER subtypes in 453 published liver cancer bulk RNA-seq data. D, Boxplot showing z-scores of signature genes for five TIMELASER subtypes in different

cancer types. Colours represents HCC (orange, _n_ = 369 cases), ICC (green, _n_ = 33 cases) and CHC (purple, _n_ = 51 cases). E, Pie charts showing the proportion of TIMELASER subtypes in C.

F, Representative CODEX results showing four different TIMELASER subtypes. For each sample, only six representative antibodies staining are displayed in the figure along with DAPI. Scale

bar, 500 μm. G, Validation of TIMELASER by a published spatial transcriptomic study of liver cancer. H&E staining and the corresponding spatial feature plots of different marker genes of

cell types are shown in different samples. In A and D, _n_ denotes biologically independent samples. Two-sided Wilcoxon rank-sum test is used. For boxplots, centre line shows median, box

limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted individually.

**, _P_ < 0.01; ***, _P_ < 0.001. EXTENDED DATA FIG. 5 L-R NETWORKS AND FEATURE SUMMARY OF FIVE TIMELASER SUBTYPES. A, Heatmap showing _R_o/e enrichment values of TIMELASER-specific

L-R pairs. B, Chord diagrams showing the interactions within each TIMELASER subtype mediated by specific L-R pairs. Line width is proportional to interaction intensity and coloured by

TIMELASER subtypes. C, Barplots showing the number of ligand-receptor pairs significantly enriched in TIME-IA and TIME-ISM modules. D, Heatmap showing the expression of chemokines and the

corresponding receptors in TIME-IA and TIME-ISM patients. Exp, normalized mean expression. E, Summary of key features across TIMELASER subtypes. F, Schematic for five TIMELASER subtypes.

Selected cell populations are shown for each TIMELASER subtype with tumour cells as background. Tex, exhausted T cell; NK, nature killer cell; TAN, tumour-associated neutrophil; TAM,

tumour-associated macrophage; DC, dendritic cell. EXTENDED DATA FIG. 6 MUTATIONAL LANDSCAPE AND GMS OF MALIGNANT CELLS. A, Heatmap showing frequencies of five TIMELASER subtypes across 111

PLC patient samples. Detailed clinical and molecular attributes of individual tumour samples are annotated. P values to the right indicate significant non-random distributions for each

attribute. Chi-square test is used for categorical variables. Two-way ANOVA test is used for continuous variables. B, Stacked barplots showing the distribution of cancer types, virus and

cirrhosis state across TIMELASER subtypes. Chi-square test. C, Boxplots showing the distribution of tumour purity, CNA and TMB inferred by WES data across TIMELASER subtypes. Two-way ANOVA

test is used for comparison of multiple groups. Two-sided Wilcoxon rank-sum test is used for comparison between any two groups. D, Heatmap showing the mutational rate of somatic mutations

enriched in different TIMELASER subtypes. E, Barplots showing mutational frequencies of _TP53_, _KRAS_, _IDH1_, and _CTNNB1_ in different TIMELASER subtypes. Colours represent different

TIMELASER subtypes. One-sided Fisher’s exact test. Tests are performed between the denoted TIME subtype (_P_ value colour coded) and a combination of all others. F, Heatmaps showing the

eight common gene modules (GMs) extracted from tumour cells. G, Boxplots showing the distributions of signature scores of GMs across tumours stratified into five TIMELASER subtypes. Overhead

asterisk is significantly higher than that of subtypes with corresponding asterisk colour. Wilcoxon rank-sum test, two-sided. In C and E, (TIME-IA, _n_ = 13 cases, TIME-ISM, _n_ = 7 cases,

TIME-ISS, _n_ = 7 cases, TIME-IE, _n_ = 32 cases, TIME-IR, _n_ = 20 cases). In G, (TIME-IA, _n_ = 18 cases, TIME-ISM, _n_ = 8 cases, TIME-ISS, _n_ = 12 cases, TIME-IE, _n_ = 42 cases,

TIME-IR, _n_ = 31 cases). In C, E, and G, _n_ denotes biologically independent patients. For boxplots, centre line shows median, box limits indicate upper and lower quartiles, and whiskers

extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted individually. *, _P_ < 0.05; **, _P_ < 0.01; ***, _P_ < 

0.001. EXTENDED DATA FIG. 7 NEUTROPHIL HETEROGENEITY IN HUMAN PLC. A, H&E and IHC plots showing the neutrophil frequencies in HCC and ICC patients. Scale bar, 20 μm. Boxplot to the right

is the quantitative result. Student’s t-test, two sided. (HCC, _n_ = 5 cases, ICC, _n_ = 8 cases, _n_ denotes biologically independent samples.) In the boxplot, centre line shows median,

box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted

individually. B, Dot heatmap showing the row-scaled expression of marker genes for neutrophil clusters. C, UMAP plots showing the expression of typical marker genes for neutrophil subsets.

Exp, normalized expression. D, Distribution of neutrophil clusters by patient. E, Monocle trajectories of neutrophils coloured by tissues (left), cluster identities (middle) and CytoTRACE

scores (right). Each dot represents a single cell. Cell orders are inferred based on the expression of the most variable genes across neutrophil clusters. F, Heatmap showing similarity

scores of peripheral blood neutrophil clusters from _Xie et al_. and lung cancer neutrophil clusters from _Zillionis et al_. compared with liver cancer neutrophil clusters inferred by

singleR. G, OS and PFS of patients stratified by the proportion of all neutrophils and neutrophil clusters (Neu_09/10/11) in TIME-ISM. Log-rank test. H, Average expression of classic

neutrophil scores and TAN-specific gene scores in neutrophil clusters. I, Gene ontology analysis showing the enrichment of specific pathways in neutrophil clusters.

Benjamini–Hochberg-adjusted hypergeometric test. Source data EXTENDED DATA FIG. 8 TRANSCRIPTION FACTORS, MIHC AND IN VITRO VALIDATION OF NEUTROPHIL CLUSTERS. A, UMAP plots showing regulon

activities of five representative transcription factors for specific neutrophil clusters. Binding motifs of these transcription factors are shown on the top. B, Normalized ATAC-seq

sequencing tracks of selected transcription factor loci in matched PBN, ALN, and TAN isolated from the same patient. ATAC peaks detected by MACS3 are denoted with the grey box above the gene

body and highlighted with light red shading. C, Workflow of co-culture experiments of PBNs with or without cell line (liver cancer cell line HepG2, HCCLM3, and MHCC97H, control cell line

HEK293T). D, Survival curve of PBN in culture condition (_n_ = 3, _n_ denotes biologically independent samples). Data are presented as mean values ± SEM. E, Expression of TAN-related

signatures in PBNs co-cultured with or without different cell lines for 0 h, 18 h, 24 h, and 30 h. F, Expression of gene signatures of different neutrophil subsets in PBNs co-cultured with

or without different cell lines. G, White arrows mark CCL4+CD66b+ neutrophils, with one cell highlighted by the four enlarged panels on the right. Middle panels show another representative

CCL4+ CD66b+ neutrophil while right panels show a representative CCL4− CD66b+ neutrophil. Scale bars are 20 μm and 2 μm. H, Expression of selected genes in PBNs co-cultured with or without

different cell lines for 0 h, 18 h, 24 h, and 30 h. I, Chord diagrams showing interactions between neutrophils and other cell types mediated by _CCL3-CCR1_ and _CCL4-CCR5_. Line width is

proportional to interaction intensity, coloured by cell types with receptors. J, Crystal violet staining of migrated monocytes co-cultured with matched TAN or non-TAN. Scale bar, 100 μm. K,

FACS analysis showing the PD-L1 expression of PBNs co-cultured with or without different cell lines for 24 h. Two-way ANOVA test is used for E and H. Source data EXTENDED DATA FIG. 9

CO-CULTURE EXPERIMENT OF CELL LINE-PBN-CD8+ T CELLS AND ANALYSES OF TWO _IFIT_+ NEUTROPHIL SUBSETS. A, Experimental workflow. B, Gating strategy separating neutrophils from CD8+ T cells in

the bottom chamber of co-culture system in A. C, FACS analysis showing the expression of CD25 (_n_ = 3), CD69 (_n_ = 3), and IFNγ (_n_ = 4) in PBNs co-cultured with different cell lines.

Student’s t-test, one-sided. Data are presented as mean values ± SEM. D, FACS analysis showing the expression of IFNγ in CD8+ T cells when anti-PD-L1 or the IgG control is added to the

co-culture system. E, FACS analysis showing the expression of CD25, IFNγ, GZMB, and PRF1 in CD8+ T cells co-cultured with matched TAN or non-TAN isolated from patients with liver cancer. F,

Volcano plot showing differentially expressed genes between Neu_03_ISG15 and Neu_09_IFIT1. Benjamini-Hochberg adjusted Wilcoxon rank-sum test, two-sided. G, Heatmap showing the predicted

ligand activity by NicheNet on genes highly expressed in Neu_09_IFIT1. Pearson correlation indicates the ability of each ligand to predict the target genes, and better predictive ligands are

thus ranked higher. H, Dot heatmap showing the selected ligand-receptor pairs between different cell populations and Neu_09_IFIT1. Benjamini-Hochberg adjusted permutation test. I, Boxplots

showing the proportion of two _IFNG_+ populations between patients with or without Neu_09_IFIT1. Wilcoxon rank-sum test, two-sided. (Yes, _n_ = 33 cases, No, _n_ = 78 cases). For boxplots,

centre line shows median, box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points

that are plotted individually. J, Pearson correlation between the expression of _CD274_ and _IFNG_ in TIME cells in this study (left) or in the collected bulk RNA-seq datasets (right). In C

and I, _n_ denotes biologically independent samples. Source data EXTENDED DATA FIG. 10 SCRNA-SEQ AND FUNCTIONAL ANALYSES OF MOUSE MODELS. A, Schematic of liver cancer mouse models.

Intrahepatic delivery of the transposable vectors pTMC (encoding _Myc_ and _∆90Ctnnb1_) or pTMK (encoding _Myc_ and _Kras__G12D_) via HDTV in _Alb-Cre_ × _Trp53__fl/fl_ mice. B,

Representative photos, H&E, and IHC staining of HCC and ICC mouse models. Rulers in the photo show a minimum unit of mm. Scale bar on the staining slides is 20 μm. C, Survival curve of

liver cancer mouse model. Log-rank test. D, UMAP plot showing major cell types of mice with liver cancer. Dots represent individual cells, and colours represent the major cell populations.

mILC: innate lymphoid cells, mNeu: neutrophils, mMph: macrophages, mMono: monocytes, mEC: endothelial cells; mFb: fibroblasts, mEpithelial: hepatocytes, biliary cells and progenitors; the

first letter m indicates mouse clusters. The two small UMAP plots show the distribution of mouse models (left) and tissue types (right). E, UMAP plot showing myeloid clusters including 5 DC,

2 monocyte and 7 macrophage clusters for liver cancer mouse models. F, Dot heatmap showing the row-scaled expression of typical marker genes for neutrophil clusters in mice. G, Stacked

barplot showing the fraction of 12 mouse neutrophil subsets across PB, AL, and tumour. H, The trajectory path of mouse neutrophil clusters inferred by Monocle2. Each dot represents a single

cell. Cell orders are inferred from the expression of the most variable genes. The trajectory direction is determined by biological prior. I, Heatmap showing Pearson’s correlations across

neutrophil clusters in human and mouse. J, UMAP plots showing the integration of mouse and human neutrophil clusters. K, Sankey plot showing the similarities of the joint clusters, mouse

tissue isolated neutrophil clusters, and human sample isolated neutrophil clusters. L, FACS analysis on neutrophil, macrophage, and CD8+ T cell populations in isotype and anti-Ly6G groups.

The right barplot shows the decreased neutrophil number in anti-Ly6G group (_n_ = 10). M, FACS analyses and coloured histogram showing reduced PD-L1 expression in TANs and reduced PD-1 and

TIM3 expression in tumour-infiltrated CD8+ T cells of the anti-Ly6G group compared with isotype control. The left barplot shows the decreased PD-L1 expression of neutrophils in anti-Ly6G

group (_n_ = 8). N, IHC of CD68 in tumour regions of mice treated with isotype control or anti-Ly6G antibody (_n_ = 6). O, FACS analysis showing the expression of surface and intracellular

Ly6G in the isotype control and anti-Ly6G treatment groups. P, Bar plot showing the statistical analysis of FACS results (_n_ = 3). In L-P, _n_ denotes biologically independent samples, data

are presented as mean values ± SEM, and two-sided Student’s t-test is used. Source data SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Figs. 1–5, Supplementary Notes. 1–9

and Supplementary Discussion. REPORTING SUMMARY SUPPLEMENTARY TABLE 1 Clinical information of the patients and library information of the samples. SUPPLEMENTARY TABLE 2 Mutations and CNVs

detected from WES. SUPPLEMENTARY TABLE 3 Statistics of 89 cell clusters and 5 TIMELASER subtypes. SUPPLEMENTARY TABLE 4 Statistics of cell clusters in mouse liver tumours. SUPPLEMENTARY

TABLE 5 Summary of antibodies and primers. SUPPLEMENTARY DATA Source Data for Supplementary Fig. 3 SUPPLEMENTARY DATA Source Data for Supplementary Fig. 4 SOURCE DATA SOURCE DATA FIG. 3

SOURCE DATA FIG. 4 SOURCE DATA EXTENDED DATA FIG. 7 SOURCE DATA EXTENDED DATA FIG. 8 SOURCE DATA EXTENDED DATA FIG. 9 SOURCE DATA EXTENDED DATA FIG. 10 RIGHTS AND PERMISSIONS Springer Nature

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Xue, R., Zhang, Q., Cao, Q. _et al._ Liver tumour immune microenvironment subtypes and neutrophil heterogeneity. _Nature_ 612, 141–147 (2022). https://doi.org/10.1038/s41586-022-05400-x

Download citation * Received: 07 September 2021 * Accepted: 30 September 2022 * Published: 09 November 2022 * Issue Date: 01 December 2022 * DOI: https://doi.org/10.1038/s41586-022-05400-x

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