Innovative breakthroughs facilitated by single-cell multi-omics: manipulating natural killer cell functionality correlates with a novel subcategory of melanoma cells

被引:55
作者
Zhao, Zhijie [1 ,2 ]
Ding, Yantao [3 ,4 ]
Tran, Lisa Jia [5 ]
Chai, Gang [1 ,2 ]
Lin, Li [1 ,2 ]
机构
[1] Shanghai Jiao Tong Univ, Shanghai Peoples Hosp 9, Dept Plast & Reconstruct Surg, Sch Med, Shanghai, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Med, Shanghai, Peoples R China
[3] Anhui Med Univ, Affiliated Hosp 1, Inst Dermatol, Dept Dermatol, Hefei, Anhui, Peoples R China
[4] Anhui Med Univ, China Key Lab Dermatol, Minist Educ, Hefei, Anhui, Peoples R China
[5] Ludwig Maximilians Univ Munchen, Dept Gen Visceral & Transplant Surg, Munich, Germany
来源
FRONTIERS IN IMMUNOLOGY | 2023年 / 14卷
关键词
single-cell sequencing; novel biomarker; tumor heterogeneity; cancer immunotherapy; melanoma; clinical outcome; SURVIVAL; CANCER; ASSOCIATION; IPILIMUMAB; EXPRESSION; INFERENCE; HALLMARK; INNATE;
D O I
10.3389/fimmu.2023.1196892
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
BackgroundMelanoma is typically regarded as the most dangerous form of skin cancer. Although surgical removal of in situ lesions can be used to effectively treat metastatic disease, this condition is still difficult to cure. Melanoma cells are removed in great part due to the action of natural killer (NK) and T cells on the immune system. Still, not much is known about how the activity of NK cell-related pathways changes in melanoma tissue. Thus, we performed a single-cell multi-omics analysis on human melanoma cells in this study to explore the modulation of NK cell activity. Materials and methodsCells in which mitochondrial genes comprised > 20% of the total number of expressed genes were removed. Gene ontology (GO), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and AUCcell analysis of differentially expressed genes (DEGs) in melanoma subtypes were performed. The CellChat package was used to predict cell-cell contact between NK cell and melanoma cell subtypes. Monocle program analyzed the pseudotime trajectories of melanoma cells. In addition, CytoTRACE was used to determine the recommended time order of melanoma cells. InferCNV was utilized to calculate the CNV level of melanoma cell subtypes. Python package pySCENIC was used to assess the enrichment of transcription factors and the activity of regulons in melanoma cell subtypes. Furthermore, the cell function experiment was used to confirm the function of TBX21 in both A375 and WM-115 melanoma cell lines. ResultsFollowing batch effect correction, 26,161 cells were separated into 28 clusters and designated as melanoma cells, neural cells, fibroblasts, endothelial cells, NK cells, CD4+ T cells, CD8+ T cells, B cells, plasma cells, monocytes and macrophages, and dendritic cells. A total of 10137 melanoma cells were further grouped into seven subtypes, i.e., C0 Melanoma BIRC7, C1 Melanoma CDH19, C2 Melanoma EDNRB, C3 Melanoma BIRC5, C4 Melanoma CORO1A, C5 Melanoma MAGEA4, and C6 Melanoma GJB2. The results of AUCell, GSEA, and GSVA suggested that C4 Melanoma CORO1A may be more sensitive to NK and T cells through positive regulation of NK and T cell-mediated immunity, while other subtypes of melanoma may be more resistant to NK cells. This suggests that the intratumor heterogeneity (ITH) of melanoma-induced activity and the difference in NK cell-mediated cytotoxicity may have caused NK cell defects. Transcription factor enrichment analysis indicated that TBX21 was the most important TF in C4 Melanoma CORO1A and was also associated with M1 modules. In vitro experiments further showed that TBX21 knockdown dramatically decreases melanoma cells' proliferation, invasion, and migration. ConclusionThe differences in NK and T cell-mediated immunity and cytotoxicity between C4 Melanoma CORO1A and other melanoma cell subtypes may offer a new perspective on the ITH of melanoma-induced metastatic activity. In addition, the protective factors of skin melanoma, STAT1, IRF1, and FLI1, may modulate melanoma cell responses to NK or T cells.
引用
收藏
页数:24
相关论文
共 50 条
  • [21] Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS
    Trimbour, Remi
    Deutschmann, Ina Maria
    Cantini, Laura
    BIOINFORMATICS, 2024, 40 (05)
  • [22] Integration Analysis of Single-Cell Multi-Omics Reveals Prostate Cancer Heterogeneity
    Bian, Xiaojie
    Wang, Wenfeng
    Abudurexiti, Mierxiati
    Zhang, Xingming
    Ma, Weiwei
    Shi, Guohai
    Du, Leilei
    Xu, Midie
    Wang, Xin
    Tan, Cong
    Sun, Hui
    He, Xiadi
    Zhang, Chenyue
    Zhu, Yao
    Zhang, Min
    Ye, Dingwei
    Wang, Jianhua
    ADVANCED SCIENCE, 2024, 11 (18)
  • [23] Single-cell multi-omics of human preimplantation embryos shows susceptibility to glucocorticoids
    Zhao, Cheng
    Biondic, Savana
    Vandal, Katherine
    Bjoerklund, Asa K.
    Hagemann-Jensen, Michael
    Sommer, Theresa Maria
    Canizo, Jesica
    Clark, Stephen
    Raymond, Pascal
    Zenklusen, Daniel R. R.
    Rivron, Nicolas
    Reik, Wolf
    Petropoulos, Sophie
    GENOME RESEARCH, 2022, 32 (09) : 1627 - 1641
  • [24] ScImmOmics: a manually curated resource of single-cell multi-omics immune data
    Li, Yan-Yu
    Zhou, Li-Wei
    Qian, Feng-Cui
    Fang, Qiao-Li
    Yu, Zheng-Min
    Cui, Ting
    Dong, Fu-Juan
    Cai, Fu-Hong
    Yu, Ting-Ting
    Li, Li-Dong
    Wang, Qiu-Yu
    Zhu, Yan-Bing
    Tang, Hui-Fang
    Hu, Bao-Yang
    Li, Chun-Quan
    NUCLEIC ACIDS RESEARCH, 2024, 53 (D1) : D1162 - D1172
  • [25] Editorial: Integrative analysis of single-cell and/or bulk multi-omics sequencing data
    Chen, Geng
    Yu, Rongshan
    Chen, Xingdong
    FRONTIERS IN GENETICS, 2023, 13
  • [26] Targeting glycolysis in esophageal squamous cell carcinoma: single-cell and multi-omics insights for risk stratification and personalized therapy
    Wang, Yan
    Shi, Yunjie
    Hu, Xiao
    Wang, Chenfang
    FRONTIERS IN PHARMACOLOGY, 2025, 16
  • [27] Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era
    Xiong, Xin
    Wang, Xin
    Liu, Cui-Cui
    Shao, Zhi-Ming
    Yu, Ke-Da
    BIOMARKER RESEARCH, 2024, 12 (01)
  • [28] iSMOD: an integrative browser for image-based single-cell multi-omics data
    Zhang, Weihang
    Suo, Jinli
    Yan, Yan
    Yang, Runzhao
    Lu, Yiming
    Jin, Yiqi
    Gao, Shuochen
    Li, Shao
    Gao, Juntao
    Zhang, Michael
    Dai, Qionghai
    NUCLEIC ACIDS RESEARCH, 2023, 51 (16) : 8348 - 8366
  • [29] Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis
    Sunny Z. Wu
    Daniel L. Roden
    Ghamdan Al-Eryani
    Nenad Bartonicek
    Kate Harvey
    Aurélie S. Cazet
    Chia-Ling Chan
    Simon Junankar
    Mun N. Hui
    Ewan A. Millar
    Julia Beretov
    Lisa Horvath
    Anthony M. Joshua
    Phillip Stricker
    James S. Wilmott
    Camelia Quek
    Georgina V. Long
    Richard A. Scolyer
    Bertrand Z. Yeung
    Davendra Segara
    Cindy Mak
    Sanjay Warrier
    Joseph E. Powell
    Sandra O’Toole
    Elgene Lim
    Alexander Swarbrick
    Genome Medicine, 13
  • [30] Leveraging Single-Cell Multi-Omics to Decode Tumor Microenvironment Diversity and Therapeutic Resistance
    Sabit, Hussein
    Arneth, Borros
    Pawlik, Timothy M.
    Abdel-Ghany, Shaimaa
    Ghazy, Aysha
    Abdelazeem, Rawan M.
    Alqosaibi, Amany
    Al-Dhuayan, Ibtesam S.
    Almulhim, Jawaher
    Alrabiah, Noof A.
    Hashash, Ahmed
    PHARMACEUTICALS, 2025, 18 (01)