High-dimensional single-cell proteomics analysis reveals the landscape of immune cells and stem-like cells in renal tumors.

Zhijian Li, Jiaxin Hu, Zhao Qin, Yuting Tao, Zhiyong Lai, Qiuyan Wang, Tianyu Li
Author Information
  1. Zhijian Li: Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China. ORCID
  2. Jiaxin Hu: Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
  3. Zhao Qin: Department of Plastic and Aesthetic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  4. Yuting Tao: Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
  5. Zhiyong Lai: Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
  6. Qiuyan Wang: Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
  7. Tianyu Li: Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.

Abstract

BACKGROUND: Renal tumors are highly heterogeneous, and identification of tumor heterogeneity is an urgent clinical need for effective treatment. Mass cytometry (MC) can be used to perform high-dimensional single-cell proteomics analysis of heterogeneous samples via cytometry by time-of-flight (CyTOF), in order to achieve more accurate observation and classification of phenotypes within a cell population. This study aimed to develop a high-dimensional MC method for the detection and analysis of heterogeneity in renal tumors.
MATERIALS AND METHODS: We collected tissue samples from 8 patients with different types of renal tumors. Single-cell suspensions were prepared and stained using a panel of 28 immune cell-centric antibodies and a panel of 21 stem-like cell-centric antibodies. The stained cells were detected using CyTOF.
RESULT: Renal tumors were divided into 25 immune cell subsets (4 CD4+ T cells, 7 CD8+ T cells, 1 B cells, 8 macrophages, 1 dendritic cells, 2 natural killer (NK) cells, 1 granulocyte, and 1 other subset) and 7 stem-like cells subsets (based on positivity of vimentin, CD326, CD34, CD90, CD13, CD44, and CD47). Different types of renal tumors have different cell subsets with significantly different characteristics.
CONCLUSION: High-dimensional single-cell proteomics analysis using MC aids in the discovery and analysis of renal tumors heterogeneity. Additionally, it can be used to accurately classify the immune cell population and analyze the expression of stem cell-related markers in renal tumors. Our findings provide a valuable resource for deciphering tumor heterogeneity and might improve the clinical management of patients with renal tumors.

Keywords

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Grants

  1. 2016GXNSFGA38006/Natural Science Foundation of Guangxi Zhuang Autonomous Region
  2. 2016JJB140183/Natural Science Foundation of Guangxi Zhuang Autonomous Region
  3. 2017JJA140260z/Natural Science Foundation of Guangxi Zhuang Autonomous Region
  4. AD17195090/Natural Science Foundation of Guangxi Zhuang Autonomous Region
  5. GXJZ201611/Talents Highland of Emergency and Medical Rescue of Guangxi Province in China
  6. 31471271/National Natural Science Foundation of China
  7. 31560311/National Natural Science Foundation of China
  8. 81760454/National Natural Science Foundation of China
  9. KY2016LX055/Promotion of Basic Ability of Young and Middle-aged Teachers in Universities in Guangxi

MeSH Term

Antigens, CD
B-Lymphocytes
Biomarkers, Tumor
CD4-Positive T-Lymphocytes
CD8-Positive T-Lymphocytes
Granulocytes
Humans
Immunophenotyping
Kidney Neoplasms
Killer Cells, Natural
Neoplastic Stem Cells
Proteomics
Single-Cell Analysis

Chemicals

Antigens, CD
Biomarkers, Tumor

Word Cloud

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