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High-dimensional single-cell proteomics analysis reveals the landscape of immune cells and stem-like cells in renal tumors.
Li, Zhijian; Hu, Jiaxin; Qin, Zhao; Tao, Yuting; Lai, Zhiyong; Wang, Qiuyan; Li, Tianyu.
Afiliação
  • Li Z; Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
  • Hu J; Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
  • Qin Z; Department of Plastic and Aesthetic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • Tao Y; Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
  • Lai Z; Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
  • Wang Q; Guangxi key laboratory for genomic and personalized medicine, Guangxi collaborative innovation center for genomic and personalized medicine, Nanning, China.
  • Li T; Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
J Clin Lab Anal ; 34(5): e23155, 2020 May.
Article em En | MEDLINE | ID: mdl-31855296
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Análise de Célula Única / Neoplasias Renais Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Análise de Célula Única / Neoplasias Renais Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article