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Single-cell metabolite profiling enables information-rich classification of lymphocyte types and subtypes.

Siyuan PanChangyi LiuHuan YaoXingyu PanJinhang LiJinlei YangMurong DuPeng LiuSichun ZhangXinrong Zhang
Published in: Chemical communications (Cambridge, England) (2023)
Lymphocytes play crucial roles in the human immune system; however, detailed metabolite characteristics need to be further investigated. Herein, we propose a lymphocyte classification method based on metabolite profiling at the single-cell level. The percentages of different lymphocyte types were calculated with a low margin of error, confirming that the metabolites could serve as a basis for lymphocyte classification. Furthermore, we analyzed the CD4/CD8 ratio in human peripheral blood to verify the feasibility of this method for the classification of lymphocyte subtypes. The proposed method is expected to be a potential tool for the clinical diagnosis of lymphocyte-related diseases.
Keyphrases
  • peripheral blood
  • single cell
  • deep learning
  • machine learning
  • endothelial cells
  • rna seq
  • healthcare
  • induced pluripotent stem cells
  • pluripotent stem cells
  • social media