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Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology.
Hu, Thomas; Allam, Mayar; Cai, Shuangyi; Henderson, Walter; Yueh, Brian; Garipcan, Aybuke; Ievlev, Anton V; Afkarian, Maryam; Beyaz, Semir; Coskun, Ahmet F.
Afiliação
  • Hu T; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
  • Allam M; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
  • Cai S; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
  • Henderson W; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
  • Yueh B; Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, USA.
  • Garipcan A; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  • Ievlev AV; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  • Afkarian M; Oak Ridge National Laboratory, Center for Nanophase Materials Sciences, Oak Ridge, TN, USA.
  • Beyaz S; Division of Nephrology, Department of Internal Medicine, University of California, Davis, CA, USA.
  • Coskun AF; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Nat Commun ; 14(1): 8260, 2023 Dec 13.
Article em En | MEDLINE | ID: mdl-38086839
ABSTRACT
Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil, and endometrium tissues, respectively. The scSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet's ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Metabolômica / Neoplasias Pulmonares Limite: Female / Humans / Male Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Metabolômica / Neoplasias Pulmonares Limite: Female / Humans / Male Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos