Your browser doesn't support javascript.
loading
Mapping cellular interactions from spatially resolved transcriptomics data.
Zhu, James; Wang, Yunguan; Chang, Woo Yong; Malewska, Alicia; Napolitano, Fabiana; Gahan, Jeffrey C; Unni, Nisha; Zhao, Min; Yuan, Rongqing; Wu, Fangjiang; Yue, Lauren; Guo, Lei; Zhao, Zhuo; Chen, Danny Z; Hannan, Raquibul; Zhang, Siyuan; Xiao, Guanghua; Mu, Ping; Hanker, Ariella B; Strand, Douglas; Arteaga, Carlos L; Desai, Neil; Wang, Xinlei; Xie, Yang; Wang, Tao.
Afiliación
  • Zhu J; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Wang Y; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Chang WY; Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
  • Malewska A; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.
  • Napolitano F; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Gahan JC; Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Unni N; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Zhao M; Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Yuan R; Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Wu F; Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Yue L; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Guo L; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Zhao Z; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Chen DZ; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Hannan R; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA.
  • Zhang S; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA.
  • Xiao G; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Mu P; Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Hanker AB; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Strand D; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Arteaga CL; Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX, USA.
  • Desai N; Hamon Center for Regenerative Science and Medicine, UT Southwestern Medical Center, Dallas, TX, USA.
  • Wang X; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Xie Y; Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Wang T; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Nat Methods ; 2024 Sep 03.
Article en En | MEDLINE | ID: mdl-39227721
ABSTRACT
Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Methods Asunto de la revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Methods Asunto de la revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos