Mass Cytometry : Methods and Protocols /

Detalles Bibliográficos
Autor Corporativo: SpringerLink (Online service)
Otros Autores: McGuire, Helen M. (Editor ), Ashhurst, Thomas M. (Editor )
Formato: eBook
Lenguaje:English
Publicado: New York, NY : Springer New York : Imprint: Humana, 2019.
Edición:1st ed. 2019.
Colección:Methods in Molecular Biology, 1989
Materias:
Tabla de Contenidos:
  • Setting up Mass Cytometry in a Shared Resource Lab Environment
  • Acquisition, Processing, and Quality Control of Mass Cytometry Data
  • Visualization of Mass Cytometry Signal Background to Enable Optimal Core Panel Customization and Signal Threshold Gating
  • Method for Tagging Antibodies with Metals for Mass Cytometry Experiments
  • Scalable Conjugation and Characterization of Immunoglobulins with Stable Mass Isotope Reporters for Single-Cell Mass Cytometry Analysis
  • Titration of Mass Cytometry Reagents
  • Cell-Surface Barcoding of Live PBMC for Multiplexed Mass Cytometry
  • Automated Cell Processing for Mass Cytometry Experiments
  • Live Cell Barcoding for Efficient Analysis of Small Samples by Mass Cytometry
  • Staining of Phosphorylated Signalling Markers Protocol for Mass Cytometry
  • Multiplex MHC Class I Tetramer Combined with Intranuclear Staining by Mass Cytometry
  • Analysis of the Murine Bone Marrow Haematopoietic System Using Mass and Flow Cytometry
  • Mass Cytometric Cell Cycle Analysis
  • Picturing Polarized Myeloid Phagocytes and Regulatory Cells by Mass Cytometry
  • Quantitative Measurement of Cell-nanoparticle Interactions Using Mass Cytometry
  • Data-Driven Flow Cytometry Analysis
  • Analysis of Mass Cytometry Data
  • Analysis of High Dimensional Phenotype Data Generated by Mass Cytometry or High Dimensional Flow Cytometry
  • Computational Analysis of High Dimensional Mass Cytometry Data from Clinical Tissue Samples
  • Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery.