Mass Cytometry : Methods and Protocols /
| Autor Corporativo: | |
|---|---|
| Otros Autores: | , |
| 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.