Flow Cytometry in Immuno-oncology
- 1st Edition, Volume 195 - April 4, 2025
- Imprint: Academic Press
- Editors: Marcello Pinti, Andrea Cossarizza
- Language: English
- Hardback ISBN:9 7 8 - 0 - 3 2 3 - 8 9 8 8 3 - 6
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 8 9 8 8 4 - 3
Flow Cytometry in Immuno-oncology, Volume 173 in the Methods in Cell Biology series, highlights advances in the field, with this new volume presenting interesting chapters on t… Read more
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Request a sales quoteFlow Cytometry in Immuno-oncology, Volume 173 in the Methods in Cell Biology series, highlights advances in the field, with this new volume presenting interesting chapters on timely topics, Basic principles of Flow cytometry in immuno-oncology, Good practice and methods for flow cytometry in immuno-oncology, Automated flow cytometry in immuno-oncology, Flow cytometric analysis of Tregs in solid tumors, Multiparametric analysis of Tumor infiltrating lymphocytes in solid tumors, Analysis of tumor-infiltrating immune cells in intracranial glioblastoma, Assessing chromosomal abnormalities in leukemias by Imaging flow cytometry, Flow cytometric analysis of cellular alkaline phosphatase in acute myeloid leukemia, and much more.
- Provides the authority and expertise of leading contributors from an international board of authors
- Presents the latest release in the Methods in Cell Biology series
- Includes the latest information on the topic of development, characterization and applications in CAR T Cells
Academic, researchers, government and industrial sectors
- Flow Cytometry in Immuno-Oncology
- Cover image
- Title page
- Table of Contents
- Series Page
- Copyright
- Contributors
- Chapter One The evolution of flow cytometry with respect to cancer
- Keywords
- 1 Background to analytical cancer detection, 1930–1985
- 2 The driving force of clinical studies
- 3 The role of DNA in cancer cell detection using flow cytometry
- 4 The relevance of conjugated antibodies
- 5 Introducing phenotyping for cancer detection by flow cytometry
- 6 Multiparameter flow cytometry for cancer diagnosis
- 7 The future for clinical cytometry
- References
- Chapter Two The contribution of automated cytometry in immuno-oncology
- Abstract
- Keywords
- 1 Introduction
- 2 Cancer immunotherapy today
- 3 Flow cytometry in clinical oncology
- 3.1 DNA ploidy and cell cycle analysis
- 3.2 Immunophenotypic analysis
- 3.3 “Rare event” analysis
- 3.4 Minimal residual disease in onco-hematology
- 3.5 Circulating dendritic cells
- 3.6 Circulating endothelial cells and endothelial progenitors
- 3.7 Circulating tumor cells
- 4 Conclusions
- References
- Chapter Three Multiparametric analysis of tumor infiltrating lymphocytes in solid tumors
- Abstract
- Keywords
- 1 Introduction
- 2 Tumor-infiltrating lymphocytes
- 2.1 Heterogeneous composition of the tumor microenvironment
- 2.2 TILs isolation from solid tumors
- 2.3 The use of single-cell technologies to characterize TILs in TME
- 2.4 Phenotypic and functional characterization of TILs
- 2.5 The role of regulatory T cells
- 2.6 Cytokine and chemokine signals regulate TILs recruitment and function within TME
- 3 Immune checkpoint proteins (ICPs) expression and tumor immune surveillance
- 4 Immune checkpoints inhibitors (ICIs): First-line therapies to fight cancer
- 5 Conclusion
- Acknowledgments
- References
- Chapter Four Assessing chromosomal abnormalities in leukemias by imaging flow cytometry
- Abstract
- Keywords
- 1 Background
- 2 Advantages
- 2.1 Technical considerations
- 3 Clinical applications
- 3.1 Chronic lymphocytic leukemia
- 3.2 Plasma cell myeloma
- 3.3 Low-level disease assessment
- 4 Conclusions
- 5 Methods/protocol/technical appendix
- 5.1 Materials
- 5.2 Buffers and solutions
- 5.3 Protocol
- Conflict of interest statement
- References
- Chapter Five Efficient discrimination of functional hematopoietic stem cell progenitors for transplantation by combining alkaline phosphatase activity and CD34+ immunophenotyping
- Abstract
- Keywords
- 1 Introduction
- 1.1 Alkaline phosphatase
- 1.2 Hematopoietic stem cell transplant
- 1.3 Rationale
- 2 Materials
- 2.1 Biological samples
- 2.2 Disposables
- 2.3 Equipment
- 2.4 Reagents and solutions
- 3 Sample preparation
- 4 DNA staining, blockading, and Alkaline Phosphatase Live Staining
- 5 Immunophenotyping
- 6 Flow cytometric acquisition and data analysis
- 6.1 Flow cytometer configuration for sample acquisition
- 6.2 Gating strategy and analysis of flow cytometry data
- 6.3 Statistical analysis
- 7 Representative results
- 7.1 Concluding remarks
- Disclosures
- References
- Chapter Six PD-L1 expression in multiple myeloma myeloid derived suppressor cells
- Abstract
- Keywords
- 1 Introduction
- 1.1 Multiple Myeloma
- 1.2 Rationale
- 2 Materials and methods
- 2.1 Biological samples
- 2.2 Reagents and solutions
- 2.3 PD-L1 immunostaining
- 2.4 Cytoplasmic PD-L1
- 2.5 Competitive binding assays
- 2.6 Flow cytometry
- 3 Representative results
- 3.1 PD-L1 shows a differential expression pattern in MDSCs after stimulation
- 3.2 PD-L1 is not expressed at cytoplasmatic level
- 3.3 PD-L1 monoclonal antibody and Durvalumab are competing for the same PD-L1 binding site
- 3.4 Dimensionality reduction and visualization of the obtained data
- 4 Discussion
- Disclosures
- References
- Chapter Seven Multiplexed cytometry for single cell chemical biology
- Abstract
- Keywords
- 1 Introduction
- 2 Before you begin
- 2.1 Designing a barcoding experiment
- 2.2 Assay setup
- 3 Materials
- 3.1 Stocks of buffers and dyes
- 4 Key resources table
- 5 Materials and equipment
- 6 Prepare plates
- 6.1 Prepare barcoding plates
- 6.2 Prepare a compound plate
- 7 Prepare cells
- 7.1 Plate cells and treat with compounds
- 7.2 Stain cells for viability, then fix and permeabilize
- 8 Barcode cells and antibody stain
- 8.1 Barcode cells with dyes
- 8.2 Stain cells with antibodies
- 9 Prepare cytometry controls
- 9.1 Create compensation controls
- 10 Expected outcomes
- 10.1 Fluorescent cell barcode quality
- 10.2 Example assay output: MAM experiment
- 11 Advantages
- 11.1 High-throughput sample multiplexing flow cytometry
- 11.2 Cost analysis of FCB
- 11.3 FCB increases data robustness
- 11.4 Modular assay composition
- 11.5 High-throughput assay multiplexing screening tool
- 12 Limitations
- 12.1 Assay throughput
- 12.2 Small molecule inputs
- 12.3 Optimization
- 13 Optimization and troubleshooting
- 13.1 Barcoding quality
- 13.2 Dead or stressed cells
- 13.3 Off scale cells and missing wells
- 14 Conclusion
- Acknowledgments
- References
- Further reading
- Edition: 1
- Volume: 195
- Published: April 4, 2025
- No. of pages (Hardback): 312
- No. of pages (eBook): 312
- Imprint: Academic Press
- Language: English
- Hardback ISBN: 9780323898836
- eBook ISBN: 9780323898843
MP
Marcello Pinti
Dr.Marcello Pinti works at the Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Affiliations and expertise
Department of Biomedical Sciences, Chair of Immunology, University of Modena and Reggio Emilia, Modena, ItalyAC
Andrea Cossarizza
Professor Andrea Cossarizza works in the Department of Medical and Surgical Sciences for Children and Adults at University of Modena and Reggio Emilia in Modena, Italy.
Affiliations and expertise
Professor, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia,
Modena, ItalyRead Flow Cytometry in Immuno-oncology on ScienceDirect