Flow Cytometry for Immuno-Oncology: Best Practices

Consider these key factors for a successful, reproducible flow cytometry assay

Emily C. Reddy, PhD

Emily C. Reddy, PhD, is an applications scientist and manager at a large academic flow cytometry shared resource laboratory in a biomedical research institution.

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Published:May 03, 2021
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Immuno-oncology, or cancer immunotherapy, is a novel approach that is transforming the cancer therapy landscape and revolutionizing the standard of care for patients, particularly for more challenging advanced and metastatic cancers. The rapid advancement of immunooncology has resulted in the demand for sensitive, accurate, and reproducible analysis technologies like flow cytometry.

Immuno-oncology basics

The fundamental basis of immuno-oncology is the dual interaction between the immune system and tumor cells. Understanding these mechanisms and identifying predictive immunological biomarkers that correlate to disease progression and treatment efficiency is critical to the development of tailored therapies.1 Immunotherapies generally function by directly stimulating the patient’s immune system to attack invading tumor cells more efficiently or by inhibiting suppression of antitumor immune responses (e.g., monoclonal antibodies, cytokines, immune checkpoint inhibitors, and cancer vaccines). Other immunotherapies involve modifying components of the immune system ex vivo, then reintroducing them into the patient (e.g., chimeric antigen receptor T-cell [CAR-T] therapy).

Flow cytometry adoption in immuno-oncology

Flow cytometry has long been the favored analysis platform in immunology and other cellular and molecular research fields. Commercial development and marketing of user-friendly benchtop instruments has resulted in the progression of flow cytometric analysis from a specialized, expensive research tool to a significant clinical methodology for routine diagnosis and prognosis of many diseases, including immunological and hematological disorders.2 Thus, the adoption of flow cytometry by immuno-oncology in pre-clinical and clinical studies was a natural evolution.

Flow cytometry is a high-throughput technology that can simultaneously analyze multiple cellular markers (surface and/or intracellular) at a single-cell level. This powerful technology can be used to accurately identify, quantify, and monitor cellular phenotypes, signaling pathways, and functional responses revealing deep insights into the complex milieu of the immune system and tumor microenvironment. Multi-parameter conventional flow cytometry can analyze up to 30 markers,3 while smaller, more focused panels of markers have traditionally been preferred in clinical settings to facilitate straightforward analyses.

The progression of flow cytometry in immuno-oncology and the clinical implications of the findings have highlighted the importance of best practices and standardized procedures to generate reliable and reproducible data.4,5 The Minimum Information about a Flow Cytometry Experiment (MIFlow- Cyt) standard6 developed by the International Society for the Advancement of Cytometry (ISAC) and the Guidelines for the Use of Flow Cytometry and Cell Sorting in Immunological Studies7 are common frameworks for research reporting in flow cytometry and immunology communities. Committees and working groups, such as the International Clinical Cytometry Society (ICCS), European Society for Clinical Cell Analysis (ESCCA), and Australasian Cytometry Society (ACS), are dedicated to establishing similar guidelines and suitable standards in clinical flow cytometry.

Steps to a successful flow cytometry assay

1. Pre-analytical


  • Appropriate collection procedure should be used depending on the sample type collected, e.g., appropriate anticoagulant for blood samples.


  • Correct identifiers and labels are required for patient samples. 
  • A sample rejection procedure should be in place for unlabeled/mismatched samples.


  • Each immuno-oncology sample source (blood, bone marrow, or tumor tissue) requires its own specific processing procedure to produce a single cell suspension. 
  • For blood and bone marrow, a standardized Ficoll density gradient separation is the gold standard for isolation of mononuclear cells. 
  • For solid tissue tumor, tissue dissociation requires mechanical and enzymatic methods to remove unwanted cell aggregates and debris; the challenge is to preserve the cells of interest and their antibody-binding epitopes.


  • If immediate staining and acquisition is not feasible, storage options must be considered. The choice between cryopreservation (method of choice) and the Fix-Freeze method is dependent on the subpopulations of interest as some cells are more sensitive and suffer greater loss of viability with cryopreservation. 
  • Remember that storage may induce functional and phenotypic changes and alter relative frequencies of subpopulation.
  • If patient sample size allows, divide samples into multiple aliquots to improve reproducibility. 
  • The sample thawing and recovery procedure is as important as freezing. It needs to be done rapidly and should also be standardized. 
  • All steps in the process, including cell counting and viability assessment, must have strict SOPs in order to avoid operator introduced variability.


  • Validation is required to ensure that antibodies are binding specifically to the intended target. It is especially important if a particular marker is critical to the data interpretation and the clinical implications therein. 
  • Titration is the process of determining the optimal concentration of an antibody to use in an assay. All antibodies should be initially titrated, and then re-titrated for every new lot due to lot-to-lot variability. For antibodies conjugated to tandem dyes, it is recommended to also re-titrate for each new aliquot within a lot.
  • Lot numbers, expiry dates, delivery dates, and in use dates should be recorded for all reagents; the use of reagents beyond the expiry date is not recommended. 
  • Antibody cocktails, or staining mixes, are often used to minimize variability between samples in flow cytometry— these should be validated, and each lot tested prior to use.


  • A key element of experimental design to help minimize variability within and between experiments, and to ensure correct data interpretation, is the inclusion of appropriate controls, including: 
    • Reference controls: A control should be prepared and run in parallel with patient samples where appropriate; a positive and/or negative control should be run daily or with each assay performed.  
    • Treatment controls: Unstimulated and stimulated controls. 
    • Panel design and gating controls: Fluorescence minus one (FMO) controls. 
    • Compensation controls. 
  • Understanding the biology (e.g., antigen expression patterns and density, and antigen co-expression), as well as the physics of the system (e.g., instrument configuration, spread, fluorochrome brightness, and emission overlap) is critical to the design of a

2. Analytical


  • It is crucial to document and monitor the performance of the flow cytometer (optics, fluidics, and electronics) in order to ensure that any variability between experiments and/or samples is due to real biological differences and not to non-biological artifacts introduced by the cytometer. 
  • Most flow cytometers have QC monitoring platforms built into their software; if not, an SOP for manual monitoring of instrument performance should be established. 
  • QC templates should be created and statistics such as CVs (coefficients of variation) and median fluorescence intensities of commercially available standardized fluorescent bead controls should be monitored over time, e.g., with Levey-Jennings graphs. DATA ACQUISITION 
  • Detailed acquisition SOPs should be established in order to ensure reproducibility and minimize variability between experiments and operators. 
  • SOPs should include the experiment layout and cell gating strategy in experimental templates. 
  • A minimum number of target events should be collected according to the assay, reporting style suited (i.e., qualitative or quantitative assay), and the criteria for rejection determined for the individual assay. Sensitivities for assays should be determined and reviewed when an assay is altered (e.g., new reagents, instrumentation), or a new assay is setup. 
  • Reference limits for antigens should be established where appropriate. 
  • Where absolute cell counts are required, a control with specified ranges should be included.

3. Post-analytical 


  • Manual gating in flow cytometry can be laborious and subjective, which can negatively affect reproducibility over time. Therefore, it is crucial to establish robust, standardized gating strategies. 
  • An alternative approach to manual gating, that is particularly useful in multi-parameter flow cytometry studies with large panels, is the use of non-biased highdimensional computational analysis tools (dimensionality reduction, e.g., tSNE, viSNE, UMAP; and clustering, e.g., FlowSOM, Phenograph, algorithms). The use of such analysis techniques may sometimes necessitate manual verification in a clinical setting; therefore, less complex automated analysis pipelines may be preferred at present.


  • Interpretation and reporting should be performed and verified by competent clinical flow cytometrists. 
  • When quantitative results are reported, reference ranges should be provided when appropriate. 
  • Reports should be completed in a timely manner, with clinical needs in mind.


  • Electronic data files (flow cytometry standard [FCS] files) should have backup copies and storage for an appropriate period of time. 
  • FCS data files should contain sample type, patient details, and date. 
  • FCS should be traceable and match correctly to individual samples from a patient. 
  • Barcoding and reference to worklist date may be useful to reduce error.

Technology advancements

Novel cytometry platforms, such mass cytometry, genomic cytometry (single-cell RNA sequencing), and spectral cytometry, have been developed to more deeply profile immune cell populations. Conventional flow cytometry measures cellular marker expression using fluorochrome-conjugated antibodies. The limitation on the number of markers traditionally analyzed in clinical flow cytometry is due primarily to the spectral overlap of fluorescence emission signals; panel design and data analysis become progressively more labor intensive and complex as the number of markers and fluorochromes increases. 

Mass cytometry is a valuable high-dimensional analysis platform that is not constrained by fluorescence emission overlap (antibodies are conjugated to metal reporters) and can be used to analyze over 40 markers;8 however, to date, it has not been widely adopted in clinical settings for real-time analysis due to slower throughput and limited availability of commercial reagents. Single-cell RNA sequencing significantly increases the dimensionality of possible markers used for analysis and allows for unbiased single-cell transcriptome profiling;9 however, the low throughput and high reagent and sequencing cost is a limitation for its use in clinical settings. 

Spectral cytometry offers high-dimensional analysis potential, comparable to mass cytometry, while retaining the real-time analysis and high-resolution properties, as well as the reasonable cost of conventional flow cytometry. While fluorescence emission overlap remains a consideration, capturing the full unique spectral signature of each fluorochrome lessens the issue. In conjunction with the release of many new and improved fluorochromes, spectral cytometry is quickly emerging as the most promising new technology in clinical flow cytometry.10,11 


With the combination of technological advancements and maintained efforts toward high standards of rigor and reproducibility, the future is exciting for immuno-oncology and flow cytometry. The number of tumors potentially amenable to immunotherapeutic intervention is growing and the field continues to make remarkable advances. 


1. Allard, Bertrand, et al. "Immuno-oncology-101: overview of major concepts and translational perspectives." Seminars in Cancer Biology. Vol. 52. Academic Press, 2018. 

2. Virgo, Paul F., and Graham J. Gibbs. "Flow cytometry in clinical pathology." Annals of Clinical Biochemistry 49.1 (2012): 17-28. 

3. Mair, Florian, and Aaron J. Tyznik. "High-dimensional immunophenotyping with fluorescence-based cytometry: a practical guidebook." Immunophenotyping. Humana, New York, NY, 2019. 1-29. 

4. Laskowski, Tamara J., et al. "Rigor and reproducibility of cytometry practices for immuno-oncology: A multifaceted challenge." Cytometry Part A 97.2 (2020): 116-125. 

5. Lambert, Claude, et al. "Flow cytometric analyses of lymphocyte markers in immune oncology: A comprehensive guidance for validation practice according to laws and standards." Frontiers in Immunology 11 (2020): 2169. 

6. Lee, Jamie A., et al. "MIFlowCyt: The minimum information about a flow cytometry experiment." Cytometry Part A: The Journal of the International Society for Analytical Cytology 73.10 (2008): 926-930. 

7. Cossarizza, Andrea, et al. "Guidelines for the use of flow cytometry and cell sorting in immunological studies." European Journal of Immunology 47.10 (2017): 1584-1797. 

8. Spitzer, Matthew H., and Garry P. Nolan. "Mass cytometry: Single cells, many features." Cell 165.4 (2016): 780-791. 

9. Papalexi, Efthymia, and Rahul Satija. "Single-cell RNA sequencing to explore immune cell heterogeneity." Nature Reviews Immunology 18.1 (2018): 35. 

10. Sanjabi, Shomyseh, and Sean Lear. "New cytometry tools for immune monitoring during cancer immunotherapy." Cytometry Part B: Clinical Cytometry 100.1 (2021): 10-18. 

11. Bonilla, Diana L., Gil Reinin, and Edmond Chua. "Full spectrum flow cytometry as a powerful technology for cancer immunotherapy research." Frontiers in Molecular Biosciences 7 (2020): 495.

Emily C. Reddy, PhD

Emily C. Reddy, PhD, is an applications scientist and manager at a large academic flow cytometry shared resource laboratory in a biomedical research institution.


ImmunotherapyOncologyFlow Cytometry