Full Spectrum Flow Cytometry as a Powerful Technology for Cancer Immunotherapy Research

被引:64
作者
Bonilla, Diana L. [1 ]
Reinin, Gil [1 ]
Chua, Edmond [1 ]
机构
[1] Cytek Biosci, Fremont, CA 94538 USA
关键词
full spectral flow cytometry; immunomonitoring; cancer immunotherapy; immunology; cytometry; SINGLE CELLS; THERAPY;
D O I
10.3389/fmolb.2020.612801
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The Nobel Prize-deserving concept of blocking inhibitory pathways in T cells, to unleash their anti-tumoral capacity, became one of the pillars of cancer treatment in the last decade and has resulted in durable clinical responses for multiple cancer types. Currently, two of the most important goals in cancer immunotherapy are to understand the mechanisms resulting in failure to checkpoint blockade and to identify predictive immunological biomarkers that correlate to treatment response, disease progression or adverse effects. The identification and validation of biomarkers for routine clinical use is not only critical to monitor disease or treatment progression, but also to personalize and develop new therapies. To achieve these goals, powerful research tools are needed. Flow cytometry stands as one of the most successful single-cell analytical tools used to characterize immune cell phenotypes to monitor solid tumors, hematological malignancies, minimal residual disease or metastatic progression. This technology has been fundamental in diagnosis, treatment and translational research in cancer clinical trials. Most recently, the need to evaluate simultaneously more features in each cell has pushed the field to implement more powerful adaptations beyond conventional flow cytometry, including Full Spectrum Flow Cytometry (FSFC). FSFC captures the full emission spectrum of fluorescent molecules using arrays of highly sensitive light detectors, and to date has enabled characterization of 40 parameters in a single sample. We will summarize the contributions of this technology to the advancement of research in immunotherapy studies and discuss best practices to obtain reliable, robust and reproducible FSFC results.
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页数:10
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