Laurien A Waaijer, Bram van Cranenbroek, Hans J P M Koenen
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引用次数: 0
Abstract
Profiling the human immune system is essential to understanding its role in disease, but it requires advanced and novel technologies. Spectral flow cytometry (SFM) enables deep profiling at the single-cell level. It is able to detect many fluorescent parameters within one measurement; therefore, it is vastly useful when patient material is limited. However, designing and analyzing these high-dimensional datasets remains complex. We optimized a 42-parameter panel (40 commercially available fluorochromes, one stacked fluorochrome and an autofluorescent (AF) parameter) that enables the identification of innate and adaptive immune cell composition. It is the first 42-parameter panel that is optimized on peripheral whole blood, and it outperforms other published OMIPs of 40 colors in terms of complexity. With this panel, we are able to identify neutrophils, basophils, eosinophils, monocytes, dendritic cells, CD4 T cells, CD8 T cells, regulatory T cells, mucosal-associated invariant T (MAIT) cells, γδ T cells, B cells, NK cells, dendritic cells, and innate lymphoid cells (ILCs). Furthermore, with the utilization of co-stimulatory, checkpoint, activation, homing, and maturation markers, this panel enables deeper phenotyping. Within one measurement, more than 80 distinct immune cell subsets were identified by FlowSOM and annotated manually. In conclusion, with this high-dimensional SFM panel, we aim to generate immune profiles to understand disease and monitor therapy response.
期刊介绍:
Cytometry Part A, the journal of quantitative single-cell analysis, features original research reports and reviews of innovative scientific studies employing quantitative single-cell measurement, separation, manipulation, and modeling techniques, as well as original articles on mechanisms of molecular and cellular functions obtained by cytometry techniques.
The journal welcomes submissions from multiple research fields that fully embrace the study of the cytome:
Biomedical Instrumentation Engineering
Biophotonics
Bioinformatics
Cell Biology
Computational Biology
Data Science
Immunology
Parasitology
Microbiology
Neuroscience
Cancer
Stem Cells
Tissue Regeneration.