Conventional fluorophore-based flow cytometry is non-destructive and can be used to sort cells for further analysis. In either case, fluorescence intensities (flow cytometry) or ion counts (mass cytometry) are assumed to be proportional to the expression level of the antibody-targeted antigens of interest.ĭue to the differences in acquisition, further distinct characteristics should be noted. CyTOF utilizes antibodies tagged with metal isotopes from the lanthanide series, which have favorable chemistry and do not occur in biological systems abundances per cell are recorded with a time-of-flight mass spectrometer. In flow cytometry, antibodies are labeled with fluorescent dyes and fluorescence intensity is measured using lasers and photodetectors. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals).įlow cytometry and the more recently introduced CyTOF (cytometry by time-of-flight mass spectrometry or mass cytometry) are high-throughput technologies that measure protein abundance on the surface or within cells. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals.
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We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations. Institute for Molecular Life Sciences, University of Zurich, 8057 Zurich, Abstract Institute for Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerlandĭepartment of Experimental Oncology, European Institute of Oncology, Via Adamello 16, I-20139 Milan, Italyĭepartment of Dermatology, University Hospital Zurich, CH-8091 Zurich, Switzerland Institute of Experimental Immunology, University of Zurich, 8057 Zurich, Switzerland Institute for Molecular Life Sciences, University of Zurich, 8057 Zurich, Carsten Krieg CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets.