Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre.

As the size and complexity of high-dimensional (HD) cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows.

Evaluating spectral cytometry for immune profiling in viral disease.

In conventional fluorescence cytometry, each fluorophore present in a panel is measured in a target detector, through the use of wide band-pass optical filters. In contrast, spectral cytometry uses a large number of detectors with narrow band-pass filters to measure a fluorophore's signal across the spectrum, creating a more detailed fluorescent signature for each fluorophore. The spectral approach shows promise in adding flexibility to panel design and improving the measurement of fluorescent signal.

High-Dimensional Fluorescence Cytometry.

The immune system consists of a complex network of cells, all expressing a wide range of surface and/or intracellular proteins. Using flow cytometry, these cells can be analyzed by labeling with fluorophore-conjugated antibodies. The recent expansion of fluorescence flow cytometry technology, in conjunction with the ever-expanding understanding of the complexity of the immune system, has led to the generation of larger high-dimensional fluorescence flow cytometry panels.