Unbiased method for spectral analysis of cells with great diversity of autofluorescence spectra

Abstract

Autofluorescence is an intrinsic feature of cells, caused by the natural emission of light by its cellular content, that can complicate analysis of flow cytometry data. Different cell types have different autofluorescence spectra and even within one cell type heterogeneity of autofluorescence spectra can be present, for example as a consequence of activation status or metabolic changes. By using full spectrum flow cytometry, the emission spectrum of a fluorochrome is captured by a set of detectors across a range of wavelengths, creating an unique spectrum for this fluorochrome, that is used to unmix the signal of a full stained sample into the signals of the different fluorochromes. Importantly, this technology can also be used to identify the aut-ofluorescence signal of an unstained sample, which can be used for unmixing purposes and to separate the autofluorescence signal from the fluorophore signals. However, this only works if the sample has one homogeneous autofluorescence spectrum. To analyze samples with a heterogeneous autofluorescence spectral profile, we here setup an unbiased workflow to detect all different autofluorescence spectra present in a sample to take them along as autofluorescence signatures during the unmixing of the full stained samples. First, clusters of cells with similar autofluorescence spectra are identified by unbiased dimensional reduction and clustering. Then, unique autofluorescence clusters are determined and are used to improve the unmixing accuracy of the full stained sample. This unbiased method allows for the identification of all autofluorescence spectra present in a sample, independent of cell types and intensity of the autofluorescence spectra. Furthermore, this method is equally useful for spectral analysis of different biological samples, including tissue cell suspensions, peripheral blood mononuclear cells and in vitro cultures of (primary) cells.