Abstract

Mass cytometry (Cytometry by Time-Of-Flight, CyTOF) is a single-cell technology that is able to quantify multiplex biomarker expressions and is commonly used in basic life science and translational research. However, the widely used Gadolinium (Gd)-based contrast agents (GBCAs) in magnetic resonance imaging (MRI) scanning in clinical practice can lead to signal contamination on the Gd channels in the CyTOF analysis. This Gd contamination greatly affects the characterization of the real signal from Gd-isotope-conjugated antibodies, severely impairing the CyTOF data quality and ruining downstream single-cell data interpretation.

To overcome this drawback, we developed a computational method named by GdClean for removing the Gd contamination induced by the usage of GBCAs. We first confirmed that the intensities of Gd signals in the Gd contamination data were highly correlated in pairs, and their pairwise intensity ratios were well accordant with the ratios of their natural abundances. Using the control CyTOF dataset of a sample stained with specifically designed antibody panels, we identified the linear composition of the observed Gd signals from Gd-labeled mAbs and GBCAs-induced Gd contamination. Based on that, we designed an optimization-based method to estimate and further remove the Gd contamination at the single-cell level. Using the real and simulated contamination CyTOF datasets, we demonstrated that our GdClean method can effectively remove the Gd contamination signal and well preserve the real signal from Gd-labeled mAbs on single cells, demonstrating its capability to ensure high data quality in CyTOF data preprocessing for better downstream single-cell analysis.

Diagram of Gd contamination signal source and GdClean algorithm design