Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells

Abstract

Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.

Fig. 1

scRNA-seq analysis. (a) Clustering result of the scRNA-seq data. (b) SingleCellNet classification score heatmap. Reference data was taken from Zheng et al. (c) Select marker gene expression.

Fig. 2

mass cytometry analysis. (a) Clustering result of the mass cytometry data. (b) Average transformed expression of select markers in each cluster. (c) Full mass cytometry panel expression.

Fig. 3

scRNA-seq and mass cytometry comparison. (a) Percentage of given cell types in scRNA-seq and mass cytometry data. (b) Percentage of given cell types in scRNA-seq, mass cytometry, and flow cytometry data. T cell percentage includes cells annotated as CD4 T, CD8 T, DN T, and DP T. Monocyte percentage includes CD16+ and CD16- monocytes. (c) Correlation of CyToF and scRNA-seq measurements in each cell type.