Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding

Abstract

Rapid progress in single-cell analysis methods allow for exploration of cellular diversity at unprecedented depth and throughput. Visualizing and understanding these large, high-dimensional datasets poses a major analytical challenge. Mass cytometry allows for simultaneous measurement of >40 different proteins, permitting in-depth analysis of multiple aspects of cellular diversity. In this article, we present one-dimensional soli-expression by nonlinear stochastic embedding (One-SENSE), a dimensionality reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data. With One-SENSE, measured parameters are grouped into predefined categories, and cells are projected onto a space composed of one dimension for each category. In contrast with higher-dimensional t-SNE, each dimension (plot axis) in One-SENSE has biological meaning that can be easily annotated with binned heat plots. We applied One-SENSE to probe relationships between categories of human T cell phenotypes and observed previously unappreciated cellular populations within an orchestrated view of immune cell diversity. The presentation of high-dimensional cytometric data using One-SENSE showed a significant improvement in distinguished T cell diversity compared with the original t-SNE algorithm and could be useful for any high-dimensional dataset.

Computational flow cytometry: helping to make sense of high-dimensional immunology data

“…More recent dimensionality reduction techniques, including t-stochastic neighbour embedding (t-SNE) 46 , apply another principle, aiming to find a lower-dimensional projection that best preserves the similarity in the original, highdimensional space. This method has been shown to work very well with flow and mass cytometry data; it is used by the viSNE 47 technique, after subsampling the data, and by One-SENSE 48 in combination with heatmaps to visualize marker expression in cell subsets. Graph-based approaches are also used, connecting similar cells in a graph and subsequently applying specific graph layout algorithms.…”

Early transcriptional and epigenetic regulation of CD8+ T cell differentiation revealed by single-cell RNA sequencing

“…tSNE is an unsupervised technique based on a non-convex objective which solves the so-called crowding problem, and has been successfully used to visualize millions of single-cell cytometry measurements where the original dimension is D≈40 approximately 54,55,56,57 . In contrast, our total RNA sequencing data for each cell gave signal for over 22,000 genes (6000 of which had a mean expression over all cells greater than 1 TPM).…”

Significant Differences in Antigen-Induced Transendothelial Migration of Human CD8 and CD4 T Effector Memory Cells

“…The modest effect of concanamycin A on CR-driven TEM of CD8 T that we observed could be explained by the presence of a relatively small subset of mature CTL in our freshly isolated peripheral blood human T EM subsets. Moreover, analysis of freshly isolated human CD8 T is likely to be complicated by the heterogeneity of subsets within this population; recent multiparameter phenotyping by CyTOF indicates that there are at least 4 subtypes of human Tc cells, and all lack expression of CCR7, like the T EM used here 27 . Nevertheless, the key point is that TCR-driven TEM of CD8 T EM does not appear to be affected by reagents that affect degranulation or TEM via the LBRC, as the same reagents effectively inhibit CD4 T EM in experiments performed in parallel.…”