Single-cell RNA sequencing identifies tumor subpopulations

Single-cell sequencing, which is used to detect clinically important tumor subpopulations, is necessary for understanding tumor heterogeneity. Here, researchers from Kangwon National University, Korea analyzed transcriptomic data obtained from 34 single cells from human lung adenocarcinoma (LADC) patient-derived xenografts (PDXs).

Schematic illustrating the preparation of coordinately co-expressed genes across the 34 single LADC cells. The upper part of the schematic illustrates the sequential procedures of LADC tissue isolation, mouse engraftment, patient-derived xenograft (PDX) cell culture, and single PDX cell preparation for RNA sequencing. The lower part of the schematic explains how the coordinately expressed genes were selected from the 34 single-cell transcriptomes.

To focus on the intrinsic transcriptomic signatures of these tumors, they filtered out genes that displayed extensive expression changes following xenografting and cell culture. Then, they performed clustering analysis using co-regulated gene modules rather than individual genes to minimize read drop-out errors associated with single-cell sequencing. This combined approach revealed two distinct intra-tumoral subgroups that were primarily distinguished by the gene module G64. The G64 module was predominantly composed of cell-cycle genes. Interestingly, the G64 module also indicated inter-tumoral heterogeneity based on its association with patient survival and other clinical variables such as smoking status and tumor stage. Taken together, these results demonstrate the feasibility of single-cell RNA sequencing and the strength of our analytical pipeline for the identification of tumor subpopulations.

Heat map combined with the hierarchical clustering analysis of G64 expression in the 34 single cells. (A) The 34 single cells were clustered into two subgroups (i.e., single cells displaying G64 down-regulation [left] and single cells displaying G64 up-regulation [right]). See also the dendrogram and black and red flat bars at the top of the heat map. The single cells displaying G64 up-regulation are labeled ‘red’ at the bottom of the heat map to ensure that the single cells exhibiting up-regulated G64 expression could be compared with the single cells shown in the other figures. (B) Principal component analysis of G64 expression in the 34 single cells, The same subgroups shown in (A) were clustered. Blue and orange dots represent single cells exhibiting G64 down- and up-regulation, respectively.