StemSC: a cross-dataset human stemness index for single-cell samples

Abstract

Background: Stemness is defined as the potential of cells for self-renewal and differentiation. Many transcriptome-based methods for stemness evaluation have been proposed. However, all these methods showed low negative correlations with differentiation time and can't leverage the existing experimentally validated stem cells to recognize the stem-like cells.

Methods: Here, we constructed a stemness index for single-cell samples (StemSC) based on relative expression orderings (REO) of gene pairs. Firstly, we identified the stemness-related genes by selecting the genes significantly related to differentiation time. Then, we used 13 RNA-seq datasets from both the bulk and single-cell embryonic stem cell (ESC) samples to construct the reference REOs. Finally, the StemSC value of a given sample was calculated as the percentage of gene pairs with the same REOs as the ESC samples.

Results: We validated the StemSC by its higher negative correlations with differentiation time in eight normal datasets and its higher positive correlations with tumor dedifferentiation in three colorectal cancer datasets and four glioma datasets. Besides, the robust of StemSC to batch effect enabled us to leverage the existing experimentally validated cancer stem cells to recognize the stem-like cells in other independent tumor datasets. And the recognized stem-like tumor cells had fewer interactions with anti-tumor immune cells. Further survival analysis showed the immunotherapy-treated patients with high stemness had worse survival than those with low stemness.

Conclusions: StemSC is a better stemness index to calculate the stemness across datasets, which can help researchers explore the effect of stemness on other biological processes.

Keywords: Cell dedifferentiation; Cross-dataset; Single-cell analysis; Stemness; Tumor microenvironment.

Fig. 1

Stability of REOs in both bulk and single-cell ESC samples. A The number of stable REOs identified from RPKM, TPM and log transformation data. B The consistency of stable REOs among 11 ESC datasets. C The correlation between the number of merged datasets for identifying stable REOs and recovery rate of REOs in the remaining datasets

Fig. 2

Overall methodology of StemSC

Fig. 3

Validation of the StemSC in the single-cell datasets with differentiation time. A The general information of validation sets. B The correlations between differentiation time and stemness index (StemSC and CytoTRACE) in all validation sets. C The changes of correlations between differentiation time and stemness index (StemSC and CytoTRACE) after combining the two batches of GSE102066. D–I The high correlations differentiation time and StemSC in each validation set. *Differentiation state of dataset GSE85066 was provided in Additional file 1: Table S5

Fig. 4

Abilities of StemSC to Identify the stemness-related genes and cellular differentiation trajectories. A The enrichment of the top 100 stemness-associated or differentiation-associated genes (the top 100 genes positively or negatively correlated with differentiation time) in the StemSC-ranked gene list. B Genes most positively or negatively correlated with StemSC. C Construction of lineage trajectory by combining Monocle 2 and StemSC. D The time-based lineage trajectory

Fig. 5

Validation of StemSC in colorectal cancer. A–C Enrichment of the 30 intestinal stem cell markers in the StemSC-ranked gene list. D–F The correlation between StemSC and the sum of gene expression values of the 30 intestinal stem cell markers. G–I The correlations between the StemSC and the gene expression values of 30 intestinal stem cell markers. J The significant difference of StemSC between tumor and normal tissue cells. The difference of stemness index between cells with different grades by using StemSC (K) and CytoTRACE (L)

Fig. 6

Validation of StemSC in glioma. A–D Enrichment of the 200 glioma stem markers in the StemSC-ranked gene list. E–H The correlation between StemSC and the sum of gene expression values of the 200 glioma stem markers. I–L The correlations between the StemSC and the gene expression values of the 200 glioma stem markers. The significant difference of StemSC between (M) different grades (N) tumor and normal tissue cells (O) CSCs and differentiated cells. P Enrichment of the 200 glioma stem markers in the gene sets ranked by the log FCs between stem-like and other common tumor cells

Fig. 7

Effect of stemness on tumor immune microenvironment. A The hierarchical cluster of the inferred copy number variation in the tumoral tissue cells of dataset GSE117891. B The expressions of the corresponding markers for the four types of immune cells in the dataset GSE117891. C–E The enrichment of the 200 stemness markers in the gene sets ranked by the log FCs between stem-like and other common tumor cells. F–H Interaction networks among immune cells, stem-like and other common tumor cells. I The higher median StemSC values in the non-responders than in the responders. J The Kaplan–Meier curves of overall survival in the high- and low-stemness groups. K The correlations between StemSC and the expressions of the 10 metastasis-associated genes in glioma