Quantitative Evaluation of Stem-like Markers of Human Glioblastoma Using Single-Cell RNA Sequencing Datasets.

Targeting glioblastoma (GBM) stem-like cells (GSCs) is a common interest in both the laboratory investigation and clinical treatment of GBM. Most of the currently applied GBM stem-like markers lack validation and comparison with common standards regarding their efficiency and feasibility in various targeting methods. Using single-cell RNA sequencing datasets from 37 GBM patients, we obtained a large pool of 2173 GBM stem-like marker candidates. To evaluate and select these candidates quantitatively, we characterized the efficiency of the candidate markers in targeting the GBM stem-like cells by their frequencies and significance of being the stem-like cluster markers. This was followed by further selection based on either their differential expression in GBM stem-like cells compared with normal brain cells or their relative expression level compared with other expressed genes. The cellular location of the translated protein was also considered. Different combinations of selection criteria highlight different markers for different application scenarios. By comparing the commonly used GSCs marker CD133 () with markers selected by our method regarding their universality, significance, and abundance, we revealed the limitations of CD133 as a GBM stem-like marker. Overall, we propose , , , etc. for laboratory-based assays with samples free of normal cells. For in vivo targeting applications that require high efficiency in targeting the stem-like subtype, the ability to distinguish GSCs from normal brain cells, and a high expression level, we recommend the intracellular marker and the surface markers and .