Cancer stem cells are critical for cancer initiation, development, and treatment resistance. Our understanding of these processes, and how they relate to glioblastoma heterogeneity, is limited. To overcome these limitations, we performed single-cell RNA sequencing on 53586 adult glioblastoma cells and 22637 normal human fetal brain cells, and compared the lineage hierarchy of the developing human brain to the transcriptome of cancer cells. We find a conserved neural tri-lineage cancer hierarchy centered around glial progenitor-like cells. We also find that this progenitor population contains the majority of the cancer’s cycling cells, and, using RNA velocity, is often the originator of the other cell types. Finally, we show that this hierarchal map can be used to identify therapeutic targets specific to progenitor cancer stem cells. Our analyses show that normal brain development reconciles glioblastoma development, suggests a possible origin for glioblastoma hierarchy, and helps to identify cancer stem cell-specific targets.

Most platforms used for the molecular reconstruction of the tumor–immune microenvironment (TIME) of a solid tumor fail to explore the spatial context of the three-dimensional (3D) space of the tumor at a single-cell resolution, and thus lack information about cell–cell or cell–extracellular matrix (ECM) interactions. To address this issue, a pipeline which integrated multiplex spatially resolved multi-omics platforms was developed to identify crosstalk signaling networks among various cell types and the ECM in the 3D TIME of two FFPE (formalin-fixed paraffin embedded) gynecologic tumor samples. These platforms include non-targeted mass spectrometry imaging (glycans, metabolites, and peptides) and Stereo-seq (spatial transcriptomics) and targeted seqIF (IHC proteomics). The spatially resolved imaging data in a two- and three-dimensional space demonstrated various cellular neighborhoods in both samples. The collection of spatially resolved analytes in a voxel (3D pixel) across serial sections of the tissue was also demonstrated. Data collected from this analytical pipeline were used to construct spatial 3D maps with single-cell resolution, which revealed cell identity, activation, and energized status. These maps will provide not only insights into the molecular basis of spatial cell heterogeneity in the TIME, but also novel predictive biomarkers and therapeutic targets, which can improve patient survival rates.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Diffuse infiltrating gliomas are aggressive tumors of the central nervous system driven by intra-tumoral heterogeneity and aberrant normal-tumor cell-cell interactions. Grade specific and locoregional metabolic dependencies driving aberrant cell-states linked to treatment resistance, seizures and infiltration of gliomas remain elusive. Here, we applied spatial transcriptomics (stRNAseq), imaging mass cytometry (IMC) and mass spectrometry imaging (MSI; metabolites, peptides and glycans) to the core and edge tumor tissue from patients with World Health Organization (WHO) grades 2-4 diffuse infiltrating gliomas including isocitrate dehydrogenase (IDH) mutant oligodendrogliomas (WHO Grades 2 and 3) and IDH wildtype astrocytomas including ‘anaplastic’ astrocytoma (prior 2016 WHO histological grade 3) and glioblastoma (GBM, WHO grade 4) stRNAseq identified regions-specific differentially expressed genes with significant overall survival implications particularly in IDH wildtype GBM. Integration of stRNA seq and MSI-derived metabolite expression demonstrated enrichment of L-glutamine in SOX4 + Neural progenitor-like (NPC-like) cells and DL-dopamine in GPNMB ⁺ Mesenchymal-like (MES-like) GBM cells at the tumor edge relative to the core. Our results uncover clinically relevant and locoregional cell state-specific metabolites that may contribute to GBM proliferation, infiltration and seizures. This comprehensive pan-diffuse infiltrating glioma multi-omics study could serve as a resource for uncovering region-specific metabolic vulnerabilities encompassing metabolites, glycans and peptides within transcriptionally defined cell states across WHO 2-4 diffuse glioma.