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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. Glioblastoma is thought to arise from neural stem cells. Here, to investigate this, the authors use single-cell RNA-sequencing to compare glioblastoma to the fetal human brain, and find a similarity between glial progenitor cells and a subpopulation of glioblastoma cells.

Background Glioblastoma (GBM) is an aggressive brain tumor that exhibits resistance to current treatment, making the identification of novel therapeutic targets essential. In this context, cellular prion protein (PrP C ) stands out as a potential candidate for new therapies. Encoded by the PRNP gene, PrP C can present increased expression levels in GBM, impacting cell proliferation, growth, migration, invasion and stemness. Nevertheless, the exact molecular mechanisms through which PRNP /PrP C modulates key aspects of GBM biology remain elusive. Methods To elucidate the implications of PRNP /PrP C in the biology of this cancer, we analyzed publicly available RNA sequencing (RNA-seq) data of patient-derived GBMs from four independent studies. First, we ranked samples profiled by bulk RNA-seq as PRNP high and PRNP low and compared their transcriptomic landscape. Then, we analyzed PRNP + and PRNP - GBM cells profiled by single-cell RNA-seq to further understand the molecular context within which PRNP /PrP C might function in this tumor. We explored an additional proteomics dataset, applying similar comparative approaches, to corroborate our findings. Results Functional profiling revealed that vesicular dynamics signatures are strongly correlated with PRNP /PrP C levels in GBM. We found a panel of 73 genes, enriched in vesicle-related pathways, whose expression levels are increased in PRNP high / PRNP + cells across all RNA-seq datasets. Vesicle-associated genes, ANXA1 , RAB31 , DSTN and SYPL1, were found to be upregulated in vitro in an in-house collection of patient-derived GBM. Moreover, proteome analysis of patient-derived samples reinforces the findings of enhanced vesicle biogenesis, processing and trafficking in PRNP high / PRNP + GBM cells. Conclusions Together, our findings shed light on a novel role for PrP C as a potential modulator of vesicle biology in GBM, which is pivotal for intercellular communication and cancer maintenance. We also introduce GBMdiscovery, a novel user-friendly tool that allows the investigation of specific genes in GBM biology.

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

Background Glioblastoma (GBM) is an aggressive brain tumor driven by glioblastoma stem cells (GSCs), which represent an appealing target for therapeutic interventions. The cellular prion protein (PrP C ), a scaffold protein involved in diverse cellular processes, interacts with various membrane and extracellular matrix molecules, influencing tumor biology. Herein, we investigate the impact of PrP C expression on GBM. Methods To address this goal, we employed CRISPR-Cas9 technology to generate PrP C knockout (KO) glioblastoma cell lines, enabling detailed loss-of-function studies. Bulk RNA sequencing followed by differentially expressed gene and pathway enrichment analyses between U87 or U251 PrP C -wild-type (WT) cells and PrP C -knockout (KO) cells were used to identify pathways regulated by PrP C . Immunofluorescence assays were used to evaluate cellular morphology and protein distribution. For assessment of protein levels, Western blot and flow cytometry assays were employed. Transwell and growth curve assays were used to determine the impact of loss-of-PrP C in GBM invasiveness and proliferation, respectively. Single-cell RNA sequencing analysis of data from patient tumors from The Cancer Genome Atlas (TCGA) and the Broad Institute of Single-Cell Data Portal were used to evaluate the correspondence between our in vitro results and patient samples. Results Transcriptome analysis of PrP C -KO GBM cell lines revealed altered expression of genes associated with crucial tumor progression pathways, including migration, proliferation, and stemness. These findings were corroborated by assays that revealed impaired invasion, migration, proliferation, and self-renewal in PrP C -KO GBM cells, highlighting its critical role in sustaining tumor growth. Notably, loss-of-PrP C disrupted the expression and localization of key stemness markers, particularly CD44. Additionally, the modulation of PrP C levels through CD44 overexpression further emphasizes their regulatory role in these processes. Conclusions These findings establish PrP C as a modulator of essential molecules on the cell surface of GSCs, highlighting its potential as a therapeutic target for GBM.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. Cell segmentation is crucial in many image analysis pipelines. This analysis compares many tools on a multimodal cell segmentation benchmark. A Transformer-based model performed best in terms of performance and general applicability.

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.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

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 + Neural progenitor-like (NPC-like) cells and DL-dopamine in 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.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

Glioblastoma (GBM) is an aggressive brain cancer with a poor survival rate. Despite hundreds of clinical trials, there is no effective targeted therapy. Glioblastoma stem cells (GSCs) are an important GBM model system. In culture, these cells form spatial structures that share morphological aspects with their source tumors. We collected 17,000 phase contrast images of 15 patient-derived GSC lines growing to confluence. We find that GSCs grow in characteristic multicellular patterns depending on their transcriptional state. Interpretable computer vision algorithms identified specific image features that predict transcriptional state across multiple cell confluency levels. This relationship will be useful in developing GSC screens where image features can be used to identify how GSC biology changes in response to perturbations simply by imaging cultured cells on plates.