Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma.

Prior studies have described the complex interplay that exists between glioma cells and neurons; however, the electrophysiological properties endogenous to glioma cells remain obscure. To address this, we employed Patch-sequencing (Patch-seq) on human glioma specimens and found that one-third of patched cells in IDH mutant (IDHmut) tumors demonstrate properties of both neurons and glia. To define these hybrid cells (HCs), which fire single, short action potentials, and discern if they are of tumoral origin, we developed the single cell rule association mining (SCRAM) computational tool to annotate each cell individually. SCRAM revealed that HCs possess select features of GABAergic neurons and oligodendrocyte precursor cells, and include both tumor and non-tumor cells. These studies characterize the combined electrophysiological and molecular properties of human glioma cells and describe a cell type in human glioma with unique electrophysiological and transcriptomic properties that may also exist in the non-tumor brain.

Leveraging single-cell sequencing to classify and characterize tumor subgroups in bulk RNA-sequencing data.

PURPOSE: Accurate classification of cancer subgroups is essential for precision medicine, tailoring treatments to individual patients based on their cancer subtypes. In recent years, advances in high-throughput sequencing technologies have enabled the generation of large-scale transcriptomic data from cancer samples. These data have provided opportunities for developing computational methods that can improve cancer subtyping and enable better personalized treatment strategies. METHODS: Here in this study, we evaluated different feature selection schemes in the context of meningioma classification. To integrate interpretable features from the bulk (n = 77 samples) and single-cell profiling (∼ 10 K cells), we developed an algorithm named CLIPPR which combines the top-performing single-cell models, RNA-inferred copy number variation (CNV) signals, and the initial bulk model to create a meta-model. RESULTS: While the scheme relying solely on bulk transcriptomic data showed good classification accuracy, it exhibited confusion between malignant and benign molecular classes in approximately ∼ 8% of meningioma samples. In contrast, models trained on features learned from meningioma single-cell data accurately resolved the sub-groups confused by bulk-transcriptomic data but showed limited overall accuracy. CLIPPR showed superior overall accuracy and resolved benign-malignant confusion as validated on n = 789 bulk meningioma samples gathered from multiple institutions. Finally, we showed the generalizability of our algorithm using our in-house single-cell (∼ 200 K cells) and bulk TCGA glioma data (n = 711 samples). CONCLUSION: Overall, our algorithm CLIPPR synergizes the resolution of single-cell data with the depth of bulk sequencing and enables improved cancer sub-group diagnoses and insights into their biology.

Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma.

Prior studies have described the complex interplay that exists between glioma cells and neurons, however, the electrophysiological properties endogenous to tumor cells remain obscure. To address this, we employed Patch-sequencing on human glioma specimens and found that one third of patched cells in IDH mutant (IDH mut ) tumors demonstrate properties of both neurons and glia by firing single, short action potentials. To define these hybrid cells (HCs) and discern if they are tumor in origin, we developed a computational tool, Single Cell Rule Association Mining (SCRAM), to annotate each cell individually. SCRAM revealed that HCs represent tumor and non-tumor cells that feature GABAergic neuron and oligodendrocyte precursor cell signatures. These studies are the first to characterize the combined electrophysiological and molecular properties of human glioma cells and describe a new cell type in human glioma with unique electrophysiological and transcriptomic properties that are likely also present in the non-tumor mammalian brain.

Targeted gene expression profiling predicts meningioma outcomes and radiotherapy responses.

Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and indications for postoperative radiotherapy are controversial. Here we develop a targeted gene expression biomarker that predicts meningioma outcomes and radiotherapy responses. Using a discovery cohort of 173 meningiomas, we developed a 34-gene expression risk score and performed clinical and analytical validation of this biomarker on independent meningiomas from 12 institutions across 3 continents (N = 1,856), including 103 meningiomas from a prospective clinical trial. The gene expression biomarker improved discrimination of outcomes compared with all other systems tested (N = 9) in the clinical validation cohort for local recurrence (5-year area under the curve (AUC) 0.81) and overall survival (5-year AUC 0.80). The increase in AUC compared with the standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval 0.07 to 0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% confidence interval 0.37 to 0.78, P = 0.0001) and suggested postoperative management could be refined for 29.8% of patients. In sum, our results identify a targeted gene expression biomarker that improves discrimination of meningioma outcomes, including prediction of postoperative radiotherapy responses.

Super-enhancer hijacking drives ectopic expression of hedgehog pathway ligands in meningiomas.

Hedgehog signaling mediates embryologic development of the central nervous system and other tissues and is frequently hijacked by neoplasia to facilitate uncontrolled cellular proliferation. Meningiomas, the most common primary brain tumor, exhibit Hedgehog signaling activation in 6.5% of cases, triggered by recurrent mutations in pathway mediators such as SMO. In this study, we find 35.6% of meningiomas that lack previously known drivers acquired various types of somatic structural variations affecting chromosomes 2q35 and 7q36.3. These cases exhibit ectopic expression of Hedgehog ligands, IHH and SHH, respectively, resulting in Hedgehog signaling activation. Recurrent tandem duplications involving IHH permit de novo chromatin interactions between super-enhancers within DIRC3 and a locus containing IHH. Our work expands the landscape of meningioma molecular drivers and demonstrates enhancer hijacking of Hedgehog ligands as a route to activate this pathway  in neoplasia.

Integrated clinical genomic analysis reveals xenobiotic metabolic genes are downregulated in meningiomas of current smokers.

INTRODUCTION: Meningiomas are the most common primary intracranial tumor. Recently, various genetic classification systems for meningioma have been described. We sought to identify clinical drivers of different molecular changes in meningioma. As such, clinical and genomic consequences of smoking in patients with meningiomas remain unexplored. METHODS: 88 tumor samples were analyzed in this study. Whole exome sequencing (WES) was used to assess somatic mutation burden. RNA sequencing data was used to identify differentially expressed genes (DEG) and genes sets (GSEA). RESULTS: Fifty-seven patients had no history of smoking, twenty-two were past smokers, and nine were current smokers. The clinical data showed no major differences in natural history across smoking status. WES revealed absence of AKT1 mutation rate in current or past smokers compared to non-smokers (p = 0.046). Current smokers had increased mutation rate in NOTCH2 compared to past and never smokers (p < 0.05). Mutational signature from current and past smokers showed disrupted DNA mismatch repair (cosine-similarity = 0.759 and 0.783). DEG analysis revealed the xenobiotic metabolic genes UGT2A1 and UGT2A2 were both significantly downregulated in current smokers compared to past (Log2FC = - 3.97, padj = 0.0347 and Log2FC = - 4.18, padj = 0.0304) and never smokers (Log2FC = - 3.86, padj = 0.0235 and Log2FC = - 4.20, padj = 0.0149). GSEA analysis of current smokers showed downregulation of xenobiotic metabolism and enrichment for G2M checkpoint, E2F targets, and mitotic spindle compared to past and never smokers (FDR < 25% each). CONCLUSION: In this study, we conducted a comparative analysis of meningioma patients based on their smoking history, examining both their clinical trajectories and molecular changes. Meningiomas from current smokers were more likely to harbor NOTCH2 mutations, and AKT1 mutations were absent in current or past smokers. Moreover, both current and past smokers exhibited a mutational signature associated with DNA mismatch repair. Meningiomas from current smokers demonstrate downregulation of xenobiotic metabolic enzymes UGT2A1 and UGT2A2, which are downregulated in other smoking related cancers. Furthermore, current smokers exhibited downregulation xenobiotic metabolic gene sets, as well as enrichment in gene sets related to mitotic spindle, E2F targets, and G2M checkpoint, which are hallmark pathways involved in cell division and DNA replication control. In aggregate, our results demonstrate novel alterations in meningioma molecular biology in response to systemic carcinogens.

Remote neuronal activity drives glioma progression through SEMA4F.

The tumour microenvironment plays an essential role in malignancy, and neurons have emerged as a key component of the tumour microenvironment that promotes tumourigenesis across a host of cancers1,2. Recent studies on glioblastoma (GBM) highlight bidirectional signalling between tumours and neurons that propagates a vicious cycle of proliferation, synaptic integration and brain hyperactivity3-8; however, the identity of neuronal subtypes and tumour subpopulations driving this phenomenon is incompletely understood. Here we show that callosal projection neurons located in the hemisphere contralateral to primary GBM tumours promote progression and widespread infiltration. Using this platform to examine GBM infiltration, we identified an activity-dependent infiltrating population present at the leading edge of mouse and human tumours that is enriched for axon guidance genes. High-throughput, in vivo screening of these genes identified SEMA4F as a key regulator of tumourigenesis and activity-dependent progression. Furthermore, SEMA4F promotes the activity-dependent infiltrating population and propagates bidirectional signalling with neurons by remodelling tumour-adjacent synapses towards brain network hyperactivity. Collectively our studies demonstrate that subsets of neurons in locations remote to primary GBM promote malignant progression, and also show new mechanisms of glioma progression that are regulated by neuronal activity.

Inhibitory input directs astrocyte morphogenesis through glial GABABR.

Communication between neurons and glia has an important role in establishing and maintaining higher-order brain function1. Astrocytes are endowed with complex morphologies, placing their peripheral processes in close proximity to neuronal synapses and directly contributing to their regulation of brain circuits2-4. Recent studies have shown that excitatory neuronal activity promotes oligodendrocyte differentiation5-7; whether inhibitory neurotransmission regulates astrocyte morphogenesis during development is unclear. Here we show that inhibitory neuron activity is necessary and sufficient for astrocyte morphogenesis. We found that input from inhibitory neurons functions through astrocytic GABAB receptor (GABABR) and that its deletion in astrocytes results in a loss of morphological complexity across a host of brain regions and disruption of circuit function. Expression of GABABR in developing astrocytes is regulated in a region-specific manner by SOX9 or NFIA and deletion of these transcription factors results in region-specific defects in astrocyte morphogenesis, which is conferred by interactions with transcription factors exhibiting region-restricted patterns of expression. Together, our studies identify input from inhibitory neurons and astrocytic GABABR as universal regulators of morphogenesis, while further revealing a combinatorial code of region-specific transcriptional dependencies for astrocyte development that is intertwined with activity-dependent processes.

Inhibitory input directs astrocyte morphogenesis through glial GABA B R.

Communication between neurons and glia plays an important role in establishing and maintaining higher order brain function. Astrocytes are endowed with complex morphologies which places their peripheral processes in close proximity to neuronal synapses and directly contributes to their regulation of brain circuits. Recent studies have shown that excitatory neuronal activity promotes oligodendrocyte differentiation; whether inhibitory neurotransmission regulates astrocyte morphogenesis during development is unknown. Here we show that inhibitory neuron activity is necessary and sufficient for astrocyte morphogenesis. We found that input from inhibitory neurons functions through astrocytic GABA B R and that its deletion in astrocytes results in a loss of morphological complexity across a host of brain regions and disruption of circuit function. Expression of GABA B R in developing astrocytes is regulated in a region-specific manner by SOX9 or NFIA and deletion of these transcription factors results in region-specific defects in astrocyte morphogenesis, which is conferred by interactions with transcription factors exhibiting region-restricted patterns of expression. Together our studies identify input from inhibitory neurons and astrocytic GABA B R as universal regulators of morphogenesis, while further revealing a combinatorial code of region-specific transcriptional dependencies for astrocyte development that is intertwined with activity-dependent processes.

Remote neuronal activity drives glioma infiltration via Sema4f.

The tumor microenvironment (TME) plays an essential role in malignancy and neurons have emerged as a key component of the TME that promotes tumorigenesis across a host of cancers. Recent studies on glioblastoma (GBM) highlight bi-directional signaling between tumors and neurons that propagates a vicious cycle of proliferation, synaptic integration, and brain hyperactivity; however, the identity of neuronal subtypes and tumor subpopulations driving this phenomenon are incompletely understood. Here we show that callosal projection neurons located in the hemisphere contralateral to primary GBM tumors promote progression and widespread infiltration. Using this platform to examine GBM infiltration, we identified an activity dependent infiltrating population present at the leading edge of mouse and human tumors that is enriched for axon guidance genes. High-throughput, in vivo screening of these genes identified Sema4F as a key regulator of tumorigenesis and activity-dependent infiltration. Furthermore, Sema4F promotes the activity-dependent infiltrating population and propagates bi-directional signaling with neurons by remodeling tumor adjacent synapses towards brain network hyperactivity. Collectively, our studies demonstrate that subsets of neurons in locations remote to primary GBM promote malignant progression, while revealing new mechanisms of tumor infiltration that are regulated by neuronal activity.

Targeted gene expression profiling predicts meningioma outcomes and radiotherapy responses.

Background: Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and current indications for postoperative radiotherapy are controversial. Recent studies have proposed prognostic meningioma classification systems using DNA methylation profiling, copy number variants, DNA sequencing, RNA sequencing, histology, or integrated models based on multiple combined features. Targeted gene expression profiling has generated robust biomarkers integrating multiple molecular features for other cancers, but is understudied for meningiomas. Methods: Targeted gene expression profiling was performed on 173 meningiomas and an optimized gene expression biomarker (34 genes) and risk score (0 to 1) was developed to predict clinical outcomes. Clinical and analytical validation was performed on independent meningiomas from 12 institutions across 3 continents (N = 1856), including 103 meningiomas from a prospective clinical trial. Gene expression biomarker performance was compared to 9 other classification systems. Results: The gene expression biomarker improved discrimination of postoperative meningioma outcomes compared to all other classification systems tested in the independent clinical validation cohort for local recurrence (5-year area under the curve [AUC] 0.81) and overall survival (5-year AUC 0.80). The increase in area under the curve compared to the current standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval [CI] 0.07-0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% CI 0.37-0.78, P = 0.0001) and re-classified up to 52.0% meningiomas compared to conventional clinical criteria, suggesting postoperative management could be refined for 29.8% of patients. Conclusions: A targeted gene expression biomarker improves discrimination of meningioma outcomes compared to recent classification systems and predicts postoperative radiotherapy responses.

Single-cell RNA sequencing reveals immunosuppressive myeloid cell diversity during malignant progression in a murine model of glioma.

Recent studies have shown the importance of the dynamic tumor microenvironment (TME) in high-grade gliomas (HGGs). In particular, myeloid cells are known to mediate immunosuppression in glioma; however, it is still unclear if myeloid cells play a role in low-grade glioma (LGG) malignant progression. Here, we investigate the cellular heterogeneity of the TME using single-cell RNA sequencing in a murine glioma model that recapitulates the malignant progression of LGG to HGG. LGGs show increased infiltrating CD4+ and CD8+ T cells and natural killer (NK) cells in the TME, whereas HGGs abrogate this infiltration. Our study identifies distinct macrophage clusters in the TME that show an immune-activated phenotype in LGG but then evolve to an immunosuppressive state in HGG. We identify CD74 and macrophage migration inhibition factor (MIF) as potential targets for these distinct macrophage populations. Targeting these intra-tumoral macrophages in the LGG stage may attenuate their immunosuppressive properties and impair malignant progression.

Glioma epileptiform activity and progression are driven by IGSF3-mediated potassium dysregulation.

Seizures are a frequent pathophysiological feature of malignant glioma. Recent studies implicate peritumoral synaptic dysregulation as a driver of brain hyperactivity and tumor progression; however, the molecular mechanisms that govern these phenomena remain elusive. Using scRNA-seq and intraoperative patient ECoG recordings, we show that tumors from seizure patients are enriched for gene signatures regulating synapse formation. Employing a human-to-mouse in vivo functionalization pipeline to screen these genes, we identify IGSF3 as a mediator of glioma progression and dysregulated neural circuitry that manifests as spreading depolarization (SD). Mechanistically, we discover that IGSF3 interacts with Kir4.1 to suppress potassium buffering and found that seizure patients exhibit reduced expression of potassium handlers in proliferating tumor cells. In vivo imaging reveals that dysregulated synaptic activity emanates from the tumor-neuron interface, which we confirm in patients. Our studies reveal that tumor progression and seizures are enabled by ion dyshomeostasis and identify SD as a driver of disease.

Social deprivation induces astrocytic TRPA1-GABA suppression of hippocampal circuits.

Social experience is essential for the development and maintenance of higher-order brain function. Social deprivation results in a host of cognitive deficits, and cellular studies have largely focused on associated neuronal dysregulation; how astrocyte function is impacted by social deprivation is unknown. Here, we show that hippocampal astrocytes from juvenile mice subjected to social isolation exhibit increased Ca2+ activity and global changes in gene expression. We found that the Ca2+ channel TRPA1 is upregulated in astrocytes after social deprivation and astrocyte-specific deletion of TRPA1 reverses the physiological and cognitive deficits associated with social deprivation. Mechanistically, TRPA1 inhibition of hippocampal circuits is mediated by a parallel increase of astrocytic production and release of the inhibitory neurotransmitter GABA after social deprivation. Collectively, our studies reveal how astrocyte function is tuned to social experience and identifies a social-context-specific mechanism by which astrocytic TRPA1 and GABA coordinately suppress hippocampal circuit function.

CXCR4 expression is associated with proneural-to-mesenchymal transition in glioblastoma.

Glioblastoma (GBM) is the most common primary intracranial malignant tumor and consists of three molecular subtypes: proneural (PN), mesenchymal (MES) and classical (CL). Transition between PN to MES subtypes (PMT) is the glioma analog of the epithelial-mesenchymal transition (EMT) in carcinomas and is associated with resistance to therapy. CXCR4 signaling increases the expression of MES genes in glioma cell lines and promotes EMT in other cancers. RNA sequencing (RNAseq) data of PN GBMs in The Cancer Genome Atlas (TCGA) and secondary high-grade gliomas (HGGs) from an internal cohort were examined for correlation between CXCR4 expression and survival as well as expression of MES markers. Publicly available single-cell RNA sequencing (scRNAseq) data was analyzed for cell type specific CXCR4 expression. These results were validated in a genetic mouse model of PN GBM. Higher CXCR4 expression was associated with significantly reduced survival and increased expression of MES markers in TCGA and internal cohorts. CXCR4 was expressed in immune and tumor cells based on scRNAseq analysis. Higher CXCR4 expression within tumor cells on scRNAseq was associated with increased MES phenotype, suggesting a cell-autonomous effect. In a genetically engineered mouse model, tumors induced with CXCR4 exhibited a mesenchymal phenotype and shortened survival. These results suggest that CXCR4 signaling promotes PMT and shortens survival in GBM and highlights its inhibition as a potential therapeutic strategy.

Hypermitotic meningiomas harbor DNA methylation subgroups with distinct biological and clinical features.

BACKGROUND: Meningiomas, the most common primary intracranial tumors, can be separated into 3 DNA methylation groups with distinct biological drivers, clinical outcomes, and therapeutic vulnerabilities. Alternative meningioma grouping schemes using copy number variants, gene expression profiles, somatic short variants, or integrated molecular models have been proposed. These data suggest meningioma DNA methylation groups may harbor subgroups unifying contrasting theories of meningioma biology. METHODS: A total of 565 meningioma DNA methylation profiles from patients with comprehensive clinical follow-up at independent discovery (n = 200) or validation (n = 365) institutions were reanalyzed and classified into Merlin-intact, Immune-enriched, or Hypermitotic DNA methylation groups. RNA sequencing from the discovery (n = 200) or validation (n = 302) cohort were analyzed in the context of DNA methylation groups to identify subgroups. Biological features and clinical outcomes were analyzed across meningioma grouping schemes. RESULTS: RNA sequencing revealed differential enrichment of FOXM1 target genes across two subgroups of Hypermitotic meningiomas. Differential expression and ontology analyses showed the subgroup of Hypermitotic meningiomas without FOXM1 target gene enrichment was distinguished by gene expression programs driving macromolecular metabolism. Analysis of genetic, epigenetic, gene expression, or cellular features revealed Hypermitotic meningioma subgroups were concordant with Proliferative or Hypermetabolic meningiomas, which were previously reported alongside Merlin-intact and Immune-enriched tumors using an integrated molecular model. The addition of DNA methylation subgroups to clinical models refined the prediction of postoperative outcomes compared to the addition of DNA methylation groups. CONCLUSIONS: Meningiomas can be separated into three DNA methylation groups and Hypermitotic meningiomas can be subdivided into Proliferative and Hypermetabolic subgroups, each with distinct biological and clinical features.

XCVATR: detection and characterization of variant impact on the Embeddings of single -cell and bulk RNA-sequencing samples.

BACKGROUND: RNA-sequencing has become a standard tool for analyzing gene activity in bulk samples and at the single-cell level. By increasing sample sizes and cell counts, this technique can uncover substantial information about cellular transcriptional states. Beyond quantification of gene expression, RNA-seq can be used for detecting variants, including single nucleotide polymorphisms, small insertions/deletions, and larger variants, such as copy number variants. Notably, joint analysis of variants with cellular transcriptional states may provide insights into the impact of mutations, especially for complex and heterogeneous samples. However, this analysis is often challenging due to a prohibitively high number of variants and cells, which are difficult to summarize and visualize. Further, there is a dearth of methods that assess and summarize the association between detected variants and cellular transcriptional states. RESULTS: Here, we introduce XCVATR (eXpressed Clusters of Variant Alleles in Transcriptome pRofiles), a method that identifies variants and detects local enrichment of expressed variants within embedding of samples and cells in single-cell and bulk RNA-seq datasets. XCVATR visualizes local "clumps" of small and large-scale variants and searches for patterns of association between each variant and cellular states, as described by the coordinates of cell embedding, which can be computed independently using any type of distance metrics, such as principal component analysis or t-distributed stochastic neighbor embedding. Through simulations and analysis of real datasets, we demonstrate that XCVATR can detect enrichment of expressed variants and provide insight into the transcriptional states of cells and samples. We next sequenced 2 new single cell RNA-seq tumor samples and applied XCVATR. XCVATR revealed subtle differences in CNV impact on tumors. CONCLUSIONS: XCVATR is publicly available to download from https://github.com/harmancilab/XCVATR .