Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma

Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer

Mapping tumor metabolic remodeling and their spatial crosstalk with surrounding non-tumor cells can fundamentally improve our understanding of tumor biology, facilitates the designing of advanced therapeutic strategies. Here, we present an integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics to hierarchically visualize the intratumor metabolic heterogeneity and cell metabolic interactions in same gastric cancer sample. Tumor-associated metabolic reprogramming is imaged at metabolic-transcriptional levels, and maker metabolites, lipids, genes are connected in metabolic pathways and colocalized in the heterogeneous cancer tissues. Integrated data from spatial multi-omics approaches coherently identify cell types and distributions within the complex tumor microenvironment, and an immune cell-dominated "tumor-normal interface" region where tumor cells contact adjacent tissues are characterized with distinct transcriptional signatures and significant immunometabolic alterations. Our approach for mapping tissue molecular architecture provides highly integrated picture of intratumor heterogeneity, and transform the understanding of cancer metabolism at systemic level.

Technical report: surgical preparation of human brain tissue for clinical and basic research

BACKGROUND

The study of the distinct structure and function of the human central nervous system, both in healthy and diseased states, is becoming increasingly significant in the field of neuroscience. Typically, cortical and subcortical tissue is discarded during surgeries for tumors and epilepsy. Yet, there is a strong encouragement to utilize this tissue for clinical and basic research in humans. Here, we describe the technical aspects of the microdissection and immediate handling of viable human cortical access tissue for basic and clinical research, highlighting the measures needed to be taken in the operating room to ensure standardized procedures and optimal experimental results.

METHODS

In multiple rounds of experiments (n = 36), we developed and refined surgical principles for the removal of cortical access tissue. The specimens were immediately immersed in cold carbogenated N-methyl-D-glucamine-based artificial cerebrospinal fluid for electrophysiology and electron microscopy experiments or specialized hibernation medium for organotypic slice cultures.

RESULTS

The surgical principles of brain tissue microdissection were (1) rapid preparation (<1 min), (2) maintenance of the cortical axis, (3) minimization of mechanical trauma to sample, (4) use of pointed scalpel blade, (5) avoidance of cauterization and blunt preparation, (6) constant irrigation, and (7) retrieval of the sample without the use of forceps or suction. After a single round of introduction to these principles, multiple surgeons adopted the technique for samples with a minimal dimension of 5 mm spanning all cortical layers and subcortical white matter. Small samples (5-7 mm) were ideal for acute slice preparation and electrophysiology. No adverse events from sample resection were observed.

CONCLUSION

The microdissection technique of human cortical access tissue is safe and easily adoptable into the routine of neurosurgical procedures. The standardized and reliable surgical extraction of human brain tissue lays the foundation for human-to-human translational research on human brain tissue.

Systems Immunology Approaches to Metabolism

Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.

Single-cell RNA sequencing highlights intratumor heterogeneity and intercellular network featured in adamantinomatous craniopharyngioma

Despite improvements in microscopically neurosurgical techniques made in recent years, the prognosis of adamantinomatous craniopharyngioma (ACP) is still unsatisfactory. Little is known about cellular atlas and biological features of ACP. Here, we carried out integrative analysis of 44,038 single-cell transcriptome profiles to characterize the landscape of intratumoral heterogeneity and tumor microenvironment (TME) in ACP. Four major neoplastic cell states with distinctive expression signatures were defined, which further revealed the histopathological features and elucidated unknown cellular atlas of ACP. Pseudotime analyses suggested potential evolutionary trajectories between specific neoplastic cell states. Notably, a distinct oligodendrocyte lineage was identified in ACP, which was associated with immunological infiltration and neural damage. In addition, we described a tumor-centric regulatory network based on intercellular communication in TME. Together, our findings represent a unique resource for deciphering tumor heterogeneity of ACP, which will improve clinical diagnosis and treatment strategies.

Single-cell heterogeneity of EGFR and CDK4 co-amplification is linked to immune infiltration in glioblastoma

Glioblastoma (GBM) is the most aggressive brain tumor, with a median survival of ∼15 months. Targeted approaches have not been successful in this tumor type due to the large extent of intratumor heterogeneity. Mosaic amplification of oncogenes suggests that multiple genetically distinct clones are present in each tumor. To uncover the relationships between genetically diverse subpopulations of GBM cells and their native tumor microenvironment, we employ highly multiplexed spatial protein profiling coupled with single-cell spatial mapping of fluorescence in situ hybridization (FISH) for EGFR, CDK4, and PDGFRA. Single-cell FISH analysis of a total of 35,843 single nuclei reveals that tumors in which amplifications of EGFR and CDK4 more frequently co-occur in the same cell exhibit higher infiltration of CD163+ immunosuppressive macrophages. Our results suggest that high-throughput assessment of genomic alterations at the single-cell level could provide a measure for predicting the immune state of GBM.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Integration of clinical and spatial data to explore lipid metabolism-related genes for predicting prognosis and immune microenvironment in gliomas

Lipid metabolism is crucial to tumor growth and immune microenvironment as well as drug sensitivity in glioma. Identifying prognostic indicators of glioma and elucidating the mechanisms of glioma progression are critical for improving the prognosis of glioma patients. In this study, we investigated the role and prognostic value of metabolism-related genes in glioma by integrative analysis of datasets from GEO, CGGA, and TCGA. Based on clinical data and transcriptome data, we found that the expression pattern of three major pathways related to lipid metabolism is fatty acidhigh-phospholipidhigh-triglyceridelow, which is associated with better prognosis and immune infiltration. The genes involved in these three pathways were used to generate a prognostic model, which showed high stability and efficiency in the test set and validation set. The spatial transcriptome of glioma patients revealed that the microenvironment of the regions with high expression of risk genes CAV1 and SCD is in a state of hypoxia, EMT, and cell cycle arrest, and thus can be used as markers of metabolic reprogramming in the tumor microenvironment. In the high-risk group, M0 macrophages and M1 macrophages were significantly enriched, and the risk score was significantly correlated with gene mutation and methylation of risk genes. We further performed drug sensitivity screening corresponding to different risk genes. This study provided novel insights into the differential immune microenvironment with different expression patterns of metablism-related genes and highlighted the spatial and temporal synergy of tumor progression and metabolic reprogramming.

Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma

Glioma represents a dominant primary intracranial malignancy in the central nervous system. Artificial intelligence that mainly includes machine learning, and deep learning computational approaches, presents a unique opportunity to enhance clinical management of glioma through improving tumor segmentation, diagnosis, differentiation, grading, treatment, prediction of clinical outcomes (prognosis, and recurrence), molecular features, clinical classification, characterization of the tumor microenvironment, and drug discovery. A growing body of recent studies apply artificial intelligence-based models to disparate data sources of glioma, covering imaging modalities, digital pathology, high-throughput multi-omics data (especially emerging single-cell RNA sequencing and spatial transcriptome), etc. While these early findings are promising, future studies are required to normalize artificial intelligence-based models to improve the generalizability and interpretability of the results. Despite prominent issues, targeted clinical application of artificial intelligence approaches in glioma will facilitate the development of precision medicine of this field. If these challenges can be overcome, artificial intelligence has the potential to profoundly change the way patients with or at risk of glioma are provided with more rational care.

Spatial immunosampling of MRI-defined glioblastoma regions reveals immunologic fingerprint of non-contrast enhancing, infiltrative tumor margins

Glioblastoma (GBM) treatment includes maximal safe resection of the core and MRI contrast-enhancing (CE) tumor. Complete resection of the infiltrative non-contrast-enhancing (NCE) tumor rim is rarely achieved. We established a safe, semi-automated workflow for spatially-registered sampling of MRI-defined GBM regions in 19 patients with downstream analysis and biobanking, enabling studies of NCE, wherefrom recurrence/progression typically occurs. Immunophenotyping revealed underrepresentation of myeloid cell subsets and CD8+ T cells in the NCE. While NCE T cells phenotypically and functionally resembled those in matching CE tumor, subsets of activated (CD69hi) effector memory CD8+ T cells were overrepresented. Contrarily, CD25hi Tregs and other subsets were underrepresented. Overall, our study demonstrated that MRI-guided, spatially-registered, intraoperative immunosampling is feasible as part of routine GBM surgery. Further elucidation of the shared and spatially distinct microenvironmental biology of GBM will enable development of therapeutic approaches targeting the NCE infiltrative tumor to decrease GBM recurrence.

Data-driven spatio-temporal modelling of glioblastoma

Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.

Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas

Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric and adult patients, respectively. Both tumors exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, and microenvironmental cues in glioma niches. However, the spatial organization of cellular states and niche-specific regulatory programs remain to be investigated. Here, we perform a spatial profiling of DMG and GBM combining short- and long-read spatial transcriptomics, and single-cell transcriptomic datasets. We identify clinically relevant transcriptional programs, RNA isoform diversity, and multi-cellular ecosystems across different glioma niches. We find that while the tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells are enriched in the neuron-rich invasive niche in both DMG and GBM. Further, we identify niche-specific regulatory programs for RG-like cells, and functionally confirm that FAM20C mediates invasive growth of RG-like cells in a neuron-rich microenvironment in a human neural stem cell derived orthotopic DMG model. Together, our results provide a blueprint for understanding the spatial architecture and niche-specific vulnerabilities of DMG and GBM.

Recent Developments in Glioblastoma Therapy: Oncolytic Viruses and Emerging Future Strategies

Glioblastoma is the most aggressive form of malignant brain tumor. Standard treatment protocols and traditional immunotherapy are poorly effective as they do not significantly increase the long-term survival of glioblastoma patients. Oncolytic viruses (OVs) may be an effective alternative approach. Combining OVs with some modern treatment options may also provide significant benefits for glioblastoma patients. Here we review virotherapy for glioblastomas and describe several OVs and their combination with other therapies. The personalized use of OVs and their combination with other treatment options would become a significant area of research aiming to develop the most effective treatment regimens for glioblastomas.

Functional characterization of TSPAN7 as a novel indicator for immunotherapy in glioma

Glioma is the most common primary malignant tumor of the central nervous system in clinical practice. Most adult diffuse gliomas have poor efficacy after standard treatment, especially glioblastoma. With the in-depth understanding of brain immune microenvironment, immunotherapy as a new treatment has attracted much attention. In this study, through analyzing a large number of glioma cohorts, we reported that TSPAN7, a member of the tetraspanin family, decreased in high-grade gliomas and low expression was associated with poor prognosis in glioma patients. Meanwhile, the expression pattern of TSPAN7 was verified in glioma clinical samples and glioma cell lines by qPCR, Western Blotting and immunofluorescence. In addition, functional enrichment analysis showed that cell proliferation, EMT, angiogenesis, DNA repair and MAPK signaling pathways were activated in the TSPAN7 lower expression subgroup. Lentiviral plasmids were used to overexpress TSPAN7 in U87 and LN229 glioma cell lines to explore the anti-tumor role of TSPAN7 in glioma. Moreover, by analyzing the relationship between TSPAN7 expression and immune cell infiltration in multiple datasets, we found that TSPAN7 was significantly negatively correlated with the immune infiltration of tumor-related macrophages, especially M2-type macrophages. Further analysis of immune checkpoints showed that, the expression level of TSPAN7 was negatively correlated with the expression of PD-1, PD-L1 and CTLA-4. Using an independent anti-PD-1 immunotherapy cohorts of GBM, we demonstrated that TSPAN7 expression may had a synergistic effect with PD-L1 on the response to immunotherapy. Based on the above findings, we speculate that TSPAN7 can serve as a biomarker for prognosis and a potential immunotherapy target in glioma patients.

Physical and biological advances in endothelial cell-based engineered co-culture model systems

Scientific knowledge in the field of cell biology and mechanobiology heavily leans on cell-based in vitro experiments and models that favor the examination and comprehension of certain biological processes and occurrences across a variety of environments. Cell culture assays are an invaluable instrument for a vast spectrum of biomedical and biophysical investigations. The quality of experimental models in terms of simplicity, reproducibility, and combinability with other methods, and in particular the scale at which they depict cell fate in native tissues, is critical to advancing the knowledge of the comprehension of cell-cell and cell-matrix interactions in tissues and organs. Typically, in vitro models are centered on the experimental tinkering of mammalian cells, most often cultured as monolayers on planar, two-dimensional (2D) materials. Notwithstanding the significant advances and numerous findings that have been accomplished with flat biology models, their usefulness for generating further new biological understanding is constrained because the simple 2D setting does not reproduce the physiological response of cells in natural living tissues. In addition, the co-culture systems in a 2D stetting weakly mirror their natural environment of tissues and organs. Significant advances in 3D cell biology and matrix engineering have resulted in the creation and establishment of a new type of cell culture shapes that more accurately represents the in vivo microenvironment and allows cells and their interactions to be analyzed in a biomimetic approach. Contemporary biomedical and biophysical science has novel advances in technology that permit the design of more challenging and resilient in vitro models for tissue engineering, with a particular focus on scaffold- or hydrogel-based formats, organotypic cultures, and organs-on-chips, which cover the purposes of co-cultures. Even these complex systems must be kept as simplified as possible in order to grasp a particular section of physiology too very precisely. In particular, it is highly appreciated that they bridge the space between conventional animal research and human (patho)physiology. In this review, the recent progress in 3D biomimetic culturation is presented with a special focus on co-cultures, with an emphasis on the technological building blocks and endothelium-based co-culture models in cancer research that are available for the development of more physiologically relevant in vitro models of human tissues under normal and diseased conditions. Through applications and samples of various physiological and disease models, it is possible to identify the frontiers and future engagement issues that will have to be tackled to integrate synthetic biomimetic culture systems far more successfully into biomedical and biophysical investigations.

Integrative analysis of single-cell transcriptomics reveals age-associated immune landscape of glioblastoma

Glioblastoma (GBM) is the most malignant tumor in center nervous system. Clinical statistics revealed that senior GBM patients had a worse overall survival (OS) comparing with that of patients in other ages, which is mainly related with tumor microenvironment including tumor-associated immune cells in particular. However, the immune heterogeneity and age-related prognosis in GBM are under studied. Here we developed a machine learning-based method to integrate public large-scale single-cell RNA sequencing (scRNA-seq) datasets to establish a comprehensive atlas of immune cells infiltrating in cross-age GBM. We found that the compositions of the immune cells are remarkably different across ages. Brain-resident microglia constitute the majority of glioblastoma-associated macrophages (GAMs) in patients, whereas dramatic elevation of extracranial monocyte-derived macrophages (MDMs) is observed in GAMs of senior patients, which contributes to the worse prognosis of aged patients. Further analysis suggests that the increased MDMs arisen from excessive recruitment and proliferation of peripheral monocytes not only lead to the T cell function inhibition in GBM, but also stimulate tumor cells proliferation via VEGFA secretion. In summary, our work provides new cues for the correlational relationship between the immune microenvironment of GBM and aging, which might be insightful for precise and effective therapeutic interventions for senior GBM patients.

MALDI Imaging Mass Spectrometry of High-Grade Gliomas: A Review of Recent Progress and Future Perspective

Glioblastoma (GBM) is the most common malignancy of the brain with a relatively short median survival and high mortality. Advanced age, high socioeconomic status, exposure to ionizing radiation, and other factors have been correlated with an increased incidence of GBM, while female sex hormones, history of allergies, and frequent use of specific drugs might exert protective effects against this disease. However, none of these explain the pathogenesis of GBM. The most recent WHO classification of CNS tumors classifies neoplasms based on their histopathological and molecular characteristics. Modern laboratory techniques, such as matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, enable the comprehensive metabolic analysis of the tissue sample. MALDI imaging is able to characterize the spatial distribution of a wide array of biomolecules in a sample, in combination with histological features, without sacrificing the tissue integrity. In this review, we first provide an overview of GBM epidemiology, risk, and protective factors, as well as the recent WHO classification of CNS tumors. We then provide an overview of mass spectrometry workflow, with a focus on MALDI imaging, and recent advances in cancer research. Finally, we conclude the review with studies of GBM that utilized MALDI imaging and offer our perspective on future research.

Impact of epigenetic reprogramming on antitumor immune responses in glioma

Epigenetic remodeling is a molecular hallmark of gliomas, and it has been identified as a key mediator of glioma progression. Epigenetic dysregulation contributes to gliomagenesis, tumor progression, and responses to immunotherapies, as well as determining clinical features. This epigenetic remodeling includes changes in histone modifications, chromatin structure, and DNA methylation, all of which are driven by mutations in genes such as histone 3 genes (H3C1 and H3F3A), isocitrate dehydrogenase 1/2 (IDH1/2), α-thalassemia/mental retardation, X-linked (ATRX), and additional chromatin remodelers. Although much of the initial research primarily identified how the epigenetic aberrations impacted glioma progression by solely examining the glioma cells, recent studies have aimed at establishing the role of epigenetic alterations in shaping the tumor microenvironment (TME). In this review, we discuss the mechanisms by which these epigenetic phenomena in glioma remodel the TME and how current therapies targeting epigenetic dysregulation affect the glioma immune response and therapeutic outcomes. Understanding the link between epigenetic remodeling and the glioma TME provides insights into the implementation of epigenetic-targeting therapies to improve the antitumor immune response.

Immune checkpoint blockade in glioblastoma: from tumor heterogeneity to personalized treatment

Immune checkpoint blockade (ICB) has revolutionized modern cancer therapy, arousing great interest in the neuro-oncology community. While several reports show that subsets of patients with glioma exhibit durable responses to immunotherapy, the efficacy of this treatment has not been observed for unselected patient populations, preventing its broad clinical implementation for gliomas and glioblastoma (GBM). To exploit the maximum therapeutic potential of ICB for patients with glioma, understanding the different aspects of glioma-related tumor immune responses is of critical importance. In this Review, we discuss contributing factors that distinguish subsets of patients with glioma who may benefit from ICB. Specifically, we discuss (a) the complex interaction between the tumor immune microenvironment and glioma cells as a potential influence on immunotherapy responses; (b) promising biomarkers for responses to immune checkpoint inhibitors; and (c) the potential contributions of peripheral immune cells to therapeutic responses.

Integration of spatial and single-cell data across modalities with weak linkage

single-cell sequencing methods have enabled the profiling of multiple types of molecular readouts at cellular resolution, and recent developments in spatial barcoding, in situ hybridization, and in situ sequencing allow such molecular readouts to retain their spatial context. Since no technology can provide complete characterization across all layers of biological modalities within the same cell, there is pervasive need for computational cross-modal integration (also called diagonal integration) of single-cell and spatial omics data. For current methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori "linked" features. When such linked features are few or uninformative, a scenario that we call "weak linkage", existing methods fail. We developed MaxFuse, a cross-modal data integration method that, through iterative co-embedding, data smoothing, and cell matching, leverages all information in each modality to obtain high-quality integration. MaxFuse is modality-agnostic and, through comprehensive benchmarks on single-cell and spatial ground-truth multiome datasets, demonstrates high robustness and accuracy in the weak linkage scenario. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, we demonstrate how MaxFuse enables the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

Single-cell computational machine learning approaches to immune-mediated inflammatory disease: New tools uncover novel fibroblast and macrophage interactions driving pathogenesis

Recent advances in single-cell sequencing technologies call for greater computational scalability and sensitivity to analytically decompose diseased tissues and expose meaningful biological relevance in individual cells with high resolution. And while fibroblasts, one of the most abundant cell types in tissues, were long thought to display relative homogeneity, recent analytical and technical advances in single-cell sequencing have exposed wide variation and sub-phenotypes of fibroblasts of potential and apparent clinical significance to inflammatory diseases. Alongside anticipated improvements in single cell spatial sequencing resolution, new computational biology techniques have formed the technical backbone when exploring fibroblast heterogeneity. More robust models are required, however. This review will summarize the key advancements in computational techniques that are being deployed to categorize fibroblast heterogeneity and their interaction with the myeloid compartments in specific biological and clinical contexts. First, typical machine-learning-aided methods such as dimensionality reduction, clustering, and trajectory inference, have exposed the role of fibroblast subpopulations in inflammatory disease pathologies. Second, these techniques, coupled with single-cell predicted computational methods have raised novel interactomes between fibroblasts and macrophages of potential clinical significance to many immune-mediated inflammatory diseases such as rheumatoid arthritis, ulcerative colitis, lupus, systemic sclerosis, and others. Third, recently developed scalable integrative methods have the potential to map cross-cell-type spatial interactions at the single-cell level while cross-tissue analysis with these models reveals shared biological mechanisms between disease contexts. Finally, these advanced computational omics approaches have the potential to be leveraged toward therapeutic strategies that target fibroblast-macrophage interactions in a wide variety of inflammatory diseases.

Macrophages and microglia in glioblastoma: heterogeneity, plasticity, and therapy

Glioblastoma (GBM) is the most aggressive tumor in the central nervous system and contains a highly immunosuppressive tumor microenvironment (TME). Tumor-associated macrophages and microglia (TAMs) are a dominant population of immune cells in the GBM TME that contribute to most GBM hallmarks, including immunosuppression. The understanding of TAMs in GBM has been limited by the lack of powerful tools to characterize them. However, recent progress on single-cell technologies offers an opportunity to precisely characterize TAMs at the single-cell level and identify new TAM subpopulations with specific tumor-modulatory functions in GBM. In this Review, we discuss TAM heterogeneity and plasticity in the TME and summarize current TAM-targeted therapeutic potential in GBM. We anticipate that the use of single-cell technologies followed by functional studies will accelerate the development of novel and effective TAM-targeted therapeutics for GBM patients.

Myeloidcells in the immunosuppressive microenvironment in glioblastoma: The characteristics and therapeutic strategies

Glioblastoma (GBM) is the most common and lethal malignant tumor of the central nervous system in adults. Conventional therapies, including surgery, radiotherapy, and chemotherapy, have limited success in ameliorating patient survival. The immunosuppressive tumor microenvironment, which is infiltrated by a variety of myeloid cells, has been considered a crucial obstacle to current treatment. Recently, immunotherapy, which has achieved great success in hematological malignancies and some solid cancers, has garnered extensive attention for the treatment of GBM. In this review, we will present evidence on the features and functions of different populations of myeloid cells, and on current clinical advances in immunotherapies for glioblastoma.

Spatial architecture of high-grade glioma reveals tumor heterogeneity within distinct domains

BACKGROUND

High-grade gliomas (HGGs) are aggressive primary brain cancers with poor response to standard regimens, driven by immense heterogeneity. In isocitrate dehydrogenase (IDH) wild-type HGG (glioblastoma, GBM), increased intratumoral heterogeneity is associated with more aggressive disease.

METHODS

Spatial technologies can dissect complex heterogeneity within the tumor ecosystem by preserving cellular organization in situ. We employed GeoMx digital spatial profiling, CosMx spatial molecular imaging, Xenium in situ mapping and Visium spatial gene expression in experimental and validation patient cohorts to interrogate the transcriptional landscape in HGG.

RESULTS

Here, we construct a high-resolution molecular map of heterogeneity in GBM and IDH-mutant patient samples to investigate the cellular communities that compose HGG. We uncovered striking diversity in the tumor landscape and degree of spatial heterogeneity within the cellular composition of the tumors. The immune distribution was diverse between samples, however, consistently correlated spatially with distinct tumor cell phenotypes, validated across tumor cohorts. Reconstructing the tumor architecture revealed two distinct niches, one composed of tumor cells that most closely resemble normal glial cells, associated with microglia, and the other niche populated by monocytes and mesenchymal tumor cells.

CONCLUSIONS

This primary study reveals high levels of intratumoral heterogeneity in HGGs, associated with a diverse immune landscape within spatially localized regions.

Dissecting the tumor microenvironment of epigenetically driven gliomas: Opportunities for single-cell and spatial multiomics

Malignant gliomas are incurable brain neoplasms with dismal prognoses and near-universal fatality, with minimal therapeutic progress despite billions of dollars invested in research and clinical trials over the last 2 decades. Many glioma studies have utilized disparate histologic and genomic platforms to characterize the stunning genomic, transcriptomic, and immunologic heterogeneity found in gliomas. Single-cell and spatial omics technologies enable unprecedented characterization of heterogeneity in solid malignancies and provide a granular annotation of transcriptional, epigenetic, and microenvironmental states with limited resected tissue. Heterogeneity in gliomas may be defined, at the broadest levels, by tumors ostensibly driven by epigenetic alterations (IDH- and histone-mutant) versus non-epigenetic tumors (IDH-wild type). Epigenetically driven tumors are defined by remarkable transcriptional programs, immunologically distinct microenvironments, and incompletely understood topography (unique cellular neighborhoods and cell-cell interactions). Thus, these tumors are the ideal substrate for single-cell multiomic technologies to disentangle the complex intra-tumoral features, including differentiation trajectories, tumor-immune cell interactions, and chromatin dysregulation. The current review summarizes the applications of single-cell multiomics to existing datasets of epigenetically driven glioma. More importantly, we discuss future capabilities and applications of novel multiomic strategies to answer outstanding questions, enable the development of potent therapeutic strategies, and improve personalized diagnostics and treatment via digital pathology.

Utility of the Cerebral Organoid Glioma 'GLICO' Model for Screening Applications

Glioblastoma, a grade IV astrocytoma, is regarded as the most aggressive primary brain tumour with an overall median survival of 16.0 months following the standard treatment regimen of surgical resection, followed by radiotherapy and chemotherapy with temozolomide. Despite such intensive treatment, the tumour almost invariably recurs. This poor prognosis has most commonly been attributed to the initiation, propagation, and differentiation of cancer stem cells. Despite the unprecedented advances in biomedical research over the last decade, the current in vitro models are limited at preserving the inter- and intra-tumoural heterogeneity of primary tumours. The ability to understand and manipulate complex cancers such as glioblastoma requires disease models to be clinically and translationally relevant and encompass the cellular heterogeneity of such cancers. Therefore, brain cancer research models need to aim to recapitulate glioblastoma stem cell function, whilst remaining amenable for analysis. Fortunately, the recent development of 3D cultures has overcome some of these challenges, and cerebral organoids are emerging as cutting-edge tools in glioblastoma research. The opportunity to generate cerebral organoids via induced pluripotent stem cells, and to perform co-cultures with patient-derived cancer stem cells (GLICO model), has enabled the analysis of cancer development in a context that better mimics brain tissue architecture. In this article, we review the recent literature on the use of patient-derived glioblastoma organoid models and their applicability for drug screening, as well as provide a potential workflow for screening using the GLICO model. The proposed workflow is practical for use in most laboratories with accessible materials and equipment, a good first pass, and no animal work required. This workflow is also amenable for analysis, with separate measures of invasion, growth, and viability.

Three-Dimensional Cell Culture Systems in Pediatric and Adult Brain Tumor Precision Medicine

Primary brain tumors often possess a high intra- and intertumoral heterogeneity, which fosters insufficient treatment response for high-grade neoplasms, leading to a dismal prognosis. Recent years have seen the emergence of patient-specific three-dimensional in vitro models, including organoids. They can mimic primary parenteral tumors more closely in their histological, transcriptional, and mutational characteristics, thus approximating their intratumoral heterogeneity better. These models have been established for entities including glioblastoma and medulloblastoma. They have proven themselves to be reliable platforms for studying tumor generation, tumor-TME interactions, and prediction of patient-specific responses to establish treatment regimens and new personalized therapeutics. In this review, we outline current 3D cell culture models for adult and pediatric brain tumors, explore their current limitations, and summarize their applications in precision oncology.

New insights into the Immune TME of adult-type diffuse gliomas

PURPOSE OF REVIEW

Adult-type diffuse gliomas are highly heterogeneous tumors. Bulk transcriptome analyses suggested that the composition of the tumor microenvironment (TME) corresponds to genetic and clinical features. In this review, we highlight novel findings on the intratumoral heterogeneity of IDH-wildtype and IDH-mutant gliomas characterized at single-cell resolution, and emphasize the mechanisms shaping the immune TME and therapeutic implications.

RECENT FINDINGS

Emergent evidence indicates that in addition to genetic drivers, epigenetic mechanisms and microenvironmental factors influence the glioma subtypes. Interactions between glioma and immune cells contribute to immune evasion, particularly in aggressive tumors. Spatial and temporal heterogeneity of malignant and immune cell subpopulations is high in recurrent gliomas. IDH-wildtype and IDH-mutant tumors display distinctive changes in their myeloid and lymphoid compartments, and D-2HG produced by IDH-mutant cells impacts the immune TME.

SUMMARY

The comprehensive dissection of the intratumoral ecosystem of human gliomas using single-cell and spatial transcriptomic approaches advances our understanding of the mechanisms underlying the immunosuppressed state of the TME, supports the prognostic value of tumor-associated macrophages and microglial cells, and sheds light on novel therapeutic options.

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.

Visualising spatial heterogeneity in glioblastoma using imaging habitats

Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glioblastoma's imaging features and heterogeneity have focussed on individual imaging biomarkers, or high-throughput radiomic approaches that consider a vast number of imaging variables across the tumour as a whole. Habitat imaging is a novel approach to cancer imaging that identifies tumour regions or 'habitats' based on shared imaging characteristics, usually defined using multiple imaging biomarkers. Habitat imaging reflects the evolution of imaging biomarkers and offers spatially preserved assessment of tumour physiological processes such perfusion and cellularity. This allows for regional assessment of treatment response to facilitate personalised therapy. In this review, we explore different methodologies to derive imaging habitats in glioblastoma, strategies to overcome its technical challenges, contrast experiences to other cancers, and describe potential clinical applications.

Injury programs shape glioblastoma

Glioblastoma is the most common and aggressive primary brain cancer in adults and is almost universally fatal due to its stark therapeutic resistance. During the past decade, although survival has not substantially improved, major advances have been made in our understanding of the underlying biology. It has become clear that these devastating tumors recapitulate features of neurodevelopmental hierarchies which are influenced by the microenvironment. Emerging evidence also highlights a prominent role for injury responses in steering cellular phenotypes and contributing to tumor heterogeneity. This review highlights how the interplay between injury and neurodevelopmental programs impacts on tumor growth, invasion, and treatment resistance, and discusses potential therapeutic considerations in view of these findings.

Lactate dehydrogenases promote glioblastoma growth and invasion via a metabolic symbiosis

Lactate is a central metabolite in brain physiology but also contributes to tumor development. Glioblastoma (GB) is the most common and malignant primary brain tumor in adults, recognized by angiogenic and invasive growth, in addition to its altered metabolism. We show herein that lactate fuels GB anaplerosis by replenishing the tricarboxylic acid (TCA) cycle in absence of glucose. Lactate dehydrogenases (LDHA and LDHB), which we found spatially expressed in GB tissues, catalyze the interconversion of pyruvate and lactate. However, ablation of both LDH isoforms, but not only one, led to a reduction in tumor growth and an increase in mouse survival. Comparative transcriptomics and metabolomics revealed metabolic rewiring involving high oxidative phosphorylation (OXPHOS) in the LDHA/B KO group which sensitized tumors to cranial irradiation, thus improving mouse survival. When mice were treated with the antiepileptic drug stiripentol, which targets LDH activity, tumor growth decreased. Our findings unveil the complex metabolic network in which both LDHA and LDHB are integrated and show that the combined inhibition of LDHA and LDHB strongly sensitizes GB to therapy.

MELK is a prognostic biomarker and correlated with immune infiltration in glioma

Objective

Glioma accounts for the vast majority of primary brain tumors with inevitable recurrence and poor prognosis. Maternal embryonic leucine zipper kinase (MELK) is overexpressed in multiple human tumors and could activate a variety of oncogenic-associated signal pathways. However, its role in the glioma microenvironment is still largely unknown.

Methods

We collected the RNA sequence data and clinical information of gliomas from the Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) databases, and investigated MELK expression and its correlation with clinicopathologic features and prognosis in glioma. Moreover, the relationship between MELK expression and immune cell infiltration in the tumor microenvironment of gliomas was explored through single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT. In addition, gene set enrichment analysis (GSEA) and Metascape online analysis were performed to find out signaling pathways enriched by differentially expressed genes (DEGs) between high- and low-MELK expression groups. Finally, immunohistochemistry was performed to validate our findings.

Results

Data analysis of CGGA and GEO datasets showed that MELK was significantly upregulated in gliomas than in normal brain tissues, and MELK expression was obviously correlated with clinicopathologic features, including age, WHO grade, histological subtype, IDH mutant status, 1p19q codeletion status, and PRS type. Stratified analysis, Cox regression analysis, and nomogram model revealed that high expression of MELK predicted poor survival; hence, MELK could serve as an independent prognostic biomarker for glioma. Moreover, results from enrichment pathway analysis indicated that the immune system process, angiogenesis, apoptosis, cell cycle, and other oncogenic-related signal pathways were significantly enriched between high- and low-MELK expression groups. Immune infiltration analysis demonstrated that increased MELK expression was significantly correlated with higher immune scores, higher fractions of immunocytes (T cells, NK cells resting, macrophages, resting mast cells, and neutrophils), and higher expression levels of immune checkpoints (B7-H3, CTLA4, LAG3, PD-1, PD-L1, and TIM3). Finally, immunohistochemistry analysis validated our findings that high expression of MELK relates to increased malignancy and poor prognosis of glioma.

Conclusion

Our findings identified that MELK could act as an independent prognostic indicator and potential immunotherapy target for glioma. In conclusion, these findings suggested that DDOST mediated the immunosuppressive microenvironment of gliomas and could be an important biomarker in diagnosing and treating gliomas.

Integration of single-cell RNA sequencing and spatial transcriptomics to reveal the glioblastoma heterogeneity

Glioblastoma (GBM), a deadly brain tumor, is still one of a few lasting challenges of contemporary oncology. Current therapies fail to significantly improve patient survival due to GBM tremendous genetic, transcriptomic, immunological, and sex-dependent heterogeneity. Over the years, clinical differences between males and females were characterized. For instance, higher incidence of GBM in males or distinct responses to cancer chemotherapy and immunotherapy between males and females have been noted. Despite the introduction of single-cell RNA sequencing and spatial transcriptomics, these differences were not further investigated as studies were focused only on revealing the general picture of GBM heterogeneity. Hence, in this mini-review, we summarized the current state of knowledge on GBM heterogeneity revealed by single-cell RNA sequencing and spatial transcriptomics with regard to genetics, immunology, and sex-dependent differences. Additionally, we highlighted future research directions which would fill the gap of knowledge on the impact of patient's sex on the disease outcome.

Integration of single-cell RNA sequencing and spatial transcriptomics to reveal the glioblastoma heterogeneity

Glioblastoma (GBM), a deadly brain tumor, is still one of the few lasting challenges of contemporary oncology. Current therapies fail to significantly improve patient survival due to GBM's tremendous genetic, transcriptomic, immunological, and sex-dependent heterogeneity. Over the years, clinical differences between males and females were characterized. For instance, higher incidence of GBM in males or distinct responses to cancer chemotherapy and immunotherapy between males and females have been noted. However, despite the introduction of single-cell RNA sequencing and spatial transcriptomics, these differences were not further investigated as studies were focused only on exposing the general picture of GBM heterogeneity. Hence, in this study, we summarized the current state of knowledge on GBM heterogeneity exposed by single-cell RNA sequencing and spatial transcriptomics with regard to genetics, immunology, and sex-dependent differences. Additionally, we highlighted future research directions which would fill the gap of knowledge on the impact of patient's sex on the disease outcome.

Spatial transcriptomics technology in cancer research

In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.

Roles of Chromatin Remodelling and Molecular Heterogeneity in Therapy Resistance in Glioblastoma

Cancer stem cells (CSCs) represent a therapy-resistant reservoir in glioblastoma (GBM). It is now becoming clear that epigenetic and chromatin remodelling programs link the stemlike behaviour of CSCs to their treatment resistance. New evidence indicates that the epigenome of GBM cells is shaped by intrinsic and extrinsic factors, including their genetic makeup, their interactions and communication with other neoplastic and non-neoplastic cells, including immune cells, and their metabolic niche. In this review, we explore how all these factors contribute to epigenomic heterogeneity in a tumour and the selection of therapy-resistant cells. Lastly, we discuss current and emerging experimental platforms aimed at precisely understanding the epigenetic mechanisms of therapy resistance that ultimately lead to tumour relapse. Given the growing arsenal of drugs that target epigenetic enzymes, our review addresses promising preclinical and clinical applications of epidrugs to treat GBM, and possible mechanisms of resistance that need to be overcome.

Transcriptome Analysis Identifies Accumulation of Natural Killer Cells with Enhanced Lymphotoxin-β Expression during Glioblastoma Progression

Glioblastomas are the most common primary brain tumors. Despite extensive clinical and molecular insights into these tumors, the prognosis remains dismal. While targeted immunotherapies have shown remarkable success across different non-brain tumor entities, they failed to show efficacy in glioblastomas. These failures prompted the field to reassess the idiosyncrasies of the glioblastoma microenvironment. Several high-dimensional single-cell RNA sequencing studies generated remarkable findings about glioblastoma-associated immune cells. To build on the collective strength of these studies, we integrated several murine and human datasets that profiled glioblastoma-associated immune cells at different time points. We integrated these datasets and utilized state-of-the-art algorithms to investigate them in a hypothesis-free, purely exploratory approach. We identified a robust accumulation of a natural killer cell subset that was characterized by a downregulation of activation-associated genes with a concomitant upregulation of apoptosis genes. In both species, we found a robust upregulation of the Lymphotoxin-β gene, a cytokine from the TNF superfamily and a key factor for the development of adaptive immunity. Further validation analyses uncovered a correlation of lymphotoxin signaling with mesenchymal-like glioblastoma regions in situ and in TCGA and CGGA glioblastoma cohorts. In summary, we identify lymphotoxin signaling as a potential therapeutic target in glioblastoma-associated natural killer cells.

Characterizing the biology of primary brain tumors and their microenvironment via single-cell profiling methods

Genomic and transcriptional heterogeneity is prevalent among the most common and aggressive primary brain tumors in children and adults. Over the past 20 years, advances in bioengineering, biochemistry and bioinformatics have enabled the development of an array of techniques to study tumor biology at single-cell resolution. The application of these techniques to study primary brain tumors has helped advance our understanding of their intra-tumoral heterogeneity and uncover new insights regarding their co-option of developmental programs and signaling from their microenvironment to promote tumor proliferation and invasion. These insights are currently being harnessed to develop new therapeutic approaches. Here we provide an overview of current single-cell techniques and discuss relevant biology and therapeutic insights uncovered by their application to primary brain tumors in children and adults.

Exploring glioblastoma stem cell heterogeneity: Immune microenvironment modulation and therapeutic opportunities

Despite its growing use in cancer treatment, immunotherapy has been virtually ineffective in clinical trials for gliomas. The inherently cold tumor immune microenvironment (TIME) in gliomas, characterized by a high ratio of pro-tumor to anti-tumor immune cell infiltrates, acts as a seemingly insurmountable barrier to immunotherapy. Glioma stem cells (GSCs) within these tumors are key contributors to this cold TIME, often functioning indirectly through activation and recruitment of pro-tumor immune cell types. Furthermore, drivers of GSC plasticity and heterogeneity (e.g., reprogramming transcription factors, epigenetic modifications) are associated with induction of immunosuppressive cell states. Recent studies have identified GSC-intrinsic mechanisms, including functional mimicry of immune suppressive cell types, as key determinants of anti-tumor immune escape. In this review, we cover recent advancements in our understanding of GSC-intrinsic mechanisms that modulate GSC-TIME interactions and discuss cutting-edge techniques and bioinformatics platforms available to study immune modulation at high cellular resolution with exploration of both malignant (i.e., GSC) and non-malignant (i.e., immune) cell fractions. Finally, we provide insight into the therapeutic opportunities for targeting immunomodulatory GSC-intrinsic mechanisms to potentiate immunotherapy response in gliomas.

Cell-Type Dependent Regulation of the Electrogenic Na+/HCO3- Cotransporter 1 (NBCe1) by Hypoxia and Acidosis in Glioblastoma

Glioblastoma multiforme (GBM) is the most common and malignant brain tumour. It is characterised by transcriptionally distinct cell populations. In tumour cells, physiological pH gradients between the intracellular and extracellular compartments are reversed, compared to non-cancer cells. Intracellular pH in tumour cells is alkaline, whereas extracellular pH is acidic. Consequently, the function and/or expression of pH regulating transporters might be altered. Here, we investigated protein expression and regulation of the electrogenic sodium/bicarbonate cotransporter 1 (NBCe1) in mesenchymal (MES)-like hypoxia-dependent and -independent cells, as well as in astrocyte-like glioblastoma cells following chemical hypoxia, acidosis and elucidated putative underlying molecular pathways. Immunoblotting, immunocytochemistry, and intracellular pH recording with the H⁺-sensitive dye 2′,7′-bis-(carboxyethyl)-5-(and-6)-carboxyfluorescein were applied. The results show NBCe1 protein abundance and active NBCe1 transport. Hypoxia upregulated NBCe1 protein and activity in MES-like hypoxia-dependent GBM cells. This effect was positively correlated with HIF-1α protein levels, was mediated by TGF-β signalling, and was prevented by extracellular acidosis. In MES-like hypoxia-independent GBM cells, acidosis (but not hypoxia) regulated NBCe1 activity in an HIF-1α-independent manner. These results demonstrate a cell-specific adaptation of NBCe1 expression and activity to the microenvironment challenge of hypoxia and acidosis that depends on their transcriptional signature in GBM.