(C) PLOS One This story was originally published by PLOS One and is unaltered. . . . . . . . . . . Understanding glioblastoma at the single-cell level: Recent advances and future challenges [1] ['Yahaya A Yabo', 'Translational Neurosurgery', 'Friedrich-Alexander University Erlangen-Nürnberg', 'Erlangen', 'Microenvironment', 'Immunology Research Laboratory', 'Dieter Henrik Heiland', 'Department Of Neurosurgery', 'University Hospital Erlangen', 'Faculty Of Medicine'] Date: 2024-06 Glioblastoma, the most aggressive and prevalent form of primary brain tumor, is characterized by rapid growth, diffuse infiltration, and resistance to therapies. Intrinsic heterogeneity and cellular plasticity contribute to its rapid progression under therapy; therefore, there is a need to fully understand these tumors at a single-cell level. Over the past decade, single-cell transcriptomics has enabled the molecular characterization of individual cells within glioblastomas, providing previously unattainable insights into the genetic and molecular features that drive tumorigenesis, disease progression, and therapy resistance. However, despite advances in single-cell technologies, challenges such as high costs, complex data analysis and interpretation, and difficulties in translating findings into clinical practice persist. As single-cell technologies are developed further, more insights into the cellular and molecular heterogeneity of glioblastomas are expected, which will help guide the development of personalized and effective therapies, thereby improving prognosis and quality of life for patients. As these technologies evolve and mature, they will likely offer more insights into the cellular and molecular heterogeneity of glioblastoma that are needed for modern personalized treatment strategies. The goal is that these advances will eventually translate into improved prognosis and quality of life for patients with glioblastoma. In this Essay, we therefore delve into recent advances, emerging technologies, and novel algorithms in the realm of single-cell genomics for glioblastoma. We focus on the paradigm shift from traditional single-cell transcriptomics to the use of integrated multiomics datasets in predictive modeling that will ultimately help to improve clinical decision processes. Our aim is to discuss the evolving landscape of single-cell genomics in glioblastoma research and provide a comprehensive overview, while highlighting the potential of these cutting-edge technologies to reshape our understanding and accelerate drug discovery and treatment success in glioblastoma, and CNS tumors in general. The benefits of single-cell genomics technologies are numerous, but major challenges hinder their use in glioblastoma research [ 18 , 19 ]. The high cost of these technologies and complexity of data analyses and interpretation, as well as the difficulty in translating findings to improve the decision-making process in clinical practice are among the hurdles researchers in this field continue to face. Up to now, only a limited number of breakthroughs in neuro-oncology and oncology research at large have been driven by single-cell genomics, leading to a somewhat critical perspective on this technology. While the potential value and opportunities presented by single-cell genomics are significant, there is an urgent need for improvements in integrating these technologies with functional validation processes to advance the field. The complexity and heterogeneity of glioblastomas has long been an obstacle to therapeutic advances, but novel technologies are offering a new way to understand these complex malignancies [ 2 ]. Among these, single-cell and spatially resolved transcriptomics have emerged as the most promising tools. These innovative approaches allow for the molecular characterization of individual cells within complex tissues, offering a level of detail previously unattainable in cancer research. Single-cell genomic studies are providing unprecedented insights into the molecular underpinnings of tumor heterogeneity and complexity to aid the development of more precisely targeted and effective therapeutic strategies. Recent studies have revealed the cellular heterogeneity and plasticity of cancer cells, their developmental trajectories, and complex interaction within the tumor ecosystem [ 8 , 12 , 14 – 16 ]. By examining individual cells, researchers can discern specific genetic and molecular features driving tumorigenesis, disease progression, and therapy resistance. These technologies have shed light on the diverse cellular landscapes within glioblastomas, illuminating the presence of distinct cellular subpopulations that contribute to tumor heterogeneity and evolution. Furthermore, novel techniques and algorithms are continually being developed to enhance the quality and interpretability of single-cell genomics data [ 17 , 18 ]. Glioblastoma is characterized by extensive inter- and intratumoral heterogeneity that was initially thought to be established by glioma stem cells (GSCs) or tumor-initiating cells. GSCs with self-renewal and differentiation capacity have traditionally been associated with tumor initiation, progression, and recurrence [ 4 – 6 ]. However, recent advances in the field have challenged the GSC model and instead highlighted the concept of cellular plasticity [ 7 – 11 ]. Glioblastoma cells exhibit a gradient of transcriptomic states and possess the ability to dynamically transit between GSC-like and differentiated states in response to microenvironmental cues and therapeutic pressure [ 12 ]. The plasticity theory therefore challenges the traditional hierarchical model of GSCs by suggesting a more adaptable and reversible tumor ecosystem [ 13 ]. This paradigm shift towards understanding the interactions between GSCs and cellular plasticity opens new avenues for targeting the elusive and dynamic nature of glioblastoma. Therapy of central nervous system (CNS) tumors, which include various malignancies affecting the brain and spinal cord, poses a remarkable challenge in the field of neuro-oncology [ 1 ]. Within this group, glioblastoma, the most aggressive and prevalent form of primary brain tumor, represents an as yet incurable disease [ 2 ]. This malignancy is known for its rapid growth and diffuse infiltration, making it highly resistant to most adjuvant therapeutic strategies. Moreover, despite the strides made in surgical and medical treatment modalities, the prognosis for patients with glioblastoma remains poor, with a modest median survival time of approximately 15 months [ 3 ]. Understanding CNS tumors at a single-cell level Tumor cell heterogeneity and plasticity Malignant brain tumors of the CNS are known for their intratumoral heterogeneity, which contributes to therapy resistance and disease recurrence [13]. Single-cell transcriptomics has emerged as a powerful tool for studying tumor heterogeneity, enabling the identification of cellular states, trajectories, and plasticity, spanning from cancer stem-like cells to differentiated tumor cells. In the early phase of the single-cell era, single-cell RNA sequencing (scRNA-seq) was used to map the heterogeneity of glioblastoma, revealing a degree of complexity and variation within these tumors that had not been fully appreciated before [14]. The previously described transcriptional phenotypes from bulk transcriptome analysis, namely, “classical,” “mesenchymal,” and “proneural,” were shown to be highly heterogeneously distributed within the tumors [20]. These investigations opened the doors for a deeper exploration of other CNS malignancies, including oligodendroglioma, where research revealed a distinct developmental hierarchy [21] that highlighted the potential for tumors to evolve and progress over time in a manner reminiscent of normal brain development. In isocitrate dehydrogenase-mutated gliomas, the influences of genetic alterations, cell lineage, and the tumor ecosystem were explored [22]. The results of this study suggest that the evolution of gliomas is not only shaped by their genetic anomalies, but also importantly stems from their cellular origins and microenvironmental interplay. Further emphasizing the role of development in glioma, an investigation into H3K27M-gliomas revealed both developmental and oncogenic processes to be important within the tumor [23]. Further research has revealed that glioblastomas mirror a normal neurodevelopmental hierarchy, suggesting that the tumor may hijack developmental pathways for its proliferation [8]. Investigating the disease from a spatial perspective, scRNA-seq has been used to study infiltrating neoplastic cells at the migrating front of human glioblastoma [24]. These data added to our understanding of how these tumors infiltrate into surrounding brain tissue. The most comprehensive single-cell study to date has suggested that glioblastoma cells exist in 4 distinct cellular states, namely, astrocyte-like (AC-like), mesenchymal-like (MES-like), oligodendrocyte progenitor cell-like (OPC-like), and neural progenitor cell-like (NPC-like), each of which mirrors a different developmental lineage [12]. Using mouse models, the authors show that these cellular states demonstrate a high degree of plasticity and are shaped by the tumor microenvironment (TME). The relative frequency of these cellular states in a glioblastoma tissue sample can be influenced by specific genetic amplifications and mutations, underlining the genetic complexity of the disease [12] (Fig 1). Through the technological advantage of spatially resolved transcriptomics, a recent study demonstrated that these 4 states are heterogeneously distributed across spatial niches and are linked to loco-regional inflammation and metabolic stress [16]. PPT PowerPoint slide PNG larger image TIFF original image Download: Fig 1. Complex interactions in the glioblastoma tumor microenvironment. Illustration of cellular differentiation and dynamic adaptation, as well as interactions in the complex microenvironment of glioblastomas. AC, astrocyte; CAF, cancer-associated fibroblast; HGF, hepatocyte growth factor; IL-10, interleukin 10; JAK, Janus kinase; MES, mesenchymal; NPC, neural progenitor cell; OPC, oligodendrocyte progenitor cell; PDGF, platelet-derived growth factor; PD-L1, programmed death-ligand 1; STAT, signal transducer and activator of transcription; TAM, tumor-associated macrophage; TGFβ, transforming growth factor β. https://doi.org/10.1371/journal.pbio.3002640.g001 In addition to the investigation of primary tumors, recent research has focused on the evolution of glioblastoma and alterations during tumor progression and resistance. A single-cell atlas of glioblastoma evolution under therapy has been constructed, which revealed both cell-intrinsic and cell-extrinsic therapeutic vulnerabilities [25]. This study, along with another report [26], elegantly demonstrated the enhanced abundance of MES-like glioblastoma cells and increased inflammation within the TME. These results further elucidate the dynamic nature of glioblastoma and underscore the critical role of the TME, especially the immune system and neural stem cells, in modulating the response to therapy. The tumor microenvironment in glioblastoma The TME, comprising a complex network of nontumor cells such as endothelial cells, immune cells, and astrocytes, as well as extracellular matrix components, has a crucial role in the progression of glioblastoma. It actively interacts with glioblastoma cells, influencing tumor growth, infiltration, and resistance to therapy (Fig 1). Single-cell genomics has emerged as a transformative tool in studying the TME, enabling a granular view of cellular heterogeneity. Recent studies in both tissue samples from patients with glioblastoma and in mouse models of the disease have started to unravel the composition of the glioblastoma TME [27,28]. The immune microenvironment in glioblastoma is characterized by a state of immunosuppression, often marked by the presence of tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells, which promote tumor progression [27,28]. Similarly, the contribution of the vascular microenvironment is also noteworthy, as glioblastomas exhibit robust angiogenesis. Several studies have provided insights into the dynamic and complex interactions within the TME of glioblastoma, focusing specifically on the role of myeloid and other immune cells [29–31]. These studies have demonstrated that myeloid cells drive the transformation towards MES-like states in glioblastoma [31], highlighting the phenotypic plasticity of cancer cells and furthering our understanding of the complex heterogeneity that is characteristic of glioblastoma. Expanding on this, tumor cells were shown to acquire myeloid-affiliated transcriptional programs through a process known as epigenetic immunoediting to elicit immune evasion [29]. By adopting this strategy, glioblastoma can evade detection and subsequent destruction by the immune system. Furthermore, myeloid cells in glioblastoma were profiled at the single-cell level showing a remarkable degree of macrophage competition and specialization, illuminating the diversity and adaptability of myeloid cells in response to tumor evolution [28]. These findings contribute to a growing body of evidence suggesting that the TME is a dynamic and adaptive system in which macrophages, for example, might adjust their behavior based on signals from their surroundings. Further broadening our understanding of the glioblastoma microenvironment, an in-depth investigation into the states of microglia, the resident immune cells of the brain, was performed through the integration of multiple high-dimensional techniques [30]. The findings of this study provide valuable insights into the functional diversity of microglia in both health and disease, further emphasizing the importance of immune elements in glioblastoma pathophysiology. scRNA-seq and cellular indexing of transcriptomes and epitopes sequencing (CITE-seq) profiling of tissue samples from patients with glioblastoma and from mouse models of glioma revealed significant compositional and expression differences between myeloid cells from males and females [27]. In the patient samples, the proportion of various cellular populations varied between men and women, with men having a higher proportion of bone marrow-derived macrophages (BDMs) and women having a higher number of microglia. Microglia and a subset of BDMs from men differentially expressed MHC class II genes in comparison to those from women. Further analysis confirmed a higher enrichment of tumor-supportive genes, in addition to MHC class II and costimulatory molecule PD-L1 expression, in men, while the myeloid cells from women were enriched for interferon gene expression. These results suggest that the difference in survival between men and women with glioblastoma may be linked to the identified differences in myeloid cell composition and gene expression. This indicates that sex difference is an important variable that should be properly accounted for in the analysis of transcriptomic data to rule out any potential bias that may be introduced by the sex of the patients. A study investigated glioma-infiltrating T cells using single-cell transcriptomics and T cell receptor (TCR) sequencing, leading to the identification of CD161 as an inhibitory receptor [32]. This receptor represents a previously unrecognized aspect of the immunosuppressive TME in glioblastomas. The presence of this inhibitory receptor explains some of the challenges encountered in stimulating effective antitumor immune responses and suggests that therapeutic approaches may need to consider methods for blocking or circumventing this inhibitory signal. Another study examined the factors contributing to T cell dysfunction in the glioblastoma TME [33]. The authors demonstrated that a defined subpopulation of heme oxygenase 1-expressing myeloid cells release the cytokine interleukin 10 (IL-10), which, in turn, mediates T cell dysfunction. The precise elucidation of immune suppression mechanisms at play within the glioblastoma TME underscores the importance of understanding cellular communication and interaction within the tumor ecosystem. Single-cell sequencing has also been instrumental to our current understanding of the complexity of neuron–glioma interactions by helping researchers to identify and describe the role of the different subpopulations involved in glioblastoma infiltration and colonization of the brain. A subpopulation of glioma cells with neuronal and neural progenitor-like cell states were found to integrate into neuronal circuits and co-opt neuronal mechanisms to fuel tumor progression and invasion in xenograft models [34]. scRNA-seq was also used to delineate glioblastoma tumors into high functionally connected (HFC) and low functionally connected (LFC) regions [35]. HFC regions had a higher expression of THBS1, which facilitates the formation of neuron–glioma connectivity, higher tumor growth, and lower survival, compared to tumors in LFC regions in glioblastoma xenografted mouse models. Interestingly, THBS1 was expressed in a compensatory manner by astrocytes and myeloid cells within the TME in the LFC tumors. These findings shed further light on the earlier findings that glioma cells physically form neuron–glioma synapses to drive tumor growth [34,36]. In glioblastoma, the influence of structural cells such as fibroblasts, pericytes, and glial cells on the growth and differentiation of tumor cells and signaling towards other cells in the tumor ecosystem is of increasing interest. Recent studies have demonstrated that tumor-associated reactive astrocytes have a key role in the evolution of an immunosuppressive environment in glioblastoma. This suggests the potential for targeting these astrocytes to alleviate immunosuppression and enhance the effectiveness of immunotherapies for treatment [37]. Immunometabolic regulation also influences the TME and drives glioblastoma pathogenicity in genetically engineered glioblastoma mouse models, emphasizing the crucial role of metabolic interactions between astrocytes and glioblastoma cells during tumor progression [38]. By integrating scRNA-seq and spatially resolved transcriptomics data, the role of cancer-associated fibroblasts (CAFs) in promoting glioblastoma has been explored. The results of this study underscore the protumoral effects of CAFs, suggesting that these cells may be a viable target for therapeutic intervention [39]. An additional interesting angle is provided by a study that illustrated the association between tumor-associated hematopoietic stem and progenitor cells and glioblastoma progression [40]. Taken together, these studies highlight the importance of the various nonmalignant cells in the TME and the need for investigating the intricate interplay between glioblastoma cells and various immune components within the TME, and their role in influencing disease progression and treatment response. The spatial architecture of the glioblastoma ecosystem Single-cell genomics data, without spatial context, are limited due to their lack of information regarding the natural embedding of cells within the complex tumor ecosystem. Spatially resolved transcriptomics data, on the other hand, are either restricted in resolution (array-based methods) or biased by a predefined gene panel (in situ sequencing). Both limitations make it currently inevitable to integrate both technologies to infer the spatial architecture of glioblastomas. Most recent studies have used array-based spatial transcriptomics or multiomics to investigate recurrent regional expression patterns across samples from patients with glioblastoma. All studies confirmed that the tumor landscape is shaped by either “reactive” niches, in which the cancer cells respond to inflammatory signals or metabolic stress, or “nonreactive” niches, which mainly contain tumor cells in developmental stages such as NPC-like or OPC-like cells that more frequently interact with the neuronal environment of the brain [16,41,42]. Each of these distinct niches consists of a defined cellular neighborhood, which can be used to predict clinical behavior [43] or response to therapy [44]. A recent spatial transcriptomics study profiled the TCR repertoires of patients with glioblastoma to further our understanding of the limited presence of T cells in glioblastomas [45]. By adding histopathological characterization and signatures of tumor regions from bulk RNA-seq profiling of glioblastoma, the authors found that T cell functional diversity was associated with different tumor niches. The addition of metabolomic information further helped in segregating different T cell phenotypes. This study demonstrates that the current resolution of spatial transcriptomics can be augmented with scRNA-seq, bulk transcriptomics, metabolomics, or imaging data to deconvolute individual cells and identify their interacting partners within tumor niches. [END] --- [1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002640 Published and (C) by PLOS One Content appears here under this condition or license: Creative Commons - Attribution BY 4.0. via Magical.Fish Gopher News Feeds: gopher://magical.fish/1/feeds/news/plosone/