Compartments in medulloblastoma with extensive nodularity are connected through differentiation along the granular precursor lineage

A simplified preparation method for single-nucleus RNA-sequencing using long-term frozen brain tumor tissues

Single-cell RNA-sequencing has provided intriguing new insights into research areas such as developmental processes and tumor heterogeneity. Most approaches, however, rely on the availability of fresh surgical specimens, thereby dramatically reducing the ability to profile particularly rare tissue types. Here, we optimized a method to isolate intact nuclei from long-term frozen pediatric glioma tissues. We performed a technical comparison between different single-nucleus RNA-sequencing (snRNA-seq) systems and applied the established nucleus isolation method to analyze frozen primary glioma tissues. The results show that our fast, simple and low-cost nuclear isolation protocol provides intact nuclei, which can be used in both droplet- and plate-based single-cell sequencing platforms - allowing the identification of distinct tumor cell populations and infiltrating microglia. Additional optimization to include shorter RNA fragments in the 3' sequencing library improved gene detection and cell type annotation. Taken together, the method dramatically increases the potential of studying rare tumor entities and is specifically tailored for using frozen brain tumor tissue.

Mitigating ambient RNA and doublets effects on single cell transcriptomics analysis in cancer research

In cancer biology, where understanding the tumor microenvironment at high resolution is vital, ambient RNA contamination becomes a considerable problem. This hinders accurate delineation of intratumoral heterogeneity, complicates the identification of potential biomarkers, and decelerates advancements in precision oncology. To solve this problem, several computational approaches are created to determine the ambient RNA contribution from scRNA-seq datasets. Techniques like SoupX and DecontX assist in assessing and eliminating ambient RNA contamination from primary gene expression profiles. Practical solutions like CellBender employ deep learning techniques to concurrently address ambient RNA contamination and background noise, offering a contemporary end-to-end strategy for data preparation. This high-quality, reliable data enables clinicians and researchers to make effective decisions that will help ensure interventions are rooted in reproducible evidence, giving hope for developing more effective targeted therapies.

The intersections between neuroscience and medulloblastoma

Medulloblastoma (MB) represents the most common malignant central nervous system tumor in childhood. The nervous system plays a critical role in the progression of MB, with interactions between the nervous system and cancer significantly influencing oncogenesis, tumor growth, invasion, stemness, and metabolism. These interactions also regulate angiogenesis, metastatic dissemination, the tumor immune microenvironment, and drug resistance. Investigating the nervous system-MB axis holds promise for identifying diagnostic markers, prognostic biomarkers, and therapeutic targets. It also provides insights into the molecular mechanisms underlying MB and informs the development of novel therapeutic strategies. This review summarizes the latest advancements in understanding the interplay between the nervous system and MB, including the role of glial cells in MB and the potential of drug repurposing targeting nervous system components for MB treatment. These findings underscore promising diagnostic and therapeutic opportunities for MB management. Additionally, we outline future research directions in neurosciences that may pave the way for innovative therapeutic approaches and deepen our understanding of this complex disease.

Characterization of single neurons reprogrammed by pancreatic cancer

The peripheral nervous system (PNS) orchestrates organ function in health and disease. Most cancers, including pancreatic ductal adenocarcinoma (PDAC), are infiltrated by PNS neurons, and this contributes to the complex tumour microenvironment (TME)1,2. However, neuronal cell bodies reside in various PNS ganglia, far from the tumour mass. Thus, cancer-innervating or healthy-organ-innervating neurons are lacking in current tissue-sequencing datasets. To molecularly characterize pancreas- and PDAC-innervating neurons at single-cell resolution, we developed Trace-n-Seq. This method uses retrograde tracing of axons from tissues to their respective ganglia, followed by single-cell isolation and transcriptomic analysis. By characterizing more than 5,000 individual sympathetic and sensory neurons, with about 4,000 innervating PDAC or healthy pancreas, we reveal novel neuronal cell types and molecular networks that are distinct to the pancreas, pancreatitis, PDAC or melanoma metastasis. We integrate single-cell datasets of innervating neurons and the TME to establish a neuron-cancer-microenvironment interactome, delineate cancer-driven neuronal reprogramming and generate a pancreatic-cancer nerve signature. Pharmacological denervation induces a pro-inflammatory TME and increases the effectiveness of immune-checkpoint inhibitors. The taxane nab-paclitaxel causes intratumoral neuropathy, which attenuates PDAC growth and, in combination with sympathetic denervation, results in synergistic tumour regression. Our multi-dimensional data provide insights into the networks and functions of PDAC-innervating neurons, and support the inclusion of denervation in future therapies.

Arrested development: the dysfunctional life history of medulloblastoma

Medulloblastoma is a heterogeneous embryonal tumor of the cerebellum comprised of four distinct molecular subgroups that differ in their developmental origins, genomic landscapes, clinical presentation, and survival. Recent characterization of the human fetal cerebellum at single-cell resolution has propelled unprecedented insights into the cellular origins of medulloblastoma subgroups, including those underlying previously elusive groups 3 and 4. In this review, the molecular pathogenesis of medulloblastoma is examined through the lens of cerebellar development. In addition, we discuss how enhanced understanding of medulloblastoma origins has the potential to refine disease modeling for the advancement of treatment and outcomes.

MAP4 kinase-regulated reduced CLSTN1 expression in medulloblastoma is associated with increased invasiveness

De-regulated protein expression contributes to tumor growth and progression in medulloblastoma (MB), the most common malignant brain tumor in children. MB is associated with impaired differentiation of specific neural progenitors, suggesting that the deregulation of proteins involved in neural physiology could contribute to the transformed phenotype in MB. Calsynthenin 1 (CLSTN1) is a neuronal protein involved in cell-cell interaction, vesicle trafficking, and synaptic signaling. We previously identified CLSTN1 as a putative target of the pro-invasive kinase MAP4K4, which we found to reduce CLSTN1 surface expression. Herein, we explored the expression and functional significance of CLSTN1 in MB. We found that CLSTN1 expression is decreased in primary MB tumors compared to tumor-free cerebellum or brain tissues. CLSTN1 is expressed in laboratory-established MB cell lines, where it localized to the plasma membrane, intracellular vesicular structures, and regions of cell-cell contact. The reduction of CLSTN1 expression significantly increased growth factor-driven invasiveness. Pharmacological inhibition of pro-migratory MAP4 kinases caused increased CLSTN1 expression and CLSTN1 accumulation in cell-cell contacts. Co-culture of tumor cells with astrocytes increased CLSTN1 localization in cell-cell contacts, which was further enhanced by MAP4K inhibition. Our study revealed a repressive function of CLSTN1 in growth-factor-driven invasiveness in MB, identified MAP4 kinases as repressors of CLSTN1 recruitment to cell-cell contacts, and points towards CLSTN1 implication in the kinase-controlled regulation of tumor-microenvironment interaction.

Spatial omics techniques and data analysis for cancer immunotherapy applications

In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.