Single-cell transcriptomics identifies potential cells of origin of MYC rhabdoid tumors.

Rhabdoid tumors (RT) are rare and highly aggressive pediatric neoplasms. Their epigenetically-driven intertumoral heterogeneity is well described; however, the cellular origin of RT remains an enigma. Here, we establish and characterize different genetically engineered mouse models driven under the control of distinct promoters and being active in early progenitor cell types with diverse embryonic onsets. From all models only Sox2-positive progenitor cells give rise to murine RT. Using single-cell analyses, we identify distinct cells of origin for the SHH and MYC subgroups of RT, rooting in early stages of embryogenesis. Intra- and extracranial MYC tumors harbor common genetic programs and potentially originate from fetal primordial germ cells (PGCs). Using PGC specific Smarcb1 knockout mouse models we validate that MYC RT originate from these progenitor cells. We uncover an epigenetic imbalance in MYC tumors compared to PGCs being sustained by epigenetically-driven subpopulations. Importantly, treatments with the DNA demethylating agent decitabine successfully impair tumor growth in vitro and in vivo. In summary, our work sheds light on the origin of RT and supports the clinical relevance of DNA methyltransferase inhibitors against this disease.

InterCellar enables interactive analysis and exploration of cell-cell communication in single-cell transcriptomic data.

Deciphering cell-cell communication is a key step in understanding the physiology and pathology of multicellular systems. Recent advances in single-cell transcriptomics have contributed to unraveling the cellular composition of tissues and enabled the development of computational algorithms to predict cellular communication mediated by ligand-receptor interactions. Despite the existence of various tools capable of inferring cell-cell interactions from single-cell RNA sequencing data, the analysis and interpretation of the biological signals often require deep computational expertize. Here we present InterCellar, an interactive platform empowering lab-scientists to analyze and explore predicted cell-cell communication without requiring programming skills. InterCellar guides the biological interpretation through customized analysis steps, multiple visualization options, and the possibility to link biological pathways to ligand-receptor interactions. Alongside convenient data exploration features, InterCellar implements data-driven analyses including the possibility to compare cell-cell communication from multiple conditions. By analyzing COVID-19 and melanoma cell-cell interactions, we show that InterCellar resolves data-driven patterns of communication and highlights molecular signals through the integration of biological functions and pathways. We believe our user-friendly, interactive platform will help streamline the analysis of cell-cell communication and facilitate hypothesis generation in diverse biological systems.

Mapping single-cell data to reference atlases by transfer learning.

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.

An extracellular vesicle-related gene expression signature identifies high-risk patients in medulloblastoma.

BACKGROUND: Medulloblastoma (MB) is a malignant brain tumor in childhood. It comprises 4 subgroups with different clinical behaviors. The aim of this study was to characterize the transcriptomic landscape of MB, both at the level of individual tumors as well as in large patient cohorts. METHODS: We used a combination of single-cell transcriptomics, cell culture models and biophysical methods such as nanoparticle tracking analysis and electron microscopy to investigate intercellular communication in the MB tumor niche. RESULTS: Tumor cells of the sonic hedgehog (SHH)-MB subgroup show a differentiation blockade. These cells undergo extensive metabolic reprogramming. The gene expression profiles of individual tumor cells show a partial convergence with those of tumor-associated glial and immune cells. One possible cause is the transfer of extracellular vesicles (EVs) between cells in the tumor niche. We were able to detect EVs in co-culture models of MB tumor cells and oligodendrocytes. We also identified a gene expression signature, EVS, which shows overlap with the proteome profile of large oncosomes from prostate cancer cells. This signature is also present in MB patient samples. A high EVS expression is one common characteristic of tumors that occur in high-risk patients from different MB subgroups or subtypes. CONCLUSIONS: With EVS, our study uncovered a novel gene expression signature that has a high prognostic significance across MB subgroups.

Macrophage-tumor cell interaction promotes ATRT progression and chemoresistance.

Atypical teratoid/rhabdoid tumors (ATRT) are known for their heterogeneity concerning pathophysiology and outcome. However, predictive factors within distinct subgroups still need to be uncovered. Using multiplex immunofluorescent staining and single-cell RNA sequencing we unraveled distinct compositions of the immunological tumor microenvironment (TME) across ATRT subgroups. CD68+ cells predominantly infiltrate ATRT-SHH and ATRT-MYC and are a negative prognostic factor for patients' survival. Within the murine ATRT-MYC and ATRT-SHH TME, Cd68+ macrophages are core to intercellular communication with tumor cells. In ATRT-MYC distinct tumor cell phenotypes express macrophage marker genes. These cells are involved in the acquisition of chemotherapy resistance in our relapse xenograft mouse model. In conclusion, the tumor cell-macrophage interaction contributes to ATRT-MYC heterogeneity and potentially to tumor recurrence.