Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing >1.2 million cells distributed in 13 atlas-level integration tasks. We evaluated methods according to scalability, usability and their ability to remove batch effects while retaining biological variation using 14 evaluation metrics. We show that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, scANVI, Scanorama, scVI and scGen perform well, particularly on complex integration tasks, while single-cell ATAC-sequencing integration performance is strongly affected by choice of feature space. Our freely available Python module and benchmarking pipeline can identify optimal data integration methods for new data, benchmark new methods and improve method development.

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.

Cell atlases often include samples that span locations, labs, and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration .Choosing a data integration method is a challenge due to the difficulty of defining integration success. Here, we benchmark 38 method and preprocessing combinations on 77 batches of gene expression, chromatin accessibility, and simulation data from 23 publications, altogether representing >1.2 million cells distributed in nine atlas-level integration tasks. Our integration tasks span several common sources of variation such as individuals, species, and experimental labs. We evaluate methods according to scalability, usability, and their ability to remove batch effects while retaining biological variation.Using 14 evaluation metrics, we find that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, BBKNN, Scanorama, and scVI perform well, particularly on complex integration tasks; Seurat v3 performs well on simpler tasks with distinct biological signals; and methods that prioritize batch removal perform best for ATAC-seq data integration. Our freely available reproducible python module can be used to identify optimal data integration methods for new data, benchmark new methods, and improve method development.2

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.

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.

Background Medulloblastoma (MB) is a malignant brain tumor in childhood. It comprises four 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 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.

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.

Atypical teratoid/rhabdoid tumors (ATRT) are pediatric brain neoplasms that are known for their heterogeneity concerning pathophysiology and outcome. The three genetically rather uniform but epigenetically distinct molecular subgroups of ATRT alone do not sufficiently explain the clinical heterogeneity. Therefore, we examined the tumor microenvironment (TME) in the context of tumor diversity. By using multiplex-immunofluorescent staining and single-cell RNA sequencing (scRNA-seq) we unveiled the pan-macrophage marker CD68 as a subgroup-independent negative prognostic marker for survival of ATRT patients. ScRNA-seq analysis of murine ATRT-SHH, ATRT-MYC and extracranial RT (eRT) provide a delineation of the TME, which is predominantly infiltrated by myeloid cells: more specifically a microglia-enriched niche in ATRT-SHH and a bone marrow-derived macrophage infiltration in ATRT-MYC and eRT. Exploring the cell-cell communication of tumor cells with tumor-associated immune cells, we found that Cd68+ tumor-associated macrophages (TAMs) are central to intercellular communication with tumor cells. Moreover, we uncovered distinct tumor phenotypes in murine ATRT-MYC that share genetic traits with TAMs. These intermediary cells considerably increase the intratumoral heterogeneity of ATRT-MYC tumors. In vitro co-culture experiments recapitulated the capability of ATRT-MYC cells to interchange cell material with macrophages extensively, in contrast to ATRT-SHH cells. We found that microglia are less involved in the exchange of information with ATRT cells and that direct contact is a prerequisite for incorporation. A relapse xenograft model implied that intermediary cells are involved in the acquisition of chemotherapy resistance. We show evidence that TAM-tumor cell interaction is one mechanism of chemotherapy resistance and relapse in ATRT.