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Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices., (© 2023. Springer Nature Limited.)

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas., (© 2023. The Author(s).)

Immune checkpoint inhibitors (ICIs) enhance anticancer immunity by releasing repressive signals into tumor microenvironments (TMEs). To be effective, ICIs require preexisting immunologically "hot" niches for tumor antigen presentation and lymphocyte recruitment. How the mutational landscape of cancer cells shapes these immunological niches remains poorly defined. We found in human and murine colorectal cancer (CRC) models that the superior antitumor immune response of mismatch repair (MMR)-deficient CRC required tumor cell-intrinsic activation of cGAS-STING signaling triggered by genomic instability. Subsequently, we synthetically enforced STING signaling in CRC cells with intact MMR signaling using constitutively active STING variants. Even in MMR-proficient CRC, genetically encoded gain-of-function STING was sufficient to induce cancer cell-intrinsic interferon signaling, local activation of antigen-presenting cells, recruitment of effector lymphocytes, and sensitization of previously "cold" TMEs to ICI therapy in vivo. Thus, our results introduce a rational strategy for modulating cancer cell-intrinsic programs via engineered STING enforcement to sensitize resistant tumors to ICI responsiveness.

Activation of CD40-signaling contributes to the initiation, progression and drug resistance of B cell lymphomas. We contributed to this knowledge by showing that constitutive CD40-signaling in B cells induces B cell hyperplasia and finally B cell lymphoma development in transgenic mice. CD40 activates, among others, the non-canonical NF-ĸB signaling, which is constitutively activated in several human B cell lymphomas and is therefore presumed to contribute to lymphopathogenesis. This prompted us to study the regulatory role of the non-canonical NF-ĸB transcription factor RelB in lymphomagenesis. To this end, we crossed mice expressing a constitutively active CD40 receptor in B cells with conditional RelB-KO mice. Ablation of RelB attenuated pre-malignant B cell expansion, and resulted in an impaired survival and activation of long-term CD40-stimulated B cells. Furthermore, we found that hyperactivation of non-canonical NF-кB signaling enhances the retention of B cells in the follicles of secondary lymphoid organs. RNA-Seq-analysis revealed that several genes involved in B-cell migration, survival, proliferation and cytokine signaling govern the transcriptional differences modulated by the ablation of RelB in long-term CD40-stimulated B cells. Inactivation of RelB did not abrogate lymphoma development. However, lymphomas occurred with a lower incidence and had a longer latency period. In summary, our data suggest that RelB, although it is not strictly required for malignant transformation, accelerates the lymphomagenesis of long-term CD40-stimulated B cells by regulating genes involved in migration, survival and cytokine signaling., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Kuhn, Valentin, Stojanovic, Strobl, Babushku, Wang, Rambold, Scheffler, Grath, John-Robbert, Blum, Feuchtinger, Blutke, Weih, Kitamura, Rad, Strobl and Zimber-Strobl.)

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., (© 2021. The Author(s).)