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Highlights d Spatial proteogenomic single-cell atlas of healthy and obese murine and human liver d Validated flow cytometry and microscopy panels for all hepatic cells d LAMs are differentially located in the lean and obese liver d Evolutionary conserved BMP9/10-ALK1 axis is essential for KC development

For more than 100 years, the fruit fly Drosophila melanogaster has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula Drosophilae , that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type–related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types between tissues, such as blood and muscle cells, reveals rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the Drosophila community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.

Tissue-resident macrophages can derive from yolk sac macrophages (YS-Macs), fetal liver monocytes (FL-MOs), or adult bone-marrow monocytes (BM-MOs). The relative capacity of these precursors to colonize a niche, self-maintain, and perform tissue-specific functions is unknown. We simultaneously transferred traceable YS-Macs, FL-MOs, and BM-MOs into the empty alveolar macrophage (AM) niche of neonatal Csf2rb(-/-) mice. All subsets produced AMs, but in competition preferential outgrowth of FL-MOs was observed, correlating with their superior granulocyte macrophage-colony stimulating factor (GM-CSF) reactivity and proliferation capacity. When transferred separately, however, all precursors efficiently colonized the alveolar niche and generated AMs that were transcriptionally almost identical, self-maintained, and durably prevented alveolar proteinosis. Mature liver, peritoneal, or colon macrophages could not efficiently colonize the empty AM niche, whereas mature AMs could. Thus, precursor origin does not affect the development of functional self-maintaining tissue-resident macrophages and the plasticity of the mononuclear phagocyte system is largest at the precursor stage.

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.

A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to group genes into co-expression modules. Because of limitations of classical clustering methods, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modelling the regulatory network, and/or allowing overlap between modules. In this study we use known regulatory networks to do a comprehensive and robust evaluation of these different methods. Overall, decomposition methods outperform all other strategies, while we do not find a clear advantage of biclustering and network inference-based approaches on large gene expression datasets. Using our evaluation workflow, we also investigate several practical aspects of module detection, such as parameter estimation and the use of alternative similarity measures, and conclude with recommendations for the further development of these methods.