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Mouse models are a critical tool for studying human diseases, particularly developmental disorders1. However, conventional approaches for phenotyping may fail to detect subtle defects throughout the developing mouse2. Here we set out to establish single-cell RNA sequencing of the whole embryo as a scalable platform for the systematic phenotyping of mouse genetic models. We applied combinatorial indexing-based single-cell RNA sequencing3 to profile 101 embryos of 22 mutant and 4 wild-type genotypes at embryonic day 13.5, altogether profiling more than 1.6 million nuclei. The 22 mutants represent a range of anticipated phenotypic severities, from established multisystem disorders to deletions of individual regulatory regions4,5. We developed and applied several analytical frameworks for detecting differences in composition and/or gene expression across 52 cell types or trajectories. Some mutants exhibit changes in dozens of trajectories whereas others exhibit changes in only a few cell types. We also identify differences between widely used wild-type strains, compare phenotyping of gain- versus loss-of-function mutants and characterize deletions of topological associating domain boundaries. Notably, some changes are shared among mutants, suggesting that developmental pleiotropy might be 'decomposable' through further scaling of this approach. Overall, our findings show how single-cell profiling of whole embryos can enable the systematic molecular and cellular phenotypic characterization of mouse mutants with unprecedented breadth and resolution.

BACKGROUND: On the basis of the mounting evidence that type 17 T (T17) cells and increased IL-17 play a key role in driving hidradenitis suppurativa (HS) lesion development, biologic agents used previously in psoriasis that block signaling of IL-17A and/or IL-17F isoforms have been repurposed to treat HS. OBJECTIVE: Our research aimed to characterize the transcriptome of HS T17 cells compared to the transcriptome of psoriasis T17 cells, along with their ligand-receptor interactions with neighborhood immune cell subsets. METHODS: Single-cell data of 12,300 cutaneous immune cells from 8 deroofing surgical HS skin samples including dermal tunnels were compared to single-cell data of psoriasis skin (19,525 cells from 11 samples) and control skin (11,920 cells from 10 samples). All single-cell data were generated by the same protocol. RESULTS: HS T17 cells expressed lower levels of IL23R and higher levels of IL1R1 and IL17F compared to psoriasis T17 cells (P < .05). HS Treg cells expressed higher levels of IL1R1 and IL17F compared to psoriasis Treg cells (P < .05). Semimature dendritic cells were the major immune cell subsets expressing IL1B in HS, and IL-1β ligand-receptor interactions between semimature dendritic cells and T17 cells were increased in HS compared to psoriasis (P < .05). HS dermal tunnel keratinocytes expressed inflammatory cytokines (IL17C, IL1A, IL1B, and IL6) that differed from the HS epidermis keratinocytes (IL36G) (P < .05). IL6, which synergizes with IL1B to maintain cytokine expression in T17 cells, was mainly expressed by fibroblasts in HS, which also expressed IL11+ inflammatory fibroblast genes (IL11, IL24, IL6, and POSTN) involved in the paracrine IL-1/IL-6 loop. CONCLUSION: The IL-1β-T17 cell cytokine axis is likely a dominant pathway in HS with HS T17 cells activated by IL-1β signaling, unlike psoriasis T17 cells, which are activated by IL-23 signaling.

Durable psoriasis improvement has been reported in a subset of psoriasis patients after treatment withdrawal of biologics blocking IL-23/Type 17 T-cell (T17) autoimmune axis. However, it is not well understood if systemic blockade of the IL-23/T17 axis promotes immune tolerance in psoriasis skin. The purpose of the study was to find translational evidence that systemic IL-17A blockade promotes regulatory transcriptome modification in human psoriasis skin immune cell subsets. We analyzed human psoriasis lesional skin 6 mm punch biopsy tissues before and after systemic IL-17A blockade using the muti-genomics approach integrating immune cell-enriched scRNA-seq (n = 18), microarray (n = 61), and immunohistochemistry (n = 61) with repository normal control skin immune cell-enriched scRNA-seq (n = 10) and microarray (n = 8) data. For the T17 axis transcriptome, systemic IL-17A blockade depleted 100% of IL17A + T-cells and 95% of IL17F + T-cells in psoriasis skin. The expression of IL23A in DC subsets was also downregulated by IL-17A blockade. The expression of IL-17-driven inflammatory mediators (IL36G, S100A8, DEFB4A, and DEFB4B) in suprabasal keratinocytes was correlated with psoriasis severity and was downregulated by IL-17A blockade. For the regulatory DC transcriptome, the proportion of regulatory semimature DCs expressing regulatory DC markers of BDCA-3 (THBD) and DCIR (CLEC4A) was increased in posttreatment psoriasis lesional skin compared to pretreatment psoriasis lesional skin. In addition, IL-17A blockade induced higher expression of CD1C and CD14, which are markers of CD1c+ CD14+ dendritic cell (DC) subset that suppresses antigen-specific T-cell responses, in posttreatment regulatory semimature DCs compared to pretreatment regulatory semimature DCs. In conclusion, systemic IL-17A inhibition not only blocks the entire IL-23/T17 cell axis but also promotes regulatory gene expression in regulatory DCs in human psoriasis skin.

Mammalian embryogenesis is characterized by rapid cellular proliferation and diversification. Within a few weeks, a single-cell zygote gives rise to millions of cells expressing a panoply of molecular programs. Although intensively studied, a comprehensive delineation of the major cellular trajectories that comprise mammalian development in vivo remains elusive. Here, we set out to integrate several single-cell RNA-sequencing (scRNA-seq) datasets that collectively span mouse gastrulation and organogenesis, supplemented with new profiling of ~150,000 nuclei from approximately embryonic day 8.5 (E8.5) embryos staged in one-somite increments. Overall, we define cell states at each of 19 successive stages spanning E3.5 to E13.5 and heuristically connect them to their pseudoancestors and pseudodescendants. Although constructed through automated procedures, the resulting directed acyclic graph (TOME (trajectories of mammalian embryogenesis)) is largely consistent with our contemporary understanding of mammalian development. We leverage TOME to systematically nominate transcription factors (TFs) as candidate regulators of each cell types specification, as well as cell-type homologs across vertebrate evolution.

Drug‐target interaction (DTI) prediction is essential for new drug design and development. Constructing heterogeneous network based on diverse information about drugs, proteins and diseases provides new opportunities for DTI prediction. However, the inherent complexity, high dimensionality and noise of such a network prevent us from taking full advantage of these network characteristics. This article proposes a novel method, NGCN, to predict drug‐target interactions from an integrated heterogeneous network, from which to extract relevant biological properties and association information while maintaining the topology information. It focuses on learning the topology representation of drugs and targets to improve the performance of DTI prediction. Unlike traditional methods, it focuses on learning the low‐dimensional topology representation of drugs and targets via graph‐based convolutional neural network. NGCN achieves substantial performance improvements over other state‐of‐the‐art methods, such as a nearly 1.0% increase in AUPR value. Moreover, we verify the robustness of NGCN through benchmark tests, and the experimental results demonstrate it is an extensible framework capable of combining heterogeneous information for DTI prediction. [ABSTRACT FROM AUTHOR]