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Highlights d hPSC-derived neuromesodermal progenitors generate functional NMOs in 3D d Functional NMJs are generated in NMOs supported by terminal Schwann cells d NMOs contract and develop central pattern generator-like circuits d NMOs can be used to model key aspects of myasthenia gravis

Single-nucleus RNA-sequencing reveals cellular heterogeneity of cardiac cells in aging. To comprehensively decipher the expected cellular responses to intrinsic cardiac aging, we applied microdroplet-based single-nucleus RNA-sequencing (9) of cross-sections of snap-frozen heart samples from 3 syngeneic young male mice (12 weeks old) and 3 aged male mice (18 months old). In total, we analyzed 14,827 nuclei from young hearts and 12,981 nuclei from old hearts (Supplemental Table 1; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.131092DS1). Using t-distributed stochastic neighbor embedding (tSNE) (10), global dimension reduction was constructed from all samples to visualize clusters that were defined by cell-specific gene markers (Figure 1A and Supplemental Table 2). Alignment of samples indicated high reproducibility across samples (Supplemental Figure 1). Most of the cells were in G 1 phase, and no influence of aging on cell cycle activity was observed (Supplemental Figure 2). Unsupervised clustering revealed 15 distinct gene expression patterns (Figure 1A and Supplemental Figure 3). Using cell type-specific gene markers (Supplemental Table 2) and published mouse single-cell gene expression data (11, 12), 7 major cell types could be annotated, including fibroblasts (A, B), cardiomyocytes (A, B, C), endothelial cells (A, B, C), immune cells (A, B, C), pericytes, epicardial cells, and adipocytes (Figure 1A and Supplemental Figure 3). In particular, for fibroblasts, the unsupervised clustering revealed 2 main clusters, fibroblast A (79.42%) and fibroblast B (20.58%). Separation of these 2 clusters was not significant (Supplemental Figure 3B), and gene markers were very similar (Supplemental Table 2); moreover, these 2 clusters were almost equally populated by young and old cells. Analysis of the cell numbers in clusters of other cell types than fibroblasts showed in part trends for changes during aging (Supplemental Figure 4) but did not reveal statistically significant differences. In general, 128 differentially expressed nonredundant genes (DEGs) were found between young and aged hearts (Figure 1B and Supplemental Table 3). Considering the DEGs in all cell clusters, 107 genes showed significantly increased expression (adjusted P < 0.1), and 21 genes showed significantly decreased expression (adjusted P < 0.1) in aged versus young hearts (Supplemental Table 3). Interestingly, aging predominantly affected gene expression patterns in fibroblasts (Figure 1B). Several highly differentially expressed genes could be confirmed by quantitative reverse transcription PCR of isolated cardiac fibroblasts (Supplemental Figure 5). Gene Ontology (GO) analysis of DEGs revealed a cell type-specific enrichment of genes associated with various pathways, such as angiogenesis, chemotaxis/migration, inflammation/immune response, and cell/matrix association (Figure 1C). Only a few coexpression networks and significantly regulated genes were shared between the main cell types. Among them, the e...

The architecture of gene regulatory networks is reminiscent of electronic circuits. Modular building blocks that respond in a logical way to one or several inputs are connected to perform a variety of complex tasks. Gene circuit engineers have pioneered the construction of artificial gene regulatory networks with the intention to pave the way for the construction of therapeutic gene circuits for next-generation gene therapy approaches. However, due to the lack of a critical amount of eukaryotic cell-compatible gene regulation systems, the field has so far been limited to prokaryotes. Recent development of several mammalian cell-compatible expression control systems laid the foundations for the assembly of transcription control modules that can respond to several inputs. Herein, three approaches to evoke combinatorial transcription control have been followed: (i) construction of artificial promoters with up to three operator sites for regulatory proteins, and (ii) parallel and (iii) serial linking of two gene regulation systems. We have combined tetracycline-, streptogramin-, macrolide-, and butyrolactone transcription control systems to engineer BioLogic gates of the NOT IF-, AND-, NOT IF IF-, NAND-, OR-, NOR-, and INVERTER-type in mammalian cells, which are able to respond to up to three different small molecule inputs. BioLogic gates enable logical transcriptional control in mammalian cells and, in combination with modern transduction technologies, could serve as versatile tools for regulated gene expression and as building blocks for complex artificial gene regulatory networks for applications in gene therapy, tissue engineering, and biotechnology. B