Villus myofibroblasts are developmental and adult progenitors of mammalian gut lymphatic musculature.

Inside the finger-like intestinal projections called villi, strands of smooth muscle cells contract to propel absorbed dietary fats through the adjacent lymphatic capillary, the lacteal, sending fats into the systemic blood circulation for energy production. Despite this vital function, mechanisms of formation, assembly alongside lacteals, and maintenance of villus smooth muscle are unknown. By combining single-cell RNA sequencing and quantitative lineage tracing of the mouse intestine, we identified a local hierarchy of subepithelial fibroblast progenitors that differentiate into mature smooth muscle fibers via intermediate contractile myofibroblasts. This continuum persists as the major mechanism for villus musculature renewal throughout adult life. The NOTCH3-DLL4 signaling axis governs the assembly of smooth muscle fibers alongside their adjacent lacteals and is required for fat absorption. Our studies identify the ontogeny and maintenance of a poorly defined class of intestinal smooth muscle, with implications for accelerated repair and recovery of digestive function following injury.

An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome.

Hormones mediate long-range cell communication and play vital roles in physiology, metabolism, and health. Traditionally, endocrinologists have focused on one hormone or organ system at a time. Yet, hormone signaling by its very nature connects cells of different organs and involves crosstalk of different hormones. Here, we leverage the organism-wide single cell transcriptional atlas of a non-human primate, the mouse lemur (Microcebus murinus), to systematically map source and target cells for 84 classes of hormones. This work uncovers previously-uncharacterized sites of hormone regulation, and shows that the hormonal signaling network is densely connected, decentralized, and rich in feedback loops. Evolutionary comparisons of hormonal genes and their expression patterns show that mouse lemur better models human hormonal signaling than mouse, at both the genomic and transcriptomic levels, and reveal primate-specific rewiring of hormone-producing/target cells. This work complements the scale and resolution of classical endocrine studies and sheds light on primate hormone regulation.

Origin and adult renewal of the gut lacteal musculature from villus myofibroblasts.

Intestinal smooth muscles are the workhorse of the digestive system. Inside the millions of finger-like intestinal projections called villi, strands of smooth muscle cells contract to propel absorbed dietary fats through the adjacent lymphatic vessel, called the lacteal, sending fats into the blood circulation for energy production. Despite this vital function, how villus smooth muscles form, how they assemble alongside lacteals, and how they repair throughout life remain unknown. Here we combine single-cell RNA sequencing of the mouse intestine with quantitative lineage tracing to reveal the mechanisms of formation and differentiation of villus smooth muscle cells. Within the highly regenerative villus, we uncover a local hierarchy of subepithelial fibroblast progenitors that progress to become mature smooth muscle fibers, via an intermediate contractile myofibroblast-like phenotype. This continuum persists in the adult intestine as the major source of renewal of villus smooth muscle cells during adult life. We further found that the NOTCH3-DLL4 signaling axis governs the assembly of villus smooth muscles alongside their adjacent lacteal, and we show that this is necessary for gut absorptive function. Overall, our data shed light on the genesis of a poorly defined class of intestinal smooth muscle and pave the way for new opportunities to accelerate recovery of digestive function by stimulating muscle repair.

Spatiotemporal transcriptomics reveals pathogenesis of viral myocarditis.

A significant fraction of sudden death in children and young adults is due to viral myocarditis, an inflammatory disease of the heart. In this study, by using integrated single-cell and spatial transcriptomics, we created a high-resolution, spatially resolved transcriptome map of reovirus-induced myocarditis in neonatal mouse hearts. We assayed hearts collected at three timepoints after infection and studied the temporal, spatial and cellular heterogeneity of host-virus interactions. We further assayed the intestine, the primary site of reovirus infection, to establish a full chronology of molecular events that ultimately lead to myocarditis. We found that inflamed endothelial cells recruit cytotoxic T cells and undergo pyroptosis in the myocarditic tissue. Analyses of spatially restricted gene expression in myocarditic regions and the border zone identified immune-mediated cell-type-specific injury and stress responses. Overall, we observed a complex network of cellular phenotypes and spatially restricted cell-cell interactions associated with reovirus-induced myocarditis in neonatal mice.

Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration.

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro-adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation, and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis.

Conventional scRNA-seq expression analyses rely on the availability of a high quality genome annotation. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, genome annotations are often incomplete, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant transcriptional activity beyond the scope of the best available genome annotation by performing scRNA-seq analysis on any region in the genome for which transcriptional products are detected. Our tool generates a single-cell expression matrix for all transcriptionally active regions (TARs), performs single-cell TAR expression analysis to identify biologically significant TARs, and then annotates TARs using gene homology analysis. This procedure uses single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.

Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis.

Single-cell RNA sequencing is a powerful tool to study developmental biology but does not preserve spatial information about tissue morphology and cellular interactions. Here, we combine single-cell and spatial transcriptomics with algorithms for data integration to study the development of the chicken heart from the early to late four-chambered heart stage. We create a census of the diverse cellular lineages in developing hearts, their spatial organization, and their interactions during development. Spatial mapping of differentiation transitions in cardiac lineages defines transcriptional differences between epithelial and mesenchymal cells within the epicardial lineage. Using spatially resolved expression analysis, we identify anatomically restricted expression programs, including expression of genes implicated in congenital heart disease. Last, we discover a persistent enrichment of the small, secreted peptide, thymosin beta-4, throughout coronary vascular development. Overall, our study identifies an intricate interplay between cellular differentiation and morphogenesis.

Simultaneous multiplexed amplicon sequencing and transcriptome profiling in single cells.

We describe droplet-assisted RNA targeting by single-cell sequencing (DART-seq), a versatile technology that enables multiplexed amplicon sequencing and transcriptome profiling in single cells. We applied DART-seq to simultaneously characterize the non-A-tailed transcripts of a segmented dsRNA virus and the transcriptome of the infected cell. In addition, we used DART-seq to simultaneously determine the natively paired, variable region heavy and light chain amplicons and the transcriptome of B lymphocytes.

(Machine-)Learning to analyze in vivo microscopy: Support vector machines.

The development of new microscopy techniques for super-resolved, long-term monitoring of cellular and subcellular dynamics in living organisms is revealing new fundamental aspects of tissue development and repair. However, new microscopy approaches present several challenges. In addition to unprecedented requirements for data storage, the analysis of high resolution, time-lapse images is too complex to be done manually. Machine learning techniques are ideally suited for the (semi-)automated analysis of multidimensional image data. In particular, support vector machines (SVMs), have emerged as an efficient method to analyze microscopy images obtained from animals. Here, we discuss the use of SVMs to analyze in vivo microscopy data. We introduce the mathematical framework behind SVMs, and we describe the metrics used by SVMs and other machine learning approaches to classify image data. We discuss the influence of different SVM parameters in the context of an algorithm for cell segmentation and tracking. Finally, we describe how the application of SVMs has been critical to study protein localization in yeast screens, for lineage tracing in C. elegans, or to determine the developmental stage of Drosophila embryos to investigate gene expression dynamics. We propose that SVMs will become central tools in the analysis of the complex image data that novel microscopy modalities have made possible. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.

Myosin II promotes the anisotropic loss of the apical domain during Drosophila neuroblast ingression.

Epithelial-mesenchymal transitions play key roles in development and cancer and entail the loss of epithelial polarity and cell adhesion. In this study, we use quantitative live imaging of ingressing neuroblasts (NBs) in Drosophila melanogaster embryos to assess apical domain loss and junctional disassembly. Ingression is independent of the Snail family of transcriptional repressors and down-regulation of Drosophila E-cadherin (DEcad) transcription. Instead, the posttranscriptionally regulated decrease in DEcad coincides with the reduction of cell contact length and depends on tension anisotropy between NBs and their neighbors. A major driver of apical constriction and junctional disassembly are periodic pulses of junctional and medial myosin II that result in progressively stronger cortical contractions during ingression. Effective contractions require the molecular coupling between myosin and junctions and apical relaxation of neighboring cells. Moreover, planar polarization of myosin leads to the loss of anterior-posterior junctions before the loss of dorsal-ventral junctions. We conclude that planar-polarized dynamic actomyosin networks drive apical constriction and the anisotropic loss of cell contacts during NB ingression.

Automated cell tracking identifies mechanically oriented cell divisions during Drosophila axis elongation.

Embryos extend their anterior-posterior (AP) axis in a conserved process known as axis elongation. Drosophila axis elongation occurs in an epithelial monolayer, the germband, and is driven by cell intercalation, cell shape changes, and oriented cell divisions at the posterior germband. Anterior germband cells also divide during axis elongation. We developed image analysis and pattern-recognition methods to track dividing cells from confocal microscopy movies in a generally applicable approach. Mesectoderm cells, forming the ventral midline, divided parallel to the AP axis, while lateral cells displayed a uniform distribution of division orientations. Mesectoderm cells did not intercalate and sustained increased AP strain before cell division. After division, mesectoderm cell density increased along the AP axis, thus relieving strain. We used laser ablation to isolate mesectoderm cells from the influence of other tissues. Uncoupling the mesectoderm from intercalating cells did not affect cell division orientation. Conversely, separating the mesectoderm from the anterior and posterior poles of the embryo resulted in uniformly oriented divisions. Our data suggest that mesectoderm cells align their division angle to reduce strain caused by mechanical forces along the AP axis of the embryo.