Cancer metastasis is no longer viewed as a linear cascade of events but rather as a series of concurrent, partially overlapping processes, as successfully metastasizing cells assume new phenotypes while jettisoning older behaviors. The lack of a systemic understanding of this complex phenomenon has limited progress in developing treatments for metastatic disease. Because metastasis has traditionally been investigated in distinct physiological compartments, the integration of these complex and interlinked aspects remains a challenge for both systems-level experimental and computational modeling of metastasis. Here, we present some of the current perspectives on the complexity of cancer metastasis, the multiscale nature of its progression, and a systems-level view of the processes underlying the invasive spread of cancer cells. We also highlight the gaps in our current understanding of cancer metastasis as well as insights emerging from interdisciplinary systems biology approaches to understand this complex phenomenon.

The lung microvasculature is essential for gas exchange and commonly considered homogeneous. We show that Vascular endothelial growth factor A (Vegfa) from the epithelium specifies a distinct endothelial cell (EC) population in the postnatal mouse lung. Vegfa is predominantly expressed by alveolar type 1 (AT1) cells and locally required to specify a subset of ECs. Single cell RNA-seq identified 15-20% lung ECs as transcriptionally distinct and marked by Carbonic anhydrase 4 (Car4), which are specifically lost upon epithelial Vegfa deletion. Car4 ECs, unlike bulk ECs, have extensive cellular projections and are separated from AT1 cells by a limited basement membrane without intervening pericytes. Without Car4 ECs, the alveolar space is aberrantly enlarged despite the normal appearance of myofibroblasts. Lung Car4 ECs and retina tip ECs have common and distinct transcriptional profiles. These findings support a signaling role of AT1 cells and shed light on alveologenesis.

Highlights d Pro-glycolytic CAFs fuel cancer cell metabolism to support breast tumor growth d CAFs attain a pro-glycolytic phenotype by epigenetic control of glycolysis d Chronic hypoxia enables epigenetic reprogramming of glycolysis in fibroblasts

Manuscript 2 SUMMARY:The lung microvasculature is essential for gas exchange and commonly considered homogeneous. We show that Vascular endothelial growth factor A (Vegfa) from the epithelium specifies a distinct endothelial cell (EC) population in the postnatal mouse lung. Vegfa is predominantly expressed by alveolar type 1 (AT1) cells and locally required to specify a subset of ECs. Single cell RNA-seq identified 15-20% lung ECs as transcriptionally distinct and marked by Carbonic anhydrase 4 (Car4), which are specifically lost upon epithelial Vegfa deletion. Car4 ECs, unlike bulk ECs, have extensive cellular projections and are separated from AT1 cells by a limited basement membrane without intervening pericytes. Without Car4 ECs, the alveolar space is aberrantly enlarged despite the normal appearance of myofibroblasts. Lung Car4 ECs and retina tip ECs have common and distinct transcriptional profiles. These findings support a signaling role of AT1 cells and shed light on alveologenesis.

Mammalian organs consist of diverse, intermixed cell types that signal to each other via ligand-receptor interactions – an interactome – to ensure development, homeostasis, and injury-repair. Dissecting such intercellular interactions is facilitated by rapidly growing single-cell RNA-seq (scRNA-seq) data; however, existing computational methods are often not sufficiently quantitative nor readily adaptable by bench scientists without advanced programming skills. Here we describe a quantitative intuitive algorithm, coupled with an optimized experimental protocol, to construct and compare interactomes in control and Sendai virus-infected mouse lungs. A minimum of 90 cells per cell type compensates for the known gene dropout issue in scRNA-seq and achieves comparable sensitivity to bulk RNA-seq. Cell lineage normalization after cell sorting allows cost-efficient representation of cell types of interest. A numeric representation of ligand-receptor interactions identifies, as outliers, known and potentially new interactions as well as changes upon viral infection. Our experimental and computational approaches can be generalized to other organs and human samples.Summary statementAn intuitive method to construct quantitative ligand-receptor interactomes using single-cell RNA-seq data and its application to normal and Sendai virus-infected mouse lungs.

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Mammalian organs consist of diverse, intermixed cell types that signal to each other via ligand-receptor interactions – an interactome – to ensure development, homeostasis, and injury-repair. Dissecting such intercellular interactions is facilitated by rapidly growing single-cell RNA-seq (scRNA-seq) data; however, existing computational methods are often not readily adaptable by bench scientists without advanced programming skills. In this Resource article, we describe a quantitative intuitive algorithm, coupled with an optimized experimental protocol, to construct and compare interactomes in control and Sendai virus-infected mouse lungs. A minimum of 90 cells per cell type compensates for the known gene dropout issue in scRNA-seq and achieves comparable sensitivity to bulk RNA-seq. Cell lineage normalization after cell sorting allows cost-efficient representation of cell types of interest. A numeric representation of ligand-receptor interactions identifies, as outliers, known and potentially new interactions as well as changes upon viral infection. Our experimental and computational approaches can be generalized to other organs and human samples.