Acid-Induced Downregulation of ASS1 Contributes to the Maintenance of Intracellular pH in Cancer.

Downregulation of the urea cycle enzyme argininosuccinate synthase (ASS1) by either promoter methylation or by HIF1α is associated with increased metastasis and poor prognosis in multiple cancers. We have previously shown that in normoxic conditions, ASS1 downregulation facilitates cancer cell proliferation by increasing aspartate availability for pyrimidine synthesis by the enzyme complex CAD. Here we report that in hypoxia, ASS1 expression in cancerous cells is downregulated further by HIF1α-mediated induction of miR-224-5p, making the cells more invasive and dependent on upstream substrates of ASS1 for survival. ASS1 was downregulated under acidic conditions, and ASS1-depleted cancer cells maintained a higher intracellular pH (pHi), depended less on extracellular glutamine, and displayed higher glutathione levels. Depletion of substrates of urea cycle enzymes in ASS1-deficient cancers decreased cancer cell survival. Thus, ASS1 levels in cancer are differentially regulated in various environmental conditions to metabolically benefit cancer progression. Understanding these alterations may help uncover specific context-dependent cancer vulnerabilities that may be targeted for therapeutic purposes. SIGNIFICANCE: Cancer cells in an acidic or hypoxic environment downregulate the expression of the urea cycle enzyme ASS1, which provides them with a redox and pH advantage, resulting in better survival.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/3/518/F1.large.jpg.

Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.

Multiple myeloma, a plasma cell malignancy, is the second most common blood cancer. Despite extensive research, disease heterogeneity is poorly characterized, hampering efforts for early diagnosis and improved treatments. Here, we apply single cell RNA sequencing to study the heterogeneity of 40 individuals along the multiple myeloma progression spectrum, including 11 healthy controls, demonstrating high interindividual variability that can be explained by expression of known multiple myeloma drivers and additional putative factors. We identify extensive subclonal structures for 10 of 29 individuals with multiple myeloma. In asymptomatic individuals with early disease and in those with minimal residual disease post-treatment, we detect rare tumor plasma cells with molecular characteristics similar to those of active myeloma, with possible implications for personalized therapies. Single cell analysis of rare circulating tumor cells allows for accurate liquid biopsy and detection of malignant plasma cells, which reflect bone marrow disease. Our work establishes single cell RNA sequencing for dissecting blood malignancies and devising detailed molecular characterization of tumor cells in symptomatic and asymptomatic patients.

Lung Single-Cell Signaling Interaction Map Reveals Basophil Role in Macrophage Imprinting.

Lung development and function arises from the interactions between diverse cell types and lineages. Using single-cell RNA sequencing (RNA-seq), we characterize the cellular composition of the lung during development and identify vast dynamics in cell composition and their molecular characteristics. Analyzing 818 ligand-receptor interaction pairs within and between cell lineages, we identify broadly interacting cells, including AT2, innate lymphocytes (ILCs), and basophils. Using interleukin (IL)-33 receptor knockout mice and in vitro experiments, we show that basophils establish a lung-specific function imprinted by IL-33 and granulocyte-macrophage colony-stimulating factor (GM-CSF), characterized by unique signaling of cytokines and growth factors important for stromal, epithelial, and myeloid cell fates. Antibody-depletion strategies, diphtheria toxin-mediated selective depletion of basophils, and co-culture studies show that lung resident basophils are important regulators of alveolar macrophage development and function. Together, our study demonstrates how whole-tissue signaling interaction map on the single-cell level can broaden our understanding of cellular networks in health and disease.

Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells.

Spatially resolved single-cell RNA sequencing (scRNAseq) is a powerful approach for inferring connections between a cell's identity and its position in a tissue. We recently combined scRNAseq with spatially mapped landmark genes to infer the expression zonation of hepatocytes. However, determining zonation of small cells with low mRNA content, or without highly expressed landmark genes, remains challenging. Here we used paired-cell sequencing, in which mRNA from pairs of attached mouse cells were sequenced and gene expression from one cell type was used to infer the pairs' tissue coordinates. We applied this method to pairs of hepatocytes and liver endothelial cells (LECs). Using the spatial information from hepatocytes, we reconstructed LEC zonation and extracted a landmark gene panel that we used to spatially map LEC scRNAseq data. Our approach revealed the expression of both Wnt ligands and the Dkk3 Wnt antagonist in distinct pericentral LEC sub-populations. This approach can be used to reconstruct spatial expression maps of non-parenchymal cells in other tissues.

Single-cell spatial reconstruction reveals global division of labour in the mammalian liver.

The mammalian liver consists of hexagon-shaped lobules that are radially polarized by blood flow and morphogens. Key liver genes have been shown to be differentially expressed along the lobule axis, a phenomenon termed zonation, but a detailed genome-wide reconstruction of this spatial division of labour has not been achieved. Here we measure the entire transcriptome of thousands of mouse liver cells and infer their lobule coordinates on the basis of a panel of zonated landmark genes, characterized with single-molecule fluorescence in situ hybridization. Using this approach, we obtain the zonation profiles of all liver genes with high spatial resolution. We find that around 50% of liver genes are significantly zonated and uncover abundant non-monotonic profiles that peak at the mid-lobule layers. These include a spatial order of bile acid biosynthesis enzymes that matches their position in the enzymatic cascade. Our approach can facilitate the reconstruction of similar spatial genomic blueprints for other mammalian organs.