The Arabidopsis thaliana lysophospholipid acyltransferase At1g78690p acylates lysocardiolipins.

The Arabidopsis thaliana lysophospholipid acyltransferase At1g78690 acylates a variety of lysophospholipids such as lyso phosphatidylglycerol, lyso phosphatidylethanolamine and lyso phosphatidylserine. Despite di-acylate phosphatidylglycerol being a substrate, overexpression of At1g78690 in Escherichia coli leads to the accumulation of acyl-PG. Here we show that cardiolipin also accumulates in cells overexpressing At1g78690. To help try and explain this observation, we show, using a liquid chromatography mass spectrometry (LC-MS) based assay, that At1g78690 utilizes both mono- and di-lyso cardiolipin as an acyl acceptor. Because At1g78690 shares high homology (∼40%) with the cardiolipin remodeling enzyme tafazzin, we also tested whether At1g78690 was able to catalyze a tafazzin-like transacylation reaction. Di-linoleoyl phosphatidylcholine was used as the acyl donor and mono-lyso cardiolipin was used as the acyl acceptor in a reaction and the reaction was monitored by LC-MS. No transfer of the linoleoyl chains was detected in an At1g78690 dependent manner suggesting that, despite the strong homology, these enzymes catalyze unique reactions.

The Arabidopsis thaliana lysophospholipid acyltransferase At1g78690p acylates a variety of lysophospholipids including bis(monoacylglycero)phosphate.

When the lysoglycerophospholipid (GPL) acyltransferase At1g78690 from Arabidopsis thaliana is over-expressed in Escherichiacoli a headgroup acylated GPL, acyl phosphatidylglycerol (PG), accumulates despite that in vitro this enzyme catalyzes the transfer of an acyl chain from acyl-CoA to the sn-2 position of 1-acyl phosphatidylethanolamine (PE) or 1-acyl PG to form the sn-1, sn-2, di acyl PE and PG respectively; it does not acylate PG to form acyl PG. To begin to understand why the overexpression of a lyso GPL acyltransferase leads to the accumulation of a headgroup acylated GPL in E. coli we investigated the headgroup specificity of At1g78690. Using membranes prepared from E. coli overexpressing At1g78690, we assessed the ability of At1g78690 to catalyze the transfer of acyl chains from acyl-coenzyme A to a variety of lyso GPL acyl acceptors including lyso-phosphatidic acid (PA), -phosphatidylcholine (PC), -phosphatidylserine (PC), -phosphatidylinositol (PI) and three stereoisoforms of bis(monoacylglycero)phosphate (BMP). The predicted products were formed when lyso PI and lyso PC were used as the acyl acceptor but not with lyso PC or lyso PA. In addition, At1g78690 robustly acylates two BMP isoforms with sn-2 and/or sn-2' hydroxyls in the R-stereoconfiguration, but not the BMP isoform with the sn-2 and sn-2' hydroxyls in the S-stereoconfiguration. This strongly suggests that At1g78690 is stereoselective for hydroxyls with R-stereochemistry. In addition, this robust acylation of BMPs by At1g78690, which yields acyl PG like molecules, may explain the mechanism by which At1g78690 so strikingly alters the lipid composition of E. coli.

Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment.

Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.

Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface.

During tumor progression, cancer cells come into contact with various non-tumor cell types, but it is unclear how tumors adapt to these new environments. Here, we integrate spatially resolved transcriptomics, single-cell RNA-seq, and single-nucleus RNA-seq to characterize tumor-microenvironment interactions at the tumor boundary. Using a zebrafish model of melanoma, we identify a distinct "interface" cell state where the tumor contacts neighboring tissues. This interface is composed of specialized tumor and microenvironment cells that upregulate a common set of cilia genes, and cilia proteins are enriched only where the tumor contacts the microenvironment. Cilia gene expression is regulated by ETS-family transcription factors, which normally act to suppress cilia genes outside of the interface. A cilia-enriched interface is conserved in human patient samples, suggesting it is a conserved feature of human melanoma. Our results demonstrate the power of spatially resolved transcriptomics in uncovering mechanisms that allow tumors to adapt to new environments.

Author Correction: Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas.

A Correction to this paper has been published: https://doi.org/10.1038/s41587-019-0392-8.

Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas.

Single-cell RNA sequencing (scRNA-seq) enables the systematic identification of cell populations in a tissue, but characterizing their spatial organization remains challenging. We combine a microarray-based spatial transcriptomics method that reveals spatial patterns of gene expression using an array of spots, each capturing the transcriptomes of multiple adjacent cells, with scRNA-Seq generated from the same sample. To annotate the precise cellular composition of distinct tissue regions, we introduce a method for multimodal intersection analysis. Applying multimodal intersection analysis to primary pancreatic tumors, we find that subpopulations of ductal cells, macrophages, dendritic cells and cancer cells have spatially restricted enrichments, as well as distinct coenrichments with other cell types. Furthermore, we identify colocalization of inflammatory fibroblasts and cancer cells expressing a stress-response gene module. Our approach for mapping the architecture of scRNA-seq-defined subpopulations can be applied to reveal the interactions inherent to complex tissues.

The Stress-Like Cancer Cell State Is a Consistent Component of Tumorigenesis.

Transcriptional profiling of tumors has revealed a stress-like state among the cancer cells with the concerted expression of genes such as fos, jun, and heat-shock proteins, though this has been controversial given possible dissociation-effects associated with single-cell RNA sequencing. Here, we validate the existence of this state using a combination of zebrafish melanoma modeling, spatial transcriptomics, and human samples. We found that the stress-like subpopulation of cancer cells is present from the early stages of tumorigenesis. Comparing with previously reported single-cell RNA sequencing datasets from diverse cancer types, including triple-negative breast cancer, oligodendroglioma, and pancreatic adenocarcinoma, indicated the conservation of this state during tumorigenesis. We also provide evidence that this state has higher tumor-seeding capabilities and that its induction leads to increased growth under both MEK and BRAF inhibitors. Collectively, our study supports the stress-like cells as a cancer cell state expressing a coherent set of genes and exhibiting drug-resistance properties.