Integrating single-cell RNA-Seq with spatial transcriptomics in pancreatic ductal adenocarcinoma using multimodal intersection analysis, bioRxiv, 2018-01-27

To understand tissue architecture, it is necessary to understand both which cell types are present and the physical relationships among them. Single-cell RNA-Seq (scRNA-Seq) has made significant progress towards the unbiased and systematic identification of cell populations within a tissue, however, the characterization of their spatial organization within it has been more elusive. The recently introduced ‘spatial transcriptomics’ method (ST) reveals the spatial pattern of gene expression within a tissue section at a resolution of a thousand 100 µm spots across the tissue, each capturing the transcriptomes of multiple cells. Here, we present an approach for the integration of scRNA-Seq and ST data generated from the same sample, and deploy it on primary pancreatic tumors from two patients. Applying our multimodal intersection analysis (MIA), we annotated the distinct micro-environment of each cell type identified by scRNA-Seq. We further found that subpopulations of ductal cells, macrophages, dendritic cells, and cancer cells have spatially restricted localizations across the tissue, as well as distinct co-enrichments with other cell types. Our mapping approach provides an efficient framework for the integration of the scRNA-Seq-defined subpopulation structure and the ST-defined tissue architecture in any tissue.

biorxiv cancer-biology 100-200-users 2018

Candidate cancer driver mutations in superenhancers and long-range chromatin interaction networks, bioRxiv, 2017-12-20

AbstractA comprehensive catalogue of the mutations that drive tumorigenesis and progression is essential to understanding tumor biology and developing therapies. Protein-coding driver mutations have been well-characterized by large exome-sequencing studies, however many tumors have no mutations in protein-coding driver genes. Non-coding mutations are thought to explain many of these cases, however few non-coding drivers besides TERT promoter are known. To fill this gap, we analyzed 150,000 cis-regulatory regions in 1,844 whole cancer genomes from the ICGC-TCGA PCAWG project. Using our new method, ActiveDriverWGS, we found 41 frequently mutated regulatory elements (FMREs) enriched in non-coding SNVs and indels (FDR<0.05) characterized by aging-associated mutation signatures and frequent structural variants. Most FMREs are distal from genes, reported here for the first time and also recovered by additional driver discovery methods. FMREs were enriched in super-enhancers, H3K27ac enhancer marks of primary tumors and long-range chromatin interactions, suggesting that the mutations drive cancer by distally controlling gene expression through threedimensional genome organization. In support of this hypothesis, the chromatin interaction network of FMREs and target genes revealed associations of mutations and differential gene expression of known and novel cancer genes (e.g., CNNB1IP1, RCC1), activation of immune response pathways and altered enhancer marks. Thus distal genomic regions may include additional, infrequently mutated drivers that act on target genes via chromatin loops. Our study is an important step towards finding such regulatory regions and deciphering the somatic mutation landscape of the non-coding genome.

biorxiv cancer-biology 0-100-users 2017

 

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