Non-small cell lung carcinoma (NSCLC) is a major cancer type whose epigenetic alteration remains unclear. We analyzed open chromatin data with matched whole-genome sequencing and RNA-seq data of 50 primary NSCLC cases. We observed high interpatient heterogeneity of open chromatin profiles and the degree of heterogeneity correlated to several clinical parameters. Lung adenocarcinoma and lung squamous cell carcinoma (LUSC) exhibited distinct open chromatin patterns. Beyond this, we uncovered that the broadest open chromatin peaks indicated key NSCLC genes and led to less stable expression. Furthermore, we found that the open chromatin peaks were gained or lost together with somatic copy number alterations and affected the expression of important NSCLC genes. In addition, we identified 21 joint-quantitative trait loci (joint-QTL) that correlated to both assay for transposase accessible chromatin sequencing peak intensity and gene expression levels. Finally, we identified 87 regulatory risk loci associated with lung cancer-related phenotypes by intersecting the QTLs with genome-wide association study significant loci. In summary, this compendium of multiomics data provides valuable insights and a resource to understand the landscape of open chromatin features and regulatory networks in NSCLC.Significance: This study utilizes state of the art genomic methods to differentiate lung cancer subtypes.

The rhesus macaque is a prime model animal in neuroscience. A comprehensive transcriptomic and open chromatin atlas of the rhesus macaque brain is key to a deeper understanding of the brain. Here we characterize the transcriptome of 416 brain samples from 52 regions of 8 rhesus macaque brains. We identify gene modules associated with specific brain regions like the cerebral cortex, pituitary, and thalamus. In addition, we discover 9703 novel intergenic transcripts, including 1701 coding transcripts and 2845 lncRNAs. Most of the novel transcripts are only expressed in specific brain regions or cortical regions of specific individuals. We further survey the open chromatin regions in the hippocampal CA1 and several cerebral cortical regions of the rhesus macaque brain using ATAC-seq, revealing CA1-and cortex-specific open chromatin regions. Our results add to the growing body of knowledge regarding the baseline transcriptomic and open chromatin profiles in the brain of the rhesus macaque.

Background Colorectal cancer (CRC) is a major cancer type whose mechanism of metastasis remains elusive. Methods In this study, we characterised the evolutionary pattern of metastatic CRC (mCRC) by analysing bulk and single-cell exome sequencing data of primary and metastatic tumours from 7 CRC patients with liver metastases. Here, 7 CRC patients were analysed by bulk whole-exome sequencing (WES); 4 of these were also analysed using single-cell sequencing. Results Despite low genomic divergence between paired primary and metastatic cancers in the bulk data, single-cell WES (scWES) data revealed rare mutations and defined two separate cell populations, indicative of the diverse evolutionary trajectories between primary and metastatic tumour cells. We further identified 24 metastatic cell-specific-mutated genes and validated their functions in cell migration capacity. Conclusions In summary, scWES revealed rare mutations that failed to be detected by bulk WES. These rare mutations better define the distinct genomic profiles of primary and metastatic tumour cell clones.

Understanding immune cell phenotypes in the tumor microenvironment (TME) is essential for explaining and predicting progression of non-small cell lung cancer (NSCLC) and its response to immunotherapy. Here we describe the single-cell transcriptomics of CD45+ immune cells from tumors, normal tissues and blood of NSCLC patients. We identified three clusters of immune cells exerting immunosuppressive effects: CD8+ T cells with exhausted phenotype, tumor-associated macrophages (TAMs) with a pro-inflammatory M2 phenotype, and regulatory B cells (B regs) with tumor-promoting characteristics. We identified genes that may be mediating T cell phenotypes, including the transcription factors ONECUT2 and ETV4 in exhausted CD8+ T cells, TIGIT and CTL4 high expression in regulatory T cells. Our results highlight the heterogeneity of CD45+ immune cells in the TME and provide testable hypotheses about the cell types and genes that define the TME.

Single-nucleotide variant (SNV) detection in the genome of single cells is affected by DNA amplification artefacts, including imbalanced alleles and early PCR errors. Existing single-cell genotyper accuracy often depends on the quality and coordination of both the target single-cell and external data, such as heterozygous profiles determined by bulk data. In most single-cell studies, information from different sources is not perfectly matched. High-accuracy SNV detection with a limited single data source remains a challenge. We developed a new variant detection method, SCOUT (Single Cell Genotyper Utilizing Information from Local Genome Territory), the greatest advantage of which is not requiring external data while base calling. By leveraging base count information from the adjacent genomic region, SCOUT classifies all candidate SNVs into homozygous, heterozygous, intermediate and low major allele SNVs according to the highest likelihood score. Compared with other genotypers, SCOUT improves the variant detection performance by 2.0–77.5% in real and simulated single-cell datasets. Furthermore, the running time of SCOUT increases linearly with sequence length; as a result, it shows 400% average acceleration in operating efficiency compared with other methods.