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Single-cell sequencing technology has enabled correlation analysis of genomic features at the cellular level. However, high levels of noise and sparsity in single-cell sequencing data make accurate assessment of correlations challenging. This study provides a toolkit, SCSC (https://github.com/thecailab/SCSC), for the estimation of correlation coefficients in single-cell sequencing data. It comprehensively assessed four strategies (classical, non-zero, dropout-weighted, imputation) and the impact of data features in various simulated scenarios. The study found that filtering zeros significantly improves estimation accuracy, and further improvement can be achieved by considering the drop-out probability. In addition, the study also identified data features including expression level, library size, and biological variations that affect correlation estimation.

Multi-regional clinical trial (MRCT) is an efficient design to accelerate drug approval globally. Once the global efficacy of test drug is demonstrated, each local regulatory agency is required to prove effectiveness of test drug in their own population. Meanwhile, the ICH E5/E17 guideline recommends using data from other regions to help evaluate regional drug efficacy. However, one of the most challenges is how to manage to bridge data among multiple regions in an MRCT since various intrinsic and extrinsic factors exist among the participating regions. Furthermore, it is critical for a local agency to determine the proportion of information borrowing from other regions given the ethnic differences between target region and non-target regions. To address these issues, we propose a discounting factor weighted Z statistic to adaptively borrow information from non-target regions. In this weighted Z statistic, the weight is derived from a discounting factor in which the discounting factor denotes the proportion of information borrowing from non-target regions. We consider three ways to construct discounting factors based on the degree of congruency between target and non-target regions either using control group data, or treatment group data, or all data. We use the calibrated power prior to construct discounting factor based on scaled Kolmogorov-Smirnov statistic. Comprehensive simulation studies show that our method has desirable operating characteristics. Two examples are used to illustrate the applications of our proposed approach.

Environmental exposures to chemicals can disrupt gene expression, and the effects could be mediated by methylation. This investigation focused on methylation of genes associated with exposure to metals. Mother-child pairs from three locations in Montana were recruited, and buccal cells were collected for genome-wide methylation assay. Four pairs were from Butte, where there is mining and a Superfund site, four pairs were from Anaconda with a Superfund site, and four pairs were from Missoula with neither a mine nor a Superfund site. Principal component analysis, linear mixed models, hierarchical clustering and heatmap, and gene set enrichment analysis were used to visualize the profiles, identify the top associated methylation loci, and investigate the involved pathways. Distinctly higher or lower methylation in samples from Butte were found at the top differentially methylated loci. The 200 genes harboring the most hypermethylated loci were significantly enriched in genes involved in actin cytoskeleton regulation, ABC transporters, leukocyte transendothelial migration, focal adhesion, and adherens junction, which plays a role in pathogenesis of disease, including autism spectrum disorders. This study lays a foundation for inquiry about genetic changes associated with environmental exposure to metals for people living in proximity to Superfund and open pit mining.

Tumor tissues are heterogeneous with different cell types in tumor microenvironment, which play an important role in tumorigenesis and tumor progression. Several computational algorithms and tools have been developed to infer the cell composition from bulk transcriptome profiles. However, they ignore the tissue specificity and thus a new resource for tissue-specific cell transcriptomic reference is needed for inferring cell composition in tumor microenvironment and exploring their association with clinical outcomes and tumor omics. In this study, we developed SCISSOR™ (https://thecailab.com/scissor/), an online open resource to fulfill that demand by integrating five orthogonal omics data of >6031 large-scale bulk samples, patient clinical outcomes and 451 917 high-granularity tissue-specific single-cell transcriptomic profiles of 16 cancer types. SCISSOR™ provides five major analysis modules that enable flexible modeling with adjustable parameters and dynamic visualization approaches. SCISSOR™ is valuable as a new resource for promoting tumor heterogeneity and tumor–tumor microenvironment cell interaction research, by delineating cells in the tissue-specific tumor microenvironment and characterizing their associations with tumor omics and clinical outcomes., Graphical Abstract Graphical AbstractSCISSOR™ preprocessed and hosted The Cancer Genome Atlas (TCGA) bulk transcriptomic data and tissue-specific single-cell transcriptomic data. Using these data, SCISSOR™ infers the proportion of tissue-specific cell types in tumor microenvironment. With the omics data and survival data from TCGA, the relationship and interaction can be tested and visualized in five modules, including Overview, Survival, Gene–TME cell correlation with two submodules for gene expression and genetic aberration, Genome-wide TME–omics association and Deconvolution. The Deconvolution module will allow users to upload their bulk transcriptome data and perform deconvolution with tissue-specific single-cell transcriptome profile references and deconvolution methods specified by users. The result will be automatically sent by email.

SummaryThe current spreading novel coronavirus SARS-CoV-2 is highly infectious and pathogenic. In this study, we screened the gene expression of three SARS-CoV-2 host receptors (ACE2, DC-SIGN and L-SIGN) and DC status in bulk and single cell transcriptomic datasets of upper airway, lung or blood of smokers, non-smokers and COVID-19 patients. We found smoking increased DC-SIGN gene expression and inhibited DC maturation and its ability of T cell stimulation. In COVID-19, DC-SIGN gene expression was interestingly decreased in lung DCs but increased in blood DCs. Strikingly, DCs shifted from cDCs to pDCs in COVID-19, but the shift was trapped in an immature stage (CD22+ or ANXA1+ DC) with MHCII downregulation in severe cases. This observation indicates that DCs in severe cases stimulate innate immune responses but fail to specifically recognize SARS-CoV-2. Our study provides insights into smoking effect on COVID-19 risk and the profound modulation of DC function in severe COVID-19.Graphical AbstractHighlightsSmoking upregulates the expression of ACE2 and CD209 and inhibits DC maturation in lungs. SARS-CoV-2 modulates the DCs proportion and CD209 expression differently in lung and blood.Severe infection is characterized by DCs less capable of maturation, antigen presentation and MHCII expression.DCs shift from cDCs to pDCs with SARS-CoV-2 infection but are trapped in an immature stage in severe cases.