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Nature Papers Study Molecular Effects of Smoking, Way of Identifying Spatial Expression Patterns of Genes

Quitting smoking may allow the lung to replenish bronchial epithelial tissue that free of the molecular impact of tobacco exposure, according to a sequencing study appearing in Nature this week. A team led by scientists from the Wellcome Trust Sanger Institute sequenced whole genomes of 632 colonies derived from single bronchial epithelial cells across 16 people including three children, four never-smokers, six former smokers, and three current smokers. They found that the cells of current smokers had significantly more — and in some cases multiple —driver mutations than non-smokers, indicating that smoking increases mutational burden, cell-to-cell heterogeneity, and driver mutations. The researchers also discovered that cell populations in former smokers had mutational burdens that were equivalent to those expected for people who had never smoked. The findings suggest that stopping smoking does not just slow lung damage but can also "reawaken cells that have not been damaged by past lifestyle choices," the authors write. GenomeWeb has more on this, here.
 
While identifying genes that display spatial expression patterns in spatially resolved transcriptomic studies is key to characterizing the spatial transcriptomic landscape in tissues, doing so requires overcoming meaningful statistical and computational challenges. To that end, researchers from the University of Michigan report in Nature Methods a statistical method for identifying spatial expression patterns of genes in data generated from various spatially resolved transcriptomic techniques. Dubbed SPARK — short for spatial pattern recognition via kernels — the approach directly models spatial count data through generalized linear spatial models. According to its developers, SPARK is scalable to analyzing tens of thousands of genes across tens of thousands spatial locations. By using it to analyze four published spatially resolved transcriptomic datasets, the scientists show it can be up to ten times more powerful than existing methods "and disclose biological discoveries that otherwise cannot be revealed by existing approaches."