A dataset of chronic nicotine-induced genes in breast cancer cells.

These data show the differentially expressed genes (DEG) from HCC38 breast cancer cell line chronically exposed to nicotine versus vehicle control. Additional data is also provided from dynamic trajectory analysis, identifying the most dynamic genes due to chronic nicotine treatment. To produce this dataset, we first performed single cell RNA sequencing from HCC38 cells chronically treated with vehicle or nicotine, followed by scanpy analysis to yield 6 discrete cell clusters at conservative resolution. We then evaluated differential gene expression between chronic nicotine and control cells for each individual cluster or in the whole sample using PyDESeq2. For dynamic trajectory analysis, Velocyto (0.6) was used to estimate the spliced and unspliced counts for each gene between chronic nicotine-treated cells and vehicle, allowing computation of gene velocities. These data are useful for analysing the expression of individual genes or gene velocities either in the whole sample or in the different clusters identified. Since the HCC38 cell line used in these experiments is heterogeneous, including cells with features of stem-like, luminal progenitor-like and more differentiated cells, this dataset allows examination of the conserved as well as disparate gene expression effects of nicotine in different breast cancer cell types. Our dataset has a great potential for re-use given the recent surge in interest surrounding the role tobacco-use plays in breast cancer progression.