3D chromatin architecture of drug-tolerant cancer cells at single-cell resolution (scDNA-seq dataset)
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ABSTRACT: Anticancer drug therapy generally elicits the drug tolerance after long-term treatment. Recent studies suggested that 3D chromatin structures of cancer cells were intimately linked to drug resistance. However, 3D chromatin structures in drug-tolerant cancer cells at single-cell resolution haven’t been elucidated. Here we performed single-cell Hi-C (scHi-C) analysis to examine the 3D chromatin structures in three stages of breast cancer cells. Then Single-cell RNA-seq data were integrated with scHi-C data. scDNA-seq data were also been generated.
Project description:Anticancer drug therapy generally elicits the drug tolerance after long-term treatment. Recent studies suggested that 3D chromatin structures of cancer cells were intimately linked to drug resistance. However, 3D chromatin structures in drug-tolerant cancer cells at single-cell resolution haven’t been elucidated. Here we performed single-cell Hi-C (scHi-C) analysis to examine the 3D chromatin structures in three stages of breast cancer cells. Then Single-cell RNA-seq data were integrated with scHi-C data.
Project description:Anticancer drug therapy generally elicits the drug tolerance after long-term treatment. Recent studies suggested that 3D chromatin structures of cancer cells were intimately linked to drug resistance. However, 3D chromatin structures in drug-tolerant cancer cells at single-cell resolution haven’t been elucidated. Here we performed single-cell Hi-C (scHi-C) analysis to examine the 3D chromatin structures in three stages of breast cancer cells.
Project description:Anticancer drug therapy generally elicits the drug tolerance after long-term treatment. Recent studies suggested that 3D chromatin structures of cancer cells were intimately linked to drug resistance. However, 3D chromatin structures in drug-tolerant cancer cells at single-cell resolution haven’t been elucidated. Here we performed single-cell Hi-C (scHi-C) analysis to examine the 3D chromatin structures in three stages of breast cancer cells. Population cells Hi-C data as the reference.
Project description:Recent advances in chromatin architecture profiling technologies, such as single-cell Hi-C (scHi-C), allow us to dissect the heterogeneity of chromosome higher-order structures across diverse cell states and different individuals. However, scHi-C experiments are still expensive and not immediately available for population-scale profiling. Here, we present scENCORE, a computational method, to reconstruct personalized and cell-type-specific higher-order chromatin structures, such as A/B compartments, directly from more cost-effective and widely available single-cell epigenetic data (e.g., scATAC-seq). We apply scENCORE on scATAC-seq data from post-mortem prefrontal cortex brains and demonstrate its utility to 1) project Mega-base scale chromatin regions into lower dimensional space by leveraging graph embedding technologies based on cell-type-specific co-variability patterns, 2) define A/B compartments via unsupervised clustering, 3) perform an alignment algorithm for multi-graph embedding to derive comparable chromatin representations and highlight dynamic chromatin compartments across cell states and individuals. Validated by Hi-C experiments using FACS-sorted cells, scENCORE can faithfully reconstruct cell-type-specific chromatin compartments. Furthermore, scENCORE uniformly constructs chromosome conformation across population-scale scATAC-seq data and discovers key 3D structural switching events associated with psychiatric disorders. In summary, scENCORE allows cost-effective cell-type-specific and personalized reconstruction that delineate higher-order chromatin structures.
Project description:The traditional method for studying cancer in vitro is to grow immortalized cancer cells in two-dimensional (2D) monolayers on plastic. However, many cellular features are impaired in these unnatural conditions and big alterations in gene expression in comparison to tumors have been reported. Three-dimensional (3D) cell culture models have become increasingly popular and are suggested to be better models than 2D monolayers due to improved cell-to-cell contacts and structures that resemble in vivo architecture. The aim of this study was to develop a simple high-throughput 3D drug screening method and to compare drug responses in JIMT1 breast cancer cells when grown in 2D, in polyHEMA coated anchorage independent 3D models and in Matrigel on-top 3D cell culture models. We screened 102 compounds with multiple concentrations and biological replicates for their effects on cell proliferation. The cells were either treated immediately upon plating or they were allowed to grow in 3D for four days prior to the drug treatment. Big variations in drug responses were observed between the models indicating that comparisons of culture model influenced drug sensitivities cannot be made based on effects of a single drug. However, we show with the 63 most prominent drugs that, in general, JIMT1 cells grown on Matrigel were significantly more sensitive to drugs than cells grown in 2D cultures, while responses of cells grown in polyHEMA resembled those of 2D. Furthermore, comparison of gene expression profiles of the cell culture models to xenograft tumors indicated that cells cultured in Matrigel and as xenografts most closely resembled each other. In this study we also suggest that 3D cultures can provide a platform for systematic experimentation of larger compound collections in a high-throughput mode and be used as alternatives for traditional 2D screens towards better comparability to in vivo state. Gene expression analysis of JIMT1 breast cancer cells cultured as xenografts for 43 days, in two dimensional cultures for seven days (2D7d), in polyHEMA three dimensional cell culture models for four and seven days (PH7d and PH7d), and in Matrigel three dimensional cultures for four and seven days (MG4d and MG7d). Two biological replicates was included for each sample.