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Cardona, C. d. J.

Publications and source records attributed to Cardona, C. d. J..

2 recordsLinked to original sources

Multi-histone ChIP-Seq Analysis with DecoDen

Epigenetic mechanisms coordinate packaging, accessibility and read-out of the DNA sequence within the chromatin context. They significantly contribute to the regulation of gene expression. Thus, they play fundamental roles during differentiation on the one hand and maintenance and propagation of cell identity on the other. Epigenetic malfunctioning is associated with a large range of diseases, from neurodevelopmental disorders to cancer progression. In humans, hundreds of known epigenetic factors and complexes are involved in establishing covalent modifications on the DNA sequence itself and on associated histone proteins. Within the cellular context, the resulting combinatorial epigenomic patterns are neither established nor interpreted independently of each other and therefore exhibit high correlations in a region-specific manner. Post-translational modifications of histone proteins can be analysed using Chromatin Immunoprecipitation followed by sequencing (ChIP-Seq). Often, several assays for a number of different histone modifications are performed as part of the same experimental design. These measurements are, however, confounded by shared biases including chromatin accessibility and mappability. Existing computational methods analyse each histone modification separately. We introduce DecoDen, a new approach that leverages replicates and multi-histone ChIP-Seq experiments for a fixed cell type to learn and remove shared biases. DecoDen (Deconvolve and Denoise) consists of two major steps: We use non-negative matrix factorisation (NMF) to learn a joint cell-type specific background signal. Half-sibling regression (HSR) is then used to correct for these biases in the histone modification signals. We demonstrate that DecoDen is a robust and interpretable method that enables the unbiased discovery of subtle peaks, which are particularly important in an individual-specific context.

bioinformatics↗

Mimicking tumor cell heterogeneity of colorectal cancer in a patient-derived organoid-fibroblast model

Patient-derived organoid (PDO) cancer models are generated from epithelial tumor cells. Although they reflect the molecular tumor characteristics, they lack the complexity of the tumor microenvironment, which is a key driver of tumorigenesis and therapy response. Here, we present a colorectal cancer (CRC) organoid model that incorporates epithelial cells and stromal fibroblasts from the same patient. Molecular characterization of primary cancer associated fibroblasts (CAFs) and matched normal fibroblasts (NF) revealed proteomic, secretome and gene expression differences in pathways associated with tumor related fibroblast function. Further, CAFs retained higher motility compared to NFs in vitro. Importantly, both CAFs and NFs supported cancer cell proliferation in 3D co-cultures, without the addition of classical niche factors. PDOs grown together with fibroblasts displayed a larger cellular heterogeneity of tumor cells compared to mono-cultures, and closely resembled the in vivo tumor morphology. This was also confirmed by the calculation of cellular proportions of epithelial cell subtypes in organoid mono-versus co-cultures, which were inferred through bioinformatics deconvolution of bulk RNA sequencing data using published single cell RNA sequencing datasets from CRC tissues. Additionally, we observed a mutual crosstalk between tumor cells and fibroblasts in the co-cultures. This was manifested by majorly deregulated pathways such as cell-cell communication and extracellular matrix remodeling in the organoids. For the fibroblasts, we observed enhanced expression of tumor induced marker genes and cytokines characteristic for myo- and immunogenic fibroblasts. This model will be vital as a physiological personalized tumor model to study disease mechanisms and therapy response in CRC. One Sentence SummaryPatient matched fibroblasts support tumor organoid growth in 3D co-culture and maintain intratumoral cellular heterogeneity and histo-morphology.

cancer biology↗