Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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The atlas of the human hypertrophied heart reveals impaired cell communication

ABSTRACT: Pathological cardiac hypertrophy is a leading cause of heart failure. The understanding of disease mechanisms is primarily based on experimental models, but knowledge of the full repertoire of cardiac cells and their gene expression profiles in the human heart is missing. Here, using large-scale single-nucleus transcriptomes, we highlight the transcriptional response of cardiomyocytes to pressure overload in humans with stenosis of the aortic valve and disclose major alterations in cellular cross-talk. Cardiomyocytes showed a reduction of incoming connections with endothelial cells and fibroblasts. Particularly Eph receptor tyrosine kinases, including the predominant EPHB1 gene, were significantly down-regulated in cardiomyocytes of the hypertrophied heart. We compared 5 AS patients to healthy patients from the human heart cell ATLAS (https://www.heartcellatlas.org). This repository contains the processed files from the single nuclei RNA-SEQ.

INSTRUMENT(S): Illumina NovaSeq 6000

ORGANISM(S): Homo sapiens

SUBMITTER: David John

PROVIDER: E-MTAB-11268 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:We developed cell2location, a principled and versatile Bayesian model that is designed to resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. To validate cell2location in real tissue, we applied the model to data from the mouse brain, which features diverse neural cell types organised in a well characterised spatial architecture across brain areas, thus presenting a canonical use case to test spatial genomics. We generated matched single nucleus (sn, this submission) and Visium spatial RNA-seq (10X Genomics) profiles of adjacent mouse brain sections that contain multiple regions from the telencephalon and diencephalon. To assess the biological and intra-organ technical variation in spatial mapping, we assayed two mouse brains and serial tissue sections from each brain (total of 3 and 2 matched sections from two animals, respectively, and an extra section for snRNA-seq), creating a rich multi-modal and replicated transcriptomic dataset. Tissue processing. Brains of wild-type adult C57BL/6 mice (postnatal day 56, 1 female and 1 male) were dissected, snap frozen, embedded in optimal cutting temperature compound (Tissue-Tek) and stored at -80oC. Brain hemispheres were cryosectioned at -20oC using a cryostat (Leica, CM3050S). To assess tissue quality, RNA was extracted from test tissue sections using the RNeasy Pico Kit (Qiagen) and yielded high RIN values (9.6 and 9.7) on an Agilent Bioanalyser, indicating high RNA quality. For matched single nuclei and Visium RNA-seq experiments, brain hemispheres were cryosectioned to adjacent thick (200 µm) and thin (10 µm) coronal sections, respectively, and processed the same day. In total, four consecutive sets of thick and thin tissue sections were collected from each brain. Five sets of tissue sections yielded both good quality single nuclei and Visium data (three adjacent sections from mouse 1 and two sections from mouse 2) while one additional section from mouse 2 yielded good single nuclei; these were considered for analysis in this study. Single nucleus RNA-sequencing. Thick (200 µm) mouse brain sections were cryosectioned, dissected from OCT and kept in a tube on dry ice until subsequent processing. Nuclei were extracted from each section as described previously. Briefly, nuclei were released from sections via Dounce homogenisation, Hoechst-stained, and isolated via fluorescence-activated cell sorting (FACS). Nuclei were then loaded into the 10X Chromium Single Cell 3′ Kit (v3) to obtain 3,000-7,000 nuclei per well, and library preparation was done per manufacturer’s protocol. Libraries were sequenced on an Illumina NovaSeq S4 system. Sequencing data were processed using 10X CellRanger version 3.0.2, aligned to mouse pre-mRNA genome reference version mm10 and mRNA count matrices were generated by adding intronic and exonic unique molecular identifier (UMI) counts for each gene in each cell. Initially, snRNA-seq counts were processed using standard Seurat V3 workflow without correcting batch effects between 6 individual samples.

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The atlas of the human hypertrophied heart reveals impaired cell communication

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