Biology subjects

Heinig, M.

Publications and source records attributed to Heinig, M..

23 records · Page 2Linked to original sources

bioRxiv2020

Tissue-specific multiOMICs analysis of atrial fibrillation

Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, novel multiOMICs approaches are needed for deciphering the underlying molecular networks. Here, we integrated genomics, transcriptomics, and proteomics of human atrial tissue which allowed for identifying widespread effects of genetic variants on both transcript (cis eQTL) and protein (cis pQTL) abundance. We further established a novel targeted trans QTL approach based on polygenic risk scores to identify candidates for AF core genes. Using this approach, we identified two trans eQTLs and four trans pQTLs for AF GWAS hits, and elucidated the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multiOMICs method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization.

genetics↗

bioRxiv2020

Cells and gene expression programs in the adult human heart

Cardiovascular disease is the leading cause of death worldwide. Advanced insights into disease mechanisms and strategies to improve therapeutic opportunities require deeper understanding of the molecular processes of the normal heart. Knowledge of the full repertoire of cardiac cells and their gene expression profiles is a fundamental first step in this endeavor. Here, using large-scale single cell and nuclei transcriptomic profiling together with state-of-the-art analytical techniques, we characterise the adult human heart cellular landscape covering six anatomical cardiac regions (left and right atria and ventricles, apex and interventricular septum). Our results highlight the cellular heterogeneity of cardiomyocytes, pericytes and fibroblasts, revealing distinct subsets in the atria and ventricles indicative of diverse developmental origins and specialized properties. Further we define the complexity of the cardiac vascular network which includes clusters of arterial, capillary, venous, lymphatic endothelial cells and an atrial-enriched population. By comparing cardiac cells to skeletal muscle and kidney, we identify cardiac tissue resident macrophage subsets with transcriptional signatures indicative of both inflammatory and reparative phenotypes. Further, inference of cell-cell interactions highlight a macrophage-fibroblast-cardiomyocyte network that differs between atria and ventricles, and compared to skeletal muscle. We expect this reference human cardiac cell atlas to advance mechanistic studies of heart homeostasis and disease.

genomics↗

bioRxiv2020

Design and power analysis for multi-sample single cell genomics experiments

BackgroundThe identification of genes associated with specific experimental conditions, genotypes or phenotypes through differential expression analysis has long been the cornerstone of transcriptomic analysis. Single cell RNA-seq is revolutionizing transcriptomics and is enabling interindividual differential gene expression analysis and identification of genetic variants associated with gene expression, so called expression quantitative trait loci at cell-type resolution. Current methods for power analysis and guidance of experimental design either do not account for the specific characteristics of single cell data or are not suitable to model interindividual comparisons. ResultsHere we present a statistical framework for experimental design and power analysis of single cell differential gene expression between groups of individuals and expression quantitative trait locus analysis. The model relates sample size, number of cells per individual and sequencing depth to the power of detecting differentially expressed genes within individual cell types. Power analysis is based on data driven priors from literature or pilot experiments across a wide range of application scenarios and single cell RNA-seq platforms. Using these priors we show that, for a fixed budget, the number of cells per individual is the major determinant of power. ConclusionOur model is general and allows for systematic comparison of alternative experimental designs and can thus be used to guide experimental design to optimize power. For a wide range of applications, shallow sequencing of high numbers of cells per individual leads to higher overall power than deep sequencing of fewer cells. The model is implemented as an R package scPower.

bioinformatics↗

bioRxiv2018

MetaMap: An atlas of metatranscriptomic reads in human disease-related RNA-seq data

BackgroundWith the advent of the age of big data in bioinformatics, large volumes of data and high performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts, but its generic nature also enables the detection of microbial and viral transcripts.\n\nFindingsWe developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-human-mapping read fraction. We validated this approach by recapitulating outcomes from 6 independent controlled infection experiments of cell line models and comparison with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from >17,000 samples from >400 studies relevant to human disease using state-of-the-art high performance computing systems. The resulting data of this large-scale re-analysis are made available in the presented MetaMap resource.\n\nConclusionsOur results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation towards the role of the microbiome in human disease.

bioinformatics↗

bioRxiv2017

Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines

The response of cancer cells to drugs is determined by various factors, including the cells mutations and gene expression levels. These factors can be assessed using next-generation sequencing. Their integration with vast prior knowledge on signaling pathways is, however, limited by the availability of mathematical models and scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. With this framework, we parameterized a mechanistic model describing major cancer-associated signaling pathways (>1200 species and >2600 reactions) using drug response data. For the parameterized mechanistic model, we found a prediction accuracy, which exceeds that of the considered statistical approaches. Our results demonstrate for the first time the massive integration of heterogeneous datasets using large-scale mechanistic models, and how these models facilitate individualized predictions of drug response. We anticipate our parameterized model to be a starting point for the development of more comprehensive, curated models of signaling pathways, accounting for additional pathways and drugs.

systems biology↗