Pietilainen, K. H. | Search bioRxiv

bioRxiv2024

Molecular Landscape of Modality-Specific Exercise Adaptation in Human Skeletal Muscle through Large-Scale Multi-OMICs Integration

We conducted a large-scale, statistically powered, meta-analysis of exercise adaptations in human skeletal muscles, integrating epigenetic, transcriptomic, transcription factors, and proteomic data across 12 independent cohorts comprising over 1000 participants and 2340 human muscle samples. Our study identified distinctive signatures associated with maximal oxygen consumption (VO2max), and identified five genes robustly intersecting multi-OMIC layers. Notably, transcription factors predominantly functioned as activators across these layers, regulating expression of target genes irrespective of whether DNA methylation levels were low or high, indicating a synergistic effect between TFs and the methylome. Analysis of distinct exercise modalities (aerobic and resistance exercise) revealed unique gene pathways, contrasting with patterns observed in inactivity (muscle disuse) studies. These findings offer a comprehensive understanding of exercise and modality-specific adaptations, shedding light on muscle health and the molecular mechanisms associated with cardiorespiratory fitness, aging, and disease prevention.

molecular biology↗

bioRxiv2019

Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

bioinformatics↗