Biology subjects

Merseburger, P.

Publications and source records attributed to Merseburger, P..

4 recordsLinked to original sources

bioRxiv2025

Deconvoluting fiber type proportions from human skeletal muscle transcriptomics and proteomics data using FibeRtypeR

Muscle fibers are the dominant, multinucleated cell type in skeletal muscle. In humans, they can be classified as slow (type 1) and fast (type 2) fibers, traditionally based on distinct properties of their contractile machinery. Slow and fast fibers are characterized by shared and specific cellular complexities that are pivotal for their adaptive capacity to exercise or role in metabolic disease progression. In this era of omics research, there is a critical need to accurately infer muscle fiber type proportions from bulk tissue omics datasets, as single-fiber approaches are not feasible in large-scale or retrospective studies. Here we present FibeRtypeR, an easy-to-use web application to accurately estimate fiber type proportions from bulk transcriptomics and proteomics datasets: https://muscleapps.ugent.be. FibeRtypeR exploits transcriptomics and proteomics profiles of 1000 fiber-typed individual human skeletal muscle fibers as a reference dataset and is validated against paired immunohistochemical fiber type determinations in 160 muscle biopsies. We show that FibeRtypeR can be applied to public datasets, illustrating the application potential across a wide range of biological contexts such as aging, disease and exercise training. This new freely accessible computational tool will prove valuable to the skeletal muscle research community. Key pointsO_LIBulk muscle omics datasets lack fiber type specific information C_LIO_LIOur new tool, FibeRtypeR, leverages in-house collected single-fiber profiles allowing for accurate fiber type inference C_LIO_LIFibeRtypeR is methodologically robust across omics technologies and workflows C_LIO_LIWe host FibeRtypeR as an intuitive open-access Shiny app, applicable to new and publicly available transcriptomics and proteomics datasets C_LI

physiology↗

bioRxiv2024

Spatial transcriptomics exploration of the primary neuroblastoma microenvironment unveils novel paracrine interactions

High-risk neuroblastomas exhibit a high degree of intratumoral heterogeneity. Single-cell RNA sequencing has greatly improved our understanding of these tumors, but the method lacks cellular tissue context and spatial information about local signaling dynamics. To address this gap, we profiled untreated and chemotherapy-treated high-risk neuroblastomas from 2 patients using spatial transcriptomics. We confirmed the transcriptional and cellular heterogeneous nature of the neuroblastoma microenvironment and identified several unique spatial niches and patterns. In one of the treated tumors, a spatially constrained cluster of undifferentiated and 11p-gained cancer cells was identified, surrounded by a rim of macrophages. A signaling interaction between the chemokine CCL18 and its receptor PITPNM3 was predicted between these cells and we experimentally demonstrated that CCL18 increases neuroblastoma cell migration. In the other tumor, we identified a stromal cluster with high transcriptional similarity to the adrenal cortex. These adrenocortical-like cells expressed the ALK ligand ALKAL2 and were predicted to communicate with neighboring ALK expressing cancer cells. We demonstrated a unique developmental pattern of adrenal medulla-specific expression of ALK and adrenocortical-specific expression of ALKAL2, suggesting a role of these signaling interactions in neuroblastoma carcinogenesis.

cancer biology↗

bioRxiv2023

Preclinical exploration of the DNA Damage Response pathway using the interactive neuroblastoma cell line explorer CLEAN.

Neuroblastoma (NB) is the most common cancer in infancy with an urgent need for more efficient targeted therapies. The development of novel (combinatorial) treatment strategies relies on extensive explorations of signaling perturbations in neuroblastoma cell lines, using RNA-Seq or other high throughput technologies (e.g., phosphoproteomics). This typically requires dedicated bioinformatics support, which is not always available. Additionally, while data from published studies are highly valuable and raw data (e.g., fastq files) are nowadays released in public repositories, data processing is time-consuming and again difficult without bioinformatics support. To facilitate NB research, more user-friendly and immediately accessible platforms are needed to explore newly generated as well as existing high throughput data. To make this possible, we developed an interactive data centralization and visualization web application, called CLEAN (the Cell Line Explorer web Application of Neuroblastoma data; https://ccgg.ugent.be/shiny/clean/). By focusing on the regulation of the DNA damage response, a therapeutic target of major interest in neuroblastoma, we demonstrate how CLEAN can be used to gain novel mechanistic insights and identify putative drug targets in neuroblastoma.

cancer biology↗

bioRxiv2022

Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data

The Human Leukocyte Antigen (HLA) genes are a group of highly polymorphic genes that are located in the Major Histocompatibility Complex (MHC) region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. While knowledge of these genotypes is of utmost importance to study differences in immune responses between cancer patients, gold standard, PCR-derived genotypes are rarely available in large Next Generation Sequencing (NGS) datasets. Therefore, a variety of methods for in silico NGS-based HLA genotyping have been developed, bypassing the need to determine these genotypes with separate experiments. However, there is currently no consensus on the best performing tool. Here, we compiled a list of 13 HLA callers and evaluated their accuracy on three different datasets. Based on these results, best-practice guidelines were constructed, and consensus HLA allele predictions were made for DNA and RNA samples from The Cancer Genome Atlas (TCGA).

bioinformatics↗