Biology subjects

Wolfien, M.

Publications and source records attributed to Wolfien, M..

5 recordsLinked to original sources

bioRxiv2022

Construction of a three-component regulatory network of transcribed ultraconserved regions for the identification of prognostic biomarkers in gastric cancer

Although altered expression and functional roles of the transcribed ultraconserved regions (T-UCRs) in the pathophysiology of neoplasms has already been investigated, relevance of the functions for T-UCRs in gastric cancer (GC) is still the subject of inquiry. In the current study, The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset was used as a resource for the RNA-sequencing data. Differential expression analysis was conducted using DESeq2. Interactions between T-UCRs, miRNAs, and mRNAs were combined into a three-component network. The Survival package was utilized to identify survival-related differentially-expressed T-UCRs (DET-UCRs). Using an in-house cohort of GC tissues, expression of two DET-UCRs was experimentally verified. Thirty-four T-UCRs were dysregulated in TCGA-STAD tumoral samples compared to non-tumoral counterparts. The network was composed of 34 DET-UCRs, 275 miRNAs and 796 mRNAs nodes. Five T-UCRs were significantly correlated with the overall survival. While no expression of uc.232 was observed in our in-house cohort of GC tissues, uc.343 showed an increased expression in gastric tumoral tissues. The constructed three-component regulatory network of T-UCRs in GC presents a comprehensive understanding of the underlying gene expression regulation processes involved in tumor development and can serve as a basis to investigate potential prognostic biomarkers and therapeutic targets. Simple summaryGastric cancer (GC) is one of the most common cancers in the world and is considered as a highly heterogeneous disease based on subtypes and genetic alterations. GC is mostly detected in the advanced stages, hence, identifying diagnostic and prognostic biomarkers is of urgent need. Transcribed ultraconserved regions (T-UCRs) are a type of long non-coding RNAs which are linked to human carcinogenesis. Their mechanisms of action and the factors regulating their expression in cancers are poorly understood. In the current study, by applying a systems biology approach and constructing a regulatory network, we have presented a T-UCR as a potential diagnostic biomarker. Additionally, five T-UCRs with significant correlation with patients overall survival were found, which can be potentially used as prognostic biomarkers in future.

cancer biology↗

bioRxiv2022

In silico Investigation of Molecular Networks Linking Gastrointestinal Diseases, Malnutrition, and Sarcopenia

Malnutrition is a common primary or secondary complication in gastrointestinal diseases. The patients nutritional status also influences muscle mass and function, which can be impaired up to the degree of sarcopenia. The molecular interactions in diseases leading to sarcopenia are complex and multifaceted, affecting muscle physiology, the intestine (nutrition), and the liver at different levels. Although extensive knowledge of individual molecular factors is available, their regulatory interplay is not yet fully understood. A comprehensive overall picture of pathological mechanisms and resulting phenotypes is lacking. In silico approaches that convert existing knowledge into computationally readable formats can help to unravel such complex systems. We compiled available experimental evidence for molecular interactions involved in the development of sarcopenia into a knowledge base, referred to as the Sarcopenia Map. By including specific diseases, namely liver cirrhosis, and intestinal dysfunction, and considering their effects on nutritional status and blood secretome, we investigated their contribution to the development of sarcopenia. The Sarcopenia Map is publicly available as an open-source, interactive online resource, providing tools that allow users to explore the information on the map and perform in silico perturbation experiments.

systems biology↗

bioRxiv2021

AMES: Automated evaluation of sarcomere structures in cardiomyocytes

BackgroundArrhythmias are severe cardiac diseases and lethal if untreated. To serve as an in vitro drug testing option for anti-arrhythmic agents, cardiomyocytes are being generated in vitro from induced pluripotent stem cells (iPSCs). Unfortunately, these generated cardiomyocytes resemble fetal cardiac tissue rather than adult cardiomyocytes. An automated tool for an unbiased evaluation of cardiomyocytes would highly facilitate the establishment of new differentiation protocols to increase cellular maturity. ResultsIn this work, a novel deep learning-based approach for this task is presented and evaluated. Different convolutional neural networks (CNNs) including 2D and 3D models were trained on fluorescence images of human iPSC-derived cardiomyocytes, which were rated based on their sarcomere content (sarcomerisation) and the orientation of sarcomere filaments (directionality) beforehand by a domain expert. The CNNs were trained to perform classifications on sarcomerisation, directionality ratings, and cell source, including primary adult and differentiated cardiomyocytes. The best accuracies are reached by a 3D model with a classification accuracy of about 90 % for sarcomerisation classification, 63 % for directionality classification, and 80 % for cell source classification. The trained models were additionally evaluated using two explanatory algorithms, IGrad and Grad-CAM. The heatmaps computed by those explainability algorithms show that the important regions in the image occur inside the cell and at the cellular borders for the classifier, and, therefore, validate the calculated regions. ConclusionIn summary, we showed that cellular fluorescence images can be analyzed with CNNs and subsequently used to predict different states of sarcomere maturation. Our developed prediction tool AMES (https://github.com/maxhillemanns/AMES) can be used to make trustworthy predictions on the quality of a cardiomyocyte, which ultimately facilitates the optimized generation of cardiomyocytes from iPSCs and improves the quality control in an automated, unbiased manner. The applied workflow of testing different CNN models, adjusting parameters, and using a variety of explanatory algorithms can be easily transferred to further image based quality control, stratification, or analysis setups.

bioinformatics↗

bioRxiv2021

Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling

The research landscape of single-cell and single-nuclei RNA sequencing is evolving rapidly, and one area that is enabled by this technology, is the detection of rare cells. An automated, unbiased and accurate annotation of rare subpopulations is challenging. Once rare cells are identified in one dataset, it will usually be necessary to generate other datasets to enrich the analysis (e.g., with samples from other tissues). From a machine learning perspective, the challenge arises from the fact that rare cell subpopulations constitute an imbalanced classification problem. We here introduce a Machine Learning (ML)-based oversampling method that uses gene expression counts of already identified rare cells as an input to generate synthetic cells to then identify similar (rare) cells in other publicly available experiments. We utilize single-cell synthetic oversampling (sc-SynO), which is based on the Localized Random Affine Shadowsampling (LoRAS) algorithm. The algorithm corrects for the overall imbalance ratio of the minority and majority class. We demonstrate the effectiveness of the method for two independent use cases, each consisting of two published datasets. The first use case identifies cardiac glial cells in snRNA-Seq data (17 nuclei out of 8,635). This use case was designed to take a larger imbalance ratio ([~]1 to 500) into account and only uses single-nuclei data. The second use case was designed to jointly use snRNA-Seq data and scRNA-Seq on a lower imbalance ratio ([~]1 to 26) for the training step to likewise investigate the potential of the algorithm to consider both single cell capture procedures and the impact of "less" rare-cell types. For validation purposes, all datasets have also been analyzed in a traditional manner using common data analysis approaches, such as the Seurat3 workflow. Our algorithm identifies rare-cell populations with a high accuracy and low false positive detection rate. A striking benefit of our algorithm is that it can be readily implemented in other and existing workflows. The code basis is publicly available at FairdomHub (https://fairdomhub.org/assays/1368) and can easily be transferred to train other customized approaches.

bioinformatics↗

bioRxiv2017

Community-driven data analysis training for biology

The primary problem with the explosion of biomedical datasets is not the data itself, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web-browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences.

bioinformatics↗