Biology subjects

Wollny, D.

Publications and source records attributed to Wollny, D..

6 recordsLinked to original sources

bioRxiv2023

Maximizing the potential of genomic and transcriptomic studies by nanopore sequencing

Nucleic acid sequencing is the process of identifying the sequence of DNA or RNA, with DNA used for genomes and RNA for transcriptomes. Deciphering this information has the potential to greatly advance our understanding of genomic features and cellular functions. In comparison to other available sequencing methods, nanopore sequencing stands out due to its unique advantages of processing long nucleic acid strands in real time, within a small portable device, enabling the rapid analysis of samples in diverse settings. Evolving over the past decade, nanopore sequencing remains in a state of ongoing development and refinement, resulting in persistent challenges in protocols and technology. This article employs an interdisciplinary approach, evaluating experimental and computational methods to address critical gaps in our understanding in order to maximize the information gain from this advancing technology. Here we present both overview and analysis of all aspects of nanopore sequencing by providing statistically supported insights. Thus, we aim to provide fresh perspectives on nanopore sequencing and give comprehensive guidelines for the diverse challenges that frequently impede optimal experimental outcomes.

bioinformatics↗

bioRxiv2023

RNA-based sensitive fungal pathogen detection

Detecting fungal pathogens, a major cause of severe systemic infections, remains challenging due to the difficulty and time-consuming nature of diagnostic methods. This delay in identification hinders targeted treatment decisions and may lead to unnecessary use of broad-spectrum antibiotics. To expedite treatment initiation, one promising approach is to directly detect pathogen nucleic acids such as DNA, which is often preferred to RNA because of its inherent stability. However, a higher number of RNA molecules per cell makes RNA a more promising diagnostic target which is particularly prominent for highly expressed genes such as rRNA. Here, we investigated the utility of a minimal input-specialized reverse transcription protocol to increase diagnostic sensitivity. This proof-of-concept study demonstrates that fungal rRNA detection by the minimal input protocol is drastically more sensitive compared to detection of genomic DNA even with high levels of human RNA background. This approach can detect several of the most relevant human pathogenic fungal genera, such as Aspergillus, Candida, and Fusarium and thus represents a powerful, cheap, and easily adaptable addition to currently available diagnostic assays.

microbiology↗

bioRxiv2023

Detection of reproducible liver cancer specific ligand-receptor signaling in blood

Cell-cell communication mediated by ligand-receptor interactions (LRI) is critical to coordinating diverse biological processes in homeostasis and disease. Lately, our understanding of these processes has greatly expanded through the inference of cellular communication, utilizing RNA extracted from bulk tissue or individual cells. Considering the challenge of obtaining tissue biopsies for these approaches, we considered the potential of studying cell-free RNA obtained from blood. To test the feasibility of this approach, we used the BulkSignalR algorithm across 295 cell-free RNA samples and compared the LRI profiles across multiple cancer types and healthy donors. Interestingly, we detected specific and reproducible LRIs particularly in the blood of liver cancer patients compared to healthy donors. We found an increase in the magnitude of hepatocyte interactions, notably hepatocyte autocrine interactions in liver cancer patients. Additionally, a robust panel of 30 liver cancer-specific LRIs presents a bridge linking liver cancer pathogenesis to discernible blood markers. In summary, our approach shows the plausibility of detecting liver LRIs in blood and builds upon the biological understanding of cell-free transcriptomes.

bioinformatics↗

bioRxiv2021

Inferring and perturbing cell fate regulomes in human cerebral organoids

Self-organizing cerebral organoids grown from pluripotent stem cells combined with single-cell genomic technologies provide opportunities to explore gene regulatory networks (GRNs) underlying human brain development. Here we acquire single-cell transcriptome and accessible chromatin profiling data over a dense time course covering multiple phases of organoid development including neuroepithelial formation, patterning, brain regionalization, and neurogenesis. We identify temporally dynamic and brain region-specific regulatory regions, and cell interaction analysis reveals emergent patterning centers associated with regionalization. We develop Pando, a flexible linear model-based framework that incorporates multi-omic data and transcription binding site predictions to infer a global GRN describing organoid development. We use pooled genetic perturbation with single-cell transcriptome readout to assess transcription factor requirement for cell fate and state regulation in organoid. We find that certain factors regulate the abundance of cell fates, whereas other factors impact neuronal cell states after differentiation. We show that the zinc finger protein GLI3 is required for cortical fate establishment in humans, recapitulating previous work performed in mammalian model systems. We measure transcriptome and chromatin accessibility in normal or GLI3-perturbed cells and identify a regulome central to the dorsoventral telencephalic fate decision. This regulome suggests that Notch effectors HES4/5 are direct GLI3 targets, which together coordinate cortex and ganglionic eminence diversification. Altogether, we provide a framework for how multi-brain region model systems and single-cell technologies can be leveraged to reconstruct human brain developmental biology.

developmental biology↗

bioRxiv2021

Characterization of RNA content in individual phase-separated coacervate microdroplets

Liquid-liquid phase separation or condensation is a form of macromolecular compartmentalization. Condensates formed by complex coacervation were hypothesized to have played a crucial part during the origin-of-life. In living cells, condensation organizes biomolecules into a wide range of membraneless compartments. Although RNA is a key component of condensation in cells and the central component of the RNA world hypothesis, little is known about what determines RNA accumulation in condensates and how single condensates differ in their RNA composition. Therefore, we developed an approach to read the RNA content from single condensates using high-throughput sequencing. We find that RNAs which are enriched for specific sequence motifs efficiently accumulate in condensates. These motifs show high sequence similarity to short interspersed elements (SINEs). We observed similar results for protein-derived condensates, demonstrating applicability across different in vitro reconstituted membraneless organelles. Thus, our results provide a new inroad to explore the RNA content of phase-separated droplets at single condensate resolution.

genomics↗

bioRxiv2016

RNA-seq library preparation from single pancreatic acinar cells

Single cell RNA sequencing technology has emerged as a promising tool to uncover previously neglected cellular heterogeneity. Multiple methods and protocols have been developed to apply single cell sequencing to different cell types from various organs. However, library preparation for RNA sequencing remains challenging for cell types with high RNAse content due to rapid degradation of endogenous RNA molecules upon cell lysis. To this end, we developed a protocol based on the SMART-seq2 technology for single cell RNA sequencing of pancreatic acinar cells, the cell type with one of the highest ribonuclease concentration measured to date. This protocol reliably produces high quality libraries from single acinar cells reaching a total of 5x106 reads / cell and [~] 80% transcript mapping rate with no detectable 3{acute}end bias. Thus, our protocol makes single cell transcriptomics accessible to cell type with very high RNAse content.

genomics↗