Biology subjects

Wiegandt, R.

Publications and source records attributed to Wiegandt, R..

3 recordsLinked to original sources

bioRxiv2025

Ischemic injury triggers a protective microglial phenotype in models of Aβ pathology

Microglia are highly plastic cells that are capable of integrating subsequent insults. As the majority of Alzheimers Disease (AD) patients also show cerebrovascular pathology, we here aimed to dissect the interactions between AD and ischemic brain injury on the microglial response to amyloid beta (A{beta}) pathology. Surprisingly, we find that ischemic stroke in the presence of cerebral {beta}-amyloidosis results in the generation of a novel neuroprotective microglial phenotype. These microglia drive a rapid accumulation of highly dense A{beta} plaques that exhibit a relatively benign nature and are strikingly similar to A{beta} plaques observed in patients that are resilient to AD pathology. Thus, our data do not only highlight the impact of a co-morbid state of brain ischemia and A{beta} pathology on the microglial phenotype but also identify novel molecular pathways that may serve to promote beneficial microglial functions in AD.

neuroscience↗

bioRxiv2025

SC-Framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets

The accelerated development of single-cell technologies has profoundly impacted the field of biological research, facilitating unparalleled insights into cellular heterogeneity. However, this progress has also produced new computational challenges in the field of bioinformatics: single-cell datasets are increasingly high-dimensional, multimodal, and large-scale, while analysis workflows often remain fragmented, data type specific, ad hoc, and difficult to reproduce. The prevailing methodologies are dependent on a combination of public tools, which hinders the reproducibility of results, limits scalability, and complicates the efforts to establish benchmarks. The necessity for a higher-level, unified framework for single-cell data analysis is paramount to address these inherent limitations. Here, we introduce the SC-Framework, providing the integration of standardized data structures, declarative workflows and standardized computational backends in a containerized environment, enabling analysts to focus on biological interpretation rather than technical overhead. SC-Framework is available at GitHub (https://github.com/loosolab/SC-Framework).

bioinformatics↗

bioRxiv2020

multicrispr: fast gRNA designer enables prime editing and parallel targeting of thousands of targets

Targeting the coding genome to introduce single nucleotide deletions/insertions via Crispr/Cas9 technology has become a standard procedure in recent years. It has quickly spawned a multitude of methods such as Prime Editing, Crispr/Cas9 assisted APEX proximity labeling of proteins, or homology directed repair (HDR), for which supporting bioinformatic tools are, however, lagging behind. New applications often require specific guide-RNA (gRNA) design functionality, and a generic gRNA design tool is critically missing. Here we review gRNA designer software and introduce multicrispr, an R based tool intended to design individual gRNAs as well as gRNA libraries targeting many genomic loci in parallel. The package is easy to use, detects, scores and filters gRNAs on both efficiency and specificity, visualizes and aggregates results per target or Crispr/Cas9 sequence, and finally returns both genomic ranges as well as sequences of preferred, off target-free gRNAs. In order to be generic, multicrispr defines and implements a genomic arithmetics framework as a basis for facile adaptation to techniques yet to arise. Its performance and new gRNA design concepts such as target set specific filtering for gRNA libraries render multicrispr the tool of choice when dealing with screening-like approaches.

bioinformatics↗