Biology subjects

Zavodszky, M. I.

Publications and source records attributed to Zavodszky, M. I..

3 recordsLinked to original sources

bioRxiv2025

Comparative snRNAseq study of C9orf72, SOD1, and sALS spinal cord

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease characterized by the loss of motor neurons, yet the cell-type specific molecular alterations within the spinal cords are not well characterized. In this study, we conducted deep molecular profiling of spinal cord tissues donated by people living with sporadic, C9orf72, and SOD1-ALS using single-nucleus RNA sequencing (snRNAseq). We observed numerous distinct gene expression patterns and enriched pathways among ALS types. However, when focusing on common features, we identified activation of stress-response and inflammatory pathways in specific microglia subtypes, as well as disrupted vesicle transport and synaptic function in a ventral inhibitory neuronal subtype. Notably, CPLX3, a SNARE regulator, was uniquely expressed in alpha-motor neurons and was commonly downregulated across all ALS types. While this study uncovers the molecular heterogeneity underlying ALS, it also highlights shared pathways within specific cell types, especially in the ventral inhibitory neurons that have been less explored in ALS research.

neuroscience↗

bioRxiv2021

OmicsView: omics data analysis through interactive visual analytics

With advances in NGS technologies, transcriptional profiling of human tissue across many diseases is becoming more routine, leading to the generation of petabytes of data deposited in public repositories. There is a need for bench scientists with little computational expertise to be able to access and mine this data to understand disease pathology, identify robust biomarkers of disease and the effect of interventions (in vivo or in vitro). To this end we release an open source analytics and visualization platform for expression data called OmicsView, http://omicsview.org. This platform comes preloaded with 1000s of samples across many disease areas and normal tissue, including the GTEx database, all processed with a harmonized pipeline. We demonstrate the power and ease-of-use of the platform by means of a Crohns disease data mining exercise where we can quickly uncover disease pathology and identify strong biomarkers of disease and response to treatment.

bioinformatics↗

bioRxiv2021

CellMap: Characterizing the types and composition of iPSC-derived cells from RNA-seq data

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

bioinformatics↗