Macha Nikolski

TileScope quantifies spatial gene expression relationships beyond simple colocalization, distinguishing regional enrichment from internal covariation in glioblastoma and other tissues.

SpaceBlocks is a workflow for reproducible, expert-supervised spatial transcriptomics analysis across platforms, identifying new cell types and differentially expressed genes in multiple datasets.

SpacePath improves spatial metabolomics analysis by handling molecular ambiguity and identifying novel lipid families in spatial datasets.

This study shows gut inflammation promotes glioblastoma growth and therapy resistance through microbiota-gut-brain axis interactions.

This study uses spatial and bulk transcriptomics to map cSCC progression, identifies stage-specific gene programs, and finds prognostic biomarkers relevant across cancers.

The paper benchmarks transcript classifiers to identify ambiguous mRNA-lncRNA classifications by analyzing uncertainty and feature contributions.

SeuratIntegrate extends Seurat with eight integration methods and performance evaluation tools to improve scRNA-seq dataset integration.

This study shows that mubritinib targets brain tumour stem cells by inhibiting complex I of the electron transport chain, delays glioblastoma tumourigenesis, and is safe in mice.

SalienceNet enhances nuclei saliency in low-light microscopy images, improving segmentation accuracy by 30% with Otsu thresholding and 26% with StarDist.

DeepSpot enhances RNA spots in smFISH images using deep learning, enabling automated detection without manual parameter tuning and achieving over 97% accuracy.

DypFISH quantitatively investigates spatial and temporal subcellular localization of RNA and protein using micropatterning and fluorescence microscopy, revealing reproducible patterns and dynamics.