Macha Nikolski

TileScope quantifies spatial gene expression relationships beyond colocalization

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

SpaceBlocks: an expert-supervised workflow for cohort-level spatial transcriptomics analysis

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 enables pathway and molecular-family analysis beyond annotation ambiguity in spatial metabolomics and lipidomics

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

Microbiota-gut-brain axis modulation drives glioblastoma progression and therapy resistance

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

Spatial and Bulk Transcriptomic Profiling Defines the Molecular Evolution of Cutaneous Squamous Cell Carcinoma and Reveals Stage-Specific Biomarkers of Clinical Relevance

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

Uncertainty-aware benchmarking reveals ambiguous transcripts in mRNA-lncRNA classification

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

SeuratIntegrate: an R package to facilitate the use of integration methods with Seurat

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

SalienceNet: an unsupervised Image-to-Image translation method for nuclei saliency enhancement in microscopy images

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

DeepSpot: a deep neural network for RNA spot enhancement in smFISH microscopy images

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

DypFISH: Dynamic Patterned FISH to Interrogate RNA and Protein Spatial and Temporal Subcellular Distribution

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