Nikos Karaiskos

pyTrance identifies co-localizing RNAs in spatial transcriptomics data with higher accuracy than existing methods, revealing biologically relevant RNA co-localization patterns in mouse brain tissue.

This study uses spatial transcriptomics to analyze tumor microenvironment remodeling dynamics in triple-negative breast cancer, identifying early stromal programs as potential therapeutic targets.

The paper introduces STIM, a framework for visualizing and aligning spatial transcriptomics data, demonstrating its use on mouse and human tissue samples.

Open-ST provides a high-resolution, 3D spatial transcriptomics method to study tissue molecular organization, revealing spatially contiguous structures and biomarkers in tumors and lymph nodes.

This paper presents a 3D spatial atlas of a human lung carcinoma, revealing tumor microenvironment niches and their associated immune interactions and survival outcomes.

Optocoder decodes spatially indexed bead arrays from microscopy images to match barcodes with sequenced data, improving accuracy and efficiency.

Spacemake processes and analyzes large-scale spatial transcriptomics data across different technologies, offering a modular and scalable pipeline with integration capabilities.

This study uses single-nuclei transcriptomics to analyze human kidney injury responses, identifying four injury-associated cell states and showing similar gene expression changes between AKI patients with and without COVID-19.

This study identifies IL-12 as the main driver of neuroinflammation in Alzheimer's disease by affecting oligodendrocyte survival and neuronal homeostasis in a mouse model.

This study shows SURF1 mutations impair human neurogenesis by generating patient-derived iPS cells and demonstrating defects in neuronal differentiation and bioenergetics.

The paper predicts cellular positions in the Drosophila embryo using scRNA-seq data by benchmarking methods against spatially annotated genes.

The paper introduces FLAM-seq, a method for full-length mRNA sequencing including poly(A) tails, and finds correlations between 3' UTR length, polyadenylation sites, and tail composition.

The paper introduces a method to reconstruct spatial positions of cells from single-cell transcriptomes without reference imaging data, leveraging expression space distances.

The paper generates a single-cell transcriptomic map of Drosophila embryos, identifies spatial gene expression patterns, and discovers patterned long non-coding RNAs.

This paper introduces a method for fixing and preserving cells for single-cell transcriptomics, demonstrating its effectiveness across species and tissue types.