Ilya Korsunsky

This paper introduces VIMA, a method for analyzing spatial molecular data to identify disease-associated spatial features, showing improved performance over existing methods in simulations and real datasets.

This study uses spatial and single-cell transcriptomics to identify immune-stromal niches in adult-onset dermatomyositis, revealing distinct cellular and cytokine signatures associated with cancer and non-cancer lesions.

SPP1hi macrophages in RA synovial fibrin deposits promote tissue hyperplasia.

This study identifies two distinct Treg states in RA synovial tissues using scRNA-seq, showing one is dysfunctional and reversible through TNFR2 engagement.

Harmony2 integrates large single-cell RNA-seq datasets efficiently and accurately, preventing overintegration in biologically diverse data.

Perivascular COMPhi fibrogenic fibroblasts expand in treatment-refractory rheumatoid arthritis synovium, where endothelial Notch signaling patterns fibroblast TGF-beta sensitivity via TGFBR3 regulation and persists despite post-treatment immune cell depletion.

The paper identifies a spatial proximity-sensing mechanism in fibroblasts that restores quiescent synovial lining during RA remission through the EGFR-CREB5 axis.

This study identifies CD4+ tissue-resident memory Th17 (TRM17) cells as the main source of IL-17A in SpA synovium, showing their role in disease pathology and potential as therapeutic targets.

Tessera improves tissue segmentation in spatial single-cell data by creating accurate, boundary-preserving tiles that capture natural tissue structures in mouse and human tissues.

This study shows Wnt signaling drives inflammation in fibroblasts of inflammatory arthritis, suggesting it as a therapeutic target.

This study identifies distinct myeloid DC subsets in synovial tissue across health and RA states, showing their roles in inflammation and tolerance.

The paper develops SCENT, a method to create cell-type-specific enhancer-gene maps from single-cell multimodal data, identifying causal variants and genes for diseases.

The paper maps cell-type-specific eQTLs for HLA genes across multiple tissues and identifies dynamic regulation of HLA-DQ genes in immune cells.

The paper deconstructs RA synovium using single-cell data to identify six cell type abundance phenotypes associated with distinct inflammatory states.

Symphony maps query cells to large reference atlases efficiently, enabling rapid identification of cell states and phenotypes across diverse datasets.

The paper identifies a common inflammatory macrophage phenotype across severe COVID-19 and other inflammatory diseases, driven by IFN-γ and TNF-α, and suggests potential therapeutic targets.

This paper introduces CNA, a method to identify disease-associated cell states by analyzing transcriptional neighborhoods across samples, showing improved performance over cluster-based approaches in simulations and real datasets.

The paper introduces scPOST, a tool for estimating statistical power in single-cell studies, and shows that larger sample sizes, reduced batch effects, and lower cell state variability increase power to detect clinically associated cell state shifts.

Harmony alternates diversity-penalized soft clustering with cluster-specific linear correction of PCA embeddings, integrating single-cell datasets over multiple covariates and handling 500,000 cells in 7.2GB of memory.

One-versus-all rank-sum across 10 groups and 1,000 features on a sparse 1-million-observation simulation finished in 17 seconds instead of 6.4 hours, with the same pass yielding auROC.

The paper introduces MASC, a method to identify disease-associated cell subsets in single-cell data, and finds an expanded Th1-skewed cytotoxic CD4+ T cell subset in rheumatoid arthritis.