Costantino Pitzalis

cellGeometry deconvolutes bulk RNA-Seq using geometric methods, offering faster and more accurate results than existing approaches.

SPP1hi macrophages in RA synovial fibrin deposits promote tissue hyperplasia.

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 immune endotypes in OA synovial tissue using integrated molecular and spatial profiling to guide precision medicine.

The paper identifies distinct cytokine signaling networks in mouse models of arthritis that mirror human RA synovial pathotypes, revealing molecular mechanisms underlying disease heterogeneity.

multiDEGGs performs differential network analysis across multi-omic data to identify biomarkers and improve predictive modeling in rheumatoid arthritis treatment response.

The paper identifies activated lymphocyte phenotypes in individuals at risk for rheumatoid arthritis using mass cytometry and develops an immunophenotype score to distinguish these individuals from controls.

This study identifies granzyme K as a driver of a novel complement activation pathway independent of classical, lectin, or alternative pathways.

The paper identifies CRTAM-expressing CD4+ T-cells in lymphoid-driven synovitis and shows that IL-6 and IL-27 inhibit their activation through STAT3 and ZEB1.

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.

BBmix improves genotype calling from RNA-sequencing by modeling RNA-specific biases, achieving higher accuracy in heterozygous calls compared to existing methods.

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

This study uses gene-gene interaction networks from RNA-Seq data to improve prediction of treatment response in rheumatoid arthritis.

M3C improves consensus clustering by simulating null distributions to reduce bias and false positives in determining the number of clusters.

Spectrum improves clustering speed and performance for single and multi-omic data by using a density-aware kernel and tensor product graph integration.