Exploring inflammatory signatures in arthritic joint biopsies with Spatial Transcriptomics.
Konstantin CarlbergMarina KorotkovaLudvig LarssonAnca I CatrinaPatrik L StåhlVivianne MalmströmPublished in: Scientific reports (2019)
Lately it has become possible to analyze transcriptomic profiles in tissue sections with retained cellular context. We aimed to explore synovial biopsies from rheumatoid arthritis (RA) and spondyloarthritis (SpA) patients, using Spatial Transcriptomics (ST) as a proof of principle approach for unbiased mRNA studies at the site of inflammation in these chronic inflammatory diseases. Synovial tissue biopsies from affected joints were studied with ST. The transcriptome data was subjected to differential gene expression analysis (DEA), pathway analysis, immune cell type identification using Xcell analysis and validation with immunohistochemistry (IHC). The ST technology allows selective analyses on areas of interest, thus we analyzed morphologically distinct areas of mononuclear cell infiltrates. The top differentially expressed genes revealed an adaptive immune response profile and T-B cell interactions in RA, while in SpA, the profiles implicate functions associated with tissue repair. With spatially resolved gene expression data, overlaid on high-resolution histological images, we digitally portrayed pre-selected cell types in silico. The RA displayed an overrepresentation of central memory T cells, while in SpA effector memory T cells were most prominent. Consequently, ST allows for deeper understanding of cellular mechanisms and diversity in tissues from chronic inflammatory diseases.
Keyphrases
- single cell
- rheumatoid arthritis
- gene expression
- rna seq
- disease activity
- oxidative stress
- genome wide
- ankylosing spondylitis
- immune response
- high resolution
- end stage renal disease
- dna methylation
- genome wide identification
- working memory
- chronic kidney disease
- ejection fraction
- systemic lupus erythematosus
- electronic health record
- interstitial lung disease
- ultrasound guided
- cell therapy
- dendritic cells
- newly diagnosed
- prognostic factors
- deep learning
- copy number
- stem cells
- convolutional neural network
- bioinformatics analysis
- bone marrow
- regulatory t cells
- optical coherence tomography
- molecular dynamics simulations
- molecular docking
- data analysis
- patient reported
- binding protein
- tandem mass spectrometry