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05.16 Transcriptome visualisation of the inflamed rheumatoid arthritis joint
  1. Konstantin Carlberg1,2,*,
  2. Sanja Vickovic1,*,
  3. Patrik L Ståhl1,3,
  4. Fredrik Salmén1,
  5. Marina Korotkova2,
  6. Vivianne Malmström2,
  7. Joakim Lundeberg1
  1. 1Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology, Sweden
  2. 2Karolinska Institute, Department of Medicine, Rheumatology Unit, Karolinska University Hospital, Sweden
  3. 3Karolinska Institute, Department of Cell and Molecular Biology, Sweden
  4. *Contributed equally to this work.

Abstract

Background The Rheumatoid Arthritis (RA) synovial tissue is heterogenous with a mix of stromal and immune cells. Macrophages and T cells are the most abundant immune cells, while B cells are more rare and often found within ectopic lymphoid structures.

Much of our understanding of the synovial inflammation is based on different immunostainings approaches. Here we have utilised the recently described Spatial Transcriptomics (ST) method to explore the RNA profile of tissue sections from RA synovial biopsies.1

Materials and methods Two snap frozen synovial biopsies from ACPA+ HLA shared epitope+ RA patients undergoing joint replacement surgery was used. Sections of 7 µm representing a single layer of cells were cut and placed on a barcoded ST slide, fixated and stained using Hematoxylin and Eosin. Thereafter permeabilization of the cells and cDNA synthesis of the captured mRNA were conducted on chip, removal of the tissue and the DNA from the surface was released for library preparation. Sequencing was performed using Next-generation sequencing. The RNA-Seq data was de-convoluted back to its original position in the section based on the barcoded information, using the ST pipeline (https://github.com/jfnavarro/st_pipeline).

Data analysis was performed using the R packages DESeq and EdgeR.2–3

Results Extracted RNA from the synovial biopsies had RIN values of 8.6 and 9.2 respectively. On average 1 M reads per sample was generated with 17 800 numbers of detected genes from each tissue section. When focusing on the lymphocyte aggregates within the tissue, some displayed features of fully developed ectopic lymphnode stuctures including expression of T cell, B cell and APC specific and related genes such as CD2, CD52, CD20 and CXCL13. Differential expression analysis revealed clusters corresponding to fibrotic areas with high expression of genes involved in protein synthesis and protein-protein interactions, areas of infiltrates with high numbers of inflammation markers and areas surrounding infiltrates with genes involved in wound repair, tissue remodelling, motility and invasion.

Conclusion The spatial transcriptomic method allows for both unbiased analysis of the transcriptional activity in tissue biopsies as well as hypothesis driven investigation of cell subsets defined by combinations of markers not easily captured by 2–3 parameters.

References 1. Ståhl PL, et al.:Visualisation and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):78–82

2. Love MI, Huber W, Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15:550.

3. Robinson MD, McCarthy DJ, Smith GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–140.

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