The inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics, where tissue-resident RNA is spatially labeled in situ with barcodes in a transcriptome-wide fashion, to study local tissue interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-Seq data coupled to cell type-specific localization patterns at and around organized structures of infiltrating leukocyte cells in the synovium. Combining morphological features and high-throughput spatially resolved transcriptomics may be able to provide higher statistical power and more insights into monitoring disease severity and treatment-specific responses in seropositive and seronegative rheumatoid arthritis.

B cells play a significant role in established Rheumatoid Arthritis (RA). However, it is unclear to what extent differentiated B cells are present in joint tissue already at the onset of disease. Here, we studied synovial biopsies (n = 8) captured from untreated patients at time of diagnosis. 3414 index-sorted B cells underwent RNA sequencing and paired tissue pieces were subjected to spatial transcriptomics (n = 4). We performed extensive bioinformatics analyses to dissect the local B cell composition. Select plasma cell immunoglobulin sequences were expressed as monoclonal antibodies and tested by ELISA. Memory and plasma cells were found irrespective of autoantibody status of the patients. Double negative memory B cells were prominent, but did not display a distinct transcriptional profile. The tissue architecture implicate both local B cell maturation via T cell help and plasma cell survival niches with a strong CXCL12–CXCR4 axis. The immunoglobulin sequence analyses revealed clonality between the memory B and plasma cell pools further supporting local maturation. One of the plasma cell-derived antibodies displayed citrulline autoreactivity, demonstrating local autoreactive plasma cell differentiation in joint biopsies captured from untreated early RA. Hence, plasma cell niches are not a consequence of chronic inflammation, but are already present at the time of diagnosis.

The RNA integrity number (RIN) is a frequently used quality metric to assess the completeness of rRNA, as a proxy for the corresponding mRNA in a tissue. Current methods operate at bulk resolution and provide a single average estimate for the whole sample. Spatial transcriptomics technologies have emerged and shown their value by placing gene expression into a tissue context, resulting in transcriptional information from all tissue regions. Thus, the ability to estimate RNA quality in situ has become of utmost importance to overcome the limitation with a bulk rRNA measurement. Here we show a new tool, the spatial RNA integrity number (sRIN) assay, to assess the rRNA completeness in a tissue wide manner at cellular resolution. We demonstrate the use of sRIN to identify spatial variation in tissue quality prior to more comprehensive spatial transcriptomics workflows.

Tendinopathy; encompassing multifactorial tendon disorders characterised by pain and functional limitation remains a significant burden in musculoskeletal medicine.1 Recent findings highlight a key role for immune mediated mechanisms in tendon disease supporting the concept that pivotal immunological and biomechanical factors conventionally associated with inflammatory rheumatic and musculoskeletal diseases (RMDs) are manifest in tendon.2 Single cell technologies3 (scRNAseq) are increasingly applied in rheumatology to identify key cellular phenotypes that drive disease pathogenesis. Despite efforts with small cell numbers and heterogenous tendon biopsies4 there remains no detailed spatial tendon cell atlas to inform translational targeting. Herein, for the first time utilising scRNAseq and spatial transcriptomics (ST), we carry out cell–cell interaction analysis to build an atlas of the dynamic cellular environment that drives the development of chronic human tendon disease. In healthy (4 biopsies, n=3040 cells) and diseased (5 biopsies, n=19 084 cells) tendon we find a mix of endothelial, immune and stromal cells (figure 1A, online supplemental file 1). Each cell type group is present in disease and healthy tissue but with distinct quantitative and qualitative characteristics. Within stromal populations we identified ‘mural type’ stromal cells (figure 1B). Mural cells, which include pericytes, are possible progenitor cells in tendon5 and interestingly, these cells are phenotypically similar to NOTCH3 high mural cells described in rheumatoid arthritis (RA) synovium which can differentiate into fibroblasts following interactions with endothelial cells (ECs) via JAG1 .6 Cell–cell interaction and ST analysis indicate a similar phenomenon could occur within tendinopathy between mural cells and SEMA3G ECs (figure 1F). In all diseased stromal cell populations, there was greater expression of genes for extracellular matrix proteins (eg, COL1A1 , COL3A1 , FN1 , BGN ) which is considered the hallmark feature of tendinopathy (online supplemental figure S2B). Furthermore, pathway analysis indicates stromal …

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.

Spatial Transcriptomics has been shown to be a persuasive RNA sequencingtechnology for analyzing cellular heterogeneity within tissue sections. Thetechnology efficiently captures and barcodes 3’ ta ...

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.

The quality of RNA is of great importance in gene expression studies. It is mostly measured using the RNA integrity number (RIN). Lately it has been shown that samples with low RIN and different fragmentation patterns could affect quality of sequencing data. For such low RIN samples a new approach has been developed by Illumina called the DV200 metric, which is the percentage of fragments >200 nucleotides. For samples with low RIN, DV200 has proved to be a better method to predict if good quality data from RNA sequencing can be generated. However, neither RIN nor DV200 provide spatial infromation on the RNA integrity. Thus, tissues with areas of heterogeneous RNA integrity, where regions of good quality RNA sequencing data could be generated from are missed. We have designed a method to spatially evaluate the RNA integrity in tissue, which we named in situ RNA QC. The method uses three probes with three different fluorophores, each bound to three specific cDNA regions synthesized from the high abundant and well conserved 18S rRNA.With the help of in-house technology from the Spatial Transcriptomics (ST) group at SciLifeLab, we enable creation of heat maps over the RNA integrity to show spatial fragmentation patterns of RNA in tissue. This could reveal the regional quality of transcripts in situ, which is crucial knowledge when selecting samples for further RNA sequencing.The assay has been tried using different tissue fixation methods in order to show a proof of concept that formalin gives shorter cDNA fragments than acetone. The generated heat-map provides a visual overview of RNA integrity in situ; hence this method could be used to select samples for sequencing by evaluation the spatial quality of RNA. For instance from fresh frozen and formalin fixated paraffin embedded (FFPE) tissue (biobanks contain large number of longterm storage FFPE samples). With this assay we will be able to determine which samples are suitable for sequencing.