Gene expression profiling of periodontitis-affected gingival tissue by spatial transcriptomics

Abstract

Periodontitis is a highly prevalent chronic inflammatory disease of the periodontium, leading ultimately to tooth loss. In order to characterize the gene expression of periodontitis-affected gingival tissue, we have here simultaneously quantified and localized gene expression in periodontal tissue using spatial transcriptomics, combining RNA sequencing with histological analysis. Our analyses revealed distinct clusters of gene expression, which were identified to correspond to epithelium, inflamed areas of connective tissue, and non-inflamed areas of connective tissue. Moreover, 92 genes were identified as significantly up-regulated in inflamed areas of the gingival connective tissue compared to non-inflamed tissue. Among these, immunoglobulin lambda-like polypeptide 5 (IGLL5), signal sequence receptor subunit 4 (SSR4), marginal zone B and B1 cell specific protein (MZB1), and X-box binding protein 1 (XBP1) were the four most highly up-regulated genes. These genes were also verified as significantly higher expressed in gingival tissue of patients with periodontitis compared to healthy controls, using reverse transcription quantitative polymerase chain reaction. Moreover, the protein expressions of up-regulated genes were verified in gingival biopsies by immunohistochemistry. In summary, in this study, we report distinct gene expression signatures within periodontitis-affected gingival tissue, as well as specific genes that are up-regulated in inflamed areas compared to non-inflamed areas of gingival tissue. The results obtained from this study may add novel information on the genes and cell types contributing to pathogenesis of the chronic inflammatory disease periodontitis.

Host‐microbe interactions: Profiles in the transcriptome, the proteome, and the metabolome

“…12 A high level of B cell and plasmacyte gene expression was also found in a study examining periodontitis-related transcriptomes in human and nonhuman primates. 13,20…”

Donor specific transcriptomic analysis of Alzheimer’s disease associated hypometabolism highlights a unique donor, microglia, and ribosomal proteins

“…It is striking that the ER translocation GO group was the most significantly enriched set in our analysis of the 10021/H0351.2002 donor brain, AD associated microglia, and two previous studies of diseased periodontal tissue (Ludden, 2015;Lundmark et al, 2018) . It is known that cytosolic ribosome genes are strongly co-expressed (Lee et al, 2004) .…”

“…Motivated by the gingipain hypothesis that links AD to P. gingivalis infection (Dominy et al, 2019;Singhrao and Olsen, 2019) , we searched for studies linking ER translocation genes and periodontitis ( P. gingivalis is a key periodontal pathogen). We found a spatial transcriptomics study that reported higher expression of the ER translocation genes in inflamed areas of periodontitis-affected gingival connective tissue compared to non-inflamed areas (Lundmark et al, 2018) . A second study that examined peri-implant soft tissue also found that ER translocation genes are expressed at higher levels in diseased mucosa samples (Ludden, 2015) .…”

“…The ER translocation genes link both the cholinergic and gingipain hypotheses of AD. Specifically, these genes are enriched for lysine and arginine residues, are highly expressed in diseased periodontal tissue (Ludden, 2015;Lundmark et al, 2018) , the substantia innominata, and cholinergic neurons. More directly, in microglia, the same genes have a staged expression pattern that increases across cellular and pathological AD associated states.…”

Transcriptional output, cell types densities and normalization in spatial transcriptomics

“…ST data share many limitations of single cell RNA-seq, including low coverage and high dropout rate. So far, ST studies have relied on preprocessing pipelines inspired by single cell RNA-seq studies (Ståhl et al, 2016;Asp et al, 2017;Giacomello et al, 2017;Berglund et al, 2018;Lundmark et al, 2018;Salmen et al, 2018;Thrane et al, 2018). These include normalization of gene-wise read counts in a cell/spot by the total number of reads collected from that cell/spot.…”