Single-Cell RNA Sequencing Identifies New Inflammation-Promoting Cell Subsets in Asian Patients With Chronic Periodontitis

Qian, Shu‐Jiao; Huang, Qing-Guo; Chen, Ruiying; Mo, Jia-Ji; Zhou, Liyun; Zhao, Yi; Li, Bin; Lai, Hong-Chang

doi:10.3389/fimmu.2021.711337

Cited by 39 publications

(38 citation statements)

References 74 publications

(88 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The activation of T and B cells is crucial in controlling chronic inflammation through constitutive cytokine secretion and modulation of osteoclastogenesis in the pathogenesis of periodontitis 111 - 113 . The proportions of immune cell subtype in the current dataset appear at odds with previous studies in periodontal tissues 64 , 66 , 114 , 115 . The inconsistency was generated mainly for two reasons.…”

Section: Discussioncontrasting

confidence: 99%

Single-cell RNA landscape of the osteoimmunology microenvironment in periodontitis

Chen¹,

Wang²,

Yang³

et al. 2022

Theranostics

View full text Add to dashboard Cite

Single-cell RNA sequencing (scRNA-seq) enables specific profiling of cell populations at single-cell resolution. The osteoimmunology microenvironment in the occurrence and development of periodontitis remains poorly understood at the single-cell level. In this study, we used single-cell transcriptomics to comprehensively reveal the complexities of the molecular components and differences with counterparts residing in periodontal tissues. Methods: We performed scRNA-seq to identify 51248 single cells from healthy controls (n=4), patients with severe chronic periodontitis (n=5), and patients with severe chronic periodontitis after initial periodontal therapy within 1 month (n=3). Uniform manifold approximation and projection (UMAP) were further conducted to explore the cellular composition of periodontal tissues. Pseudotime cell trajectory and RNA velocity analysis, combined with gene enrichment analysis were used to reveal the molecular pathways underlying cell fate decisions. CellPhoneDB were performed to identify ligand-receptor pairs among the major cell types in the osteoimmunology microenvironment of periodontal tissues. Results: A cell atlas of the osteoimmunology microenvironment in periodontal tissues was characterized and included ten major cell types, such as fibroblasts, monocytic cells, endothelial cells, and T and B cells. The enrichment of TNFRSF21 + fibroblasts with high expression of CXCL1, CXCL2, CXCL5, CXCL6, CXCL13 , and IL24 was detected in patients with periodontitis compared to healthy individuals. The fractions of CD55 + mesenchymal stem cells (MSCs), APOE + pre-osteoblasts (pre-OBs), and IBSP + osteoblasts decreased significantly in response to initial periodontal therapy. In addition, CXCL12 + MSC-like pericytes could convert their identity into a pre-OB state during inflammatory responses even after initial periodontal therapy confirmed by single-cell trajectory. Moreover, we portrayed the distinct subtypes of monocytic cells and abundant endothelial cells significantly involved in the immune response. The heterogeneity of T and B cells in periodontal tissues was characterized. Finally, we mapped osteoblast/osteoclast differentiation mediators to their source cell populations by identifying ligand-receptor pairs and highlighted the effects of Ephrin-Eph signaling on bone regeneration after initial periodontal therapy. Conclusions: Our analyses uncovered striking spatiotemporal dynamics in gene expression, population composition, and cell-cell interactions during periodontitis progression. These findings provide insights into the cellular and molecular underpinning of periodontal bone regeneration.

show abstract

Section: Discussioncontrasting

confidence: 99%

Single-cell RNA landscape of the osteoimmunology microenvironment in periodontitis

Chen¹,

Wang²,

Yang³

et al. 2022

Theranostics

View full text Add to dashboard Cite

show abstract

“…To our knowledge, this was the first study to identify ERS-related biomarkers of periodontitis based on microarray datasets. Single-cell sequencing and RNA-seq data that do not rely on predesigned probes are now increasingly used because of their high sensitivity and ability to detect novel genes [ 33 , 34 ]. However, the samples of periodontitis using these two methods are now too few in the public platform to be analyzed on a large scale.…”

Section: Discussionmentioning

confidence: 99%

Identification of Endoplasmic Reticulum Stress-Related Biomarkers of Periodontitis Based on Machine Learning: A Bioinformatics Analysis

Jiao

Zhang

et al. 2022

Disease Markers

View full text Add to dashboard Cite

Objective. To screen for potential endoplasmic reticulum stress- (ERS-) related biomarkers of periodontitis using machine learning methods and explore their relationship with immune cells. Methods. Three datasets of periodontitis (GSE10334, GES16134, and GES23586) were obtained from the Gene Expression Omnibus (GEO), and the samples were randomly assigned to the training set or the validation set. ERS-related differentially expressed genes (DEGs) between periodontitis and healthy periodontal tissues were screened and analyzed for GO, KEGG, and DO enrichment. Key DEGs were screened by two machine learning algorithms, LASSO regression and support vector machine-recursive feature elimination (SVM-RFE); then, the potential biomarkers were identified through validation. The infiltration of immune cells of periodontitis was calculated using the CIBERSORT algorithm, and the correlation between immune cells and potential biomarkers was specifically analyzed through the Spearman method. Results. We obtained 36 ERS-related DEGs of periodontitis from the training set, from which 11 key DEGs were screened by further machine learning. SERPINA1, ERLEC1, and VWF showed high diagnostic values ( AUC > 0.85 ), so they were considered as potential biomarkers for periodontitis. According to the results of the immune cell infiltration analysis, these three potential biomarkers showed marked correlations with plasma cells, neutrophils, resting dendritic cells, resting mast cells, and follicular helper T cells. Conclusions. Three ERS-related genes, SERPINA1, ERLEC1, and VWF, showed valuable biomarker potential for periodontitis, which provide a target base for future studies on early diagnosis and treatment of periodontitis.

show abstract

“…The research from Caetano et al identified that one epithelial subtype driving the epithelial inflammation reaction increased, and a fibroblast‐like cluster had high expression in pro‐inflammatory genes such as amphiregulin (AREG) 39 . The scRNA‐seq study by Qian et al reported that Major Histocompatibility Complex class II cell surface receptor HLA‐DR+ endothelial cells, CXCL13+ fibroblasts, and NLR Family Pyrin Domain Containing 3 (NLRP3) + macrophages were involved in the development of periodontitis 113 . In addition, another study using scRNA‐seq revealed heterogeneity in macrophage populations between periodontitis‐affected and normal gingival tissues in individuals with and without type 2 diabetes 114 .…”

Section: In‐depth Study Of Oral Diseases Using Scrna‐seqmentioning

confidence: 99%

Single‐cell RNA sequencing in oral science: Current awareness and perspectives

Ding

Wang

et al. 2022

Cell Proliferation

View full text Add to dashboard Cite

The emergence of single-cell RNA sequencing enables simultaneous sequencing of thousands of cells, making the analysis of cell population heterogeneity more efficient. In recent years, single-cell RNA sequencing has been used in the investigation of heterogeneous cell populations, cellular developmental trajectories, stochastic gene transcriptional kinetics, and gene regulatory networks, providing strong support in life science research. However, the application of single-cell RNA sequencing in the field of oral science has not been reviewed comprehensively yet. Therefore, this paper reviews the development and application of single-cell RNA sequencing in oral science, including fields of tissue development, teeth and jaws diseases, maxillofacial tumors, infections, etc., providing reference and prospects for using single-cell RNA sequencing in studying the oral diseases, tissue development, and regeneration. | INTRODUCTIONOn the analysis of mixed samples of thousands of cells, bulk RNA sequencing is prone to uncover gene expression by sequencing on average in the absence of the cellular transcriptome heterogeneity of individual cells. Traditional RNA sequencing can reveal information of dominant cell subpopulations, but the transcriptome characteristics of rare cell subpopulations cannot be shown because the results are † Authors contributing equally to this article.

show abstract

Single-Cell RNA Sequencing Identifies New Inflammation-Promoting Cell Subsets in Asian Patients With Chronic Periodontitis

Cited by 39 publications

References 74 publications

Single-cell RNA landscape of the osteoimmunology microenvironment in periodontitis

Single-cell RNA landscape of the osteoimmunology microenvironment in periodontitis

Identification of Endoplasmic Reticulum Stress-Related Biomarkers of Periodontitis Based on Machine Learning: A Bioinformatics Analysis

Single‐cell RNA sequencing in oral science: Current awareness and perspectives

Contact Info

Product

Resources

About