BACKGROUND: Immune cells promote periodontal bone loss through an unresolved inflammatory response to bacterial pathogens. The limited availability of ex vivo gingival immune cells severely impedes identification of cell types and cell-specific functions
that drive human periodontitis and thus impedes the development of effective pharmacotherapeutics. Previous studies have largely relied on mRNA analysis and confocal microscopy to imprecisely estimate gingival immune cell function. The aim of the study was to develop a cell type-specific technique to quantitate function of resident gingival immune cells.
METHODS: Diseased tissues from chronic periodontitis in non-diabetes or type 2 diabetes subjects or relatively healthy gingival tissues were removed during standard-of-care surgery for pocket reduction surgery or crown lengthening, respectively. Gingiva was dissociated with collagenase to generate single cell suspensions, then 9-color flow cytometry was used quantitate and/or isolate myeloid cells (CD11b+), B cells (CD20+), T cells (CD4+ or CD8+) and natural killer (NK) cells (CD56+). We stimulated the sorted cells with lineage-appropriate activators for 36 hrs and measured cytokine production by ELISPOT, an assay that identifies individual cytokine-producing cells by fixed “spots” on a solid support.
RESULTS: A higher proportion of gingival CD4+ T helper cells and not CD8+cytoxic T cells from subjects with periodontal disease with or without type 2 diabetes produce pro-inflammatory cytokines compared to CD4+ T cells from crown lengthening subjects. CD4+ T cells were the dominant cell population in gingiva from all three groups, and all groups contained similar proportions of cytotoxic (CD8+) T cells, myeloid cells (CD11b+), B cells (CD20+) and natural killer cells (CD56+).
CONCLUSION: The combination of flow cytometry, cell sorting and ELISPOT identified CD4+ T cells as dominant immune cells in human periodontal lesions, and identified T cell cytokines that may uniquely promote periodontitis in type 2 diabetes.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/26373 |
| Date | 25 October 2017 |
| Creators | Azer Refaat, Michel E. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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