Research Day


Times of Peak Innate and Adaptive Immunity in Peripheral Blood Subsets: Potential Implications for Study Design, Assessment of Immune Function, and Therapeutic Intervention in Type 1 Diabetes

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INTRODUCTION: Our current understanding regarding the potential influence of circadian rhythms on cellular immune populations thought key to type 1 diabetes (T1D) development is quite limited. Hence our objective for this study was to characterize normal daily fluctuations in both innate and adaptive immune cellular subsets. We hypothesized such efforts would improve our understanding of the pathogenesis of T1D, while indicating optimal times to sample peripheral blood mononuclear cells (PBMC). METHODS: Venous blood samples (10 cc) were drawn from 10 healthy volunteers every 4 hours over a 24 hr inpatient period, followed by extensive flow cytometric phenotyping. The timing of peak peripheral blood frequencies was determined with a statistical method for fitting a cosine curve to 24 hr data (COSINOR). RESULTS: We observed many major cell populations with significant (p<0.05) circadian patterns. Such cell populations and their time of peak (military) were: CD4Tcell (00:30); Classical Monocytes (02:00); Treg(02:00); CD4+CD8+ (02:00); CD56bright NK (03:00); CD8Tnaive (03:00); CD8T (05.30); CD4Tnaive (06.30); CD4-CD8- (11:00); Monocytes (12:00); CD4Temra (12:00); CD8Temra (12:30); NKT(13:00); CD4Tem (13:00); CD8Tem (13:00); DC (14:00); CD56dim NK (15:00); NK-T (15:00); CD4Tcm (20:00); CD8Tcm (21:00); B-Cells (22:00); Monocytes (23:00). CONCLUSIONS: We conclude there is appreciable heterogeneity in the times of peak of circulating immune cells measured by PBMC sampling. This heterogeneity suggests that T1D autoimmunity studies need to carefully consider PBMC timing as an important part of study design, and suggests the idea of multiple sampling throughout the day. These findings are also expected to have important implications for further studies and therapies. Future studies of pathways dysregulated in autoimmunity could consider circadian influence, while profiles of transcriptional genes could vary depending upon the time of day in which transcription is active. Additionally, in clinical practice, this heterogeneity could indicate optimal times to deliver immunomodulatory therapies or even suggest pre-disease alterations in the immune milieu.

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