DNA Methylation Profiles of Purified Cell Types in Bronchoalveolar Lavage: Applications for Mixed Cell Paediatric Pulmonary Studies

Exploring the use of immunomethylomics in the characterization of depressed patients: A proof-of-concept study

Alterations in DNA methylation and inflammation could represent valid biomarkers for the stratification of patients with major depressive disorder (MDD). This study explored the use of DNA-methylation based immunological cell-type profiles in the context of MDD and symptom severity over time. In 119 individuals with MDD, DNA-methylation was assessed on whole blood using the Illumina Infinium MethylationEPIC 850 k BeadChip. Quality control and data processing, as well as cell type estimation was conducted using the RnBeads package. The cell type composition was estimated using epigenome-wide DNA methylation signatures, applying the Houseman method, considering six cell types (neutrophils, natural killer cells (NK), B cells, CD4+ T cells, CD8+ T cells and monocytes). Two cytokines (IL-6 and IL-1β) and hsCRP were quantified in serum. We performed a hierarchical cluster analysis on the six estimated cell-types and tested the differences between these clusters in relation to the two cytokines and hsCRP, depression severity at baseline, and after 6 weeks of treatment (celecoxib/placebo + vortioxetine). We performed a second cluster analysis with cell-types and cytokines combined. ANCOVA was used to test for differences across clusters. We applied the Bonferroni correction. After quality control, we included 113 participants. Two clusters were identified, cluster 1 was high in CD4+ cells and NK, cluster 2 was high in CD8+ T-cells and B-cells, with similar fractions of neutrophils and monocytes. The clusters were not associated with either of the two cytokines and hsCRP, or depression severity at baseline, but cluster 1 showed higher depression severity after 6 weeks, corrected for baseline (p = 0.0060). The second cluster analysis found similar results: cluster 1 was low in CD8+ T-cells, B-cells, and IL-1β. Cluster 2 was low in CD4+ cells and natural killer cells. Neutrophils, monocytes, IL-6 and hsCRP were not different between the clusters. Participants in cluster 1 showed higher depression severity at baseline than cluster 2 (p = 0.034), but no difference in depression severity after 6 weeks. DNA-methylation based cell-type profiles may be valuable in the immunological characterization and stratification of patients with MDD. Future models should consider the inclusion of more cell-types and cytokines for better a prediction of treatment outcomes.

Uncertainty quantification of reference-based cellular deconvolution algorithms

The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample's DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to >6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).