Cell Type Composition In Samples Research Articles

P106 Single-cell transcriptomic analysis reveals insights into the mechanism of action of Granulocyte-monocyte apheresis

Abstract Background Granulocyte-monocyte apheresis (GMA) is a non-pharmacological treatment approved for the management of ulcerative colitis (UC), particularly steroid-dependent cases. The exact mechanism of action and immunological changes associated with GMA remain undescribed. Gene expression analysis at the single-cell level (through scRNA-Seq) has emerged as a key tool of choice to characterize drug response at the molecular level. We used scRNA-Seq to characterize the transcriptomic effects and immune cell population alterations in GMA. Methods We generated scRNA-Seq from peripheral blood mononuclear cells (PBMC) of two ulcerative colitis (UC) patients undergoing GMA treatment. We compared the gene expression profile before and after 5 sessions of GMA treatment. The analytical pipeline included quality control and classical filtering steps, cell-type annotation, differential gene expression analysis and pathway enrichment profiling. Results We report three main results. First, we observed significant reductions of cell types directly affected by GMA treatment in UC patients. This includes classical CD14+ monocytes and Natural killers, which are central components of the innate system. Of note, we observed a remarkable increase in Double-negative T cells (dnT) after 5 sessions of GMA treatment, suggesting potential expansion of protective populations involved in decreased inflammation. Second, we observe a variety of genes and regulatory pathways altered by GMA treatment. In total, we detect 86 differentially expressed genes (DEGs), which overall are biased towards downregulation (63%). Of note, we detected dnT-s exhibiting upregulation of NEFL that is associated with the MAPK cascade and downregulation of genes related to immune response and signaling pathways. Finally, the effects of GMA treatment extend beyond the above mentioned populations, with particular alterations in CD4+ T cell populations such as CD4+ central memory and CD4+ Naive (7 and 13 DEGs, respectively). Conclusion For the first time, we generated single-cell transcriptomic profiles to characterize the effects of GMA treatment in peripheral blood of UC patients. Our preliminary analysis detects important alterations in the gene regulation and cell type composition in samples obtained after 5 sessions of GMA. Through expansion of this dataset to include more time points and profiles for more individuals, we will discuss the longitudinal changes and molecular mechanisms involved in response to GMA treatment.

Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi.

SummaryThe minfi package is widely used for analyzing Illumina DNA methylation array data. Here we describe modifications to the minfi package required to support the HumanMethylationEPIC (‘EPIC’) array from Illumina. We discuss methods for the joint analysis and normalization of data from the HumanMethylation450 (‘450k’) and EPIC platforms. We introduce the single-sample Noob (ssNoob) method, a normalization procedure suitable for incremental preprocessing of individual methylation arrays and conclude that this method should be used when integrating data from multiple generations of Infinium methylation arrays. We show how to use reference 450k datasets to estimate cell type composition of samples on EPIC arrays. The cumulative effect of these updates is to ensure that minfi provides the tools to best integrate existing and forthcoming Illumina methylation array data.Availability and ImplementationThe minfi package version 1.19.12 or higher is available for all platforms from the Bioconductor project.Supplementary information Supplementary data are available at Bioinformatics online.

Cell type specific DNA methylation in cord blood: A 450K-reference data set and cell count-based validation of estimated cell type composition

ABSTRACTEpigenome-wide association studies of prenatal exposure to different environmental factors are becoming increasingly common. These studies are usually performed in umbilical cord blood. Since blood comprises multiple cell types with specific DNA methylation patterns, confounding caused by cellular heterogeneity is a major concern. This can be adjusted for using reference data consisting of DNA methylation signatures in cell types isolated from blood. However, the most commonly used reference data set is based on blood samples from adult males and is not representative of the cell type composition in neonatal cord blood. The aim of this study was to generate a reference data set from cord blood to enable correct adjustment of the cell type composition in samples collected at birth. The purity of the isolated cell types was very high for all samples (>97.1%), and clustering analyses showed distinct grouping of the cell types according to hematopoietic lineage. We explored whether this cord blood and the adult peripheral blood reference data sets impact the estimation of cell type composition in cord blood samples from an independent birth cohort (MoBa, n = 1092). This revealed significant differences for all cell types. Importantly, comparison of the cell type estimates against matched cell counts both in the cord blood reference samples (n = 11) and in another independent birth cohort (Generation R, n = 195), demonstrated moderate to high correlation of the data. This is the first cord blood reference data set with a comprehensive examination of the downstream application of the data through validation of estimated cell types against matched cell counts.