M cell maturation and cDC activation determine the onset of adaptive immune priming in the neonatal Peyer's patch.

Early-life immune development is critical to long-term host health. However, the mechanisms that determine the pace of postnatal immune maturation are not fully resolved. Here, we analyzed mononuclear phagocytes (MNPs) in small intestinal Peyer's patches (PPs), the primary inductive site of intestinal immunity. Conventional type 1 and 2 dendritic cells (cDC1 and cDC2) and RORgt+ antigen-presenting cells (RORgt+ APC) exhibited significant age-dependent changes in subset composition, tissue distribution, and reduced cell maturation, subsequently resulting in a lack in CD4+ T cell priming during the postnatal period. Microbial cues contributed but could not fully explain the discrepancies in MNP maturation. Type I interferon (IFN) accelerated MNP maturation but IFN signaling did not represent the physiological stimulus. Instead, follicle-associated epithelium (FAE) M cell differentiation was required and sufficient to drive postweaning PP MNP maturation. Together, our results highlight the role of FAE M cell differentiation and MNP maturation in postnatal immune development.

PyMix--the python mixture package--a tool for clustering of heterogeneous biological data.

BACKGROUND: Cluster analysis is an important technique for the exploratory analysis of biological data. Such data is often high-dimensional, inherently noisy and contains outliers. This makes clustering challenging. Mixtures are versatile and powerful statistical models which perform robustly for clustering in the presence of noise and have been successfully applied in a wide range of applications. RESULTS: PyMix - the Python mixture package implements algorithms and data structures for clustering with basic and advanced mixture models. The advanced models include context-specific independence mixtures, mixtures of dependence trees and semi-supervised learning. PyMix is licenced under the GNU General Public licence (GPL). PyMix has been successfully used for the analysis of biological sequence, complex disease and gene expression data. CONCLUSIONS: PyMix is a useful tool for cluster analysis of biological data. Due to the general nature of the framework, PyMix can be applied to a wide range of applications and data sets.

Hematopoietic stem and progenitor cells acquire distinct DNA-hypermethylation during in vitro culture.

Hematopoietic stem and progenitor cells (HPCs) can be maintained in vitro, but the vast majority of their progeny loses stemness during culture. In this study, we compared DNA-methylation (DNAm) profiles of freshly isolated and culture-expanded HPCs. Culture conditions of CD34(+) cells - either with or without mesenchymal stromal cells (MSCs) - had relatively little impact on DNAm, although proliferation is greatly increased by stromal support. However, all cultured HPCs - even those which remained CD34(+) - acquired significant DNA-hypermethylation. DNA-hypermethylation occurred particularly in up-stream promoter regions, shore-regions of CpG islands, binding sites for PU.1, HOXA5 and RUNX1, and it was reflected in differential gene expression and variant transcripts of DNMT3A. Low concentrations of DNAm inhibitors slightly increased the frequency of colony-forming unit initiating cells. Our results demonstrate that HPCs acquire DNA-hypermethylation at specific sites in the genome which is relevant for the rapid loss of stemness during in vitro manipulation.