Meng Wang

Getting around the blood–brain barrier The meninges comprise three membranes that surround and protect the central nervous system (CNS). Recent studies have noted the existence of myeloid cells resident there, but little is known about their ontogeny and function, and whether other meningeal immune cell populations have important roles remains unclear (see the Perspective by Nguyen and Kubes). Cugurra et al. found in mice that a large proportion of continuously replenished myeloid cells in the dura mater are not blood derived, but rather transit from cranial bone marrow through specialized channels. In models of CNS injury and neuroinflammation, the authors demonstrated that these myeloid cells have an immunoregulatory phenotype compared with their more inflammatory blood-derived counterparts. Similarly, Brioschi et al. show that the meninges host B cells that are also derived from skull bone marrow, mature locally, and likely acquire a tolerogenic phenotype. They further found that the brains of aging mice are infiltrated by a second population of age-associated B cells, which come from the periphery and may differentiate into autoantibody-secreting plasma cells after encountering CNS antigens. Together, these two studies may inform future treatment of neurological diseases. Science , abf7844, abf9277, this issue p. eabf7844 , p. eabf9277 ; see also abj8183, p. 396

Single-cell RNA-sequencing data has revolutionized our ability to understand of the patterns of cell-cell and ligand-receptor connectivity that influence the function of tissues and organs. However, the quantification and visualization of these patterns in a way that informs tissue biology are major computational and epistemological challenges. Here, we present Connectome, a software package for R which facilitates rapid calculation and interactive exploration of cell-cell signaling network topologies contained in single-cell RNA-sequencing data. Connectome can be used with any reference set of known ligand-receptor mechanisms. It has built-in functionality to facilitate differential and comparative connectomics, in which signaling networks are compared between tissue systems. Connectome focuses on computational and graphical tools designed to analyze and explore cell-cell connectivity patterns across disparate single-cell datasets and reveal biologic insight. We present approaches to quantify focused network topologies and discuss some of the biologic theory leading to their design.

Robust biomarkers of aging have been developed from DNA methylation in humans and more recently, in mice. This study aimed to generate a novel epigenetic clock in rats-a model with unique physical, physiological, and biochemical advantages-by incorporating behavioral data, unsupervised machine learning, and network analysis to identify epigenetic signals that not only track with age, but also relates to phenotypic aging. Reduced representation bisulfite sequencing (RRBS) data was used to train an epigenetic age (DNAmAge) measure in Fischer 344 CDF (F344) rats. This measure correlated with age at (r = 0.93) in an independent sample, and related to physical functioning (p=5.9e-3), after adjusting for age and cell counts. DNAmAge was also found to correlate with age in male C57BL/6 mice (r = 0.79), and was decreased in response to caloric restriction. Our signatures driven by CpGs in intergenic regions that showed substantial overlap with H3K9me3, H3K27me3, and E2F1 transcriptional factor binding.