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Dynamic three-dimensional chromatin conformation is a critical mechanism for gene regulation during development and disease. Despite this, profiling of three-dimensional genome structure from complex tissues with cell-type specific resolution remains challenging. Recent efforts have demonstrated that cell-type specific epigenomic features can be resolved in complex tissues using single-cell assays. However, it remains unclear whether single-cell chromatin conformation capture (3C) or Hi-C profiles can effectively identify cell types and reconstruct cell-type specific chromatin conformation maps. To address these challenges, we have developed single-nucleus methyl-3C sequencing to capture chromatin organization and DNA methylation information and robustly separate heterogeneous cell types. Applying this method to >4,200 single human brain prefrontal cortex cells, we reconstruct cell-type specific chromatin conformation maps from 14 cortical cell types. These datasets reveal the genome-wide association between cell-type specific chromatin conformation and differential DNA methylation, suggesting pervasive interactions between epigenetic processes regulating gene expression.

Microglia are immune system cells that function in protecting and maintaining the brain. Gosselin et al. examined the epigenetics and RNA transcripts from single microglial cells and observed consistent profiles among samples despite differences in age, sex, and diagnosis. Mouse and human microglia demonstrated similar microglia-specific gene expression profiles, as well as a shared environmental response among microglia collected either immediately after surgery (ex vivo) or after culturing (in vitro). Interestingly, those genes exhibiting differences in expression between humans and mice or after culturing were often implicated in neurodegenerative diseases. Science , this issue p. eaal3222 Single-cell sequencing of brain microglia reveals ex vivo and in vitro differences in transcription. Microglia play essential roles in central nervous system (CNS) homeostasis and influence diverse aspects of neuronal function. However, the transcriptional mechanisms that specify human microglia phenotypes are largely unknown. We examined the transcriptomes and epigenetic landscapes of human microglia isolated from surgically resected brain tissue ex vivo and after transition to an in vitro environment. Transfer to a tissue culture environment resulted in rapid and extensive down-regulation of microglia-specific genes that were induced in primitive mouse macrophages after migration into the fetal brain. Substantial subsets of these genes exhibited altered expression in neurodegenerative and behavioral diseases and were associated with noncoding risk variants. These findings reveal an environment-dependent transcriptional network specifying microglia-specific programs of gene expression and facilitate efforts to understand the roles of microglia in human brain diseases.

Single-cell DNA methylome profiling has enabled the study of epigenomic heterogeneity in complex tissues and during cellular reprogramming. However, broader applications of the method have been impeded by the modest quality of sequencing libraries. Here we report snmC-seq2, which provides improved read mapping, reduced artifactual reads, enhanced throughput, as well as increased library complexity and coverage uniformity compared to snmC-seq. snmC-seq2 is an efficient strategy suited for large-scale single-cell epigenomic studies. Single-cell DNA methylome profiling allows the study of epigenomic heterogeneity in tissues but has been impeded by library quality. Here the authors demonstrate snmC-seq2 which improves mapping, throughput and library complexity.

Single-cell sequencing methods have emerged as powerful tools for identification of heterogeneous cell types within defined brain regions. Application of single-cell techniques to study the transcriptome of activated neurons can offer insight into molecular dynamics associated with differential neuronal responses to a given experience. Through evaluation of common whole-cell and single-nuclei RNA-sequencing (snRNA-seq) methods, here we show that snRNA-seq faithfully recapitulates transcriptional patterns associated with experience-driven induction of activity, including immediate early genes (IEGs) such as Fos , Arc and Egr1 . SnRNA-seq of mouse dentate granule cells reveals large-scale changes in the activated neuronal transcriptome after brief novel environment exposure, including induction of MAPK pathway genes. In addition, we observe a continuum of activation states, revealing a pseudotemporal pattern of activation from gene expression alone. In summary, snRNA-seq of activated neurons enables the examination of gene expression beyond IEGs, allowing for novel insights into neuronal activation patterns in vivo. The molecular dynamics associated with neuronal activation patterns in vivo are unclear. Lacar et al . perform single-nuclei RNA-sequencing of hippocampal neurons from mice exposed to a novel environment, and identify large-scale transcriptome changes in individual neurons associated with the experience.

Activity-induced remodeling of neuronal circuits is critical for memory formation. This process relies in part on transcription, but neither the rate of activity nor baseline transcription is equal across neuronal cell types. In this study, we isolated mouse hippocampal populations with different activity levels and used single nucleus RNA-seq to compare their transcriptional responses to activation. One hour after novel environment exposure, sparsely active dentate granule (DG) neurons had a much stronger transcriptional response compared to more highly active CA1 pyramidal cells and vasoactive intestinal polypeptide (VIP) interneurons. Activity continued to impact transcription in DG neurons up to 5 h, with increased heterogeneity. By re-exposing the mice to the same environment, we identified a unique transcriptional signature that selects DG neurons for reactivation upon re-exposure to the same environment. These results link transcriptional heterogeneity to functional heterogeneity and identify a transcriptional correlate of memory encoding in individual DG neurons. Single nuclei RNA-seq has been used to characterize transcriptional signature of environment-related activity in cells of the dentate gyrus. Here the authors use this approach to show that whether a neuron will be reactivated in response to re-exposure to a previous environment can be predicted by its transcriptional signature.

Bovine milk proteins account for 10% of the global protein supply, which justifies the importance of thoroughly understanding their digestive processes. Extensive research on digestion is being conducted both in vivo and in vitro. However, interpretations and comparisons across different studies require a thorough understanding of the methodologies used. Both the rate and extent of milk protein digestion can be affected by several intrinsic and extrinsic factors with potential implications for overall digestibility and physiological responses. Among intrinsic factors, the impact of genetic variants in native milk proteins has emerged as a growing research area. To these, further complexity is added by the processing conditions frequently applied to milk prior to consumption. The main aim of this work is to provide an overview of the current knowledge on the impact of variations in milk protein profiles (particularly whey: casein ratio and protein polymorphisms), the treatments applied during processing (pasteurisation, homogenisation) and consumption (temperature changes) on protein digestion. To support the interpretation of the current literature, this manuscript also presents a historical perspective into research in this field and summarizes the protocols that are most frequently used, presently, on in vitro digestion studies.

The LHCb Performance Regression (LHCbPR) framework allows for periodic software testing to be performed in a reproducible manner. LHCbPR provides a JavaScript based web front-end service, built atop industry standard tools such as AngularJS, Bootstrap and Django. This framework records the evolution of tests over time allowing for this data to be extracted for end-user analysis. The LHCbPR framework has been expanded to integrate the nightly testing and profiling. These developments allow for key performance metrics within the Trigger software to be monitored over time. Additionally, tests of the full physics reconstruction have been integrated into LHCbPR. These allow for tracking the effect that optimization work has on physics reconstruction performance. This presentation will describe the integration of these tests into LHCbPR as well as describing the structure and new features developed for the frontend web service.

The upgraded LHCb detector, due to start datataking in 2022, will have to process an average data rate of 4 TB/s in real time. Because LHCb’s physics objectives require that the full detector information for every LHC bunch crossing is read out and made available for real-time processing, this bandwidth challenge is equivalent to that of the ATLAS and CMS HL-LHC software read-out, but deliverable five years earlier. Over the past six years, the LHCb collaboration has undertaken a bottom-up rewrite of its software infrastructure, pattern recognition, and selection algorithms to make them better able to efficiently exploit modern highly parallel computing architectures. We review the impact of this reoptimization on the energy efficiency of the realtime processing software and hardware which will be used for the upgrade of the LHCb detector. We also review the impact of the decision to adopt a hybrid computing architecture consisting of GPUs and CPUs for the real-time part of LHCb’s future data processing. We discuss the implications of these results on how LHCb’s real-time power requirements may evolve in the future, particularly in the context of a planned second upgrade of the detector.

Nature Communications 7: Article number: 11022 (2016); Published 19 April 2016; Updated 17 March 2017 An incorrect version of Supplementary Data 1, in which normalized counts were analysed instead of raw counts, resulting in a smaller number of differentially expressed genes, was inadvertently published with this article.

Nature Communications 7: Article number:11022 (2016); Published 19 April 2016; Updated 14 June 2016 In the original version of this Article, the middle names or initials of the authors Suguna Rani Krishnaswami, Jerika J. Barron, Martijn J.E. Kelder, Sarah L. Parylak, Apuã C.M. Paquola and Jennifer A. Erwin were omitted from the author information.