SpatialExperiment is a new data infrastructure for storing and accessing spatially-resolved transcriptomics data, implemented within the R/Bioconductor framework, which provides advantages of modularity, interoperability, standardized operations and comprehensive documentation. Here, we demonstrate the structure and user interface with examples from the 10x Genomics Visium and seqFISH platforms, and provide access to example datasets and visualization tools in the STexampleData, TENxVisiumData and ggspavis packages. The SpatialExperiment, STexampleData, TENxVisiumData and ggspavis packages are available from Bioconductor. The package versions described in this manuscript are available in Bioconductor version 3.15 onwards. Supplementary data are available at Bioinformatics online.

Autism Spectrum Disorder (ASD) is the most prevalent neurodevelopmental disorder in the United States and often co-presents with sleep problems. Sleep problems in ASD predict the severity of ASD core diagnostic symptoms and have a considerable impact on the quality of life of caregivers. Little is known, however, about the underlying molecular mechanisms of sleep problems in ASD. We investigated the role of Shank3, a high confidence ASD gene candidate, in sleep architecture and regulation. We show that mice lacking exon 21 of Shank3 have problems falling asleep even when sleepy. Using RNA-seq we show that sleep deprivation increases the differences in prefrontal cortex gene expression between mutants and wild types, downregulating circadian transcription factors Per3, Bhlhe41, Hlf, Tef, and Nr1d1. Shank3 mutants also have trouble regulating wheel-running activity in constant darkness. Overall, our study shows that Shank3 is an important modulator of sleep and clock gene expression.

Introduction: Coronavirus disease 2019 (COVID‐19) emerged in China, leading to worldwide morbidity and mortality, including depression and anxiety. As the pandemic spread throughout Italy, mental health concerns increased for people with cystic fibrosis (pwCF), who are at greater risk. The aim was to pilot a Telehealth Psychological Support Intervention for pwCF and caregivers to reduce stress, depression, and anxiety during the lockdown in Italy in March 2020.Methods: This intervention utilized cognitive behavioral skills (e.g., cognitive reframing). Participants included 16 pwCF and 14 parents, who completed four individual telehealth sessions with a psychologist. Stress ratings, Patient Health Questionnaire and General Anxiety Disorder, PHQ‐8 and GAD‐7, were completed, in addition to Feasibility and Satisfaction ratings.Results: Ratings of stress significantly decreased from pre‐ to post‐testing for pwCF (paired t(14) = −4.06, p < .01) and parents (paired t = −5.2, p < .001). A large percentage of both groups scored in the clinical range for depression and anxiety at baseline (pwCF: depression/anxiety = 71%; parents: depression = 57%; anxiety = 79%); a large proportion (20%–40%) reported moderate to severe symptomatology. Significant reductions in depression for pwCF were found (pre: M = 8.0 to post: M = 4.7; paired t(14) = 2.8, p < .05) but not anxiety (pre: M = 6.9 to post: M = 5.6, t(14) = 1.2, p = NS—non‐significant). Parental depression decreased for parents (pre: M = 6.4 to post: M = 5.1, t(14) = −2.5, p < .05), but not anxiety (pre: M = 8.1 to post: M = 7.9, t(14) = −0.2, p = NS). Feasibility and Satisfaction were positive.Conclusion: This telehealth intervention yielded reductions in stress and depression for participants. Anxiety did not significantly decrease, possibly because COVID was ongoing. This feasible, satisfactory intervention was effective for improving mental health.

The High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers. Moreover, most of the available tools are designed for expert users, who have great confidence with command-line applications. In this paper, we present HiCeekR (https://github.com/lucidif/HiCeekR), a novel R Graphical User Interface (GUI) that allows researchers to easily perform a complete Hi-C data analysis. With the aid of the Shiny libraries, it integrates several R/Bioconductor packages for Hi-C data analysis and visualization, guiding the user during the entire process. Here, we describe its architecture and functionalities, then illustrate its capabilities using a publicly available dataset.