A digital atlas of ion channel expression patterns in the two-week-old rat brain

Ionic mechanisms involved in arginine vasopressin-mediated excitation of auditory cortical and thalamic neurons

The axons containing arginine vasopressin (AVP) from the hypothalamus innervate a variety of structures including the cerebral cortex, thalamus, hippocampus and amygdala. A plethora amount of evidence indicates that activation of the V1a subtype of the vasopressin receptors facilitates anxiety-like and fear responses. As an essential structure involved in fear and anxiety responses, the amygdala, especially the lateral nucleus of amygdala (LA), receives glutamatergic innervations from the auditory cortex and auditory thalamus where high density of V1a receptors have been detected. However, the roles and mechanisms of AVP in these two important areas have not been determined, which prevents the understanding of the mechanisms whereby V1a activation augments anxiety and fear responses. Here, we used coronal brain slices and studied the effects of AVP on neuronal activities of the auditory cortical and thalamic neurons. Our results indicate that activation of V1a receptors excited both auditory cortical and thalamic neurons. In the auditory cortical neurons, AVP increased neuronal excitability by depressing multiple subtypes of inwardly rectifying K+ (Kir) channels including the Kir2 subfamily, the ATP-sensitive K+ channels and the G protein-gated inwardly rectifying K+ (GIRK) channels, whereas activation of V1a receptors excited the auditory thalamic neurons by depressing the Kir2 subfamily of the Kir channels as well as activating the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels and a persistent Na+ channel. Our results may help explain the roles of V1a receptors in facilitating fear and anxiety responses. Categories: Cell Physiology.

Growth and development in monogenic forms of neonatal diabetes

PURPOSE OF REVIEW

Neonatal diabetes mellitus (NDM) is a rare disorder in which 80-85% of infants diagnosed under 6 months of age will be found to have an underlying monogenic cause. This review will summarize what is known about growth and neurodevelopmental difficulties among individuals with various forms of NDM.

RECENT FINDINGS

Patients with NDM often have intrauterine growth restriction and/or low birth weight because of insulin deficiency in utero and the severity and likelihood of ongoing growth concerns after birth depends on the specific cause. A growing list of rare recessive causes of NDM are associated with neurodevelopmental and/or growth problems that can either be related to direct gene effects on brain development, or may be related to a variety of co-morbidities. The most common form of NDM results in spectrum of neurological disability due to expression of mutated KATP channels throughout the brain.

SUMMARY

Monogenic causes of neonatal diabetes are characterized by variable degree of restriction of growth in utero because of deficiency of insulin that depends on the specific gene cause. Many forms also include a spectrum of neurodevelopmental disability because of mutation-related effects on brain development. Longer term study is needed to clarify longitudinal effects on growth into adulthood.

Spatial distribution of marker gene activity in the mouse lung during alveolarization

This data is a curated collection of visual images of gene expression patterns from the pre- and post-natal mouse lung, accompanied by associated mRNA probe sequences and RNA-Seq expression profiles. Mammalian lungs undergo significant growth and cellular differentiation before and after the transition to breathing air. Documenting normal lung development is an important step in understanding abnormal lung development, as well as the challenges faced during a preterm birth. Images in this dataset indicate the spatial distribution of mRNA transcripts for over 500 different genes that are active during lung development, as initially determined via RNA-Seq. Images were systematically acquired using high-throughput in situ hybridization with non-radioactive digoxigenin-labeled mRNA probes across mouse lungs from developmental time points E16.5, E18.5, P7, and P28. The dataset was produced as part of The Molecular Atlas of Lung Development Program (LungMAP) and is hosted at https://lungmap.net. This manuscript describes the nature of the data and the protocols for generating the dataset.

A chicken embryo cardiac outflow tract atlas for registering changes due to abnormal blood flow

Subdivision-based image registration has previously been applied to co-localize digital information extracted from rigid structures in biological specimens, such as the brain. Here, we describe and demonstrate the creation and application of a two-dimensional subdivision-based atlas representing a dynamic structure: the outflow tract of the developing chicken heart. The atlas is designed to segment three different anatomical layers of the outflow tract, and is demonstrated on the characterization of collagen XIV in both control and induced abnormal flow specimens. Abnormal blood flow in the embryonic developing heart can lead to congenital heart disease. Comparing local cellular and sub-cellular changes that are caused by abnormal flow can assist in understanding the molecular pathways involved in maladaptations of the heart and congenital defects. This study demonstrates the approach and potential for more extensive applications of the subdivision-based atlas for the embryonic chicken heart.

Towards the automatic classification of neurons

The classification of neurons into types has been much debated since the inception of modern neuroscience. Recent experimental advances are accelerating the pace of data collection. The resulting growth of information about morphological, physiological, and molecular properties encourages efforts to automate neuronal classification by powerful machine learning techniques. We review state-of-the-art analysis approaches and the availability of suitable data and resources, highlighting prominent challenges and opportunities. The effective solution of the neuronal classification problem will require continuous development of computational methods, high-throughput data production, and systematic metadata organization to enable cross-laboratory integration.