All-optical electrophysiology—spatially resolved simultaneous optical perturbation and measurement of membrane voltage—would open new vistas in neuroscience research. We evolved two archaerhodopsin-based voltage indicators, QuasAr1 and 2, which show improved brightness and voltage sensitivity, microsecond response times, and produce no photocurrent. We engineered a novel channelrhodopsin actuator, CheRiff, which shows improved light sensitivity and kinetics, and spectral orthogonality to the QuasArs. A co-expression vector, Optopatch, enabled crosstalk-free genetically targeted all-optical electrophysiology. In cultured neurons, we combined Optopatch with patterned optical excitation to probe back-propagating action potentials in dendritic spines, synaptic transmission, sub-cellular microsecond-timescale details of action potential propagation, and simultaneous firing of many neurons in a network. Optopatch measurements revealed homeostatic tuning of intrinsic excitability in human stem cell-derived neurons. In brain slice, Optopatch induced and reported action potentials and subthreshold events, with high signal-to-noise ratios. The Optopatch platform enables high-throughput, spatially resolved electrophysiology without use of conventional electrodes.

We describe a child with onset of command auditory hallucinations and behavioral regression at 6 yr of age in the context of longer standing selective mutism, aggression, and mild motor delays. His genetic evaluation included chromosomal microarray analysis and whole-exome sequencing. Sequencing revealed a previously unreported heterozygous de novo mutation c.385G>A in ATP1A3, predicted to result in a p.V129M amino acid change. This gene codes for a neuron-specific isoform of the catalytic α-subunit of the ATP-dependent transmembrane sodium–potassium pump. Heterozygous mutations in this gene have been reported as causing both sporadic and inherited forms of alternating hemiplegia of childhood and rapid-onset dystonia parkinsonism. We discuss the literature on phenotypes associated with known variants in ATP1A3, examine past functional studies of the role of ATP1A3 in neuronal function, and describe a novel clinical presentation associated with mutation of this gene.

The elucidation of disease etiologies and establishment of robust, scalable, high-throughput screening assays for autism spectrum disorders (ASDs) have been impeded by both inaccessibility of disease-relevant neuronal tissue and the genetic heterogeneity of the disorder. Neuronal cells derived from induced pluripotent stem cells (iPSCs) from autism patients may circumvent these obstacles and serve as relevant cell models. To date, derived cells are characterized and screened by assessing their neuronal phenotypes. These characterizations are often etiology-specific or lack reproducibility and stability. In this manuscript, we present an overview of efforts to study iPSC-derived neurons as a model for autism, and we explore the plausibility of gene expression profiling as a reproducible and stable disease marker.

The advent of optogenetics and genetically encoded photosensors has provided neuroscience researchers with a wealth of new tools and methods for examining and manipulating neuronal function in vivo. There exists now a wide range of experimentally validated protein tools capable of modifying cellular function, including light-gated ion channels, recombinant light-gated G protein-coupled receptors, and even neurotransmitter receptors modified with tethered photo-switchable ligands. A large number of genetically encoded protein sensors have also been developed to optically track cellular activity in real time, including membrane-voltage-sensitive fluorophores and fluorescent calcium and pH indicators. The development of techniques for controlled expression of these proteins has also increased their utility by allowing the study of specific populations of cells. Additionally, recent advances in optics technology have enabled both activation and observation of target proteins with high spatiotemporal fidelity. In combination, these methods have great potential in the study of neural circuits and networks, behavior, animal models of disease, as well as in high-throughput ex vivo studies. This review collects some of these new tools and methods and surveys several current and future applications of the evolving field of optophysiology.

BackgroundFragile X syndrome (FXS) is a neurodevelopmental disorder whose biochemical manifestations involve dysregulation of mGluR5-dependent pathways, which are widely modeled using cultured neurons. In vitro phenotypes in cultured neurons using standard morphological, functional, and chemical approaches have demonstrated considerable variability. Here, we study transcriptomes obtained in situ in the intact brain tissues of a murine model of FXS to see how they reflect the in vitro state.MethodsWe used genome-wide mRNA expression profiling as a robust characterization tool for studying differentially expressed pathways in fragile X mental retardation 1 (Fmr1) knockout (KO) and wild-type (WT) murine primary neuronal cultures and in embryonic hippocampal and cortical murine tissue. To study the developmental trajectory and to relate mouse model data to human data, we used an expression map of human development to plot murine differentially expressed genes in KO/WT cultures and brain.ResultsWe found that transcriptomes from cell cultures showed a stronger signature of Fmr1KO than whole tissue transcriptomes. We observed an over-representation of immunological signaling pathways in embryonic Fmr1KO cortical and hippocampal tissues and over-represented mGluR5-downstream signaling pathways in Fmr1KO cortical and hippocampal primary cultures. Genes whose expression was up-regulated in Fmr1KO murine cultures tended to peak early in human development, whereas differentially expressed genes in embryonic cortical and hippocampal tissues clustered with genes expressed later in human development.ConclusionsThe transcriptional profile in brain tissues primarily centered on immunological mechanisms, whereas the profiles from cell cultures showed defects in neuronal activity. We speculate that the isolation and culturing of neurons caused a shift in neurological transcriptome towards a “juvenile” or “de-differentiated” state. Moreover, cultured neurons lack the close coupling with glia that might be responsible for the immunological phenotype in the intact brain. Our results suggest that cultured cells may recapitulate an early phase of the disease, which is also less obscured with a consequent “immunological” phenotype and in vivo compensatory mechanisms observed in the embryonic brain. Together, these results suggest that the transcriptome of cultured primary neuronal cells, in comparison to whole brain tissue, more robustly demonstrated the difference between Fmr1KO and WT mice and might reveal a molecular phenotype, which is typically hidden by compensatory mechanisms present in vivo. Moreover, cultures might be useful for investigating the perturbed pathways in early human brain development and genes previously implicated in autism.Electronic supplementary materialThe online version of this article (doi:10.1186/s13229-015-0061-9) contains supplementary material, which is available to authorized users.

We provide a regularization framework for subject transfer learning in which we seek to train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent representation and the subject label. We introduce three notions of independence and corresponding penalty terms using mutual information or divergence as a proxy for independence. For each penalty term, we provide several concrete estimation algorithms, using analytic methods as well as neural critic functions. We provide a hands-off strategy for applying this diverse family of regularization algorithms to a new dataset, which we call "AutoTransfer". We evaluate the performance of these individual regularization strategies and our AutoTransfer method on EEG, EMG, and ECoG datasets, showing that these approaches can improve subject transfer learning for challenging real-world datasets.

Complex phenotypes may represent novel syndromes that are the composite interaction of several genetic and environmental factors. We describe an 9-year old male with high functioning autism spectrum disorder and Muckle-Wells syndrome who at age 5  years of age manifested perseverations that interfered with his functioning at home and at school. After age 6, he developed intermittent episodes of fatigue and somnolence lasting from hours to weeks that evolved over the course of months to more chronic hypersomnia. Whole exome sequencing showed three mutations in genes potentially involved in his clinical phenotype. The patient has a predicted pathogenic de novo heterozygous p.Ala681Thr mutation in the ATP1A3 gene (chr19:42480621C>T, GRCh37/hg19). Mutations in this gene are known to cause Alternating Hemiplegia of Childhood, Rapid Onset Dystonia Parkinsonism, and CAPOS syndrome, sometimes accompanied by autistic features. The patient also has compound heterozygosity for p.Arg490Lys/p.Val200Met mutations in the NLRP3 gene (chr1:247588214G>A and chr1:247587343G>A, respectively). NLRP3 mutations are associated in an autosomal dominant manner with clinically overlapping auto-inflammatory conditions including Muckle-Wells syndrome. The p.Arg490Lys is a known pathogenic mutation inherited from the patient's father. The p.Val200Met mutation, inherited from his mother, is a variant of unknown significance (VUS). Whether the de novoATP1A3mutation is responsible for or plays a role in the patient's episodes of fatigue and somnolence remains to be determined. The unprecedented combination of two NLRP3 mutations may be responsible for other aspects of his complex phenotype.

As important decisions about the distribution of society's resources become increasingly automated, it is essential to consider the measurement and enforcement of fairness in these decisions. In this work we build on the results of Dwork and Ilvento in [1], which laid the foundations for the study of fair algorithms under composition.In particular, we study the cohort selection problem, where we wish to use a fair classifier to select k candidates from an arbitrarily ordered set of size n > k, while preserving individual fairness and maximizing utility. We define a linear utility function to measure performance relative to the behavior of the original classifier. We develop a fair, utility-optimal O(n)-time cohort selection algorithm for the offline setting, and our primary result, a solution to the problem in the streaming setting that keeps no more than O(k) pending candidates at all time.