Human cortical spheroids with a high diversity of innately developing brain cell types

BACKGROUND

Three-dimensional (3D) human brain spheroids are instrumental to study central nervous system (CNS) development and (dys)function. Yet, in current brain spheroid models the limited variety of cell types hampers an integrated exploration of CNS (disease) mechanisms.

METHODS

Here we report a 5-month culture protocol that reproducibly generates H9 embryonic stem cell-derived human cortical spheroids (hCSs) with a large cell-type variety.

RESULTS

We established the presence of not only neuroectoderm-derived neural progenitor populations, mature excitatory and inhibitory neurons, astrocytes and oligodendrocyte (precursor) cells, but also mesoderm-derived microglia and endothelial cell populations in the hCSs via RNA-sequencing, qPCR, immunocytochemistry and transmission electron microscopy. Transcriptomic analysis revealed resemblance between the 5-months-old hCSs and dorsal frontal rather than inferior regions of human fetal brains of 19-26 weeks of gestational age. Pro-inflammatory stimulation of the generated hCSs induced a neuroinflammatory response, offering a proof-of-principle of the applicability of the spheroids.

CONCLUSIONS

Our protocol provides a 3D human brain cell model containing a wide variety of innately developing neuroectoderm- as well as mesoderm-derived cell types, furnishing a versatile platform for comprehensive examination of intercellular CNS communication and neurological disease mechanisms.

Key role for lipids in cognitive symptoms of schizophrenia

Schizophrenia (SZ) is a psychiatric disorder with a convoluted etiology that includes cognitive symptoms, which arise from among others a dysfunctional dorsolateral prefrontal cortex (dlPFC). In our search for the molecular underpinnings of the cognitive deficits in SZ, we here performed RNA sequencing of gray matter from the dlPFC of SZ patients and controls. We found that the differentially expressed RNAs were enriched for mRNAs involved in the Liver X Receptor/Retinoid X Receptor (LXR/RXR) lipid metabolism pathway. Components of the LXR/RXR pathway were upregulated in gray matter but not in white matter of SZ dlPFC. Intriguingly, an analysis for shared genetic etiology, using two SZ genome-wide association studies (GWASs) and GWAS data for 514 metabolites, revealed genetic overlap between SZ and acylcarnitines, VLDL lipids, and fatty acid metabolites, which are all linked to the LXR/RXR signaling pathway. Furthermore, analysis of structural T₁-weighted magnetic resonance imaging in combination with cognitive behavioral data showed that the lipid content of dlPFC gray matter is lower in SZ patients than in controls and correlates with a tendency towards reduced accuracy in the dlPFC-dependent task-switching test. We conclude that aberrations in LXR/RXR-regulated lipid metabolism lead to a decreased lipid content in SZ dlPFC that correlates with reduced cognitive performance.

Symbol Digit Modalities Test Variant in a Smartphone App for Persons With Multiple Sclerosis: Validation Study

Background

The decline of cognitive processing speed (CPS) is a common dysfunction in persons with multiple sclerosis (MS). The Symbol Digit Modalities Test (SDMT) is widely used to formally quantify CPS. We implemented a variant of the SDMT in MS sherpa, a smartphone app for persons with MS.

Objective

The aim of this study was to investigate the construct validity and test-retest reliability of the MS sherpa smartphone variant of the SDMT (sSDMT).

Methods

We performed a validation study with 25 persons with relapsing-remitting MS and 79 healthy control (HC) subjects. In the HC group, 21 subjects were matched to the persons with MS with regard to age, gender, and education and they followed the same assessment schedule as the persons with MS (the “HC matched” group) and 58 subjects had a less intense assessment schedule to determine reference values (the “HC normative” group). Intraclass correlation coefficients (ICCs) were determined between the paper-and-pencil SDMT and its smartphone variant (sSDMT) on 2 occasions, 4 weeks apart. Other ICCs were determined for test-retest reliability, which were derived from 10 smartphone tests per study participant, with 3 days in between each test. Seven study participants with MS were interviewed regarding their experiences with the sSDMT.

Results

The SDMT scores were on average 12.06% higher than the sSDMT scores, with a standard deviation of 10.68%. An ICC of 0.838 was found for the construct validity of the sSDMT in the combined analysis of persons with MS and HC subjects. Average ICCs for test-retest reliability of the sSDMT for persons with MS, the HC matched group, and the HC normative group were 0.874, 0.857, and 0.867, respectively. The practice effect was significant between the first and the second test of the persons with MS and the HC matched group and trivial for all other test-retests. The interviewed study participants expressed a positive attitude toward the sSDMT, but they also discussed the importance of adapting a smartphone cognition test in accordance with the needs of the individual persons with MS.

Conclusions

The high correlation between sSDMT and the conventional SDMT scores indicates a very good construct validity. Similarly, high correlations underpin a very good test-retest reliability of the sSDMT. We conclude that the sSDMT has the potential to be used as a tool to monitor CPS in persons with MS, both in clinical studies and in clinical practice.

Sensitivity to Stimulus Irregularity Is Inherent in Neural Networks

Behavior is controlled by complex neural networks in which neurons process thousands of inputs. However, even short spike trains evoked in a single cortical neuron were demonstrated to be sufficient to influence behavior in vivo. Specifically, irregular sequences of interspike intervals (ISIs) had a more reliable influence on behavior despite their resemblance to stochastic activity. Similarly, irregular tactile stimulation led to higher rates of behavioral responses. In this study, we identify the mechanisms enabling this sensitivity to stimulus irregularity (SSI) on the neuronal and network levels using simulated spiking neural networks. Matching in vivo experiments, we find that irregular stimulation elicits more detectable network events (bursts) than regular stimulation. Dissecting the stimuli, we identify short ISIs-occurring more frequently in irregular stimulations-as the main drivers of SSI rather than complex irregularity per se. In addition, we find that short-term plasticity modulates SSI. We subsequently eliminate the different mechanisms in turn to assess their role in generating SSI. Removing inhibitory interneurons, we find that SSI is retained, suggesting that SSI is not dependent on inhibition. Removing recurrency, we find that SSI is retained due to the ability of individual neurons to integrate activity over short timescales ("cell memory"). Removing single-neuron dynamics, we find that SSI is retained based on the short-term retention of activity within the recurrent network structure ("network memory"). Finally, using a further simplified probabilistic model, we find that local network structure is not required for SSI. Hence, SSI is identified as a general property that we hypothesize to be ubiquitous in neural networks with different structures and biophysical properties. Irregular sequences contain shorter ISIs, which are the main drivers underlying SSI. The experimentally observed SSI should thus generalize to other systems, suggesting a functional role for irregular activity in cortex.

Autism spectrum disorders and autistic traits share genetics and biology

Autism spectrum disorders (ASDs) and autistic traits in the general population may share genetic susceptibility factors. In this study, we investigated such potential overlap based on common genetic variants. We developed and validated a self-report questionnaire of autistic traits in adults. We then conducted genome-wide association studies (GWASs) of six trait scores derived from the questionnaire through exploratory factor analysis in 1981 adults from the general population. Using the results from the Psychiatric Genomics Consortium GWAS of ASDs, we observed genetic sharing between ASDs and the autistic traits 'childhood behavior', 'rigidity' and 'attention to detail'. Gene-set analysis subsequently identified 'rigidity' to be significantly associated with a network of ASD gene-encoded proteins that regulates neurite outgrowth. Gene-wide association with the well-established ASD gene MET reached significance. Taken together, our findings provide evidence for an overlapping genetic and biological etiology underlying ASDs and autistic population traits, which suggests that genetic studies in the general population may yield novel ASD genes.

Autism spectrum disorders and autistic traits share genetics and biology

This corrects the article DOI: 10.1038/mp.2017.98.

Integrated molecular landscape of Parkinson's disease

Parkinson’s disease is caused by a complex interplay of genetic and environmental factors. Although a number of independent molecular pathways and processes have been associated with familial Parkinson’s disease, a common mechanism underlying especially sporadic Parkinson’s disease is still largely unknown. In order to gain further insight into the etiology of Parkinson’s disease, we here conducted genetic network and literature analyses to integrate the top-ranked findings from thirteen published genome-wide association studies of Parkinson’s disease (involving 13.094 cases and 47.148 controls) and other genes implicated in (familial) Parkinson’s disease, into a molecular interaction landscape. The molecular Parkinson’s disease landscape harbors four main biological processes—oxidative stress response, endosomal-lysosomal functioning, endoplasmic reticulum stress response, and immune response activation—that interact with each other and regulate dopaminergic neuron function and death, the pathological hallmark of Parkinson’s disease. Interestingly, lipids and lipoproteins are functionally involved in and influenced by all these processes, and affect dopaminergic neuron-specific signaling cascades. Furthermore, we validate the Parkinson’s disease -lipid relationship by genome-wide association studies data-based polygenic risk score analyses that indicate a shared genetic risk between lipid/lipoprotein traits and Parkinson’s disease. Taken together, our findings provide novel insights into the molecular pathways underlying the etiology of (sporadic) Parkinson’s disease and highlight a key role for lipids and lipoproteins in Parkinson’s disease pathogenesis, providing important clues for the development of disease-modifying treatments of Parkinson’s disease.

Lipids and lipoproteins play a central role in four key biological processes underlying Parkinson’s disease (PD). Using bioinformatics and other extensive analyses of previously published data, Geert Poelmans, Cornelius Klemann and colleagues in The Netherlands, Germany and the USA have mapped the interactions of proteins that are encoded by genes associated with both familial and sporadic forms of PD. They identify the oxidative stress response, lysosomal function, endoplasmic reticulum stress response and immune response activation as the main mechanisms leading to the death of dopaminergic neurons. Lipid signaling is implicated in all four of these processes and the authors find a link between the levels of particular lipids and lipoproteins and the risk of PD. These findings suggest that compounds that regulate lipid or lipoprotein levels offer a potential new treatment strategy for PD.

Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks

Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution.

A Developmental Switch for Hebbian Plasticity

Hebbian forms of synaptic plasticity are required for the orderly development of sensory circuits in the brain and are powerful modulators of learning and memory in adulthood. During development, emergence of Hebbian plasticity leads to formation of functional circuits. By modeling the dynamics of neurotransmitter release during early postnatal cortical development we show that a developmentally regulated switch in vesicle exocytosis mode triggers associative (i.e. Hebbian) plasticity. Early in development spontaneous vesicle exocytosis (SVE), often considered as 'synaptic noise', is important for homogenization of synaptic weights and maintenance of synaptic weights in the appropriate dynamic range. Our results demonstrate that SVE has a permissive, whereas subsequent evoked vesicle exocytosis (EVE) has an instructive role in the expression of Hebbian plasticity. A timed onset for Hebbian plasticity can be achieved by switching from SVE to EVE and the balance between SVE and EVE can control the effective rate of Hebbian plasticity. We further show that this developmental switch in neurotransmitter release mode enables maturation of spike-timing dependent plasticity. A mis-timed or inadequate SVE to EVE switch may lead to malformation of brain networks thereby contributing to the etiology of neurodevelopmental disorders.

Neurotransmitter release is the principal form of chemical communication in the brain. When an action potential reaches a synapse, calcium influx activates the machinery for neurotransmitter release. During early neuronal development this machinery matures such that neurotransmitter release becomes time-locked to action potentials. By modeling this change in neurotransmitter release, we mechanistically show that the maturation process can be solely responsible for switching on associative (i.e. Hebbian) plasticity in the brain. The relevant proteins of the release machinery can thereby regulate the rate at which neural circuits represent sensory input, providing a novel mechanism to control the learning rate and onset. Appropriately timing of the onset of Hebbian plasticity is important because during early development sensory experience fine-tunes, often irreversibly, the neural wiring in our brain.

Separating burst from background spikes in multichannel neuronal recordings using return map analysis

We propose a preprocessing method to separate coherent neuronal network activity, referred to as “bursts”, from background spikes. High background activity in neuronal recordings reduces the effectiveness of currently available burst detection methods. For long-term, stationary recordings, burst and background spikes have a bimodal ISI distribution which makes it easy to select the threshold to separate burst and background spikes. Finite, nonstationary recordings lead to noisy ISIs for which the bimodality is not that clear. We introduce a preprocessing method to separate burst from background spikes to improve burst detection reliability because it efficiently uses both single and multichannel activity. The method is tested using a stochastic model constrained by data available in the literature and recordings from primary cortical neurons cultured on multielectrode arrays. The separation between burst and background spikes is obtained using the interspike interval return map. The cutoff threshold is the key parameter to separate the burst and background spikes. We compare two methods for selecting the threshold. The 2-step method, in which threshold selection is based on fixed heuristics. The iterative method, in which the optimal cutoff threshold is directly estimated from the data. The proposed preprocessing method significantly increases the reliability of several established burst detection algorithms, both for simulated and real recordings. The preprocessing method makes it possible to study the effects of diseases or pharmacological manipulations, because it can deal efficiently with nonstationarity in the data.

Applying double-magnetic induction to measure head-unrestrained gaze shifts: calibration and validation in monkey

The double magnetic induction (DMI) method has successfully been used to record head-unrestrained gaze shifts in human subjects (Bremen et al., J Neurosci Methods 160:75-84, 2007a, J Neurophysiol, 98:3759-3769, 2007b). This method employs a small golden ring placed on the eye that, when positioned within oscillating magnetic fields, induces orientation-dependent voltages in a pickup coil in front of the eye. Here we develop and test a streamlined calibration routine for use with experimental animals, in particular, with monkeys. The calibration routine requires the animal solely to accurately follow visual targets presented at random locations in the visual field. Animals can readily learn this task. In addition, we use the fact that the pickup coil can be fixed rigidly and reproducibly on implants on the animal's skull. Therefore, accumulation of calibration data leads to increasing accuracy. As a first step, we simulated gaze shifts and the resulting DMI signals. Our simulations showed that the complex DMI signals can be effectively calibrated with the use of random target sequences, which elicit substantial decoupling of eye- and head orientations in a natural way. Subsequently, we tested our paradigm on three macaque monkeys. Our results show that the data for a successful calibration can be collected in a single recording session, in which the monkey makes about 1,500-2,000 goal-directed saccades. We obtained a resolution of 30 arc minutes (measurement range [-60,+60]°). This resolution compares to the fixation resolution of the monkey's oculomotor system, and to the standard scleral search-coil method.