NGN2 induces diverse neuron types from human pluripotency.

Human neurons engineered from induced pluripotent stem cells (iPSCs) through neurogenin 2 (NGN2) overexpression are widely used to study neuronal differentiation mechanisms and to model neurological diseases. However, the differentiation paths and heterogeneity of emerged neurons have not been fully explored. Here, we used single-cell transcriptomics to dissect the cell states that emerge during NGN2 overexpression across a time course from pluripotency to neuron functional maturation. We find a substantial molecular heterogeneity in the neuron types generated, with at least two populations that express genes associated with neurons of the peripheral nervous system. Neuron heterogeneity is observed across multiple iPSC clones and lines from different individuals. We find that neuron fate acquisition is sensitive to NGN2 expression level and the duration of NGN2-forced expression. Our data reveal that NGN2 dosage can regulate neuron fate acquisition, and that NGN2-iN heterogeneity can confound results that are sensitive to neuron type.

Comparison of induced neurons reveals slower structural and functional maturation in humans than in apes.

We generated induced excitatory neurons (iNeurons, iNs) from chimpanzee, bonobo, and human stem cells by expressing the transcription factor neurogenin-2 (NGN2). Single-cell RNA sequencing showed that genes involved in dendrite and synapse development are expressed earlier during iNs maturation in the chimpanzee and bonobo than the human cells. In accordance, during the first 2 weeks of differentiation, chimpanzee and bonobo iNs showed repetitive action potentials and more spontaneous excitatory activity than human iNs, and extended neurites of higher total length. However, the axons of human iNs were slightly longer at 5 weeks of differentiation. The timing of the establishment of neuronal polarity did not differ between the species. Chimpanzee, bonobo, and human neurites eventually reached the same level of structural complexity. Thus, human iNs develop slower than chimpanzee and bonobo iNs, and this difference in timing likely depends on functions downstream of NGN2.

CSS: cluster similarity spectrum integration of single-cell genomics data.

It is a major challenge to integrate single-cell sequencing data across experiments, conditions, batches, time points, and other technical considerations. New computational methods are required that can integrate samples while simultaneously preserving biological information. Here, we propose an unsupervised reference-free data representation, cluster similarity spectrum (CSS), where each cell is represented by its similarities to clusters independently identified across samples. We show that CSS can be used to assess cellular heterogeneity and enable reconstruction of differentiation trajectories from cerebral organoid and other single-cell transcriptomic data, and to integrate data across experimental conditions and human individuals.

Selective serotonin reuptake inhibitors versus placebo in patients with major depressive disorder. A systematic review with meta-analysis and Trial Sequential Analysis.

BACKGROUND: The evidence on selective serotonin reuptake inhibitors (SSRIs) for major depressive disorder is unclear. METHODS: Our objective was to conduct a systematic review assessing the effects of SSRIs versus placebo, 'active' placebo, or no intervention in adult participants with major depressive disorder. We searched for eligible randomised clinical trials in The Cochrane Library's CENTRAL, PubMed, EMBASE, PsycLIT, PsycINFO, Science Citation Index Expanded, clinical trial registers of Europe and USA, websites of pharmaceutical companies, the U.S. Food and Drug Administration (FDA), and the European Medicines Agency until January 2016. All data were extracted by at least two independent investigators. We used Cochrane systematic review methodology, Trial Sequential Analysis, and calculation of Bayes factor. An eight-step procedure was followed to assess if thresholds for statistical and clinical significance were crossed. Primary outcomes were reduction of depressive symptoms, remission, and adverse events. Secondary outcomes were suicides, suicide attempts, suicide ideation, and quality of life. RESULTS: A total of 131 randomised placebo-controlled trials enrolling a total of 27,422 participants were included. None of the trials used 'active' placebo or no intervention as control intervention. All trials had high risk of bias. SSRIs significantly reduced the Hamilton Depression Rating Scale (HDRS) at end of treatment (mean difference -1.94 HDRS points; 95% CI -2.50 to -1.37; P < 0.00001; 49 trials; Trial Sequential Analysis-adjusted CI -2.70 to -1.18); Bayes factor below predefined threshold (2.01*10-23). The effect estimate, however, was below our predefined threshold for clinical significance of 3 HDRS points. SSRIs significantly decreased the risk of no remission (RR 0.88; 95% CI 0.84 to 0.91; P < 0.00001; 34 trials; Trial Sequential Analysis adjusted CI 0.83 to 0.92); Bayes factor (1426.81) did not confirm the effect). SSRIs significantly increased the risks of serious adverse events (OR 1.37; 95% CI 1.08 to 1.75; P = 0.009; 44 trials; Trial Sequential Analysis-adjusted CI 1.03 to 1.89). This corresponds to 31/1000 SSRI participants will experience a serious adverse event compared with 22/1000 control participants. SSRIs also significantly increased the number of non-serious adverse events. There were almost no data on suicidal behaviour, quality of life, and long-term effects. CONCLUSIONS: SSRIs might have statistically significant effects on depressive symptoms, but all trials were at high risk of bias and the clinical significance seems questionable. SSRIs significantly increase the risk of both serious and non-serious adverse events. The potential small beneficial effects seem to be outweighed by harmful effects. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42013004420.

Improved membrane-based sensor network for reliable gas monitoring in the subsurface.

A conceptually improved sensor network to monitor the partial pressure of CO(2) in different soil horizons was designed. Consisting of five membrane-based linear sensors (line-sensors) each with 10 m length, the set-up enables us to integrate over the locally fluctuating CO(2) concentrations (typically lower 5%(vol)) up to the meter-scale gaining valuable concentration means with a repetition time of about 1 min. Preparatory tests in the laboratory resulted in a unexpected highly increased accuracy of better than 0.03%(vol) with respect to the previously published 0.08%(vol). Thereby, the statistical uncertainties (standard deviations) of the line-sensors and the reference sensor (nondispersive infrared CO(2)-sensor) were close to each other. Whereas the uncertainty of the reference increases with the measurement value, the line-sensors show an inverse uncertainty trend resulting in a comparatively enhanced accuracy for concentrations >1%(vol). Furthermore, a method for in situ maintenance was developed, enabling a proof of sensor quality and its effective calibration without demounting the line-sensors from the soil which would disturb the established structures and ongoing processes.

Membrane Based Measurement Technology for in situ Monitoring of Gases in Soil.

The representative measurement of gas concentration and fluxes in heterogeneous soils is one of the current challenges when analyzing the interactions of biogeochemical processes in soils and global change. Furthermore, recent research projects on CO(2)-sequestration have an urgent need of CO(2)-monitoring networks. Therefore, a measurement method based on selective permeation of gases through tubular membranes has been developed. Combining the specific permeation rates of gas components for a membrane and Dalton's principle, the gas concentration (or partial pressure) can be determined by the measurement of physical quantities (pressure or volume) only. Due to the comparatively small permeation constants of membranes, the influence of the sensor on its surrounding area can be neglected. The design of the sensor membranes can be adapted to the spatial scale from the bench scale to the field scale. The sensitive area for the measurement can be optimized to obtain representative results. Furthermore, a continuous time-averaged measurement is possible where the time for averaging is simply controlled by the wall-thickness of the membrane used. The measuring method is demonstrated for continuous monitoring of O(2) and CO(2) inside of a sand filled Lysimeter. Using three sensor planes inside the sand pack, which were installed normal to the gas flow direction and a reference measurement system, we demonstrate the accuracy of the gas-detection for different flux-based boundary conditions.