DataSheet1_Hyperoxygenation During Mid-Neurogenesis Accelerates Cortical Development in the Fetal Mouse Brain.pdf

Oxygen tension is well-known to affect cortical development. Fetal brain hyperoxygenation during mid-neurogenesis in mice (embryonic stage E14.5. to E16.5) increases brain size evoked through an increase of neuroprecursor cells. Nevertheless, it is unknown whether these effects can lead to persistent morphological changes within the highly orchestrated brain development. To shed light on this, we used our model of controlled fetal brain hyperoxygenation in time-pregnant C57BL/6J mice housed in a chamber with 75% atmospheric oxygen from E14.5 to E16.5 and analyzed the brains from E14.5, E16.5, P0.5, and P3.5 mouse embryos and pups via immunofluorescence staining. Mid-neurogenesis hyperoxygenation led to an acceleration of cortical development by temporal expansion of the cortical plate with increased NeuN+ neuron counts in hyperoxic brains only until birth. More specifically, the number of Ctip2+ cortical layer 5 (L5) neurons was increased at E16.5 and at birth in hyperoxic brains but normalized in the early postnatal stage (P3.5). The absence of cleaved caspase 3 within the extended Ctip2+ L5 cell population largely excluded apoptosis as a major compensatory mechanism. Timed BrdU/EdU analyses likewise rule out a feedback mechanism. The normalization was, on the contrary, accompanied by an increase of active microglia within L5 targeting Ctip2+ neurons without any signs of apoptosis. Together, hyperoxygenation during mid-neurogenesis phase of fetal brain development provoked a specific transient overshoot of cortical L5 neurons leading to an accelerated cortical development without detectable persistent changes. These observations provide insight into cortical and L5 brain development.</p

Human TDP-43 and FUS selectively affect motor neuron maturation and survival in a murine cell model of ALS by non-cell-autonomous mechanisms

<p>TAR DNA-binding protein 43 (TDP-43) and fused in sarcoma (FUS) were recently found to cause familial and sporadic amyotrophic lateral sclerosis (ALS). The mechanisms by which mutations within these genes cause ALS are not understood. We established murine embryonic stem cell (ESC)-based cell models that stably express the human wild-type (WT) and various ALS causing mutations of <i>TDP-43</i> (A315T) and <i>FUS</i> (R514S, R521C and P525L). We investigated their effect on pan-neuron as well as motor neuron degeneration. Finally, non-cell-autonomous mediated neurodegeneration by muscle cells was investigated. Expression of mutant hTDP-43, but not wild-type TDP-43, as well as wild-type and mutant hFUS proteins induced neuronal degeneration with partial selectivity for motor neurons. Motor neuron loss was accompanied by abnormal neurite morphology and length. In chimeric coculture experiments with control motor neurons and mutant muscle cells (as their major target cells), we detected that mutant hTDP-43 A315T as well as wild-type and hFUS P525L expression only in muscle cells is sufficient to exert degenerative effects on control motor neurons. In conclusion, our data indicate that a selective vulnerability of motor neurons expressing the pathogenic ALS-causing genes <i>TDP-43</i> and <i>FUS</i>, is, at least in part, mediated through non-cell-autonomous mechanisms.</p

Comparison of PLI distributions derived from the first (pre-ictal) time interval (blue curve) and an interval during the seizure attack (red curve).

<p>Distributions from seizure intervals tend to exhibit longer periods of phase-locking resulting in a deviation from a power-law of the distribution's tail. Plots are shown for scale 3 corresponding to the frequency band 25-12.5 Hz for patients 1–7 (P1–P7) and 32-16 Hz for patient 8 (P8), respectively.</p

The distribution of phase-locking intervals deviates from a power-law during epileptic seizures.

<p>Top: The electrocorticogram (ECoG) recording shows the onset of a focal epileptic seizure attack around 300 seconds time. Bottom: Cumulative distributions of phase-locking intervals (PLI) are obtained during three time intervals of 150 seconds: pre-ictal (left), ictal (middle) and post-ictal (right). Dashed lines indicate a power-law with exponent −3.1. While the distribution appears to follow a power-law during the pre-ictal period, intervals of increased phase-locking disturb this characteristic distribution with the onset of seizure activity. Data shown are from patient 1 at scale 3, corresponding to the frequency band 25–12.5 Hz.</p

Distribution of PLI in a model exhibiting self-organized criticality.

<p>A Through an adaptive interplay of network dynamics and topology, the Bornholdt model self-organizes toward a characteristic connectivity independent of initial conditions. The plot shows the evolution to a characteristic connectivity of approximately in a network of 1024 nodes for three different initial connectivities, , and . B At this self-organized connectivity the network exhibits a phase transition between order and disorder. The plot shows the frozen component defined as the fraction of nodes that do not change their state along the attractor as a function of networks' average connectivities for a network of 1024 nodes. The data were measured along the dynamical attractor reached by the system, averaged over 100 random topologies for each value of . A transition around a value can be observed. C After a period of self-organization based on the adaptive interplay between topology and dynamics (aSO on, full black line), links were added and deleted solely with a certain probability independent of node activity (aSO off, dashed line: links were added with and deleted with , point-dashed line: links added with , deleted with ). Each iteration marks a topological update of the network, between iterations network activity was limited to 1000 time steps where topology was not changed. Phase-lock intervals between 20 randomly chosen nodes were calculated for scale 1 from 100 consecutive iterations at three time points: at the self-organized connectivity (bottom left), at a connectivity lower (bottom middle) and higher (bottom right) than the evolved connectivity. The distribution of PLI follows a power-law only at the self-organized connectivity (bottom left). All depicted distributions are cumulative distributions. The dashed line marks a power-law with exponent −1.5 to guide the eye.</p

Development of the deviation from a power-law.

<p>ECoG recordings from 8 patients showing a focal seizure attack are shown along with values for consecutive time windows of 150 seconds duration overlapping by 100 seconds. The power-law fit of data in the first time window was taken as the reference to calculate . Although different in extent, an increase of quantifying the deviation from the initial pre-ictal distribution can be observed during seizures for all patients and different scales (scale 2 red, scale 3 blue, scale 4 green).</p

Dimensionality of Rolled-up Nanomembranes Controls Neural Stem Cell Migration Mechanism

We employ glass microtube structures fabricated by rolled-up nanotechnology to infer the influence of scaffold dimensionality and cell confinement on neural stem cell (NSC) migration. Thereby, we observe a pronounced morphology change that marks a reversible mesenchymal to amoeboid migration mode transition. Space restrictions preset by the diameter of nanomembrane topography modify the cell shape toward characteristics found in living tissue. We demonstrate the importance of substrate dimensionality for the migration mode of NSCs and thereby define rolled-up nanomembranes as the ultimate tool for single-cell migration studies

Video2_Using a Digital Twin of an Electrical Stimulation Device to Monitor and Control the Electrical Stimulation of Cells in vitro.FLV

Electrical stimulation for application in tissue engineering and regenerative medicine has received increasing attention in recent years. A variety of stimulation methods, waveforms and amplitudes have been studied. However, a clear choice of optimal stimulation parameters is still not available and is complicated by ambiguous reporting standards. In order to understand underlying cellular mechanisms affected by the electrical stimulation, the knowledge of the actual prevailing field strength or current density is required. Here, we present a comprehensive digital representation, a digital twin, of a basic electrical stimulation device for the electrical stimulation of cells in vitro. The effect of electrochemical processes at the electrode surface was experimentally characterised and integrated into a numerical model of the electrical stimulation. Uncertainty quantification techniques were used to identify the influence of model uncertainties on relevant observables. Different stimulation protocols were compared and it was assessed if the information contained in the monitored stimulation pulses could be related to the stimulation model. We found that our approach permits to model and simulate the recorded rectangular waveforms such that local electric field strengths become accessible. Moreover, we could predict stimulation voltages and currents reliably. This enabled us to define a controlled stimulation setting and to identify significant temperature changes of the cell culture in the monitored voltage data. Eventually, we give an outlook on how the presented methods can be applied in more complex situations such as the stimulation of hydrogels or tissue in vivo.</p

Video1_Using a Digital Twin of an Electrical Stimulation Device to Monitor and Control the Electrical Stimulation of Cells in vitro.FLV

Electrical stimulation for application in tissue engineering and regenerative medicine has received increasing attention in recent years. A variety of stimulation methods, waveforms and amplitudes have been studied. However, a clear choice of optimal stimulation parameters is still not available and is complicated by ambiguous reporting standards. In order to understand underlying cellular mechanisms affected by the electrical stimulation, the knowledge of the actual prevailing field strength or current density is required. Here, we present a comprehensive digital representation, a digital twin, of a basic electrical stimulation device for the electrical stimulation of cells in vitro. The effect of electrochemical processes at the electrode surface was experimentally characterised and integrated into a numerical model of the electrical stimulation. Uncertainty quantification techniques were used to identify the influence of model uncertainties on relevant observables. Different stimulation protocols were compared and it was assessed if the information contained in the monitored stimulation pulses could be related to the stimulation model. We found that our approach permits to model and simulate the recorded rectangular waveforms such that local electric field strengths become accessible. Moreover, we could predict stimulation voltages and currents reliably. This enabled us to define a controlled stimulation setting and to identify significant temperature changes of the cell culture in the monitored voltage data. Eventually, we give an outlook on how the presented methods can be applied in more complex situations such as the stimulation of hydrogels or tissue in vivo.</p

Change of olfactory function after 12 weeks as expressed by the TDI score (comprehensive score of threshold, discrimination, and identification abilities) in the training group compared to controls without training.

<p>Change of olfactory function after 12 weeks as expressed by the TDI score (comprehensive score of threshold, discrimination, and identification abilities) in the training group compared to controls without training.</p