Emerging evidence suggests that Parkinson's disease (PD), besides being an age-associated disorder, might also have a neurodevelopment component. Disruption of mitochondrial homeostasis has been highlighted as a crucial cofactor in its etiology. Here, we show that PD patient-specific human neuroepithelial stem cells (NESCs), carrying the LRRK2-G2019S mutation, recapitulate key mitochondrial defects previously described only in differentiated dopaminergic neurons. By combining high-content imaging approaches, 3D image analysis, and functional mitochondrial readouts we show that LRRK2-G2019S mutation causes aberrations in mitochondrial morphology and functionality compared with isogenic controls. LRRK2-G2019S NESCs display an increased number of mitochondria compared with isogenic control lines. However, these mitochondria are more fragmented and exhibit decreased membrane potential. Functional alterations in LRRK2-G2019S cultures are also accompanied by a reduced mitophagic clearance via lysosomes. These findings support the hypothesis that preceding mitochondrial developmental defects contribute to the manifestation of the PD pathology later in life.

Human stem cell-derived organoids have great potential for modelling physiological and pathological processes. They recapitulate in vitro the organization and function of a respective organ or part of an organ. Human midbrain organoids (hMOs) have been described to contain midbrain-specific dopaminergic neurons that release the neurotransmitter dopamine. However, the human midbrain contains also additional neuronal cell types, which are functionally interacting with each other. Here, we analysed hMOs at high-resolution by means of single-cell RNA sequencing (scRNA-seq), imaging and electrophysiology to unravel cell heterogeneity. Our findings demonstrate that hMOs show essential neuronal functional properties as spontaneous electrophysiological activity of different neuronal subtypes, including dopaminergic, GABAergic, glutamatergic and serotonergic neurons. Recapitulating these in vivo features makes hMOs an excellent tool for in vitro disease phenotyping and drug discovery.

Increasing evidence suggests that neurodevelopmental alterations might contribute to increase the susceptibility to develop neurodegenerative diseases. We investigate the occurrence of developmental abnormalities in dopaminergic neurons in a model of Parkinson's disease (PD). We monitor the differentiation of human patient-specific neuroepithelial stem cells (NESCs) into dopaminergic neurons. Using high-throughput image analyses and single-cell RNA sequencing, we observe that the PD-associated LRRK2-G2019S mutation alters the initial phase of neuronal differentiation by accelerating cell-cycle exit with a concomitant increase in cell death. We identify the NESC-specific core regulatory circuit and a molecular mechanism underlying the observed phenotypes. The expression of NR2F1, a key transcription factor involved in neurogenesis, decreases in LRRK2-G2019S NESCs, neurons, and midbrain organoids compared to controls. We also observe accelerated dopaminergic differentiation in vivo in NR2F1-deficient mouse embryos. This suggests a pathogenic mechanism involving the LRRK2-G2019S mutation, where the dynamics of dopaminergic differentiation are modified via NR2F1.

Developing measures for rapid and early detection of disease re-emergence is important to perform science-based risk assessment of epidemic threats. In the past few years, several early warning signals (EWS) from complex systems theory have been introduced to detect impending critical transitions and extend the set of indicators. However, it is still debated whether they are generically applicable or potentially sensitive to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data has, so far, been limited. Hence, verifying EWS performance remains a challenge. In this study, we tackle this question by analyzing the performance of common EWS, such as increasing variance and autocorrelation, in detecting the emergence of COVID-19 outbreaks in various countries. Our work illustrates that these EWS might be successful in detecting disease emergence when some basic assumptions are satisfied: a slow forcing through the transitions and not-fat-tailed noise. In uncertain cases, we observe that noise properties or commensurable time scales may obscure the expected early warning signals. Overall, our results suggest that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to certain features of the underlying dynamics. Our findings thus pave a connection between theoretical and empirical studies, constituting a further step towards the application of EWS indicators for informing public health policies.

Direct detection (DD) of dark matter (DM) candidates in the $$\lesssim${}10\text{ }\text{ }\mathrm{GeV}$ mass range is very sensitive to the tail of their velocity distribution. The important quantity is the maximum weakly interacting massive particle speed in the observer's rest frame, i.e. in average the sum of the local Galactic escape speed ${v}_{\text{esc}}$ and of the circular velocity of the Sun ${v}_{c}$. While the latter has been receiving continuous attention, the former is more difficult to constrain. The RAVE Collaboration has just released a new estimate of ${v}_{\text{esc}}$ [T. Piffl et al., Astron. Astrophys. 562, A91 (2014)] that supersedes the previous one [M. C. Smith, et al. Mon. Not. R. Astron. Soc. 379, 755 (2007)], which is of interest in the perspective of reducing the astrophysical uncertainties in DD. Nevertheless, these new estimates cannot be used blindly as they rely on assumptions in the dark halo modeling which induce tight correlations between the escape speed and other local astrophysical parameters. We make a self-consistent study of the implications of the RAVE results on DD assuming isotropic DM velocity distributions, both Maxwellian and ergodic. Taking as references the experimental sensitivities currently achieved by LUX, CRESST-II, and SuperCDMS, we show that (i) the exclusion curves associated with the best-fit points of P14 may be more constraining by up to $$\sim${}40%$ with respect to standard limits, because the underlying astrophysical correlations induce a larger local DM density, and (ii) the corresponding relative uncertainties inferred in the low weakly interacting massive particle mass region may be moderate, down to 10\char21{}15% below 10 GeV. We finally discuss the level of consistency of these results with other independent astrophysical constraints. This analysis is complementary to others based on rotation curves.

The ability of phototrophs to colonise different environments relies on robust protection against oxidative stress, a critical requirement for the successful evolutionary transition from water to land. Photosynthetic organisms have developed numerous strategies to adapt their photosynthetic apparatus to changing light conditions in order to optimise their photosynthetic yield, which is crucial for life on Earth to exist. Photosynthetic acclimation is an excellent example of the complexity of biological systems, where highly diverse processes, ranging from electron excitation over protein protonation to enzymatic processes coupling ion gradients with biosynthetic activity, interact on drastically different timescales from picoseconds to hours. Efficient functioning of the photosynthetic apparatus and its protection is paramount for efficient downstream processes, including metabolism and growth. Modern experimental techniques can be successfully integrated with theoretical and mathematical models to promote our understanding of underlying mechanisms and principles. This review aims to provide a retrospective analysis of multidisciplinary photosynthetic acclimation research carried out by members of the Marie Curie Initial Training Project, AccliPhot, placing the results in a wider context. The review also highlights the applicability of photosynthetic organisms for industry, particularly with regards to the cultivation of microalgae. It intends to demonstrate how theoretical concepts can successfully complement experimental studies broadening our knowledge of common principles in acclimation processes in photosynthetic organisms, as well as in the field of applied microalgal biotechnology.