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Cellular complexity in the brain has been a central area of study since the birth of cellular neuroscience over a hundred years ago. Several different classification systems have been put forward based on emerging techniques. It is still largely unclear if and how the classification system produced using recent single-cell transcriptomics corresponds to previous classification systems. The interneurons of the hippocampus has been extensively characterised on physiological and morphological basis and we used this classification as a basis to compare single-cell RNA sequencing data from the CA1 hippocampus. We show, using the in situ sequencing technique “pciSeq” that the predictions made from scRNAseq data corresponds existing classification. Furthermore, we leverage the rich data from scRNAseq and combined it with GWAS data from patients to begin to elucidate the cellular origin of genetic heritability of brain disorders. Although many of these disorders are genetically complex it seems that specific and sometimes non-overlapping cell types underlie the ethology of these disorders. For instance we show a largely ignored role of oligodendrocytes in Parkinson’s disease which can be confirmed in patient material. This proves the feasibility to link modern transcriptomics with genetics to leverage the recent advances in understanding of genetic structure of brain disorders to yield actionable targets.

The predictions for a warmer and drier climate and for increased likelihood of climate extremes raise high concerns about the possible collapse of dryland ecosystems, and about the formation of new drylands where native species are less tolerant to water stress. Using a dryland-vegetation model for plant species that display different tradeoffs between fast growth and tolerance to droughts, we find that ecosystems subjected to strong seasonal variability, typical for drylands, exhibit a period-doubling route to chaos that results in early collapse to bare soil. We further find that fast-growing plants go through period doubling sooner and span wider chaotic ranges than stress-tolerant plants. We propose the detection of period-doubling signatures in power spectra as early indicators of ecosystem collapse that outperform existing indicators in their ability to warn against climate extremes and capture the heightened vulnerability of newly-formed drylands.

Peripheral neurons extend long axons that connect to target cells in skin or muscle. Axonal transcriptomes and proteomes have distinct characteristics and are modified by local stimuli. The regulation of axonal protein translation is still poorly understood. Here we show that mTOR, an important kinase in cell growth signaling, is locally translated in axons following injury, and regulates most of the local protein synthesis in axons. Moreover, we identified RNA sequences and RNA binding proteins involved in mTOR mRNA axonal localization. These results reveal critical roles for mTOR local translation in neuronal growth and regeneration.

According to the long-lasting computational scheme each neuron sums the incoming electrical signals via its dendrites and when the membrane potential reaches a certain threshold the neuron typically generates a spike. We present three types of experiments indicating that each neuron functions as a collection of independent threshold units, where the neuron is activated following the origin of the arriving signals. In addition, experimental and theoretical results reveal a new underlying mechanism for the fast brain learning process, dendritic learning, as opposed to learning which is based solely on slow synaptic plasticity. The learning occurs in closer proximity to the neuron, dendritic strengths are self-oscillating, and weak synapses play a key role in the dynamics.