A community-based transcriptomics classification and nomenclature of neocortical cell types

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

Neuronal hypertrophy dampens neuronal intrinsic excitability and stress responsiveness during chronic stress

KEY POINTS

The hypothalamic-pituitary-adrenal (HPA) axis habituates to repeated stress exposure. We studied hypothalamic corticotropin-releasing hormone (CRH) neurons that form the apex of the HPA axis in a mouse model of stress habituation using repeated restraint. The intrinsic excitability of CRH neurons decreased after repeated stress in a time course that coincided with the development of HPA axis habituation. This intrinsic excitability plasticity co-developed with an expansion of surface membrane area, which increased a passive electric load and dampened membrane depolarization in response to the influx of positive charge. We report a novel structure-function relationship for intrinsic excitability plasticity as a neural correlate for HPA axis habituation.

ABSTRACT

Encountering a stressor immediately activates the hypothalamic-pituitary-adrenal (HPA) axis, but this stereotypic stress response also undergoes experience-dependent adaptation. Despite the biological and clinical importance, how the brain adjusts stress responsiveness in the long term remains poorly understood. We studied hypothalamic corticotropin-releasing hormone neurons that form the apex of the HPA axis in a mouse model of stress habituation using repeated restraint. Using patch-clamp electrophysiology in acute slices, we found that the intrinsic excitability of these neurons substantially decreased after daily repeated stress in a time course that coincided with their loss of stress responsiveness in vivo. This intrinsic excitability plasticity co-developed with an expansion of surface membrane area, which increased a passive electric load, and dampened membrane depolarization in response to the influx of positive charge. Multiphoton imaging and electron microscopy revealed that repeated stress augmented ruffling of the plasma membrane, suggesting an ultrastructural plasticity that may efficiently accommodate the membrane area expansion. Overall, we report a novel structure-function relationship for intrinsic excitability plasticity as a neural correlate for adaptation of the neuroendocrine stress response.

Non-monotonic accumulation of spike time variance during membrane potential oscillations

A spike-phase neural code has been proposed as a mechanism to encode stimuli based on the precise timing of spikes relative to the phase of membrane potential oscillations. This form of coding has been reported in both in vivo and in vitro experiments across several regions of the brain, yet there are concerns that such precise timing may be compromised by an effect referred to as variance accumulation, wherein spike timing variance increases over the phase of an oscillation. Here, we provide a straightforward explanation of this effect based on the theoretical spike time variance. The proposed theory is consistent with recordings of mitral neurons. It shows that spike time variance can increase in a nonlinear fashion with spike number, in a way that is dependent upon the frequency and amplitude of the oscillation. Further, non-monotonic accumulation of variance can arise from different combinations of oscillation parameters. Nonlinear accumulation sometimes leads to lower variance than that of a mean rate-matched homogeneous Poisson process, particularly for spikes that occur in later phases of oscillation. However, such an advantage is limited to a narrow range of oscillation amplitudes and frequencies. These results suggest fundamental constraints on spike-phase coding, and reveal how certain spikes in a sequence may exhibit increased firing time precision relative to their neighbors.

Burst predicting neurons survive an in vitro glutamate injury model of cerebral ischemia

Neuronal activity in vitro exhibits network bursts characterized by brief periods of increased spike rates. Recent work shows that a subpopulation of neurons reliably predicts the occurrence of network bursts. Here, we examined the role of burst predictors in cultures undergoing an in vitro model of cerebral ischemia. Dissociated primary cortical neurons were plated on multielectrode arrays and spontaneous activity was recorded at 17 days in vitro (DIV). This activity was characterized by neuronal avalanches where burst statistics followed a power law. We identified burst predictors as channels that consistently fired immediately prior to network bursts. The timing of these predictors relative to bursts followed a skewed distribution that differed sharply from a null model based on branching ratio. A portion of cultures were subjected to an excitotoxic insult (DIV 18). Propidium iodine and fluorescence imaging confirmed cell death in these cultures. While the insult did not alter the distribution of avalanches, it resulted in alterations in overall spike rates. Burst predictors, however, maintained baseline levels of activity. The resilience of burst predictors following excitotoxic insult suggests a key role of these units in maintaining network activity following injury, with implications for the selective effects of ischemia in the brain.