How Sleep Deprivation Leads to Disrupted Neural Processing

Post by Meredith McCarty

The takeaway

Sleep deprivation leads to changes in sensory perception and arousal levels. The measured increase in neural population synchrony and decreased responses to auditory stimuli are similar across NREM sleep and sleep deprivation states. 

What's the science?

Sleep deprivation is known to alter cognitive performance in numerous ways, including the impairment of working memory, vigilance, cognitive speed, and executive attention. Despite the apparent cognitive impairments associated with sleep deprivation, the extent to which sleep deprivation alters neural processing remains underexplored. This week in Current Biology, Marmelshtein and colleagues recorded the auditory cortex of rats during different states of vigilance to determine what changes in neural activity are associated with sleep deprivation. 

How did they do it?

A total of 7 adult male rats were implanted with microwire arrays, and EEG and EMG electrodes and placed in a motorized running wheel apparatus for a ten-hour experimental paradigm. The microwire arrays allow for sampling of single neuron spiking activity, whereas the EEG and EMG electrodes allow for monitoring of slower brain rhythms across larger networks, as is relevant for determining arousal state. In order to induce vigilant and sleep-deprived states, the authors programmed the wheel to alternate between 3 seconds of forced running and 12-18 seconds of fixed wheel position over the first 5-hour experimental period. Following this 5 hours of sleep deprivation, the wheel was fixed for the final 5 hours in order to allow for a recovery sleep opportunity. Throughout the experiment, auditory stimuli trains were presented intermittently via speakers throughout the apparatus. This experimental design allows for the comparison of neural responses in the auditory cortex to auditory stimuli across vigilant, tired, and NREM and REM sleep. 

What did they find?

The authors compared many features of auditory processing across experimental conditions to determine whether arousal level had any effect on auditory processing. First, they found no significant effect of sleep deprivation on mean frequency tuning, onset responses, and spontaneous firing rate, which suggests that the rats’ arousal state had no effect on these neural responses. However, they found significant differences in population coupling measures, including increased population synchrony, and decreased entrainment to rapid auditory stimuli trains. These results suggest that sleep deprivation significantly affected how correlated individual neuronal firing rate was with the local population. When comparing neural activity during sleep deprivation and the recovery sleep experimental stages, they found that the neural effects of sleep deprivation - specifically increases in population synchrony - were very similar to NREM sleep. This suggests that low-arousal states, such as sleep deprivation and NREM sleep, lead to disrupted cortical processing of faster auditory inputs. 

What's the impact?

This study found that sleep deprivation leads to altered neuronal activity in early auditory sensory regions. While many aspects of neural processing were not affected by arousal level, the authors did reveal significant changes in population synchronization measures due to arousal level. The authors found similar increases in population synchronization and disrupted rapid sensory processing in both NREM and sleep-deprived states. These results have practical implications in the accurate monitoring of arousal levels, and theoretical implications in the continued study of how arousal and brain state influence brain activity.

Access the original scientific publication here. 

A Novel Subtype of Hypothalamic-Habenula Neurons Drives Aversive Behavior

Post by Trisha Vaidyanathan

The takeaway

The population of glutamatergic excitatory neurons that project from the lateral hypothalamic area (LHA) to the lateral habenula (LHb) is composed of six molecularly, physiologically, and functionally distinct cellular subtypes. One specific subtype, characterized by the expression of estrogen receptor 1 (Esr1+), mediates aversive behavior and a sex-specific maladaptive response to stress.  

What's the science?

The LHA, the LHb, and the prefrontal cortex (PFC) are key nodes in the neural circuit that controls emotional behavior. The LHA is the primary input to the LHb and this LHA-LHb pathway has been shown to send negative signals that mediate avoidance behavior and depression. While recent work has demonstrated that neural populations in other hypothalamic regions are heterogenous, the LHA-LHb cells are still thought to be one homogenous population. This week in Nature Neuroscience, Calvigioni and colleagues used a combination of ex vivo electrophysiology, single-cell RNA sequencing, and mouse genetics to determine if the LHA-LHb population is heterogeneous, characterize any subtypes of LHA-LHb cells, and determine their function in mediating emotional behavior.

How did they do it?

The authors characterized the heterogeneity of LHA-LHb cells using a powerful technique called Patch-Seq, which allowed them to characterize both the electrophysiological properties (via patch clamp recordings) and the gene expression pattern (via single-cell RNA sequencing) of an individual LHA-LHb cell. The authors then used this gene expression data to create Cre mouse lines that enabled them to genetically modify specific LHA-LHb subtypes.

Next, the authors confirmed previous findings that activation of the entire LHA-LHb population leads to aversive behavior using a two-chamber real-time preference test, in which one chamber is associated with optogenetic stimulation and, if aversive, mice will avoid entering that chamber. They next repeated these experiments but only activated specific LHA-LHb subtypes, to identify which subtype is responsible for the aversive behavior. To confirm, they also silenced cells by inhibiting neurotransmitter release via expression of tetanus toxin. 

Lastly, the authors focused on the cellular subtype marked by expression of estrogen receptor 1 (Esr1+). First, they used high-density electrodes in vivo to compare the PFC response to Esr1+ cell activation with the PFC response to an aversive stimulus in the form of an air puff to the eye. Next, the authors explored the role of Esr1+ cells in a sex-specific maladaptive stress response by exposing male and female mice to an unpredictable foot shock while simultaneously inhibiting Esr1+ cells or, in separate experiments, performing patch clamp electrophysiology to characterize changes in Esr1+ cell properties following the shock stressor.    

What did they find?

First, the authors identified six subtypes of LHA-LHb cells based on the electrophysiological properties obtained with patch-clamp recordings and demonstrated each subtype has a topographical organization across LHA, a unique anatomical projection to LHb, and a unique morphology. Further, single-cell RNA sequencing data revealed unique expression markers for many of these subtypes which the authors then used to generate Cre mouse lines for subtype-specific genetic manipulation.

Next, using optogenetics, the authors demonstrated that only activation of the Esr1+ subtype, but not any other subtype, recapitulated the avoidance behavior caused by activating the entire LHA-LHb population. Further, silencing Esr1+ cells while simultaneously activating the rest of the LHA-LHb population prevented avoidance behavior. This demonstrated that the Esr1+ subtype is necessary and sufficient for mediating the LHA-LHb avoidance behavior. 

Lastly, the authors found that, similar to an external aversive stimulus (air puff to the eye), Esr1+ activation had specific and profound effects on PFC activity, suggesting that Esr1+ cells are a critical component of the broader emotional behavior circuit. The authors also found Esr1+ cells mediate a sex-specific stress response. They demonstrated that unpredictable shocks induced a maladaptive stress response specifically in female mice and this response was reduced if Esr1+ cells were silenced. They also found that unpredictable shocks shifted the intrinsic firing properties of Esr1+ burst-firing cells in female, but not male, mice, suggesting this shift underlies a female-specific susceptibility to stress.

What's the impact?

This study is the first to show that LHA-LHb cells are a heterogenous population and identified six distinct subtypes based on unique physiological, molecular, morphological, and anatomical markers. Further, they demonstrate that a specific subtype of LHA-LHb cells marked by Esr1 expression is necessary and sufficient for aversive behavior and sex-specific stress responses. Broadly, this research reveals the importance of characterizing the diversity of neuron subtypes that underlie complex emotional behaviors. 

Access the original scientific publication here

Transcriptomic Changes in Cellular Communities In the Brain Contributes to Alzheimer’s Disease

Post by Soumilee Chaudhuri

The takeaway

Major brain cell types — neurons, oligodendrocytes, endothelial cells, etc. — individually and synergistically contribute towards molecular changes seen in the aging human brain in Alzheimer’s Disease (AD). A high-resolution transcriptomic map in the aging human brain unraveled 1) diverse cell populations associated with AD and 2) how networks of cellular communities coordinate to alter biological pathways, ultimately leading to AD.

What's the science?

Alzheimer’s Disease is an irreversible, neurodegenerative illness, with a complex pathophysiology. Amongst many unknowns in the molecular mechanisms of AD, is the specific contribution of major cell types in the aging brain and how this might trigger AD-related dementia. Even so, knowledge of the contribution of different brain cells in AD pathogenesis has advanced over the last decade due to the advent of high-resolution technologies such as single-cell RNA sequencing. We know that perturbation in transcriptomes (i.e., full range of mRNA molecules produced) of major brain cell types and subtypes like excitatory and inhibitory neurons, oligodendrocytes or microglia for example, synergize to cause events that lead to molecular changes seen in AD. However, we do not know the specific contributions of each cell subtype to this disease due to limited sample size and a lack of robust and sensitive technologies powerful enough to capture interindividual as well as cell-specific diversity in AD brain. This week in Nature Neuroscience, Dr. Cain and colleagues unravel the distinct cellular architecture of the aging brain prefrontal cortex in AD as well as how cells interact, using combined bulk and single cell RNA sequencing analyses and novel bioinformatics pipelines.

How did they do it?

The authors used a powerful approach to get insights about coordinated multicellular communities in the AD brain. They used a) integrative transcriptomics (bulk and single-cell RNA sequencing) as well as b) a robust analytical approach (CellMod: a deconvolution tool that allows estimation and projection of single cellular landscapes from a limited set of individuals to a higher number of individuals). The authors used single-cell data from 24 individuals from the ROSMAP cohort to generate a cellular map of the aging Dorsolateral Prefrontal Cortex (DLPFC) brain region, and used that as input to their novel CellMod deconvolution algorithm to estimate cellular compositions in an independent set of 638 individuals with bulk RNAseq data. Then, network analysis within this model revealed cellular sets and subsets and interacting cellular communities across individuals with AD vs. controls; and advanced statistical modeling associated known AD traits and risk factors to these identified cell subsets and communities across the case (AD) vs control (no AD) groups.

What did they find?

The authors identified specific subpopulations of cells associated with AD pathophysiology. Primarily, the authors discovered oligodendrocyte transcriptional pathways and a downregulation in somatostatin-producing neurons (SST neurons) as perturbed in AD pathogenesis. From the innovative cellular map of the neocortex that the authors constructed, they found interesting insights about oligodendrocyte diversity and interactions, implicating oligodendrocytes  —the myelinating cells of the brain — as a strong cellular contributor to AD. Further, they identified that oligodendrocyte expression from both positive (Oli.4) and negative oligodendrocyte (Oli.1) cells was strongly associated with tau pathology and cognitive decline in AD. Multiple shared pathways within cellular communities were identified and related to known risk factors of AD, as a way of validating that AD indeed, is a pathophysiologic process with multiple interacting cell types. Overall, the findings of this study pinpoint the importance of approaching our understanding of AD through a lens of interacting multicellular communities and networks. 

What's the impact?

This study is the first to show that cell specific as well as coordinated cellular and sub-cellular interactions in the aging brain may contribute to a diseased microenvironment in AD. Additionally, the authors find evidence of the contribution of communities of oligodendrocytes to cognitive decline and tau burden in Alzheimer’s Disease. The result of this study extends research on cellular and subcellular heterogeneity in the diseased aging brain and helps to inform therapeutic targets for AD and dementia.

Access the original scientific publication here