Biology subjects

Wallace, E. P.

Publications and source records attributed to Wallace, E. P..

5 recordsLinked to original sources

bioRxiv2022

Autofluorescence is a biomarker of neural stem cell activation state

Neural stem cells (NSCs) in the adult brain are primarily quiescent but can activate and enter the cell cycle to produce newborn neurons. NSC quiescence can be regulated by disease, injury, and age, however our understanding of NSC quiescence is limited by technical limitations imposed by the bias of markers used to isolate each population of NSCs and the lack of live-cell labeling strategies. Fluorescence lifetime imaging (FLIM) of autofluorescent metabolic cofactors has previously been used in other cell types to study shifts in cell states driven by metabolic remodeling that change the optical properties of these endogenous fluorophores. Here we asked whether autofluorescence could be used to discriminate NSC activation state. We found that quiescent NSCs (qNSCs) and activated NSCs (aNSCs) each have unique autofluorescence intensity and fluorescence lifetime profiles. Additionally, qNSCs specifically display an enrichment of a specific autofluorescent signal localizing to lysosomes that is highly predictive of cell state. These signals can be used as a graded marker of NSC quiescence to predict cell behavior and track the dynamics of quiescence exit at single cell resolution in vitro and in vivo. Through coupling autofluorescence imaging with single-cell RNA sequencing in vitro and in vivo, we provide a high-resolution resource revealing transcriptional features linked to rapid NSC activation and deep quiescence. Taken together, we describe a single-cell resolution, non-destructive, live-cell, label-free strategy for measuring NSC activation state in vitro and in vivo and use this tool to expand our understanding of adult neurogenesis.

neuroscience↗

bioRxiv2021

Sleep deprivation exacerbates seizures and diminishes GABAergic tonic inhibition

Patients with epilepsy report that sleep deprivation is a common trigger for breakthrough seizures. The basic mechanism of this phenomenon is unknown. In the Kv1.1-/- mouse model of epilepsy, daily sleep deprivation indeed exacerbated seizures though these effects were lost after the 3rd day. Sleep deprivation also accelerated mortality in ~52% of Kv1.1-/- mice, not observed in controls. Voltage-clamp experiments on the day after recovery from sleep deprivation showed reductions in GABAergic tonic inhibition in dentate granule cells both in Kv1.1-/- and wild-type mice. Our results suggest that sleep deprivation is detrimental to seizures and survival, possibly due to reductions in GABAergic tonic inhibition.

neuroscience↗

bioRxiv2020

Sleep-wake Characteristics in a Mouse Model of Severe Traumatic Brain Injury: Relation to Post-Traumatic Epilepsy.

Study objectivesTraumatic brain injury (TBI) results in sequelae that include post-traumatic epilepsy (PTE) and sleep-wake disturbances. Here we sought to determine whether sleep characteristics could predict development of PTE in a model of severe TBI. MethodsFollowing controlled cortical impact (CCI), sham injury (craniotomy only) or no craniotomy (NC), CD-1 mice were implanted with epidural electroencephalography (EEG) and nuchal electromyography (EMG) electrodes. Acute (1st week) and chronic (months 1, 2 and 3 after injury) 1-week long video-EEG/EMG recordings were examined for epileptiform activity. We analyzed sleep-wake patterns manually and extracted high amplitude interictal events from EEG using an automated method. Sleep spindles and EEG delta power were derived from non-rapid eye movement (NREM) sleep epochs. Brain CTs (computerized tomography) were performed to quantify the extent of brain lesions in cohorts of sham and CCI. ResultsPosttraumatic seizures were seen with CCI, whereas interictal epileptiform activity as well as sleep-wake disruptions (shorter wake or NREM bout lengths, shorter duration or lower power for spindles, and increased NREM EEG delta power) were seen in CCI and sham groups. No sleep feature predicted PTE. Follow up brain CTs showed a small lesion in the sham injury group suggesting a milder form of TBI that may account for their interictal activity and sleep changes. ConclusionsIn our model, interictal epileptiform activity and sleep disruptions resulted from CCI and sham and thus, sham injury was not an optimal negative control. Further work is necessary to determine the relationship between sleep-wake disturbances and PTE. Statement of significanceTraumatic brain injury (TBI) results in sequelae such as post-traumatic seizures and sleep-wake disturbances but it is difficult to predict which individuals will develop these symptoms. Our study is novel in that we characterized epileptiform activity and multiple sleep characteristics in a mouse model of severe TBI (Controlled cortical impact-CCI) and explored whether any specific sleep disturbance can predict post-traumatic epilepsy. Specifically, post-traumatic seizures were seen after CCI only whereas epileptiform activity other than seizures as well as sleep-wake disruptions in mice that received a TBI and their sham injury controls. CT imaging showed that the sham injury group also had small brain lesions suggesting that a more optimal control in TBI research is to perform no craniotomy. No single sleep characteristic was predictive of post-traumatic epilepsy although NREM delta power was different in chronic recordings between TBI mice that developed seizures and those that did not. These studies are relevant to further research in TBI models, to develop a sleep biomarker for PTE. The work is also relevant to humans with TBI as monitoring sleep phenotypes may predict risk, but may also help develop therapies to prevent post-traumatic epilepsy.

neuroscience↗

bioRxiv2020

Deficits in Behavioral and Neuronal Pattern Separation in Temporal Lobe Epilepsy

In temporal lobe epilepsy, the ability of the dentate gyrus to limit excitatory cortical input to the hippocampus breaks down, leading to seizures. The dentate gyrus is also thought to help discriminate between similar memories by performing pattern separation, but whether epilepsy leads to a breakdown in this neural computation, and thus to mnemonic discrimination impairments, remains unknown. Here we show that temporal lobe epilepsy is characterized by behavioral deficits in mnemonic discrimination tasks, in both humans (females and males) and mice (C57Bl6 males, systemic low-dose kainate model). Using a recently developed assay in brain slices of the same epileptic mice, we reveal a decreased ability of the dentate gyrus to perform certain forms of pattern separation. This is due to a subset of granule cells with abnormal bursting that can develop independently of early EEG abnormalities. Overall, our results linking physiology, computation and cognition in the same mice advance our understanding of episodic memory mechanisms and their dysfunction in epilepsy. Significance StatementPeople with temporal lobe epilepsy (TLE) often have learning and memory impairments, sometimes occurring earlier than the first seizure, but those symptoms and their biological underpinnings are poorly understood. We focused on the dentate gyrus, a brain region that is critical to avoid confusion between similar memories and is anatomically disorganized in TLE. We show that both humans and mice with TLE experience confusion between similar situations. This impairment coincides with a failure of the dentate gyrus to disambiguate similar input signals because of pathological bursting in a subset of neurons. Our work bridges seizure-oriented and memory-oriented views of the dentate gyrus function, suggests a mechanism for cognitive symptoms in TLE and supports a long-standing hypothesis of episodic memory theories.

neuroscience↗

bioRxiv2018

A Predictive Epilepsy Index Based on Probabilistic Classification of Interictal Spike Waveforms

Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model. We calculated the odds-ratio of events from kainate-versus saline-treated mice within each cluster, converted these values to probability scores, P(kainate), and calculated an Hourly Epilepsy Index for each animal by summing the probabilities for events where the cluster P(kainate) > 0.5 and dividing the resultant sum by the record duration. This Index is predictive of whether an animal received an epileptogenic treatment (i.e., kainate), even if a seizure was never observed. We applied this method to an out-of-sample dataset to assess epileptiform spike morphologies in five kainate mice monitored for ~1 month. The magnitude of the Index increased over time in a subset of animals and revealed changes in the prevalence of epileptiform (P(kainate) > 0.5) spike morphologies. Importantly, in both data sets, animals that had electrographic seizures also had a high Index. This analysis is fast, unbiased, and provides information regarding the salience of spike morphologies for disease progression. Future refinement will allow a better understanding of the definition of interictal spikes in quantitative and unambiguous terms.

neuroscience↗