Human glial fibrillary acidic protein-delta (GFAP-delta) is a GFAP protein isoform that is encoded by an alternative splice variant of the GFAP-gene. As a result, GFAP-delta protein differs from the predominant splice form, GFAP-alpha, by its C-terminal protein sequence. In this study, we show that GFAP-delta protein is not expressed by all GFAP-expressing astrocytes but specifically by a subpopulation located in the subpial zone of the cerebral cortex, the subgranular zone of the hippocampus, and, most intensely, by a ribbon of astrocytes following the ependymal layer of the cerebral ventricles. Therefore, at least in the sub ventricular zone (SVZ), GFAP-delta specifically marks the population of astrocytes that contain the neural stem cells in the adult human brain. Interestingly, the SVZ astrocytes actively splice GFAP-delta transcripts, in contrast to astrocytes adjacent to this layer. Furthermore, we show that GFAP-delta protein, unlike GFAP-alpha, is not upregulated in astrogliosis. Our data therefore indicate a different functional role for GFAP-delta in astrocyte physiology. Finally, transfection studies showed that GFAP-delta protein expression has a negative effect on GFAP filament formation, and therefore could be important for modulating intermediate filament cytoskeletal properties, possibly facilitating astrocyte motility. Further studies on GFAP-delta and the cells that express it are important for gaining insights into its function during differentiation, migration and during health and disease.

Glial fibrillary acidic protein (GFAP) is considered to be a highly specific marker for glia. Here, we report on the expression of GFAP in neurons in the human hippocampus. Intriguingly, this neuronal GFAP is coded by out-of-frame splice variants and its expression is associated with Alzheimer pathology. We identified three novel GFAP splice forms: Delta 135 nt, Delta exon 6 and Delta 164 nt. Neuronal GFAP is mainly observed in the pyramidal neurons of the hippocampus of Alzheimer and Down syndrome patients and aged controls, but not in neurons of patients suffering from hippocampal sclerosis. Apparently, the hippocampal neurons in patients with Alzheimer's disease pathology are capable of expressing glia-specific genes.

The reproducibility crisis (or replication crisis) in biomedical research is a particularly existential and under-addressed issue in the field of behavioral neuroscience, where, in spite of efforts to standardize testing and assay protocols, several known and unknown sources of confounding environmental factors add to variance. Human interference is a major contributor to variability both within and across laboratories, as well as novelty-induced anxiety. Attempts to reduce human interference and to measure more "natural" behaviors in subjects has led to the development of automated home-cage monitoring systems. These systems enable prolonged and longitudinal recordings, and provide large continuous measures of spontaneous behavior that can be analyzed across multiple time scales. In this review, a diverse team of neuroscientists and product developers share their experiences using such an automated monitoring system that combines Noldus PhenoTyper® home-cages and the video-based tracking software, EthoVision® XT, to extract digital biomarkers of motor, emotional, social and cognitive behavior. After presenting our working definition of a “home-cage”, we compare home-cage testing with more conventional out-of-cage tests (e.g., the open field) and outline the various advantages of the former, including opportunities for within-subject analyses and assessments of circadian and ultradian activity. Next, we address technical issues pertaining to the acquisition of behavioral data, such as the fine-tuning of the tracking software and the potential for integration with biotelemetry and optogenetics. Finally, we provide guidance on which behavioral measures to emphasize, how to filter, segment, and analyze behavior, and how to use analysis scripts. We summarize how the PhenoTyper has applications to study neuropharmacology as well as animal models of neurodegenerative and neuropsychiatric illness. Looking forward, we examine current challenges and the impact of new developments. Examples include the automated recognition of specific behaviors, unambiguous tracking of individuals in a social context, the development of more animal-centered measures of behavior and ways of dealing with large datasets. Together, we advocate that by embracing standardized home-cage monitoring platforms like the PhenoTyper, we are poised to directly assess issues pertaining to reproducibility, and more importantly, measure features of rodent behavior under more ethologically relevant scenarios.

Automated gait assessment tests are used in studies of disorders characterized by gait impairment. CatWalk XT is one of the first commercially available automated systems for analyzing the gait of rodents and is currently the most used system in peer-reviewed publications. This automated gait analysis system can generate a large number of gait parameters. However, this creates a new challenge in selecting relevant parameters that describe the changes within a particular disease model. Here, for the first time, we performed a multi-disorder review on published CatWalk XT data. We identify commonly reported CatWalk XT gait parameters derived from 91 peer-reviewed experimental studies in mice, covering six disorders of the central nervous system (CNS) and peripheral nervous system (PNS). The disorders modeled in mice were traumatic brain injury (TBI), stroke, sciatic nerve injury (SNI), spinal cord injury (SCI), Parkinson’s disease (PD), and ataxia. Our review consisted of parameter selection, clustering, categorization, statistical evaluation, and data visualization. It suggests that certain gait parameters serve as potential indicators of gait dysfunction across multiple disease models, while others are specific to particular models. The findings also suggest that the more site-specific the injury is, the fewer parameters are reported to characterize its gait abnormalities. This study strives to present a clearly organized picture of gait parameters used in each one of the different mouse models, potentially helping novel CatWalk XT users to apply this information to similar or related mouse models they are working on.