Probing sporadic and familial Alzheimer's disease using induced pluripotent stem cells.

Our understanding of Alzheimer's disease pathogenesis is currently limited by difficulties in obtaining live neurons from patients and the inability to model the sporadic form of the disease. It may be possible to overcome these challenges by reprogramming primary cells from patients into induced pluripotent stem cells (iPSCs). Here we reprogrammed primary fibroblasts from two patients with familial Alzheimer's disease, both caused by a duplication of the amyloid-β precursor protein gene (APP; termed APP(Dp)), two with sporadic Alzheimer's disease (termed sAD1, sAD2) and two non-demented control individuals into iPSC lines. Neurons from differentiated cultures were purified with fluorescence-activated cell sorting and characterized. Purified cultures contained more than 90% neurons, clustered with fetal brain messenger RNA samples by microarray criteria, and could form functional synaptic contacts. Virtually all cells exhibited normal electrophysiological activity. Relative to controls, iPSC-derived, purified neurons from the two APP(Dp) patients and patient sAD2 exhibited significantly higher levels of the pathological markers amyloid-β(1-40), phospho-tau(Thr 231) and active glycogen synthase kinase-3β (aGSK-3β). Neurons from APP(Dp) and sAD2 patients also accumulated large RAB5-positive early endosomes compared to controls. Treatment of purified neurons with β-secretase inhibitors, but not γ-secretase inhibitors, caused significant reductions in phospho-Tau(Thr 231) and aGSK-3β levels. These results suggest a direct relationship between APP proteolytic processing, but not amyloid-β, in GSK-3β activation and tau phosphorylation in human neurons. Additionally, we observed that neurons with the genome of one sAD patient exhibited the phenotypes seen in familial Alzheimer's disease samples. More generally, we demonstrate that iPSC technology can be used to observe phenotypes relevant to Alzheimer's disease, even though it can take decades for overt disease to manifest in patients

Aleatoric Uncertainty Estimation of Overnight Sleep Statistics Through Posterior Sampling Using Conditional Normalizing Flows

In sleep staging, a polysomnography is visually scored by a human expert, who creates a hypnogram that classifies the measurement into a sequence of sleep stages, from which overnight sleep statistics, such as total sleep time, are derived. Because inter-scorer agreement between humans is limited, deep learning methods trained to automate sleep staging have aleatoric uncertainty about both hypnogram and overnight statistics. We would like to estimate this aleatoric uncertainty, which can be achieved by means of posterior sampling. Current approaches model the hypnogram through a time-based factorization of categorical distributions over sleep stages. This discards time-dependent information, invalidating posterior sampling of the overnight statistics. Instead of factorizing, we propose to jointly model the sequence of sleep stages, by introducing U-Flow, a conditional normalizing flow network. We compare U-Flow to factorized baselines, leveraging 921 recordings, and show that it achieves similar performance in terms of accuracy and Cohen’s kappa on the majority voted hypnograms, while outperforming in terms of uncertainty estimation of the overnight sleep statistics

Modeling the Impact of Inter-Rater Disagreement on Sleep Statistics using Deep Generative Learning

Sleep staging is the process by which an overnight polysomnographic measurement is segmented into epochs of 30 seconds, each of which is annotated as belonging to one of five discrete sleep stages. The resulting scoring is graphically depicted as a hypnogram, and several overnight sleep statistics are derived, such as total sleep time and sleep onset latency. Gold standard sleep staging as performed by human technicians is time-consuming, costly, and comes with imperfect inter-scorer agreement, which also results in inter-scorer disagreement about the overnight statistics. Deep learning algorithms have shown promise in automating sleep scoring, but struggle to model inter-scorer disagreement in sleep statistics. To that end, we introduce a novel technique using conditional generative models based on Normalizing Flows that permits the modeling of the inter-rater disagreement of overnight sleep statistics, termed U-Flow. We compare U-Flow to other automatic scoring methods on a hold-out test set of 70 subjects, each scored by six independent scorers. The proposed method achieves similar sleep staging performance in terms of accuracy and Cohen's kappa on the majority-voted hypnograms. At the same time, U-Flow outperforms the other methods in terms of modeling the inter-rater disagreement of overnight sleep statistics. The consequences of inter-rater disagreement about overnight sleep statistics may be great, and the disagreement potentially carries diagnostic and scientifically relevant information about sleep structure. U-Flow is able to model this disagreement efficiently and can support further investigations into the impact inter-rater disagreement has on sleep medicine and basic sleep research.</p

Certainty about Uncertainty in Sleep Staging: a Theoretical Framework

Sleep stage classification is an important tool for the diagnosis of sleep disorders. Because sleep staging has such a high impact on clinical outcome, it is important that it is done reliably. However, it is known that uncertainty exists in both expert scorers and automated models. On average, the agreement between human scorers is only 82.6%. In this study, we provide a theoretical framework to facilitate discussion and further analyses of uncertainty in sleep staging. To this end, we introduce two variants of uncertainty, known from statistics and the machine learning community: aleatoric and epistemic uncertainty. We discuss what these types of uncertainties are, why the distinction is useful, where they arise from in sleep staging, and provide recommendations on how this framework can improve sleep staging in the future

Neuropathy-induced spinal GAP-43 expression is not a main player in the onset of mechanical pain hypersensitivity.

Structural plasticity within the spinal nociceptive network may be fundamental to the chronic nature of neuropathic pain. In the present study, the spatiotemporal expression of growth-associated protein-43 (GAP-43), a protein which has been traditionally implicated in nerve fiber growth and sprouting, was investigated in relation to mechanical pain hypersensitivity. An L5 spinal nerve transection model was validated by the presence of mechanical pain hypersensitivity and an increase in the early neuronal activation marker cFos within the superficial spinal dorsal horn upon innocuous hindpaw stimulation. Spinal GAP-43 was found to be upregulated in the superficial L5 dorsal horn from 5 up to 10 days after injury. GAP-43 was co-localized with calcitonin-gene related peptide (CGRP), but not vesicular glutamate transporter-1 (VGLUT-1), IB4, or protein kinase-γ (PKC-γ), suggesting the regulation of GAP-43 in peptidergic nociceptive afferents. These GAP-43/CGRP fibers may be indicative of sprouting peptidergic fibers. Fiber sprouting largely depends on growth factors, which are typically associated with neuro-inflammatory processes. The putative role of neuropathy-induced GAP-43 expression in the development of mechanical pain hypersensitivity was investigated using the immune modulator propentofylline. Propentofylline treatment strongly attenuated the development of mechanical pain hypersensitivity and glial responses to nerve injury as measured by microglial and astroglial markers, but did not affect neuropathy-induced levels of spinal GAP-43 or GAP-43 regulation in CGRP fibers. We conclude that nerve injury induces structural plasticity in fibers expressing CGRP, which is regarded as a main player in central sensitization. Our data do not, however, support a major role of these structural changes in the onset of mechanical pain hypersensitivity

Amelioration of motor/sensory dysfunction and spasticity in a rat model of acute lumbar spinal cord injury by human neural stem cell transplantation

INTRODUCTION: Intraspinal grafting of human neural stem cells represents a promising approach to promote recovery of function after spinal trauma. Such a treatment may serve to: I) provide trophic support to improve survival of host neurons; II) improve the structural integrity of the spinal parenchyma by reducing syringomyelia and scarring in trauma-injured regions; and III) provide neuronal populations to potentially form relays with host axons, segmental interneurons, and/or α-motoneurons. Here we characterized the effect of intraspinal grafting of clinical grade human fetal spinal cord-derived neural stem cells (HSSC) on the recovery of neurological function in a rat model of acute lumbar (L3) compression injury. METHODS: Three-month-old female Sprague–Dawley rats received L3 spinal compression injury. Three days post-injury, animals were randomized and received intraspinal injections of either HSSC, media-only, or no injections. All animals were immunosuppressed with tacrolimus, mycophenolate mofetil, and methylprednisolone acetate from the day of cell grafting and survived for eight weeks. Motor and sensory dysfunction were periodically assessed using open field locomotion scoring, thermal/tactile pain/escape thresholds and myogenic motor evoked potentials. The presence of spasticity was measured by gastrocnemius muscle resistance and electromyography response during computer-controlled ankle rotation. At the end-point, gait (CatWalk), ladder climbing, and single frame analyses were also assessed. Syrinx size, spinal cord dimensions, and extent of scarring were measured by magnetic resonance imaging. Differentiation and integration of grafted cells in the host tissue were validated with immunofluorescence staining using human-specific antibodies. RESULTS: Intraspinal grafting of HSSC led to a progressive and significant improvement in lower extremity paw placement, amelioration of spasticity, and normalization in thermal and tactile pain/escape thresholds at eight weeks post-grafting. No significant differences were detected in other CatWalk parameters, motor evoked potentials, open field locomotor (Basso, Beattie, and Bresnahan locomotion score (BBB)) score or ladder climbing test. Magnetic resonance imaging volume reconstruction and immunofluorescence analysis of grafted cell survival showed near complete injury-cavity-filling by grafted cells and development of putative GABA-ergic synapses between grafted and host neurons. CONCLUSIONS: Peri-acute intraspinal grafting of HSSC can represent an effective therapy which ameliorates motor and sensory deficits after traumatic spinal cord injury