Opening the black box of traumatic brain injury: a holistic approach combining human 3D neural tissue and an in vitro traumatic brain injury induction device.

Traumatic brain injury (TBI) is caused by a wide range of physical events and can induce an even larger spectrum of short- to long-term pathophysiologies. Neuroscientists have relied on animal models to understand the relationship between mechanical damages and functional alterations of neural cells. These in vivo and animal-based in vitro models represent important approaches to mimic traumas on whole brains or organized brain structures but are not fully representative of pathologies occurring after traumas on human brain parenchyma. To overcome these limitations and to establish a more accurate and comprehensive model of human TBI, we engineered an in vitro platform to induce injuries via the controlled projection of a small drop of liquid onto a 3D neural tissue engineered from human iPS cells. With this platform, biological mechanisms involved in neural cellular injury are recorded through electrophysiology measurements, quantification of biomarkers released, and two imaging methods [confocal laser scanning microscope (CLSM) and optical projection tomography (OPT)]. The results showed drastic changes in tissue electrophysiological activities and significant releases of glial and neuronal biomarkers. Tissue imaging allowed us to reconstruct the injured area spatially in 3D after staining it with specific nuclear dyes and to determine TBI resulting in cell death. In future experiments, we seek to monitor the effects of TBI-induced injuries over a prolonged time and at a higher temporal resolution to better understand the subtleties of the biomarker release kinetics and the cell recovery phases.

The Role of Interstitial Fluid Pressure in Cerebral Porous Biomaterial Integration.

Recent advances in biomaterials offer new possibilities for brain tissue reconstruction. Biocompatibility, provision of cell adhesion motives and mechanical properties are among the present main design criteria. We here propose a radically new and potentially major element determining biointegration of porous biomaterials: the favorable effect of interstitial fluid pressure (IFP). The force applied by the lymphatic system through the interstitial fluid pressure on biomaterial integration has mostly been neglected so far. We hypothesize it has the potential to force 3D biointegration of porous biomaterials. In this study, we develop a capillary hydrostatic device to apply controlled in vitro interstitial fluid pressure and study its effect during 3D tissue culture. We find that the IFP is a key player in porous biomaterial tissue integration, at physiological IFP levels, surpassing the known effect of cell adhesion motives. Spontaneous electrical activity indicates that the culture conditions are not harmful for the cells. Our work identifies interstitial fluid pressure at physiological negative values as a potential main driver for tissue integration into porous biomaterials. We anticipate that controlling the IFP level could narrow the gap between in vivo and in vitro and therefore decrease the need for animal screening in biomaterial design.

Statistical distortion of supervised learning predictions in optical microscopy induced by image compression.

The growth of data throughput in optical microscopy has triggered the extensive use of supervised learning (SL) models on compressed datasets for automated analysis. Investigating the effects of image compression on SL predictions is therefore pivotal to assess their reliability, especially for clinical use. We quantify the statistical distortions induced by compression through the comparison of predictions on compressed data to the raw predictive uncertainty, numerically estimated from the raw noise statistics measured via sensor calibration. Predictions on cell segmentation parameters are altered by up to 15% and more than 10 standard deviations after 16-to-8 bits pixel depth reduction and 10:1 JPEG compression. JPEG formats with higher compression ratios show significantly larger distortions. Interestingly, a recent metrologically accurate algorithm, offering up to 10:1 compression ratio, provides a prediction spread equivalent to that stemming from raw noise. The method described here allows to set a lower bound to the predictive uncertainty of a SL task and can be generalized to determine the statistical distortions originated from a variety of processing pipelines in AI-assisted fields.

SpikeOnChip : A Custom Embedded Platform for Neuronal Activity Recording and Analysis.

In this paper we present SpikeOnChip, a custom embedded platform for neuronal activity recording and online analysis. The SpikeOnChip platform was developed in the context of automated drug testing and toxicology assessments on neural tissue made from human induced pluripotent stem cells. The system was developed with the following goals: to be small, autonomous and low power, to handle micro-electrode arrays with up to 256 electrodes, to reduce the amount of data generated from the recording, to be able to do computation during acquisition, and to be customizable. This led to the choice of a Field Programmable Gate Array System-On-Chip platform. This paper focuses on the embedded system for acquisition and processing with key features being the ability to record electrophysiological signals from multiple electrodes, detect biological activity on all channels online for recording, and do frequency domain spectral energy analysis online on all channels during acquisition. Development methodologies are also presented. The platform is finally illustrated in a concrete experiment with bicuculline being administered to grown human neural tissue through microfluidics, resulting in measurable effects in the spike recordings and activity. The presented platform provides a valuable new experimental instrument that can be further extended thanks to the programmable hardware and software.

Mass Generation, Neuron Labeling, and 3D Imaging of Minibrains.

Minibrain is a 3D brain in vitro spheroid model, composed of a mixed population of neurons and glial cells, generated from human iPSC derived neural stem cells. Despite the advances in human 3D in vitro models such as aggregates, spheroids and organoids, there is a lack of labeling and imaging methodologies to characterize these models. In this study, we present a step-by-step methodology to generate human minibrain nurseries and novel strategies to subsequently label projection neurons, perform immunohistochemistry and 3D imaging of the minibrains at large multiplexable scales. To visualize projection neurons, we adapt viral transduction and to visualize the organization of cell types we implement immunohistochemistry. To facilitate 3D imaging of minibrains, we present here pipelines and accessories for one step mounting and clearing suitable for confocal microscopy. The pipelines are specifically designed in such a way that the assays can be multiplexed with ease for large-scale screenings using minibrains and other organoid models. Using the pipeline, we present (i) dendrite morphometric properties obtained from 3D neuron morphology reconstructions, (ii) diversity in neuron morphology, and (iii) quantified distribution of progenitors and POU3F2 positive neurons in human minibrains.

Soft Printable Electrode Coating for Neural Interfaces.

The mechanical mismatch between implantable interfaces and neural tissues may be reduced by employing soft polymeric materials. Here, we report on a simple strategy to prepare and pattern a soft electrode coating of neural interfacing devices based on a screen-printable conducting hydrogel. The coating formulation, based on polyacrylamide and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, is suitable to additive manufacturing and exhibits excellent adhesion to polydimethylsiloxane, an elastomer commonly used as a substrate in soft neural interfaces. The soft conductive coating displays a tunable elastic modulus in the 10-100 kPa range and electrochemical properties on a par with stiff conductive inks while supporting good neural cell attachment and proliferation in vitro. Next, the soft printable hydrogel is integrated within a 4 × 4 microelectrode array for electrocorticography with 250 µm-diameter contacts. Acute recording of cortical local field potentials and electrochemical characterization preimplantation and postimplantation highlight the stability of the soft organic conductor. The overall robustness of the soft coating and its patterning method provide a promising route for a range of implantable bioelectronic applications.

Unravelling the effects of multiple experimental factors in metabolomics, analysis of human neural cells with hydrophilic interaction liquid chromatography hyphenated to high resolution mass spectrometry.

This work introduces a strategy for decomposing variable contributions within the data obtained from structured metabolomic studies. This approach was applied in the context of an in vitro human neural model to investigate biochemical changes related to neuroinflammation. Neural cells were exposed to the neuroinflammatory toxicant trimethyltin at different doses and exposure times. In the frame of an untargeted approach, cell contents were analysed using HILIC hyphenated with HRMS. Detected features were annotated at level 1 by comparison against a library of standards, and the 126 identified metabolites were analysed using a recently proposed chemometric tool dedicated to multifactorial Omics datasets, namely, ANOVA multiblock OPLS (AMOPLS). First, the total observed variability was decomposed to highlight the contribution of each effect related to the experimental factors. Both the dose of trimethyltin and the exposure time were found to have a statistically significant impact on the observed metabolic alterations. Cells that were exposed for a longer time exhibited a more mature and differentiated metabolome, whereas the dose of trimethyltin was linked to altered lipid pathways, which are known to participate in neurodegeneration. Then, these specific metabolic patterns were further characterised by analysing the individual variable contributions to each effect. AMOPLS was highlighted as a useful tool for analysing complex metabolomic data. The proposed strategy allowed the separation, quantitation and characterisation of the specific contribution of the different factors and the relative importance of every metabolite to each effect with respect to the total observed variability of the system.

Localized hypertrophic neuropathy of the sciatic nerve in children: MRI findings.

BACKGROUND: Localized hypertrophic neuropathy (LHN) of the sciatic nerve in children is a rare condition characterized by a painless neurological deficit in the sciatic nerve territory. OBJECTIVE: To demonstrate the role of MRI using a specific protocol and describe the primary findings in LHN. MATERIALS AND METHODS: Imaging in four children (age 2 years to 12 years) is presented. All children presented with lower limb asymmetry. Three had a steppage gait. LHN was confirmed by electrophysiological studies and by MRI of the whole sciatic nerve with a dedicated protocol covering the lumbar spine and the lower limb. RESULTS: There were four direct MRI findings: (1) linear and focal hypertrophy with progressive enlargement of a peripheral nerve or plexus diameter, (2) abnormal hyperintensity of the nerve on T2-weighted images, (3) preserved fascicular configuration, and (4) variable enhancement after intravenous gadolinium administration. In addition there were atrophy and fatty infiltration of innervated muscles. MRI was helpful for determining the extent of lesions and in excluding peripheral nerve compression or tumour. CONCLUSION: MRI of the whole sciatic nerve is the method of choice for diagnosing LHN of the sciatic nerve.