Continuous monitoring of tissue microphysiology is a key enabling feature of the organ-on-chip (OoC) approach for in vitro drug screening and disease modeling. Integrated sensing units are particularly convenient for microenvironmental monitoring. However, sensitive in vitro and real-time measurements are challenging due to the inherently small size of OoC devices, the characteristics of commonly used materials, and external hardware setups required to support the sensing units. Here we propose a silicon-polymer hybrid OoC device that encompasses transparency and biocompatibility of polymers at the sensing area, and has the inherently superior electrical characteristics and ability to house active electronics of silicon. This multi-modal device includes two sensing units. The first unit consists of a floating-gate field-effect transistor (FG-FET), which is used to monitor changes in pH in the sensing area. The threshold voltage of the FG-FET is regulated by a capacitively-coupled gate and by the changes in charge concentration in close proximity to the extension of the floating gate, which functions as the sensing electrode. The second unit uses the extension of the FG as microelectrode, in order to monitor the action potential of electrically active cells. The layout of the chip and its packaging are compatible with multi-electrode array measurement setups, which are commonly used in electrophysiology labs. The multi-functional sensing is demonstrated by monitoring the growth of induced pluripotent stem cell-derived cortical neurons. Our multi-modal sensor is a milestone in combined monitoring of different, physiologically-relevant parameters on the same device for future OoC platforms.

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Organ-on-a-chip (OoC) and microfluidic devices are conventionally produced using microfabrication procedures that require cleanrooms, silicon wafers, and photomasks. The prototyping stage often requires multiple iterations of design steps. A simplified prototyping process could therefore offer major advantages. Here, we describe a rapid and cleanroom-free microfabrication method using maskless photolithography. The approach utilizes a commercial digital micromirror device (DMD)-based setup using 375 nm UV light for backside exposure of an epoxy-based negative photoresist (SU-8) on glass coverslips. We show that microstructures of various geometries and dimensions, microgrooves, and microchannels of different heights can be fabricated. New SU-8 molds and soft lithography-based polydimethylsiloxane (PDMS) chips can thus be produced within hours. We further show that backside UV exposure and grayscale photolithography allow structures of different heights or structures with height gradients to be developed using a single-step fabrication process. Using this approach: (1) digital photomasks can be designed, projected, and quickly adjusted if needed; and (2) SU-8 molds can be fabricated without cleanroom availability, which in turn (3) reduces microfabrication time and costs and (4) expedites prototyping of new OoC devices@en

We present preliminary recordings on chip of three-dimensional (3D) electric neuronal activity from cultures of cortical neurons derived from human-induced pluripotent stem cells (hiPSCs). The recordings were obtained through 3D microelectrode arrays (MEAs) composed of truncated, 90 μm-high Si micropyramids endowed with multiple, electrically distinct, and vertically arranged TiN microelectrodes. The unique design and implementation of the 3D microelectrodes, complemented by a 60-electrode readout interface, allow for 3D spatial recording of neuronal activity, as well as single-unit recordings in high throughput, which are currently not possible with commercial MEA platforms. Future work will aim at optimizing extended 3D MEAs over optically transparent substrates for electro-physiological investigation of 3D neuronal tissues and organoids.@en

Continuous monitoring of tissue microphysiology is a key enabling feature of the organ-on-chip (OoC) approach for drug screening and disease modeling. Sensing charged species in OoC tissue microenvironments is thereby essential. However, the inherently small (i.e., cm) size of OoC devices poses the challenging requirement to integrate miniaturized and highly sensitive in situ charge sensing components to maximize signal extraction from small volumes (nL to L, range) of media used in these devices. Here we meet this need by presenting a novel dual-gate field-effect transistor-based charge sensor integrated within an optically transparent microelectromechanical (MEM) OoC device. Post-process mask-less decoration of Ti sensing electrodes by spark-ablated Au nanoparticle films significantly increases the effective electrode surface area and thus sensor sensitivity while retaining the CMOS-compatibility of the wafer-level fabrication process. We validate the biocompatibility of the sensor and its selective response to poly-D-lsine and KC1, and provide a perspective on monitoring cultures and differentiation of hiPSC-derived cortical neurons on our OoC device.

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Organ-on-chip (OoC) devices are in rising demand for high-throughput and low-cost development and toxico-logical screening of chemicals and pharmaceuticals, as they accurately mimic in vitro physiological conditions as in the human body. In particular, OoCs are urgently needed for screening cardiovascular drug toxicity. Physiological relevance of cardiovascular cell cultures requires moving substrates. To date cell culture substrates have been commonly actuated by pneumatic systems, which are bulky, expensive and non-user-friendly, and may thus limit the adoption of OoCs by researchers and industry. In this paper we propose the first actuating and sensing smart material-based OoC device and demonstrate its functionality by culturing human vascular smooth muscle cells (vSMC). Our device utilizes a single ionic polymer metal composite (IPMC)-based transducer to provide both actuation and sensing. The soft IPMC substrate allows to intermittently apply cyclic loading to tissues and to sense their spontaneous contractions. We integrated the transducer within a compact, easy-to-operate, economically affordable and scalable OoC prototype, which achieves an actuation range of 0.2 mm and 0.72 V/mm sensing resolution. The 0.1 % strain induced by actuation on cells accurately corresponds to in vitro strains for vSMCs. We successfully grew vSMCs on the IPMC substrate, and actuated them for 150 min at 1 Hz. Fluorescent staining showed no evidence of adverse effects. These results are a major step towards versatile OoCs for a wide variety of biological modelling of human tissues.@en

In vitro study of high-level neurobiological systems requires three-dimensional (3D) neuronal cultures [1]. Meas-uring responses along all three spatial dimensions is critical to record electric activity inside 3D neuronal models, such as organoids and other 3D brain tissue constructs. However, this lies beyond the capacity of 2D microelec-trode arrays (MEAs) [2]. We present planar arrays of 3D micro-pyramids, whereby each micro-pyramid supports multiple, electrically distinct and vertically stacked microelectrodes. The 3D microarrays were produced by wafer-scale micromachining and assembled onto printed circuit boards (PCBs) conforming to MEA readout standards.@en

•Actuation has been succesfully performed for 2h30 with no side effects nor
delimination of the human tissue
•0.1 % strain has been achieved during the actutaion mode, close to the
strain experienced in vivo by vSMCs
•0.72 V/mm sensitivity has been shown on the sensing mode
•Batch fabrication and downscaling will be targetted in the near future
•Actual sensing of the cells’ contraction will be reserved for further work @en

Familial hemiplegic migraine type 1 (FHM1) is a rare monogenic subtype of migraine with aura caused by mutations in CACNA1A that encodes the α1A subunit of voltage-gated CaV2.1 calcium channels. Transgenic knock-in mice that carry the human FHM1 R192Q missense mutation (‘FHM1 R192Q mice’) exhibit an increased susceptibility to cortical spreading depression (CSD), the mechanism underlying migraine aura. Here, we analysed gene expression profiles from isolated cortical tissue of FHM1 R192Q mice 24 h after experimentally induced CSD in order to identify molecular pathways affected by CSD. Gene expression profiles were generated using deep serial analysis of gene expression sequencing. Our data reveal a signature of inflammatory signalling upon CSD in the cortex of both mutant and wild-type mice. However, only in the brains of FHM1 R192Q mice specific genes are up-regulated in response to CSD that are implicated in interferon-related inflammatory signalling. Our findings show that CSD modulates inflammatory processes in both wild-type and mutant brains, but that an additional unique inflammatory signature becomes expressed after CSD in a relevant mouse model of migraine.@en

In this data report we make available to the community a highly variable longitudinal MRI mouse brain data set of ischemic lesion after transient middle cerebral artery occlusion (tMCAo). Together with the provided semi-automated and automated segmentations, these data can be used to further improve the method proposed by Mulder et al. (2017) and also to serve as a benchmark for comparison between different approaches to segment ischemic lesions in MRI mouse brain data. It can also be used to develop and validate algorithms that further classify the stroke area into core and penumbra. • The data were collected from mice: (i) of different ages, (ii) of two different strains, (iii) at different time points after the ischemic infarct induction, (iv) from two laboratories, (v) using two different MRI systems, and (vi) using three different sets of acquisition parameters. • Segmentations of the ischemic lesions are provided as well. These were obtained by: (i) two observers using a semi-automated method and (ii) using the novel automated segmentation approach described by Mulder et al. (2017). • Type/format of data: raw files, MetaImage files, text/Excel files, analyzed data. • The following set of images associated with each of the 121 scans is included: raw Bruker MRI data (reference scan, T2 scan with all echoes, calculated T2-weighted map), automated segmentations of the ischemic lesions and semi-automated segmentations by two observers. • For 99 of these scans, an accompanying set of Bruker MR diffusion maps, containing the Diffusion-Weighted Image (DWI) and calculated Apparent Diffusion Coefficient (ADC) maps, is included. • Acquisition hardware: small-animal Bruker MRI systems (7 T and 11.7 T). • Experimental set-up: infarct was induced in male mice of different age and background, using the tMCAo model. After that, MRI scans at different time points after infarct induction were acquired. • Data sources: Leiden, Netherlands; Cologne, Germany. • Data accessibility: all related data sets (121 T2 scans + template + 99 diffusion scans) were deposited in the public Dryad Digital Repository (https://doi.org/10.5061/dryad.1m528).@en

Magnetic resonance imaging (MRI) has become increasingly important in ischemic stroke experiments in mice, especially because it enables longitudinal studies. Still, quantitative analysis of MRI data remains challenging mainly because segmentation of mouse brain lesions in MRI data heavily relies on time-consuming manual tracing and thresholding techniques. Therefore, in the present study, a fully automated approach was developed to analyze longitudinal MRI data for quantification of ischemic lesion volume progression in the mouse brain. We present a level-set-based lesion segmentation algorithm that is built using a minimal set of assumptions and requires only one MRI sequence (T2) as input. To validate our algorithm we used a heterogeneous data set consisting of 121 mouse brain scans of various age groups and time points after infarct induction and obtained using different MRI hardware and acquisition parameters. We evaluated the volumetric accuracy and regional overlap of ischemic lesions segmented by our automated method against the ground truth obtained in a semi-automated fashion that includes a highly time-consuming manual correction step. Our method shows good agreement with human observations and is accurate on heterogeneous data, whilst requiring much shorter average execution time. The algorithm developed here was compiled into a toolbox and made publically available, as well as all the data sets.

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Migraine is a common brain disorder, with a heritability of 50%. Genome-wide association studies have identified several loci, but interpretation remains challenging. We integrated migraine GWAS data with spatial gene expression data of adult brains from the Allen Human Brain Atlas, to identify specific brain regions and molecular pathways involved in migraine. We used two complementary methods. First, we clustered all genes into co-expression modules and identified those associated with migraine. Second, we constructed local co-expression networks around high-confidence migraine genes. Both approaches converge on functions and anatomy.@en