Speakers

Discover the expertise of key opinion leaders and leading scientists at the MxW Summit 2024! Our speakers, pioneers in neuroscience, cell biology, and bioengineering, will share insights on iPSC-derived neurons, neural organoids, and MEAs. Learn more about their innovative talks and the impact on neuroscience research.

Confirmed Keynote Speakers:

Prof. Dr. Cedric Bardy
South Australian Health and Medical Research Institute (SAHMRI), Australia

Prof. Dr. Feng Guo
Indiana University Bloomington, USA

Prof. Dr. Nako Nakatsuka
Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland

Prof. Dr. Tom Nowakowski
University of California San Francisco (UCSF), USA

Confirmed Invited Speakers:

Confirmed Short Talks:

Confirmed Sponsored Talks:

Tom Brown
bit.bio, United Kingdom

Raymond Price
Neuroservices Alliance, USA

Confirmed Invited Panel Speakers for Special Session “Pioneering Stories that Shaped MaxWell Biosystems”:

Dr. Douglas Bakkum
Shift Crypto AG, Switzerland

Dr. Sadik Hafizovic
Zurich Instruments, Switzerland

Prof. Dr. Andreas Hierlemann
Bio Engineering Laboratory, ETH Zurich, Switzerland

Speaker Information – Scientific Abstracts & Short Bios:

Prof. Dr. Cedric Bardy
South Australian Health and Medical Research Institute (SAHMRI), Australia

Title | Modeling Functional Human Brain Circuits In Vitro with iPSC from Patients with Neurodegenerative Disorders.

Abstract | Medical research opportunities are arising with unprecedented access to live tissue from patients via induced pluripotent stem cells (iPSC) and the advance of organoid methods. In this talk, Cedric will give an overview of recent steps his team has taken to improve human neuronal circuits in vitro for electrophysiological assays and reproducible drug screens.

^{References: https://www.bardylab.com/publications.html}

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Prof. Dr. Feng Guo
Indiana University Bloomington, USA

Title | Brain Organoid Computing for Artificial Intelligence

Abstract | In recent years, the demand for computational power has surged due to the rapid advancements in artificial intelligence (AI), encompassing machine learning and artificial neural network models. Nonetheless, existing computing hardware faces hurdles concerning energy efficiency and processing capacity, especially when handling intricate models. In addressing these obstacles, neuromorphic computing systems are being developed, drawing inspiration from the structure and functionality of the human brain. One intriguing avenue involves utilizing human brain organoids—three-dimensional brain-like tissues derived from human stem cells. These organoids can recapitulate certain the structure and function of a human brain. Herein, we develop a hybrid neuromorphic computing system by melding conventional silicon chips with a human brain organoid. We implement a human brain organoid into a reservoir computing framework, a kind of artificial neural networks. The computation is performed by sending and receiving information from the brain organoid using a high-density Maxwell multielectrode array (MEA). This new approach enables predictions or classifications from the original input data. To showcase the versatility of this hybrid system, we demonstrated its efficacy in tasks such as speech recognition and nonlinear equation prediction. This innovative fusion of traditional computing chips and organoids holds potential for advancing biocomputing, brain-machine interfaces, and translational medicine. The hybrid neuromorphic computing system introduced here paves the way for further exploration, offering insights into the synergy between artificial and biological systems.

^{References:
[1] Cai, H. et al. Brain organoid reservoir computing for artificial intelligence. Nat Electron 6, 1032–1039 (2023)}

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Prof. Dr. Nako Nakatsuka
Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland

Title | Interfacing Chemical Nanotechnologies with Biosystems

Abstract | Neurotechnologies that can monitor chemical signaling in complex biological systems are a necessity to expand our understanding of brain (dys)function. Our work fills this technological gap by harnessing DNA-based recognition elements called aptamers, which can be designed to capture neurotransmitters such as dopamine and serotonin with unprecedented sensitivity and selectivity. We functionalize DNA aptamers inside nanoscale pipettes with openings of ca. 10 nm. Upon target binding, aptamers undergo a structure switching, which is transduced as measurable changes in current through the nanopore of the sensors. Nanoscale confinement of the sensor surface mitigates nonspecific binding in complex environments, overcoming a critical bottleneck for biosensors. We have demonstrated the detection of neurotransmitters released by live neurons in complex media as well as in brain slices with unprecedented sensitivity. Further, we are combining biosensors with electrophysiology to use an integrative approach to tackle the complexity of the brain. Continuous monitoring of serotonin in live tissue have been conducted in tandem with electrical recordings from microelectrode arrays.

^{References:
[1] Stuber, A. et al. Aptamer Renaissance for Neurochemical Biosensing. ACS NANO 18, 2552-2563 (2024)
[2] Stuber, A. et al. Interfacing Aptamer-Modified Nanopipettes with Neuronal Media and Ex Vivo Brain Tissue. ACS Measurement Science 4, 92-103 (2024)
[3] Duru, J. et al. Driving electrochemical reactions at the microscale using CMOS microelectrode arrays. Lab Chip 23, 5047-5058 (2023)
[4] Stuber, A. et al. Aptamer Conformational Dynamics Modulate Neurotransmitter Sensingin Nanopores. ACS NANO 17, 19168-19179 (2023)
[5] Frutiger, A. et al. Nonspecific Binding—Fundamental Concepts and Consequences for Biosensing Applications. Chemical Reviews 121, 8095-8160 (2021)}

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Prof. Dr. Tom Nowakowski
University of California San Francisco (UCSF), USA

Title | Establishing Tools for Human Neuroscience

Abstract | Human brain contains an astonishing diversity of cell types distributed across hundreds of anatomical regions. To study their development and dysfunction, we have developed protocols that maintain human tissue in ex vivo for multiple days and allow us to systematically interrogate the development and the emergence of functional properties directly in human tissue. Moreover, by leveraging scalable and high throughput techniques for library generation and selection, we can develop new tools with increased specificity and efficiency of targeting human brain circuits that could ultimately lead to the development of refined strategies for circuit therapeutics.

^{References:
[1] Zhu, D. et al. Optimal trade-off control in machine learning-based library design, with application to adeno- associated virus (AAV) for gene therapy. Sci Adv. 10, eadj3786 (2024)}

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Prof. Dr. Magdalini Polymenidou
Department of Quantitative Biomedicine, University of Zurich (UZH), Switzerland

Title | Studying Toxic Mechanisms of TDP-43 in Human Neural Networks

Abstract | Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are two symptomatically different manifestations of the same progressive neurodegenerative proteinopathy for which no cure exists. A key player in the pathogenesis of both diseases is the RNA-binding protein TDP-43, which becomes aggregated and thereby dysfunctional in affected neurons of patient brains. Devising faithful human neuronal models of TDP-43 proteinopathies has been challenging, as most studies using iPSC-derived neurons have reported low to no TDP-43 pathology, potentially due to the early maturation state of human neurons in culture. To overcome this problem and study neurodegeneration phenotypes in human neurons, we recently developed a methodology for generating human neuronal networks with remarkable maturity, longevity and reproducibility, which were denominated iNets. Combining high density multielectron arrays and single cell RNA sequencing, we demonstrated the potential of iNets for modeling neurodegeneration (Hruska-Plochan et al, Nature, 2024). Overexpression of wild-type TDP-43 in a minority of neurons within iNets led to progressive fragmentation and aggregation of the protein, resulting in a partial loss of function and neurotoxicity. Single-cell transcriptomics revealed a novel set of misregulated RNA targets in TDP-43-overexpressing neurons and in patients with TDP-43 proteinopathies exhibiting a loss of nuclear TDP-43. The strongest misregulated target encoded the synaptic protein NPTX2, the levels of which are controlled by TDP-43 binding on its 3′ untranslated region. When NPTX2 was overexpressed in iNets, it exhibited neurotoxicity, whereas correcting NPTX2 misregulation partially rescued neurons from TDP-43-induced neurodegeneration. More recently, we attempted to trigger TDP-43 pathology in iNets, by exposing them to pathological TDP-43 species, either generated in vitro, or obtained from FTD patient brains by SarkoSpin (Laferriere et al, Nature Neurosc, 2018), a method we previously developed to enrich pathological TDP-43 from tissues. Our data suggest that exogenous pathological TDP-43 species are consistently internalized by iNet neurons and recruit endogenous TDP-43, sometimes leading to apparent nuclear clearance, reminiscent of FTD brain pathology. Moreover, we show that we can successfully co-culture iPSC-derived myeloid precursors with differentiating iNets, leading to the integration of ramified microglia in iNets.

^{References:
[1] Hruska-Plochan, M. et al. A model of human neural networks reveals NPTX2 pathology in ALS and FTLD. Nature 626, 1073-1083 (2024)
[2] Laferrière, F. et al. TDP-43 extracted from frontotemporal lobar degeneration subject brains displays distinct aggregate assemblies and neurotoxic effects reflecting disease progression rates. Nature Neuroscience 22, 65-77 (2019)
[3] Rossi, P. et al. FTLD-TDP assemblies seed neoaggregates with subtype-specific features via a prion-like cascade. EMBO reports 22, e53877 (2021)}

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Prof. Dr. Hirokazu Takahashi
Graduate School of Information Science and Technology, The University of Tokyo, Japan

Title | The Brain as a Spontaneously Active Physical Reservoir

Abstract | The rich repertoire and non-linear dynamics of stimulus-driven responses in the neural system are considered computational resources. We attempted to estimate the information processing capacity (IPC) of the living brain based on the theory of reservoir computing. IPC is derived from the normalized least-square error of a linear readout of a nonlinear system, or reservoir, with respect to a target function. The sum of IPCs for complete orthogonal systems is theoretically equal to the dimension of the observed internal states, and thus is considered as the amount of computational resources. In the present study, we empirically estimated the total IPCs in the dissociated culture of living neurons and in the living sensory cortex [1]. We then discuss how the growth of IPC is related to the development of self-organized criticality (SoC) in neural systems with spontaneous activity [2][3].

^{References:
[1] N. Ishida, et al. Quantification of information processing capacity in living brain as physical reservoir. Applied Physics Letters 122, 233702 (2023)
[2] Y. Yada, et al. Development of neural population activity toward self-organized criticality. Neuroscience 343, 55-65 (2017)
[3] N. Ikeda, et al. Noise and spike-time-dependent plasticity drive self-organized criticality in spiking neural network: Toward neuromorphic computing. Applied Physics Letters 123, 023701 (2023)}

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Prof. Dr. Fabio Benfenati
Istituto Italiano di Tecnologia, Genova, Italy

Title | Non-Genetic Neuron Optostimulation by Biophotonic Organic Transducers for Vision Restoration

Abstract | The grand challenge in biomedical engineering is to develop methods for interfacing optical stimuli with the nervous system. Our research aims at engineering new phototransducers following two main strategies: interfacing neurons with nano-actuators to establish a sort of hybrid synapses; and (ii) inserting molecular transducers in the neuronal membrane. Goal is to interrogate specific neuronal circuits and compensate for nervous system pathologies in which neuronal degeneration has induced a specific loss of function (e.g., photoreceptor degeneration in Retinitis pigmentosa). Current retinal implant technologies using microelectrodes for delivering electrical signals meet several problems that limit the performance and safety of the implants, while optogenetics, a breakthrough in optical stimulation, requires gene therapy. The innovation consists in using biocompatible organic actuators sensitive to visible light for light-driven neuronal activation via seamless interfaces that can recapitulate the physiological activation of neural circuits in the retina. We will describe the most recent approaches to visual restoration in experimental models of retinal degeneration by using photovoltaic polymeric nanoparticles, membrane targeted photochromic compounds and glutamate-releasing nanostructure for generating hybrid interfaces able to induce light-evoked activation of inner retinal neurons. After testing these devices in vitro and ex vivo in retinal explants, we have implanted them in vivo and assessed visual restoration with an array of behavioral and electrophysiological tests. Remarkably, both photovoltaic polymeric nanoparticles and membrane targeted photochromic compounds restore visual performances also in end-stages of the disease and represent promising strategies for the translation to the therapy of human Retinitis pigmentosa.

^{References:
[1] Francia S, Shmal D, Di Marco S, Chiaravalli G, Maya-Vetencourt JF, Manterol G, Michetti C, Cupini S, Manfredi G, DiFrancesco ML, Rocchi A, Perotto S, Sacco R, Bisti S, Pertile G, Lanzani G, Colombo E, Benfenati F (2022) Light-induced charge generation in polymeric nanoparticles restores vision in advanced-stage retinitis pigmentosa rats. Nature Communications 13: 3677.
[2] Benfenati F, Lanzani G (2021) Clinical translation of nanoparticles for neural stimulation. Nature Rev. Materials 6: 1-4.
[3] Maya-Vetencourt JF, Manfredi G, Mete M, Colombo E, Bramini M, Di Marco S, Shmal D, Mantero G, Dipalo M, Rocchi A, DiFrancesco ML, Papaleo ED, Russo A, Barsotti J, Eleftheriou C, Di Maria F, Piazza F, Cossu V, Emionite L, Ticconi F, Marini C, Sambuceti G, Pertile G, Lanzani G, Benfenati F (2020) Subretinally injected semiconducting polymer nanoparticles fully rescue vision in a rat model of retinal dystrophy. Nature Nanotechnology 15: 698-708.
[4] DiFrancesco ML, Lodola F, Colombo E, Maragliano L, Bramini M, Paternò GM, Baldelli P, Serra MD, Lunelli L, Marchioretto M, Grasselli G, Cimò S, Colella L, Fazzi D, Ortica F, Vurro V, Eleftheriou CG, Shmal D, Maya-Vetencourt JF, Bertarelli C, Lanzani G, Benfenati F (2020) Neuronal firing modulation by a membrane-targeted photoswitch. Nature Nanotechnology 15: 296-306.
[5] Maya-Vetencourt JF, Ghezzi D, Antognazza MR, Colombo E, Mete M, Feyen P, Desii A, Buschiazzo A, Di Paolo M, Di Marco S, Ticconi F, Emionite L, Shmal D, Marini C, Donelli I, Freddi G, Maccarone R, Bisti S, Sambuceti G, Pertile G, Lanzani G, Benfenati F (2017) A fully organic retinal prosthesis restores vision in a rat model of degenerative blindness. Nature Materials 16, 681.}

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Dr. Michele Bertacchi
Studer Lab, Institut de Biologie Valrose (iBV), France

Title | Using Human Organoids to Model Normal and Pathological Brain Development and Function.

Abstract | Human 3D organoids generated from induced pluripotent stem cells offer a unique opportunity to investigate normal and pathological human development in vitro, circumventing obvious technical and ethical limitations associated with the use of human foetal material. From examining the effect of FGF8 signalling on the regional identity of neocortical organoids, to assessing the impact of NR2F1 gene deletion on retinal organoid differentiation, and concluding with an in vitro model of glioblastoma invasion in brain organoids, I will show how we attempt at modelling brain development and function under both normal and disease-like conditions. From a technical point of view, we will discuss and troubleshoot common challenges encountered during the use of HD-MEA detection of spontaneous electrical activity to explore the maturation and human brain organoids in a dish.

^{References:
[1] Bertacchi, M., Maharaux, G., Loubat, A., Jung, M. & Studer, M. FGF8-mediated gene regulation affects regional identity in human cerebral organoids. (2023) doi:10.1101/2023.12.22.572974.}

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Dr. Annalisa Bucci
Franke Lab, IOB – Institute of Molecular and Clinical Ophthalmology Basel, Switzerland

Title | Propagation Speeds of Action Potentials in the Human Retina Compensate for Traveling Distances

Abstract | Precise timing of action potentials is crucial for processing sensory information. Axonal length and propagating speed largely determine the time necessary for action potentials to reach postsynaptic neurons. Within the retina, retinal ganglion cell (RGC) axons form the retinal nerve fiber layer (RNFL), a highly organized layer with species-specific axonal arrangements. The human RNFL is characterized by unmyelinated axons, resulting in slow signal transmission, and by the presence of the fovea, a specialized region enabling high-resolution vision, which is located temporal to the optic nerve head (i.e., optic disc). Its central part, known as fovea centralis, is mostly devoid of RGC somas and axons. As a result, foveal cones establish connections with RGCs that are radially displaced on a ring-like structure encircling the fovea centralis. Thus, RGC axons originating temporal to the fovea are significantly longer than axons originating nasally, which extend directly towards the optic disc. This leads to different paths for visual information from adjacent cones in the fovea centralis to reach the optic disc, either through direct routes or longer trajectories circumnavigating the fovea centralis. We investigated whether different axonal lengths in the human RNFL entail distinct action potential traveling speeds, thereby synchronizing visual information. To measure traveling speeds precisely, we recorded action potentials of foveal RGCs in ex vivo human retinal explants at subcellular resolution by means of high-density microelectrode-arrays (HD-MEAs). We found that action potential propagation speeds varied based on the location of RGC somas relative to the fovea centralis. Foveal RGC axons originating temporal to the fovea centralis propagated action potentials more than 40% faster than those originating nasally. Moreover, peripheral RGCs exhibited action potentials propagating up to three times faster than those in the foveal retina. Both foveal and peripheral retina showed a bimodal distribution of propagation speeds among the two predominant cell types in primate retina: midget and parasol cells. By developing a comprehensive model of the human RNFL, we successfully predicted the entire paths and lengths of RGC axons, which strongly correlated with observed lengths and propagation speeds. Our findings suggest a compensatory mechanism in the human retina that contributes to synchronizing the arrival times of visual signals in the brain.

^{This work was financially supported by the Swiss National Science Foundation (SNSF) under Sinergia Grant (CRSII5_173728) and Eccellenza Grant (PCEFP3_187001), and by the European Commission under ERC Advanced Grant 694829 (“neuroXscales”).}

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Prof. Dr. Edna Grünblatt
Psychiatric University Hospital Zurich, University of Zurich, Switzerland

Title | Modeling ADHD Neurodevelopmental Alterations and Treatment Effects

Abstract | ADHD is a complex neurodevelopmental condition with a polygenic and phenotypic spectrum. Neurodevelopmental deficits linked to Wnt pathways could contribute to ADHD. Methylphenidate (MPH) has one of the greatest response rates in mental disorders, yet some do not respond, and mechanism of action is unclear. We modelled ADHD using patient-derived iPSCs, neural stem cells (NSCs), and forebrain cortical neurons (FCNs) to exactly determine the altered time points (growth, Wnt, and synaptic puncta) and treatment-specific response. Our ADHD NSCs exhibited increased Wnt activity, as evidenced by higher levels of active b-catenin (Mann-Whitney, *p=0.018), and enhanced response to Wnt3a treatment in Wnt reporter assays, with lower EC50 values (Unpaired t test with Welch’s correction, *p=0.037). Additionally, there were evidence of higher IC50 values after treatment with the antagonist DKK1. MPH at a concentration of 10 nM enhanced Wnt activity specifically in ADHD NSCs compared to vehicle (Ordinary Two-Way ANOVA with Dunnett’s posthoc tests, *p=0.012). The 7-day chronic treatment showed a tendency to increase active b-catenin levels. At the early stage of iPSC, there were no changes between groups. However, ADHD NSCs showed significantly lower proliferation compared to controls when analysed using two different methods: xCELLigence and WST-1 (Mann-Whitney, *p=0.014 and unpaired t test with Welch’s correction, *p=0.012, respectively). Interestingly, MPH at 10 nM modestly increased their growth rates after a 4-day treatment. This effect was no longer observable after fully blocking the Wnt signalling 30 minutes prior to MPH treatment, suggesting that MPH-induced improvements in the proliferation of ADHD NSCs occurs in a Wnt-dependent manner. In FCNs, quantifying colocalizations between Synapsin-1 and Homer-1 (pre- and post-synaptic proteins, respectively) revealed higher synaptic connectivity in ADHD (Mann Whitney, *p=0.032). In exploratory analyses of Spearman correlations, b-catenin levels had a positive association withADHD polygenic risk scores (PRS). Interestingly, growth rates of NSCs negatively correlated with clinical scores of ADHD symptomatology and ADHD-PRS while correlating positively with Wnt-related PRS. Overall, we showed the model’s translationability to clinical and treatment outcomes, identified the involvement of the Wnt-pathway in ADHD, and highlighted a novel target of MPH.

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Dr. Takuya Isomura
RIKEN Center for Brain Science, Japan

Title | Experimental Validation of the Free-Energy Principle with In Vitro Neural Networks

Abstract | Empirical applications of the free-energy principle at the cellular and synaptic levels are not straightforward because they entail a commitment to a particular neuronal basis. We addressed this issue by developing a reverse engineering technique that allows precise linking of quantities in neuronal networks to those in Bayesian inference. By combining with an in vitro causal inference paradigm that we previously established, we experimentally validated the free-energy principle by showing its ability to predict the quantitative self-organisation of in vitro neural networks.

^{References:
[1] Isomura, T. Bayesian mechanics of self-organising systems. arXiv.org https://arxiv.org/abs/2311.10216 (2023).
[2] Isomura, T., Kotani, K., Jimbo, Y. & Friston, K. J. Experimental validation of the free-energy principle with in vitro neural networks. Nature Communications vol. 14 https://www.nature.com/articles/s41467-023-40141-z (2023).
[3] Isomura, T., Shimazaki, H. & Friston, K. J. Canonical neural networks perform active inference. Communications biology vol. 5 https://www.nature.com/articles/s42003-021-02994-2 (2022).}

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Title | Validation of Novel Targeted Therapy for KCNQ2-Related Disorders in iPSC-Derived Neuronal Model Using High-Density MEA

Abstract | KCNQ2 encodes for a voltage-gated potassium channel subunit of the Kv7 channel, known as Kv7.2, which regulate the resting membrane potential and dampen repetitive neuronal firing. Human pathogenic variants in KCNQ2 are implicated in a range of neurodevelopmental and epileptic disorders. Loss-of-Function variants cause Self-limited Neonatal Epilepsy (KCNQ2-SLNE), Dominant Negative (DN) variants cause Developmental Delay with neonatal seizures (KCNQ2-DN), while Gain-of-Function (GOF) variants cause DD with or without later onset seizures (KCNQ2-GOF). Currently, there are no targeted therapies that improve the DD aspect of KCNQ2-related disorders. To address this problem, we developed patient-derived human induced pluripotent stem cell (hiPSC) lines from 1 KCNQ2-SLNE, 2 KCNQ2-GOF, 3 KCNQ2-DN patients and 4 controls. These hiPSC lines were differentiated into excitatory cortical neurons using NGN overexpression. We identified genotype specific network characteristics over time using high density Multi Electrode Arrays (MEA). Using this network fingerprint we validated new potential precision gene- and pharmacological therapies, that partially rescued the in vitro KCNQ2-DN and KCNQ2-GOF phenotypes, offering new avenues for therapeutic intervention in KCNQ2-related disorders.

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Daniel Lloyd-Davies-Sánchez
Lancaster Lab, MRC Laboratory of Molecular Biology (LMB), United Kingdom

Title | Of Mice And Men:- Cerebral Organoids For The Study Of Brain Development

Abstract | Cerebral organoids are a useful model to gain further understanding of human brain development which can otherwise be difficult to study. Technical and ethical limitations and thus an absence of meaningful or accessible in-vivo approaches for human, mean that animal models such as mouse, as well as 2D culture, have traditionally been utilised in order to gain access to questions of brain development. Indeed, there exists a large literature for such studies, but how translatable their findings are, and what such studies may mean for human specific brain development, is not entirely clear. To be able to properly understand human-specific aspects of brain development requires us to understand our phylogenetic and evolutionary context, and how we differ from closely related species such as apes, as well as more distantly related species such as rodents. New models may enable us to do so. Cerebral organoids grown under minimally guided protocols can recapitulate differences in species-specific speeds of brain development which can be observed in vivo. We adapted the cerebral organoid protocol, previously used on human-derived stem cells, for other species including mouse and observed developmental trajectories and neural lineage differentiation that was intrinsically faster compared to human. Further long-term culture at the air-liquid interface facilitated further differentiation of more mature cell populations, and the establishment of neuron and axonal connections and dynamics.

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Haley Moore
Konopka Lab, University of Texas Southwestern Medical Center, USA

Title | Exploring Single Neuron and Network Activity in Cultured Human Cortex Slices

Abstract | Though nonhuman model organisms are invaluable to our field, studying human neuroscience in the context of human brain tissue is paramount. However, human brain tissue is considerably difficult to acquire, especially if living specimens are required for functional studies like electrophysiology. Even when tissue is available, it is challenging to work with. Many studies have shown the ability to culture human cortex slices for some time, but only a portion include electrophysiological analyses to support the claim of slice viability. Innovations in multielectrode array (MEA) technology have made it possible to glean information about groups of cells – separately and together – without the need to patch many individual neurons. Linking the activity of individual cell types with their corresponding gene expression profiles has been done using a low-throughput, high-effort technique in which nuclei are isolated from a single cell after patch clamp recordings. To overcome the limitations of this technique and increase overall information yield, we have employed MEA recordings of cultured human cortex slices followed by single-nucleus combined RNA and ATAC sequencing. Using similar techniques to those we have employed to link gene expression and oscillatory profiles of human neurosurgery patients undergoing intracranial EEG recordings, we aim to correlate specific features of MEA data with gene expression in specific cell-types. In our recent experiments, we aim to understand the impact of direct electrical stimulation on neural dynamics, transcription, and chromatin structure. The goal of this project is to identify gene targets and pathways that may be relevant to nascent human neuromodulation strategies to improve memory function. This talk will address technical aspects of conducting and analyzing MEA recordings of cultured human cortex slices, then give a brief overview of our preliminary results linking gene expression and MEA activity in single cells.

^{References:
[1] Berto, S., Fontenot, M.R., Seger, S. et al. Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding. Nat Neurosci 24, 554–564 (2021). https://doi.org/10.1038/s41593-021-00803-x}

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Prof. Dr. Shoi Shi
International Institute for Integrative Sleep Medicine, University of Tsukuba, Japan

Title | Synaptic Strength in Prefrontal Cortex Regulates Homeostatic Sleep Need

Abstract | Sleep is essential for memory consolidation, yet the connection to synaptic strength is not fully understood. In this presentation, I will discuss our latest findings, demonstrating a causal relationship between cellular synaptic strength and the macro-level sleep need. Our research uniquely integrates a mathematical model, cell cultures on microelectrode arrays (MEAs), and in vivo experiments to conclusively establish this causal link.

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Prof. Dr. Kenta Shimba
Graduate School of Frontier Sciences, The University of Tokyo, Japan

Title | Integration of Microfabrication Techniques for Enhanced Signal Measurement in Neuronal Networks

Abstract | The brain is composed of a network formed by numerous neurons, transmitting signals through axons. A comprehensive understanding of brain function necessitates measuring phenomena across a broad scale, from axons to networks. Recently, high-density microelectrode arrays (HD-MEAs) have enabled electrical measurements at various scales, including axons, single cells, and networks. To efficiently and accurately measure signals, combining HD-MEAs with other techniques proves effective. We investigate changes in axonal conduction properties through selective pharmacological stimulation and signal transmission between subnetworks composed of different types of neurons using microfabrication techniques. In my talk, various microdevice fabrication methods will be introduced to suit the intended use.

^{References:
[1] K. Shimba, T. Asahina, K. Sakai, K. Kotani, and Y. Jimbo, Recording Saltatory Conduction along Sensory Axons Using a High-Density Microelectrode Array, Frontiers in Neuroscience, Vol. 16, 854637, 2022.
[2] CH Chang, T Furukawa, T Asahina, K Shimba, K Kotani, and Y Jimbo, Coupling of in vitro neocortical-hippocampal coculture bursts induces different spike rhythms in individual networks, Frontiers in Neuroscience, Vol. 16, 873664, 2022.
[3] K Shimba, K Kotani, Y Jimbo, Microfabricated Device to Record Axonal Conduction under Pharmacological Treatment for Functional Evaluation of Axon Ion Channel, IEEE Transactions on Biomedical Engineering, Vol. 68, No. 12, 2021.}

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Prof Dr. Lena Smirnova
Johns Hopkins University, Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), USA

Title | Brain Microphysiological Systems Towards Cognition-on-a Dish Model

Abstract | Recent advances in human stem-cell-derived brain organoids promise to replicate critical aspects of learning and memory in vitro. Coining the term Organoid Intelligence (OI) to encompass these technical developments, we are showcasing the new multi-disciplinary scientific and engineering field of OI We define OI as a new frontier of a biocomputing revolution. As such, we provide a vision for its development over the coming decade and highlight the important societal and ethical considerations it entails. We delineate its principles and potential benefits over computational AI by using biological learning to vastly improve the speed, quality and energy efficiency of computing for the benefit of science and society. Here, we describe the scientific and technological basis of OI – bringing to the fore the latest collaborative brain cell culture/bioengineering advances, providing the foundation for the new OI paradigm (allowing production of myelinated brain organoids encapsulated in to the multielectrode cages, EEG). We share a comprehensive vision of a multidisciplinary research and development trajectory that aims to further scale up the production of organoids housed in novel electrode arrays. We present the challenges and nascent solutions being developed with the potential to pioneer novel biocomputing models via stimulus-response training and organoid-computer interfaces – assessing the true learning potential of OI We delineate the necessary roles of the various disciplines involved in this inherently multidisciplinary new field – including electrophysiology, bioengineering, brain modelling, AI/big data, and bioethics. OI application goes beyond modeling of learning and memory to biological and hybrid computing as well as disease modeling.

^{References:
[1] Smirnova L, Caffo B.S, Gracias D.H, Morales Pantoja I, Zack D.J, Baker L, Berlinicke C.A, Harris T, Huang Q, Tang B, Kahn J, Muotri A.R, Szalay A.S., Vogelstein J.T, Worley P.F, and Hartung T. Organoid Intelligence (O.I.): the new frontier in biocomputing and intelligence-in-a-dish. Frontiers in Science, 2023 Feb 28. 10.3389/fsci.2023.1017235
[2] Smirnova L, Hartung T The Promise and Potential of Brain Organoids. Adv. Healthcare Mater. 2024, 2302745. https://doi.org/10.1002/adhm.202302745.}

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Title | Automated Experimentation and Characterization of Brain Organoid Neural Networks

Abstract | The complexity of neural network activity within brain organoids requires a meticulous approach to long-term culture and electrophysiological characterization. We propose an automated platform that enhances the consistency and precision of organoid studies by combining remote-controlled media maintenance, imaging, and electrophysiology. We analyze the organoids’ response to constant and dynamic stimuli by integrating targeted electrical stimulation during electrical recordings. The framework employs adaptive feedback to refine stimulation strategies, with the aim of modulating neural responsiveness throughout the experiment. The results indicate that exposing organoids to modulation signals can deliberately influence their reactions, highlighting the potential for goal-directed adaptation.

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Dr. Dai Akita
Takahashi Lab, The University of Tokyo, Japan

Title | Information Processing Capacity Of Neuronal Reservoir Computing Using HD-MEA

Abstract | Neurons, which constitute the brain, are pivotal in the information processing of many organisms. Their functionality, as emulated by algorithms in artificial neural networks, is widely applied across various domains. However, utilizing living neurons for computing in practical, everyday applications has remained a challenge, despite their potential for energy-efficient processing. Recently, a technique known as physical reservoir computing (PRC) has garnered attention as a method to employ information processing of cultured neurons.
PRC is a computational paradigm that exploits the inherent dynamics of physical systems for complex information processing tasks. This approach is rooted in reservoir computing (RC), a specialized recurrent neural network strategy. RC simplifies the learning process by keeping the weights of the recurrent layer fixed and only adjusting the output layer’s weights. In PRC, a physical system serves as the reservoir, analogous to the fixed-weight recurrent layer. Inputs are sequentially introduced to the reservoir, and the evolving state of the reservoir is recorded and transformed into output through optimized linear readout.
Several studies have successfully implemented PRC systems using cultured neurons as reservoirs, achieving tasks such as speech recognition, robot control, logical operations, and more. Despite these advances, the critical factors for enhancing performance and optimal PRC system configurations using cultured neurons remain elusive. In this study, we employed rat primary cortical neurons cultured on high-density microelectrode arrays (HD-MEA) and experimented with various PRC setups. We explored both electrical and optical stimulation, varying the time-step intervals and intensities. Furthermore, we artificially expanded the size of the neural network by interconnecting multiple cultures with mutual feedback stimulation. The impact of these variables on PRC’s capabilities was assessed using an index known as information processing capacity (IPC), which measures the comprehensive performance of the reservoir beyond specific tasks. Finally, we discuss these results with mathematical theory of IPC.

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Prof. Dr. Paulo Aguiar
Institute for Research and Innovation in Health, University of Porto, Portugal

Title | Closed-Loop Neuromodulation in MEAs – Disrupting Abnormal Neuronal Oscillations with Adaptive Delayed Feedback Control

Abstract | Closed-loop neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson’s disease. However, at the moment, standard stimulation protocols rely on continuous open-loop stimulation and the design of adaptive controllers is an active field of research. Delayed Feedback Control (DFC), a popular method used to control chaotic systems, has been proposed as a closed-loop technique for desynchronization of neuronal populations but, so far, was only tested in computational studies. We implement DFC for the first time in neuronal populations and access its efficacy in disrupting unwanted neuronal oscillations. To analyse in detail the performance of this activity control algorithm we used specialized in vitro platforms with high spatiotemporal monitoring/stimulating capabilities. We show that the conventional DFC in fact worsens the neuronal population oscillatory behaviour, which was never reported before. Conversely, we present an improved control algorithm, adaptive DFC (aDFC), which monitors the ongoing oscillation periodicity and self-tunes accordingly. aDFC effectively disrupts collective neuronal oscillations restoring a more physiological state. Overall, these results support aDFC as a better candidate for therapeutic closed-loop brain stimulation.

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Daniele Bellantoni
Netti Lab, Università degli Studi di Napoli Federico II, Italy

Title | Recapitulation of Electrophysiological Spontaneous Activity in An In-Vitro Innervated Human Skin Equivalent

Abstract | Current models for chemotherapeutic induced peripheral neuropathy (CIPN) rely on animal-based approaches [1]. While these models can recapitulate some features of the pathology, ethical and economical sustainable alternatives capable to capture the onset and the development of the human pathology along with quantitative measurements of pain response are required. Tissue engineering approaches provide human derived models in-vitro for drug testing and quantitative measurements of tissue alteration starting from primary cells. In this context, to correctly recapitulate in vitro the neurosensory peripheral system, the interactions between sensory neurons and keratinocytes, which has been well documented by the literature [2],[3] need to be considered. Our research group has significant experience in developing functional and endogenous 3D tissues in vitro. [4],[5].
Here we fabricated a 3D full thickness and innervated human skin equivalents (IHSE) with the MaxWell high density microelectrode arrays (HD-MEA). At the best of our knowledge, this represent the first attempt ever tried to integrated a tissue engineered IHSE with a HD-MEA system. The use of the MaxOne allowed to monitor the spontaneous activity of the neurons innervating the engineered tissue, providing non-invasive measurements of neuronal activity and morphological information. Activity before and after the coculture of the sensory neurons with the human skin equivalent and monitor functional and morphological changes will be presented.

^{References:
[1] Kumar A., Harshpreet K., and Arti S. Neuropathic pain models caused by damage to central or peripheral nervous system. Pharmacological Reports 70.2, 206-216 (2018)
[2] Talagas M. et al. Keratinocytes communicate with sensory neurons via synaptic-like contacts. Annals of neurology 88.6, 1205-1219 (2020)
[3] Talagas M. et al. Cutaneous nociception: role of keratinocytes. Experimental Dermatology 28.12,1466-1469 (2019)
[4] Palmiero C. et al. Engineered dermal equivalent tissue in vitro by assembly of microtissue precursors. Acta biomaterialia 6.7, 2548- 2553 (2010)
[5] Martorina F. et al. In vitro activation of the neuro-transduction mechanism in sensitive organotypic human skin model. Biomaterials 113, 217-229 (2017)}

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Dr. Martina Brofiga
Massobrio Lab, The University of Genova, Italy

Title | In vitro MEA-based model to investigate the electrophysiological features of the Cortical-Striatal-Thalamic (CST) circuit

Abstract | The human brain comprises billions of neurons organized in well-defined spatial locations that define clusters (modules). Each cognitive and motor function is possible due to the correct interaction among them. The disruption or loss of these connections can produce pathological conditions. To understand how the information is transmitted and computed, we need to investigate the communication among the different modules. The development of reliable in vitro models is particularly important to understand the basic physiology of the nervous system, and consequently to advance the knowledge on neurological disorders and possible therapeutic approaches. In vitro neuronal cultures that recreate the in vivo microenvironment (e.g., modularity and heterogeneity) may also provide a unique model for investigating the pathophysiology of specific diseases. In this work, we developed an engineered in vitro model to recreate the cortical-striatal-thalamic (CST) circuit, whose malfunctioning is at the basis of neurological disorders such as obsessive compulsive disorder and schizophrenia. We exploited a three-compartment polymeric device and investigated the spontaneous electrophysiological activity of dissociated rat neurons coupled to Micro-Electrode Arrays (MEAs). The analysis of key parameters such as the spiking and bursting activity rates underlined how the different neuronal networks exhibited different behaviors when the in vivo microenvironment was partially reproduced. Indeed, all three neuronal populations were modulated both by the different spatial configuration (modularity, a basic characteristic of the human brain) and by the presence of physiological inputs (heterogeneity). Moreover, we proved that in CST, despite the absence of constraints on the directionality of the connections, the different neuronal populations self-organize in such a way as to reproduce the flow of information observed in vivo. Moreover, CST network spontaneously generate repeated motifs and thus it is able to maintain long-term memory. In summary, the results highlighted how oversimplified models that do not take into account the physiological condition of the brain, in which different modules (cortex, striatum, and thalamus) interact with each other, can lead to the investigation of only partial (incomplete) aspects. A model that better recreates the in vivo microenvironment can help the understanding of the still unclear pathophysiology of neurodegenerative diseases.

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Ummi Ciptasari
Kasri Lab, Radboud University Medical Center, Netherlands

Title | Integrating network activity with transcriptomic profiling in hiPSCs-derived neuronal networks to understand the molecular drivers of functional heterogeneity in the context of neurodevelopmental disorders

Abstract | Neurodevelopmental disorders (NDDs) represent a large and heterogeneous group of rare disorders. Individual types of NDDs with a known genetic etiology are typically rare, owing to the very high number of individual genes causative for such conditions, but their aggregate societal impact is dramatic. To understand the functional heterogeneity in neuronal activity associated with NDDs and identify its molecular basis, we integrated RNA-sequencing and micro-electrode array (MEA) recordings in hiPSCs-derived neurons carrying NDD-related mutations. Clustering analysis of 345 recordings from 35 NDD-related cell lines at 28 days in-vitro (DIVs) revealed seven discrete activity phenotypes. While a continuous phenotypic landscape of network alterations emerged, specific clusters were identified for certain disorders. At DIV49, stronger associations between activity phenotypes and syndromes were observed. Simultaneous RNA-sequencing and network activity recording at DIV49 were performed for seven cell lines, including four lines with variants in chromatin remodeling genes (ADNP, YY1, CHD8, and EHMT1). Weighted Gene Correlation Network Analysis identified co-regulated gene modules potentially influencing the activity patterns. We built a Bayesian Network to integrate information on the expression level of each gene module and the functional clusters allocated to the samples, finding three parent nodes of the “cluster” variable that differentially combined to modulate the assigned clusters. Since the expression activity of these three parent gene modules could be influenced to induce particular network phenotypes and eventually to rescue disease-related activity patterns toward a control phenotype, we explored the Library of Integrated Network-based Cellular Signatures (LINCS) database for compounds enhancing the transcriptomic signature of specific clusters. In conclusion, our framework provides a platform to bridge the gap between molecular and functional aspects of normal and abnormal neurodevelopment, holding the potential to contribute to the development of treatments for NDDs.

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Blandine Clément
Laboratory of Biosensors and Bioelectronics, ETH Zürich, Switzerland

Title | Nerve Model To Study The Diverse Electrophysiological Properties Of Human Ipsc-Derived Nociceptive Neuron Subtypes

Abstract | Neuropathic pain results from various types of nerve damage and is often inadequately treated. Most available in vitro tools to search for new therapeutic approaches fail to generate human translatable results. Nociceptors are a specialized subpopulation of sensory neurons conveying the perception of pain and are morphologically and functionally distinct from other neurons. The individual sensation of pain can be triggered by different subpopulations and may vary between patients. However, currently available painkillers against neuropathic pain have no cell specificity resulting in unwanted side effects. Thus, the development of tools that can differentiate nociceptive fibres would enable a more targeted compound screening.
In this work, we present a multi-compartment nerve model platform combining a CMOS-based high-density microelectrode array with a polydimethylsiloxane (PDMS) guiding microstructure that aims to capture the electrophysiological responses of individual dorsal root ganglion (DRG) subtypes. Human iPSC-derived sensory neurons were cultured at low density inside the microstructure and only a few axons grew in multiple parallel 4 x 10 μm microchannels before converging to a bigger nerve bundle-forming channel. This configuration allowed the measurement of propagation speeds and stimulation-induced responses of close-to-individual axons in microchannels after electrical stimulation of the fiber bundle. The responses exhibited differences not only in the latency, but also in the frequency-dependent slowing of conduction and conduction failure profiles, highlighting the diversity of nociceptive fibres within a single network. The variety of electrophysiological signatures suggest that such a platform might be suitable to evaluate nociceptor-specific drug response for a given subtype of sensory neurons.

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Dr. Fikret Emre Kapucu
Narkilahti Lab, Faculty of Medicine and Health Technology, Tampere University, Finland

Title | MEA Embedded Microphysiological Platform for Studying Parkinson’s Disease Related Α–Synuclein Pathology

Abstract | Studying α–synuclein (α-s) pathology and its effects on neuronal functionality is essential for understanding the mechanisms underlying the development and progression of Parkinson’s Disease (PD). Earlier functional and structural changes, such as the reduction of synaptic proteins, progressive impairments in neuronal excitability, synaptic activity, and network connectivity, have been reported with α–s aggregation in neuronal cells.
In this study, we utilized an engineered circular tripartite neuronal network model integrated with microelectrode arrays (MEAs) as a platform to study functional markers during α-s aggregation and propagation. We focused on human induced pluripotent stem cell (hiPSC)-derived neuronal cultures. A gradual aggregation of α-s in conventional cultures and in the proximal compartments of circular tripartite networks was achieved after exposing neurons to fibrillar forms of α-s (PFFs). The axonal propagation of aggregated α-s to the distal networks was revealed while MEA recordings were collected from proximal and distal networks and assessed spatiotemporally. Additionally, for parallel functional assessment, calcium and mitochondrial readouts obtained from conventional single culture wells were analyzed for the same time points. We observed the impacts of α-s aggregation on both spontaneous neuronal activity and glutamatergic receptor channel-mediated neuronal activity, while calcium and mitochondrial functionality exhibited more complex patterns.
Our platform enabled the assessment of the early, middle, and late phases of α-s aggregation and propagation over a 13-day follow-up period. Alterations in protein expression, along with structural and functional changes at the cellular level in neuronal cultures, suggest a complex interplay during in vitro propagation of α-s aggregates. The suggested platform has the potential to be used in future pharmacological studies by enabling distinct treatment locations for preventive and therapeutic medications within studied networks.

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Tjitse van der Molen
Kosik Lab, University of California Santa Barbara, USA

Title | RT-Sort: An Action Potential Propagation Based Algorithm for Real Time Spike Detection and Sorting

Abstract | Here, we present RT-Sort (Real Time Sorting), an action potential propagation based algorithm that enables real time spike detection and single unit sorting across 1020 recording electrodes. RT-Sort achieves sorting latencies smaller than 10ms from the waveform trough to the sorted detection. By utilizing high-fidelity sequential action potential detection on multiple electrodes with sub millisecond but nonzero time delays, RT-Sort opens a new paradigm for spike sorting on high density multi-electrode arrays. Instead of waveform shape template matching, RT-Sort’s focus on action potential propagation makes it robust against waveform shape changes and overlapping waveforms. As a result, RT-Sort yields good accuracy in addition to its unique ability to spike sort with latencies on the range of mono-synaptic delay times.
Neural computation on a chip has received significant interest. However, until now a choice has to be made between getting single unit resolution for computations but doing so only after the end of the experiment [1] or doing real time computations in a closed-loop manner but with multi-unit activity as most granular input [2]. RT-Sort provides the solution to this problem, pushing the boundaries of experiments that can be designed for your culture system of interest. Developed by the Maxwell user community for the Maxwell user community, RT-Sort is optimized for use on Maxwell Biosystems multi-electrode arrays under a wide variety of electrode configurations. RT-Sort will soon be made publicly available for you to implement in your experiments.

^{References:
[1] Cai H. et al. Brain organoid reservoir computing for artificial intelligence. Nature Electronics 6.12, 1032-1039 (2023)
[2] Kagan B. J. et al. In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron 110.23, 3952-3969 (2022)}

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Dr. Karin Stecher
Neurolentech, Austria

Title | Neurodevelopmental Disease Modelling for Drug Discovery Using Patient-Derived Neuronal Co-Cultures

Abstract | Developing therapeutics for neurodevelopmental disorders has historically posed significant challenges, primarily due to the absence of accurate disease models. At Neurolentech, we utilize an innovative pipeline to generate reliable neural models from our extensive patient-derived iPSC biobank with the purpose of developing new treatments for neurodevelopmental diseases such as autism and epilepsy.
Our neural models consist of cocultures of GABAergic and glutamatergic neurons on rat astrocytes and are characterized by transcriptomic and functional analysis. Electrophysiology data from various individuals is obtained via the MaxTwo device from Maxwell. Our analysis of the recordings was pivotal for optimizing the generation and maturation of iNeurons as well as protocol steps to reduce technical variability, increasing reproducibility and minimizing data size.
Our findings suggest that HD-MEA recordings constitute a robust method for assay development. A pilot study that induced homeostatic plasticity in co-culture showed that PTX administration changed the bursting pattern of the healthy control. Expanding MEA analysis is currently the main emphasis at Neurolentech in order to create high-throughput assays capable of capturing shared phenotypes across our patient cohorts. Ultimately, we aim to employ these assays for target identification in an effort to advance the field of drug discovery for neurodevelopmental disorders.

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Nicolai Winter-Hjelm
Sandvig Lab, Norwegian University of Science and Technology (NTNU), Norway

Title | Advanced Interfaces for Investigating the Impact of Topology on the Functional Dynamics of Engineered Neural Networks

Abstract | Engineered neural networks are invaluable tools for studying neural function and dysfunction at the micro- and mesoscale in a highly controllable microenvironment. In recent years, advanced engineered interfaces, such as multinodal microfluidic platforms and 3D scaffolding technologies, have opened a broad avenue for manipulating the physical dimensionality of such networks to support the emergence of circuit motifs akin to those seen in vivo. Yet, the impact of such topologies on the functional dynamics of the networks remains largely underexplored. In this work, we demonstrate the utilization of microfluidic platforms with up to 12 interconnected nodes for structuring hierarchical multinodal neural network configurations in vitro. In this way, we aim to recapitulate brain-region specific microarchitectures, such as the laminar organization of the neocortex, or the cortical-hippocampal projection. We furthermore control the direction of axonal outgrowth between the nodes by implementing geometrical constraints inspired by a Tesla valve within the microtunnels. This selective control of afferent-efferent connectivity supports the emergence of neural networks with directional information flow, similar to networks in the brain. Additionally, we demonstrate fabrication and utilization of biocompatible 3D interfaces of the polymer SU8 for supporting 3D network growth, readily compatible with the PDMS-based microfluidics. Using electron microscopy and immunocytochemistry, we show that neurons utilize these scaffolds for self-assembly into complex multi-layered networks. All platforms are compatible with our in-house developed nanoporous microelectrode arrays, supporting electrophysiological characterization of network dynamics using information- and graph theoretical approaches. The relevant analyses show that implementation of microtopologies gives rise to networks exhibiting hallmarks of high computational complexity, with prominent segregated and integrated functional dynamics compared to single population unstructured networks. In summary, we present highly versatile, complementary neuroengineering solutions for recapitulating in vivo topological neural circuit configurations, compatible with a range of theoretical concepts and techniques for investigating dynamic structural and functional network attributes with high temporal and spatial precision. Utilization of these technologies can provide new insights into the relationship between neural network topology and function and significantly broaden the scope of advanced preclinical neural network models.

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Dr. Cristina Zivko
Machairaki Lab, Johns Hopkins University School of Medicine, USA

Title | Drug Response Heterogeneity for Alzheimer’s Disease Patients Using iPSC-Derived Organoids as Pre-Clinical Platforms

Abstract | The number of people suffering from Alzheimer’s Disease (AD) around the globe is estimated to exceed 150 million by 2050. AD is an incurable neurodegenerative disease, and patients show high degrees of variability in its onset, progression, and symptoms.[1] Crucially, responsiveness to the limited options is also variable, and 98% of clinical trials for AD have failed in the past two decades.[2] There is thus an urgent, unmet medical need to drastically improve pre-clinical scientific efforts, in order to find better predictors of drug responsiveness.
A precision medicine approach holds groundbreaking promise in this field.
Human peripheral blood mononuclear cells from healthy volunteers (n=6) and AD patients (n=24) were reprogrammed into induced pluripotent stem cells (iPSCs) and subsequently differentiated into hindbrain organoids with serotonergic neurons within.[3,4] Extensive characterization and validation experiments were conducted to assess the quality of the iPSCs, as well as the quality of the 3D hindbrain organoids derived from those iPSC. The presence of pluripotency (OCT4, NANOG, TRA-1-60) and specific neuronal markers (TUJ1, MAP2, TPH2) was evaluated by fluorescence microscopy, real time PCR and flow cytometry. Electrophysiological functionality by microelectrode array is currently being evaluated.
A new drug screening in vitro assay was meticulously developed in biologically independent triplicates, and it was then used to test the responsiveness organoids from all 30 iPSC lines to the FDA approved drug Escitalopram, a selective serotonin reuptake inhibitor. Acute (1 h) and prolonged (6 d) effects were determined by monitoring serotonin levels, and by extensive proteomic profiling.
Our results show that it is indeed possible to develop personalized, iPSC-derived, neuronal in vitro platforms from individuals’ blood samples. These versatile platforms, customized for targeted scientific questions, revealed highly heterogeneous drug responses across people, paving the way to pre-clinical stratification of patients who are likely to benefit or not from a drug before its administration.

^{References:
[1] Kim C. K. et al. Alzheimer’s disease: key insights from two decades of clinical trial failures. Journal of Alzheimer’s Disease 87.1, 83- 100 (2022)
[2] Lyketsos C. G. et al. Neuropsychiatric symptoms in Alzheimer’s disease. Alzheimer’s & Dementia 7.5, 532-539 (2011)
[3] Sagar R. et al. Excitatory neurons derived from human-induced pluripotent stem cells show transcriptomic differences in Alzheimer’s patients from controls. Cells 12.15, 1990 (2023)}

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Tom Brown
bit.bio, United Kingdom

Title | Utilising Functional Human iPSC-Derived Neurons As A Robust Foundation for Generating Consistent, Physiologically Relevant HD-MEA Assays

Abstract | High-density microelectrode arrays (HD-MEAs) are a powerful tool to measure the electrophysiological properties of human neurons in vitro, and are a cornerstone method in the development of new therapeutics for neurological diseases. High-density electrophysiology experiments that produce publishable, translatable data rely on high- quality, functional, and physiologically relevant cells as input.
bit.bio has developed opti-oxTM, an iPSC reprogramming technology that enables the consistent generation of defined, mature, functional human cell types at scale.
In this talk, we will explore how the MaxWell MaxTwo HD-MEA system has been used in the functional characterisation of opti-ox precision reprogrammed ioMotor Neurons, ioGlutamatergic NeuronsTM, and ioSensory NeuronsTM.
We dive into data generated on the MaxWell MaxTwo characterising the electrophysiological properties of these three cell types and show how their properties are aligned with their known biological function. We will also discuss how you could use ioCellsTM to set up similar experiments with a MaxTwo system in your own lab.

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Raymond Price
Neuroservices Alliance, USA

Title | High Density MEA Recording of Primary Rat Neuron Cultures and Human iPSC-derived Neuron Cultures Growing at Low Density on Astrocyte Feeder Layers

Abstract | Drug discovery projects rely on primary rodent neuronal cultures as well as human iPSC-derived neurons as model systems. However, it is important to characterize the cell properties to understand and qualify either system as a ‘fit-for-purpose’ model to interrogate the efficacy and potency of novel therapeutic molecules. We have found that neurons growing at low density on a monolayer of astrocytes are ideal for developing the mature neuronal phenotype in vitro. Astrocytes provide the optimum substrate for neuronal survival and differentiation, and neurons express a full repertoire of voltage-gated and ligand-gated ion channels as well as GPCRs that modulate neuronal excitability. These cultures also show both intrinsic and synaptic excitability. While the gold standard for electrophysiology, the data throughput of patch clamp recording is limited to 1 or 2 neurons at a time. High density microelectrode arrays (MEAs) from MaxWell promise to greatly increase the data throughput of functional endpoints to 100s to 1,000s of neurons at a time. We present MEA data recorded from low density rat cortical neuron cultures as wellas as iPSC-derived neurons (both at 25-100 cells/mm2) plated on astrocytes. These culture conditions more closely resemble the conditions used for patch clamp recording. Neuronal activity (number of active electrodes, firing rate, bursting) increase with development time in vitro. We also demonstrate that neurons show the expected pharmacological responses to molecules designed to increase or decrease neuronal activity.

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Dr. Douglas Bakkum
Shift Crypto AG, Switzerland

Speaker for Special Session | “Pioneering Stories that Shaped MaxWell Biosystems”

Short Bio | Douglas is a founder and CEO of Shift Crypto and creator of the original BitBox cryptocurrency hardware wallet. He has a diverse academic background with a PhD in neuroengineering and bachelor and master’s degrees in mechanical engineering, where he researched learning & memory in the brain, AI, cognitive science, robotics, and bioart.

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Dr. Sadik Hafizovic
Zurich Instruments, Switzerland

Speaker for Special Session | “Pioneering Stories that Shaped MaxWell Biosystems”

Short Bio | Sadik Hafizovic is co-founder and CEO of Zurich Instruments. He received his PhD in Electrical Engineering from ETH Zurich in 2006 for research at the Institute for Quantum Electronics. After a postdoctoral experience at the Bio Engineering Laboratory of ETH, he co-founded the spin off Zurich Instruments in 2008. Since then, he has developed the company from an innovative lock-in manufacturer to a major T&M player and leading instrumentation provider for quantum computing. Entrepreneurship, organization development, FPGA technologies, quantum computing, experimental physics, and many things outdoors on land or on water resonate with him.

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Prof. Dr. Andreas Hierlemann
Bio Engineering Laboratory, ETH Zurich, Switzerland

Speaker for Special Session | “Pioneering Stories that Shaped MaxWell Biosystems”

Short Bio | Andreas Hierlemann got his college education in chemistry at the University of Tübingen, Germany and a Ph.D. degree in 1996. He held Postdoc positions 1997 at Texas A & M University, College Station, TX and 1998 at Sandia National Laboratories, Albuquerque, NM, USA. He joined the Department of Physics of ETH Zurich 1999, where he was appointed Associate Professor 2004. In 2008, he became Full Professor in the Department of Biosystems Science and Engineering of ETH Zurich in Basel.

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The speaker lineup for the MxW Summit 2024 is continuously being updated. Stay tuned for the latest information on our confirmed speakers!