MaxWell Biosystems at ISSCR 2024

MaxWell Biosystems at ISSCR 2024

ISSCR 2024Discover all our events and activities that will be taking place during the ISSCR Annual Meeting 2024 in Hamburg from Wednesday July 10 – Saturday July 13!

Meet the Team | Booth #802 Innovation Showcase | Thursday 11 | 12:00 PM CEST Poster Presentations | Thursday 11 July | 15:45 PM CEST
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Meet the Team at ISSCR:

Our team will be at ISSCR and would be happy to meet you!

Dr. Anastasiia Oryshchuk

Dr. Zhuoliang (Ed) Li

Dr. Silvia Oldani
 

Dr. Laura D’Ignazio

Dr. Diana Freire
 

Dr. Marie Obien


Booth:

Visit us at booth #802 to learn more about how our HD-MEA systems MaxOne and MaxTwo can further your research.


Innovation Showcase:

Thursday, July 11th | 12:00 – 13:00 CEST | Hall 4, Entrance Level

Title | Advanced Functional Characterization of iPSC-Derived In Vitro Models in Disease Modelling and Artificial Intelligence

Abstract | Two-dimensional (2D) and three-dimensional (3D) cell cultures, designed to mimic in vivo conditions to varying extents, particularly when derived from human induced pluripotent stem cells (iPSCs), serve as a valuable substitute for conventional animal models. These cellular models not only provide a crucial avenue in disease modeling but also offer significant opportunities in artificial intelligence. Our guest speakers will explore innovative applications and methodologies of iPSC-derived neural cultures for modeling neurodegeneration and showcasing the computational capabilities of 3D biological neural networks. Following the seminar, we will engage in an interactive Q&A session to shed light on diverse perspectives regarding the future of iPSC in vitro models.

Speakers

Prof. Dr. Feng Guo

Prof. Dr. Feng Guo
Indiana University Bloomington, USA

Presentation 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.

Short Bio:
Dr. Feng Guo is an Associated Professor of Intelligent Systems Engineering at Indiana University Bloomington (IUB).
Before joining IUB in 2017, he received his Ph.D. in Engineering Science and Mechanics at Penn State and his postdoc training at Stanford University.
His group is developing intelligent medical devices, sensors, and systems with the support of multiple NIH and NSF awards.
He is a recipient of the NIH Director’s New Innovator Award, the Outstanding Junior Faculty Award at IU, Early Career Award at Penn State, the Dean Postdoctoral Fellowship at Stanford School of Medicine, etc.

Marian

Dr. Marián Hruška-Plocháň
Polymenidou Lab, University of Zurich, Switzerland

Presentation Title:
iNets – novel human neural networks to study neurodegenerative diseases

Abstract:
Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are fatal neurodegenerative diseases. Pathogenesis of these TDP-43 proteinopathies cannot be studied in animal models due to human-specific TDP-43 mRNA binding targets. Therefore, we generated human iPSC-derived, colony morphology neural stem cells (iCoMoNSCs). Differentiated iCoMoNSCs formed self-organized long-lived neural networks consisting of glia and synaptically connected and electrophysiologically active neurons – iNets.
Overexpression of wild-type TDP-43 in a minority of iNet neurons led to its progressive fragmentation and aggregation, resulting in neurotoxicity. scRNA-seq revealed misregulation of synaptic protein NPTX2, the mRNA levels of which are controlled by TDP-43 binding on NPTX2 3’UTR. When NPTX2 was overexpressed in iNets, it exhibited neurotoxicity, whereas correcting NPTX2 misregulation partially rescued neurons from TDP-43-ignited neurodegeneration. Importantly, we confirmed NPTX2 misaccumulation in ALS and FTLD patient neurons with TDP-43 pathology. Our work directly links TDP-43 misregulation and NPTX2 accumulation, thereby indicating a new pathway of neurotoxicity.
Next, we plan to utilize iNets and MacNets (iNets integrated with iPSC-derived microglia) to study the mechanisms and consequences of TDP-43 aggregation, their spread from neuron to neuron and the link between these events and TDP-43 loss of function.

Short Bio:
Dr. Marián Hruška-Plocháň is passionate about fighting neurodegenerative diseases. His current research focuses on TDP-43 and C9orf72 ALS/FTD-associated proteinopathies.
To tackle these diseases, he developed iNets, a unique human neural network model. iNets lets him study ALS/FTD pathology directly in human neurons, astrocytes, and microglia upon their embedding. This makes it ideal for deciphering disease mechanisms and testing potential therapies – including gene therapies, small molecules, and immunotherapies. iNets are also compatible with HD-MEA applications. His background spans molecular biology, cell and developmental biology, neuroscience, and gene/cell therapy.


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Poster Presentations:

Thursday, July 11th | 15:45 – 16:45 CEST


Dr. Laura D’Ignazio

Senior Key Account Manager
Poster Number: 425

Title | Next-generation Electrophysiology for Functional Characterization of Human Neural Organoids

Abstract | Human induced pluripotent stem cell (hiPSC)-derived neural models have emerged as invaluable tools for studying neurological disorders, such as epilepsy, Alzheimer’s, and Parkinson’s disease.
Real-time, label-free measurement of electrical activity in self-organizing in vitro cellular models provides critical insight into the complexity of their neuronal networks. High-density microelectrode arrays (HD-MEAs) enable non-invasive electrophysiological recordings from various electrogenic samples, including iPSC-derived neurons, retinal explants, brain slices, and neural organoids.
In this study, we used MaxOne and MaxTwo high-density MEA platforms (MaxWell Biosystems AG, Switzerland), with 26,400 electrodes per well, to record extracellular action potentials in neural organoids at different scales, ranging from cell population networks to single-cell resolution and subcellular levels. We showcased the flexible selection of electrodes for recording neural activity, increasing the reproducibility and statistical power of the data collected. Key metrics such as firing rate, spike amplitude, and network burst profile were extrapolated in a parallelized manner to capture even the smallest neuronal signals.
Furthermore, we characterized axonal function and structure using the AxonTracking Assay, which allows measurement of action potential conduction velocity, latency, axonal length, and branching. This automated assay facilitates high-throughput characterization of disease models targeting axon initial segments, axonal branching, development, and conduction.
MaxWell Biosystems’ HD-MEA platforms, along with automatically generated plots and extracted metrics, provide a unique, user-friendly approach to identifying and isolating functionally active regions in 3D cultures. These powerful platforms enable long-term in vitro disease modeling and compound testing in acute recordings and/or longitudinal studies.


Dr. Zhuoliang (Ed) Li

Field Application Scientist
Poster Number: 149

Title | The Importance of High-Density Microelectrode Arrays for Recording Multi-Scale Extracellular Potential and Label-Free Characterization of Network Dynamics in iPSC-Derived Neurons

Abstract | Advances in the development of microelectrode arrays (MEAs) for in-vitro electrophysiological recordings have enabled the characterization of multi-scale behavior in neuronal networks, ranging from subcellular level to network dynamics. Such devices are fundamental for studying the phenotype of neurological disorders and for drug discovery, providing unique insights into the complexity of neuronal networks. Electrode density, spacing, and size influence the signal quality, noise level, and sensitivity. To properly characterize the full behavior of neuronal networks, MEAs must combine single-cell and subcellular resolution with high-throughput assays, while maintaining sensitivity to small extracellular action potentials to describe the full range of network dynamics.
In this study, the MaxOne and MaxTwo high-density (HD) MEA systems (MaxWell Biosystems, Switzerland) were used to record activity from induced pluripotent stem cell derived neurons, demonstrating the advantages of having 26,400 electrodes per well, which is key to increasing the statistical power of data collected longitudinally. HD-MEA recordings were compared with simulated low-density recordings, in which larger, low-density electrodes were mimicked by clustering adjacent electrodes on HD-MEAs. Additionally, the AxonTracking Assay, an automated tool for recording and analyzing individual axonal arbors from many neurons in parallel, was used to characterize the function and axonal structure of recorded cultures.
Results indicated that higher density and smaller electrodes provided greater sensitivity, enabling the detection of smaller spikes, and covering the full spectrum of network behavior. The high-resolution analysis of network dynamics, coupled with the AxonTracking Assay’s subcellular insights, provide powerful insights into drug screening and disease modelling.

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