Publications
Selected Publications

High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels
Presenting measurements of neuronal preparations with a novel CMOS-based microelectrode array at high-spatiotemporal-resolution on subcellular, cellular, and network level.
J. Müller, M. Ballini, P. Livi, Y. Chen, M. Radivojevic, A. Shadmani, V. Viswam, I. L. Jones, M. Fiscella, R. Diggelmann, A. Stettler, U. Frey, D. J. Bakkum, and A. Hierlemann, “High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels,” Lab Chip, vol. 15, no. 13, pp. 2767–2780, May 2015.

Revealing Neuronal Function through Microelectrode Array Recordings
Reviewing the current understanding of microelectrode signals and the techniques for analyzing them, with focus on the ongoing advancements in microelectrode technology (in vivo and in vitro) and recent advanced microelectrode array measurement methods that facilitate the understanding of single neurons and network function.
M. E. J. Obien, K. Deligkaris, T. Bullmann, D. J. Bakkum, and U. Frey, “Revealing Neuronal Function through Microelectrode Array Recordings,” Front. Neurosci., 8:423, Jan 2015.
A 1024-Channel CMOS Microelectrode Array With 26,400 Electrodes for Recording and Stimulation of Electrogenic Cells In Vitro
A high-resolution CMOS-based microelectrode array featuring 1,024 low-noise readout channels, 26,400 electrodes at a density of 3,265 electrodes per mm2, including on-chip 10bit ADCs and consuming only 75 mW.
M. Ballini, J. Muller, P. Livi, Y. Chen, U. Frey, A. Stettler, A. Shadmani, V. Viswam, I. L. Jones, D. Jackel, M. Radivojevic, M. K. Lewandowska, W. Gong, M. Fiscella, D. J. Bakkum, F. Heer, and A. Hierlemann, “A 1024-Channel CMOS Microelectrode Array With 26,400 Electrodes for Recording and Stimulation of Electrogenic Cells In Vitro,” IEEE Journal of Solid-State Circuits, vol. 49, no. 11, pp. 2705-2719, 2014.
Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites
Demonstrating a method to electrically visualize action potential propagation on axons and revealing
large variations in velocity.
D. J. Bakkum, U. Frey, M. Radivojevic, T. L. Russell, J. Muller, M. Fiscella, H. Takahashi, and A. Hierlemann, “Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites,” Nature Communications, 4:2181, Jul 2013.
Microelectronic System for High-Resolution Mapping of Extracellular Electric Fields Applied to Brain Slices
Recording and modeling extracellular action potentials of Purkinje cells at subcellular resolution.
U. Frey, U. Egert, F. Heer, S. Hafizovic, and A. Hierlemann, “Microelectronic System for High-Resolution Mapping of Extracellular Electric Fields Applied to Brain Slices,” Biosensors and Bioelectronics, vol. 24, no. 7, pp. 2191-2198, 2009.
Modulation of Cardiomyocyte Electrical Properties Using Regulated Bone Morphogenetic Protein-2 Expression
Controlling BMP-2 expression to modulate the electrophysiological properties of cardiomyocytes using an HD-MEA for detailed monitoring.
C. D. Sanchez-Bustamante, U. Frey, J. M. Kelm, A. Hierlemann, and M. Fussenegger,
“Modulation of Cardiomyocyte Electrical Properties Using Regulated Bone Morphogenetic Protein-2 Expression,” Tissue Engineering Part A, vol. 14, no. 12, pp. 1969-1988, 2008.
All Publications
Habibollahi, Forough; Kagan, Brett J; Burkitt, Anthony N; French, Chris Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks Journal Article Nature Communications, 2023. Abstract | Links | BibTeX | Tags: 2D Neuronal Culture, HD-MEA, IPSC, MaxOne, MEA Metrics, MEA Technology, Primary Neuronal Cell Culture Radivojevic, Milos; Punga, Anna Rostedt Functional imaging of conduction dynamics in cortical and spinal axons Journal Article eLife, 2023. Abstract | Links | BibTeX | Tags: 2D Neuronal Culture, Axon Tracking Assay, MaxOne, MEA Technology, Primary Neuronal Cell Culture Girardi, Gregory; Zumpano, Danielle; Goshi, Noah; Raybould, Helen; Seker, Erkin Cultured Vagal Afferent Neurons as Sensors for Intestinal Effector Molecules Journal Article biosensors, 2023. Abstract | Links | BibTeX | Tags: 2D Neuronal Culture, HD-MEA, MaxOne, MEA Technology, Primary Neuronal Cell Culture Bartram, Julian; Franke, Felix; Kumar, Sreedhar Saseendran; Buccino, Alessio Paolo; Xue, Xiaohan; Gänswein, Tobias; Schröter, Manuel; Kim, Taehoon; Kasuba, Krishna Chaitanya; Hierlemann, Andreas Parallel reconstruction of the excitatory and inhibitory inputs received by single neurons reveals the synaptic basis of recurrent spiking Journal Article eLife, 2023. Abstract | Links | BibTeX | Tags: HD-MEA, MaxOne, MEA Metrics, MEA Technology, Modeling, Primary Neuronal Cell Culture, Spike Sorting Duru, Jens; Maurer, Benedikt; Doran, Ciara Giles; Jelitto, Robert; Küchler, Joël; Ihle, Stephan J; Ruff, Tobias; John, Robert; Genocchi, Barbara; Vörös, János Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays Journal Article SSRN, 2023. Abstract | Links | BibTeX | Tags: HD-MEA, MaxOne, MEA Technology, Primary Neuronal Cell Culture, Spike Sorting, Stimulation Xu, He Jax; Yao, Yao; Yao, Fenyong; Chen, Jiehui; Li, Meishi; Yang, Xianfa; Li, Sheng; Lu, Fangru; Hu, Ping; He, Shuijin; Peng, Guangdun; Jing, Naihe Generation of functional posterior spinal motor neurons from hPSCs-derived human spinal cord neural progenitor cells Journal Article Cell Regeneration, 2023. Abstract | Links | BibTeX | Tags: 2D Neuronal Culture, Activity Scan Assay, Axon Tracking Assay, HD-MEA, IPSC, MaxOne, MEA Technology, Network Assay, Organoids Cai, Hongwei; Ao, Zheng; Tian, Chunhui; Wu, Zhuhao; Liu, Hongcheng; Tchieu, Jason; Gu, Mingxia; Mackie, Ken; and Guo, Feng Brain Organoid Computing for Artificial Intelligence Journal Article bioRxiv, 2023. Abstract | Links | BibTeX | Tags: HD-MEA, Machine Learning, MaxOne, MEA Technology, Modeling, Organoids, Stimulation Qian, Junming; Guan, Xiaonan; Xie, Bing; Xu, Chuanyun; Niu, Jacqueline; Tang, Xin; Li, Charles H; Colecraft, Henry M; Jaenisch, Rudolf; Liu, Shawn X Multiplex epigenome editing of MECP2 to rescue Rett syndrome neurons Journal Article Science Translational Medicine, 2023. Abstract | Links | BibTeX | Tags: 2D Neuronal Culture, HD-MEA, IPSC, MaxTwo, MEA Technology Akarca, Danyal; Dunn, Alexander W E; Hornauer, Philipp J; Ronchi, Silvia; Fiscella, Michele; Wang, Congwei; Terrigno, Marco; Jagasia, Ravi; Vértes, Petra E; Mierau, Susanna B; Paulsen, Ole; Eglen, Stephen J; Hierlemann, Andreas; Astle, Duncan E; Schröter, Manuel Homophilic wiring principles underpin neuronal network topology in vitro Journal Article BioRxiv, 2022. Abstract | Links | BibTeX | Tags: MaxOne, MaxTwo, MEA Technology Kumar, Sreedhar S; Gänswein, Tobias; Buccino, Alessio P; Xue, Xiaohan; Bartram, Julian; Emmenegger, Vishalini; Hierlemann, Andreas Tracking axon initial segment plasticity using high-density microelectrode arrays: A computational study Journal Article Frontiers in Neuroinformatics, 2022. Abstract | Links | BibTeX | Tags: CMOS, HD-MEA, MEA Technology Lee, Jihyun; Gänswein, Tobias; Ulusan, Hasan; Emmenegger, Vishalini; Saguner, Ardan M; Duru, Firat; and Hierlemann, Andreas Repeated and On-Demand Intracellular Recordings of Cardiomyocytes Derived from Human Induced Pluripotent Stem Cells Journal Article ACS Sensors, 2022. Abstract | Links | BibTeX | Tags: Cardiomyocytes, CMOS, HD-MEA, MaxOne, MEA Technology Hruska-Plochan, Marian; Betz, Katharina M; Ronchi, Silvia; Wiersma, Vera I; Maniecka, Zuzanna; Hock, Eva-Maria; Laferriere, Florent; Sahadevan, Sonu; Hoop, Vanessa; Delvendahl, Igor; Panatta, Martina; van der Bourg, Alexander; Bohaciakova, Dasa; Frontzek, Karl; Aguzzi, Adriano; Lashley, Tammaryn; Robinson, Mark D; Karayannis, Theofanis; Mueller, Martin; Hierlemann, Andreas; Polymenidou, Magdalini Human neural networks with sparse TDP-43 pathology reveal NPTX2 misregulation in ALS/FTLD Journal Article BioRxiv, 2021. Abstract | Links | BibTeX | Tags: Inhibitory Neurons, MEA Technology, Neuronal Networks Yuan, Xinyue; Hierlemann, Andreas; Frey, Urs Extracellular Recording of Entire Neural Networks Using a Dual-Mode Microelectrode Array With 19,584 Electrodes and High SNR Journal Article IEEE, 2021. Abstract | Links | BibTeX | Tags: ETH-CMOS-MEA, MEA Technology Ricci, Chiara; Frey, Urs; Obien, Marie Engelene J IEEE, 2020. Abstract | Links | BibTeX | Tags: Activity Scan Assay, HD-MEA, MaxOne, MEA Technology Yuan, Xinyue; Schröter, Manuel; Obien, Marie Engelene J; Fiscella, Michele; Gong, Wei; Kikuchi, Tetsuhiro; Odawara, Aoi; Noji, Shuhei; Suzuki, Ikuro; Takahashi, Jun; Hierlemann, Andreas; Frey, Urs Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level Journal Article Nature Communications, 11 (4854), 2020. Abstract | Links | BibTeX | Tags: ETH-CMOS-MEA, MEA Technology Diggelmann, Roland; Fiscella, Michele; Hierlemann, Andreas; Franke, Felix Automatic Spike Sorting Algorithm for High-Density Microelectrode Arrays Journal Article Journal of Neurophysiology, 120 (4), 2018. Abstract | Links | BibTeX | Tags: MEA Technology Dragas, Jelena; Viswam, Vijay; Shadmani, Amir; Chen, Yihui; Bounik, Raziyeh; Stettler, Alexander; Radivojevic, Milos; Geissler, Sydney; Obien, Marie Engelene J; Müller, Jan; Hierlemann, Andreas IEEE journal of solid-state circuits, 52 (6), pp. 1576-1590, 2017, ISSN: 0018-9200. Abstract | Links | BibTeX | Tags: ETH-CMOS-MEA, MEA Technology Obien, Marie Engelene J; Deligkaris, Kosmas; Bullmann, Torsten; Bakkum, Douglas J; Frey, Urs Revealing neuronal function through microelectrode array recordings Journal Article Frontiers in Neuroscience, 9 , pp. 423, 2015, ISSN: 1662453X. Abstract | Links | BibTeX | Tags: MEA Technology, Review Ballini, Marco; Müller, Jan; Livi, Paolo; Chen, Yihui; Frey, Urs; Stettler, Alexander; Shadmani, Amir; Viswam, Vijay; Jones, Ian L; Jäckel, David; Radivojevic, Milos; Lewandowska, Marta K; Gong, Wei; Fiscella, Michele; Bakkum, Douglas J; Heer, Flavio; Hierlemann, Andreas A 1024-channel CMOS microelectrode array with 26,400 electrodes for recording and stimulation of electrogenic cells in vitro Journal Article IEEE Journal of Solid-State Circuits, 49 (11), pp. 2705-2719, 2014, ISSN: 00189200. Abstract | Links | BibTeX | Tags: MaxOne, MEA Technology Müller, Jan; Bakkum, Douglas J; Hierlemann, Andreas Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons. Journal Article Frontiers in Neural Circuits, 6 , pp. 121, 2013, ISSN: 1662-5110. Abstract | Links | BibTeX | Tags: ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation Hierlemann, Andreas; Frey, Urs; Hafizovic, Sadik; Heer, Flavio Growing cells atop microelectronic chips: Interfacing electrogenic cells in vitro with CMOS-based microelectrode arrays Journal Article Proceedings of the IEEE, 99 (2), pp. 252-284, 2011, ISSN: 00189219. Abstract | Links | BibTeX | Tags: ETH-CMOS-MEA, MEA Technology, Review Livi, Paolo; Heer, Flavio; Frey, Urs; Bakkum, Douglas J; Hierlemann, Andreas Compact voltage and current stimulation buffer for high-density microelectrode arrays Journal Article IEEE Transactions on Biomedical Circuits and Systems, 4 (6), pp. 372-378, 2010, ISSN: 19324545. Abstract | Links | BibTeX | Tags: ETH-CMOS-MEA, MEA Technology, Stimulation Frey, Urs; Sedivy, Jan; Heer, Flavio; Pedron, Rene; Ballini, Marco; Müller, Jan; Bakkum, Douglas J; Hafizovic, Sadik; Faraci, Francesca D; Greve, Frauke; Kirstein, Kay Uwe; Hierlemann, Andreas Switch-matrix-based high-density microelectrode array in CMOS technology Journal Article IEEE Journal of Solid-State Circuits, 45 (2), pp. 467-482, 2010, ISSN: 00189200. Abstract | Links | BibTeX | Tags: ETH-CMOS-MEA, MEA Technology Heer, Flavio; Hafizovic, Sadik; Franks, Wendy; Blau, Axel; Ziegler, Christiane; Hierlemann, Andreas CMOS microelectrode array for bidirectional interaction with neuronal networks Journal Article IEEE Journal of Solid-State Circuits, 41 (7), pp. 1620-1629, 2006, ISSN: 00189200. Abstract | Links | BibTeX | Tags: ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation Franks, Wendy; Schenker, Iwan; Schmutz, Patrik; Hierlemann, Andreas Impedance characterization and modeling of electrodes for biomedical applications Journal Article IEEE Transactions on Biomedical Engineering, 52 (7), pp. 1295-1302, 2005, ISSN: 00189294. Abstract | Links | BibTeX | Tags: MEA Technology Jenkner, Martin; Tartagni, Marco; Hierlemann, Andreas; Thewes, Roland Cell-based CMOS sensor and actuator arrays Journal Article IEEE Journal of Solid-State Circuits, 39 (12), pp. 2431-2437, 2004, ISSN: 00189200. Abstract | Links | BibTeX | Tags: MEA Technology, Review Heer, Flavio; Franks, Wendy; Blau, Axel; Taschini, S; Ziegler, Christiane; Hierlemann, Andreas; Baltes, Henry CMOS microelectrode array for the monitoring of electrogenic cells Journal Article Biosensors & Bioelectronics, 20 (2), pp. 358-366, 2004, ISSN: 0956-5663. Abstract | Links | BibTeX | Tags: ETH-CMOS-MEA, MEA Technology2023
title = {Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks},
author = {Forough Habibollahi and Brett J. Kagan and Anthony N. Burkitt and Chris French },
url = {https://www.nature.com/articles/s41467-023-41020-3},
doi = {https://doi.org/10.1038/s41467-023-41020-3},
year = {2023},
date = {2023-08-30},
journal = {Nature Communications},
abstract = {Understanding how brains process information is an incredibly difficult task. Amongst the metrics characterising information processing in the brain, observations of dynamic near-critical states have generated significant interest. However, theoretical and experimental limitations associated with human and animal models have precluded a definite answer about when and why neural criticality arises with links from attention, to cognition, and even to consciousness. To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of ‘Pong’ to demonstrate Synthetic Biological Intelligence (SBI). We demonstrate that critical dynamics emerge when neural networks receive task-related structured sensory input, reorganizing the system to a near-critical state. Additionally, better task performance correlated with proximity to critical dynamics. However, criticality alone is insufficient for a neuronal network to demonstrate learning in the absence of additional information regarding the consequences of previous actions. These findings offer compelling support that neural criticality arises as a base feature of incoming structured information processing without the need for higher order cognition.},
keywords = {2D Neuronal Culture, HD-MEA, IPSC, MaxOne, MEA Metrics, MEA Technology, Primary Neuronal Cell Culture},
pubstate = {published},
tppubtype = {article}
}
title = {Functional imaging of conduction dynamics in cortical and spinal axons},
author = {Milos Radivojevic and Anna Rostedt Punga},
url = {https://elifesciences.org/articles/86512},
doi = {https://doi.org/10.7554/eLife.86512},
year = {2023},
date = {2023-08-22},
journal = {eLife},
abstract = {Mammalian axons are specialized for transmitting action potentials to targets within the central and peripheral nervous system. A growing body of evidence suggests that, besides signal conduction, axons play essential roles in neural information processing, and their malfunctions are common hallmarks of neurodegenerative diseases. The technologies available to study axonal function and structure integrally limit the comprehension of axon neurobiology. High-density microelectrode arrays (HD-MEAs) allow for accessing axonal action potentials at high spatiotemporal resolution, but provide no insights on axonal morphology. Here, we demonstrate a method for electrical visualization of axonal morphologies based on extracellular action potentials recorded from cortical and motor neurons using HD-MEAs. The method enabled us to reconstruct up to 5-cm-long axonal arbors and directly monitor axonal conduction across thousands of recording sites. We reconstructed 1.86 m of cortical and spinal axons in total and found specific features in their structure and function.},
keywords = {2D Neuronal Culture, Axon Tracking Assay, MaxOne, MEA Technology, Primary Neuronal Cell Culture},
pubstate = {published},
tppubtype = {article}
}
title = {Cultured Vagal Afferent Neurons as Sensors for Intestinal Effector Molecules},
author = {Gregory Girardi and Danielle Zumpano and Noah Goshi and Helen Raybould and Erkin Seker},
url = {https://www.mdpi.com/2079-6374/13/6/601},
doi = {10.3390/bios13060601},
year = {2023},
date = {2023-05-31},
journal = {biosensors},
abstract = {The gut–brain axis embodies the bi-directional communication between the gastrointestinal tract and the central nervous system (CNS), where vagal afferent neurons (VANs) serve as sensors for a variety of gut-derived signals. The gut is colonized by a large and diverse population of microorganisms that communicate via small (effector) molecules, which also act on the VAN terminals situated in the gut viscera and consequently influence many CNS processes. However, the convoluted in vivo environment makes it difficult to study the causative impact of the effector molecules on VAN activation or desensitization. Here, we report on a VAN culture and its proof-of-principle demonstration as a cell-based sensor to monitor the influence of gastrointestinal effector molecules on neuronal behavior. We initially compared the effect of surface coatings (poly-L-lysine vs. Matrigel) and culture media composition (serum vs. growth factor supplement) on neurite growth as a surrogate of VAN regeneration following tissue harvesting, where the Matrigel coating, but not the media composition, played a significant role in the increased neurite growth. We then used both live-cell calcium imaging and extracellular electrophysiological recordings to show that the VANs responded to classical effector molecules of endogenous and exogenous origin (cholecystokinin serotonin and capsaicin) in a complex fashion. We expect this study to enable platforms for screening various effector molecules and their influence on VAN activity, assessed by their information-rich electrophysiological fingerprints.},
keywords = {2D Neuronal Culture, HD-MEA, MaxOne, MEA Technology, Primary Neuronal Cell Culture},
pubstate = {published},
tppubtype = {article}
}
title = {Parallel reconstruction of the excitatory and inhibitory inputs received by single neurons reveals the synaptic basis of recurrent spiking},
author = {Julian Bartram and Felix Franke and Sreedhar Saseendran Kumar and Alessio Paolo Buccino and Xiaohan Xue and Tobias Gänswein and Manuel Schröter and Taehoon Kim and Krishna Chaitanya Kasuba and Andreas Hierlemann},
url = {https://elifesciences.org/reviewed-preprints/86820},
doi = {10.7554/eLife.86820},
year = {2023},
date = {2023-05-17},
journal = {eLife},
abstract = {Self-sustained recurrent activity in cortical networks is thought to be important for multiple crucial processes, including circuit development and homeostasis. Yet, the precise relationship between the synaptic input patterns and the spiking output of individual neurons remains largely unresolved. Here, we developed, validated and applied a novel in vitro experimental platform and analytical procedures that provide – for individual neurons – simultaneous excitatory and inhibitory synaptic activity estimates during recurrent network activity. Our approach combines whole-network high-density microelectrode array (HD-MEA) recordings from rat neuronal cultures with patch clamping and enables a comprehensive mapping and characterization of active incoming connections to single postsynaptic neurons. We found that, during network states with excitation(E)-inhibition(I) balance, postsynaptic spiking coincided precisely with the maxima of fast fluctuations in the input E/I ratio. These spike-associated E/I ratio escalations were largely due to a rapid bidirectional change in synaptic inhibition that was modulated by the network-activity level. Our approach also uncovered the underlying circuit architecture and we show that individual neurons received a few key inhibitory connections – often from special hub neurons – that were instrumental in controlling postsynaptic spiking. Balanced network theory predicts dynamical regimes governed by small and rapid input fluctuation and featuring a fast neuronal responsiveness. Our findings – obtained in self-organized neuronal cultures – suggest that the emergence of these favorable regimes and associated network architectures is an inherent property of cortical networks in general.},
keywords = {HD-MEA, MaxOne, MEA Metrics, MEA Technology, Modeling, Primary Neuronal Cell Culture, Spike Sorting},
pubstate = {published},
tppubtype = {article}
}
title = {Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays},
author = {Jens Duru and Benedikt Maurer and Ciara Giles Doran and Robert Jelitto and Joël Küchler and Stephan J. Ihle and Tobias Ruff and Robert John and Barbara Genocchi and János Vörös},
url = {https://www.ssrn.com/abstract=4427959},
doi = {DOI: 10.2139/ssrn.4427959},
year = {2023},
date = {2023-04-24},
journal = {SSRN},
abstract = {Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks’ early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation.},
keywords = {HD-MEA, MaxOne, MEA Technology, Primary Neuronal Cell Culture, Spike Sorting, Stimulation},
pubstate = {published},
tppubtype = {article}
}
title = {Generation of functional posterior spinal motor neurons from hPSCs-derived human spinal cord neural progenitor cells},
author = {He Jax Xu and Yao Yao and Fenyong Yao and Jiehui Chen and Meishi Li and Xianfa Yang and Sheng Li and Fangru Lu and Ping Hu and Shuijin He and Guangdun Peng and Naihe Jing},
url = {https://cellregeneration.springeropen.com/articles/10.1186/s13619-023-00159-6},
doi = {10.1186/s13619-023-00159-6},
year = {2023},
date = {2023-03-23},
journal = {Cell Regeneration},
abstract = {Spinal motor neurons deficiency results in a series of devastating disorders such as amyotrophic lateral sclerosis (ALS), spinal muscular atrophy (SMA) and spinal cord injury (SCI). These disorders are currently incurable, while human pluripotent stem cells (hPSCs)-derived spinal motor neurons are promising but suffered from inappropriate regional identity and functional immaturity for the study and treatment of posterior spinal cord related injuries. In this study, we have established human spinal cord neural progenitor cells (hSCNPCs) via hPSCs differentiated neuromesodermal progenitors (NMPs) and demonstrated the hSCNPCs can be continuously expanded up to 40 passages. hSCNPCs can be rapidly differentiated into posterior spinal motor neurons with high efficiency. The functional maturity has been examined in detail. Moreover, a co-culture scheme which is compatible for both neural and muscular differentiation is developed to mimic the neuromuscular junction (NMJ) formation in vitro. Together, these studies highlight the potential avenues for generating clinically relevant spinal motor neurons and modeling neuromuscular diseases through our defined hSCNPCs.},
keywords = {2D Neuronal Culture, Activity Scan Assay, Axon Tracking Assay, HD-MEA, IPSC, MaxOne, MEA Technology, Network Assay, Organoids},
pubstate = {published},
tppubtype = {article}
}
title = {Brain Organoid Computing for Artificial Intelligence},
author = {Hongwei Cai and Zheng Ao and Chunhui Tian and Zhuhao Wu and Hongcheng Liu and Jason Tchieu and Mingxia Gu and Ken Mackie and and Feng Guo},
url = {https://www.biorxiv.org/content/10.1101/2023.02.28.530502v1},
doi = {10.1101/2023.02.28.530502},
year = {2023},
date = {2023-03-01},
journal = {bioRxiv},
abstract = {Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we develop Brainoware, living AI hardware that harnesses the computation power of 3D biological neural networks in a brain organoid. Brain-like 3D in vitro cultures compute by receiving and sending information via a multielectrode array. Applying spatiotemporal electrical stimulation, this approach not only exhibits nonlinear dynamics and fading memory properties but also learns from training data. Further experiments demonstrate real-world applications in solving non-linear equations. This approach may provide new insights into AI hardware.
},
keywords = {HD-MEA, Machine Learning, MaxOne, MEA Technology, Modeling, Organoids, Stimulation},
pubstate = {published},
tppubtype = {article}
}
title = {Multiplex epigenome editing of MECP2 to rescue Rett syndrome neurons},
author = {Junming Qian and Xiaonan Guan and Bing Xie and Chuanyun Xu and Jacqueline Niu and Xin Tang and Charles H. Li and Henry M. Colecraft and Rudolf Jaenisch and X. Shawn Liu},
url = {https://www.science.org/doi/10.1126/scitranslmed.add4666},
doi = {10.1126/scitranslmed.add4666},
year = {2023},
date = {2023-01-18},
journal = {Science Translational Medicine},
abstract = {Rettsyndrome(RTT)isanX-linkedneurodevelopmental disorder caused byloss-of-function heterozygous mutationsofmethyl CpG-binding protein2(MECP2) ontheXchromosome inyoungfemales. Reactivationofthe silent wild-type MECP2 allelefromtheinactiveXchromosome (Xi)represents apromising therapeutic opportunity forfemale patients withRTT.Here,weapplied amultiple xepigenome editing approachtoreactivate MECP2 fromXiinRTThuman embryonicstemcells(hESCs) andderivedneurons.Demethyla tionofthe MECP2 promoter bydCas9-T et1withtarget single-guide RNAreactivatedMECP2 fromXiinRTThESCs without detectable off-target effects atthetranscriptional level.Neuronsderivedfrommethyla tion-edited RTThESCs maintained MECP2 reactivationandreversedthesmaller somasizeandelectrophysiological abnormalities, twohallmarks ofRTT.InRTTneurons,insulationofthemethyla tion-edited MECP2 locusbydCpf1-CT CF (acatalytically deadCpf1fusedwithCCCTC-binding factor)withtarget CRISPR RNAenhanced MECP2 reactivationandrescued RTT-relatedneuronaldefects, providing aproof-of-concept studyforepigenome editing to treatRTTandpotentially otherdominant X-linkeddiseases.},
keywords = {2D Neuronal Culture, HD-MEA, IPSC, MaxTwo, MEA Technology},
pubstate = {published},
tppubtype = {article}
}2022
title = {Homophilic wiring principles underpin neuronal network topology in vitro},
author = {Danyal Akarca and Alexander W. E. Dunn and Philipp J. Hornauer and Silvia Ronchi and Michele Fiscella and Congwei Wang and Marco Terrigno and Ravi Jagasia and Petra E. Vértes and Susanna B. Mierau and Ole Paulsen and Stephen J. Eglen and Andreas Hierlemann and Duncan E. Astle and Manuel Schröter},
url = {https://www.biorxiv.org/content/10.1101/2022.03.09.483605v2.abstract},
doi = {https://doi.org/10.1101/2022.03.09.483605},
year = {2022},
date = {2022-12-01},
journal = {BioRxiv},
abstract = {Economic efficiency has been a popular explanation for how networks self-organize within the developing nervous system. However, the precise nature of the economic negotiations governing this putative organizational principle remains unclear. Here, we address this question further by combining large-scale electrophysiological recordings, to characterize the functional connectivity of developing neuronal networks in vitro, with a generative modeling approach capable of simulating network formation. We find that the best fitting model uses a homophilic generative wiring principle in which neurons form connections to other neurons which are spatially proximal and have similar connectivity patterns to themselves. Homophilic generative models outperform more canonical models in which neurons wire depending upon their spatial proximity either alone or in combination with the extent of their local connectivity. This homophily-based mechanism for neuronal network emergence accounts for a wide range of observations that are described, but not sufficiently explained, by traditional analyses of network topology. Using rodent and human monolayer and organoid cultures, we show that homophilic generative mechanisms can accurately recapitulate the topology of emerging cellular functional connectivity, representing an important wiring principle and determining factor of neuronal network formation in vitro.},
keywords = {MaxOne, MaxTwo, MEA Technology},
pubstate = {published},
tppubtype = {article}
}
title = {Tracking axon initial segment plasticity using high-density microelectrode arrays: A computational study},
author = {Sreedhar S. Kumar and Tobias Gänswein and Alessio P. Buccino and Xiaohan Xue and Julian Bartram and Vishalini Emmenegger and Andreas Hierlemann},
url = {https://www.frontiersin.org/articles/10.3389/fninf.2022.957255/full},
doi = {10.3389/fninf.2022.957255},
year = {2022},
date = {2022-10-03},
journal = {Frontiers in Neuroinformatics},
abstract = {Despite being composed of highly plastic neurons with extensive positive feedback, the nervous system maintains stable overall function. To keep activity within bounds, it relies on a set of negative feedback mechanisms that can induce stabilizing adjustments and that are collectively termed “homeostatic plasticity.” Recently, a highly excitable microdomain, located at the proximal end of the axon—the axon initial segment (AIS)—was found to exhibit structural modifications in response to activity perturbations. Though AIS plasticity appears to serve a homeostatic purpose, many aspects governing its expression and its functional role in regulating neuronal excitability remain elusive. A central challenge in studying the phenomenon is the rich heterogeneity of its expression (distal/proximal relocation, shortening, lengthening) and the variability of its functional role. A potential solution is to track AISs of a large number of neurons over time and attempt to induce structural plasticity in them. To this end, a promising approach is to use extracellular electrophysiological readouts to track a large number of neurons at high spatiotemporal resolution by means of high-density microelectrode arrays (HD-MEAs). However, an analysis framework that reliably identifies specific activity signatures that uniquely map on to underlying microstructural changes is missing. In this study, we assessed the feasibility of such a task and used the distal relocation of the AIS as an exemplary problem. We used sophisticated computational models to systematically explore the relationship between incremental changes in AIS positions and the specific consequences observed in simulated extracellular field potentials. An ensemble of feature changes in the extracellular fields that reliably characterize AIS plasticity was identified. We trained models that could detect these signatures with remarkable accuracy. Based on these findings, we propose a hybrid analysis framework that could potentially enable high-throughput experimental studies of activity-dependent AIS plasticity using HD-MEAs.},
keywords = {CMOS, HD-MEA, MEA Technology},
pubstate = {published},
tppubtype = {article}
}
title = {Repeated and On-Demand Intracellular Recordings of Cardiomyocytes Derived from Human Induced Pluripotent Stem Cells},
author = {Jihyun Lee and Tobias Gänswein and Hasan Ulusan and Vishalini Emmenegger and Ardan M. Saguner and Firat Duru and and Andreas Hierlemann},
url = {https://pubs.acs.org/doi/10.1021/acssensors.2c01678},
doi = {https://doi.org/10.1021/acssensors.2c01678},
year = {2022},
date = {2022-09-27},
journal = {ACS Sensors},
abstract = {Pharmaceutical compounds may have cardiotoxic properties, triggering potentially life-threatening arrhythmi- as. To investigate proarrhythmic effects of drugs, the patch clamp technique has been used as the gold standard for charac- terizing the electrophysiology of cardiomyocytes in vitro. However, the applicability of this technology for drug screening is limited, as it is complex to use and features low throughput. Recent studies have demonstrated that 3D-nanostructured electrodes enable to obtain intracellular signals from many cardiomyocytes in parallel; however, the tedious electrode fab- rication and limited measurement duration still remain major issues for cardiotoxicity testing. Here, we demonstrate how porous Pt-black electrodes, arranged in high-density microelectrode arrays, can be used to record intracellular-like signals of cardiomyocytes at large-scale repeatedly over an extended period of time. The developed technique, which yields highly parallelized electroporations by using stimulation voltages around 1 Volt peak-to-peak amplitude, enabled intracellular-like recordings at high success rates without causing significant alteration in key electrophysiological features. In a proof of concept study, we investigated electrophysiological modulations induced by two clinically applied drugs, nifedipine and quinidine. As the obtained results were in good agreement with previously published data, we are confident that the devel- oped technique has the potential to be routinely used in in vitro platforms for cardiotoxicity screening.},
keywords = {Cardiomyocytes, CMOS, HD-MEA, MaxOne, MEA Technology},
pubstate = {published},
tppubtype = {article}
}2021
title = {Human neural networks with sparse TDP-43 pathology reveal NPTX2 misregulation in ALS/FTLD},
author = {Marian Hruska-Plochan and Katharina M. Betz and Silvia Ronchi and Vera I. Wiersma and Zuzanna Maniecka and Eva-Maria Hock and Florent Laferriere and Sonu Sahadevan and Vanessa Hoop and Igor Delvendahl and Martina Panatta and Alexander van der Bourg and Dasa Bohaciakova and Karl Frontzek and Adriano Aguzzi and Tammaryn Lashley and Mark D. Robinson and Theofanis Karayannis and Martin Mueller and Andreas Hierlemann and Magdalini Polymenidou},
url = {https://doi.org/10.1101/2021.12.08.471089},
doi = {10.1101/2021.12.08.471089},
year = {2021},
date = {2021-12-09},
journal = {BioRxiv},
abstract = {Human cellular models of neurodegeneration require reproducibility and longevity, which is necessary for simulating these age-dependent diseases. Such systems are particularly needed for TDP-43 proteinopathies1,2, which involve human-specific mechanisms3–6 that cannot be directly studied in animal models. To explore the emergence and consequences of TDP-43 pathologies, we generated iPSC-derived, colony morphology neural stem cells (iCoMoNSCs) via manual selection of neural precursors7. Single-cell transcriptomics (scRNA-seq) and comparison to independent NSCs8, showed that iCoMoNSCs are uniquely homogenous and self-renewing. Differentiated iCoMoNSCs formed a self-organized multicellular system consisting of synaptically connected and electrophysiologically active neurons, which matured into long-lived functional networks. Neuronal and glial maturation in iCoMoNSC-derived cultures was similar to that of cortical organoids9. Overexpression of wild-type TDP-43 in a minority of iCoMoNSC-derived neurons led to progressive fragmentation and aggregation, resulting in loss of function and neurotoxicity. scRNA-seq revealed a novel set of misregulated RNA targets coinciding in both TDP-43 overexpressing neurons and patient brains exhibiting loss of nuclear TDP-43. The strongest misregulated target encoded for the synaptic protein NPTX2, which was consistently misaccumulated in ALS and FTLD patient neurons with TDP-43 pathology. Our work directly links TDP-43 misregulation and NPTX2 accumulation, thereby highlighting a new pathway of neurotoxicity.},
keywords = {Inhibitory Neurons, MEA Technology, Neuronal Networks},
pubstate = {published},
tppubtype = {article}
}
title = {Extracellular Recording of Entire Neural Networks Using a Dual-Mode Microelectrode Array With 19,584 Electrodes and High SNR},
author = {Xinyue Yuan and Andreas Hierlemann and Urs Frey},
url = {https://ieeexplore.ieee.org/document/9385387},
doi = {10.1109/JSSC.2021.3066043},
year = {2021},
date = {2021-03-24},
journal = {IEEE},
abstract = {Electrophysiological research on neural networks and their activity focuses on the recording and analysis of large data sets that include information of thousands of neurons. CMOS microelectrode arrays (MEAs) feature thousands of electrodes at a spatial resolution on the scale of single cells and are, therefore, ideal tools to support neural-network research. Moreover, they offer high spatio-temporal resolution and signal-to-noise ratio (SNR) to capture all features and subcellular-resolution details of neuronal signaling. Here, we present a dual-mode (DM) MEA, which enables simultaneous: 1) full-frame readout from all electrodes and 2) high-SNR readout from an arbitrarily selectable subset of electrodes. The DM-MEA includes 19,584 electrodes, 19,584 full-frame recording channels with noise levels of 10.4 μVrms in the action potential (AP) frequency band (300 Hz-5 kHz), 246 low-noise recording channels with noise levels of 3.0 μVrms in the AP band and eight stimulation units. The capacity to simultaneously perform full-frame and high-SNR recordings endows the presented DM-MEA with great flexibility for various applications in neuroscience and pharmacology.},
keywords = {ETH-CMOS-MEA, MEA Technology},
pubstate = {published},
tppubtype = {article}
}2020
title = {MAPSYNE: Miniaturized micropipette system combined with high-density microelectrode arrays for automated manipulation of neuronal networks in-vitro},
author = {Chiara Ricci and Urs Frey and Marie Engelene J. Obien},
url = {https://ieeexplore.ieee.org/document/9175797/},
doi = {10.1109/EMBC44109.2020.9175797},
year = {2020},
date = {2020-10-08},
journal = {IEEE},
abstract = {We present MAPSYNE, a miniaturized and automated system combining a high-density microelectrode array (HD-MEA) and a movable micropipette for studying, monitoring, and perturbing neurons in vitro. The system involves an all-electrical approach to automatically move a glass micropipette towards a target location on the HD-MEA surface, without the need for a microscope. Two methods of performing blind navigation are employed, (i) stop-measure-go approach wherein the pipette moves for a predefined distance before measuring its location then the process is repeated until the pipette reaches its destination, and (ii) predictive approach wherein the pipette is continuously tracked and moved. This automated system can be applied for unsupervised single-cell manipulation of neurons in a network, such as electroporation and local delivery of compounds.},
keywords = {Activity Scan Assay, HD-MEA, MaxOne, MEA Technology},
pubstate = {published},
tppubtype = {article}
}
title = {Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level},
author = {Xinyue Yuan and Manuel Schröter and Marie Engelene J. Obien and Michele Fiscella and Wei Gong and Tetsuhiro Kikuchi and Aoi Odawara and Shuhei Noji and Ikuro Suzuki and Jun Takahashi and Andreas Hierlemann and Urs Frey},
url = {https://www.nature.com/articles/s41467-020-18620-4#citeas},
doi = {10.1038/s41467-020-18620-4},
year = {2020},
date = {2020-09-25},
journal = {Nature Communications},
volume = {11},
number = {4854},
abstract = {Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Current labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescent indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended time scales. We report on a dual-mode high-density microelectrode array, which can simultaneously record in (i) full-frame mode with 19,584 recording sites and (ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we develop reliable analysis tools, which drastically increase the throughput to infer axonal morphology and conduction speed.},
keywords = {ETH-CMOS-MEA, MEA Technology},
pubstate = {published},
tppubtype = {article}
}2018
title = {Automatic Spike Sorting Algorithm for High-Density Microelectrode Arrays},
author = {Roland Diggelmann and Michele Fiscella and Andreas Hierlemann and Felix Franke},
url = {https://www.physiology.org/doi/pdf/10.1152/jn.00803.2017},
doi = {10.1152/jn.00803.2017},
year = {2018},
date = {2018-09-12},
journal = {Journal of Neurophysiology},
volume = {120},
number = {4},
abstract = {High-density microelectrode arrays (HD-MEAs) can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike-sorters have to be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multi-electrodes, however, suffer from the "curse of dimensionality", and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal-component-analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike-sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently using classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data pre-whitening before the principal-component-analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and which were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation were not required.},
keywords = {MEA Technology},
pubstate = {published},
tppubtype = {article}
}2017
title = {A Multi-Functional Microelectrode Array Featuring 59760 Electrodes, 2048 Electrophysiology Channels, Stimulation, Impedance Measurement and Neurotransmitter Detection Channels},
author = {Jelena Dragas and Vijay Viswam and Amir Shadmani and Yihui Chen and Raziyeh Bounik and Alexander Stettler and Milos Radivojevic and Sydney Geissler and Marie Engelene J Obien and Jan Müller and Andreas Hierlemann},
url = {http://ieeexplore.ieee.org/document/7913669/},
doi = {10.1109/JSSC.2017.2686580},
issn = {0018-9200},
year = {2017},
date = {2017-04-27},
journal = {IEEE journal of solid-state circuits},
volume = {52},
number = {6},
pages = {1576-1590},
abstract = {Biological cells are characterized by highly complex phenomena and processes that are, to a great extent, interdependent. To gain detailed insights, devices designed to study cellular phenomena need to enable tracking and manipulation of multiple cell parameters in parallel; they have to provide high signal quality and high spatiotemporal resolution. To this end, we have developed a CMOS-based microelectrode array system that integrates six measurement and stimulation functions, the largest number to date. Moreover, the system features the largest active electrode array area to date (4.48×2.43 mm(2)) to accommodate 59,760 electrodes, while its power consumption, noise characteristics, and spatial resolution (13.5 mum electrode pitch) are comparable to the best state-of-the-art devices. The system includes: 2,048 action-potential (AP, bandwidth: 300 Hz to 10 kHz) recording units, 32 local-field-potential (LFP, bandwidth: 1 Hz to 300 Hz) recording units, 32 current recording units, 32 impedance measurement units, and 28 neurotransmitter detection units, in addition to the 16 dual-mode voltage-only or current/voltage-controlled stimulation units. The electrode array architecture is based on a switch matrix, which allows for connecting any measurement/stimulation unit to any electrode in the array and for performing different measurement/stimulation functions in parallel.},
keywords = {ETH-CMOS-MEA, MEA Technology},
pubstate = {published},
tppubtype = {article}
}2015
title = {Revealing neuronal function through microelectrode array recordings},
author = {Marie Engelene J Obien and Kosmas Deligkaris and Torsten Bullmann and Douglas J Bakkum and Urs Frey},
url = {https://www.frontiersin.org/articles/10.3389/fnins.2014.00423/full},
doi = {10.3389/fnins.2014.00423},
issn = {1662453X},
year = {2015},
date = {2015-01-06},
journal = {Frontiers in Neuroscience},
volume = {9},
pages = {423},
abstract = {Microelectrode arrays and microprobes have been widely utilized to measure neuronal activity, both in vitro and in vivo. The key advantage is the capability to record and stimulate neurons at multiple sites simultaneously. However, unlike the single-cell or single-channel resolution of intracellular recording, microelectrodes detect signals from all possible sources around every sensor. Here, we review the current understanding of microelectrode signals and the techniques for analyzing them. We introduce the ongoing advancements in microelectrode technology, with focus on achieving higher resolution and quality of recordings by means of monolithic integration with on-chip circuitry. We show how recent advanced microelectrode array measurement methods facilitate the understanding of single neurons as well as network function.},
keywords = {MEA Technology, Review},
pubstate = {published},
tppubtype = {article}
}2014
title = {A 1024-channel CMOS microelectrode array with 26,400 electrodes for recording and stimulation of electrogenic cells in vitro},
author = {Marco Ballini and Jan Müller and Paolo Livi and Yihui Chen and Urs Frey and Alexander Stettler and Amir Shadmani and Vijay Viswam and Ian L Jones and David Jäckel and Milos Radivojevic and Marta K Lewandowska and Wei Gong and Michele Fiscella and Douglas J Bakkum and Flavio Heer and Andreas Hierlemann},
url = {http://ieeexplore.ieee.org/document/6923484/},
doi = {10.1109/JSSC.2014.2359219},
issn = {00189200},
year = {2014},
date = {2014-10-14},
journal = {IEEE Journal of Solid-State Circuits},
volume = {49},
number = {11},
pages = {2705-2719},
abstract = {To advance our understanding of the functioning of neuronal ensembles, systems are needed to enable simultaneous recording from a large number of individual neurons at high spa-tiotemporal resolution and good signal-to-noise ratio. Moreover, stimulation capability is highly desirable for investigating, for example, plasticity and learning processes. Here, we present a microelectrode array (MEA) system on a single CMOS die for in vitro recording and stimulation. The system incorporates 26,400 platinum electrodes, fabricated by in-house post-processing, over a large sensing area (3.85 2.10 mm) with sub-cellular spatial resolution (pitch of 17.5 µm). Owing to an area and power efficient implementation, we were able to integrate 1024 readout channels on chip to record extracellular signals from a user-specified selection of electrodes. These channels feature noise values of 2.4 µV in the action-potential band (300 Hz–10 kHz) and 5.4 µV in the local-field-potential band (1 Hz–300 Hz), and provide programmable gain (up to 78 dB) to accommodate various biological preparations. Amplified and filtered signals are digitized by 10 bit parallel single-slope ADCs at 20 kSamples/s. The system also includes 32 stimulation units, which can elicit neural spikes through either current or voltage pulses. The chip consumes only 75 mW in total, which obviates the need of active cooling even for sensitive cell cultures.},
keywords = {MaxOne, MEA Technology},
pubstate = {published},
tppubtype = {article}
}2013
title = {Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons.},
author = {Jan Müller and Douglas J Bakkum and Andreas Hierlemann},
url = {https://www.frontiersin.org/articles/10.3389/fncir.2012.00121/full},
doi = {10.3389/fncir.2012.00121},
issn = {1662-5110},
year = {2013},
date = {2013-01-10},
journal = {Frontiers in Neural Circuits},
volume = {6},
pages = {121},
abstract = {We present a system to artificially correlate the spike timing between sets of arbitrary neurons that were interfaced to a complementary metal-oxide-semiconductor (CMOS) high-density microelectrode array (MEA). The system features a novel reprogrammable and flexible event engine unit to detect arbitrary spatio-temporal patterns of recorded action potentials and is capable of delivering sub-millisecond closed-loop feedback of electrical stimulation upon trigger events in real-time. The relative timing between action potentials of individual neurons as well as the temporal pattern among multiple neurons, or neuronal assemblies, is considered an important factor governing memory and learning in the brain. Artificially changing timings between arbitrary sets of spiking neurons with our system could provide a "knob" to tune information processing in the network.},
keywords = {ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation},
pubstate = {published},
tppubtype = {article}
}2011
title = {Growing cells atop microelectronic chips: Interfacing electrogenic cells in vitro with CMOS-based microelectrode arrays},
author = {Andreas Hierlemann and Urs Frey and Sadik Hafizovic and Flavio Heer},
url = {http://ieeexplore.ieee.org/document/5594982/},
doi = {10.1109/JPROC.2010.2066532},
issn = {00189219},
year = {2011},
date = {2011-02-01},
journal = {Proceedings of the IEEE},
volume = {99},
number = {2},
pages = {252-284},
abstract = {Complementary semiconductor-metal-oxide (CMOS) technology is a very powerful technology that can be more or less directly interfaced to electrogenic cells, like heart or brain cells in vitro. To this end, the cells are cultured directly atop the CMOS chips, which usually undergo dedicated postprocessing to obtain a reliable bidirectional interface via noble-metal microelectrodes or high-k dielectrics. The big advantages of using CMOS integrated circuits (ICs) include connectivity, the possibility to address a large number of microelectrodes on a tiny chip, and signal quality, the possibility to condition small signals right at the spot of their generation. CMOS will be demonstrated to constitute an enabling technology that opens a route to high-spatio-temporal-resolution and low-noise electrophysiological recordings from a variety of biological preparations, such as brain slices, or cultured cardiac and brain cells. The recording technique is extracellular and noninvasive, and the CMOS chips do not leak out any toxic compounds, so that the cells remain viable for extended times. In turn, the CMOS chips have been demonstrated to survive several months of culturing while being fully immersed in saline solution and being exposed to cellular metabolic products. The latter requires dedicated passivation and packaging techniques as will be shown. Fully integrated, monolithic microelectrode systems, which feature large numbers of tightly spaced microelectrodes and the associated circuitry units for bidirectional interaction (stimulation and recording), will be in the focus of this review. The respective dense microelectrode arrays (MEAs) with small pixels enable subcellular-resolution investigation of regions of interest in, e.g., neurobiological preparations, and, at the same time, the large number of electrodes allows for studying the activity of entire neuronal networks . Application areas include neuroscience, as the devices enable fundamental neurophysiological insights at the cellular and circuit level, as well as medical diagnostics and pharmacology.},
keywords = {ETH-CMOS-MEA, MEA Technology, Review},
pubstate = {published},
tppubtype = {article}
}2010
title = {Compact voltage and current stimulation buffer for high-density microelectrode arrays},
author = {Paolo Livi and Flavio Heer and Urs Frey and Douglas J Bakkum and Andreas Hierlemann},
url = {http://ieeexplore.ieee.org/document/5617318/},
doi = {10.1109/TBCAS.2010.2080676},
issn = {19324545},
year = {2010},
date = {2010-11-01},
journal = {IEEE Transactions on Biomedical Circuits and Systems},
volume = {4},
number = {6},
pages = {372-378},
abstract = {We report on a compact (0.02 mm2 ) buffer for both voltage and current stimulation of electrogenic cells on a complementary metal-oxide semiconductor microelectrode array. In voltage mode, the circuit is a high-current class-AB voltage follower, based on a local common-mode feedback (LCMFB) amplifier. In current mode, the circuit is a current conveyor of type II, using the same LCMFB amplifier with cascode stages to increase the gain. The circuit shows good linearity in the 0.5-3.5 V input range and has extensively been used for stimulation of neuronal cultures.},
keywords = {ETH-CMOS-MEA, MEA Technology, Stimulation},
pubstate = {published},
tppubtype = {article}
}
title = {Switch-matrix-based high-density microelectrode array in CMOS technology},
author = {Urs Frey and Jan Sedivy and Flavio Heer and Rene Pedron and Marco Ballini and Jan Müller and Douglas J Bakkum and Sadik Hafizovic and Francesca D Faraci and Frauke Greve and Kay Uwe Kirstein and Andreas Hierlemann},
url = {http://ieeexplore.ieee.org/document/5405139/},
doi = {10.1109/JSSC.2009.2035196},
issn = {00189200},
year = {2010},
date = {2010-02-02},
journal = {IEEE Journal of Solid-State Circuits},
volume = {45},
number = {2},
pages = {467-482},
abstract = {We report on a CMOS-based microelectrode array (MEA) featuring 11, 011 metal electrodes and 126 channels, each of which comprises recording and stimulation electronics, for extracellular bidirectional communication with electrogenic cells, such as neurons or cardiomyocytes. The important features include: (i) high spatial resolution at (sub)cellular level with 3150 electrodes per mm2 (electrode diameter 7 um, electrode pitch 18 um); (ii) a reconflgurable routing of the recording sites to the 126 channels; and (iii) low noise levels.},
keywords = {ETH-CMOS-MEA, MEA Technology},
pubstate = {published},
tppubtype = {article}
}2006
title = {CMOS microelectrode array for bidirectional interaction with neuronal networks},
author = {Flavio Heer and Sadik Hafizovic and Wendy Franks and Axel Blau and Christiane Ziegler and Andreas Hierlemann},
url = {http://ieeexplore.ieee.org/document/1644873/},
doi = {10.1109/ESSCIR.2005.1541628},
issn = {00189200},
year = {2006},
date = {2006-06-26},
journal = {IEEE Journal of Solid-State Circuits},
volume = {41},
number = {7},
pages = {1620-1629},
abstract = {A CMOS metal-electrode-based micro system for bidirectional communication (stimulation and recording) with neuronal cells in vitro is presented. The chip overcomes the interconnect challenge that limits today's bidirectional microelectrode arrays. The microsystem has been fabricated in an industrial CMOS technology with several post-CMOS processing steps to realize 128 biocompatible electrodes and to ensure chip stability in physiological saline. The system comprises all necessary control circuitry and on-chip A/D and D/A conversion. A modular design has been implemented, where individual stimulation- and signal-conditioning circuitry units are associated with each electrode. Stimulation signals with a resolution of 8 bits can be sent to any subset of electrodes at a rate of 60 kHz, while all electrodes of the chip are continuously sampled at a rate of 20 kHz. The circuitry at each electrode can be individually reset to its operating point in order to suppress artifacts evoked by the stimulation pulses. Biological measurements from cultured neuronal networks originating from dissociated cortical tissue of fertilized chicken eggs with amplitudes of up to 500 muVpp are presented.},
keywords = {ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation},
pubstate = {published},
tppubtype = {article}
}2005
title = {Impedance characterization and modeling of electrodes for biomedical applications},
author = {Wendy Franks and Iwan Schenker and Patrik Schmutz and Andreas Hierlemann},
url = {http://ieeexplore.ieee.org/document/1440608/},
doi = {10.1109/TBME.2005.847523},
issn = {00189294},
year = {2005},
date = {2005-06-13},
journal = {IEEE Transactions on Biomedical Engineering},
volume = {52},
number = {7},
pages = {1295-1302},
abstract = {A low electrode-electrolyte impedance interface is critical in the design of electrodes for biomedical applications. To design low-impedance interfaces a complete understanding of the physical processes contributing to the impedance is required. In this work a model describing these physical processes is validated and extended to quantify the effect of organic coatings and incubation time. Electrochemical impedance spectroscopy has been used to electrically characterize the interface for various electrode materials: platinum, platinum black, and titanium nitride; and varying electrode sizes: 1 cm2, and 900 mu m2. An equivalent circuit model comprising an interface capacitance, shunted by a charge transfer resistance, in series with the solution resistance has been fitted to the experimental results. Theoretical equations have been used to calculate the interface capacitance impedance and the solution resistance, yielding results that correspond well with the fitted parameter values, thereby confirming the validity of the equations. The effect of incubation time, and two organic cell-adhesion promoting coatings, poly-L-lysine and laminin, on the interface impedance has been quantified using the model. This demonstrates the benefits of using this model in developing better understanding of the physical processes occurring at the interface in more complex, biomedically relevant situations.},
keywords = {MEA Technology},
pubstate = {published},
tppubtype = {article}
}2004
title = {Cell-based CMOS sensor and actuator arrays},
author = {Martin Jenkner and Marco Tartagni and Andreas Hierlemann and Roland Thewes},
url = {http://ieeexplore.ieee.org/document/1362853/},
doi = {10.1109/JSSC.2004.837082},
issn = {00189200},
year = {2004},
date = {2004-11-30},
journal = {IEEE Journal of Solid-State Circuits},
volume = {39},
number = {12},
pages = {2431-2437},
abstract = {In recent years, increasing knowledge about in vitro cell handling and culturing has encouraged a variety of CMOS-based approaches to stimulate and detect electrical activity of biological cells. This paper outlines in a topical review the scope of cell-based biosensors and actuators for in vitro applications ranging from single-cell detection to multisite probing of complex neural tissue. Recent examples are selected to demonstrate how standard CMOS processes have been used to engineer arrays with different functionality.},
keywords = {MEA Technology, Review},
pubstate = {published},
tppubtype = {article}
}
title = {CMOS microelectrode array for the monitoring of electrogenic cells},
author = {Flavio Heer and Wendy Franks and Axel Blau and S Taschini and Christiane Ziegler and Andreas Hierlemann and Henry Baltes},
url = {http://www.sciencedirect.com/science/article/pii/S0956566304000806?via%3Dihub},
doi = {10.1016/j.bios.2004.02.006},
issn = {0956-5663},
year = {2004},
date = {2004-03-19},
journal = {Biosensors & Bioelectronics},
volume = {20},
number = {2},
pages = {358-366},
abstract = {Signal degradation and an array size dictated by the number of available interconnects are the two main limitations inherent to standalone microelectrode arrays (MEAs). A new biochip consisting of an array of microelectrodes with fully-integrated analog and digital circuitry realized in an industrial CMOS process addresses these issues. The device is capable of on-chip signal filtering for improved signal-to-noise ratio (SNR), on-chip analog and digital conversion, and multiplexing, thereby facilitating simultaneous stimulation and recording of electrogenic cell activity. The designed electrode pitch of 250 mu m significantly limits the space available for circuitry: a repeated unit of circuitry associated with each electrode comprises a stimulation buffer and a bandpass filter for readout. The bandpass filter has corner frequencies of 100 Hz and 50 kHz, and a gain of 1000. Stimulation voltages are generated from an 8-bit digital signal and converted to an analog signal at a frequency of 120 kHz. Functionality of the read-out circuitry is demonstrated by the measurement of cardiomyocyte activity. The microelectrode is realized in a shifted design for flexibility and biocompatibility. Several microelectrode materials (platinum, platinum black and titanium nitride) have been electrically characterized. An equivalent circuit model, where each parameter represents a macroscopic physical quantity contributing to the interface impedance, has been successfully fitted to experimental results.},
keywords = {ETH-CMOS-MEA, MEA Technology},
pubstate = {published},
tppubtype = {article}
}