Stretchable mesh-electrodes interfacing human iPSC brain organoids

Projektinformationen

STRELECOID

ID Finanzhilfevereinbarung: 846567

DOI

10.3030/846567

Projekt abgeschlossen

EK-Unterschriftsdatum 3 Mai 2019

Startdatum 1 Dezember 2019

Enddatum 19 Juli 2023

Finanziert unter

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Gesamtkosten

€ 246 669,12

EU-Beitrag

€ 246 669,12

Koordiniert durch

FORSCHUNGSZENTRUM JULICH GMBH
Germany

Periodic Reporting for period 2 - STRELECOID (Stretchable mesh-electrodes interfacing human iPSC brain organoids)

Berichtszeitraum: 2021-12-01 bis 2022-11-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Human induced pluripotent stem-cells are a technology which allows to take a piece of human skin or a bit of blood, and turn that tissue into stem cells. Stem cells have the particularity of being able to become any cell in the human body given the correct environment, e.g tissues like lung, heart, liver, brain etc. It is therefore possible to take a bit of the patient's skin or blood and create a piece of brain tissue in the petri dish, by making the stem cells generated from the patient's skin or blood, turn into neurons, the cells which make up the brain.
This is very advantageous because it is unfeasible to perform experiments on human's brain as it would involve opening up the skull and perform experiments which may not fail due to ethical and safety concerns. Using human induced pluripotent stem cells allows one to duplicate real, human neural tissue outside of the patient's body. In the case that this patient carries a genetic neuropsychiatric disorder like certain kinds of epilepsies, one can create a multiple copies of the afflicted brain tissue and study therapeutic approaches on it without risking to harm the patient. This constitutes a paradigm shift in studying human neuropsychiatric disorders and opens the door not only for therapeutics but also personalized medicine.

Many neuropsychiatric disorders are characterized by abnormal electrical activity of the neurons making up the brain tissue. One therefore needs means of measuring such electrical activity. These neural tissues which we call organoids do not possess an immune system or a blood vessel network. Therefore, one cannot insert instruments and create tissue damage without inducing the death of the entire organoid, as it is incapable of shuffling out cell debris from itself. A possibility to circumvent the insertion of an electrical measuring device is to have the organoid proliferate and grow on top of a device and create a sort of symbiote: organoid and electrode-array.
To minimize mechanical mismatch, which could lead to inflammation of the tissue, and to make the measuring device as imperceptible as possible to the neurons making up the organoid, it is important to reduce the footprint of the measuring device as much as possible while making it able to deform and reshape itself under the strain impacted by growing cells.

To fulfill these characteristics, I develop mesh-electrode-arrays akin to fishnets which have a very low footprint and are able to deform and reconfigure their shapes when exposed to external stresses imposed by the cells. Cells can then grow freely around this mesh-electrode-array and incorporate it into themselves on the long term. In this way, the measurement device is constantly inside the organoid and no insertion needs to ever happen. Because this paradigm allows for continuous monitoring of cellular activity, we envision to couple the mesh-electrode-array with learning algorithms that over time figure out how to, for example, send in small electrical stimuli through various electrodes in order to disrupt epileptic seizures in such organoids. Hopefully, such therapeutic approaches can then be translated to humans in the form of implantable devices.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

The creation of mesh-electrode-arrays requires many moving parts from design to production. We have successfully designed a end-to-end design framework in the Python programming language. This offers several advantages such as ease of use given the popularity of Python, the absence of any software licensing fees enabling anyone regardless of funding to use the framework. It consists of a versatile module for designing complex shapes and connecting them with ease unlike most traditional, commercial computer-aided-design (CAD) software. The designed shape can be then put through a physical simulation of deformation in order to verify how the strain distributes itself inside the mesh-electrode-array as it would get twisted and deformed by the cells. Once we have a satisfactory design, we can move it to production, hopefully saving countless manufacturing hours by pre-testing the designs in simulation.

This yielded the creation of a mesh-electrode-array that is able to embed itself seamlessly without breaking in neural organoids (balls of cells) for more than 180 days (half a year). We expect these devices to be able to chronically integrate inside the organoid and measure activity all the time.

In conjunction with such a device which is able to record electrical activity continuously, we designed a software interface to detect neural activity within the recordings automatically and on-the-fly. This allows us to only save part of the data which is meaningful, and provides about a 99 percent compression factor of the data. The method relies on a simple, lightweight artificial-intelligence framework, again implemented in Python, that is called InceptionTime. The hardware necessary to record electrical activity comes from IntanTechnologies who provide all the source code to operate on their hardware freely, making our work full reproducible for anyone able to purchase the system.

We are currently working on a closed-loop stimulation approach with a reinforcement learning algorithm tasked with stimulating electrically the neural organoid based on the organoid's activity. This could be used for example to find out how to prevent the onset of an epileptic seizure (too much and too synchronized electrical activity) by electrical stimulation. The learning algorithm would be tasked with keeping the neural activity normal (its goal) and would have to find the proper stimulation strategy to ensure that. This would pave the way towards personalized therapeutics as every organoid would be representative of a specific patient for whom the algorithm would find out the best suited stimulation strategy.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

Owing the framework we developed, we have been able to push the state-of-the-art in terms of deformable micro-electrode-array design far exceeding capabilities of usual groups at Harvard or Northwestern University both in terms of electrode performance and mechanical resilience of electrode array.
This is not only brought on by the speed with which we can develop new design, but we have an integrated design-simulation loop that allows us to quickly fine-tune and optimize the designs to ours needs. For instance, we have managed to design a device that can be taken from a humid to a dry environment without breaking, which usually occurs because of water's surface tension applying large forces to these meshes. Such mechanical stability while being deformable is unprecedented. Furthermore, our devices can be embedded more than 180 days inside the organoids, a feat that has yet to be demonstrated elsewhere.

We expect to release the framework as open source and detail how to make and assemble these devices in an effort to popularize the technology but also benefit from the creativity of many other researchers around the globe. With reliable, long-term embedded electrode arrays in human induced-pluripotent-stem-cell organoids, we can hope to accelerate the speed at which therapeutics for neurological disorders can be developed.

Until the end of the project we expect to understand what reinforcement learning algorithm one should use to perform simple tasks such as disrupting the onset of bursting activity in the diseases neural organoids.

Overall state of the project