The remotely accessible hardware includes all the systems which are required to preserve homeostasis, monitor environmental parameters and perform electrophysiological experiments. These systems can be controlled interactively using our custom Graphical User Interface (GUI) or via Python scripts. All data is stored in a time-series database (InfluxDB), which can be accessed either via a GUI or via Python scripts. The users typically connect to the system using the Remote Desktop Protocol (RDP).

The platform is composed of several sub-systems, which can be accessed remotely via API calls over the internet, typically through Python.

Our current platform features 4 MEAs. The MEAs were designed by Prof. Roux’s Lab form Haute Ecole du Paysage, d’Ingénierie et d’Architecture (HEPIA) and are described in Wertenbroek et al. (2021). Each MEA can accommodate 4 organoids, with 8 electrodes per organoid (Figure 1E).

The MEA setup utilizes an Air-Liquid-Interface (ALI) approach (Stoppini et al., 1991), in which the organoids are directly placed on electrodes located atop of a permeable membrane (Figure 1D), with the medium flowing beneath this membrane in a 170 μL chamber. As a result, a thin layer of medium, created by surface tension, separates the upper side of the organoids from the humidified incubator air. This arrangement is further protected by a lid partially covering the MEA (Figure 1F). This ALI method enables a higher throughput and higher stability compared to submerged approaches, since no dedicated coating is required, and it is less prone to have the organoids detaching from the electrodes.

The electrodes in our system enable both stimulation and recording. The respective digital-to-analog and analog-to-digital conversions are performed by Intan RHS 32 headstages. Stimulations are executed using a current controller that ranges from 10 nA to 2.5 mA, and recordings are obtained by measuring the voltage on each electrode at a 30 kHz sampling frequency with a 16 bits resolution giving an accuracy of 0.15 μV. The headstages are connected to an Intan RHS controller, which in turn is connected to a computer via a USB port. The Figure 2A shows the electrical activity recorded for each of the 32 electrodes. It can be noticed that the recorded activity is different between each electrode. This difference comes from the facts that each set of 8 electrodes records a different FO and that for a given FO, electrodes record at a different location. This display is refreshed in real-time and also available 24/7 on our website at the URL https://finalspark.com/live/. We compared the recording characteristics of this ALI setup to MCS MEA (60MEA200/30iR-Ti) monitoring a submerged FO, using the exact same Intan system for voltage conversion. The overlays of an action potential recorded, respectively, with the ALI and submerged versions are shown in Figures 2C,D and show similar signal characteristics.

To sustain the life of the organoids on the MEA, Neuronal Medium (NM) needs to be constantly supplied. Our Neuroplatform is equipped with a closed-loop microfluidic system that allows for a 24/7 medium supply. The medium is circulating at a rate of 15uL/min. The medium flow rate is controlled by a BT-100 2 J peristaltic pump and is continuously adjusted according to needs, for instance during experimental runs. The peristaltic pump is connected to the PC-control software using an RS485 interface, for programmed (i.e., in Python) or manual operations (Figure 2B). Additionally, Figure 3A depicts this microfluidic closed-loop circuit.

The microfluidic circuit is made of 0.8 mm (inside diameter, ID) tubing. Continuous monitoring of the microfluidic circuit and flow rate is achieved by using Fluigent flow-rate sensors, which connect to the Neuroplatform control center via USB. Data related to medium flow rate is stored in a database for later access. Figure 2E shows the cyclic variations in flow induced by the cams of the peristaltic pump.

A secondary microfluidic system is used to replace the medium in the closed-loop with fresh medium every 24 h, a process illustrated in Figure 3A. This replacement is fully automated through a Python script and performed in the following consecutive steps:

Each MEA is equipped with a 12.3-megapixel camera that can be controlled interactively or programmatically (i.e., through a Raspberry Pi) for still image capture or video recording. The camera is positioned below the MEA, while illumination is provided by a remotely controlled LED situated above the MEA. Figures 3B,C illustrate this assembly (the aluminum wrapping is used in order to minimize the noise). This setup is particularly useful for detecting various changes, such as cell necrosis, possible organoid displacement caused by microfluidics, variations in medium acidity (using color analysis since our medium contains Phenol red), contamination, neuromelanin production (which can happen when uncaging dopamine), overflows (where the medium inadvertently fills the chamber above the membrane), or bubbles in the medium. For the latter two events, dedicated algorithms automatically detect these issues and trigger an alert to the on-site operator.

Changes of acidity, for example, can be detected by measuring the average color over a pre-defined window. Figure 2F shows the evolution of the medium’s red color component, with data points recorded hourly. The noticeable sudden drop is attributed to the pumping of medium with a slightly different acidity. This change in acidity results in a color alteration of the phenol red present in the medium.

It is also possible to release molecules at specific timings using a process called uncaging. In this method, a specific wavelength of light is employed to break open a molecular “cage” that contains a neuroactive molecule, such as Glutamate, NMDA or Dopamine. A fiber optic of 1,500 μm core diameter and a numerical aperture of 0.5 is used to direct light in the medium within the MEA chamber. The current system, Prizmatix Silver-LED, operates at 365 nm with an optical power of 260 mW. The uncaging system is fully integrated into the Neuroplatform and can be programmatically controlled during experiment runs via our API (see section 5.3).

The environmental conditions are monitored within two incubators. In both incubators, the following parameters are recorded: CO2, O2 concentrations, humidity, atmospheric pressure and temperature. Door-opening events are also logged since they have a major impact on measurements. The primary purpose of this monitoring is to ensure that experiments are performed in stable and reproducible environmental conditions.

All these parameters are displayed in real-time in a graphic interface showing both instant values as well as variations versus time of noise and flowrates (Figure 4A).

Incubator 1 houses the MEAs and the organoids used for electrophysiological experiments. In addition to the mentioned parameters, flowmeters are also utilized to report the actual flow rate of the microfluidic for each MEA, as depicted in the graph labelled “Pump” in Figure 4A. The system’s state is indirectly monitored through the noise level of each MEA, as shown in the graph labelled “Noise Intan” in Figure 4A. The noise level is calculated based on the standard deviation of the electrical signals recorded by the electrodes over a 30 ms period.

Incubator 2 houses the organoids which are kept in orbital shakers. Piezoelectric gyroscopes are used to measure the actual rotation speed of the orbital shakers.

Since all the data is logged in the database, it is also possible to access the historical measurements through a dedicated GUI (Figure 4B).

The core of the system relies on a computational notebook which provides access to 3 resources (Figure 5A):

The Neuroplatform records monitored data 24/7 using InfluxDB, a database designed for time series. Other options are also available.

This database contains all the data coming from the hardware listed in Section 3.

The electrical activity of the neurons is also recorded 24/7 at a sampling rate of 30 kHz. To minimize the volume of stored data, we designed a dedicated process that focuses on significant events, such as threshold crossings that are likely to be due to action potentials (spikes). The following pseudo code illustrates the implemented approach:

Additionally, a timestamp corresponding to each detected event is also stored in the database, along with the maximum value of voltage during the 3 ms spike waveform recording.

The threshold T is computed directly from voltage values sampled each 33 μs, according to the following formula:

Where σ i is the standard deviation computed over a set i of 30 ms consecutive voltage values, and M d n represents the median function computed over 101 consecutive σ i values. The use of the median reduces the sensitivity to outliers, which is typically caused by action potentials. In our current setup, a multiplier of 6 on the median has proven to be a good compromise for achieving reliable spike detection.

Besides electric tension data, the number spikes recorded per minute is also computed and stored in the database every minute by a batch process.

As previously discussed, the communication among neurons is captured by the MEA and converted into a voltage signal sampled at 30 kHz. The Neuroplatform offers two basic access modes to the recorded neural activity:

In addition to the aforementioned features, the Neuroplatform offers even more advanced methods. For instance, it includes counting spikes over a fixed time period of 200 ms following stimulation, with a 10 ms delay suppressing the stimulation artifact.

From a technical perspective, accessing the number of spikes can be accomplished in two different ways:

The second approach is required when the stimulation protocol demands real-time responsiveness. This is typically the case for certain closed-loop strategies. For instance, closed-loop stimulation strategies have been deployed in primary cortical cultures for effective burst control (Wagenaar et al., 2005a,b) and for goal-directed learning (Samsi et al., 2023).

Programmatically stimulating the FO on the Neuroplatform is accomplished by sending an electrical current to the MEA electrodes. The electrical current profile can be parameterized in a variety of ways, which is partly shown in Figure 5B. These parameters and controls include:

send_stim_param (electrodes, params)

The spontaneous electrical activity of the FO can be represented by the concept of “Center of Activity” (CA) (Bakkum et al., 2008) which is defined as a virtual position C on the MEA described by:

Where X k Y k define the spatial position of the 8 electrodes and F k is the number of spontaneous spikes detected. The interest of the concept of CA is that its position provides statistical information about the average location of the activity over the surface of the FO. The ability to change the position of the CA is interesting because it also shows the ability to memorize information in the state of the FO.

The coordinates of the CA can be modified using a high frequency stimulation. In the following experiment we use the following protocol:

Figure 6A displays the 100 measured positions of the CA corresponding to the spontaneous activity before the 20 Hz stimulation in blue, and after the high-frequency stimulation in red (the average position is indicated by a cross). A close-up is shown in Figure 6B. The timestamps of the spontaneous activity, before and after stimulation, are presented in Figures 6C,D, respectively. Each graph shows one example of the 100 records of 500 ms used to compute the CA location (showing a decrease of spontaneous firing activity of electrodes 3, 4 and 6). A noticeable shift in the average position (shown by a cross) of the CA can be observed before and after the high-frequency stimulation (as seen in Figure 6A), indicating a change of state of the biological network. A classifier based on a simple logistic regression is employed to predict if the network has received the 20 Hz stimulation. In this particular experiment, the classification accuracy, computed from the confusion matrix, is 95.5%.

The Neuroplatform allows users to perform both the experimental part (including stimulation and reading operations) and the visualization of the CA displacement within the same Python source code. The 500 ms 20 Hz signal is generated directly by the Python source code shown below. The first trigger.send instruction sends the trigger for the stimulation on a specific electrode and time.sleep pauses the execution for 50 ms.

Despite the common perception of Python as being less than ideal for real-time signal processing due to its inherent latency, our empirical data reveals a time accuracy of under 1 ms (on an Intel Xeon CPU E5-2690 v2 @ 3.00GHz), a level of precision that is satisfactory for the generation of tetanic signals.

In this example, the objective is to identify the set of stimulation parameters that can elicit the maximum number of action potentials within 200 ms after a stimulation.

Depending on the FOs, their composition, and maturity, only specific combinations of electrodes and parameters can elicit spikes. In our experiment, we use an 8-electrode MEA and cycle through several stimulation signal parameters as shown in Figure 7A. Consequently, we need to test a total of 342 different parameter-electrode combinations. The following pseudo code illustrates the Python script used in this experiment.

The aim of probabilistic stimulation and no stimulation in step 5 is to evaluate the difference between elicited and spontaneous spikes in a way that ensures there is no bias.

The bar chart in Figure 7A displays a segment of the experimental results. It shows a 15-s recording from a single electrode, corresponding to one execution of step 4 in the pseudo code above. Each bar represents the spike count during a 200 ms period, repeated every 250 ms. The orange bars in this plot are the result of the parameters selected in step 1 of the pseudo code. The blue bars represent no-stimulation periods, thus corresponding to the spontaneous activity of the neurons.

From Figure 7A, we can see that this particular combination of electrode and parameters reliably elicits responses.

In practice, the Python script can also be used to automatically display the 342 graphs similar to Figure 7A, allowing the operator to select the optimal set of parameters. Additionally, it can compute a scalar metric to characterize the “efficiency” of the parameters, and automatically identify the optimal parameters.

An example of a parameter maximization metric is given in the equation below. Let us denote μ r and μ s the average number of spikes recorded spontaneously or after a stimulation, respectively, and σ r and σ s as their standard deviations. The following metric is used:

The set of parameters that maximize this metric can then be utilized to perform other experiments requiring elicited spikes, such as investigating the effect of pharmacological agents on a biological network’s ability to react quickly to stimulation.

‘Uncaging’ is a pivotal technique in cellular biology, enabling the precise control of molecular interactions within cells (Gienger et al., 2020). It involves the use of photolabile caged compounds that are activated by specific light wavelengths, releasing bioactive molecules in a targeted and timely manner. This method is particularly valuable for studying dynamic processes in neural networks and intracellular signaling, offering real-time insights into complex biological mechanisms.

Our Neuroplatform is equipped with all necessary components to perform uncaging. In this example, we investigate closed-loop stimulation, where dopamine is used to reward the network when more spikes are elicited by the same stimulation. The release of the dopamine is achieved through the uncaging of CNV-dopamine using the UV system described in section 3.6.

Figure 7B shows the flow chart of the closed-loop uncaging process. The optimal stimulation parameters are first found using the technique shown in 5.2 (in this case, a current of 4uA, biphasic with 100uS per phase), which is sent successively to each of the 8 electrodes with a delay of 10 ms between each electrode.

Figure 7C shows the response timestamps of the 8 electrodes for a period of 1,200 ms, 600 ms before and after the stimulation. The stimulation event is indicated by the vertical red line. It is interesting to observe that in this particular case, most of the elicited spikes originate from 2 electrodes, specifically electrode 112 and electrode 119.

The Python source code implementing the closed-loop process illustrated in Figure 7B is provided below. We would like to highlight here how concise the code is. With only 13 lines of code, the entire closed-loop process has been implemented.

The graph in Figure 7D shows the variation in the number of spikes elicited during the execution of the script above across 5 h. A general increase in the number of elicited spikes can be observed. However, it is obviously not possible to establish causality between the closed-loop strategy and the observed increase with this single experiment alone. The primary purpose of this closed-loop experiment is to demonstrate the flexibility offered by the Neuroplatform.

Human forebrain organoids were originated as described in Govindan et al. (2021). Briefly, Human Neural Stem Cells derived from the human induced pluripotent stem (hiPS) cell line (ThermoFisher), were plated in flasks coated with CellStart (Fisher Scientific) and amplified in Stempro NSC SFM kit (ThermoFischer) complete medium: KnockOut D-MEM/F12, 2 mM of GlutaMAX, 2% of StemPro Neural supplement, 20 ng/mL of Human FGF-basic (FGF-2/bFGF) Recombinant Protein, and 20 ng/mL of EGF Recombinant Human Protein (Fisher Scientific). Cells were then detached with StemPro^™ Accutase (Gibco) and plated in p6 at the concentration of 250,000 cells/well. The plates were sealed with breathable adhesive paper and leads, placed on an orbital shaker at 80 rpm, and culture for 7 days at 37°C 5% CO2. After one week the newly formed spheroids were put in differentiation medium I (Diff I), containing DMEM/F-12, GlutaMAX^™ supplement (Gibco), 2% BSA, 1X of Stempro® hESC Supplement, 20 ng/mL of BDNF Recombinant Human Protein (Invitrogen), 20 ng/mL of GDNF Recombinant Human Protein (Gibco), 100 mM of N6,2′-O-Dibutyryladenosine 3′,5′-cyclic monophosphate sodium salt, and 20 mM of 2-Phospho-L-ascorbic acid trisodium salt. After one week, brain spheroids were put in differentiation medium II (Diff II) made of 50% of Diff I and 50% of Neurobasal Plus (Invitrogen). After 3 weeks of culture in Diff II, brain organoids were plated in Neurobasal Plus and kept in the orbital shaker until the transfer on the MEA. Medium was change once per week.

Mature FOs were fixed in 2.5% Glutaraldehyde in 0.1 M phosphate buffer pH 7.4, at RT. After 24 h the samples were processed as described in Cakir et al. (2019) at the Electron Microscopy Facility of University of Lausanne. The whole FO images were acquired with Quanta FEG 250 Scanning Electron Microscope.

MEA connected with the microfluid system was moved from the incubator and placed on a 12.3-megapixel camera system (with an optical lens of 16 mm of focal, giving a magnification power of 21x) inside the cell culture hood. The lid was removed to access the top of the liquid/air interface. Sterile Hydrophilic PTFE MEMBRANE Hole ‘confetti’ (diameter 2.5 mm, diameter of the hole 0.7 mm) (HEPIA) were positioned on top of each electrode and left there 2 min to absorb the medium. FOs were collected from the plate using wide bore pipette tips (Axygen) and placed in the middle of confetti, in a 10 μL drop of medium. The position of the organoids was adjusted with the help of sterile forceps. After all the organoids were put on place, the chamber was covered with the plate sealer Greiner Bio-One^™ BREATHseal^™ Sealer (Fisher Scientific), and with the MEA lid. MEA containing the organoids were placed immediately back in the cell incubator and were ready to be used for recording and stimulation. A similar procedure was used for the positioning of organoids on MCS MEA (60MEA200/30iR-Ti). In this case the Hydrophilic PTFE MEMBRANE was not used and organoids were directly laid on the electrodes in a 30 μL drop of medium. Recording of organoid activity was performed immediately afterwards.

Cell culture media was stored in a 50 mL Falcon tube with a multi-port delivery cap (ElveFlow) and stored at 4°C. Each reservoir delivery cap contained a single 0.8 mm ID × 1.6 mm OD PTFE tubing (Darwin Microfluidics), sealed by a two-piece PFA Fittings and ferrule threaded adapter (IDEX), extending from the bottom of the reservoir to an inlet port on the 4-port valve head of the RVM Rotary Valve (Advance Microfluidics SA). Sterile air is permitted to refill the reservoir through a 0.22-μm filter (Milian) fixed to the cap to compensate for syringe pump medium withdrawal. A similar PTFE tubing and PFA Fittings and adapters were used to connect the syringe pump to the 4-port valve head of the RVM Rotary Valve (Advance Microfluidics SA). Each PTFE tubing coming from the distribution valve connects with a 50 mL falcon tube inside the cell culture incubator (Binder) and to a borosilicate glass bottle (Milian) to collect discarded cell culture medium.

A secondary microfluid system made of 0.8 mm ID × 1.6 mm OD PTFE tubing, were used to connect each 50 mL falcon tube inside the cell culture incubator with its own MEA (HEPIA). The connection was through a precise peristaltic pump BT100-2 J (Darwin Microfluidics) containing 10 rollers. A compute module (Raspberry Pi 4) controlled the peristaltic pump and the Rotary Valve, through a custom application program interface (API), using RS485 interface and RS-232 interface, respectively. A Fluigent flow-rate sensor connected via USB to the Raspberry Pi 4 allowed the monitoring of the flow rate inside the microfluidic system between the peristaltic pump and the MEA. Python was used to develop the software required to carry out automation protocols.

Carboxynitroveratryl (CNV)-caged dopamine (Tocris Bioscience) was dissolved in Neurobasal Plus at the concentration of 1 mM, and injected in the fluidic system. After 3 h from the injection, the uncaging experiment started as described in paragraph 5.3. UV Silver-LED fiber-coupled LED (Prizmatix) was used to uncage the dopamine at the wavelength of 365 nm for 800 ms each time.