Construction of in vitro neural networks (NN)

The topology for the microfluidic NNs was designed as a dual-compartment architecture separated by microchannels and a middle chamber, as described in Fig.1a and b. The microfluidic design includes large channels (teal area) on both sides of the microfluidic circuit, which are for seeding somas. Physical barriers prevent the somas from migrating outside these large chambers. However, the 5-m-tall microchannels and a middle chamber (red area) enable neurites to spread and connect the fluidic compartments along defined pathways. Because of the enhanced growth kinetics of the axons, long, straight microchannels (>500m in length) are expected to favor them and to prevent dendrites from connecting distant populations.

Figure1c illustrates the possible neurite guidance and connection schemes. From left to right, the first and shortest microchannels should favor neurite outgrowth from the somatic to the synaptic chamber. From there, dendrites are expected to spread over this 3-mm-wide middle chamber, while the axons, in contrast, may grow straight ahead toward the opposite channels or turn back toward the somatic chamber. At one entrance of the long axon microchannels, short dead-end microchannels should prevent an axonal closed loop, which would lock axons into the long microchannel. Those traps should guide the axons toward the short microchannel and the somatic chamber. The last schematic illustrates a simple, inexhaustive list of examples of connectivity that may result from these guiding rules in the cases of one or two nodes located in a somatic chamber. Active and hidden nodes (blue and gray circles, respectively) can both be involved.

The microfluidic circuits are then assembled with electronic chips on which microelectrode arrays are accurately aligned with the fluidic compartments and microchannels (Fig.2). Thus, several recording devices can efficiently track spike propagation within the neurites while simultaneously monitoring soma activation.

Optical and fluorescent micrographs of random and microfluidic networks showing the homogeneous distribution of somas within the random area of both control (a) and microfluidic (b,c) samples and the wide exploration of neurites within all fluidic compartments, including the somatic chamber (c), the microchannels and the synaptic chamber (df). Immunofluorescence staining was performed after 14days in culture. DAPI, anti-synapsin, and anti-tubulin (YL1/2) were chosen as markers for labeling the cell nuclei, synapses and cytoskeleton, respectively.

For both growth conditions, primary cells extracted from hippocampal neurons were seeded on poly-l-lysine-coated microelectrode arrays and cultured in glial-conditioned media (same culture for both conditions). Thus, the substrate properties and culture conditions remained the same for the two batches of samples (details in Materials and methods). In the somatic chamber, neurons were well dispersed, and neurites homogeneously covered the underlying substrate surface, forming a highly entangled mesh (Fig.2b). Additionally, the synaptic chamber was widely explored by the neurites (Fig.2d), confirming their efficient spreading within the short microchannels as well as the efficient filtering of somas (Fig.2e). Figure2f gives a closer view of the junction with the synaptic chamber. The intricate entanglement of neurites and their proximity within the microchannels is expected to reinforce the neurite coupling efficiency and the networks modularity. These first results assessed the healthy and efficient outgrowth of neurons in the microfluidic compartments, which succeeded to provide the expected network structure, mainly by keeping the soma and neurite compartments in the desired location.

Figure3 shows the representative activity recorded within the random and organized networks on Day 6 in vitro (DIV6). As clearly observed, the number of active electrodes and the spike rate are significantly higher in the organized microfluidic NN (Fig.3a and b). Additionally, the number of isolated spikes as opposed to burst events was higher than that in controls (Fig.3c). Thus, the modularity of microfluidic NNs appears enhanced within the microfluidic network (dual-compartment configuration shown in Fig. S1).

Activity patterns of random and organized NNs. Comparison of the neuronal activity of cultured hippocampal neurons cultured in random configuration (left column) and on a microfluidic chip (right column). Recordings were acquired 6days after seeding (6days in vitro). (a) Typical 50s time course of one recording channel of the MEA within the control random sample (left) and inside an axonal microchannel (right). (b) Raster plots of all events crossing the negative threshold of 5 mean absolute deviations for the 64 recording channels of the MEA in the control and microfluidic conditions (left and right resp.). Red dots highlight examples of collective bursts. (c) Evolution of neural activity during the culture time for random (blue) and organized (red) NNs, in terms of the following (from left to right): mean spike rate per active electrode (min 0.1Hz mean firing rate), number of active electrodes, mean burst rate and burst duration. The mean spike and burst rates are extracted from the voltage traces for each recording channel and averaged among all active electrodes (60 electrodes total, same culture for all conditions). Statistical significance ***p<0.001 (Students t test).

Note that the electrodes located within the microchannels are expected to have a high sealing resistance because the channel cross section is small and filled with cellular material. As a result, the detection efficiency of such electrodes is believed to be increased compared to that of their synaptic and somatic chamber counterparts44. This effect related only to the measurement condition could artificially increase the activity level observed in microfluidic NNs. However, the spiking rate measured in the synaptic chamber did not follow that trend. While this compartment was similar to the somatic chamber in terms of growth conditions, the spiking rate was significantly higher, being rather comparable to that of the microchannels. Thus, the recording conditions could not explain the higher electrical activity. The electrical activity was enhanced independently of the MEAs detection efficiency, revealing the impact of the NN structure on the cell activity and the discrepancy in the spiking dynamics of the soma and the neurite.

The mid-term evolution of the electrical activity remained the same for both conditions, with all electrophysiological features globally increasing over time up to Day 15 (Fig.3c). Interestingly, the maximal number of active electrodes was reached earlier for the confined microfluidic NN (i.e. 4 days earlier than for the open NN, Fig. S2). Additionally, the number of active electrodes was significantly higher, in agreement with the raster plots (Fig.3b). Thus, more electrodes were active, and their activation occurred earlier in cell development. The confinement and geometrical constraints of the microfluidic environment reinforce the establishment of electrical activity, which agrees with the accelerated maturation of neuronal cells previously observed by immunohistochemistry within a similar microfluidic chip24.

The evolution of the burst rate followed a similar trend, increasing up to Day 14. Values ranged from 24 to 34Hz for the microfluidic networks, greatly exceeding the bursting rate of random NN (10 times higher). The burst duration was, however, similar for control and microfluidic networks, slightly increasing with the culture time (from 50 to 250ms) and as expected for hippocampal neurons4, confirming the reliability of the microfluidic NNs.

Neurite compartments exhibited dense activity patterns compared to the somatic chamber, with the highest spiking rates being located within the proximal compartments that were the closest to the somatic chamber (Fig.4). Within these short microchannels, spike patterns were characterized by the highest spike amplitude and shape variability. This variability remained within the synaptic chamber, but spike amplitudes were lowered. In those short and synaptic compartments, both dendrites and axons can be expected. However, in the distal and long microchannels, spike amplitude and shape were almost perfectly constant, which is as expected for action potentials carried by axons. These discrepancies were observed under the same growth conditions, all within the microchannels, and stem from the physiological properties of neurites.

Spike forms acquired in each microfluidic compartment. Data are sourced from the same recording at DIV 11, with the 50s time trace on the left and the superposed cutouts extracted by a spike sorting algorithm (detailed in methods). From top to bottom, the figure shows the typical voltage time trace and spike forms within the long and distant axonal microchannel; the synaptic middle chamber (without somas); the short neurite (dendrites and axons) microchannels; and the somatic chamber.

Interestingly, the activity in the somatic chamber resembled that of the control samples in terms of spike shape and spike rate (Fig.3a). When the activity within the somatic chamber was isolated, the spiking rate closely followed the trend observed in control samples, ranging from 0.9 to 2.5Hz from 6 to 11days (Fig. S2), which is a typical value for hippocampal neurons. Thus, the areas containing the soma (within the random and organized NNs, respectively) exhibited comparable spike patterns regardless of the growth condition (opened or confined). Previous works reported similar differences between somatic and axonal spikes (without the microfluidic environment)42, which agrees with our observations and further highlights the physiological relevance of the observations. Here, the microchannels provided a unique way to identify and study neurite activity in proximal and distant areas, presumably corresponding to dendrites and axons, respectively.

The cross-correlation (CC) analysis (Fig.5) provided a functional cartography of the random and organized networks at several stages of their development (detailed in materials and methods, and see Fig. S3 for the dual-somatic chamber). For the control sample, correlations became significant at DIV11 between electrode clusters randomly dispersed over the whole sample (Fig.5a). Their amplitude was weak but remained constant over the network. In contrast, cross-correlations were spatially defined and more intense in term of amplitude and number within the organized networks (Fig.5b), also emerging earlier at DIV5.

Correlations within random and organized NNs. The cross-correlation matrix (CCM) was extracted from the 60 recording channels of the MEAs during the culture time (one electrode per line and per column; bin size<5ms). From top to bottom: CCM obtained at DIV11 and DIV14 for the control sample (left) and at DIV6 and DIV11 for the microfluidic sample (right). (Bottom right) Schematics illustrate the position of the recording channels within the microfluidic compartments. The bottom colored bar is then used in the (xy) axes of the CC maps to highlight the position of each microelectrode: (filled, teal) in the large chamber containing all the soma, (filled, red) in the microchannels and the synaptic chamber, and (open, teal) in the empty large chamber for axon outputs only (no soma).

Maximal values were found within the long and distal microchannels, with mean correlation coefficients close to 1 and 0.5, respectively. Indeed, strong correlations can be expected when measuring spike propagation within the axonal compartment, which is more highlighted within the distal and long microchannels.

Somatic signals were correlated with some electrodes located in the microchannels and the synaptic chamber, revealing long-range synchrony as well (Fig.5b). Their amplitudes increased with time (Fig.5d), revealing a reinforcement of network synchrony and connectivity, especially between the microchannels and the synaptic and somatic chambers. They were concomitant with a modulation of short-range correlations, which became higher between neighboring electrodes. This effect could have several origins, such as time selection of the master node and a reinforcement of selected connections. Additionally, it could result from inhibitory activity, glutamatergic and GABAergic neurons being expected in similar proportions in our culture, and their maturation could explain the appearance of silent electrodes at the final stage of electrical maturation.

Thus, groups of spatially confined electrodes revealed a synchronization of the subpopulation consistent with the geometrical constraints. Somatic and synaptic chambers and neurite microchannels exhibited specific spiking patterns (Figs.3 and 4) and correlation landscapes (Fig.5) that enabled the identification of each network compartment. In that way, microfluidic circuits are capable of inducing significant differences in the spatiotemporal dynamics of in vitro neural networks.

The short-term cross-correlations between each microelectrode were then assessed to track signal propagation between each compartment (Fig.6). Figure6a first assesses the connectivity of the somatic chamber. The main feature was that there were higher correlation and synchrony levels between soma and neurite than between somas. Most of the correlations occurred with the proximal microchannels. This explains the synchrony and correlation between proximal neurites (Fig.6b, purple column). The analysis also reveals long-range correlations with both the synaptic chamber and the axonal microchannels (orange and yellow columns). Thus, somatic signals efficiently activated the emission of spikes within distant axonal microchannels (up to a few mm).

Immediate correlation of spike trains within the organized NN. Mapping of short-term correlations (signal delay is2.5ms max) extracted from the MEA recordings of 11-day-old microfluidic NNs. Arrows represent a significant correlation between the 5ms-binned spike trains of two electrodes. The maximum delay between correlated electrodes is2.5ms. The four panels (ad) distinguish the interactions between (a) somas and neurites (blue arrow) and (bd) along neurites. (b) Correlation between the electrodes of the same MEA column but within different microchannels (purple arrows), showing backward and forward propagation between adjacent neurite channels or synchrony between proximal neurites resulting from the same excitation. (c) Correlation between electrodes of the same MEA line, thus within the same or aligned microchannels (green arrows), showing straight spike propagation; (d) Correlation between each electrode located within the microchannels and the synaptic chamber (red arrows), showing entangled neurite-neurite interactions. Straight correlations (green arrows, in Panel (c) are excluded.

Between different microchannels (Fig.6b, purple arrow), the correlations appeared strongest in the synaptic chamber (n=3.9 per electrode, orange column), where there was no physical barrier to restrict communication between neurites. Then, the correlation within different microchannels (purple, yellow, red columns) could reveal backward and forward propagation between adjacent neurite channels or synchrony resulting from the same excitation. This could stem, respectively, from closed loops of neurites (Fig.1c) or the proximity between microchannels and the somatic or synaptic chambers. The number of these correlations was higher for proximal microchannels, both in terms of number and length of correlation, up to electrodes separated by 5 pitches (n+5). If we consider the neural architecture as we designed it, this would suggest a higher level of connectivity for the dendrites and proximal axons (both present within the short microchannels) than for the distant axon (long microchannel). Further studies should assess this point with immunostaining to identify dendrites and axons and excitatory and inhibitory neurons, for instance. In fact, we must not neglect other possibilities, such as the impact of dendritic signals (e.g. EPSPs and IPSPs from inhibitory and excitatory neurons), which may hide activity within distant microchannels.

Figure6c shows straight propagation along aligned microchannels (green arrow) and presumably along the same or connected neurites. Again, more signals propagated to the left than to the right side of the synaptic chamber, which agrees with the expected position of the dendrites and axons and the filtering effect of the synaptic chamber. These propagations were dominated by short-distance correlations, essentially between neighboring electrodes (n+1 or n+2). Long-range interactions were, however, clearly distinguished between misaligned electrodes (Fig.6d, red arrow), with each active site being correlated on average with three distant (>n+1) electrodes and one neighboring (n+1) electrode. The spatial range of the correlation reached several millimeters (up to n+6). Generally, those panels show that straight propagation involved axonal channels, while propagation between dendrites and within the synaptic chamber was more spatially distributed, which is indeed as expected for hippocampal neurons. The design architecture of the microfluidic NN is functionally relevant.

The directionality of neural communications was then assessed by picturing the delayed cross-correlations (between 5 and 25ms). Thus, the correlated spike trains were expected to share a similar origin. We assume that a positive delay between correlated electrodes (A and B) indicates the direction of propagation (from A to B), regardless of the propagation pathway (possibly indirect with hidden nodes). Under this assumption, most of the short-range correlations observed previously were suppressed, while long-range correlations are numerous despite the distance between electrodes and the background noise (Fig.7).

Long-term correlation of spike trains within organized NN. Mapping of delayed correlations (signal delay is25ms max) extracted from the MEA recordings of 11-day-old microfluidic NNs. Arrows represent significant correlation with a delay between25ms and 25ms between 5-ms-binned spike trains of two electrodes. Short-term correlations with a delay less than 5ms are excluded. The four panels (ad) distinguish the interactions between (a) somas and neurites (blue arrow) and (bd) along neurites. The same representation as in Fig.6 is used for the purple, green and red arrows.

The temporality of events was clear within aligned microchannels (Fig.7c). Signals propagated from the short to the long microchannels toward the axons and seemed to originate from the somatic chamber (Fig.7a). Additionally, the same somatic electrode seemed to activate several neurite channels, which could explain the correlation observed between those microchannels (Fig.7b). Within adjacent and parallel microchannels (Fig.7b), signals could be carried by the same neurites (in a closed loop configuration), but the delay (525ms) suggests indirect communications, presumably by dendrites. As illustrated in Fig.7d, communications were highly intricate between short and long channels, which confirms efficient neurite mixing within the synaptic chamber. The directionality was also mitigated, as 50% of propagations occurred in both directions for the purple and red columns (short and long microchannels). This dual directionality agrees with the emergence of both input and output nodes in the same somatic chamber (greenandblue columns Fig.7a). For that reason, we can barely distinguish backpropagation events, if any, and their impact on signal processing within such microfluidic circuits.

Interestingly, we observed only one efferent node and few (34) afferents (output and input nodes, respectively) for both conditions within organized and random NNs (Fig.7a and Fig. S4, respectively). However, the number of correlated spike trains was significantly reduced in control cultures of the same age, which confirms intense activity underlying the accelerated maturation within the microfluidic environments. The microchannels are shown to enhance the detection efficiency and amplitude of recorded signals. However, high levels of activity and synchrony were also observed in the wider synaptic chamber, which excludes an isolated effect of the enhanced detection efficiency within the microchannels. Differences in encoding properties between random and organized NNs are thus demonstrated, leveraging a high level of connectivity. While somas and neurites could be isolated, this analysis indeed underlines the complexity of neural communications and the rich encoding possibility even within a basic one-node architecture.

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