Spontaneous Cell Cluster Formation in Human iPSC-Derived Neuronal Spheroid Networks Influences Network Activity

Abstract

Three-dimensional neuronal culture systems such as spheroids, organoids, and assembloids constitute a branch of neuronal tissue engineering that has improved our ability to model the human brain in the laboratory. However, the more elaborate the brain model, the more difficult it becomes to study functional properties such as electrical activity at the neuronal level, similar to the challenges of studying neurophysiology in vivo We describe a simple approach to generate self-assembled three-dimensional neuronal spheroid networks with defined human cell composition on microelectrode arrays. Such spheroid networks develop a highly three-dimensional morphology with cell clusters up to 60 µm in thickness and are interconnected by pronounced bundles of neuronal fibers and glial processes. We could reliably record from up to hundreds of neurons simultaneously per culture for ≤90 d. By quantifying the formation of these three-dimensional structures over time, while regularly monitoring electrical activity, we were able to establish a strong link between spheroid morphology and network activity. In particular, the formation of cell clusters accelerates formation and maturation of correlated network activity. Astrocytes both influence electrophysiological network activity as well as accelerate the transition from single cell layers to cluster formation. Higher concentrations of astrocytes also have a strong effect of modulating synchronized network activity. This approach thus represents a practical alternative to often complex and heterogeneous organoids, providing easy access to activity within a brain-like 3D environment.Significance StatementNeuronal "organoid" cultures with multiple cell types grown on elaborate three-dimensional scaffolds have become popular tools to generate brain-like properties in vitro but bring with them similar problems concerning access to physiological function as real brain tissue. Here, we developed a new approach to form simple brain-like spheroid networks from human neurons, but using the normal supporting cells of the brain, astrocytes, as the scaffold. By growing these cultures on conventional microelectrode arrays, we were able to observe development of complex patterns of electrical activity for months. Our results highlight how formation of three-dimensional structures accelerated the formation of synchronized neuronal network activity and provide a promising new simple model system for studying interactions between known human cell types in vitro.

Keywords: 3D neuronal culture; astrocytes; induced pluripotent stem cells; microelectrode arrays; neuronal networks.

Figure 1.

Cell cluster formation and phenotypic characteristics. A, Phase contrast images showing cell distribution and organoids formation. At culture day 4, cells are spread out across the electrode area. B, At 88 d, distinct clusters are formed, with distinct processes interdigitating the clusters. C, Confocal maximum intensity projections (MIPs) of fluorescent immunolabeled B-III-tubulin, a marker for neurons: top (top) and side (bottom) views of a representative cluster. Cluster width often exceeds 100 μm and thickness can be up to 60 μm. D, Confocal cross section of a large cluster, showing that fluorescently labeled neuronal (B-III-tubulin, yellow), astrocyte (GFAP, magenta), and nucleic (DAPI, cyan) markers are found throughout the volume of the clusters. E, Confocal MIPs of fluorescently labeled neurons (B-III-tubulin, yellow), astrocytes (GFAP, magenta), and nuclei (DAPI, cyan). F, Confocal MIP of fluorescently stained neurons (B-III-tubulin, yellow), astrocytes (GFAP, magenta), and nuclei (DAPI, cyan) at the base of the projections that interdigitate the clusters. Neurons and glia are typically seen growing in very tight association both in projections and in clusters. G, DIV 34 fluorescently stained postsynaptic and presynaptic markers PSD-95 (yellow) and synaptophysin (magenta) respectively show rich expression of both markers. Inset, The two markers are occasionally observed a small distance apart from each other, showing that putative NMDA receptors are forming at synapses. H, Confocal MIPs of fluorescently stained neurons (B-III-tubulin, yellow), astrocytes (GFAP, magenta), and nuclei (DAPI, cyan), showing how clusters organize with interdigitating projections. I, Confocal MIPs of fluorescently stained neurons growing in the absence of astrocytes at 14 DIV, showing no clustering. J, Confocal MIPs of fluorescently stained neurons (B-III-tubulin, yellow), astrocytes (GFAP, magenta), and nuclei (DAPI, cyan), showing the base of the projections. It is clear that the projections are sparsely populated by nuclei, which mostly reside inside clusters. K, Confocal MIPs of fluorescently stained DAPI (cyan), MAP-2 (yellow), and GAD-65 (magenta) showing two neurons; one indicated by an arrow is expressing GAD-65, which was classified as a GABAergic neuron. L, Confocal MIPs of fluorescently stained DAPI (cyan), MAP-2 (yellow), and GAD-65 (magenta) indicated by an arrow showing a band of GAD-65 puncta, possibly from a GABAergic synapses. Scale bars: A, 200 μm; B, 200 μm; C, 100 μm; D, 50 μm; E, 100 μm; F, 50 μm; G; 20 μm; inset. 2.3 μm; H, 200 μm; I, 150 μm; J, 50 μm; K, 20 μm; L, μm.

Figure 2.

Principle for spike sorting and burst detection. A, Example of filtered data from one electrode in a 25-d-old culture. Spontaneous activity in the form of spikes is seen throughout the recording. Two bursts of variable durations are visible in this segment. B, Sorted spike waveforms for two different units are colored by the unit of origin. C, Colored boxes showing periods identified as bursts for the two different units seen in A and B. D, PCA of spikes obtained from a filtered signal. Units appear as distinct clusters in PCA space, and are potentially spikes from two different neurons. E, Aligned spike waveforms showing waveforms of classified units, highlighting the difference in the shape of action potentials for the two units. F, Determination of threshold for burst detection. A separation between two modes of a log ISI histogram is identified (vertical lines) and used as a threshold for burst detection.

Figure 3.

Raster plots from an example MEA recording showing detected spikes for all electrodes individually (black and red dots) and summed spikes across all electrodes binned into 100 ms bins (green line). Spikes classified within bursts are highlighted in red. When bursts overlap sufficiently between units, they are classified as network bursts, shown here as green boxes. A, Day 4 recording showing spontaneous asynchronous activity on many electrodes. B, At ∼3 weeks, characteristic busts are seen, which results in an increased spike rate on electrodes synchronously, giving rise to a peak in summed binned spike rate. C, D, As cultures continue to mature, the dynamics of network bursts change, typically increasing in occurrence frequency and decreasing in duration.

Figure 4.

Network characteristics of neuronal cultures. All figures show confidence intervals obtained from bootstrapping. A, Overall activity in spikes per second for active units and four different culture compositions. B, A burstiness metric (see Materials and Methods) giving the ratio of spikes occurring during bursts to total number of spikes. C, Network burstiness metric giving the fraction of total spikes occurring during network burst events. D, Number of network bursts per 10 min recording. E, Duration of network bursts. F, Entropy of spike trains. G, Correlation coefficient in spike rates between units on the same MEA.

Figure 5.

Activity maps for all channels of a MEA during a network burst. Colors show the amplitude envelope of the filtered signal averaged across the time points indicated above each pictogram. A, The network burst typically includes a large subset of electrodes and often shows some response on every electrode. B, The same network burst as in A, but at a finer temporal scale. This highlights the characteristic “fuzziness” of an electrode during a network burst event. Network burst events typically occur simultaneously on almost all channels, with little to no obvious initiation site.

Figure 6.

An example showing correlation between units on each electrode. A, A phase contrast image montage taken immediately after the recording. B, Black circles represent individual neurons sorted from the electrodes, with diameter scaled to the relative activity (in spikes per second). The curved line width and color are proportional to the correlation coefficient between each unit pair. We see that strong correlations are not confined to local clusters of cells, but span long distances, which morphologically appear only to be connected by the characteristic “strands” or “bundles” of glial and axonal projections.

Figure 7.

Quantifying cluster formation. A, Stitched montages from phase contrast images show that organoids have a brownish tint. B, Dividing the red value of every pixel with the corresponding blue value gives a new image that indicates relative values of red and blue. Electrodes were removed simply by finding very dark pixels and effectively applying pixel values of nearby pixels to electrode pixels. C, The background light intensity sometimes varies across the full stitched image, so a bandpass filter was applied, which also removes uninteresting small features. This bandpass-filtered image was used in two approaches. D, E, Simple segmentation to acquire the degree of cluster formation over the entire electrode array (D), and estimating a per-electrode clusterness metric or cluster score of the proximity of every electrode individually (E). The latter was obtained by multiplying a small Gaussian kernel on the positions of the electrodes. F, Cluster ratio normalized to the cluster ratio of the first day. When comparing high astrocyte and low astrocyte ratios, we can see that high astrocyte ratios tend to cluster more and faster. G, Cluster ratio normalized to the cluster ratio of the first day, showing that used MEAs develops faster in high astrocyte ratios than in low astrocyte ratios. The difference is not seen on new MEAs, which show greater cluster formation than the low astrocyte ratio in used MEAs.

Figure 8.

Relationship between cluster formation and burstiness for all electrodes of all MEAs of high and low cell density. A, At high cell density, we often see that the per-electrode clusterness is different depending on how bursty the electrodes are. This effect appears after the third week and is sustained through most of the full duration of the culture cycle. In general, the most bursty electrodes from all MEAs show higher cluster scores, meaning that the bursty electrodes are commonly found where cell clusters are the largest and most pronounced. B, This effect is only occasionally seen in low cell density, likely because of the smaller sample size of this dataset, as low cell density often shows a smaller number of active electrodes.

Figure 9.

Effect of cluster formation on network burst durations and correlation coefficient. The cluster ratio was derived from the ratio between pixels segmented as being part of a cluster to the total number of pixels and represents the extent that clusters have formed on individual MEAs. A, Network bursts durations, averaged across one recording for all MEAs, are influenced by the extent to which the MEAs have formed clusters. The lower tertile (0−33%) of burst durations are relatively more confined to MEAs, which have formed pronounced cluster (i.e., cluster formation negatively influences network burst durations). B, Correlation coefficient, again averaged for all unit pairs on individual MEAs, is also influenced by cluster formation. Correlation coefficients above the median of all MEAs are generally confined to MEAs with stronger cluster score. This implies that cluster formation is positively correlated with the formation of strongly correlated networks of neurons.