Preconfigured neuronal firing sequences in human brain organoids

27 min read Original article ↗

Data availability

The data supporting the findings of this study are available within the article and its supplementary information. Raw and curated electrophysiology recordings can be found here https://dandiarchive.org/dandiset/001603. scRNA-seq data have been deposited and are publicly available in the NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) with accession GSE290330.

Code availability

Spike sorting was performed in Python 3.6 using SpikeInterface 0.13.0 and previously published63, which can be found at https://github.com/SpikeInterface/spikeinterface. Custom code for electrophysiology analysis is available at https://github.com/braingeneers/Protosequences

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Acknowledgements

We would like to thank the members of the Braingeneers consortium for helpful discussions and D. Haussler for insightful comments. We would also like to thank members of the UC Santa Cruz Genomics Institute for helping with computing resources, in particular D. Parks for assistance with archiving the neurophysiology data. This study was supported by the National Science Foundation (NSF) Emerging Frontiers in Research and Innovation under award (NSF 2515389 to T.S.), UC Santa Cruz Baskin Engineering Seed Grant (to T.S.), Schmidt Futures Foundation (SF857 to M.T.), National Human Genome Research Institute under award (1RM1HG011543 to M.T.), German Research Foundation FOR5159 TP1 (437610067 to I.L.H.-O.), European Research Council advanced grant ‘neuroXscales’ (694829 to A.H.), Swiss NSF project (205320_188910/1 to A.H.), National Institutes of Health (NIH; T32 ES007141 to D.-M.A.E.D.) and International Foundation for Ethical Research (to D.-M.A.E.D.), Hopkins Discovery and Johns Hopkins SURPASS (to L.S.), John Douglas French Alzheimer’s Foundation (to K.S.K.), NIH BRAIN Initiative (R01NS118442 to K.B.H.) and National Institute of Mental Health grant (1U24MH132628 to M.A.M.-R.). Through the National Research Platform, this work was supported in part by NSF awards (CNS-1730158, ACI-1540112, ACI-1541349, OAC-1826967, OAC-2112167, CNS-2100237 and CNS-2120019), the University of California Office of the President and the University of California San Diego’s California Institute for Telecommunications and Information Technology/Qualcomm Institute.

Author information

Authors and Affiliations

  1. Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA

    Tjitse van der Molen, Gregory A. Kaurala, Cole Duncan, Sawyer McKenna, Jesus Gonzalez-Ferrer & Tal Sharf

  2. UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA

    Tjitse van der Molen, Alex Spaeth, Sebastian Hernandez, Gregory A. Kaurala, Hunter E. Schweiger, Cole Duncan, Jinghui Geng, Jesus Gonzalez-Ferrer, Bradley M. Colquitt, Mohammed A. Mostajo-Radji, Mircea Teodorescu & Tal Sharf

  3. Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA

    Tjitse van der Molen, Max Lim, Paul K. Hansma & Kenneth S. Kosik

  4. Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA

    Tjitse van der Molen, Max Lim & Kenneth S. Kosik

  5. Department of Electrical and Computer Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA

    Alex Spaeth, Sebastian Hernandez, Jinghui Geng & Mircea Teodorescu

  6. Institute of Developmental Neuroscience, Center for Molecular Neurobiology, Hamburg Center of Neuroscience, University Medical Center Hamburg–Eppendorf, Hamburg, Germany

    Mattia Chini & Ileana L. Hanganu-Opatz

  7. Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, USA

    Hunter E. Schweiger, Cole R. K. Harder & Bradley M. Colquitt

  8. Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland

    Julian Bartram, Tobias Gänswein & Andreas Hierlemann

  9. Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA, USA

    Aditya Dendukuri & Linda R. Petzold

  10. Department of Physics, University of California, Santa Barbara, Santa Barbara, CA, USA

    Zongren Zhang & Paul K. Hansma

  11. Department of Biology, Washington University in St. Louis, St. Louis, MO, USA

    Kiran Bhaskaran-Nair & Keith B. Hengen

  12. Department of Physics, University of California, Santa Cruz, Santa Cruz, CA, USA

    Aidan L. Morson

  13. Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Dowlette-Mary Alam El Din, Jason Laird, Maren Schenke & Lena Smirnova

  14. Institute for the Biology of Stem Cells, University of California, Santa Cruz, Santa Cruz, CA, USA

    Bradley M. Colquitt & Tal Sharf

Authors

  1. Tjitse van der Molen
  2. Alex Spaeth
  3. Mattia Chini
  4. Sebastian Hernandez
  5. Gregory A. Kaurala
  6. Hunter E. Schweiger
  7. Cole Duncan
  8. Sawyer McKenna
  9. Jinghui Geng
  10. Max Lim
  11. Julian Bartram
  12. Tobias Gänswein
  13. Aditya Dendukuri
  14. Zongren Zhang
  15. Jesus Gonzalez-Ferrer
  16. Kiran Bhaskaran-Nair
  17. Aidan L. Morson
  18. Cole R. K. Harder
  19. Linda R. Petzold
  20. Dowlette-Mary Alam El Din
  21. Jason Laird
  22. Maren Schenke
  23. Lena Smirnova
  24. Bradley M. Colquitt
  25. Mohammed A. Mostajo-Radji
  26. Paul K. Hansma
  27. Mircea Teodorescu
  28. Andreas Hierlemann
  29. Keith B. Hengen
  30. Ileana L. Hanganu-Opatz
  31. Kenneth S. Kosik
  32. Tal Sharf

Contributions

T.S. designed, conceived and supervised the study. M.C., I.L.H.-O., K.B.H. and K.S.K. offered numerous suggestions and comments. T.v.d.M., A.S. and M.C. performed computational analysis and statistics on electrophysiology recordings. J.B. and T.G. performed extracellular recordings on acute brain slices under the supervision of A.H. S.H., G.A.K., H.E.S., C.D. and S.M. cultured mouse brain organoids and performed electrophysiology measurements under the supervision of T.S. and M.A.M.-R. S.H., G.A.K. and H.E.S. performed single-cell RNA sequencing and immunohistochemistry of mouse organoids under the supervision of M.A.M.-R., B.M.C. and T.S. C.R.K.H. performed additional immunohistochemistry and fluorescence microscopy under the supervision of T.S. S.H., H.E.S. and J.G.-F. performed analysis on single-cell RNA sequencing data from mouse organoids under the supervision of M.A.M.-R. and B.M.C. D.-M.A.E.D., J.L. and M.S. performed electrophysiology measurements and bulk RNA sequencing of additional human brain organoids under the supervision of L.S. A.D., Z.Z. and M.L. performed additional electrophysiology analysis under the supervision of T.v.d.M., L.R.P. and P.K.H. K.B.-N. performed computational analysis under the supervision of K.B.H. A.L.M. and J.G. contributed to spike sorting and archiving neurophysiology datasets under the supervision of T.S. and M.T. T.S. wrote the first draft of the paper. M.C., I.L.H.-O., K.B.H., T.v.d.M. and K.S.K. provided valuable edits to subsequent drafts, and all authors discussed the results and commented on the paper.

Corresponding author

Correspondence to Tal Sharf.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks Giorgia Quadrato and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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Extended data

Extended Data Fig. 1 Backbone units occupy the tail of skewed firing rate distributions.

a, A histogram of the distribution of average firing rates for all units in organoid 1. The majority of units have low average firing rates, while a long tail in the distribution contains a small subset of units with high average firing rates. A lognormal distribution is fitted to the histogram. The inset shows the histogram for the logarithm of the average firing rates of the same organoid. A normal distribution is fitted to the histogram. b, Normal distributions fit to the logarithm of the average firing rate per unit for the 8 different organoids. c, The R2 values for the fitted normal distributions shown in b. R2 = 0.97 ± 0.04 (mean ± s.d.) across the 8 human brain organoids. Backbone neurons alone are not well described by a lognormal distribution. R2 values are 0.45 ± 0.30 across the 8 human brain organoids. d, The distribution of average firing rates per organoid for backbone and non-rigid units separated. Red bars mark the distribution medians. The backbone units populate the tail of the skewed average firing rate distributions in all organoids. See Fig. 7a for statistical comparisons.

Extended Data Fig. 2 Reproducible firing patterns in human brain organoids.

a, Raster plot visualization of single-unit spiking (blue dots) measured across the surface of a human brain organoid slice from ref. 27 (HO5), positioned on top of the same Maxwell Biosystems microelectrode array as used for the organoid recordings included in the main Figs. 1–5. The population firing rate is shown by the red solid line. Population bursts are marked by sharp increases in the population rate. Burst peak events are denoted by local maxima (black dots) that exceed 4× r.m.s. fluctuations in the population rate. The shaded gray regions denote the burst duration window as defined by the time interval in which the population rate remains above 10% of its peak value in the burst. b, The instantaneous firing rate of single-unit activity from a after reordering. The backbone units are plotted above the dashed line, while non-rigid units are plotted below the dashed line. In each category, units are ordered based on their median firing rate peak time relative to the burst peak, considered over all bursts in the recording. c, The average burst peak-centered firing rate measured across all burst events for the example recording of which part is shown in a. The burst peak is indicated by the dotted line. The unit order is the same as b. Please note the progressive increase in the firing rate peak time relative to the burst peak, as well as a spread in the active duration for units having their peak activity later in the burst. The average firing rate is normalized per unit to aid in visual clarity. d, The distributions of the log of the average firing rate per unit, separated for backbone and non-rigid units. All units from the four recordings mentioned in ref. 22 are pooled together and all units from the four recordings mentioned in ref. 27 are pooled together (P < 10−20 and P = 0.40, respectively, two-sided linear mixed-effect model, n = 4 organoids each). e, The distributions of the average burst-to-burst correlations per unit after average rate normalization, separated for backbone and non-rigid units. All units from the four recordings mentioned in ref. 22 are pooled together and all units from the four recordings mentioned in ref. 27 are pooled together. Please note the significant difference between backbone and non-rigid units present for both sets of recordings (P = 1 × 10−10 and P = 0.0092, respectively, two-sided linear mixed-effect model, n = 4 organoids each). f, The distributions of the pairwise correlations per unit pair after average rate normalization, separated for backbone pairs, backbone and non-rigid combinations and non-rigid pairs. All unit pairs from the four recordings mentioned in ref. 22 are pooled together and all unit pairs from the four recordings mentioned in ref. 27 are pooled together. Please note that only the normalized correlation for backbone pairs are significantly larger than 0 for both sets of recordings (P = 0.0001, 0.145 and 1; P = 7 × 10−11, 1 and 1, respectively), one-sided linear mixed-effect model, n = 4 organoids each).

Extended Data Fig. 3 Burst clustering distinguishes non-rigid from backbone unit variability.

a, Pairwise firing rate correlations per burst (computed over a window ranging from −250 ms until 500 ms relative to the burst peak) projected onto the first two principal components, labeled by the identified clusters, show a clear separation between different burst clusters. The results, for example, recording Or5, are shown. b, The population rate for a snippet of the recording for Or5 covering several bursts labeled by their cluster. c, Firing rates per unit for 8 different example bursts per cluster. d, The average firing rate per unit for the different detected burst clusters. e, A selection of non-rigid units is most variable in their activity between the different burst clusters as reflected by a higher CV score for their firing rate in the different burst clusters (CV scores are computed per row for the 4 columns shown in d). f, The CV scores for the firing rate over the different burst clusters are significantly higher for non-rigid units compared to backbone units (P ≤ 10−20 for difference between backbone and non-rigid, two-sided linear mixed-effect model).

Extended Data Fig. 4 Pharmacological modulation of excitatory and inhibitory signaling impacts bursts and sequences.

a, Raster plot visualization of single-unit spiking (blue dots) measured across the surface of a murine organoid (MO10), positioned on top of the same type of microelectrode array as used for the recordings included in the main figures. The population firing rate is shown by the red solid line. Population bursts are marked by sharp increases in the population rate. The shaded gray regions denote the burst duration window as defined by the time interval in which the population rate remains above 10% of its peak value in the burst. Top: baseline recording. Middle: recording of the same organoid using the same electrode configuration, after treatment with 10 μM gabazine to inhibit inhibitory signaling by blocking GABAA receptors. Bottom: recording of the same organoid slice using the same electrode configuration, after blocking AMPA and NMDA receptors with bath application of NBQX (10 µM) and R-CPP (20 µM) to inhibit components of excitatory synaptic transmission. b, Number of detected population bursts for 5 different murine organoids (MO10-14) under baseline conditions, after treatment with gabazine and after treatment with NBQX and R-CPP. Please note that bursting disappears after NBQX and R-CPP treatment reflected as a significant decrease in bursting compared to baseline conditions (P < 0.001, two-sided linear mixed-effect model). Meanwhile, the number of bursts increase after gabazine treatment (P < 0.05, two-sided linear mixed-effect model). c, Fraction of bursts in which a unit fires at least 2 spikes for the 5 different murine organoids under baseline conditions compared to the gabazine treatment. The fraction of bursts in which units are active increases significantly after gabazine treatment (P < 10−7, two-sided linear mixed-effect model). d, Normalized Spearman rank-order correlations comparing the sequential order of backbone sequences for all burst pairs of 5 different murine organoids (MO10-14) under baseline conditions and after treatment with gabazine. Correlation scores are z-scored relative to shuffled spike matrices such that values above 0 indicate a more consistent backbone sequence than the shuffled data. There is a significant increase in backbone sequence order similarity after treatment with gabazine (P < 10−20, two-sided linear mixed-effect model).

Extended Data Fig. 5 Sequential activations and burst-to-burst similarity are not present after shuffling.

a, Same raster plot visualization as Fig. 1a after shuffling. The population firing rate remains the same after shuffling and is shown by the red solid line. Population bursts exist in the same frames after shuffling and are denoted by local maxima (black dots) that exceed 4× r.m.s. fluctuations in the population rate. The burst duration windows remain the same after shuffling and are marked by the shaded gray regions, which denote the interval in which the population rate remains above 10% of its peak value in the burst. The average firing rate per unit remains the same after shuffling. b, Same instantaneous firing rate visualization as Fig. 2b. The same ordering is used as in Fig. 2b. c, Same average burst peak-centered firing rate visualization as Fig. 2c after shuffling. The burst peak is indicated by the dotted line. The unit order is the same as Fig. 2c. Please note that the progressive increase in the firing rate peak time relative to the burst peak, as well as a spread in the active duration for units having their peak activity later in the burst are not present anymore after shuffling. The average firing rate is normalized per unit to aid in visual clarity. d, Same burst-peak-centered spike times and pairwise burst-to-burst correlations as in Fig. 2d after shuffling. For (i) and (ii), the consistent firing patterns relative to the burst peak as exemplified in Fig. 2d are not present anymore and the average burst-to-burst correlation scores have decreased from 0.96 to 0.69 and from 0.82 to 0.53, respectively. Meanwhile, the average burst-to-burst correlation for the non-rigid unit exemplified in (iii) decreased from 0.51 to 0.49.

Extended Data Fig. 6 Intrinsic activity in murine primary cultures resembles organoids after shuffling.

a, Raster plot visualization of single-unit spiking (blue dots) measured across a 2D murine primary culture from ref. 28 (Pr1) recorded on a high-density microelectrode array. The population firing rate is shown by the red solid line. Population bursts are marked by sharp increases in the population rate. Burst peak events are denoted by local maxima (black dots) that exceed 4× r.m.s. fluctuations in the population rate. The shaded gray regions denote the burst duration window as defined by the time interval in which the population rate remains above 10% of its peak value in the burst. b, The instantaneous firing rate of single-unit activity from a after reordering. The backbone units are plotted above the dashed line, while non-rigid units are plotted below the dashed line. In each category, units are ordered based on their median firing rate peak time relative to the burst peak, considered over all bursts in the recording. c, The average burst peak-centered firing rate measured across all burst events for the example recording of which part is shown in a. The burst peak is indicated by the dotted line. The unit order is the same as b. Please note that the progressive increase in the firing rate peak time relative to the burst peak, as well as a spread in the active duration for units having their peak activity later in the burst are not present in the murine primary recording, similar to the organoid data after shuffling as shown in Extended Data Fig. 5. The average firing rate is normalized per unit to aid in visual clarity.

Extended Data Fig. 7 Consistent results in brain slices from different animals.

a, Raster plot visualization of single-unit spiking (blue dots) measured across the surface of a murine neonatal cortical slice from a different animal (M3S1) dissected at P13, positioned on top of the same type of microelectrode array as used for the recordings included in main Fig. 6. The population firing rate is shown by the red solid line. Population bursts are marked by sharp increases in the population rate. Burst peak events are denoted by local maxima (black dots) that exceed 4× r.m.s. fluctuations in the population rate. The shaded gray regions denote the burst duration window as defined by the time interval in which the population rate remains above 10% of its peak value in the burst. b, The instantaneous firing rate of single-unit activity from a after reordering. The backbone units are plotted above the dashed line, while non-rigid units are plotted below the dashed line. In each category, units are ordered based on their median firing rate peak time relative to the burst peak, considered over all bursts in the recording. c, The average burst peak-centered firing rate measured across all burst events for the example recording of which part is shown in a. The burst peak is indicated by the dotted line. The unit order is the same as b. Please note the progressive increase in the firing rate peak time relative to the burst peak, as well as a spread in the active duration for units having their peak activity later in the burst. The average firing rate is normalized per unit to aid in visual clarity.

Extended Data Fig. 8 Backbone sequences observed during spontaneous population bursts in murine cortical organoids.

a, Raster plot visualization of single-unit spiking (blue dots) measured across the surface of a murine organoid (MO1) recorded at 42 DIV, positioned on top of the same type of microelectrode array as used for the recordings included in the main figures. The population firing rate is shown by the red solid line. Population bursts are marked by sharp increases in the population rate. Burst peak events are denoted by local maxima (black dots) that exceed 4x-r.m.s. fluctuations in the population rate. The shaded gray regions denote the burst duration window as defined by the time interval in which the population rate remains above 10% of its peak value in the burst. b, The instantaneous firing rate of single-unit activity from panel a after reordering. The backbone units are plotted above the dashed line, while non-rigid units are plotted below the dashed line. In each category, units are ordered based on their median firing rate peak time relative to the burst peak, considered over all bursts in the recording. c, The average burst peak-centered firing rate measured across all burst events for the example recording of which part is shown in a. The burst peak is indicated by the dotted line. The unit order is the same as b. Please note the progressive increase in the firing rate peak time relative to the burst peak, as well as a spread in the active duration for units having their peak activity later in the burst. The average firing rate is normalized per unit to aid in visual clarity.

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van der Molen, T., Spaeth, A., Chini, M. et al. Preconfigured neuronal firing sequences in human brain organoids. Nat Neurosci (2025). https://doi.org/10.1038/s41593-025-02111-0

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