科技科学

Stimulation modulates gene-linked cell assemblies in the human brain

Nature作者：Haley Moore2026年8月5日正文已收录本站

Data availability

All raw MEA snRNA-seq, snATAC-seq and IVS snRNA-seq data were deposited to the Gene Expression Omnibus (GEO) archive under the accession number GSE288939. The data presented in this Article are available through interactive Shiny applications that enable users to explore the datasets in detail, including gene expression profiles, dimensionality reduction and clustering results, cell-type annotations, marker gene expression and additional features. Users can also generate custom visualizations directly from the datasets. These applications include data from MEA excitatory, inhibitory and non-neuronal cells: https://biocm-moore-etal-mea-multiome-subglia.share.connect.posit.cloud/https://biocm-moore-etal-mea-multiome-inhneurons.share.connect.posit.cloud/ and https://biocm-moore-etal-mea-multiome-excneurons.share.connect.posit.cloud/. These applications include data from IVS excitatory inhibitory, and non-neuronal cells: https://biocm-moore-etal-ivs-snrna.share.connect.posit.cloud/, https://biocm-moore-etal-ivs-excneurons.share.connect.posit.cloud/, https://biocm-moore-etal-ivs-inhneurons.share.connect.posit.cloud and https://biocm-moore-etal-ivs-subglia.share.connect.posit.cloud.

Code availability

Figures and supplementary tables were generated using custom scripts in R (v4.2.1) and Python (v3.8.20, v3.11.8, v3.11.10 and v3.12.4). The code used for data analysis in this study is available on GitHub: https://github.com/BioinformaticsMUSC/Moore_etal_MEA/.

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Acknowledgements

Essential conceptual feedback was provided by M. Chahrour, B. Evers, R. W. Greene and S. Choi. Code and feedback on neuronal assembly analysis were provided by G. Umbach. SCENIC+ analyses and visualization were informed with feedback from A. Gogate.

Funding

H.M., M.D., A.F., A.K., B.C.L. and G.K. were supported by the O’Donnell Brain Institute at UTSW. S.S., B.G. and S.B. were supported by the CNDD Genomics and Bioinformatics Core at MUSC and by the Biorepository and Tissue Analysis Shared Resource at the Hollings Cancer Center at MUSC. Additional funding was provided by the National Institutes of Health: NS132443 (H.M.), NS126143 (B.C.L. and G.K.), GM148302 (S.S., B.G. and S.B.) and CA138313 (S.S., B.G. and S.B.).

Author information

Authors and Affiliations

  1. Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Haley Moore, Mantre Dehnad, Ashwinikumar Kulkarni & Genevieve Konopka

  2. Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Haley Moore & Bradley C. Lega

  3. O’Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Haley Moore, Mantre Dehnad, Anne Freelin, Ashwinikumar Kulkarni, Bradley C. Lega & Genevieve Konopka

  4. Department of Neurobiology, University of California Los Angeles, Los Angeles, CA, USA

    Anne Freelin & Genevieve Konopka

  5. Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA

    Bryan Granger, Suganya Subramanian & Stefano Berto

  6. Bioinformatics Core, Medical University of South Carolina, Charleston, SC, USA

    Bryan Granger, Suganya Subramanian & Stefano Berto

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

    Tjitse van der Molen

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

    Tjitse van der Molen

Authors

  1. Haley Moore
  2. Mantre Dehnad
  3. Anne Freelin
  4. Bryan Granger
  5. Suganya Subramanian
  6. Tjitse van der Molen
  7. Ashwinikumar Kulkarni
  8. Stefano Berto
  9. Bradley C. Lega
  10. Genevieve Konopka

Contributions

Conceptualization: H.M., B.C.L. and G.K. Methodology: H.M., S.B., B.C.L. and G.K. Investigation: H.M., M.D. and A.F. Formal analysis: H.M., B.G., S.S., T.v.d.M., A.K. and S.B. Resources: A.K., S.B., B.C.L. and G.K. Software: H.M., B.G., S.S., A.K. and S.B. Visualization: H.M., B.G., S.S., T.v.d.M. and S.B. Supervision: S.B., B.C.L. and G.K. Writing—original draft: H.M. Writing—review and editing: H.M., M.D., A.F., S.B., B.C.L. and G.K.

Corresponding authors

Correspondence to Bradley C. Lega or Genevieve Konopka.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Raag Airan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Unique electrophysiological responses across putative cell types.

a, The 5-burst stimulation pulse cycle is repeated for 15 min, with different stimulation electrode configurations for each cycle. b, Mean 90th percentile spike amplitude at all electrodes and percent total active electrodes are unaffected by stimulation. c, Units identified per DIV. d, Units identified per subject. e, Selection of WaveMAP parameters. Each data point is the average modularity or number of clusters identified in 25 iterations using different samples of 80% of the 494 total units. f, Proportion of WaveMAP clusters across upper and lower cortical layers according to x-coordinate of highest spike amplitude. g, WaveMAP clusters exhibit unique features. h, Increase in change in cofiring on the order of 3-5 ms when comparing pre-stimulation to post-stimulation versus the first half to the second half of the pre-stimulation scan (two-sided paired Wilcoxon signed-rank test. W = 1753298, p = 1.23 × 10−137, n = 3660 pairs). i, Pyramidal-pyramidal dyads show the most prominent increase in cofiring changes (One-Way ANOVA for effect of unit pair subpopulation on induced correlation difference: F = 10.05, p = 4.4 × 10−5. Tukey post-hoc test: pyr-pyr change larger than pyr-int change p = 3.1 × 10−5, pyr-pyr change larger than int-int change p = 2.7 × 10−3, pyr-int change versus int-int change p = 0.70. n = 852, 1753 and 1055 pyr-pyr, pyr-int and int-int pairs). j, No difference in co-firing changes between different layers (One-Way ANOVA for effect of unit pair spatial location on induced correlation difference: F = 1.04, p = 0.35. Tukey post-hoc test: upper-upper change versus upper-lower change p = 0.32, upper-upper change versus lower-lower change p = 0.53, upper-lower change versus lower-lower change p = 0.83. n = 463, 1467 and 1730 upper-upper, upper-lower and lower-lower pairs with distinct nonzero synaptic delay times respectively). k, Overall increase in cofiring in pyr-pyr pairs after stimulation (two-sided paired Wilcoxon signed-rank test n = 852 pyr-pyr pairs with distinct nonzero synaptic delay times, W = 151482, p = 4.15 × 10−17).

Extended Data Fig. 2 Identification and validation of cell assemblies in human temporal cortex slices.

a, Raster plots and normalized z-scored spike rate matrix for assembly example in Fig. 2. Highlighted regions correspond with plots outlined in the same color. The width of each bin is 25 ms. b, Number of units identified per subject and DIV. c, The number of observed assemblies exceeds the number of assemblies identified by chance in all 1000 shuffle trials (p < 0.0001, permutation test). d, Assembly members are equally distributed between WaveMAP clusters (Fisher’s exact tests all p > 0.05). e, Assembly members are overrepresented in the MEA location roughly corresponding to cortical layer 4 (Fisher’s exact test, odds ratio = 1.93, p = 0.026). f, Permutation tests for assembly plasticity shuffle controls. The total number of observed positive, negative, and all significant correlations were all significantly greater than chance. Null distributions represent the significant correlations identified across 19 assemblies per shuffle iteration. g, Permutation tests for neuron drift shuffle controls with the total drift fraction across 19 assemblies per shuffle iteration. Red vertical lines in (C), (F), and (G) indicate observed value.

Extended Data Fig. 3 Quality control metrics and differential accessibility for MEA stimulation multiome experiments.

a, UMAP clustering and annotation of snRNA-seq only, snATAC-seq only, and combined snRNA-seq plus snATAC-seq data. b, Normalized expression of marker genes for each cell class. c, Normalized chromatin accessibility of marker genes for each cell class. d, Single nucleus quality control metrics split by cell class. e, Number of differentially accessible regions (DARs) in MEA stimulated versus control tissue. f, Proportion of features found in MEA DARs.

Extended Data Fig. 4 Impact of MEA-based stimulation on interneuron and non-neuron gene regulation.

a, UMAP of subclustered interneurons. b, Number of differentially expressed genes per interneuron subcluster. c, No enrichment of differentially expressed activity regulated genes. d, Stimulation differentially activates few regulons in interneurons. e, UMAP of subclustered non-neurons. f, Number of differentially expressed genes per non-neuron subcluster. g, Augur cell type prioritization. h, Number of chromatin-to-gene linkages increases after stimulation in Micro/PVM_1a cells determined by scMultiMap. i, Stimulation differentially activates many regulons in non-neurons.

Extended Data Fig. 5 Overlap between excitatory and inhibitory neuron transcriptional responses to MEA stimulation.

a, Upregulated genomic repertoire in L2/3_IT_1a excitatory neurons is similar to differentially expressed genes in VIP_13, PVALB_3, and LAMP5_1 inhibitory neurons in the MEA stimulation paradigm. b, L3_IT_1, L4_IT_1, and L5_IT_1 exhibit significant convergence across up and downregulated genes compared to SST_24, SST_11, and PVALB_5 interneurons in the MEA stimulation paradigm.

Extended Data Fig. 6 hdWGCNA reveals excitatory cell type and layer specific links between firing rate and gene expression networks.

a, Network dendrogram. b, Significance of each module correlated with mean firing rate fold change in pyramidal neurons. c, Correlation of each module with cell type and mean firing rate of upper- and lower-layer pyramidal neurons (MFRU and MFRL, respectively). d, Feature plot of gene expression in black and green modules in the original multiomic UMAP space. e, Visualization of the top hub genes in the EM1 and EM5 modules. Node color indicates log2(fold change) of gene expression in stimulated versus control cells. f, Degree of EGR1 and NFIA binding enrichment in each module. EM = excitatory module.

Extended Data Fig. 7 hdWGCNA reveals inhibitory cell type and layer specific links between firing rate and gene expression networks.

a, Network dendrogram. b, Significance of each module correlated with mean firing rate fold change in interneurons. c, Correlation of each module with cell type and mean firing rate of upper- and lower-layer interneurons (MFRU and MFRL, respectively). d, Feature plot of gene expression in the IM5 module in the original multiomic UMAP space. e, Degree of FOS binding enrichment in each module. f, Visualization of the top hub genes in the IM5 module. Node color indicates log2(fold change) of gene expression in stimulated versus control cells. IM = inhibitory module.

Extended Data Fig. 8 Quality control metrics for IVS snRNA-seq and hypergeometric overlap analysis of IVS and MEA stimulation on neuronal transcriptomes.

a, UMAP of cell class clusters. b, Normalized marker gene expression for each cell class. c, Single nucleus quality control metrics split by cell class. d, The transcriptomic effect of IVS and MEA stimulation is strikingly similar in excitatory neurons and e, also similar in inhibitory neurons.

Extended Data Fig. 9 In vivo stimulation engages SST interneuron subtypes similarly to MEA-based stimulation.

a, UMAP of subclustered interneurons in the IVS paradigm. b, Normalized expression of key marker genes for each interneuron subcluster. c, Number of differentially expressed genes per interneuron subcluster. d, Upregulation of rPRGs EGR1 and FOS in SST+ interneurons. e, Enrichment of synaptic gene ontology terms downregulated DEGs only (blues), and upregulated DEGs only (reds) by cell type. Fisher’s exact test with FDR correction was used in accordance with the SynGO documentation (syngoportal.org). f, RRHO2 comparison of SST subtypes from MEA and IVS paradigms shows significant convergence in differential gene regulation.

Extended Data Fig. 10 Impact of in vivo stimulation on non-neuronal cell types.

a, UMAP of subclustered non-neurons in the IVS paradigm. b, Normalized expression of key marker genes for each non-neuron subcluster. c, Number of differentially expressed genes per non-neuron subcluster. d, Enrichment of differentially expressed activity regulated genes in several non-neuron subclusters. e, Upregulation of rPRGs in astrocytes. f, Convergence and divergence of transcriptional responses in different non-neuronal cell types in MEA and IVS paradigms.

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Moore, H., Dehnad, M., Freelin, A. et al. Stimulation modulates gene-linked cell assemblies in the human brain. Nature (2026). https://doi.org/10.1038/s41586-026-10879-9

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