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Increased neural excitability and glioma synaptic activity drives glioma proliferation in human cortex

Nature Neuroscience (2025)Cite this article

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Abstract

Adult gliomas are incurable primary brain cancers that infiltrate healthy brain and incorporate into neural networks. Gliomas can be classified as low grade or high grade based on histopathological and molecular features, which broadly predicts their aggressiveness. Here we performed patch-clamp electrophysiological recordings from pyramidal neurons and glioma cells from individuals with either low- or high-grade glioma. We find that the biophysical properties of human pyramidal neurons within glioma-infiltrated cortex differ according to tumor grade, with neurons from high-grade glioma being more excitable than those from low-grade glioma. Additionally, glioma cells within high-grade tumors have smaller, longer synaptic responses. Increased neuron–glioma network activity within human high-grade tumor tissue leads to increased glioma proliferation, suggesting that the hyperexcitability of pyramidal neurons in human high-grade glioma may drive tumor growth. Combined, our findings illustrate that high- and low-grade glioma differentially hijack neural networks.

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Fig. 1: Biophysical properties of human pyramidal neurons in high- versus low-grade glioma.
Fig. 2: Intrinsic properties of human glioma cells recorded in tissue from tumor-bearing brain.
Fig. 3: Synaptic activity differs between high- and low-grade glioma.
Fig. 4: Increasing neural activity leads to an increase in glioma synaptic activity and proliferation.

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Data availability

Access to the data is restricted because of the use of human participants, and datasets generated during and/or analyzed in the current study are available from the corresponding authors on request.

Code availability

Classifier code is available via GitHub at https://github.com/lucympalmer/GlioPathoPNClassifers.git.

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Acknowledgements

We thank G. McDougall for assistance with the glioma cell morphological analysis, and C. Chavez, A. Soch and the Florey Microscopy Facility for assistance with image processing. This work was supported by the NHMRC (grant no. APP2003114, K.J.D. and L.M.P.; grant no. APP2026307, L.M.P.), the Sylvia and Charles Viertel Charitable Foundation (L.M.P.), the Brain Cancer Centre (H.M., K.J.D., L.M.P.), the Warren Haynes Fellowship and the Royal Melbourne Hospital Neuroscience Foundation (H.M.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. The Florey Institute of Neuroscience and Mental Health, Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia

    Heidi McAlpine, Marius Rosier, Jordan Rozario, Xiaoyu Wang, Robertas Guzulaitis, Hefei Guan, Yi Hu & Lucy Maree Palmer

  2. Department of Neurosurgery, Royal Melbourne Hospital, Melbourne, Victoria, Australia

    Heidi McAlpine, Jordan Rozario & Katharine Jann Drummond

  3. Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia

    Verena C. Wimmer

  4. Life Sciences Center, Vilnius University, Vilnius, Lithuania

    Robertas Guzulaitis

  5. Department of Biostatistics, University of Melbourne, Melbourne, Victoria, Australia

    Leonid Chirlov

  6. Department of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia

    Christian Davey & Sue Finch

  7. Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia

    Katharine Jann Drummond

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Contributions

H.M., K.J.D. and L.M.P. conceptualized and designed the study; H.M. along with M.R., X.W., R.G. and H.G. performed the electrophysiological recordings; J.R., H.M., M.R. and H.G. analyzed all recordings; X.W. and H.G performed the neural reconstructions; Y.H. performed the computational modeling; L.C., C.D. and S.F. provided statistical assistance. V.C.W. assisted with imaging. H.M., K.J.D. and L.M.P. interpreted the results and wrote the paper.

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Correspondence to Heidi McAlpine, Katharine Jann Drummond or Lucy Maree Palmer.

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Nature Neuroscience thanks Jayeeta Basu, Shawn Hervey-Jumper and Frank Winkler for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Intrinsic properties of pyramidal neurons in high- and low-grade glioma.

a, Principal component analysis (PCA) was used for dimension reduction to visualize the collective differences in the biophysical properties of pyramidal neurons from different glioma grades. b-k, Comparison between the properties of pyramidal neurons recorded in high- (blue; n = 104 neurons) and low-grade (green; n = 50 neurons) glioma for (b) Action potential threshold (p = 0.001), (c) Membrane resistance (p = 0.008), (d) Inter-spike interval (p = 0.037), (e) Resting Membrane Potential (RMP; p = 0.602), (f) Total number of action potentials (p = 0.146), (g) Voltage sag (p = 0.217), (h) Action potential rise-time (p = 0.329), (i) Adaptation ratio (p = 0.656), (j) Action potential half-width (p = 0.187) and (k) Action potential delay (p = 0.126). Mean values are represented as bold lines. All data were analyzed using a linear mixed-effect model. ns, p > 0.05; *, p < 0.05; **, p < 0.001.

Extended Data Fig. 2 Biophysical properties of human pyramidal neurons in high- vs low-grade glioma from individual patients.

Electrophysiological recordings were averaged and assessed according to individual patients diagnosed with high-grade (n = 32) and low-grade (n = 16) glioma. Trends in Rheobase, Maximum Action Potential (AP) Rate of Rise, AP amplitude and Resting Membrane Potential (RMP) were similar to the analysis performed in individual recordings. a, AP Firing Rate of pyramidal neurons from high- and low-grade glioma in response to somatic current step injections (20 pA, 1200 ms; n = 32 (high), 16 (low) patients). b, Rheobase in pyramidal neurons from high- and low-grade glioma (n = 32 (high), 16 (low) patients; p = 0.015, Mann-Whitney test). c, Maximum AP Rate of Rise (dV/dT) in pyramidal neurons recorded from high- and low-grade glioma (n = 32 (high), 16 (low) patients; p = 0.02, Mann-Whitney test). d, AP phase plot of membrane potential relative to threshold in high- and low-grade glioma (n = 32 (high), 16 (low) patients). e, Peak AP Amplitude in pyramidal neurons in high- and low-grade glioma (n = 32 (high), 16 (low) patients; p = 0.017, Mann-Whitney test). f, RMP in pyramidal neurons from high- and low-grade glioma (n = 32 (high), 16 (low) patients; p = 0.474, Mann-Whitney test). Data are presented as mean values ± SEM. ns, p > 0.05; *, p < 0.05.

Extended Data Fig. 3 Intrinsic properties of pyramidal neurons classified according to IDH status.

Biophysical properties of neurons recorded from tissue clinically determined as IDH wildtype (WT) or IDH-mutant (mutant). (a-f) Comparison between IDH-WT (light blue; n = 69 cells) and IDH-mutant (light green; n = 85 cells) for action potential (AP) (a) Amplitude (p = 0.0003), (b) Threshold (p = 0.005), (c) Max rate of rise (p = 0.01), (d) Rheobase (p = 0.178), (e) Half-Width (p = 0.328), (f) Rise-time (p = 0.924), (g) Adaptation ratio (p = 0.633), (h) Total number of APs (up to 510 pA), (i) Resting membrane potential (RMP, p = 0.458). Mean values are represented as bold lines. All data were analyzed using a linear mixed-effect model. ns, p > 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001.

Extended Data Fig. 4 Classifier models and their performance in classifying neurons as recorded from high- or low-grade glioma.

a, Two-dimensional projection of training data using DFAC illustrating the separation of high- (blue) and low-grade (green) recordings, in the optimised discriminant (dimension-reduced) space. Performance for one classification test, out of 100 tests with random training data is shown. Large data points represent the centroids for high- (blue) and low-grade (green). b, DFACf projected discriminant scores for both training and test data for the sample shown in (a) illustrating the separation of training and test sets. c, The architecture of the LRC presents that multiple input features (or trace values) are first combined linearly by assigned weights and an intercept (bias), forming a weighted sum, and then this output is transformed by the sigmoid (logistic) activation function, mapping the value to a probability between 0 and 1. A binary prediction can ultimately be made to the model’s confidence on distinguishing the classes. d, The architecture of MLPC depicts an input layer that receives the raw data, which then feeds into a single hidden layer comprising 100 neurons. Each neuron in this hidden layer applies a non-linear activation function (ReLU) to its weighted sum of inputs, whose outputs are then passed to an output layer, producing the final class prediction. e, The classification performance in training strategies (n = 100 tests), including those fed with extracted features (DFACf and LRCf) and raw data (LRCt and MLPCt) (One-way ANOVA). Data are presented as mean values ± SEM. f, The performance of DFAC against the number of neurons used for training. Linear regression, R2 = 0.8847. **, p < 0.01; ****, p < 0.0001.

Extended Data Fig. 5 Morphological reconstruction of high- and low-grade neurons.

Pyramidal neurons (average somatic depth below pia, 381 ± 21 μm) were filled with the fluorescence marker biocytin-TMR via the patch pipette. Neuron morphology was imaged using confocal microscopy and reconstructed before Sholl analysis was performed. (top) Neuron morphology and (bottom) Number of intersections versus Sholl radius for pyramidal neurons within (a) high-grade and (b) low-grade tissue. c, Comparison between dendritic length and distance from soma for neurons in high-grade (blue; n = 8 cells) ad low-grade (green; n = 6 cells) glioma (p = 0.498, Komogorov-Smirnov test). d, Differences in total length (top; p = 0.852, Mann Whitney test) and intersections (bottom; p = 0.883, Mann Whitney test) for neurons in high-grade (blue; n = 8 cells) and low-grade (green; n = 6 cells) glioma. Data are presented as mean values ± SEM. ns, p > 0.05.

Extended Data Fig. 6 Glioma cell identification using morphological and electrophysiological properties, immunostaining and sample targeting.

a, Glioma cells were filled with the fluorescent marker biocytin-TMR via the patch pipette and immunostained for the neuro-oncology diagnostic marker, Glial Fibrillary Acidic Protein (GFAP). Confocal image of an example glioma cell filled with biocytin-TMR (left), GFAP (middle) and overlay of TMR and GFAP (right). b, Voltage response to somatic current injection in the glioma cell shown in (a). Inset, zoom of boxed region illustrating spikelet (arrow). c, Voltage clamp recording from the glioma cell shown in (a) and (b) highlighting a spontaneous synaptic current in boxed region. Inset, zoom of boxed region. d, Example histopathology of glioma tissue used for patch clamp recordings which was stained with hematoxylin and eosin (H&E). H&E staining was repeated on three independent samples. e, Voltage response to somatic current injection in an example glioma cell from within the tissue sample shown in (d). f, Differential Interference Contrast (DIC) image of a human brain slice highlighting a putative glioma cell (yellow), glia (salmon) and neurons (white).

Extended Data Fig. 7 Glioma cell morphology.

a, Sholl analysis was performed on glioma cells filled with biocytin-TMR. (left) Reconstruction of glioma cell and (right) Number of intersections at distances from the soma (radius of Sholl). The tumor type, grade and sample type (tumor or cortex) are provided for each glioma cell. b, Overlay of Sholl analysis for all reconstructed glioma cells.

Extended Data Fig. 8 Identification of glioma cells.

a, Soma diameter (maximum width) of glioma cells (red; n = 13 cells) and layer 2/3 pyramidal neurons (black; n = 14 cells). p < 0.0001, Mann Whitney test. b, Maximum length of somatic projection in glioma cells (red; n = 13 cells) and glia (black; n = 5 cells). p = 0.025, Mann Whitney test. c, Example voltage response to 330pA somatic current injection in glioma cell (top), glia (middle), layer 2/3 pyramidal neuron (bottom). d, Examples of biocytin-TMR filled glioma. d, (top) Three examples of network connected glioma cells. A single glioma cell was patched and filled with biocytin-TMR (yellow asterisks) which resulted in non-patched cells up to 100 µm away to become fluorescent (pink asterisks), presumably by diffusion of the fluorescent intracellular solution though a tumour microtube connected network. (bottom) Four examples of glioma cells where the TMR was confined to a single cell. Data are presented as mean values ± SEM.

Extended Data Fig. 9 Neural activity is associated with increased glioma proliferation in high-grade glioma.

Confocal images of brain slices from high-grade glioma patients stained with DAPI (blue, for total cell numbers) and Ki67 (orange, to indicate cells replicating). a, Brain slices from patient 2 diagnosed with glioblastoma were incubated for 6 hours in (top) 4AP, (middle) aCSF only, or (bottom) 4AP and tetrodotoxin (4AP + TTX). Ki67 and DAPI staining was performed in three slices. b, The percentage of Ki67 positive cells in slices shown in (a). c, Brain slices from patient 3 (glioblastoma) were incubated for 6 hours in (top) 4AP or (bottom) aCSF. Ki67 and DAPI staining was performed in two slices. d, The percentage of Ki67 positive cells in slices shown in (c). e, Brain slices from patient diagnosed with astrocytoma WHO grade 4 were incubated for 6 hours in (top) 4AP or (bottom) aCSF. Ki67 and DAPI staining was performed in two slices. f, The percentage of Ki67 positive cells in slices shown in (e). Scale bars, 50 µm.

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McAlpine, H., Rosier, M., Rozario, J. et al. Increased neural excitability and glioma synaptic activity drives glioma proliferation in human cortex. Nat Neurosci (2025). https://doi.org/10.1038/s41593-025-02149-0

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  • DOI: https://doi.org/10.1038/s41593-025-02149-0

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