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A human brain map of mitochondrial respiratory capacity and diversity

Nature volume 641, pages 749–758 (2025)Cite this article

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An Author Correction to this article was published on 22 May 2025

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Abstract

Mitochondrial oxidative phosphorylation (OXPHOS) powers brain activity1,2, and mitochondrial defects are linked to neurodegenerative and neuropsychiatric disorders3,4. To understand the basis of brain activity and behaviour, there is a need to define the molecular energetic landscape of the brain5,6,7,8,9,10. Here, to bridge the scale gap between cognitive neuroscience and cell biology, we developed a physical voxelization approach to partition a frozen human coronal hemisphere section into 703 voxels comparable to neuroimaging resolution (3 × 3 × 3 mm). In each cortical and subcortical brain voxel, we profiled mitochondrial phenotypes, including OXPHOS enzyme activities, mitochondrial DNA and volume density, and mitochondria-specific respiratory capacity. We show that the human brain contains diverse mitochondrial phenotypes driven by both topology and cell types. Compared with white matter, grey matter contains >50% more mitochondria. Moreover, the mitochondria in grey matter are biochemically optimized for energy transformation, particularly among recently evolved cortical brain regions. Scaling these data to the whole brain, we created a backwards linear regression model that integrates several neuroimaging modalities11 to generate a brain-wide map of mitochondrial distribution and specialization. This model predicted mitochondrial characteristics in an independent brain region of the same donor brain. This approach and the resulting MitoBrainMap of mitochondrial phenotypes provide a foundation for exploring the molecular energetic landscape that enables normal brain function. This resource also relates to neuroimaging data and defines the subcellular basis for regionalized brain processes relevant to neuropsychiatric and neurodegenerative disorders. All data are available at http://humanmitobrainmap.bcblab.com.

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Fig. 1: Human brain voxelization and mapping.
Fig. 2: Mitochondrial density and activity of respiratory complexes.
Fig. 3: snRNA-seq analysis of selected voxels.
Fig. 4: Projected mitochondrial activities of the entire brain.

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Data availability

All data supporting the findings, including the individual mitochondrial features data, correlations coefficients with related PET brain maps and the markdown of the snRNA-seq analysis, are provided as Supplementary Information and are available at https://neurovault.org/collections/16418/ and are centralized at http://humanmitobrainmap.bcblab.com.

Code availability

The computer codes used in this study are provided as Supplementary Information (R Markdown for snRNA-seq data in Supplementary File 1) and are available at https://neurovault.org/collections/16418.

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Acknowledgements

This project was supported by NIH grant RF1AG076821 and the Baszucki Brain Research Fund to M.P., a pilot award from the Columbia University Department of Psychiatry to M.P., E.V.M. and M. Boldrini, and the JPB Foundation to E.V.M. and D.S. M.T.d.S. is supported by HORIZON-INFRA-2022 SERV (grant number 101147319) ‘EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health’, by the European Union’s Horizon 2020 research and innovation programme under the European Research Council (ERC) Consolidator grant agreement number 818521 (DISCONNECTOME), the University of Bordeaux’s IdEx ‘Investments for the Future’ program RRI ‘IMPACT’, and the IHU ‘Precision & Global Vascular Brain Health Institute–VBHI’ funded by the France 2030 initiative (ANR-23-IAHU-0001). P.L.D.J. acknowledges support from the RF1AG057473 grant.

Author information

Author notes

  1. These authors contributed equally: Ayelet M. Rosenberg, Anna S. Monzel

Authors and Affiliations

  1. Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA

    Eugene V. Mosharov, Ayelet M. Rosenberg, Anna S. Monzel, Snehal Bindra, Alex Junker, David Sulzer & Martin Picard

  2. New York State Psychiatric Institute, New York, NY, USA

    Eugene V. Mosharov, Gorazd B. Rosoklija, Andrew J. Dwork, Madeline B. Mariani, Mihran Bakalian, David Sulzer, J. John Mann, Mark D. Underwood, Maura Boldrini & Martin Picard

  3. Department of Medicine, Endocrinology, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Corey A. Osto, Linsey Stiles & Orian S. Shirihai

  4. Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA

    Gorazd B. Rosoklija, Andrew J. Dwork, Madeline B. Mariani, Mihran Bakalian, J. John Mann, Mark D. Underwood & Maura Boldrini

  5. Department of Pathology and Cell Biology, Columbia University, New York, NY, USA

    Andrew J. Dwork

  6. Center for Translational and Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA

    Ya Zhang, Masashi Fujita, Philip L. De Jager & Vilas Menon

  7. Departments of Neurology and Pharmacology, Columbia University Irving Medical Center, New York, NY, USA

    David Sulzer

  8. Brain Connectivity and Behaviour Laboratory, Paris, France

    Michel Thiebaut de Schotten

  9. Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France

    Michel Thiebaut de Schotten

  10. Department of Neurology, H. Houston Merritt Center, Columbia Translational Neuroscience Initiative, Columbia University Irving Medical Center, New York, NY, USA

    Martin Picard

  11. Robert N. Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA

    Martin Picard

Authors
  1. Eugene V. Mosharov
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Contributions

E.V.M. and M.P. designed the study and supervised data collection, analyses and interpretation of results. G.B.R., A.J.D., M.B.M., M. Bakalian, J.J.M., M.D.U. and M. Boldrini (Quantitative Brain Biology, Brain QUANT Institute) collected and stored the donor brain, psychiatric autopsy and imaging of thin brain slices. E.V.M. oversaw the hardware and software for physical partitioning (voxelization) of the frozen human brain. A.M.R. and S.B. collected samples and performed biochemical and molecular assays with A.J. C.A.O., L.S. and O.S.S. performed respirometry assays. A.S.M., Y.Z., M.F., V.M. and P.L.D.J. performed snRNA-seq analyses. M.T.d.S. registered the data to MNI coordinates, developed the regression model with MRI parameters and created the brain maps. E.V.M., M.T.d.S., A.S.M. and A.M.R. prepared the figures. E.V.M., M.T.d.S. and M.P. drafted the manuscript. All authors reviewed the final version of the manuscript.

Corresponding authors

Correspondence to Michel Thiebaut de Schotten or Martin Picard.

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The authors declare no competing interests.

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Nature thanks Vilhelm Bohr, Marco Palombo, Russell Swerdlow and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Workflow of tissue collection.

a, The right hemisphere coronal slab was mounted on a metal plate with an OCT compound with the top (rostral) surface of the slab parallel to the plate. Created in BioRender. Mosharov, E. (2025) https://BioRender.com/z524941. b, After affixing the plate to the computer numerical control (CNC) cutting area, the top surface was cleaned with a 12.7 mm flat-tip drill bit rotating at 100 RPM and moving horizontally at 300 mm/sec. After cleaning 1 mm from the top, morphological brain structures were clearly visible and the surface was parallel to the plane of drill bit movement, ensuring that voxels will be of a uniform height. c, A 3 × 3 mm grid was milled with a 0.4 mm drill bit rotating at 10,000 RPM and moving horizontally at 250 mm/min. During a single pass, 0.2 mm of the depth was milled, requiring 15 passes to reach the desired 3 mm of cut depth and a total distance of ~70 m. Total milling time for a 130.8 ×61.25 mm hemisphere was 4 h and 38 min. d, Fully milled slab was placed on dry ice. First, four samples of shavings were collected from the surface above one white and three gray areas (Extended Data Fig. 2). Next, shavings were gently removed from the surface with a brush and a pre-chilled scalpel and forceps were used to collect 703 individual voxels. e, Following sample collection, slab surface was cleaned once more with a 12.7 mm drill bit. f, The metal plate with the slab was then mounted on a freezing microtome and several 50 µm-thick cryosections were collected for histological evaluation. g, Summary of the steps during the collection of brain voxels and thin sections. Letters on the right refer to images shown on the corresponding panels. h and i, Thin brain section stained with Nissl to show neurons and glia either alone (h) or in combination with an immunostaining against neuronal nuclear antigen NeuN to highlight neuron-enriched areas (i).

Extended Data Fig. 2 Analysis of tissue shavings and the workflow for mitochondrial colorimetric and respiratory assays.

a, Positions of collection sites of the four tissue shavings. Samples 1–3 were above the grey matter areas, while 4 was above a white matter area. b, Comparison of mitochondrial activities between the four ‘dust’ samples and corresponding tissue samples collected below after tissue voxelization. Values are mean ± SD. While all mitochondrial features (MitoD, TRC and MRC) were lower in all dust samples compared to tissue collected below, MitoD was less different between brain dust and tissue block. In sample 4, brain dust and voxels features appear to be very similar, but this possibly indicates that colorimetry and respirometry assays are at their detection limit when measuring mitochondria complex activities in the white matter. c, Brain voxel collection and preparation. d, Voxel plating and data collection for colorimetric mitochondrial assay and qPCR. e, Frozen tissue respirometry plate layouts derived from the assay plates on d to accommodate all samples and ran in duplicate. Samples were loaded into Seahorse plates at a constant volume rather than constant protein content. Parts c–e created in BioRender. Rosenberg, A. (2025) https://BioRender.com/k26p643.

Extended Data Fig. 3 Raw and transformed mitochondrial features measured by colorimetric and respirometry assays.

a, Image of the brain slab before voxelization. b, Distributions of non transformed values (i.e., values are in a linear scale) of citrate synthase (CS) activity, mitochondrial DNA (mtDNA) density and mitochondrial density (MitoD). c and d, Mapping of mitochondria complexes I, II and IV activities measured by colorimetry (c) and respirometry (d) assays. e and f, Tissue Respiratory Capacity (TRC) and Mitochondria Respiratory Capacity (MRC) calculated from raw (non-transformed) values of enzymatic activities. Same maps derived from power transformed values are shown on Fig. 2b–d. g-j, Distributions of power transformed values for CS activity and mtDNA density (g) mtDNA copy number (mtDNAcn) derived as a ratio of mitochondrial over nuclear DNA (mtDNA/nDNA; h), and CI, CII and CIV activities measured by colorimetry (i) and respirometry (j) assays. Because of the high variability of CI activities measured by colorimetry (c and i), these data were not used for TRC calculation. All values except mtDNAcn are normalized to each group’s mean. Bar graphs on the left of each panel show distributions of repeated measures of control Gray, White and Mixed matter samples on different assay plates (i.e., data points are technical replicates). *,**,*** P < 0.05, 0.01 and 0.001 by 1-way ANOVA with Tukey’s post-hoc.

Extended Data Fig. 4 Volumetric transformation of mitochondrial parameters normalizes their distributions.

a, Distributions of complexes I, II and IV activities, CS activity and mtDNA density. Complex activities are averages from colorimetry and respirometry assays. b, Volumetric transformations of the data on a. Mitochondria density metrics and OxPhos complexes activities were calculated as averages of cube root and square root values from colorimetric and respirometry assays. c, Same data as b with voxels assigned to Gray matter (G, n = 339) and White matter (W, n = 169) clusters based on their anatomical location. Voxels with mixed identity (n = 194) are not shown for simplicity. d, Coefficient of variation (CV), skewness and Kolmogorov-Smirnov (K-S) distance of distributions of raw and transformed mitochondrial values from a-c.

Extended Data Fig. 5 Correlations between mitochondrial parameters assayed using different techniques.

a, CS activity and mtDNA density, which are both related to mitochondria mass, are positively correlated. Because of the high variability in MitoTracker Deep Red measurements (MTDR, a far-red fluorescent probe used to chemically mark and visualize mitochondria in cells), they correlated poorly with CS and mtDNA, thus MTDR was not used for further analysis. b and c, Relationship between CII and CIV activities measured by colorimetric and respirometry assays. d, Correlations between CI, CII and CIV activities (an average between colorimetric and respirometry assays). a-d, Pearson’s r2 values show how well datapoints follow the linear regression; p values indicate if the slope of the regression line is significantly different from zero; shaded areas represent 90% confidence interval. e, Relationship between TRC, MRC and MitoD values (see Fig. 2a for definitions) in gray and white matter voxels. *** - the slopes of linear regressions are different with p < 0.001.

Extended Data Fig. 6 Morphing the partitioned brain slice into Montreal Neurological Institute (MNI) space.

a, Image of the partitioned brain slice with an overlaid milling grid. b, Conversion of the brain slice into Neuroimaging Informatics Technology Initiative (NIfTI) format and identification of 34 anatomical landmarks (see below). c, Manual creation of NIfTI grid corresponding to the milling grid with the same coordinates. d, Identification of the anatomical landmarks as on b in the magnetic resonance imaging stereotaxic space (MNI152). e, Warping and deformation of the coordinates from b to d. f, Application of this deformation to c displayed in color onto the MNI152 for visual convenience. The following 34 anatomical landmarks were identified: top of the interhemispheric fissure (0), surface (1) and deepest point (2) of the cingulate sulcus, surface (3) and deepest point (4) of the superior frontal sulcus (6), middle of the middle frontal gyrus (7), surface of the inferior frontal sulcus, middle surface of the inferior frontal gyrus (8), surface (5) and deepest point (9) of the lateral fissure, highest (10) and lowest point (11) of the circular insular sulcus, surface (12) and deepest point (13) of the superior temporal sulcus, surface (14) and deepest point (15) of the inferior temporal sulcus, surface (16) and deepest point (17) of the accessory temporopolar sulcus, surface (18) and deepest point (19) of the entorhinal sulcus, middle of the entorhinal cortex (20), most medial points of the amygdala (21), superior (22), inferior (23), lateral (24), and medial (25) putamen, most medial internal globus pallidus inferior (26), superior (27), inferior (28) lateral (29) and medial (30) caudate, highest point of the lateral ventricles (31), deepest point of the callosal sulcus (32) and middle of the cingulum gyrus (33).

Extended Data Fig. 7 Clustering of brain voxels by similarities of mitochondrial density and OxPhos activities.

a, Three visually defined UMAP clusters (upper) obtained after dimensionality reduction of mitochondrial features of all brain voxels (Fig. 2e) were used to predict whether voxels originated from GM, WM or a mixed sample (middle). The table lists prediction accuracy with the number of voxels identified as each type and the fractions that were identified correctly. Note that most localization errors occurred due to the presence of voxels with mixed composition, which complicates their anatomical mapping. Χ2(2, N = 1031) = 10.3, p < 0.01. b, Both MitoD and TRC values were higher in GM than the WM voxels (same data as on Fig. 2f; mean ± SD), but the TRC decreases more than MitoD. *** P < 0.0001 by 2-way ANOVA (F1,1260 = 18.67). c, Distribution of z-score values of each mitochondrial feature on the UMAP plot of GM voxels only (Fig. 2g).

Extended Data Fig. 8 Histological evaluation of brain areas used for snRNAseq analysis.

Staining of 50 µm sections obtained after brain voxelization (see Extended Data Fig. 1) with Nissl stain (blue, neurons and glial cells) and NeuN (brown, neuronal cells). Two panels in black frames show Nissl-only stained sections with annotated cortical (layers I-II) and hippocampal (dentate gyrus, hilus and CA3) areas. The entire Nissl-stained brain section is shown on Extended Data Fig. 1h.

Extended Data Fig. 9 Workflow of data processing with stepwise linear regression model.

a, Voxels that had all 6 mitochondrial parameters (4 independent and 2 derived) and with a sum of GM and WM probabilities more than 70% (n = 539 ‘observed’ voxels) were randomly split into 80% training (n = 431) and 20% testing (n = 108) datasets. A model was built by predicting each of the 6 mitochondrial values in the training dataset using stepwise backward linear regression of neuroimaging values (Extended Data Table 1). The model was then applied to the testing dataset to verify predictions accuracy. Correlations between observed and predicted values of the testing dataset are shown on Fig. 4a. Next, the predictive model was extended to all the brain voxels of the MNI space to produce a whole brain estimated map for each of the mitochondrial values (Fig. 4b–e). b, Correlation between TRC and MRC values either predicted by the model or calculated from predicted CI, CII, CIV and MitoD values (see formulas on Fig. 2a). Strong correlation between predicted and calculated values for TRC and MRC shows the robustness of the model as it can accurately predict derived parameters (TRC and MRC) without the knowledge of the independent parameters they were derived from (CI, CII, CIV and MitoD). c, Scatterplots of 20% out-of-sample prediction of mitochondrial profiles (same data as on Fig. 4a) after the pairing between voxels observed and predicted values was scrambled. Slopes of all linear regressions are not different from zero (p > 0.7; Pearson’s r2 is shown on each graph). d, Predicted (right) vs. observed (left, same as Fig. 2b–d) maps of TRC, MitoD and MRC values from the same coronal plane. Both sides show the right hemisphere (i.e., the predicted image is mirrored). Scale bars are in z-score units.

Extended Data Fig. 10 Higher mitochondrial activity in phylogenetically younger brain areas.

a-b, Comparison between predicted mitochondrial parameters (raw TRC, MRC and MitoD values for 80,0039 brain voxels) and indirect measures of brain evolution such as grey matter variability (a) and monkey-to-humans areal expansion (b).

Extended Data Table 1 List of neuroimaging metrics and their standardized beta coefficient relationship with the mitochondrial features indicating the unique contribution of each predictor while accounting for the influence of all other variables in the model
Full size table

Supplementary information

Supplementary File 1

Extended discussion of the loadings for MRI-based metrics and mitochondria biology from Extended Table 1.

Reporting Summary

Peer Review File

Supplementary Data 1

snRNA-seq markdown (HTML file). Analysis of snRNA-seq data. Detailed markdown of the snRNA-seq analysis, including the code for data pre-processing, visualization, cluster identification and mitochondrial analysis. This file also shows additional figures supporting the analysis. A HTML file also available at http://humanmitobrainmap.bcblab.com.

Supplementary Data 2

MRI and PET data correlations (xlsx file). A complete dataset of mitochondrial parameters (CI, CII and CIV activities, MitoD, TRC and MRC) and their correlations with MRI and PET metrics. An Excel spreadsheet with MRI and PET values for each voxel is also included.

Supplementary Data 3

IGOR Pro CNC manager (ipf file). Software for the voxelization of the tissue. Igor Pro procedure file for (1) defining the parameters to clean and partition a frozen brain slab, (2) generating G-codes used to control the CNC router, and (3) randomizing samples and assigning them to specific assay plates.

Supplementary Data 4

IGOR Pro Sample unscramble (ipf file). Software for the voxelization of the tissue. Igor Pro procedure file for (1) derandomizing the samples after the assays are complete, and (2) plotting the assay data on the topological map of the brain.

Supplementary Data 5

Python code for NIfTI file conversion (py file). Morphing the image of the brain onto MNI space coordinates. Python code used for the conversion of the image of the brain slab to a NIfTI format.

Supplementary Video 1

Video of human brain voxelization with a CNC cutter. Human brain slab voxelization using a CNC cutter. Milling was done over about 5 h at –25 °C (freezer room) with routing of the drill bit controlled by a computer in the next room (4 °C).

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Mosharov, E.V., Rosenberg, A.M., Monzel, A.S. et al. A human brain map of mitochondrial respiratory capacity and diversity. Nature 641, 749–758 (2025). https://doi.org/10.1038/s41586-025-08740-6

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