Connectomic Analysis of Mitochondria in the Central Brain of Drosophila
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eLife Assessment
This study provides a valuable resource describing the size, density, and synaptic proximity of mitochondria across thousands of neurons in the Drosophila brain, drawn from a well-annotated electron microscopy dataset. The evidence supporting the main descriptive findings is mostly solid, though some measured parameters contain internal inconsistencies and the proposed links between mitochondrial properties and neural activity or energy use remain speculative. The work will interest neuroscientists studying mitochondrial biology, energetics, and connectomics, and lays a foundation for future studies connecting mitochondrial organization to neural circuit function.
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Abstract
Mitochondria are integral to the metabolism and cell biology of a neuron. Electron microscopy images of fly brain volumes, taken for connectomics, can be analyzed for mitochondria as well as the cells and synapses already reported. Here, from the Drosophila Hemibrain connectome dataset, we extract, classify, and measure approximately 6 million mitochondria, with the majority located among more than 20 thousand neurons and over 5500 cell types. Each mitochondrion is annotated with its location, orientation, voxel size, and appearance (dark and dense, light and sparse, or medium), and each synapse is linked to its closest mitochondrion. Using these data, we show how the most basic characteristics of mitochondria—volume, distance from synapses, and appearance—vary considerably between cell types and between brain regions. Mitochondria are larger and closer at presynapses than at postsynapses, and presynapses typically have a mitochondrion within one micron. However, cells important for learning and memory, Kenyon cells, have unusually small and sparse mitochondria that are placed with unusual precision near only half of presynapses. We find that mitochondria occupy a greater fraction of cell volume in inhibitory neurons than excitatory cells, dopaminergic neurons have a distinct synaptic positioning of mitochondria, and that glutamatergic neurons have a high fraction of mitochondria with a light appearance. We also find that synapses with more postsynaptic partners have larger presynaptic mitochondria and more distant postsynaptic mitochondria. Finally, we have extended the data model of our public web interface, neuPrint, to record our mitochondria data as searchable subcellular components of neurons in a connectomic database. These results indicate wide-ranging principles for how mitochondrial organization varies by cell type in a dataset of unprecedented size and coverage.
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eLife Assessment
This study provides a valuable resource describing the size, density, and synaptic proximity of mitochondria across thousands of neurons in the Drosophila brain, drawn from a well-annotated electron microscopy dataset. The evidence supporting the main descriptive findings is mostly solid, though some measured parameters contain internal inconsistencies and the proposed links between mitochondrial properties and neural activity or energy use remain speculative. The work will interest neuroscientists studying mitochondrial biology, energetics, and connectomics, and lays a foundation for future studies connecting mitochondrial organization to neural circuit function.
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Reviewer #1 (Public review):
Summary:
In this manuscript, the authors build a connectome-scale resource for studying mitochondria in the Drosophila Hemibrain. The authors segment and classify millions of mitochondria, quantify their size, abundance, appearance, and spatial relationship to synapses across thousands of neuron types, and extend neuPrint so that these organelles can be queried together with neuronal connectivity. The analyses identify substantial mitochondrial variation across cell types, systematic differences between pre- and postsynaptic mitochondrial organization, associations with neurotransmitter class, and changes in mitochondrial properties with synaptic fan-out.
Strengths:
The scale and accessibility of the resource are the major strengths of this work. The mitochondrial annotations are integrated with an …
Reviewer #1 (Public review):
Summary:
In this manuscript, the authors build a connectome-scale resource for studying mitochondria in the Drosophila Hemibrain. The authors segment and classify millions of mitochondria, quantify their size, abundance, appearance, and spatial relationship to synapses across thousands of neuron types, and extend neuPrint so that these organelles can be queried together with neuronal connectivity. The analyses identify substantial mitochondrial variation across cell types, systematic differences between pre- and postsynaptic mitochondrial organization, associations with neurotransmitter class, and changes in mitochondrial properties with synaptic fan-out.
Strengths:
The scale and accessibility of the resource are the major strengths of this work. The mitochondrial annotations are integrated with an established connectomic dataset and can be queried through neuPrint, which should make the study useful to researchers beyond the immediate biological questions addressed here. The manuscript also describes the underlying data model and how mitochondrial information is linked to neurons and synapses.
The manuscript compares mitochondrial organization across multiple levels, including individual cells, cell types, brain regions, synaptic polarity, neurotransmitter class, and synaptic fan-out. Several descriptive results are particularly clear, including the tendency for mitochondria to be closer and larger at presynaptic than postsynaptic sites and the identification of cell-type-specific patterns.
The biological interpretations are generally placed in the context of prior experimental literature on synaptic energy demand, calcium handling, and mitochondrial positioning. In many places, the authors appropriately use cautious language such as "may," "possible," and "consistent with," which helps distinguish mechanistic hypotheses from direct measurements. The cited literature therefore provides a reasonable biological framework for interpreting the structural observations, even where the corresponding physiological variables are not measured in this specimen.
The authors also acknowledge important limitations of the Hemibrain sample, including incomplete neurons, regional differences in reconstruction quality, and the fact that the analysis represents a single physiological snapshot. The public code and data infrastructure further increase the utility and reproducibility of the study.
Weaknesses:
The dataset has extraordinary cellular coverage but derives from one Hemibrain specimen. Claims about "natural ranges," generality across Drosophila neurons, or organizational principles extending beyond this specimen should therefore be phrased with some caution. This is particularly relevant for cell types represented by only one or a few instances. For example, MBON13 is represented by a single cell in this dataset, so its unusually high mitochondrial investment is well supported for that instance, but variability of this property within the cell type cannot be assessed here. This is consistent with the authors' own acknowledgement in the Discussion that detailed conclusions for individual cell types may require larger numbers of cells.
The mitochondrial appearance classes support several biological conclusions, particularly the differences associated with brain region and neurotransmitter class. The manuscript describes how dark, medium, and light training labels were generated and reports strong validation of mitochondrial detection and nearby-mitochondrion recall, but I could not find a corresponding independent validation of the three appearance labels themselves. Some assessment of classification reproducibility, for example using a held-out manually assessed set, a confusion matrix, or inter-annotator agreement, would strengthen confidence in conclusions that depend on these categories.
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Reviewer #2 (Public review):
Summary:
Electron microscopy (EM) offers resolution of neuronal ultrastructure not available through any other technique, but it has a limited "field of view". Also, an EM study's value is limited by the ability to identify neuron types within that field of view. This data set largely overcomes both limitations, first, through the analysis of contiguous blocks of SEM-FIB data spanning almost half a millimeter in extent, serially milled at an exceptionally fine 8 nm to create isotropic 8 nm voxels, and, secondly, through efforts that identified most neurons within the sectioned volume. In overcoming the usual limitations, the authors can offer an overview of the capacity of different classes of neurons to control the size and distribution of their mitochondria - a non-trivial outcome considering the essential …
Reviewer #2 (Public review):
Summary:
Electron microscopy (EM) offers resolution of neuronal ultrastructure not available through any other technique, but it has a limited "field of view". Also, an EM study's value is limited by the ability to identify neuron types within that field of view. This data set largely overcomes both limitations, first, through the analysis of contiguous blocks of SEM-FIB data spanning almost half a millimeter in extent, serially milled at an exceptionally fine 8 nm to create isotropic 8 nm voxels, and, secondly, through efforts that identified most neurons within the sectioned volume. In overcoming the usual limitations, the authors can offer an overview of the capacity of different classes of neurons to control the size and distribution of their mitochondria - a non-trivial outcome considering the essential role of mitochondria in generating cellular ATP and neurotransmitter precursors. The science community is doubly served by the authors' website "neuPrint" which is accessible to the public, where the data can be visualized and interrogated according to various hypotheses. As it stands, this study fails to reach its otherwise exceptional potential as analysis errors throw a shadow over the findings.
Strengths:
(1) This is the first comprehensive analysis of mitochondrial morphometrics and ultrastructural positioning across a substantial portion of an insect brain. The adult fruit fly brain was previously milled at 8 nm intervals and imaged at 4 nm/pixel (Scheffer et al., 2020), offering an unprecedented data set for mining mitochondrial parameters relevant to neuronal function (this manuscript).
(2) The value of the data set has been amplified through systematic identification of neuronal identities and likely neurotransmitter types. The numbers of mitochondria (>6,000,000), neurons (>20,000), and neuron types (5,555) included in the study are similarly unprecedented.
(3) The language of the manuscript is straightforward and accessible.
Weaknesses:
(1) The data describing mean median (mean of the medians derived from many neuron types) mitochondrial volume (Fig.2B) and mean median length (Fig.2C) are not internally consistent, i.e. an ellipsoid with volume 0.15 cubic microns requires a longest axis of at least ~0.66 microns to remain a valid prolate shape (i.e., for the long axis to exceed the two minor axes); the reported value of 0.55 microns falls well short of this, indicating the two panels are not describing a geometrically coherent object. This inconsistency can be reconciled only by looking at the supplementary data, where the data are reported for each cell type and the relevant column is titled "Median miitochondrion long axis (r0)" (sic). Here it is evident that the parameter being reported on each plot in Figure 2C is in fact the radius of the long axis and not mitochondrion "length" as currently stated.
(2) However, even with the correction referred to above, the mean median mitochondrial diameter, i.e., minor axes, would be 0.5 microns, a robust mitochondrion for otherwise fine central processes, and double the diameters reported from C.elegans motor neurons (Riboul et al., 2024) and interneurons within the optic lobe of the miniature parasitic wasp (Fischer et al., 2017). While it is a dubious exercise to reverse engineer possible ellipsoid body dimensions from the mean of medians of both longest axis and volume, the inconsistency remains when referring to the mean of medians of smaller groups of neurons in the supplementary data.
(3) The estimates of mitochondrial volume density are uncommonly high, i.e., 4%-24%, with a mean median of 10%. These values are 2-3 fold greater than estimates from comparable EM studies, e.g. 4-6% for C.elegans motor neurons (Riboul et al., 2024); 4-6% for Drosophila larval motor neuron terminals (Justs et al., 2022); and 4-8% for lamina-medulla and medial-medulla interneurons in the miniature parasitic wasp (Fischer et al., 2017). Estimates from rat brain are similarly small, not exceeding 8% (e.g., Santuy et al., 2018), leading to concerns that, just like the two-fold error in reporting mitochondrial lengths, a two-fold error has propagated in mitochondrial volume density.
(4) The internal inconsistency in reported mitochondrial axis length can be identified from the manuscript's own numbers alone, using simple arithmetic. By contrast, the plausibility of the resulting mitochondrial minor-axis diameter (~0.5 microns), and of the reported mitochondrial volume density (4-24%, median 10%), can only be assessed by comparison with the wider literature - where comparable EM-based estimates across several species consistently fall in the 0.18-0.24 micron (diameter) and 4-8% (density) range, respectively. Both this literature comparison and the simple arithmetic used to establish the axis-length inconsistency were available to a reviewer. The other parameters reported throughout the manuscript are inscrutable, i.e., not readily checked by arithmetic or by comparison with existing literature. Nevertheless, the errors and implausibilities identified above throw a shadow over every other piece of data.
(5) Putting errors to one side, if the authors wish to report values in an unprecedented range, they need to do so with due diligence and defend the novelty of their data in the context of precedence in the field.
Assessment of whether aims were met:
The authors did not achieve their aims, but those aims remain well within their reach, as processing data sets is generally trivial relative to the physical act of repeating experiments and recapturing ideal experimental conditions.
Discussion of the likely impact:
This manuscript has great bones, and should it receive the attention/rigor it deserves, it is likely to become the go-to reference for mitochondrial morphometrics and positioning across whole central neurons in Drosophila, specifically, and in insects in general.
Several of the findings in this study, should they persist through reanalysis, are likely to be impactful. A particularly interesting finding is that the size of the closest mitochondrion is larger at pre- rather than post-synaptic compartments (line 309; Figure 7). The dogma in neuroscience, mostly stemming from studies on vertebrate neurons and partly supported by the misguided use of fluorescence microscopy to assess mitochondrial size and distribution, is that mitochondria are larger in dendrites than in axons. The ability to assess a compartment as either pre- or post-synaptic in the present study gets to the heart of the matter about pre- vs post-synaptic dependence on mitochondrial function without getting drawn into arguments about more macroscopic cell structures that may or may not be preserved between insects and vertebrates. This finding is worth discussing in the context of the mouse data from Sager et al., 2026, Science, but it is a discussion that requires consideration of the relative volume of the pre- vs post-synaptic compartments, and whether mitochondria have the same opportunity to closely attend postsynaptic compartments.
A second finding, and one that might have received more attention in the discussion, was the observation: "KC presynapses with smaller median distances to mitochondria have larger nearest mitochondria, ... . For KCs and MBONs, the mitochondrial volume is inversely correlated with the distance". There are several avenues here that might have been explored. Firstly, in the context of data processing, and secondly, in the context of bioenergetics, the implication of greater intermittence of input at any particular postsynapse, and less intermittence of output at any particular presynapse. Lastly, implications with regard to the relative reliance on mitochondria for neurotransmitter synthesis between pre- vs post-synaptic compartments, an implication that might be addressed through reference to the particular neurotransmitters of each neuron class and whether that neurotransmitter is directly dependent on neighboring mitochondria for precursors.
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Reviewer #3 (Public review):
Summary:
This manuscript presents a resource describing the morphology and distribution of mitochondria across a large part of the central brain of Drosophila. In addition to providing a valuable resource for the community, the authors analyze their dataset in great detail. These analyses reveal interesting differences in mitochondria across cell types and brain regions with regard to morphological parameters, their spatial relationship to pre- and post-synapses, differences in mitochondrial characteristics between neurons that use different neurotransmitters, and between synapses with multiple postsynaptic partners.
Strengths:
The main strength of the paper is the large size of the dataset comprising ca. 6 million segmented mitochondria distributed across more than 20000 neurons. This dataset provides …
Reviewer #3 (Public review):
Summary:
This manuscript presents a resource describing the morphology and distribution of mitochondria across a large part of the central brain of Drosophila. In addition to providing a valuable resource for the community, the authors analyze their dataset in great detail. These analyses reveal interesting differences in mitochondria across cell types and brain regions with regard to morphological parameters, their spatial relationship to pre- and post-synapses, differences in mitochondrial characteristics between neurons that use different neurotransmitters, and between synapses with multiple postsynaptic partners.
Strengths:
The main strength of the paper is the large size of the dataset comprising ca. 6 million segmented mitochondria distributed across more than 20000 neurons. This dataset provides enough statistical power to detect differences in mitochondrial characteristics between brain regions and cell types. An additional strength is the easy accessibility of the data through the neuPrint web interface, enabling the community to explore the dataset and perform further analyses.
Weaknesses:
The study is primarily descriptive. While this is not necessarily a weakness, the manuscript could more clearly discuss potential applications of the dataset beyond pure characterization. Potential improvements include neurotransmitter predictions, cell type classification, or sites of gap junction coupling.
Conclusion:
Taken together, I consider this manuscript to provide a highly valuable resource and analysis that will be useful to researchers interested in mitochondria and neuronal cell biology in Drosophila. Furthermore, the dataset provides potential for researchers to link mitochondrial characteristics to neuronal physiology.
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Reviewer #4 (Public review):
Summary:
Longden et al. present a comprehensive analysis of all the mitochondria in the hemibrain dataset of the Drosophila brain. They quantify variations in mitochondrion size, spatial distribution, and proximity to pre- and post-synapses that reveal stark differences between cell types and brain regions. Similar to findings from other model systems, their results indicate that mitochondria are larger and closer to presynapses than to postsynapses. They also show that inhibitory neurons have more mitochondria by volume than excitatory neurons. Furthermore, they have made all this data available through neuPrint, making it a valuable resource for the broader research community. The evidence for their conclusions is compelling based on the unprecedented breadth of analysis across cell types and brain regions.
Reviewer #4 (Public review):
Summary:
Longden et al. present a comprehensive analysis of all the mitochondria in the hemibrain dataset of the Drosophila brain. They quantify variations in mitochondrion size, spatial distribution, and proximity to pre- and post-synapses that reveal stark differences between cell types and brain regions. Similar to findings from other model systems, their results indicate that mitochondria are larger and closer to presynapses than to postsynapses. They also show that inhibitory neurons have more mitochondria by volume than excitatory neurons. Furthermore, they have made all this data available through neuPrint, making it a valuable resource for the broader research community. The evidence for their conclusions is compelling based on the unprecedented breadth of analysis across cell types and brain regions.
Strengths:
The comprehensive nature of their analyses across all of the mitochondria in the dataset enables Longden et al. to describe each observation in the context of individual neurons, cell types, and brain regions. The authors use this analysis structure to highlight cell types that possess unique mitochondrial metrics, such as the Kenyon cells
The reconstructions and results from this paper have been incorporated into neuPrint, making these data accessible to a wider set of researchers and scientific investigations.
Weaknesses:
The motivation for this study could be enhanced with a more general overview in the introduction. Answering some of the following questions would provide more context as to the interpretation of their results and the value of extending neuPrint to include mitochondria. How does the distribution of mitochondria in a cell enable function? What is the biological implication of having mitochondria near a synapse? What do we know about mitochondria from other modalities, and how does the information from the connectome extend or corroborate what we already know?
Similarly, this paper would benefit from more contextualization of their results within the broader study of mitochondria, both in the field of fly neuroscience and connectomics across species. A few of these studies are rightfully referenced; however, a more direct discussion of how the results from Longden et al. sit within the current landscape would highlight the value of this effort and broaden the readership of the manuscript.
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Author response:
We thank the Editors and reviewers for their close reading of our work and valuable comments. We appreciate their recognition of the foundational nature of our study. The reviews suggest multiple improvements that we plan to implement in a revised version, including the following: validation of appearance labels; short self-contained example code for analysing the dataset; greater comparison with prior work in the discussion; more details in the methods. The reviews also make many suggestions for improving the presentation, wording, and clarity of the work. In particular, we acknowledge that we used incorrect terminology in referring to the axes rather than the semi-axes of fitted ellipsoids. The revision will correct this error and provide the full parameters of the ellipsoid fits (r0, r1, r2) in the supplementary …
Author response:
We thank the Editors and reviewers for their close reading of our work and valuable comments. We appreciate their recognition of the foundational nature of our study. The reviews suggest multiple improvements that we plan to implement in a revised version, including the following: validation of appearance labels; short self-contained example code for analysing the dataset; greater comparison with prior work in the discussion; more details in the methods. The reviews also make many suggestions for improving the presentation, wording, and clarity of the work. In particular, we acknowledge that we used incorrect terminology in referring to the axes rather than the semi-axes of fitted ellipsoids. The revision will correct this error and provide the full parameters of the ellipsoid fits (r0, r1, r2) in the supplementary data, allowing the consistency of these fits with mitochondrial volume to be appreciated. We did identify that the values of one of the mitochondrial variables not used for the analysis in the paper (r2) had been corrupted in the uploading of the publicly available hemibrain:v1.2.1 dataset to neuprint. Accordingly, we have generated hemibrain:v1.2.3 which restores the original, correct and uncorrupted values of this variable.
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