Dynamic changes in mitochondria support phenotypic flexibility of microglia

Domain: biology
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
Microglial capacity to adapt to tissue needs is a hallmark feature of these cells. New studies show that mitochondria critically regulate the phenotypic adaptability of macrophages. To determine whether these organelles play similar roles in shaping microglial phenotypes, we generated transgenic mouse crosses to accurately visualize and manipulate microglial mitochondria. We find that brain-region differences in microglial attributes and responses to aging are accompanied by regional differences in mitochondrial mass and aging-associated mitochondrial remodeling. Microglial mitochondria are also altered within hours of LPS injections and microglial expression of inflammation-, trophic-, and phagocytosis-relevant genesis strongly correlated with expression of mitochondria-relevant genes. Finally, direct genetic manipulation of microglial mitochondria alters microglial morphology and leads to brain-region specific effects on microglial gene expression. Overall, this study advances our understanding of microglial mitochondria and supports the idea that mitochondria influence basal microglial phenotypes and phenotypic remodeling that takes place over hours to months.

Introduction
An essential and defining characteristic of microglia is their ability to rapidly remodel their attributes and shift into a wide array of distinct functional states1–3. Historically, microglial cellular phenotypes were viewed in a binary fashion as either pro-inflammatory (M1, activated) or anti-inflammatory/tissue-reparative (M2, homeostatic)4. Work over the past 10-15 years has refined our understanding of microglial functions, showing that they can adopt a wide spectrum of transcriptional signatures, morphologies, and secretory/proliferative/phagocytic/ migratory profiles5. Moreover, the particular combination of microglial properties observed depends on brain region, age, and the presence or absence of chronic stress, high fat diet, and pollution exposure. Distinct tissue injury, infection, and disease processes also elicit unique sets of properties in responsive microglia5–17, meaning that binary categorization of microglial functional states is far too simplistic and inaccurate.

As the field works to catalogue the spectrum of cellular phenotypes that microglia display in distinct circumstances, efforts have also been made to identify central or master regulators of microglial phenotypes. Key examples include transcription factors that turn on entire programs of microglial gene expression and associated downstream functional capabilities. Other examples include chromatin remodelers that can similarly enable or suppress expression of large numbers of genes that will shape downstream microglial attributes and functions10,18. Identifying such regulators of microglial phenotypes that have the capacity to modulate numerous microglial attributes is of high utility for the field. It can assist with defining and categorizing microglial functional states, as they can be determined on the basis of the central regulator rather than panels of 10s-100s of microglial genes, proteins, or functional readouts. These central regulators are also of great interest as potential therapeutic targets that could be utilized to elicit broad changes in microglial function via coordinated regulation of multiple microglial attributes. Recent findings raise the possibility that mitochondria-as organelles-may have the capability to regulate microglial phenotypes in this fashion.

Classically thought of as the ATP producing powerhouse of the cell, mitochondria are now also recognized as critical signaling hubs19–22. Mitochondria can influence calcium-dependent intracellular signaling pathways via their ability to buffer intracellular calcium. They exert strong influence on signaling that involves protein post-translational modifications, as mitochondrial metabolic pathways generate many of the metabolites needed for these modifications. Mitochondria can influence the function of cellular endoplasmic reticulum and lysosomes via physical interactions with these organelles, and mitochondria can signal to the nucleus to impact gene expression19–22. Research in macrophages supports the idea that mitochondria act as signaling hubs, demonstrating that mitochondria regulate macrophage responses to pathology and ability to shift into distinct inflammatory states23–29. Key observations suggest a similarly important role for these organelles in regulating changes in microglial cellular phenotypes. Regional differences in microglial phenotypes are accompanied by prominent differences in expression of mitochondrial-relevant genes30, and the state of mitochondria appears to be linked to microglial responses to early life immune challenge, brain injury, and protein aggregates in neurodegenerative disease31–38. Yet, direct and rigorous study of these organelles in microglia in vivo is minimal and gaps persist in our knowledge about potential mitochondrial regulation of microglial attributes.

To begin addressing this knowledge gap, we sought to analyze the relationship between mitochondria and key microglial attributes in both physiological and pathological contexts in mice. We used multiple transgenic crosses and multidisciplinary approaches to determine whether differences in microglial properties across brain regions during normative aging were accompanied by regional differences in microglial mitochondria. To explore the relationship between these organelles and microglial properties during CNS challenges, we chose pathological insults that differ in their modality (tissue injury vs inflammatory challenge) and time course over which microglia exhibit phenotypic remodeling (mins vs hours/days). Finally, to probe causal links between mitochondrial state and microglial properties we generated transgenic crosses to selectively manipulate microglial mitochondria and link organelle state to cellular properties. Collectively, these studies advance the field by revealing the mitochondrial landscape within microglia and the relationship of these organelles to microglial phenotypic remodeling across brain regions, in response to inflammatory insult, and during aging.

Results
Region-specific phenotypes of microglia are accompanied by regional differences in mitochondrial mass and number
Mitochondrial abundance, morphology, and molecular composition vary dramatically across cell types39, suggesting that the relationship between mitochondria and cellular function cannot be generalized across cell types and need to be defined on a cell by cell basis. To unequivocally visualize microglial mitochondria, we crossed mice that express inducible cre recombinase in microglia (CX3CR1CreER/+) mice that express Cre-dependent mitochondrial-targeted GFP (mitoGFP) (Fig. 1A)40,41. In fixed tissue from young adult (2 mo) double transgenic CX3CR1CreER/+;mitoGFP mice (MG-mitoGFP mice hereafter), numerous GFP+ structures could be observed throughout the somas and cell processes of microglia in the nucleus accumbens (NAc) and ventral tegmental area (VTA), two interconnected brain regions where we have extensively mapped microglial phenotypes across the lifespan (Fig. 1B and Fig. S1A, B)30,42,43. Consistent with reports of tamoxifen-independent recombination when using CX3CR1CreER/+ mice and reporters with minimal distance between loxP sites44,45, recombination was observed in approximately 80% of microglia in MG-mitoGFP mice without tamoxifen administration (Fig. S1A–C) or in oil-injected controls (Fig. S1D, E). FACS-based analysis indicated that 4-hydroxytamoxifen treatment increased recombination to approximately 90% of microglia (Fig. S1F, G). Given the high level of tamoxifen-independent recombination, we elected to carry out analyses without tamoxifen-treatment, unless otherwise noted. In terms of cellular and organelle specificity of mitoGFP, FACS analyses indicated that GFP expression was not observed in non-microglial CNS cells (Fig. S1H, I). Moreover, in acute brain sections from MG-mitoGFP mice, GFP+ structures were consistently colocalized with tetramethylrhodamine (TMRM), a mitochondrial membrane potential indicator, confirming that GFP-tagged structures are mitochondria (Fig. S1J, K). Together these results indicate that MG-mitoGFP mice accurately label microglial mitochondria.

Microglia show prominent differences in cell density, branching complexity, and gene expression across the NAc and VTA30. To determine if these regional differences in baseline microglial attributes were accompanied by regional differences in microglial mitochondria, we carried out high resolution confocal imaging and 3D volumetric reconstruction of both microglia and their mitochondria in the NAc and VTA of young adult mice (Fig. 1B and Fig. S2A). Consistent with previous findings, microglial tissue coverage in the VTA was lower than that observed in NAc (Fig. 1C)30. Yet, field of view (FOV) mitochondrial mass (mitochondrial volume relative to microglial volume) and FOV mitochondrial number relative to microglial volume were greater in the VTA compared to the NAc (Fig. 1D, E). The FOV sphericity, median volume, max volume, and aspect ratio (longest axis/shortest axis - measure of elongation) of individual mitochondria were relatively consistent across NAc and VTA (Fig. S2B–E). Together, these observations indicate that regional differences in microglial cellular phenotypes are accompanied by regional differences in some but not all mitochondrial metrics.

Mitochondrial number is partially correlated with morphological complexity of microglia
Microglial capacity to survey the surrounding brain tissue is influenced by the degree of their cell branching complexity. To define how mitochondrial networks relate to microglial branching structure, we crossed MG-mitoGFP mice to Ai14 reporter mice to drive cytosolic expression of TdTomato (TdTom) along with GFP tagging of microglial mitochondria (Fig. 1A)40,41,46. In the absence of tamoxifen injection, incidence of mitoGFP+ TdTom+ cells was low enough to allow rigorous and unequivocal reconstruction of the branching patterns of individual microglial cells and their mitochondrial networks in VTA and NAc as well as prefrontal cortex (PFC) (Fig. 1F and Fig. S2F). Consistent with previous findings, microglia display morphological differences across these brain regions, with NAc microglia exhibiting greater total process length than VTA microglia (Fig. 1G)30. PFC microglia displayed total process length comparable to that of VTA microglia. To relate these regional differences in total process length to mitochondria of the cells, we used regression analyses and found that greater process length was significantly associated with higher total volume of mitochondria in NAc microglia (Fig. 1H). Both VTA and PFC microglia showed trends toward greater process length being correlated with a greater number of mitochondria (Fig. 1H). These findings suggest that a greater quantity/volume of mitochondria are needed to support a greater mass of cell processes.

Microglia also displayed regional differences in morphology in terms of total number of branch points, with this metric being elevated in NAc microglia compared to VTA and PFC microglia (Fig. 1I). Robust regression analyses revealed that greater branching was associated with a greater number of mitochondria for VTA microglia (Fig. 1J). Trends toward greater branching being associated with a larger total volume of mitochondria were also evident for NAc and VTA microglia. Intriguingly, these findings suggest that the architecture of cell processes, rather than simply the total amount of cell processes, may be related to mitochondrial networks within microglia (Fig. 1J). Consistent with this idea, Sholl analysis revealed that both the number of intersections (reflecting the number of cell process branches/morphological complexity) and the number of mitochondria reached a maximum at approximately 20–25 µm from the cell soma in the PFC, NAc, and VTA microglia (Fig. S2G–I). Hence, the subcellular distribution of the organelles aligns with branching architecture, as opposed to mitochondria being primarily clustered in the soma or at the motile tips of microglial processes. In addition, the maximum number of Sholl intersections was significantly correlated with the total number of mitochondria in PFC, with NAc and VTA microglia showing trends toward significance in this metric (Fig. S2J). Altogether, these analyses suggest that baseline microglial morphology and features of their mitochondria are partially interlinked.

Mitochondrial number and mitochondrial motility are not aligned with microglial tissue surveillance
Microglia engage in continual morphological remodeling as they survey the surrounding tissue. Microglial tissue surveillance can also vary across brain regions and is assumed to be an energy-intensive process7,47. However, the relationship between microglial cell process motility and mitochondrial abundance has not been explored. We performed two-photon imaging in acute brain sections prepared from young adult (2-3 mo) MG-mitoGFP;Ai14 mice and recorded motility of NAc and VTA microglial cell processes (TdTom) and attributes of mitochondria (GFP) within these cells (Fig. 2A, B and Fig. S3A–C). Microglia were imaged at least 60 µm below the surface of the brain section. Although microglia show more simplified branching in acute brain sections compared to fixed tissue, total mitochondrial volume and total number of microglial Sholl intersections were positively correlated (Fig. S3D), indicating that alignment of mitochondria with the morphological architecture of the cells was preserved in acute brain sections. Moreover, microglial motility and mitochondrial motility were not correlated with time elapsed since preparation of brain sections (Fig. S3E, F), suggesting that this preparation is well suited for initial exploration of relationships between mitochondria and microglial cell dynamics.

Although NAc and VTA microglia displayed regional differences in morphology and mitochondrial mass in fixed tissue (Fig. 1), microglial cell process motility, defined as percent pixel change over a 10 min period, was similar across NAc and VTA in acute brain sections (Fig. 2B, C and Fig. S3B). In addition, despite several associations between microglial morphology and features of their mitochondria in fixed tissue (Fig. 1H, J and Fig. S2J), there was no correlation between total mitochondrial volume or mitochondrial mass and microglial motility in NAc or VTA (Fig. 2D, E) in acute brain sections. These observations suggest that there is a more robust relationship between mitochondrial abundance and baseline cell morphology than with dynamic remodeling of that morphology.

Microglial mitochondria themselves also exhibited motility that included jitter, concerted directional movement, and occasional examples of fission and fusion (Fig. 2F, G and Fig. S3C). These types of mitochondrial motility are consistent with what has been reported in other cell types29,48. 3D reconstruction and tracking of mitochondria in Imaris revealed that motility of individual mitochondria (total displacement over 10 mins) was similar in NAc and VTA microglia, with the majority of mitochondria displaying modest movement (approximately 1 µm) and a smaller number of mitochondria showing larger movements of 2-6 µm (Fig. 2H). To begin exploring whether mitochondrial motility was related to cell process remodeling of individual microglia, we used robust regression analyses and found that mitochondrial motility (percent pixel change for all mitochondria in the cell) was not significantly correlated with microglial motility in either the NAc or the VTA (Fig. 2I). This was also the case at the level of primary branches of microglia. Even within one microglial cell, some primary branches contained few or no mitochondria while others contained multiple mitochondria. However, there was no significant correlation between motility of primary microglial branches and either mitochondrial motility (Fig. 2J) or mitochondrial mass within those branches (Fig. S3G). Altogether, these data further reinforce the idea that mitochondria are not tightly linked to baseline microglial tissue surveillance within a 10 min time frame.

Mitochondrial distribution is stable during early phases of microglial response to focal injury
In addition to homeostatic tissue surveillance, microglia exhibit rapid and robust cell process extension toward focal sites of CNS injury49–51. In acute brain sections from MG-mitoGFP;Ai14 mice, we induced focal laser lesions and imaged the response of both microglia and their mitochondria to this insult. Consistent with previous findings in the field49,52, microglia rapidly extended cell processes toward and contacted the lesion within 5 min (Fig. 3A–G). However, these rapidly extending cell processes did not contain any mitochondria and no mitochondria were trafficked into lesion-responsive cell processes during the imaging period (Fig. 3A–E). Quantitative analyses confirmed minimal change in mitochondrial (GFP+) signals within 30 µm of the lesion over a 20 min period (Fig. 3H, I). Qualitative observations suggested minimal remodeling of mitochondria in the somas and non-extending processes of microglia that responded to laser lesion (Fig. 3A–E, zoom).

Mitochondrial remodeling is evident at the earliest stages of microglial responses to inflammatory challenge
Microglia respond to a wide variety of CNS insults that vary substantially in both the nature of the insult (injury vs. infection vs. disease process vs. pathological electrical activity, etc.) as well as the time course over which microglial responses emerge and evolve (minutes to months). Given lack of clear evidence for mitochondrial involvement in rapid microglial responses to tissue injury, we sought to further explore the relationship between mitochondria and microglial phenotypic remodeling using a pathological insult of distinct modality as well as time course. Intraperitoneal (i.p.) injections of lipopolysaccharide (LPS), a component of the cell wall of gram-negative bacteria, were used to model inflammatory responses that accompany infection. Microglia show prominent changes in gene expression within hours of LPS injection and changes in morphology by 24–48 hours post-injection53. In vitro LPS exposure alters microglial mitochondrial structure and function54–57 and, in macrophages, mitochondrial alterations play an essential role in enabling and regulating changes in macrophage attributes as they respond to inflammatory challenges24–27,29,58,59. All together, these findings suggest that there may be important relationships between mitochondria, as organelles, and microglial responses to inflammatory challenge.

Young adult (3-4 mo) MG-MitoGFP mice were given i.p. injections of saline or LPS and microglial gene- and protein-expression were analyzed via FACS, using the previously described gating strategy to identify and isolate microglia (Fig. S1D), followed by downstream qPCR (Fig. 4A). High expression of key microglial genes (Cx3cr1 and P2ry12) and little to no expression of oligodendrocyte-lineage, astrocyte-, and neuron-specific genes (Fig. S4A) confirmed fidelity of our microglial isolation. Within 4 h of LPS challenge, microglia from both striatum (STR, containing NAc) and midbrain (MB, containing VTA) upregulated transcripts for pro-inflammatory factors (Il1β, Tnfα), and downregulated transcripts for homeostatic microglial genes (Tgfβ1, P2ry12, Cx3cr1), consistent with previous studies53,60. Upregulation of genes associated with microglial phagocytic function (Cd45, Itgam) was evident by 24 h post-injection (Fig. 4B, S4B and Table S1, S2). Overall, STR and MB microglia responded similarly to LPS challenge at the gene expression level.

Microglial expression of mitochondrial-function related genes was also altered at both 4 h and 24 h post injection (Figs. 4B, S4B and Tables S1, S2). Within 4 h of LPS challenge, genes related to mitochondrial ROS handling (Sod2, Cat), mitochondrial fission (Dnm1l), and mitochondrial biogenesis (Nrf1) were significantly altered. Some of these gene expression changes were maintained into 24 h post injection (Sod2, Dnm1l), while others appeared more restricted to the 4 h time point (Cat, Nrf1). At 24 h post injection, prominent upregulation of electron transport chain component genes Ndufa12 (Complex I), Cox4i1 (Complex IV), and Atp5d (Complex V) emerged. Genes related to mitochondrial calcium handling (Vdac1, Micu1) were also significantly altered. Again, most gene expression changes were similar across STR and MB (Figs. 4B, S4B and Tables S1, S2).

To probe the relationship between mitochondrial state and key features of microglia as they respond to inflammatory challenge, we used Principal Component Analysis (PCA) and found that PCA based solely on expression patterns of mitochondrial function-relevant genes can explain 64.7% of sample variation with the first 2 components and 81% of variation with the first 3 components (Fig. 4C and Fig. S4C). Moreover, 24 h post-injection samples cluster away from Saline samples (Fig. 4C) and 4 h post-injection samples are interspersed among Saline samples or skewed toward the 24 h samples. Hence, without even considering microglial inflammatory genes or homeostatic genes, microglia that are responding to LPS challenge can be clearly identified on the basis of their mitochondrial function profiles. We also used robust regression analyses and found numerous correlations between individual microglial-state and mitochondrial-state genes (Fig. 4D and Table S3). For example, expression of homeostatic microglial gene P2ry12 showed significant correlation with electron transport chain (ETC) genes (Ndufa12, Sdha) as well as Tfam, which influences expression of mitochondrial DNA encoded ETC genes, in saline-treated animals, but the majority of these correlations were lost at 4 h post-LPS. Microglial pro-inflammatory genes Il1β and Tnfα showed significant correlation with mitochondrial biogenesis genes (Nrf1, Tfam), mitochondrial fusion and fission genes (Mfn1, Dnm1l) and the ROS handling gene Sod2 at 4 h post LPS (Fig. 4D and Table S3). Microglial- and mitochondrial-gene correlations remained abundant and continued to evolve at 24 h post LPS.

Together these data indicate that numerous facets of mitochondrial function are remodeled during microglial responses to inflammatory challenge and that mitochondrial remodeling begins at the earliest stages of microglial responses to inflammatory challenges. Moreover, examination of Cd68, a key microglial lysosome gene, hints that mitochondria, as organelles, may be uniquely positioned to regulate microglial functional state during inflammatory challenge. While LPS treatment increased the correlation between most mitochondria-relevant genes and key microglial function genes (Cd45, Cx3cr1, P2ry12, Tgfβ1) (Fig. 4D), LPS treatment did not modulate correlations between Cd68 and Cx3cr1, P2ry12, Tgfβ1 (correlations between Cd68 and Cd45 increased at 24 h post LPS) (Fig. S4D and Table S4). Moreover, while multiple mitochondria-relevant genes were positively correlated with expression of the microglial pro-inflammatory factors Tnfα and Il1β at 4 h post-LPS, Cd68 expression was not correlated with Tnfα and Il1β in any of the conditions examined (Fig. S4D).

The transcript-level, qPCR-based analyses presented thus far show high sensitivity for the detection of early changes in cell and organelle state following LPS challenge. To test protein-level changes following the LPS challenge, we analyzed data recorded during FACS isolation of microglia. These analyses revealed that the intensity of GFP, reflecting mitochondrial mass, was significantly increased at 24 h post-injection in both the STR and MB, with hints of emerging increases at 4 h post-injection (Fig. 4E, F). In contrast, mitochondrial membrane potential, as detected by TMRM intensity, decreased significantly at 24 h post LPS (Fig. 4E, F). These changes in mitochondrial mass and mitochondrial membrane potential were accompanied by significant protein-level increases in CD45 by 24 h in STR microglia and trends toward increased CD45 in MB microglia. Protein level expressions of CX3CR1 and P2RY12 differed across regions but not across treatment (Fig S4E). Microglial size, as indicated by forward scatter, was significantly elevated in both STR and MB by 24 h post LPS, while internal complexity (side scatter) remained unchanged (Fig S4E). These data confirm that changes in mitochondrial state at the protein and functional level are associated with changes in microglial protein-level expression and cell structure.

Increased mitochondrial mass and mitochondrial elongation are associated with microglial responses to inflammatory challenge
To investigate how LPS challenge impacts the morphology of microglial mitochondria, we analyzed fixed tissue from 3-4 mo MG-MitoGFP mice (Fig. 5A, Fig. S5A) at 24 h post-LPS, when protein-level changes in microglia and their mitochondria were most evident via FACS (Fig. 4). 3D reconstruction of both microglia and their mitochondria in NAc and VTA revealed significant increases in mitochondrial mass at 24 h post LPS (Fig. 5B), consistent with FACS-based analyses of GFP and qPCR-based increases in mitochondrial biogenesis factor Nrf1. No significant changes in mitochondrial number (relative to cell volume) were detected (Fig. 5C) and, instead, microglial mitochondria showed significant elongation, as detected by increases in aspect ratio (Fig. 5D). Elongation of mitochondria has been associated with increased capacity for oxidative phosphorylation61–63, which would be consistent with the qPCR-based observation of robust increases in electron transport chain components (Ndufa12, Atp5d, Cox4i1) at 24 h post LPS (Fig. 4B). Collectively, these findings indicate that mitochondrial structural remodeling is a key component of microglial responses to inflammatory challenge.

Microglial mitochondria undergo morphological and functional changes during aging
Microglia exhibit numerous changes during aging, including altered morphology and inflammatory profile13,43,64–66. Altered mitochondrial function is heavily implicated in CNS aging, but aging-associated changes in mitochondria have not been investigated with cellular specificity67–69. Hence, whether and how mitochondrial remodeling is linked to changing microglial attributes during aging is unknown. We also showed previously that microglial responses to aging vary across brain regions, with VTA microglia displaying proliferative and inflammatory changes months before microglia in the NAc43. Whether there are links between mitochondrial state and early VTA microglial aging has also not been investigated.

To determine if aging-associated changes in microglial morphology and inflammatory profile are accompanied by changes in their mitochondria, we analyzed NAc and VTA microglial mitochondria in fixed tissue from middle-aged (12-13 mo) and late middle-aged (16-18 mo) MG-MitoGFP mice (Fig. 6A) and compared them to microglial mitochondria in young adult (2-3 mo) mice. Tissues were treated with TrueBlack to quench autofluorescent aggregates (lipofuscin) that emerge during aging and any dim residual signals in microglia could be clearly distinguished from MG-MitoGFP signal during analysis (Fig. S6A)70. With age, mitochondrial mass tended to increase, particularly in the VTA, and significant increases in mitochondrial number (relative to cell volume) were observed in both NAc and VTA microglia (Fig. 6B, C). VTA microglia also showed significant increases in mitochondrial elongation at 16-18 mo (Fig. 6B, C). Hence, at the level of mitochondrial structure, LPS challenge and aging both elicit similar types of mitochondrial remodeling.

Although aging impacts microglia in both the NAc and VTA, key regional differences in microglial aging are also apparent. VTA microglia show more prominent lysosome swelling, proliferation, and inflammatory profiles, while NAc microglia show more prominent reductions in cell branching complexity43. To further explore how regional differences in microglial aging relate to mitochondrial remodeling during aging, within-mouse comparisons were carried out using paired t-tests. This analysis revealed that the elevated mitochondrial mass and mitochondrial number in VTA microglia relative to NAc microglia that were observed at 2-3 mo were no longer significant at 12-13 mo and 16-18 mo, likely due to increased mouse-to-mouse variation (Fig. S6B, C) and increased mitochondrial number (relative to cell volume) in NAc microglia. Finally, VTA microglia exhibited an elevated aspect ratio (greater mitochondrial elongation) relative to NAc microglia at 16-18 mo (Fig. S6D). Therefore, some regional differences in microglial mitochondria that are evident in young adulthood (mitochondrial mass and number) are lost during aging, while other regional differences in microglial mitochondria are gained (elongation).

To further explore how aging impacts microglial mitochondria, we used the previously described gating strategy (Fig. S1D) for FACS-based analysis of microglia from 3 mo and 18 mo MG-MitoGFP mice. This analysis revealed that the median fluorescence intensity of GFP was significantly increased with age in both STR (containing NAc) and MB (containing VTA) microglia (Fig. S6E), pointing to increases in mitochondrial mass. Conversely, mitochondrial membrane potential (TMRM intensity normalized to GFP) was significantly reduced with age in both regions (Fig. 6D, E). To confirm changes to mitochondrial membrane potential and further elucidate the time course of such changes, we analyzed TMRM intensity of microglia from WT mice and included an intermediate 12–13 mo time point. In this analysis, MB microglia exhibited slightly more depolarized mitochondrial membrane potential (lower TMRM intensity) than STR microglia in 2–3 mo old mice (Fig. 6F). These regional differences grew progressively larger in 12-13 mo old mice (Fig. 6F) and 16–18 mo old mice (Fig. S6F). Examining the time course of TMRM changes within each region revealed subtle but significant depolarization in 12-13 mo MB microglia and pronounced depolarization in 16-18 mo MB microglia, compared to MB microglia from 2-3 mo mice. Interestingly, STR microglia exhibited hyperpolarized mitochondrial membrane potential (higher TMRM intensity) in 12–13 mo mice compared to 2–3 mo mice. At 16-18 mo, however, they showed depolarized mitochondrial membrane potential, relative to microglia from 2-3 mo mice (Fig. 6F). Together, these findings indicate that the functional status of microglial mitochondria changes significantly during aging, with key differences in the time course and magnitude of these changes across brain regions. Moreover, they reveal that remodeling of microglial mitochondrial status begins in middle age, rather than in the late stages of aging.

Targeted genetic manipulation of microglial mitochondria elicits changes in microglial morphology and gene expression
Results presented thus far indicate that there are correlations between region-specific microglial morphology and mitochondrial mass/number. They also reveal correlations between mitochondrial mass, mitochondrial elongation, mitochondrial membrane potential, and changes in microglial morphology and inflammatory profiles during responses to LPS challenge and aging. To further probe whether there are causal links between mitochondria and these microglial phenotypic attributes, we crossed CX3CR1CreER/+ mice and CX3CR1CreER/+;mitoGFP mice with Tfamfl/fl mice to achieve targeted deletion of mitochondrial transcription factor A (TFAM) in microglia (MG-TFAMKO or MG-mitoGFP-TFAMKO) (Fig. 7A). TFAM deletion in diverse cell types leads to reduced expression of electron transport chain subunits encoded by mitochondrial DNA and gives rise to changes in mitochondrial membrane potential and mitochondrial morphology, making this an ideal genetic manipulation to further elucidate the relationship between mitochondrial and microglial phenotypes71–75.

For these experiments, mice were injected with 4-hydroxytamoxifen and analyzed 2 months later to allow for both TFAM deletion and turnover of existing mitochondrial components that will be impacted by TFAM deletion, as mitochondrial proteins are among the most long-lived within the cell76. This interval also allows for the replacement of any peripheral CX3CR1+ cells that experience TFAM deletion in these mice. FACS followed by qPCR confirmed that this treatment paradigm resulted in downregulation of Tfam in both STR and MB microglia in MG-TFAMKO mice (Fig. 7B). Histology in fixed tissue revealed that microglial TFAM loss resulted in increased mitochondrial volume, mitochondrial number, and mitochondrial sphericity at the tissue (FOV) level in MG-mitoGFP-TFAMKO mice (Fig. 7C–F, Fig. S7A). Mean intensity of the mitoGFP signal was also elevated in MG-mitoGFP-TFAMKO mice in both NAc and VTA (Fig. S7A–C). This increase in GFP intensity appeared to vary substantially from cell-to-cell in MG-mitoGFP-TFAMKO mice and may reflect incomplete TFAM recombination in some microglia. Analysis of individual cells confirmed increases in mitochondrial number and trends toward increased mitochondrial sphericity in microglia from MG-mitoGFP-TFAMKO mice (Fig. S7D, E). This analysis also highlighted increased cell-cell variation in microglia from MG-mitoGFP-TFAMKO mice. Some properties of microglial mitochondria, such as FOV median and max mitochondrial volume, were unchanged following TFAM loss (Fig. S7F). Together, these findings confirm that this genetic manipulation induces changes to some of the same mitochondrial attributes (number, sphericity, GFP intensity) that differ across brain regions and that are remodeled during microglial responses to LPS and aging.

To determine how genetically-induced changes in mitochondrial status relate to microglial properties, we analyzed microglial density, tissue coverage, and individual microglial morphological complexity. Microglial density did not differ significantly between MG-mitoGFP-TFAMKO and control mice (Fig. 7G). However, microglial tissue coverage was significantly increased in both NAc and VTA microglia from MG-mitoGFP-TFAMKO mice (Fig. 7H), suggesting that this manipulation caused changes to microglial morphology. To investigate this possibility, we also reconstructed individual microglial cells and assessed branching complexity via Sholl analysis. The total number of Sholl intersections was elevated in microglia from MG-mitoGFP-TFAMKO mice, particularly in the NAc (Fig. 7I), supporting the idea that there are important links between mitochondrial state and microglial morphology. Moreover, this increase in morphological complexity suggests that TFAM deletion does not simply make microglia “reactive” or compromise their health. Finally, no significant changes were observed in FOV mitochondrial number (relative to FOV microglial volume) or mitochondrial mass in MG-mitoGFP-TFAMKO mice (Fig. 7J–K), which would be expected if both microglial volume and mitochondrial number/volume are increased in these mice.

To further elucidate how genetically targeted manipulation of microglial mitochondria affects microglial properties, we used FACS analysis of STR and MB microglia (gated via the previously described strategy, Fig. S1D) as well as qPCR for a panel of microglial- and mitochondrial-function related genes. FACS analysis revealed significant increases in microglial forward scatter (FSC, cell size) and side scatter (SSC, internal cellular complexity) in MG-TFAMKO samples (Fig. 8A), consistent with observed increases in microglial tissue coverage/volume (Fig. 7H). Protein level expression of CD45 and CX3CR1 (median fluorescence intensity, MFI, measured during FACS) differed across brain regions, but was not significantly altered by TFAM loss.

qPCR analysis revealed that multiple mitochondria-related genes were altered in response to TFAM knockout. This included increased expression of genes coding for components of the electron transport chain (Ndufa12, Atp5d, Cox4i1, Sdha), as well as genes related to mitochondrial ROS (Sod2 and Cat) and calcium handling (Micu1, Fig. 8B, Fig. S8A and Table S5). Mitochondrial biogenesis genes Nrf1 and Ppargc1a also showed significant changes, but in opposite directions (Fig. 8B, Fig. S8A and Table S5). Mitochondrial fusion gene Dnm1l was significantly decreased and there were no significant changes in expression of mitochondrial fission-associated gene Mfn1 (Fig. 8B, Fig. S8A and Table S5). Together, these data indicate that loss of microglial TFAM induces broad changes to expression of nuclear-encoded, mitochondria-relevant genes and suggest that multiple aspects of mitochondrial function are impacted by this manipulation.

These alterations in mitochondrial state upon TFAM knockout were accompanied by significant changes in genes related to key microglial functions. Proinflammatory transcripts Il1β and Tnfα were significantly increased in MG-TFAMKO microglia while Ifnβ1 was significantly decreased. For multiple microglial genes that show regional differences in expression (Cd45, Cd68, P2ry12), these regional differences were preserved in TFAM knockout mice, and were not significantly modulated by TFAM loss. Instead, Tgfβ1 and Cx3cr1, which have both been implicated in modulation of overall microglial phenotypes, were significantly increased in microglia from MG-TFAMKO mice (Fig. 8C). Post hoc analysis revealed that many of the gene expression changes associated with TFAM KO occurred in a brain region-specific manner. Increases in Tnfα were primarily observed in MB TFAM knockout samples, while increases in Tgfβ1 and Cx3cr1 were only prominent in STR TFAM knockout microglia (Fig. 8C and Table S5). Leveraging PCA analysis to examine these gene expression changes in an integrated fashion revealed that MG-TFAMKO samples from STR and MB appeared to cluster separately from one another (Fig. 8C and Fig. S8B–C), providing further evidence that manipulating mitochondrial function exerts distinct effects on STR and MB microglia.

Loss of TFAM causes subtle changes in LPS-induced mitochondrial remodeling and microglial responses to inflammatory challenge
In addition to defining how mitochondrial manipulation impacts basal microglial phenotypes, we sought to investigate the impact of TFAM knockout on microglial ability to respond to an inflammatory challenge. To this end, MG-mitoGFP-TFAMKO and MG-mitoGFP control mice were given i.p. injections of LPS, and microglial gene- and protein-expression were analyzed via FACS and downstream qPCR. To maximize ability to detect protein level alterations, as well as catch potential differences in time course of microglial transcriptional responses, samples were collected and analyzed at an intermediate time point (12 h) between those examined previously (4 h and 24 h, Figs. 4, 5). On the whole, TFAM loss did not appear to substantially impair remodeling of microglial mitochondria following LPS treatment. For example, both microglia from MG-mitoGFP control and MG-mitoGFP-TFAMKO mice showed increased mitoGFP at 12 h post LPS (Fig. 8D), consistent with previous observations of increased mitoGFP at 24 h post LPS (Fig. 4F). Moreover, the magnitude of the mitoGFP increase did not differ across genotypes. TMRM levels, which were seen to decrease at 24 h post LPS (Fig. 4F), had not yet declined at 12 h post LPS and did not differ across genotypes (Fig. S8D). Similarly, at the transcript level, LPS-induced changes in expression of mitochondria-relevant genes appeared to proceed irrespective of TFAM loss (Table S6). For example, Sod2 (up-regulated at 4 h and 24 h compared to saline) and Vdac1 (down-regulated at 4 h compared to saline) were still up- and down-regulated in TFAM KO microglia at 12 h post LPS (Fig. S8F, G). In cases where significant differences were observed across genotypes, many of these were already evident without LPS challenge. For example, Cox4i was significantly decreased and there were no significant changes in expression of mitochondrial fission-associated gene Mfn1 (Fig. 8B, Fig. S8A and Table S5). Together, these data indicate that loss of microglial TFAM induces broad changes to expression of nuclear-encoded, mitochondria-relevant genes and suggest that multiple aspects of mitochondrial function are impacted by this manipulation.

These alterations in mitochondrial state upon TFAM knockout were accompanied by significant changes in genes related to key microglial functions. Proinflammatory transcripts Il1β and Tnfα were significantly increased in MG-TFAMKO microglia while Ifnβ1 was significantly decreased. For multiple microglial genes that show regional differences in expression (Cd45, Cd68, P2ry12), these regional differences were preserved in TFAM knockout mice, and were not significantly modulated by TFAM loss. Instead, Tgfβ1 and Cx3cr1, which have both been implicated in modulation of overall microglial phenotypes, were significantly increased in microglia from MG-TFAMKO mice (Fig. 8C), and it remained elevated relative to controls after LPS (Fig. S8H).

The microglial response to LPS-challenge was also largely preserved at both the protein- and gene-expression, irrespective of TFAM loss. Microglia from both MG-mitoGFP control and MG-mitoGFP-TFAMKO mice showed increases in FSC (cell size) at 12 h post injection (Fig. 8D), consistent with observations at 24 h post LPS (Fig. 4F). TFAM KO microglia showed significantly higher FSC than control microglia, but this increased FSC was already evident in the absence of LPS treatment (Fig. 8A). Regional differences in expression of CD45 and CX3CR1 across STR and MB that were present at baseline in TFAM KO and control microglia (Fig. 4F, Fig. S4E, Fig. 8A) remained present at 12 h post LPS (Figs. 8D, and S8D). At the transcript level, the prominent increases in Tnfα and Il1β that are observed following LPS injection (Fig. 4B), were still observed in TFAM KO microglia (Fig. 8E and Fig. S8H) and did not differ significantly in magnitude from responses in control microglia. Similarly, LPS-induced downregulation of homeostatic transcripts Tgfβ1 and Cx3cr1 did not differ across genotypes (Fig. 8E and Fig. S8I). Expression levels of Cd68 and Itgam did differ significantly across genotypes, with TFAM KO microglia showing more muted reductions in expression compared to control samples at 12 h post LPS injection (Fig. 8E).

Altogether, these subtle differences in response to LPS challenge were evident in PCA analysis comparing the gene expression patterns of saline-treated MG-mitoGFP samples (Fig. 4B), untreated MG-TFAMKO samples (Fig. 8B), and 12 h LPS-treated control and MG-mitoGFP-TFAMKO samples. This analysis revealed that LPS-treatment was the strongest driver of sample clustering (Fig. 8G) on PC1, with genotype effects being evident on PC2. Together, these data suggest that microglial mitochondrial remodeling and changes in microglial attributes in response to acute inflammatory challenge remain largely intact in MG-mitoGFP-TFAMKO mice. Future studies using repeated or chronic inflammatory challenges will be needed to determine whether the subtle differences in mitochondrial remodeling that were observed lead to more prominent downstream shifts in microglial function.

Methods
Mouselines
C57Bl6 WT mice were obtained from the NIA Aged Rodent Colony and housed in the UCLA vivarium for at least one month before experiments. CX3CR1CreER/+ mice were originally obtained from Jackson Labs (stock #025524) and subsequently maintained in our colony41. Rosa26-lsl-mito-GFP mice (mitoGFP mice) were obtained from Dr. Jeremy Nathans (Johns Hopkins University) and are available at Jackson Labs (stock #021429). These mice enable cre-dependent expression of GFP that is fused to an N-terminal signal derived from mouse cytochrome c oxidase, subunit VIIIa, enabling specific localization to mitochondria40. Ai14 TdTomato reporter mice were obtained from Jackson Labs (stock #007914) and subsequently maintained in our colony46. Mice used for experiments were heterozygous for these transgenes. Tfamfl/fl floxed mice were obtained from Jackson Laboratories and have loxP sites flanking exons 6-7 of the transcription factor A, mitochondrial (Tfam) gene (stock #026123)75. These mice were crossed to the CX3CR1CreER mice with a final breeding strategy to generate CX3CR1CreER;Tfamfl/fl and CX3CR1CreER;Tfam+/+ littermate controls. In all experiments, balanced numbers of male and female mice were used. Mice were housed in a normal light/dark cycle and had ad libitum access to food and water. All experiments were performed in strict accordance to an animal use protocol (ARC−2018-103) approved by the Animal Care and Use Committees at UCLA.

Tamoxifen injections
In general, experiments were carried out without treating mice with tamoxifen. For select experiments as noted, 4-Hydroxytamoxifen (H7904-25mg, 95% pure z-isomer) was dissolved at 20mg/mL in ethanol. For injections preparation, 4-OHT was redissolved in ethanol and diluted in a 1:4 mixture of castor oil:sunflower seed oil (Sigma, Cat #s 259853 and S5007) to give a final concentration of 1mg/kg. CX3CR1CreER/+;mitoGFP mice at 3-4 mos received intraperitoneal injections of 4-OHT or vehicle (oil mixture) twice a day for 3 days at 1mg/kg. Mice were euthanized 3 weeks later for immunohistochemistry and flow cytometry. CX3CR1CreER/+;mitoGFP;Tfamfl/fl mice and CX3CR1CreER/+;Tfamfl/fl at 3-4 mos received injections of 4-Hydroxytamoxifen twice a day for 3 days at 1mg/kg. The mice were housed for 2 months after injections to allow for turnover of long-lived mitochondrial proteins76,91. Mice were then euthanized for immunohistochemistry or flow cytometry.

Lipopolysaccharide injections
CX3CR1CreER/+;mitoGFP and CX3CR1CreER/+;mitoGFP;Tfamfl/fl mice 3 to 7 months (mos) of age received single intraperitoneal injections of 1mg/kg of Lipopolysaccharide (LPS) (Millipore Sigma, strain O55:B5, cat# L2880-25MG) or saline. All injections were performed around ~10am. For immunohistochemistry, mice were deeply anesthetized via isoflurane inhalation and transcardially perfused with 1M phosphate-buffered saline (PBS; pH 7.4) followed by ice-cold 4% paraformaldehyde (PFA) in 1M PBS. For flow cytometry, mice were deeply anesthetized via isoflurane inhalation and transcardially perfused with 10 mL of chilled 1M PBS. Time points for experiments were 4h, 12h, or 24h post-injection.

Tissue collection and immunohistochemistry
Mice were deeply anesthetized via isoflurane inhalation and transcardially perfused with 1M PBS followed by ice-cold 4% PFA in 1M PBS. Brain tissue was isolated and postfixed in PFA for ~4 h at 4°C and then stored in 1M PBS with 0.1% sodium azide until sectioning. Coronal brain sections were prepared using a vibratome at 60 µm thickness. For immunostaining, free-floating brain sections were washed with 1M PBS (5 min) and then permeabilized and blocked in a solution of 2% bovine serum albumin (BSA), 3% normal donkey serum (NDS), and 0.01% Triton-X for 2 h with rotation at room temperature. Sections were then incubated overnight with primary antibodies prepared in 2% bovine serum albumin (BSA) and 3% normal donkey serum (NDS) at 4°C with rotation. Sections from aged mice were incubated in TrueBlack lipofuscin autofluorescence quencher, (5%; Biotium cat: 23007) for 60 s followed by a 3×5 min rinse in 1M PBS prior to incubation with primary antibodies. Following primary antibody incubation, sections were washed 3x with 1M PBS and then incubated with secondary antibodies prepared in 2% bovine serum albumin (BSA) and 3% normal donkey serum (NDS) at room temperature for 2 h with rotation. This was followed by washes in 1M PBS (3x) with a second wash containing 1:4000 dilution of DAPI in 1M PBS. Sections were mounted using Aqua-Poly/Mount (Polysciences cat: 18606) and cover-slipped. Primary antibodies used include: rabbit anti-Iba1 (1:800; Wako, catalog #019-19741), rat anti-CD68 (1:200; clone FA11, AbD Serotec, catalog #MCA1957), chicken anti-TH (1:500; Aves, catalog #TYH). Secondary antibody combinations include rabbit AlexaFluor-647, chicken AlexaFluor-594, rat AlexaFluor-594, or chicken AlexaFluor-405 (used at 1:1000; all raised in donkey; Jackson ImmunoResearch Laboratories). For each set of analyses, three brain sections containing nucleus accumbens (NAc) and prefrontal cortex (PFC) and three brain sections containing ventral tegmental area (VTA) were chosen from each mouse. Brain sections were selected based on well-defined anatomical parameters and were matched for anterior-posterior location.

Confocal image acquisition and analysis
Fixed tissue slides were imaged using a Zeiss LSM880 or Zeiss LSM700 confocal microscope. Within the NAc, images were acquired at the boundary between core and shell (identified anatomically) and include both subregions. In the VTA, images captured were medial to the medial lemniscus. For quantification of MitoGFP recombination within microglia, stacks of confocal images (z-stacks) with a z interval of 1 µm were taken using a 20x objective and imported into ImageJ software for analysis. Maximum intensity projections were counted manually for the % of microglia exhibiting MitoGFP expression. For 3D volumetric reconstruction of microglia and mitochondria, confocal z-stacks were acquired with a 63x objective and z interval of 0.3 µm and imported into Imaris (Bitplane) software for analysis. The surface reconstruction module was used for volumetric reconstruction of mitochondria and microglia within the entire field of view (FOV). Measures of the average volume of individual mitochondria, mitochondrial sphericity (degree of circularity, with 1 representing a perfect circle), and aspect ratio (longest axis of mitochondria/shortest axis of mitochondria) were generated within the Imaris surfaces module and exported for graphing and statistical testing. For FOV-based analyses, two or three images from separate brain sections were analyzed per mouse to obtain an average value for that mouse. 3 to 7 mice were analyzed per brain region, per age and/or per condition. In some cases, further analysis of individual microglia was carried out by selecting Imaris surfaces from an individual cell and the mitochondria within that cell. In these cases, 4-6 cells were analyzed per mouse. To select individual microglia for full 3D morphological reconstruction in MG-mitoGFP;Ai14 mice, the brain region of interest was located and centered using only TH staining and anatomical landmarks with 20x objectives. After switching to 63x objectives, TdTom+mitoGFP+ microglia within that field of view were imaged. Slight X-Y adjustments were made, if needed, to bring a TdTom+mitoGFP+ microglial cell away from the edge of the FOV to avoid truncating processes. Cells were excluded from reconstruction if their somas fell near the top or bottom edge of the brain slice rather than being situated near the middle of the z-stack. The Imaris filament tracer module was used for reconstruction and analysis of the branching morphology of individual cells, and surfaces were used to reconstruct mitochondria as described above. 1 or 2 cells were reconstructed per FOV per brain region, yielding 3-6 morphologically reconstructed cells per mouse. The Imaris filament tracer tool was also used for Sholl analysis, where each concentric circle was 1 µm apart. For analysis of mitoGFP signal intensity in MG-mitoGFP-TFAMKO and MG-mitoGFP control mice, mean fluorescence intensities for each individual, Imaris-reconstructed mitochondria in a field of view were extracted. Mean fluorescence intensity values for all reconstructed mitochondria from one mouse were then pooled (n=3 FOVs per mouse per brain region) and a cumulative distribution plot for that mouse and brain region was generated. Cumulative distribution plots from all mice from one genotype and brain region were then averaged to generate an average cumulative distribution for that group.

Acute brain slice preparation
CX3CR1CreER/+;mitoGFP or CX3CR1 CreER/+; mitoGFP;Ai14 mice at 1.5 to 2.5 months were anesthetized with isoflurane and perfused transcardially with 10mL of oxygenated, ice-cold N-methyl-D-glucamine (NMDG)-based solution containing the following (in mM): 92 NMDG, 20 HEPES, 30 NaHCO3, 1.2 NaHPO4, 2.5 KCl, 5 sodium ascorbate, 3 sodium pyruvate, 2 thiourea, 10 MgSO4, and 0.5 CaCl2, 10 glucose, pH 7.4 (310 mOsm). Brains were then rapidly dissected free and horizontal midbrain sections (230 µm thick) and coronal forebrain sections (300 µm thick) were prepared using a vibratome in ice cold NMDG-based cutting solution bubbled continuously with 95% O2/5% CO2. Following sectioning, sections were transferred to a holding chamber containing NMDG solution at 34°C for 5 min and then transferred to artificial cerebrospinal fluid (aCSF) solution at room temperature for 30 min recovery containing the following (in mM): 125 NaCl, 2.5 KCl, 1.25 NaH2PO4·2H2O, 1 MgCl2·6H2O, 26 NaHCO3, 11 Glucose and 2.4 CaCl2·2H2O and bubbled continuously with 95% O2/5% CO2.

Acute brain slice multiphoton imaging
Acute brain slices from NAc and VTA were imaged using a Leica Stellaris 8 Dive, multiphoton microscope equipped with a Coherent Chameleon Ultra II laser and a Leica 25x/1.00 NA W motCORR objective. During video acquisition, acute brain slices were continuously perfused with aCSF bubbled with 95% O2/5% CO2. Videos were acquired at least 60 µm below the surface of the slice and video acquisition was carried out within 1–3 h after sectioning. For analysis of microglial mitochondria only in CX3CR1CreER/+;mitoGFP mice, an excitation wavelength of 930 nm was used and GFP was detected via a non-descanned detector (463–537 nm). For analysis of microglial mitochondria and microglial cell process motility in CX3CR1CreER/+;mitoGFP;Ai14 mice, an excitation wavelength of 930 nm was used, and GFP and TdTom signals were detected simultaneously via two non-descanned detectors (detector 1: 463–537 nm, detector 2: 558–668 nm). Stacks of images encompassing entire microglial cells (z-step interval 1 µm) were acquired at 0.02 Hz (for mitochondrial motility only) and 0.008 Hz (mitochondrial motility + microglial cell process motility). To induce a focal laser lesion, exposure to high power illumination of 750 nm for 10 sec was applied 60 µm below the surface with maximal zoom in the space between 4-5 GFP+TdTom+ microglial cells. Imaging of microglia and their mitochondria post-lesion was then carried out as described above.

Analysis of microglial and mitochondrial motility
To analyze microglial and mitochondrial motility, 3Dmorph, a previously published automated analysis pipeline by York et al.92, was adapted for 2D analysis. Movies were pre-processed in ImageJ. Pre-processing included: background subtraction, median filter, drift correction (MultiStackReg ImageJ plugin), and bleach correction (Histogram matching ImageJ plugin). Individual cells were highlighted by ROI of maximum projections at each time point and the signal outside of ROI was removed. The adapted 3Dmorph script was applied to the final 2D movie to determine added/subtracted pixels, total pixels changed, and the area of TdTomato (microglia) and GFP (mitochondria) channels. To calculate microglial and mitochondrial motility, the total number of pixels altered during a 10-min interval were normalized to the total number of pixels making up the cell (cell area). For quantification focused on individual microglial branches, preprocessed videos were cropped to isolate entire primary branches of the cell from the point at which the primary branch connects to the cell soma out to the terminal endings of branches emanating from that primary process. Analysis and quantification were then carried out in the same manner as for entire cells. Mitochondrial mass of whole microglia or primary branching processes was calculated as GFP area divided by TdTomato area in final frame. Morphological complexity of microglia was analyzed using Sholl analysis through the Simple Neurite Tracer plugin in ImageJ42. Each concentric circle was 1 µm apart, like the morphological analysis conducted in fixed tissue. Values from Sholl analysis of cell branching in the first and last frame of 10-min videos were averaged to obtain an overall assessment of cell complexity throughout the imaging period. To analyze mitochondrial motility in 3D, additional analyses were carried out within Imaris (Bitplane) Software. The surface reconstruction module was used to create surfaces for GFP+ mitochondria in microglial cell processes where individual mitochondria can be easily tracked; microglial somas contain large mitochondrial networks, complicating tracking of individual mitochondria. 4D movement tracks were then generated for each reconstructed mitochondrion. Some manual editing of tracks was performed to ensure accurate tracking of reconstructed mitochondria across time. 3D positional information at the start and end of 10 min videos was used to calculate displacement of individual mitochondria. Individual mitochondrial displacements were averaged per cell to obtain average mitochondrial displacement within individual microglia. The volume for individual mitochondria was averaged across the initial frame and the final frame and volume of all mitochondria in a cell was summed to obtain the mitochondrial volume for each microglial cell.

Analysis of laser lesion response
Videos were preprocessed in ImageJ for background subtraction, median filter, maximum projection, and thresholding. Using an analysis strategy similar to that employed by others in the field50,51, circular ROIs were used to create three rings within images at defined times post-lesion (0, 5, 10, and 20 mins), with the first ring enclosing the lesion, the second ring at 15 µm from the first, and the third ring at 30 µm from the first. ROI rings were used as masks to extract pixels within each ring at each time point and the measure function was used to extract ring area and percent of area covered by thresholded pixels. The percent change of pixels within a ring was calculated as initial pixels minus final pixels divided by initial pixels.

Tissue dissociation and flow cytometry
2-4 mo, 12-13 mo, and 16-18 mo WT, CX3CRCreER/+;mitoGFP and CX3CR1CreER/+;Tfamfl/fl mice were deeply anesthetized with isoflurane and transcardially perfused with 10 mL of chilled 1M PBS. The brain was rapidly removed, cut coronally with a razor blade using anatomical landmarks and midbrain and striatum were dissected free. Samples were thoroughly minced with a razor blade on a chilled glass surface and transferred to 1.7 mL tubes containing chilled 1 mL Hibernate A Low-Fluorescence (BrainBits, Half100). Samples were then mechanically dissociated using gentle sequential trituration with fire-polished glass pipettes of decreasing diameter. Cell suspensions were pelleted via centrifugation and resuspended in 1M PBS + Debris Removal Solution (Miltenyi, 130-109-398). Debris removal was carried out according to the manufacturer’s instructions. Following debris removal, samples were resuspended in small volumes of 1M PBS and incubated with primary antibodies on ice for 20 min. Tetramethylrhodamine (TMRM) (Biotium, 70017) was used in some experiments to assess mitochondrial membrane potential. For these experiments, samples were incubated with TMRM 50nM for 20 min at room temperature, prior to antibody incubation. Following incubation, samples were washed with 1x PBS and filtered through a 35 µm filter (Falcon, 352235) prior to sorting. With the exception of TMRM incubation, throughout the experiment, samples were kept at 4°C. Samples were sorted using a FACSAria I cell sorter (BD Biosciences). The population of cells containing microglia could be readily identified based on forward scattering (FSC) and side scattering (SSC) properties. A gating strategy based on FSC and SSC width and height was used to select single cells and reduce dead cells and doublets. Microglial cells within this population were then identified and sorted based on combinations of microglial antibodies. Antibody combinations used included anti-CD45 (1:400; Biolegend, 147703), anti-P2RY12 (1:500; Biolegend, 848005), and anti-CX3CR1 (1:800; Biolegend, 149023). Microglia from CX3CR1CreER/+;mitoGFP mice were further gated from microglial antibodies to sort GFP+ cells. Placement of gates was verified using unstained samples as well as single color controls and Fluorescence Minus One (FMOs) controls. Gated Microglia were FACS sorted into 1.7 mL tubes containing 50 µl Pico Pure RNA extraction buffer and, following the sort, samples were incubated at 42°C for 30 min, and stored in RNase-free tubes at −80°C until further processing. Total sample processing time from brain dissection to completion of FACS purification ranged from 2-4 h depending on the sort order of individual samples. Samples were collected in batches with each day of sorting including a young adult, saline injected, or TFAM+/+ control mouse. Sort order of samples from specific ages, brain regions, treatment conditions, or genotypes was interleaved to avoid introduction of bias related to tissue processing length. FACS-isolation itself was carried out at 4°C. FACS-isolation of all samples for a given set of experiments was completed prior to downstream processing to allow simultaneous RNA isolation and cDNA preparation as described below.

Flow cytometry analyses
FCS files from sorting experiments were imported into FlowJo software for additional analyses of flow cytometry data. After placement of gates as described above, median fluorescence intensity measures were exported for populations of interest. Graphing and statistical analyses of MFI values were performed in OriginPro. To examine TMRM values normalized for mitochondrial volume/abundance, TMRM signal for each individual cell in a given sample was normalized to mitoGFP signal for that cell. Median values for this TMRM/mitoGFP measure were then calculated across all cells for a given sample. In some experiments, fluorescence signals across the entire population of cells from one sample were examined via cumulative distribution plots. For these analyses, a cumulative distribution plot was generated for each sample. Cumulative distribution curves from all samples in a group were then averaged to generate an average plot for that group.

RNA Extraction and qPCR
Total RNA was extracted from sorted cells using Pico Pure RNA isolation kit (Arcturus Bioscience) according to the manufacturer’s instructions. Single strand cDNAs were synthesized using Superscript III first-strand cDNA synthesis kit (Invitrogen, 18080051), according to the manufacturer’s protocol. Samples were subjected to PreAmplification-RT-PCR. TaqMan PreAmp Master Mix Kit was used for cDNA preamplification (Cat# 4391128; Applied Biosystems, Life Technologies), using pooled primer mixes of 20x dilution of TaqMan Gene Expression Assay and 80nM of custom-designed primer sets. cDNAs were pre-amplified in an Veriti 96-Well Fast Thermal Cycler (ThermoFisher Scientific, 4375305) using the program: 95°C hold for 10 min, 14 cycles of 90°C denaturation for 15 s, and 60°C annealing and extension for 4 min. Pre-amplification PCR products were immediately diluted five times with molecular biology grade water and stored at −20°C, or immediately processed for RT-PCR. For qPCR analysis of gene expression, single plex qPCR assays were performed on technical duplicates using TaqMan probes for each gene target or for endogenous control gene (Gapdh for LPS experiments, Gusb for MG-TFAMKO/MG-mitoGFP-TFAMKO experiments). TaqMan Advanced Fast PCR Master Mix was used (catalog #4444963; Invitrogen). To avoid amplification of any genomic DNA contamination, primers and probes that amplify across target gene exon–exon junctions were selected when possible. qPCRs reactions were run in a QuantStudio 5 using the program: 95°C hold for 20 s, followed by 40 cycles of 95°C denaturation for 3 s, and 60°C annealing and extension for 30 s. Calculations of relative expression and expression fold change from Ct data were conducted according to User Bulletin #2 for ABI Prism 7900 Sequence Detection System. For each target gene, the average Ct value for the endogenous control gene (Gapdh/Gusb) was subtracted from the average Ct value for the target gene, to obtain ΔCt. The relative expression was then plotted as 2-ΔΔCt. ΔΔCt was obtained by subtracting the average ΔCt of the control group from the average ΔCt of a given sample. Expression fold change was then plotted as 2-ΔΔCt. The average ΔCt of the control group was calculated separately for STR and MB samples. All TaqMan Assays and custom primers/probes that were used are detailed in Table S8.

Statistical analyses
Statistical analysis was performed in OriginPro, Matlab, and R. Heatmaps were generated in R using the ComplexHeatmap package by Gu Z et al93. PCAs were generated in R using the ggplot2 package by Wickham H94. Robust regression with False Discovery Rate control analyses were performed in R or Matlab. Cumulative distribution plots were analyzed for significant differences using Kolmogorov-Smirnov tests in OriginPro or Matlab. All graphed values are shown as mean ± SEM. Statistical details of experiments (statistical tests used, exact value of N, what N represents) can be found in results and the figure legends. In general, statistical significance was assessed using one- or two-way ANOVA. Post hoc comparisons were conducted using Student’s t-test with Bonferroni correction for multiple comparisons. Data are sufficiently normal and variance within groups is sufficiently similar for use of parametric tests. 2 tailed paired t-tests were used to compare trends across two different brain regions within individual mice. Statistical significance was considered if P<0.05. Asterisks were used to delineate statistical significance.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this research paper, the authors studied the role of microglial mitochondria in surveillance, response to injury, inflammatory stimulus or aging. They identify region-specific differences in the distribution of microglial mitochondria, together with a correlation of mitochondrial content with microglial morphological complexity. They also show that mitochondrial distribution is stable during microglial reaction to focal lesion, and interestingly, that there is no, or even negative correlation between mitochondrial mass and microglial motility. They also show mitochondrial remodeling at the early stages of microglial response to inflammatory challenge, and morphological/functional changes during aging. Finally, they show that genetic manipulation of microglial mitochondria leads to changes of mitochondrial morphology and gene expression.\nOverall, the topic of the paper is important and timely. The authors used largely appropriate experimental models and control measurements. While the main observations made by using microglial MG-mitoGFP and MG-TFAMKO mice concerning the role of mitochondria in microglial phenotype changes and responses are novel, some additional functional studies would be required to provide deeper mechanistic insight into morphological and gene expression changes related to mithochondrial function and TFAM in microglia.\nSpecific points:\n- The authors studied the nucleus accumbens (NAc) and ventral tegmental area (VTA), two brain regions where microglia display distinct cellular phenotypes. While this is a fair comparison to make, most studies assessing microglial morphology and function were performed in the cerebral cortex. The authors should not be requested to replicate all data sets in the cortex, but the statement “these observations indicate that regional differences in microglial cellular phenotype are accompanied by regional differences in mitochondrial metrics” would be highly supported by morphometry comparisons of NAc and VTA microglia with microglia in the cerebral cortex. The same applies to mitochondria numbers that appear to correlate with microglial morphological complexity primarily in the VTA.\n- One limitation of this work is that imaging studies were performed in brain slices ex vivo and slice preparation markedly impacts on microglial morphology and responses. The authors are aware of this issue, but the fact that microglial motility (total process remodeling, normalized to cell area) not correlated with time elapsed since preparation of brain sections or number of Sholl intersections, does not resolve the expected changes in cellular complexity of microglia upon slice preparation. Do the authors have data to show how microglial morphologies and mitochondrial networks related to microglial branching structure change in brain slices in different areas compared to the in vivo situation?\n- As the authors claim that mitochondrial content is correlated with microglial morphological complexity, it would be interesting to see whether microglial 3D morphology would be altered after deletion of mitochondrial transcription factor A (TFAM). Also, it would be good to test the possible influence of TFAM downregulation on microglial process motility and microglial reaction to focal injury. The results of these experiments would greatly enhance our understanding of the role of microglial mitochondria.\n- The authors show that changes in microglial function genes and those for some inflammatory mediators are region specific after TFAM deletion. While these data sets are interesting, in functional terms it would be more important to show how TFAM deletion influences mitochondrial genes and inflammation after LPS injection and how these changes correlate with microglial morphological states.", "reviewer_2": "Espinoza and colleagues use a similar approach with mitochondria-targeted GFP to profile mitochondrial dynamics in microglia from multiple brain regions in mouse acute brain slices in the steady state, in the course of laser lesions, normal aging, in response to LPS, and following prolonged knockout of the mitochondrial transcription factor TFAM. The authors reveal many interesting insights, including regions variability in mitochondrial density and morphology, a lack of mitochondrial dynamics during process extension, and increased mitochondrial number in aging. While these findings are valuable to the field, it should be noted that they are descriptive, and causal links between microglial function and mitochondrial dynamics cannot be made from these data. However, the authors are generally careful to acknowledge this point. Additionally, transcriptomic experiments are fraught with potential issues in experimental design and execution, and would be significantly improved with the suggestions laid out below.\nA major concern is lack of proper RNA seq from TFAM null microglia. TFAM is known to control mtDNA-cGAS-Sting (see work of West and Shadel). Any insight into whether perturbing TFAM is affecting this pathway is necessary and important for the field.\n• Major comments\no The choice to forgo tamoxifen in many experiments with the Cx3cr1-CreER is questionable. While leaky expression is indeed observed with this driver, and this can, in theory, lead to mosaic expression, it is very unclear if this yields an unbiased representation of microglia. Reconstruction of these cells from animals treated with tamoxifen also should not be prohibitive due to crowding. As autofluorescence in green channels is a genuine concern in aged mice or in cases of neurodegeneration, the value of this method comes into question if tamoxifen truly cannot be used. Figure S1 should include: 1) a representative gating strategy for tamoxifen-treated mice using the same voltages and instrument as in Fig. S1D (where higher GFP expression may be observed), and 2) quantification of GFP+ microglia from tamoxifen treated and untreated animals to show even representation. Alternatively, these cells could be FACS sorted from treated and untreated mice to show that there is no bias toward different microglial states in the untreated mice.\no The authors should acknowledge that the Cx3cr1-CreER driver as well as IBA1 staining also targets border-associated macrophages which are not easily visually distinguished from microglia.\no Correlations which are nonsignificant (P > 0.05), such as in S2H-K should be clearly described as being nonsignificant. In these cases, and in S3D-F, p-value correction, such as FDR correction, should also be applied to account for multiple comparisons. Likely none of these associations will still be significant after correction.\no Figure 4 is largely unconvincing, perhaps as a result of incomplete microglia purification. Is the entire FACS gating strategy for microglia shown in Fig. 4A? If so, ignoring FSC and SSC has almost certainly introduced a great deal of myelin debris into the final lysate. Additionally, far too many cells (the vast majority) appear to be CD45+, which is extremely unlikely unless pre-enrichment was performed (not mentioned in methods). The authors should show the complete gating strategy in a supplemental figure and should include representative gating for unstained controls and fluorescence-minus-one (FMO) controls to ensure that these antibodies worked correctly and voltages were set to reasonable values. Furthermore, were voltages set correctly, microglia should be CD45 intermediate, not CD45 high. The P2RY12+, CD45int population seen outside the gate may actually be true microglia. This does not appear to be the same scheme shown in Fig. S1D, which is far more rigorous.\n  As a result, upregulation of a number of pro-inflammatory genes, including the pro-inflammatory cytokine genes Il1b and Tnf is very modest, just one standard deviation above the mean at 4hrs post-LPS, and indistinguishable from saline controls at 24hrs. With the exception of Cd45 and Itgam, all gene comparisons are <2 SD. Relative expression should be shown as fold-increases as is the standard for qPCR, which will likely highlight these modest changes, and all samples should be shown. Curiously this is performed in Fig. 8 (albeit strangely), so clearly the authors are aware of this convention. Normalization to Gapdh may have been an issue here, given the potential metabolic shifts observed in these samples. Finally, improper statistics were performed. ANOVAs were performed without multiple testing correction, and no post-hoc analysis was performed.\n  PCA and correlation plots are largely uninformative given the small number of genes sampled\n  All of these issues would likely be remedied with a well-designed RNA-seq experiment on purified microglia using the sorting scheme shown in Fig. S1D.\no While the motivation to ablate TFAM in microglia is not clear from Fig. 4, it is still a very interesting experiment for the field, as a whole. The finding of increased coverage of microglia in Fig. 7I is very interesting and somewhat unexpected but would be bolstered significantly by counting individual microglia using either the existing images or flow cytometry to determine if these is proliferation or increases in size or process extension.\no For Fig. 8 to be considered reliable, Tfam must be shown to demonstrate effective knockout and also microglia purity. Unlike in Fig. 4, ∆∆Ct values are shown, rather than Z-scores, but again fold-change would be much more interpretable. Here again the use of Gapdh as a normalization control is questionable given the direct targeting of metabolism with Tfam knockout. Again, this experiment would be significantly improved with a well-designed RNA-seq experiment on purified microglia using the sorting scheme shown in Fig. S1D.\n• Minor comments\no If the authors have set alpha to 0.05 for these experiments, P = 0.05 should not be marked as significant (see Fig. 1L)\no Figure callouts frequently occur out of order. Panels should appear in the order they are mentioned in the text. Similarly, some panels appear to never be referenced and should be either mentioned or removed, e.g. S2H, S2J-K.\no The use of multiple Y-axis scales (as in S2F-G) is not ideal data visualization and should be avoided. Faceting or simply presenting a scatter plot is far more interpretable.\no All heatmaps should show all samples, rather than just the group average.\no Flow cytometry data should be displayed as contour plots with outliers shown. At present, with scatter plots, it appears that many populations are split arbitrarily. While this may not be the case, this is important for showing the true distribution.\no Data presentation in Figs. 6F and 7J is nonstandard and makes use of pseudoreplication by treating each cell or mitochondrion as an independent measurement. Instead, comparisons of median fluorescence intensity for each mouse / region should be displayed (ideally as boxplots) and compared statistically. These findings are potentially very interesting and proper analysis would better highlight them.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The manuscript presents valuable findings about the location of mitochondria in microglia and will, therefore, have some implications for the microglia community. The results align with previous in vivo microglia data analysis (Maes et al. 2023, cited by the authors) from a different mitochondria labeling mouse model using fixed and live in vivo imaging in the nucleus accumbens (NAc) and the ventral tegmental area (VTA). The data presented is solid and broadly supports the claims. The most compelling data is that dynamic microglia processes often lack mitochondria, which is counterintuitive to likely energy-demanding microglial processes and the concept of mitochondria as an energy source. The manuscript contains a lot of experiments yet does not go beyond this observation. The manuscript remains mainly descriptive of the microglia and mitochondria phenotypes. The rationale for exploring different environmental alterations and their impact on the microglia-mitochondria network is not straightforwardly defined, leaving the impression of a phenotype search.\nIntroduction:\nThe introduction needs to provide more confidence and perspective on the goal of the manuscript. Although the first paragraph introduces microglia and their ability to shift between functional states, the concept of a microglia state must be defined. The second paragraph introduces the expression of different cell surface receptors, finishing with a question of how microglia interpret different signals. From the third paragraph, it is not intuitive how mitochondria as “cell attributes” are involved in the previously introduced perspective of functional states and interpreting different signals. What impact would the mitochondrial network remodeling have on the microglia? Furthermore, if the authors consider “cell attributes” as intrinsic properties of microglia to define their functional state, then this should be clearly outlined and justified. Lysosomes, for example, would be a more intuitive “attribute” to distinguish the different microglial states in the conditions tested within this manuscript compared to mitochondria.\nIn addition, the introduction across all three paragraphs consists mainly of attribute lists not followed up in the manuscript, such as high-fat diet, pollution exposure, and TLR4 and TGFBR2. Also, the list of mitochondrial parameters needs to be more focused since the study does not evaluate buffering calcium, metabolites, or interactions with other organelles.\nOverall, the introduction only loosely connects mitochondria distribution and potential microglia state and does not provide a clear rationale for justifying the study.\nResults:\nFig 1/S1-2:\ni) The authors performed a detailed verification of their mouse model to ensure that mitochondria were labeled. This data set looks convincing. However, without ultrastructural analysis, it cannot be ensured that MG-mitoGFP mice “accurately renew the in vivo distribution of microglia mitochondria.” If the authors seek this accuracy as they write, they must perform ultrastructural analysis.\nii) Since the main text states that mitoGFP recombination was substantial without tamoxifen injection, is the data in Fig. S1 without tamoxifen injection, while Fig. 1 with tamoxifen? For clarification, the authors should annotate in the figure legend if they have administered tamoxifen. If Fig. S1 is without tamoxifen, the authors should validate the model again with tamoxifen since tamoxifen could potentially affect the mitoGFP distribution.\nFurthermore, for Fig. 1F, the analysis has been done without tamoxifen for rigorous analysis. This is surprising since microglia coverage across tissue is not overlapping, and confocal images should be sufficient to resolve the mitochondria signature for each microglia.\niii) How relevant is the correlation between mitochondrial number and microglia complexity/number of intersections? Are these just larger cells with more mitochondria? Based on the data in Fig 1E, the data point variation for each region is small. Furthermore, how have the microglia for reconstruction been selected?\nWhat is the rationale for choosing the “maximum number of Sholl intersections”? Which radius size has been used? This can severely impact the readout. Furthermore, was this maximum always at a similar distance from the soma, or has there been some variability? This could explain potential variability in the data. The same question applies to Fig. S3E. The value used for sholl analysis in Fig. S3D is also unclear.\niv) The choice for looking at the NAc and VTA needs better clarification, also in perspective for the later analysis with LPS and aging. Are those brain regions particularly vulnerable?\nv) Based on the figure legend description in Fig 1. It is understood that microglial volume is calculated per field of view (tissue coverage)- and mitochondrial number, volume, and mass are then calculated based on this FOV microglial volume. However, the text states, “greater number of mitochondria relative to cell volume, as well as a greater total mitochondrial volume relative to cell volume (mitochondrial mass)”. If the quantification is relative to total microglial volume per FOV, and not on a per-cell basis, stating ‘cell volume’ is confusing. For example, Fig. 1D-E, is the relative ‘microglia volume’ in 1D the same denominator as ‘cell volume’ in 1E? In addition, does the number of mitochondria within a FOV bring us important information? Since many microglial processes not connected to a cell body are present within a brain volume, these averages do not indicate information on a per-cell basis. The authors should identify precise nomenclature and analysis methods, describe this in detail in the methods, and use the terminology consistently throughout the manuscript.\nvi) The mitochondrial field focuses more on measuring network connectivity, i.e., fragmented or connected networks. This metric is not shown throughout the manuscript but would be an essential feature to describe when considering mitochondrial structural network changes.\nFig 2:\ni) The conclusion of Fig. 2C needs to be clarified. If VTA and NAc show the same motility, how can the conclusion be made that the VTA is surveying less based on their morphology?\nii) Do the motile processes contain mitochondria? Or are motile mitochondria only found in primary processes that are more stable? (also related to Fig. 3).\nFig 3:\ni) This data is very interesting and raises exciting new questions; however, no quantification supports the concluding statement.\nii) At this resolution, it seems difficult to quantify and confirm that the mitochondrial networks remain stable after focal injury. Can the authors image at the resolution seen in Fig 2 and provide data analysis of mitochondrial network motility before and after injury? Do all mitochondria stop moving, or only a few travel within the extending process?\nFig 4/5:\ni) If mitochondria are not linked to microglial tissue surveillance and injury, as the authors conclude in Fig. 2J and Fig. 3, respectively, how do they rationalize using LPS? Based on live imaging, mitochondria are not linked in a “direct fashion to baseline surveillance” or injury-induced damage. Why would it suddenly be related to an inflammatory response induced by LPS?\nii) QPCR of Dnml1 is used to indicate fission- however, this data should also be reflected in the image analysis in Fig 5. Could the authors indicate changes in the median volume of organelles (per cell) to compare alongside the mitochondrial number and aspect ratio? If the mitochondria are indeed elongated as indicated by an aspect ratio measure, the median volume of organelles per cell should increase (aligning with a mass increase) since the number of mitochondria did not change between saline and LPS for both brain regions.\niii) Do the authors have the details of the post-hoc analysis of 5B-D?\niv) Technical aspect related to sampling for flow cytometry. The method section must clarify whether the samples were processed sequentially or in batches. Furthermore, information related to the total time from tissue removal to sorting must be included. It has been shown that the microglia transcriptional profile adapts within 30 minutes of incubation on ice (Marsh 2022. DOI: 10.1038/s41593-022-01022-8). This can result in misleading results. Therefore, the authors should add the animal grouping strategy and total processing time to the methods and, if necessary, repeat the experiments when the timing is longer than 30 min.\nv) How was the correlation analysis performed in Fig. 4D? Cherry-picked genes were connected to putative microglial states, an aspect that is also not well-defined, introducing a bias. Also, the explanation of the “few CD68 correlations with other genes” does not seem solid (Fig. S4B) since the heatmap shows much more correlation than any of the other plots in Fig. 4.\nFig 6:\nMicroglia are known to show a sex-specific response also in adulthood and, especially, during aging. Therefore, the authors must precisely annotate the data plots, indicating which data points come from male or female animals.\nFig 7:\ni) Related to the mitochondrial quantification in 7D: Is this data represented as an average volume of all organelles within the cell? If yes, it would be better to use a median, as small mitochondria can skew the average value and misrepresent the data.\nii) Mitochondrial analysis: In Fig. 7, the analysis focused on volume, number, and sphericity, whereas earlier figures describing the mitochondria in fixed tissue focused on mass, number, aspect ratio, volume, sphericity. To allow comparison between the datasets, the authors should be consistent and include all metrics for all figures. Furthermore, the methods section needs to include a detailed description of the analysis method for each parameter.\nFig 8:\nHow do the microglia change after TFAM KO? A two-month waiting period may allow time for compensatory mechanisms to circumvent initial damage.\nDiscussion:\nIn line with the loose introduction, the discussion is also only superficial. It mostly repeats the results without putting the research data into the context of the literature. For example, a reported result is increased mitochondrial mass. What does increased mitochondrial mass imply? The manuscript would enormously benefit from a complete revision of the introduction and discussion to make it coherent.\nOverstatements:\nThe manuscript suffers from several overstatements and preliminary conclusions, which must be fixed. A few examples are listed below:\ni) “First time” in the introduction.\nii) The authors regularly write “in vivo” within their manuscript, yet it becomes quickly apparent that the majority of the experiments are performed in a fixed tissue environment. This should be clarified in the text to avoid misunderstandings.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We thank the reviewer for recognizing the timely contributions of our study to the field and for agreeing that experiments are well designed. We also fully agree that additional functional studies that probe mechanistic links between discrete mitochondrial properties and specific microglial attributes are a critical future direction for our work and the field more broadly. We hope the reviewer will agree that, with our additional experiments and analyses, this study provides an essential foundation for that future work.\nSpecific points:\n- The authors studied the nucleus accumbens (NAc) and ventral tegmental area (VTA), two brain regions where microglia display distinct cellular phenotypes. While this is a fair comparison to make, most studies assessing microglial morphology and function were performed in the cerebral cortex. The authors should not be requested to replicate all data sets in the cortex, but the statement “these observations indicate that regional differences in microglial cellular phenotype are accompanied by regional differences in mitochondrial metrics” would be highly supported by morphometry comparisons of NAc and VTA microglia with microglia in the cerebral cortex. The same applies to mitochondria numbers that appear to correlate with microglial morphological complexity primarily in the VTA.\nThe reviewer rightly points to the utility of analyzing additional brain regions to better place our findings from the VTA and NAc in context.\n→ To respond to this concern, we used triple transgenic MG-mitoGFP;Ai14 mice to analyze microglia and their mitochondria in prefrontal cortex (Fig. 1G-J, and Fig. S2G,J). These triple transgenic mice allow for rigorous reconstruction of the branching morphology of individual microglial cells, together with reconstruction of that cell’s mitochondria. This analysis revealed that morphological complexity of PFC microglia (total process length and total branch points) was similar to that of VTA microglia. Morphological complexity of NAc microglia was elevated compared to both of these other regions. In terms of the relationship between morphology and mitochondria, the abundance of mitochondria in PFC microglia peaked at the same distance from the soma as the max number of Sholl intersections (Fig. S2G), similar to what was observed for VTA and NAc microglia (Fig. S2H,I). The correlation between mitochondrial and cell morphological parameters continued to be most evident in VTA microglia, with one significant association (P = 0.02) and three nearly significant associations (P = 0.05, P = 0.07, P = 0.08, Fig. S2J). The correlation between mitochondrial and cell morphological parameters was not as prominent in PFC or NAc microglia. PFC microglia showed one significant association (P = 0.02) and one nearly significant (P = 0.05). NAc microglia showed one significant association (P = 0.03), and two nearly significant associations (P = 0.08, P = 0.08, Fig. S2J). All together, these data suggest that there are links between microglial mitochondria and baseline morphological features of these cells, with some regional variation in the strength of this association.\nWe would also like to refer the reviewer to a study by Pietramale et. al (BioRxiv 2024.10.15.618505) that was co-submitted with our study. In this work, the authors carry out a number of similar analyses within somatosensory cortex, providing additional contextual information for whether what we observe in NAc and VTA is similar or distinct from what is observed in cortex.\n- One limitation of this work is that imaging studies were performed in brain slices ex vivo and slice preparation markedly impacts on microglial morphology and responses. The authors are aware of this issue, but the fact that microglial motility (total process remodeling, normalized to cell area) not correlated with time elapsed since preparation of brain sections or number of Sholl intersections, does not resolve the expected changes in cellular complexity of microglia upon slice preparation. Do the authors have data to show how microglial morphologies and mitochondrial networks related to microglial branching structure change in brain slices in different areas compared to the in vivo situation?\nWe are glad that our awareness of the limitations of acute brain slice preparations is evident to the reviewer. The reviewer is correct that microglia in acute brain sections exhibit simplified morphology compared to that seen in vivo. This is widely known in the field and is evident in direct comparisons of Sholl intersections analyzed in fixed tissue compared to in acute brain sections (Reviewer Figure 1, see also PMID 28689984 with fixed tissue reconstructions of VTA/SNc/SNr microglia in Fig. 2C and dye filled VTA/SNc/SNr microglia from acute brain sections in Fig. S2I). Despite this reduced complexity, core features of microglial cell process motility are preserved in acute brain sections. This includes ability to respond to repeated changes in neuronal activity (PMID 25100586), chemotaxis toward point sources of ATP (PMID 25100587, PMID 29290552), and impact of potassium on ramification and tissue surveillance (PMID 29290552). In the present study, our objective is to gain initial insight into whether features of microglial mitochondria are clearly linked to motility of these cells. Given that correlations between microglial branching and mitochondrial volume are still evident in acute brain sections (Fig. S3D) and that microglial and mitochondrial motility are not significantly correlated with time since brain slice preparation (Fig. S3E,F), we feel that this preparation is appropriate for initial investigations of the relationship between microglial motility and their mitochondria.\n→ To address the reviewer’s concern, we modified the wording of the results to say “Although microglia show more simplified branching in acute brain sections, total mitochondrial volume and total number of microglial Sholl intersections were positively correlated (Fig. S3D), indicating that alignment of mitochondria with morphological architecture of the cells was preserved in acute brain sections.”\n- As the authors claim that mitochondrial content is correlated with microglial morphological complexity, it would be interesting to see whether microglial 3D morphology would be altered after deletion of mitochondrial transcription factor A (TFAM). Also, it would be good to test the possible influence of TFAM downregulation on microglial process motility and microglial reaction to focal injury. The results of these experiments would greatly enhance our understanding of the role of microglial mitochondria.\nWe completely agree that assessment of microglial cell process motility and responses to laser lesion in the context of microglial TFAM knockout would be extremely useful analyses to carry out. At present, we do not have transgenic lines that enable microglial TFAM knockout and live imaging of microglia. This would require generation of CX3CR1CreER/+;TFAMfl/fl;Ai14 mice or similar, making these experiments a key future direction but beyond the scope of the present study.\n→ To address the reviewer’s first point, we carried out additional analysis of microglial attributes following TFAM knockout in fixed tissue. Density of microglia in NAc and VTA was not significantly altered by loss of TFAM (Fig. 7G). We also carried out Sholl analysis of individual microglia (Fig. 7I) and found a significant increase in total number of intersections in TFAM KO compared to control mice, particularly in the NAc. These findings support the conclusion that a change in mitochondrial status is accompanied by a change in morphological attributes of microglia. Moreover, this increase in morphological complexity upon TFAM loss argues against the idea that this manipulation simply makes the microglia “sick” or “reactive.”\n- The authors show that changes in microglial function genes and those for some inflammatory mediators are region specific after TFAM deletion. While these data sets are interesting, in functional terms it would be more important to show how TFAM deletion influences mitochondrial genes and inflammation after LPS injection and how these changes correlate with microglial morphological states.\nUnderstanding how mitochondrial state regulates key microglial functions, including their responses to pathological challenges, is absolutely an important future direction for this work and for the field.\n→ As the reviewer suggested, we have treated MG–TFAM-KO mice and controls with LPS and used a combination of flow cytometry and qPCR to analyze the microglial response to this inflammatory challenge. In our initial FACS analyses of microglial responses to LPS injection, we noted a substantial increase in intensity of the mitoGFP signal in both striatum (STR) and midbrain (MB) microglia (Fig. 4F). This increase in mitoGFP was still observed in TFAM KO mice at 12hrs post-LPS injection (Fig. 8D), suggesting that loss of TFAM does not prevent microglia from mounting a substantial mitochondrial biogenesis response to LPS challenge. In our initial qPCR analysis of responses to LPS, 10/14 mitochondria-relevant genes showed significant changes in expression level following LPS (Fig. 4B). The majority of these mitochondria-relevant genes behaved similarly in this new set of LPS experiments irrespective of genotype, suggesting, again, that microglial capacity to engage in mitochondrial remodeling in response to LPS challenge is largely intact in TFAM KO mice. Only 5 mitochondria-relevant genes (Cox4i1, Vdac, Micu, Mfn1, and Mavs) showed significant differences across genotype in the context of LPS challenge. In most cases TFAM KO microglia showed the same type of change in expression as control microglia following LPS treatment, and simply differed in the magnitude of up- or down-regulated expression compared controls.\nPerhaps somewhat predictably, as capacity to remodel mitochondria is largely intact, changes in microglial attributes in response to LPS were also largely similar across MG-TFAMKO mice and controls. Inflammatory factors such as Tnfα and Il1β were elevated following LPS across brain regions and genotypes. Decreases in Cd68 were slightly more muted in KO mice, as were decreases in Itgam (CD11b). When taking expression of all analyzed genes into account, PCA analysis shows that LPS is the strongest driver of cell phenotype (driving variance on PC1) and genotype is a secondary determinant of variance on PC2. These observations suggest that the impact of TFAM loss on microglial responses to inflammatory challenge are subtle and might be more apparent in the context of repeated or chronic inflammatory challenges.\nIt is worth remembering that TFAM KO alters mitochondrial state via impacting the mitochondrial DNA. Out of 96 genes that code for components of the electron transport chain (ETC), 13 reside in the mtDNA rather than nuclear DNA. Altered ETC function as a result of TFAM KO can impact mitochondrial respiration, mitochondrial calcium buffering, and reactive oxygen species production, among other mitochondrial attributes. This broad impact on multiple aspects of mitochondrial state is precisely what made this manipulation attractive as a tool for probing links between mitochondria and microglial phenotypic attributes. However, TFAM KO will not impact all aspects of mitochondrial state and may exert only mild effects on mobilization of nuclear-encoded mitochondrial genes, particularly during an acute pathology response. As a field, we ideally need to generate synergistic information about how distinct manipulations of mitochondrial function regulate microglial attributes and functions in physiological and pathological contexts.\nWe thank the reviewer for the recognition that our work provides valuable insights for the field. We have carried out additional experiments, additional analyses, and made changes to the figures to try and address concerns about qPCR-based analyses.\nA major concern is lack of proper RNA seq from TFAM null microglia. TFAM is known to control mtDNA-cGAS-Sting (see work of West and Shadel). Any insight into whether perturbing TFAM is affecting this pathway is necessary and important for the field.\nThis is a valuable suggestion, and we have added qPCR of cGAS (Mb21d1) and Sting (Tmem173) to the panel of genes being analyzed following TFAM KO (Fig. 8B). cGas differed significantly across brain regions (higher in midbrain, P = 0.03, Table S5) but did not differ across TFAM KO vs control samples. Sting showed trends toward downregulation in KO microglia from both brain regions, but these did not reach significance (P = 0.416, Table S5).\n• Major comments\no The choice to forgo tamoxifen in many experiments with the Cx3cr1-CreER is questionable. While leaky expression is indeed observed with this driver, and this can, in theory, lead to mosaic expression, it is very unclear if this yields an unbiased representation of microglia. Reconstruction of these cells from animals treated with tamoxifen also should not be prohibitive due to crowding. As autofluorescence in green channels is a genuine concern in aged mice or in cases of neurodegeneration, the value of this method comes into question if tamoxifen truly cannot be used. Figure S1 should include: 1) a representative gating strategy for tamoxifen-treated mice using the same voltages and instrument as in Fig. S1D (where higher GFP expression may be observed), and 2) quantification of GFP+ microglia from tamoxifen treated and untreated animals to show even representation. Alternatively, these cells could be FACS sorted from treated and untreated mice to show that there is no bias toward different microglial states in the untreated mice.\nWe appreciate the reviewer’s desire to ensure that analyzed cells are accurately representative for the overall microglial cell population. Given that our initial histology and FACS-based quantification of mitoGFP positive microglia indicated that greater than 80% of the cells were expressing mitoGFP in the absence of tamoxifen injection, we felt confident that we were not analyzing a limited subset of microglia and that results should be representative of the microglial population as a whole.\n→ At the reviewer’s suggestion, we have carried out additional experiments to treat mitoGFP mice with 4-hydroxytamoxifen (4HT) and compare microglial properties from 4HT- and oil-injected mice. FACS-based analyses indicate that 4HT injection increased the % of recombined cells from ~85% to ~94% (Fig. S1D-G). Reviewer Figure 2 also confirms that FSC (cell size), SSC (internal complexity), mitoGFP, TMRM, CX3CR1, CD45, and P2RY12 median fluorescence intensities (MFI) are not different between microglia from 4HT-injected and oil-injected mice. Based on these additional analyses, we feel that analysis of microglia and their mitochondria in untreated mice does not yield a biased assessment of these cells or their organelles.\n→ To address the reviewer’s comment about autofluorescence in aging mice (which is indeed a concern for accurate analysis of microglial properties and functions), we added representative images to Fig. S6A. These images illustrate that our tissue processing with True Black eliminates the majority of autofluorescent signals from tissue of aging mice. Residual dim autofluorescence in the somas of some microglia colocalize with CD68, and mitoGFP signals are clearly distinguished above these residual signals.\no The authors should acknowledge that the Cx3cr1-CreER driver as well as IBA1 staining also targets border-associated macrophages which are not easily visually distinguished from microglia. We have added the following statement to the discussion:\n“Border associated macrophages (BAMs) can also be labeled by Iba1 immunostaining and are expected to experience recombination and mitoGFP expression in CX3CR1CreER/+;mitoGFP mice. As BAMs are primarily concentrated in the meninges and vascular interfaces and are significantly less abundant than microglia, we are confident that our results are focused on parenchymal microglia. Nonetheless, future studies employing markers of BAMs can explicitly analyze the mitochondria of BAMs and identify similarities and differences of BAM and microglial mitochondria and how these organelles regulate cell function.”\no Correlations which are nonsignificant (P > 0.05), such as in S2H-K should be clearly described as being nonsignificant. In these cases, and in S3D-F, p-value correction, such as FDR correction, should also be applied to account for multiple comparisons. Likely none of these associations will still be significant after correction. We thank the reviewer for helping to ensure rigor of our analyses. We have followed the reviewer’s request to add “n.s.” to more clearly indicate linear regression analyses where no significance was present. We also re-analyzed all linear regressions in the manuscript using robust regression with false discovery rate control and now report statistics based on these analyses.\no Figure 4 is largely unconvincing, perhaps as a result of incomplete microglia purification. Is the entire FACS gating strategy for microglia shown in Fig. 4A? If so, ignoring FSC and SSC has almost certainly introduced a great deal of myelin debris into the final lysate. Additionally, far too many cells (the vast majority) appear to be CD45+, which is extremely unlikely unless pre-enrichment was performed (not mentioned in methods). The authors should show the complete gating strategy in a supplemental figure and should include representative gating for unstained controls and fluorescence-minus-one (FMO) controls to ensure that these antibodies worked correctly and voltages were set to reasonable values. Furthermore, were voltages set correctly, microglia should be CD45 intermediate, not CD45 high. The P2RY12+, CD45int population seen outside the gate may actually be true microglia. This does not appear to be the same scheme shown in Fig. S1D, which is far more rigorous.\nWe thank the reviewer for pointing out aspects of this figure that could be confusing or misleading. As described in the methods of the original submission, our tissue dissociation workflow includes use of Miltenyi debris removal solution, which substantially reduces debris (including myelin debris) in tissue suspensions. As described in the methods, FSC and SSC gates are then used to isolate the population likely to contain cells. This is further refined using gates for FSC-H and FSC-W as well as SSC-H and SSC-W to reduce dead cells and doublets. The Fig. 4A panel in the original submission was only meant to be a schematic to indicate that FACS-purification of microglia was employed. However - as the reviewer indicates - this could be misinterpreted as being meant to illustrate the full gating strategy used.\n→ To respond to this concern, we have replaced the gating “schematic” in Fig. 4A with a more general cartoon of FACS sorting.\n→ We have added text throughout the results to clarify that the gating strategy shown in Figure S1 is being used. For example, the description of FACS experiments in Fig. 4 now reads “Young adult (3-4mo) MG-MitoGFP mice were given i.p. injections of saline or LPS and microglial gene- and protein- expression were analyzed via FACS, using the previously described gating strategy to identify and isolate microglia (Fig. S1D), followed by downstream qPCR (Fig. 4A).”\n→ We have added the following statement to the methods “Placement of gates was verified using unstained samples as well as single color controls and Fluorescence Minus One (FMOs) controls.”\nAs we are already stretched for space in supplementary figures, we feel it should be sufficient to describe these standard approaches and controls in the methods. We also include a representative example in Reviewer Figure 3.\nRegarding observed CD45 expression levels - we perfume animals prior to tissue dissociation and observe very few CD45 high macrophages using the gating strategy described above. We routinely use this and similar FACS gating strategies in the lab and have carried out RNAseq analysis on isolated cells for multiple projects. Mining data from one of those projects (PMID 28689984) illustrates that cells isolated using our workflows express high levels of microglial markers and low levels of macrophage markers (Reviewer Figure 4).\n§ As a result, upregulation of a number of pro-inflammatory genes, including the pro-inflammatory cytokine genes Il1b and Tnf is very modest, just one standard deviation above the mean at 4hrs post-LPS, and indistinguishable from saline controls at 24hrs. With the exception of Cd45 and Itgam, all gene comparisons are <2 SD. Relative expression should be shown as fold-increases as is the standard for qPCR, which will likely highlight these modest changes, and all samples should be shown. Curiously this is performed in Fig. 8 (albeit strangely), so clearly the authors are aware of this convention. Normalization to Gapdh may have been an issue here, given the potential metabolic shifts observed in these samples. Finally, improper statistics were performed. ANOVAs were performed without multiple testing correction, and no post-hoc analysis was performed.\nWe thank the reviewer for the detailed assessment of our qPCR data. First, as the reviewer can hopefully appreciate, we are attempting to probe the relationship between microglia (as cells - represented by expression of genes and proteins associated with key microglial functions) and their mitochondria (dynamic organelles - represented by expression of genes and proteins associated with diverse mitochondrial functions), in the context of an evolving response to inflammatory challenge. There are numerous valid ways to carry out and present these analyses. In specific sets of experiments, we have chosen analysis and display strategies that we feel most effectively highlight the central findings of the data set. For example, by using heat maps and PCA plots in Figure 4B and C, we aim to highlight the overall temporal dynamics of gene expression changes during LPS response, rather than focusing in depth on individual genes. Presenting the qPCR values as relative expression (2-ΔCt) rather than as a fold change relative to saline (2-ΔΔCt) allows the reader to more fully appreciate overall temporal evolution of a panel of genes that represent microglial function, as well as a panel of genes that represent mitochondrial function. We feel this allows the reader to appreciate whether changes in microglial gene expression occur first, and mitochondrial gene expression changes lag behind, or whether changes to expression of mitochondrial genes are observed as early as changes to microglial genes. In our opinion, the heatmap approach is the best way to capture this. Use of PCA analysis based only on mitochondrial genes further highlights that overall cellular state in response to LPS treatment can be fully captured by looking only at the mitochondrial relevant genes of that cell (24hr samples cluster clearly away from others and 4hr samples show trends of moving toward the 24hr samples).\nUse of relative expression (2-ΔCt) to quantify and display qPCR data is explained in the Applied Biosystems User Bulletin #2, which describes in detail multiple approaches for quantification of qPCR data. Relative expression (2-ΔCt) is widely used in the field (A few examples - PMID: 23603814 Nature Medicine; PMID: 38104136 Molecular Neurodegeneration; PMID: 36581670 Scientific Reports; PMID: 34266459 Molecular Neurodegeneration).\nRegarding the magnitude and time course of the inflammatory response to LPS treatment, these responses can vary widely and are impacted by the specific dose used, route of administration (i.p. versus s.c. versus intracranial), LPS type (O111:B4 vs. O127:B8 vs O55:B5 vs O128:B12), age of the mice, background strain of mice, and the vivarium in which the animals live (PMID: 11723144; PMID: 38183948, PMID: 31075861). The increases in pro-inflammatory factor mRNA that we report are within this wide range of magnitudes/time courses that have been previously observed. Moreover, it’s worth noting that our z-score heatmaps emphasize changes in expression of individual genes across time and do not emphasize the relative magnitude of gene expression changes across individual genes. The increases we observed in Tnfα and Il1β at 4hrs were highly significant (see Supplementary Table 2) and, if converted into a fold change relative to saline (2-ΔΔCt), are, by far, the largest fold changes we observed among all interrogated genes (Reviewer Figure 5).\nRegarding testing for statistical significance - as stated in our methods in the original submission, Bonferroni correction for multiple comparisons were included for all post-hoc tests following ANOVA or two-way ANOVAs.\n→ To address the reviewer’s concern and ensure that our statistical approaches are clear, we have added statements to relevant figure legends that correction for multiple comparisons was employed.\nRegarding the choice of endogenous control gene for qPCR, the reviewer brings up the excellent point that one needs to be cautious regarding Gapdh in some contexts.\n→ To address this concern, we repeated most of the qPCR experiments from MG-TFAM KO and control mice using Gusb as a housekeeping gene. We also used Gusb for the new qPCR experiments involving LPS challenge of MG-TFAM KO and control mice (Fig. 8 and Fig. S8). PCA plots using the gene expression results from Gusb (Fig. 8C) and from Gapdh (Fig. S8C) both indicate that TFAM KO is a key driver of observed variance, accounting for microglial distribution on PC1 or PC2, and both PCAs show that midbrain and striatum TFAM KO microglia exhibit distinct responses to this manipulation.\n§ PCA and correlation plots are largely uninformative given the small number of genes sampled\nWe respectfully disagree with the reviewer on this point. PCA helps probe and illustrate the main “overall take home message” of any dataset that has a “large” number of variables. We have interrogated 23 different genes in samples taken from two different brain regions (MB and STR) and three different treatment conditions (saline, LPS 4hr, LPS 24hr). What is the overall main driver of microglial state in this situation? Is it the brain region from which the cells were isolated? Is it the treatment group that the animal was part of? Is it the state of inflammatory factor transcription? Carrying out dimensionality reduction via principle component analysis can help sort this out. We feel that the PCA in Fig. 4C helps convey the message that mitochondrial genes alone are enough to identify which samples are in different phases of responding to LPS challenge.\nOne does not need to sample 1000s of genes for PCA to be useful. Essentially anytime you have 3 or more continuous variables, dimensionality reduction can be useful. We have 23 variables when including all genes analyzed and 14 variables when only including mitochondria-relevant genes. Other important conditions for using PCA include having a sufficient number of samples per variable. SPSS recommends at least 5-10 samples per variable. We have 41 samples total, 13-14 samples per treatment group, and 20-21 samples per brain region. Examples of PCA being used for qPCR data (rather than RNAseq or other -omics datasets) are abundant in the literature. A few examples - PMID: 20142997 (lung cancer) PMID: 35692040 (plant biology) PMID: 35401509 (atlantic salmon) PMID: 29926971(achilles tendon) PMID: 36499629 (mesenchymal stem cells) PMID: 36992300 (avian influenza).\n§ All of these issues would likely be remedied with a well-designed RNA-seq experiment on purified microglia using the sorting scheme shown in Fig. S1D.\nWe fully agree that more comprehensive analysis of the microglial transcriptome in the context of targeted manipulation of microglial mitochondria is of great interest. (RNAseq of microglia during responses to LPS have already been published, e.g. PMID 26884166, PMID 30477578). We have carried out RNAseq of microglia in TFAM KO and control animals and analysis of this dataset is underway. However, this dataset is part of a follow-up project that investigates the contribution of microglial mitochondria to risk for Parkinson’s Disease. We feel that inclusion of RNAseq data is well beyond the scope of this current (foundational) manuscript which reveals the microglial mitochondrial landscape in multiple contexts in which these cells exhibit phenotypic remodeling.\no While the motivation to ablate TFAM in microglia is not clear from Fig. 4, it is still a very interesting experiment for the field, as a whole. The finding of increased coverage of microglia in Fig. 7I is very interesting and somewhat unexpected but would be bolstered significantly by counting individual microglia using either the existing images or flow cytometry to determine if these is proliferation or increases in size or process extension. We apologize that the rationale for targeting TFAM was not clearly described. The text of the manuscript originally said “TFAM deletion in diverse cell types leads to reduced expression of electron transport chain subunits encoded by mitochondrial DNA, as well as changes in mitochondrial membrane potential and mitochondrial morphology”\n→ To address the reviewer’s concern we have expanded our explanation of the rationale for the TFAM manipulation with the following text “Results presented thus far indicate that there are correlations between region-specific microglial morphology and mitochondrial mass/number. They also reveal correlations between mitochondrial mass, mitochondrial elongation, mitochondrial membrane potential and changes in microglial morphology and inflammatory profiles during responses to LPS challenge and aging. To further probe whether there are causal links between mitochondria and these microglial phenotypic attributes, we crossed CX3CR1CreER/+ mice and CX3CR1CreER/+;mitoGFP mice with Tfamfl/fl mice to achieve targeted deletion of mitochondrial transcription factor A (TFAM) in microglia (MG-TFAMKO or MG-mitoGFP-TFAMKO)(Fig. 7A). TFAM deletion in diverse cell types leads to reduced expression of electron transport chain subunits encoded by mitochondrial DNA, and gives rise to changes in mitochondrial membrane potential and mitochondrial morphology (62,63), making this an ideal genetic manipulation to further elucidate the relationship between mitochondria and microglial phenotype.”\n→ To expand on our observation of increased tissue coverage in TFAM KO microglia we have analyzed microglial density and process branching of microglia in TFAM KO mice. See responses to Reviewer #1, point 3.\no For Fig. 8 to be considered reliable, Tfam must be shown to demonstrate effective knockout and also microglia purity. Unlike in Fig. 4, ΔΔCt values are shown, rather than Z-scores, but again fold-change would be much more interpretable. Here again the use of Gapdh as a normalization control is questionable given the direct targeting of metabolism with Tfam knockout. Again, this experiment would be significantly improved with a well-designed RNA-seq experiment on purified microglia using the sorting scheme shown in Fig. S1D.\nWe thank the reviewer for pointing out that we neglected to include qPCR data indicating purity of the collected cells. We carry out analysis of cell purity at the start of any qPCR experiment, and had intended to include these data in the manuscript. These data have now been added to Figure S4A and indicate that microglia purified with the indicated FACS gating workflow show high expression of microglial markers Cx3cr1 and P2ry12, and negligible expression of markers for other cell types (Olig1, Olig2, Aldh1l1, Slc1a2 [GLT1], Ppp1r1b [DARPP-32], Slc32a1 [VIAAT]). Demonstration of TFAM knockout was shown by qPCR in Figure 7B in the initial submission. It is now also shown in Figure S8A.\nThe qPCR data shown in Fig. 8 in the original submission were displaying fold change of TFAM KO microglia relative to control microglia from the same brain region, calculated using 2-ΔΔCt. We thank the reviewer for pointing out that y-axis labels of “ΔΔCt” could cause confusion in this regard. In the updated Fig. 8, y-axes are labeled “fold change” in any instance where 2-ΔΔCt has been used to computed displayed values.\n→ To address the reviewer’s concern regarding choice of endogenous housekeeping gene, we repeated most of the qPCR experiments from MG-TFAM KO and control mice using Gusb as a housekeeping gene. We also used Gusb for the new qPCR experiments involving LPS challenge of MG-TFAM KO and control mice (Fig. 8 and Fig. S8).\n• Minor comments\no If the authors have set alpha to 0.05 for these experiments, P = 0.05 should not be marked as significant (see Fig. 1L) We thank the reviewer for highlighting this important detail. We have re-checked all figures to make sure that P = 0.05 was not marked as significant.\no Figure callouts frequently occur out of order. Panels should appear in the order they are mentioned in the text. Similarly, some panels appear to never be referenced and should be either mentioned or removed, e.g. S2H, S2J-K. We thank the reviewer for pointing this out and have adjusted figure organization and results text to address this issue.\no The use of multiple Y-axis scales (as in S2F-G) is not ideal data visualization and should be avoided. Faceting or simply presenting a scatter plot is far more interpretable.\nWe now plot data on separate histograms to address this concern (Figure S2G-I).\no All heatmaps should show all samples, rather than just the group average.\nWhile we agree with the reviewer in principle, given the that we aim to reveal and emphasize underlying gene expression patterns across two brain regions, three treatment groups (or two genotypes), and two major categories of genes (mitochondria-function relevant genes and microglial function relevant genes), we feel that inclusion of heat maps with all samples is not the most effective method for visually highlighting overall gene expression patterns. However, to address the reviewer’s concern, we have added heatmaps that include all samples to Figure S4 and Figure S8.\no Flow cytometry data should be displayed as contour plots with outliers shown. At present, with scatter plots, it appears that many populations are split arbitrarily. While this may not be the case, this is important for showing the true distribution.\nWe have updated all FACS data to be displayed as contour plots. In most cases, we are not attempting to split populations for quantification. Rather, quadrant boundaries have been placed to allow shifts in the overall population to be detected. For example, in Fig. 4E, all microglia are TMRM+ and the boundary for GFP intensity was placed just above the population center in saline samples. This highlights increases in mitoGFP intensity that emerge at 4hrs and 24hrs following LPS treatment.\no Data presentation in Figs. 6F and 7J is nonstandard and makes use of pseudoreplication by treating each cell or mitochondrion as an independent measurement. Instead, comparisons of median fluorescence intensity for each mouse / region should be displayed (ideally as boxplots) and compared statistically. These findings are potentially very interesting and proper analysis would better highlight them.\nWe respectfully disagree that cumulative distributions are a non-standard data presentation. These distributions are generated by first plotting a cumulative distribution for each mouse (in essence, the shape of the distribution for one mouse represents one datapoint). Distribution curves from all mice in a group are then averaged to generate the average distribution curve for a given age/ brain region / genotype, which is what is being compared statistically. We feel this largely mitigates the concern about pseudo replication.\n→ To address the reviewer’s concern, we have included a new section in the methods “Flow cytometry analyses” that includes the following description of how cumulative distributions for Figure 6 were generated:\n“In some experiments, fluorescence signals across the entire population of cells from one sample were examined via cumulative distribution plots. For these analyses, a cumulative distribution plot was generated for each sample. Cumulative distribution curves from all samples in a group were then averaged to generate an average plot for that group.”\n→ We also added the following description to the methods section describing “Confocal image acquisition and analysis”: “For analysis of mitoGFP signal intensity in MG-mitoGFP-TFAMKO and MG-mitoGFP control mice, mean fluorescence intensities for each individual, Imaris-reconstructed mitochondria in a field of view were extracted. Mean fluorescence intensity values for all reconstructed mitochondria from one mouse were then pooled (n=3 FOVs per mouse per brain region) and a cumulative distribution plot for that mouse and brain region was generated. Cumulative distribution plots from all mice from one genotype and brain region were then averaged to generate an average cumulative distribution for that group.”\nWe thank the reviewer for recognizing that our data make valuable contributions to the field and that the presented data are solid. We are sorry to hear that the rationale for analyzing mitochondrial properties and how they relate to key microglial attributes was not easily appreciated and understood. Our launching point for this study was precisely the emerging recognition that mitochondria do more than simply produce energy. In fact, for some cell types such as macrophages and other immune cells, this organelle plays essential roles in regulating dynamic changes in the overall functional state of the cell. Given that very little is known about microglial mitochondria, to explore whether there are important relationships between this organelle and overall microglial functional state, we aimed to analyze key properties of microglia and their mitochondria in instances where the functional state of these cells changes. We specifically chose examples of microglial phenotypic remodeling that operate at distinct temporal scales (laser lesion - minutes; LPS - hours to days; aging - months to years) and degrees of physiological vs pathological (baseline regional differences in microglial phenotype - physiological; baseline microglial tissue surveillance - physiological; normative aging - arguably physiological; laser lesion - pathological; LPS - pathological). We also chose pathological challenges that are distinct in modality (tissue injury vs. immunological/ inflammatory insult vs. lifespan tissue maintenance) Also by design, we wished to interrogate the relationship between microglial mitochondria and microglial attributes in paradigms where abundant data regarding microglial phenotype are available (from our own prior studies and the broader field). It is disappointing to hear that our study design leaves the impression of a phenotype search, but this means that we need to do a better job of articulating rationale behind study design in the abstract/introduction/results.\n→ To address this concern, we have made changes to wording of the abstract, introduction, and beginning of each section in the results. We hope that the reviewers find that this highlights and justifies study design rationale more effectively.\nWe also acknowledge that much of our study is descriptive, but we would argue that it is precisely these types of studies that are needed to enable follow-up mechanistic studies in the field. Mitochondria can impact cellular function and phenotype through energy/ATP production, but they also impact cellular function and phenotype via calcium buffering (which impacts intracellular signaling pathways), mitochondrial-nucleus signaling (which regulates gene expression), production of reactive oxygen species (which have important intracellular signaling roles), production of metabolites used for protein posttranslational modifications, and lipid metabolism (which can shape cell membrane properties and dynamics). These are observations about mitochondrial function built up from many fields and disparate cell types. Without foundational guidepost observations regarding which microglial attributes are most tightly linked to which aspects of mitochondrial status, we can’t, as a field, make focused hypotheses or design optimal mechanistic interventions that test causation. Given the state of knowledge in the field regarding microglial mitochondria, it would be a very high bar to request both the foundational mapping and the mechanistic manipulations in the same study."}, {"round": 2, "reviewer_comments": {"reviewer_1": "No further questions.", "reviewer_2": "It is much improved.\nThey should cite and discuss their findings with other previous studies on mitochondria and microglia.\nPMID: 36787364\nPMID: 39048800\nPMID: 38480879\nPMID: 39048801", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "Overall, the revised manuscript is significantly improved in clarity, making it accessible to a broader readership. It offers insights into the role of mitochondria in microglia across a range of physiological, pathological, and aging conditions, providing an orientation for future studies in this direction.\nOur specific concerns have been adequately addressed, with only a few minor points remaining.\nOur comments for each argument are uploaded as .pdf in green.\n[Editorial Note: See end of file]", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We thank the reviewer for their time and feedback.\nWe thank the reviewer for their time and feedback, and for pointing out additional citations that needed to be included. We have revised our discussion and introduction to incorporate the suggested studies listed above.\nWe thank the reviewer for recognizing that our data make valuable contributions to the field and that the presented data are solid. We are sorry to hear that the rationale for analyzing mitochondrial properties and how they relate to key microglial attributes was not easily appreciated and understood. Our launching point for this study was precisely the emerging recognition that mitochondria do more than simply produce energy. In fact, for some cell types such as macrophages and other immune cells, this organelle plays essential roles in regulating dynamic changes in the overall functional state of the cell. Given that very little is known about microglial mitochondria, to explore whether there are important relationships between this organelle and overall microglial functional state, we aimed to analyze key properties of microglia and their mitochondria in instances where the functional state of these cells changes. We specifically chose examples of microglial phenotypic remodeling that operate at distinct temporal scales (laser lesion - minutes; LPS - hours to days; aging - months to years) and degrees of physiological vs pathological (baseline regional differences in microglial phenotype - physiological; baseline microglial tissue surveillance - physiological; normative aging - arguably physiological; laser lesion - pathological; LPS - pathological). We also chose pathological challenges that are distinct in modality (tissue injury vs. immunological/ inflammatory insult vs. lifespan tissue maintenance) Also by design, we wished to interrogate the relationship between microglial mitochondria and microglial attributes in paradigms where abundant data regarding microglial phenotype are available (from our own prior studies and the broader field). It is disappointing to hear that our study design leaves the impression of a phenotype search, but this means that we need to do a better job of articulating rationale behind study design in the abstract/introduction/results.\n→ To address this concern, we have made changes to wording of the abstract, introduction, and beginning of each section in the results. We hope that the reviewers find that this highlights and justifies study design rationale more effectively.\nWe also acknowledge that much of our study is descriptive, but we would argue that it is precisely these types of studies that are needed to enable follow-up mechanistic studies in the field. Mitochondria can impact cellular function and phenotype through energy/ATP production, but they also impact cellular function and phenotype via calcium buffering (which impacts intracellular signaling pathways), mitochondrial-nucleus signaling (which regulates gene expression), production of reactive oxygen species (which have important intracellular signaling roles), production of metabolites used for protein posttranslational modifications, and lipid metabolism (which can shape cell membrane properties and dynamics). These are observations about mitochondrial function built up from many fields and disparate cell types. Without foundational guidepost observations regarding which microglial attributes are most tightly linked to which aspects of mitochondrial status, we can’t, as a field, make focused hypotheses or design optimal mechanistic interventions that test causation. Given the state of knowledge in the field regarding microglial mitochondria, it would be a very high bar to request both the foundational mapping and the mechanistic manipulations in the same study.\nIntroduction:\nThe introduction needs to provide more confidence and perspective on the goal of the manuscript. Although the first paragraph introduces microglia and their ability to shift between functional states, the concept of a microglia state must be defined. The second paragraph introduces the expression of different cell surface receptors, finishing with a question of how microglia interpret different signals. From the third paragraph, it is not intuitive how mitochondria as “cell attributes” are involved in the previously introduced perspective of functional states and interpreting different signals. What impact would the mitochondrial network remodeling have on the microglia? Furthermore, if the authors consider “cell attributes” as intrinsic properties of microglia to define their functional state, then this should be clearly outlined and justified. Lysosomes, for example, would be a more intuitive “attribute” to distinguish the different microglial states in the conditions tested within this manuscript compared to mitochondria.\nIn addition, the introduction across all three paragraphs consists mainly of attribute lists not followed up in the manuscript, such as high-fat diet, pollution exposure, and TLR4 and TGFBR2. Also, the list of mitochondrial parameters needs to be more focused since the study does not evaluate buffering calcium, metabolites, or interactions with other organelles.\nOverall, the introduction only loosely connects mitochondria distribution and potential microglia state and does not provide a clear rationale for justifying the study.\nWe are sorry to hear that the reviewer did not feel that the introduction clearly and cohesively set up the rationale for the study.\n→ We have edited and expanded the introduction to try and address many of the points raised by the reviewer. Some of the information (e.g. description of multiple functional roles for mitochondria as organelles) is necessary background information for readers who may not be familiar with the fact that mitochondria do more than simply produce ATP.\nWe acknowledge the adjustment of the introduction to cover the points. We did not question the necessity of explaining the mitochondria as organelles; yet, the initial draft suffered from being insufficiently concise listed facts that are not relevant for the manuscript.\nWe are glad to hear the reviewer feels we have adequately addressed concerns about introduction content and organization.\nResults:\nFig 1/S1-2:\ni) The authors performed a detailed verification of their mouse model to ensure that mitochondria were labeled. This data set looks convincing. However, without ultrastructural analysis, it cannot be ensured that MG-mitoGFP mice “accurately reveal the in vivo distribution of microglia mitochondria.” If the authors seek this accuracy as they write, they must perform ultrastructural analysis.\nUse of this tool to accurately label mitochondria has been published previously (PMID 28132831). We would also like to refer the reviewer to studies by Pietramale et. al (BioRxiv 2024.10.15.618505) and Maes et. al (PMID 37731609) that also use a cre-dependent mitochondrial label (PhAM; JAX #018385) in microglia and find very similar distribution and morphological features of microglial mitochondria as what we report using mitoGFP (JAX #021429). Pietramale et. al also carry out analysis of human cortical EM datasets and found very similar somatic vs cell process distribution of microglial mitochondria as what is revealed by both cre-dependent fluorescent reporters.\nTogether with our acute brain slice experiments using TMRM to evaluate colocalization with microglial mitoGFP signal (Fig. S1J,K), we feel that we are justified in stating that this label accurately reveals the distribution of microglial mitochondria. In addition, we have carried out experiments in which bone marrow derived macrophages (BDBMs) were generated from CX3CR1CreER/+;mitoGFP mice. Visualization of all mitochondria in these in vitro preps is very clear and indicates that all TMRE labeled mitochondria in the BMDMs are also labeled by GFP. Moreover, all GFP+ structures are TMRE+ (i.e. GFP is not labeling other organelles) and the full extent of the TMRE labeled mitochondria is also captured by GFP (Reviewer Figure 6). If the reviewer feels strongly that these data are necessary to demonstrate fidelity of our mitochondrial labeling, we can incorporate these data into supplementary figures.\nWe agree that the accuracy of the cytochrome c oxidase Subunit VIIIa -based fluorescence in the mitochondria mouse model is valid. We also agree that the distribution of mitochondrial GFP tag co-localizes with the mitochondrial dye TMRM in both Fig S1J,K as well as the reviewer supplied figure 6. We appreciate the extra effort to confirm this observation.\nOur initial comment was primarily directed at the text in the original version, which stated:\n“Together these results indicate that MG-mitoGFP mice accurately reveal the in vivo distribution of microglial mitochondria.” (End of results paragraph 1). The authors have revised this to conclude: “Together these results indicate that MG-mitoGFP mice accurately label microglial mitochondria.” to which we agree completely, and appreciate the text amendment to reflect the data.\nWe thank the reviewer for acknowledging our additional efforts to validate the accuracy of the mito-GFP tag and are glad they agree with our amended text.\nii) Since the main text states that mitoGFP recombination was substantial without tamoxifen injection, is the data in Fig. S1 without tamoxifen injection, while Fig. 1 with tamoxifen? For clarification, the authors should annotate in the figure legend if they have administered tamoxifen. If Fig. S1 is without tamoxifen, the authors should validate the model again with tamoxifen since tamoxifen could potentially affect the mitoGFP distribution.\nFurthermore, for Fig. 1F, the analysis has been done without tamoxifen for rigorous analysis. This is surprising since microglia coverage across tissue is not overlapping, and confocal images should be sufficient to resolve the mitochondria signature for each microglia.\nAll experiments were carried out without tamoxifen injection except experiments to induce TFAM knockout from microglia.\n→ We have added clarification to this effect in the results and methods sections. We have also added clarification to all figure legends about whether tamoxifen was used for that specific set of analyses. The decision to forgo tamoxifen injection in MG-MitoGFP mice was motivated by primarily by the observation that 80-90% of microglia showed recombination and mitoGFP expression in the absence of tamoxifen injection. With such a high percentage of cells expressing the mitoGFP tag, we did not feel that there was substantial risk that we were only sampling a subset of cells.\n→ We have also carried out additional experiments to treat mice with tamoxifen and compare the properties of the mitoGFP+ population of microglia with and without tamoxifen treatment (Figure S1D-H and Reviewer Figure 2). Tamoxifen injection resulted in a slightly higher percentage of microglia expressing mitoGFP but did not result in any significant changes in GFP intensity, FSC, SSC, CX3CR1, CD45, or P2RY12, confirming that forgoing tamoxifen treatment does not result in biased sampling of only a subset of microglia.\nWith regard to experiments for Figure 1F using the triple transgenic MG-MitoGFP;Ai14 animals, the presence of two cre-dependent transgenes results in sparser recombination and lower densities of mitoGFP+ only, TdTom+ only, and mitoGFP+TdTom+ cells. Although we agree with the reviewer that reasonable reconstruction of microglia can be carried out when the microglia are labeled with Iba1 staining or a universally expressed fluorophore (e.g. CX3CR1-EGFP mice), it can be tricky to unequivocally distinguish which terminal processes belong to which cell, particularly in brain regions where microglia present at higher density and show greater morphological complexity, such as the nucleus accumbens (NAc). Hence, we maintain that 3D reconstruction of individual cells is more rigorous when the cells are sparsely labeled.\nWe appreciate the clarification of tamoxifen administration in figure legends, as it makes it straightforward to follow the presented conditions. Given the additional experiments suggested by Reviewer 1 and presented in Reviewer Fig. 2, the authors have satisfied our initial comments.\nWe are glad that our revisions have satisfied this specific reviewer concern.\niii) How relevant is the correlation between mitochondrial number and microglia complexity/number of intersections? Are these just larger cells with more mitochondria? Based on the data in Fig 1E, the data point variation for each region is small. Furthermore, how have the microglia for reconstruction been selected?\nWhat is the rationale for choosing the “maximum number of Sholl intersections”? Which radius size has been used? This can severely impact the readout. Furthermore, was this maximum always at a similar distance from the soma, or has there been some variability? This could explain potential variability in the data. The same question applies to Fig. S3E. The value used for sholl analysis in Fig. S3D is also unclear.\n→ To clarify how cells were selected for 3D reconstruction the following text was added to the methods:\n“To select microglia for 3D reconstruction, the brain region of interest was located and centered using only TH staining and anatomical landmarks with 20x objectives. After switching to 60x objectives, TdTom+mitoGFP+ microglia within that field of view were imaged. Slight X-Y adjustments were made, if needed, to bring a TdTom+mitoGFP+ microglial cell away from the edge of the FOV to avoid truncating processes. Cells were excluded from reconstruction if their somas fell near the top or bottom edge of the brain slice rather than being situated near the middle of the z-stack.”\nRegarding maximum number of Sholl intersections - in the original submission we chose to highlight the relationship between this morphological metric and mitochondria because this nicely captures the largest degree of branching complexity that a cell displays, irrespective of whether that branching complexity is close to the soma or further away at distal processes. As the reviewer points out, each individual cell will likely achieve its maximum number of intersections at a different radius from the soma. On its own, this metric doesn’t offer any special advantage over other morphological metrics, which is why we also analyze total process length and sum of branch points (obtained directly from Imaris reconstructions).\n→ In the revised manuscript, we highlight total process length and sum of branch points in the main figure (Fig. 1G-J) and also present data from max # of Sholl intersections in the table in Fig. S2J. Data from PFC microglia are now also presented in addition to NAc and VTA microglia.\nRegarding data presented in Fig. S3D, total number of intersections is the sum of all intersections at all radius lengths. This metric is meant to capture the total branching complexity of the cell. → To clarify this point, the text of the figure legend has been changed to “Robust regression plot showing relationship between total mitochondrial volume per cell and microglial morphological complexity (total Sholl intersections across all radii).”\nRegarding the biological significance or relevance of the correlations between microglial mitochondria and morphological metrics - Nothing is known about whether energy production or any other facet of mitochondrial function plays a critical role in determining the type of morphology that a microglial cell can adopt. If the mitochondria do play some role in regulating cell morphology, one might expect to find a correlation between mitochondrial number/volume and morphological complexity. If, instead, mitochondria were only important for regulating microglial production and secretion of trophic/inflammatory factors, there might be no correlation between the morphological complexity of an individual cell and the abundance of mitochondria in that cell. In Reviewer Figure 6, above, one can appreciate that a fairly amorphous Bone Marrow Derived Macrophage (BMDM) has tons of mitochondria. But it would be difficult to relate mitochondrial abundance to morphological complexity of BMDMs in any fashion. In this regard, the relationship between mitochondria and microglial morphological complexity may be more akin to what is observed in neurons, where mitochondria are linked to dendritic spine morphology and remodeling (PMID 32714603).\n→ Elaboration of some of these points has been added to the discussion.\nThe addition of methodological details, microglial metrics including branch points and process length, and the further discussion of these aspects satisfy our initial comments.\nMinor comment: Please clarify in the method section whether for the Sholl radius 1 um has been used.\nWe have clarified in the methods section that for Sholl analysis “each concentric circle was 1 um apart”.\niv) The choice for looking at the NAc and VTA needs better clarification, also in perspective for the later analysis with LPS and aging. Are those brain regions particularly vulnerable? → We have added text throughout the results section to explain in greater depth our rationale for focusing on these two brain regions. These additions include:\n“In fixed tissue from young adult (2mo) double transgenic CX3CR1CreER/+;mitoGFP mice (MG-mitoGFP mice hereafter), numerous GFP+ structures could be observed throughout the somas and cell processes of microglia in the nucleus accumbens (NAc) and ventral tegmental area (VTA), two interconnected brain regions where we have extensively mapped microglial phenotypes across the lifespan (Fig. 1B and Fig. S1A,B)”\n“Microglia show prominent differences in cell density, branching complexity, and gene expression across the NAc and VTA30. To determine if these regional differences in baseline microglial attributes were accompanied by regional differences in microglial mitochondria, we carried out high resolution confocal imaging and 3D reconstruction of both microglia and their mitochondria in the NAc and VTA of young adult mice (Fig. 1B and Fig. S2A).”\n“We also showed previously that microglial responses to aging vary across brain region, with VTA microglia displaying proliferative and inflammatory changes months before microglia in the NAc. Whether there are links between mitochondrial state and early VTA microglial aging has also not been investigated.”\nWe appreciate the changes that improved the clarity of the manuscript.\nWe are pleased to hear that the reviewer feels that clarity was improved.\nv) Based on the figure legend description in Fig 1. It is understood that microglial volume is calculated per field of view (tissue coverage)- and mitochondrial number, volume, and mass are then calculated based on this FOV microglial volume. However, the text states, “greater number of mitochondria relative to cell volume, as well as a greater total mitochondrial volume relative to cell volume (mitochondrial mass)”. If the quantification is relative to total microglial volume per FOV, and not on a per-cell basis, stating ‘cell volume’ is confusing. For example, Fig. 1D-E, is the relative ‘microglia volume’ in 1D the same denominator as ‘cell volume’ in 1E? In addition, does the number of mitochondria within a FOV bring us important information? Since many microglial processes not connected to a cell body are present within a brain volume, these averages do not indicate information on a per-cell basis. The authors should identify precise nomenclature and analysis methods, describe this in detail in the methods, and use the terminology consistently throughout the manuscript.\nWe performed analyses using a FOV approach to obtain information about microglia as a cell population in that particular brain region. We feel that carrying out FOV analyses AND individual cell focused analyses is more rigorous than either approach alone. FOV analyses can smooth out the effects of cell-to-cell variation and remove concerns about any unintended bias in selecting individual cells for analysis.\n→ We have updated descriptions in the methods, figure legends and text to ensure consistency and avoid confusion about the distinction between FOV-based versus individual cell-based analyses.\nThese additions satisfy our initial comments.\nWe are glad that our revisions have satisfied this specific reviewer concern.\nvi) The mitochondrial field focuses more on measuring network connectivity, i.e., fragmented or connected networks. This metric is not shown throughout the manuscript but would be an essential feature to describe when considering mitochondrial structural network changes.\nThe reviewer is correct that measures of “fragmentation” vs interconnected “networks” are commonly seen in the mitochondrial field. The vast majority of these studies are carried out in cultured cells where genetically-encoded mitochondrial reporters and/or mitochondrial dyes can easily be used with fantastic signal-to-noise, allowing automated reconstruction and analysis of mitochondria using machine learning platforms like Avia. In fixed tissue, however, even with optimal confocal imaging, distinguishing whether a cluster of mitochondria are fully, physically interconnected or separate is much more challenging. This is particularly true in the cell soma, where mitochondria are often very closely packed. For this reason, we feel it is more accurate to analyze mitochondrial elongation, mitochondrial sphericity, mitochondrial number, and mitochondrial mass which are also widely used in the mitochondrial field and to avoid classifications like “networked” or “fragmented,” which carry functional implications.\n→ To address the reviewer’s point, we have included new material in the discussion, commenting on the limitations of light microscopy for resolving whether mitochondria are truly networked or discrete, and additional future approaches that can be used to determine whether small mitochondria are have high ROS / low TMRM and are likely to be undergoing mitophagy (which is often assumed when employing the term fragmented) versus they have undergone fission to facilitate trafficking.\nThese additions satisfy our initial comments.\nWe are glad that our revisions have satisfied this specific reviewer concern.\nFig 2:\ni) The conclusion of Fig. 2C needs to be clarified. If VTA and NAc show the same motility, how can the conclusion be made that the VTA is surveying less based on their morphology?\nWe originally advanced this interpretation because, in vivo, VTA microglia are less dense and less branched than NAc microglia (Fig 1C,G,I and PMID: 28689984 and PMID: 35396327). Hence, if microglia in both brain regions show similar motility, it will take VTA microglia longer to execute a complete surveillance of the surrounding tissue than the more abundant, more highly branched NAc microglia.\n→ However, given that our observations of motility are made in an acute brain section preparation, we removed this statement to address any concerns that it is too speculative.\nThis amendment satisfies our initial comments.\nWe are glad that our revisions have satisfied this specific reviewer concern.\nii) Do the motile processes contain mitochondria? Or are motile mitochondria only found in primary processes that are more stable? (also related to Fig. 3). The reviewer brings up the very interesting question of how subcellular location relates to the motility of individual microglial cell processes and the motility of the mitochondria themselves.\n→ To address this point, we have carried out additional analyses focusing on individual microglial cell processes (Fig. 2J). Similar to our findings at the level of the whole microglial cell, this analysis revealed that there was not a significant correlation between the abundance of mitochondria in a primary cell branch and the motility of that branch. There was also no significant correlation between the motility of mitochondria in a branch and the motility of the branch itself (or vice versa). Hence, it is not the case that microglial process branches that have more mitochondria exhibit any heightened capacity for cell process remodeling, at least over the time frame we are analyzing. We draw the reviewer’s attention to the study by Pietramale et. al (BioRxiv 2024.10.15.618505) co-submitted with our study that suggest that this result may differ if focused on even smaller branchlets. By focusing on smaller branchlets (rather than entire arbors branching out from one primary process), they found that microglial branchlets with fewer mitochondria were more motile. There could also be important brain region differences in this mitochondria-branch motility relationship. This will be an important area for the field to investigate further, and we elaborate on some of these considerations further in the discussion.\nWe appreciate the consideration.\nMinor comment: The outlined description fits into a discussion and the authors should cite the co-submitted study/biorxiv Pietramale, which investigates the question in more depth.\nWe have added descriptions of the findings and implications of the Pietramale et. al study to the discussion.\nFig 3:\ni) This data is very interesting and raises exciting new questions; however, no quantification supports the concluding statement.\nWe are glad that the reviewer finds these data intriguing.\n→ In this resubmission, we have included quantification of microglial process motility and mitochondrial motility following laser lesion (Fig. 3F-I). These analyses revealed that abundance of microglial processes close to the lesion increases over time, whereas abundance of microglial mitochondria close to the lesion does not increase, confirming our qualitative observations from the initial manuscript submission.\nWe appreciate the addition of quantification as it significantly strengthens the figure.\nWe are glad that our revisions have satisfied this specific reviewer concern.\nii) At this resolution, it seems difficult to quantify and confirm that the mitochondrial networks remain stable after focal injury. Can the authors image at the resolution seen in Fig 2 and provide data analysis of mitochondrial network motility before and after injury? Do all mitochondria stop moving, or only a few travel within the extending process?\nIn this set of experiments, we elected to image a larger field of view in order to sample a larger number of microglia to determine how they and their mitochondrial networks respond to laser lesion. While repeat experiments to image smaller fields of view at higher resolution could be carried out, we felt this would best be accomplished via in vivo multiphoton imaging that would also permit mitochondria to be monitored over a much longer time frame. Using such in vivo multiphoton approaches, Pietramale et. al (BioRxiv 2024.10.15.618505) found similar responses of microglia and their mitochondria in cortex, with negligible mitochondrial remodeling within 40min of the laser lesion. Intriguingly, they did observe mitochondrial remodeling at 6hrs following lesion.\nWe acknowledge the shortcomings, which should be outlined in the discussion and referenced to the Pietramale study.\nWe have added descriptions of the findings and implications of the Pietramale et. al study to the discussion.\nFig 4/5:\ni) If mitochondria are not linked to microglial tissue surveillance and injury, as the authors conclude in Fig. 2J and Fig. 3, respectively, how do they rationalize using LPS? Based on live imaging, mitochondria are not linked in a “direct fashion to baseline surveillance” or injury-induced damage. Why would it suddenly be related to an inflammatory response induced by LPS? We are sorry to hear that the rationale for utilizing LPS to further interrogate the relationship between mitochondria and microglial responses to tissue challenge was not clearly communicated. Laser lesion is a focal tissue injury that elicits a rapid (within minutes) response from microglia. Purinergic signaling from the injured tissue to microglial P2RY12 receptors plays a critical role in this rapid injury response. Our data indicate that rapid extension of microglial processes is not accompanied by rapid remodeling of mitochondrial networks (at least on a morphological level). Given this result, to continue testing whether mitochondria play an important role in regulating microglial phenotypic remodeling in response to pathological challenges, it was desirable to examine microglial responses that occur over a longer time scale (hours to days) as well as microglial responses to a distinct modality of challenge (inflammatory challenge rather than tissue damage).\n→ As described above, we have made changes to wording of the abstract, introduction, and beginning of each section in the results to better explain rationale for study design.\nWe appreciate the changes that improved the clarity of the manuscript.\nWe are pleased to hear that the reviewer feels that clarity was improved.\nii) QPCR of Dnml1 is used to indicate fission- however, this data should also be reflected in the image analysis in Fig 5. Could the authors indicate changes in the median volume of organelles (per cell) to compare alongside the mitochondrial number and aspect ratio? If the mitochondria are indeed elongated as indicated by an aspect ratio measure, the median volume of organelles per cell should increase (aligning with a mass increase) since the number of mitochondria did not change between saline and LPS for both brain regions.\nThe reviewer is correct that dynamin-related protein 1 (DRP1; encoded by the gene Dnml1) plays a key role in promoting mitochondrial fission. In both our FACS-based analyses in Fig. 4F and Fig. 8D, the prominent increase in GFP median fluorescence intensity suggests that mitochondrial biogenesis accompanies microglial responses to LPS challenge.\nThe increase in mitochondrial mass detected via histology (Fig. 5B), further indicates an overall increase in mitochondria within microglia following LPS challenge. The reviewer is correct that, in general, mean volume of individual mitochondria trended toward increases at 24hrs following LPS (Reviewer Figure 7), particularly in the NAc, so the FACS-based increase in GFP and the increase in mitochondrial mass via histology is likely driven by larger, longer mitochondria.\nThese findings don’t necessarily represent an inconsistency with the increase in Dnml1 transcript found by qPCR. Indeed, Dnml1 may be getting upregulated precisely because there are larger, longer mitochondria and increased fission will be needed to return the mitochondrial network to its pre-LPS state.\nThe image analysis data support the concept that there is an increase in mitochondrial mass, accompanied by more elongated mitochondria. The support of the reviewer figure 7 helps clarify this point. The finding that mitochondria are elongated after LPS challenge is in contrast to the reported effect of LPS on microglial mitochondria in the in vitro setting PMID: 30637805, which is an interesting point to highlight in the discussion.\nWe have included a statement in our discussion highlighting this difference between in vitro results and our findings and suggesting potential reasons for this discrepancy.\niii) Do the authors have the details of the post-hoc analysis of 5B-D?\nAs described in the methods in the original submission, all one-way and two-way ANOVAs were performed with Bonferroni correction for multiple comparisons being applied to all post-hoc comparisons.\n→ We have added information about post-hoc corrections to the figure legends.\nWe appreciate the addition.\nWe are glad that our revisions have satisfied this specific reviewer concern.\niv) Technical aspect related to sampling for flow cytometry. The method section must clarify whether the samples were processed sequentially or in batches. Furthermore, information related to the total time from tissue removal to sorting must be included. It has been shown that the microglia transcriptional profile adapts within 30 minutes of incubation on ice (Marsh 2022. DOI: 10.1038/s41593-022-01022-8). This can result in misleading results. Therefore, the authors should add the animal grouping strategy and total processing time to the methods and, if necessary, repeat the experiments when the timing is longer than 30 min.\nThe authors are correct that microglia are extremely sensitive and adaptable cells that will respond to tissue dissociation. Hence, care needs to be taken in the tissue processing workflow to avoid artifacts. We are very familiar with the Marsh et. al paper to which the reviewer refers. In this study, the key finding is that exposing microglia to elevated temperature (37oC) to carry out enzymatic digestion causes them to exhibit altered transcriptional profiles. See Figure 1C of the paper. This panel shows that you can prevent aberrant microglial transcriptional signatures by keeping the entire workflow at 4oC (i.e. on ice) OR by including transcriptional inhibitors. For all experiments in our study, the entire workflow was carried out at 4 oC as described in the methods. In our prior studies (PMID: 28689984), RNAseq confirms that this workflow does not elicit upregulation of inflammatory factors typically associated with microglial reactivity. Moreover, this workflow does not induce increased expression of the specific genes highlighted in the Marsh et. al study (Reviewer Figure 8).\nOf note, none of the workflows in Marsh et al are completed in under 30min. All of their tissue processing workflows take 70-130min as indicated by their workflow diagrams in Figures 1 and 2. 30 minutes refers to the portion of their workflow during which microglia were exposed to elevated temperature for enzymatic dissociation OR maintained at 4 degrees, with the latter treatment preserving in vivo gene expression signatures. Moreover, their stated workflow time does not include time for sorting, but rather indicates time prior to when sorting begins.\n→ As requested by the reviewer, we have added the following information to the methods regarding our sample processing:\n“Total sample processing time from brain dissection to completion of FACS purification ranged from 2-4hrs depending on sort order of individual samples. Samples were collected in batches with each day of sorting including a young adult, saline injected, or TFAM-wt control mouse. Sort order of samples from specific ages, brain regions, treatment conditions, or genotypes was interleaved to avoid introduction of bias related to tissue processing length. FACS-isolation itself was carried out at 4oC. FACS-isolation of all samples for a given set of experiments was completed prior to downstream processing to allow simultaneous RNA isolation and cDNA preparation as described below.”\nWe appreciate the addition.\nWe are glad that our revisions have satisfied this specific reviewer concern.\nv) How was the correlation analysis performed in Fig. 4D? Cherry-picked genes were connected to putative microglial states, an aspect that is also not well-defined, introducing a bias. Also, the explanation of the “few CD68 correlations with other genes” does not seem solid (Fig. S4B) since the heatmap shows much more correlation than any of the other plots in Fig. 4.\nTo test for potential correlations between microglial genes reflective of overall microglial functional state and our entire panel of 14 mitochondria-relevant genes, we carried out linear regression analyses for each microglial gene and all of the mitochondrial genes. For considerations of space, we included results for 6 / 9 microglial genes in the main figure and an additional microglial gene in Figure S4. We respectfully disagree that showing data from 7/9 microglial genes constitutes “cherry-picking” genes.\nIt also seems that we have not done an optimal job of explaining the significance of the Cd68 result. Expression levels of Cd68 are indeed correlated with expression levels of multiple other microglial genes (Tgfβ1, Cx3cr1, P2ry12, Fig. S4D). However, the important point here is that this degree of correlation was not modulated by LPS challenge (i.e. these significant correlations were present at baseline and remained present at both 4hrs and 24hrs post-LPS). Also importantly, expression levels of Tnfα and Il1β were correlated with expression levels of multiple mitochondrial genes, particularly at 4hrs post LPS. Yet, no correlation was observed between Cd68 expression levels and those of Tnfα or Il1β. Together, these findings suggest a more robust relationship between the state of mitochondria (as organelles) and acute microglial responses to LPS compared to the relationship between lysosomes and acute microglial responses to LPS. (*Please note that in response to Reviewer 2’s comment, we re-executed our linear regression analyses using Robust regression with false discovery rate control; using this more rigorous approach, some of the correlations that were found to be significant in the original submission are no longer significant in this revised analysis).\n→ To address the reviewer’s comment, we have modified the text of the results:\n“Moreover, examination of Cd68, a key microglial lysosome gene, hints that mitochondria, as organelles, may be uniquely positioned to regulate microglial functional state during inflammatory challenge. While LPS treatment increased the correlation between most mitochondria-relevant genes and key microglial function genes (Cd45, Cx3cr1, P2ry12, Tgfβ1)(Fig. 4D), LPS treatment did not modulate correlations between Cd68 and Cx3cr1, P2ry12, Tgfβ1 (correlations between Cd68 and Cd45 increased at 24hrs post LPS)(Fig. S4D and Table S4). Moreover, while multiple mitochondria-relevant genes were positively correlated with expression of the microglial pro-inflammatory factors Tnfα and Il1β at 4hrs post-LPS, Cd68 expression was not correlated with Tnfα and Il1β in any of the conditions examined (Fig. S4D).”\nWe appreciate the addition.\nWe are glad that our revisions have satisfied this specific reviewer concern.\nFig 6:\nMicroglia are known to show a sex-specific response also in adulthood and, especially, during aging. Therefore, the authors must precisely annotate the data plots, indicating which data points come from male or female animals.\nWe agree that probing for sex-differences is important for the microglial field and we included both male and female mice in all our analyses.\n→ To address the reviewer’s comment, we have added a supplementary figure that replicates key findings from each of our analyses with the annotation of datapoints that come from male and female mice (Fig. S9) We have also added commentary to the discussion that future research should continue to investigate possible sex differences in microglial mitochondria.\nWe appreciate the addition, which sets an essential criterion for the transparency of the research data and will enable the community to acknowledge sex-specific differences to be follow up in the future, especially in neurodevelopmental and neurodegenerative diseases.\nMinor point: For sampling with at least 3males/females, it would be appreciated to see which metrics did carry significant sex-specific differences.\nWe agree that this is an important topic for future investigation and we anticipate delving into this in greater detail in future work.\nFig 7:\ni) Related to the mitochondrial quantification in 7D: Is this data represented as an average volume of all organelles within the cell? If yes, it would be better to use a median, as small mitochondria can skew the average value and misrepresent the data. Figure 7D shows the sum total volume of all the mitochondria in a field of view (FOV). This value was obtained for 3 FOVs for each mouse to compute a mouse average, which is displayed in the graph.\n→ We have adjusted the label of the y axis and the description of this panel in the figure legend to clearly specify that these are FOV measurements. We have also added FOV median mitochondrial volume and FOV maximum volume of an individual mitochondrion to Figure S7F. This highlights that increases in the total number of mitochondria within the FOV are driving increases in total FOV mitochondrial volume, rather than increases in the size of individual mitochondria.\nii) Mitochondrial analysis: In Fig. 7, the analysis focused on volume, number, and sphericity, whereas earlier figures describing the mitochondria in fixed tissue focused on mass, number, aspect ratio, volume, sphericity. To allow comparison between the datasets, the authors should be consistent and include all metrics for all figures. Furthermore, the methods section needs to include a detailed description of the analysis method for each parameter.\n→ We have added analysis of mitochondrial mass to this figure (Fig. 7K). We have expanded the description in the methods of how these metrics were generated within Imaris analysis software.\nThis amendment satisfies our initial comments.\nWe are glad that our revisions have satisfied this specific reviewer concern.\nFig 8:\nHow do the microglia change after TFAM KO? A two-month waiting period may allow time for compensatory mechanisms to circumvent initial damage. Understanding how TFAM KO impacts microglia is indeed the primary goal of these experiments. → In addition to the histology in the original submission (previous Fig 7I, now Fig 7H) which showed that microglial tissue coverage within the field of view (FOV) increases after microglial TFAM KO, we now show that morphological complexity of individual microglia (analyzed via Sholl analysis) increases in MG-TFAM KO mice (Fig. 7I).\n→ We also show that microglial density is unchanged (Fig. 7G).\n→ In addition to the qPCR in the original submission that showed multiple changes in microglial gene expression in MG-TFAM KO mice (previous Fig. 8C, now Fig. 8B), we now also show that protein level expression of CD45 and CX3CR1 (MFI during FACS) are unchanged in MG-TFAM KO microglia. Cell size (FSC) and internal complexity (SSC) are increased in microglia from MG-TFAM KO mice (Fig. 8A).\n→ Finally, in this resubmission, we challenge MG-TFAM KO mice with LPS and utilize FACS and qPCR to assess the capacity of the cells to mount a mitochondrial and cellular response to this challenge (see detailed description in response to reviewer #1, point 4).\nAs described in the results, we elected to wait for 2 months following tamoxifen injection for the following reasons. 1) This will allow for any peripheral monocytes/macrophages impacted by this manipulation to cycle out and be replaced. 2) Mitochondrial proteins are among the most long-lived proteins in the CNS (PMID: 30315172). Hence, it is estimated that approximately 1 month would be needed for existing mitochondrial proteins to cycle out. Moreover, TFAM exerts its broad effects on the mitochondria by altering stability of the mitochondrial DNA. This impacts capacity of the mitochondria to produce key components of the electron transport chain, with additional downstream consequences for other aspects of mitochondrial function. In line with this, virtually all studies that have used cell-specific TFAM KO have either used constitutive Cre lines (PMID 31269437, PMID 17227870, PMID 9916807, PMID 25005176, PMID 23168219, PMID 18945718), or waited at least a month (PMID 35165165, PMID 40210116) . The reviewer is correct that some compensatory responses may be elicited and some of the changes we observe in expression of nuclear-encoded mitochondrial components may represent attempts at compensation. Nonetheless, we feel that our choice of 2 months is well justified, and it will fall to future studies to tease apart direct vs compensatory effects on the organelle.\nDiscussion:\nIn line with the loose introduction, the discussion is also only superficial. It mostly repeats the results without putting the research data into the context of the literature. For example, a reported result is increased mitochondrial mass. What does increased mitochondrial mass imply? The manuscript would enormously benefit from a complete revision of the introduction and discussion to make it coherent.\nWe are sorry to hear that the reviewer did not find the discussion informative.\n→ We have updated the discussion to include some potential implications of key observations based on what is known in the literature from mitochondrial behavior in other cell types. Multiple follow-up studies will be required to fully elucidate what a particular mitochondrial attribute means for microglial function in a given context. Our aim with this study was to inspire those types of follow-up studies by revealing how closely linked the general state of the organelle is with the overall state of the cell.\nOverstatements:\nThe manuscript suffers from several overstatements and preliminary conclusions, which must be fixed. A few examples are listed below:\ni) “First time” in the introduction. We have removed this type of wording from the manuscript.\nii) The authors regularly write “in vivo” within their manuscript, yet it becomes quickly apparent that the majority of the experiments are performed in a fixed tissue environment. This should be clarified in the text to avoid misunderstandings. We have adjusted wording in the manuscript to avoid confusion as the reviewer suggests.\nOverall, the revised manuscript is significantly improved in clarity, making it accessible to a broader readership. It offers insights into mitochondria in microglia across a range of physiological, pathological, and aging conditions, providing the foundation for many future studies. Our specific concerns have been adequately addressed, with only a few minor points remaining.\nWe are pleased to hear that the reviewer feels that clarity was improved.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nWe thank the reviewer for their time and feedback."}]

Decision letter