Energetic diversity in retinal ganglion cells is modulated by neuronal activity and correlates with resilience to degeneration

Domain: biology
Published: 2026-03-27
Editorial outcome: major_revision

Abstract
Neuronal function requires high energy expenditure that is likely customized to meet specific signaling demands. However, little is known about diversity of metabolic homeostasis among divergently-functioning types of neurons. To this end, we examined retinal ganglion cells (RGCs), a population of closely related, yet electrophysiologically distinct excitatory projection neurons. Using in vivo 2-photon imaging to measure ATP with single cell resolution, we identified differential homeostatic energy maintenance in the RGC population that correspond to distinct RGC types. In the presence of circuit activity, the most active RGC type (Alpha RGCs), had lower homeostatic ATP levels than other types and exhibited the greatest magnitude of ATP decline when ATP synthesis was inhibited. By simultaneously manipulating circuit activity and mitochondrial function, we found that while oxidative phosphorylation was required to meet ATP demands during circuit activity, it was expendable to maintain resting ATP levels. We also examined ATP signatures associated with survival and injury response after axotomy and report a correlation between low homeostatic ATP and increased survival. In addition, we observed transient ATP increases in RGCs following axon injury. Together, these findings identify diversity of energy handling capabilities of dynamically active neurons with implications for neuronal resilience.

Introduction
Our nervous system is energetically demanding as it consumes a disproportionate amount of fuel relative to the rest of our bodies, and neurons consume most of that energy quota 1-3. The near-stoichiometric relationship between oxygen and glucose consumption in the brain at rest suggests that oxidative phosphorylation (OXPHOS) is the primary mode of ATP generation by most neurons 4, yet glycolysis is well-documented in specific neurons or under conditions of energetic stress 5-9. Despite or perhaps because of the extreme energy expenditure of neurons, evolutionary pressures have forced energetic savings where possible into the diverse properties of individual neurons, circuit wiring patterns, and glial/vascular support processes 10-12. Therefore, metabolic specializations likely exist across different types of neurons to best accommodate their unique signaling requirements. Indeed, remarkable cases like the high level of mitochondrial activity in the whisker barrel cortex pyramidal neurons, or the aerobic glycolysis of rod photoreceptors are well-documented and were identified nearly a century ago 7,13,14. More recent large scale studies have identified diversity in mitochondrial characteristics that correlate with brain region and network connectivity 12. While progress has been made in understanding the metabolic characteristics of neurons in general and their interplay with glial cells, energetic diversity across neuronal populations is much less understood. We were accordingly motivated to query the intrinsic differences in energetic properties across physiologically diverse but otherwise closely related neurons as it relates to their signaling function.

In this study we examine the energetics of retinal ganglion cells (RGCs), a population of excitatory projection neurons that share the same milieu, and are closely related in developmental origins as well as general synaptic and morphological features. Yet RGCs are comprised of electrophysiologically divergent types, thus allowing direct comparison of their energetic properties. Like most neurons, RGC type differences are likely driven by pressures of energetic efficiency. For example, human high-acuity RGCs that encode dark (i.e. OFF) or light (i.e. ON) conditions respectively have asymmetric contrast-responses that are tuned to minimize spiking and maximize energetic efficiency of feature encoding 15. In this study, we focus on three RGC type families, Alpha RGCs (\alphaRGCs), intrinsically photosensitive RGCs (ipRGCs) and ON-OFF direction selective RGCs (ooDSGCs) because they represent a range of signaling properties that may exhibit differential homeostatic and activity -related energetic characteristics 16-19. Importantly, these families also contrast in their resilience to neurodegeneration, allowing for inferences of the relationship between energetic homeostasis and RGC resiliency in the face of injury 20-25. Indeed, metabolic dysfunction is a major driver of neurodegeneration 26-29, including for RGCs in conditions like glaucoma and traumatic optic neuropathy 30,31.

Previous studies of RGC metabolism have relied on whole retinal tissue or the enriched total RGC population 32-36. Although such results have been insightful, they are limited by the small proportional representation of RGCs in the whole retina (1% of cells) and the inability to examine differences among RGCs 37. Furthermore, the complement of vasculature, glia, and interstitial fluid composition in the retina likely creates a metabolic microenvironment difficult to faithfully recapitulate ex vivo 38.

In this work, we examined metabolic heterogeneity among RGCs in their native environment, we used transpupillary 2-photon in vivo imaging of ATeam, a well-established genetically-encoded fluorescent biosensor that measures ATP concentration 39, to measure ATP of RGCs at individual-cell resolution 40,41. Correlating this in vivo metabolic imaging with immunostaining, we characterize ATP heterogeneity in the RGC population and identify type-specific ATP differences. Through intraocular injection of metabolic inhibitors under varying activity states, we also describe the role of activity in energy consumption in vivo and identify metabolic requirements for maintaining energetic homeostasis during activity-related ATP consumption. Finally, we induced neurodegeneration using an optic nerve crush model and examined energetic features that correlate with survival, as well as observed the longitudinal energetic response to degenerative conditions. Through studying these RGCs, we describe heterogeneity in ATP metabolism of a population of neuron types in their shared native microenvironment, characterize the role of activity and OXPHOS in ATP homeostasis, and identify energetic responses to neurodegeneration along with features of well-surviving neurons.

Results
ATP levels vary across RGC types
To directly measure ATP levels in individual RGCs, we expressed ATeam1.03-nD/nA (referred to as ATeam throughout), a genetically-encoded FRET-based biosensor, in murine RGCs by delivering a Cre-dependent AAV2 expression construct bilaterally into the eyes of VGlut2-Cre transgenic mice 42,43. This strategy labels RGCs and horizontal cells in the retina; since horizontal cells in the retinal inner nuclear layer are deep to the RGCs, they were not included for imaging 41. We measured ATP levels (YFP/CFP \propto [ATP]) by time-lapse in vivo imaging through the pupil using 2-photon microscopy (Fig1a). High-throughput quantification was accomplished by neural network-based segmentation of RGC somas using Cellpose, run on a custom model trained on our dataset 44. YFP and CFP were pseudocolored as magenta and green respectively, such that higher ATP RGCs will appear magenta and lower ATP RGCs will appear green; white pixels indicate display saturation in either or both channels. ATP levels varied across the RGC population, with somas exhibiting a range of YFP/CFP values distributed normally within each retina (Fig1b-d). The coefficient of variation across these samples was 18.9 +/- 4.5% (mean +/- s.d.), which represents about 13% of the total dynamic range of ATeam1.03 and 0.5 mM ATP for differences over the linear range of the biosensor 39. We do not expect pH to drive baseline differences in Ateam1.03 measurements since mVenus has a pKa of 5.5 45, and pH recordings from various neurons under tetanic stimulation range from pH 6.65 to 7.05 46-48. To verify that ATP differences within a population represent true heterogeneity characteristic to individual RGCs, we performed repeat imaging of the same cells one week later, which showed consistent baseline ATP differences (R = 0.65, p < 0.001) (Fig1e-f).

To determine whether baseline ATP heterogeneity was related to RGC type, we measured ATP in vivo and then collected retinas to perform immunostaining of the imaged regions for RGC type markers. We labeled \alphaRGCs, ipRGCs, and ooDSGCs using antibodies for SPP1, TBR2 and CART, respectively (Fig1g) 22,49,50. Landmark-based deformable alignment of the in vivo and immunostained images using BigWarp enabled efficient matching of ATP measurements to RGC type 51. Our analyzed RGCs were comprised of 29.2 +/- 4.8% CART+ RGCs, 7.6 +/- 2.4% TBR2+ RGCs, 9.3 +/- 5.7% SPP1+ RGCs, consistent with previously reported native distributions by immunostaining 22,49,50,52,53. We found that ooDSGCs and ipRGCs had higher baseline ATP compared to \alphaRGCs (Fig1h-i). \alphaRGCs are comprised of four subtypes corresponding to their light response properties (ON- or OFF- depolarizing and transient vs sustained firing). Importantly, sustained \alphaRGCs demonstrate more activity than their transient counterparts; thus we examined if heterogeneity in ATP levels may be present within the \alphaRGC family 16. Notably, one \alphaRGC subtype, the \alphaON-sustained RGC (\alphaON-S), is also the M4 subtype of ipRGC, and expresses both SPP1 and TBR2 54. We found that baseline ATP levels in \alphaON-S RGCs were not statistically different compared to other \alphaRGCs, but were significantly lower than for the other ipRGC subtypes (Fig1h-i). Taken together, our results demonstrate that homeostatic ATP levels can differ between RGCs within the same microenvironment in vivo, and that these differences are in part related to RGC type.

\alphaRGCs exhibit the greatest ATP depletion under mitochondrial inhibition
To further characterize differential energy maintenance across RGCs, we investigated the effect of inhibiting ATP synthesis on steady-state ATP levels. Since neurons largely rely on OXPHOS for ATP production, we blocked various components of the electron transport chain (ETC) in vivo with intraocular injection of pharmacological inhibitors (1\mu l in 50/50 PBS/DMSO) (Fig2a). We first tested rotenone (ROT), an inhibitor of Complex I (CI) of the ETC. To our surprise, ROT injection (10 \mu M) resulted in precipitous ATP decline in a subset of RGCs while others were less affected (Fig2b,f). Post hoc immunofluorescence of these retinas revealed that \alphaRGCs, especially \alphaON-S, exhibited the greatest extent of ATP depletion (Fig2n,r,v). We observed similar results following injections of 10 mM etomoxir (FigS1), which inhibits Complex I at high concentrations 55. We do not expect that RGCs with lower baseline ATeam1.03 should artificially report a stronger response to partial mitochondrial inhibition since we are likely recording from the linear response range of ATeam1.03 39 since we observed a symmetrical normal distribution at rest (Fig1d), and we were able to increase ATP measurements with circuit activity inhibition (see below Fig3g-l). Similarly, inhibition of ETC Complex II (CII) and Complex III (CIII) by thenoyltrifluoroacetone (TTFA, 30 mM) and antimycin A (AA, 100 \mu M), respectively, also caused the greatest ATP decline in \alphaRGCs and \alphaON-S, albeit with different temporal dynamics, possibly due to differences in the pharmacodynamics of the inhibitors (Fig2c-x). Inhibition of ETC Complex IV (CIV), the common endpoint of electron transport, by potassium cyanide (KCN, 25 mM) elicited ATP decline across the RGC population (Fig2e -y). While RGC types showed similar ATP phenotypes across biological replicates, the magnitude of inhibition depended on the overall inhibition achieved by the individual injection (FigS2). Finally, we examined the effect of glycolysis inhibition by injection of 200 mM 2-deoxyglucose (2-DG), but did not observe alterations in RGC ATP levels as a result (FigS3). Thus, specific metabolic inhibition blocking catabolic components CI-III that have anapleurotic pathways disproportionately depleted ATP in \alphaRGCs and particularly \alphaON-S. In contrast, inhibition of CIV, a severe metabolic chokepoint, caused energetic depletion across all RGCs. These results show that \alphaRGCs not only have a lower homeostatic ATP set-point, but demonstrate greater sensitivity to metabolic challenge, and suggest that \alphaRGCs may have reduced ATP production capacity or higher energetic demands.

\alphaRGCs have higher protein expression of mitochondrial ETC components
Lower baseline ATP and greater susceptibility to mitochondrial inhibition in \alphaRGCs could be due to decreased expression of mitochondrial ETC components. Thus, we examined protein expression for mitochondria and key components of OXPHOS by performing quantitative confocal microscopy of retina wholemounts immunostained for TOM20 (mitochondrial membrane translocase reflecting mitochondrial abundance), NDUFB8 (Complex I), SDHA (Complex II), UQCRC2 (Complex III), and ATP5A (Complex V) (FigS4). We used high-resolution volumetric confocal scanning of immunostained samples to 3D segment RGC somas labelled by RBPMS using Cellpose. Mitochondrial marker intensity was measured and SPP1+ cell masks were identified, allowing for automated immunofluorescence measurements in thousands of Alpha and non-Alpha RGCs in total. Despite lower baseline ATP and greater sensitivity to mitochondrial inhibitors, \alphaRGCs exhibited higher staining intensity for TOM20, NDUFB8, SDHA, UQCRC2, and ATP5A, showing that \alphaRGCs have more mitochondrial volume and are enriched for ETC components (FigS4b). Paradoxically higher mitochondrial expression in \alphaRGCs indicates that their lower ATP is less likely due to decreased production capacity, potentially reflecting adaptation to a greater steady state ATP demand.

Homeostatic ATP levels are stable under physiologic alterations in circuit activity
Lower ATP levels in \alphaRGCs could be the result of greater ATP consumption, especially given that \alphaRGCs are fast-spiking neurons 16,17,56. We first assessed our ability to alter circuit activity levels in vivo. In a separate cohort of mice, we expressed the Ca2+ FRET biosensor Twitch2B in RGCs, in which YFP/CFP \propto [Ca2+] as a readout of RGC activity 41,57. As a positive control for activity modulation, we examined the effect of increasing or decreasing retinal circuit activity by intraocular injection of strychnine/bicuculline (Str/Bic) or NBQX/AP5 to block inhibitory glycine/GABA or excitatory glutamate receptors respectively. As expected, disinhibition by Str/Bic raised RGC Ca2+ levels (FigS5a-f) 40. Conversely, blocking glutamatergic input with NBQX/AP5 lowered RGC Ca2+ levels (FigS5g-l). These pharmacologic experiments demonstrate that Str/Bic and NBQX/AP5 altered RGC activity sufficiently to elicit sustained changes in intracellular Ca2+. We next tested the effect of physiologic activity alterations. Since 2-photon laser scanning microscopy is known to drive circuit activity in retinal explants and likely contributes to RGC activity in vivo (Euler et al., 2019; Wang et al., 2021), we used the onset of image acquisition as an activity-altering light stimulus. In RGCs expressing Twitch2B, onset of image acquisition after 2 minutes of dark adaptation resulted in transient Ca2+ elevations in a subset of RGCs, consistent with our previous reports (FigS5m-n; Wang et al., 2021). To quantify this light-driven circuit activation, we calculated the absolute maximum change in Ca2+ during the first 10 seconds of scanning and found greater Ca2+ dynamics compared to apparent steady-state conditions reached after 2 minutes of continuous imaging (FigS5o).

We then performed these experiments in mice with ATeam-expressing RGCs. As expected, pharmacologically increasing activity by Str/Bic caused a population-wide decrease in ATP, which was followed by recovery toward baseline levels by 15 min (Fig3a-f), despite sustained calcium elevations at this time (FigS5b-f). Notably, ATP levels were reduced uniformly as the relative differences in ATP within the RGC population were maintained throughout, evidenced by the correlation of pre- and post-injection ATP levels (R = 0.68, R = 0.80; p < 0.001) (Fig3e,f). Conversely, NBQX/AP5 resulted in a slight and delayed population-wide ATP elevation (Fig3g-l). Once again, relative ATP differences were preserved during this population-wide elevation (R = 0.87, R = 0.85; p < 0.001) (Fig3k,l). Together, these results show that pharmacologic manipulation of retinal circuit activity alters ATP in all RGCs to a similar extent, possibly via affecting substrate availability in the local micro-milieu supported by the observations that alterations were similar across the RGC population, and ATP build up to circuit inhibition was not apparent until 15 min after glutamate receptor blockade. We next examined the effect of physiologic activity changes on ATP levels. Despite demonstrating Ca2+ alterations during light-stimulation at the onset of 2-photon imaging, we did not observe changes in ATP levels in this timeframe (Fig3m-o). This finding suggests that the intracellular ATP pool in RGCs is robust enough to respond to acute physiological changes in activity-related demand that occur while processing light signals, possibly due to mechanisms in place for real-time metabolic homeostasis. This possibility is further supported by the observation of ATP recovery toward homeostatic levels after Str/Bic injection despite sustained elevations in Ca2+ (FigS5b-f, Fig3b-f).

Selective ATP depletion after OXPHOS inhibition is driven by neuronal activity
Larger ATP decline in \alphaRGCs, and especially \alphaON-S, after CI-III inhibition could indicate higher energetic demand by these RGC types. Since \alphaRGCs are also among the most strongly activated under limited light conditions 58,59, we tested whether neuronal activity may contribute to their selective ATP decline. We first measured ATP after ROT injection, or after ROT co-injected with NBQX/AP5 to simultaneously block neuronal activity (Fig4a-f). Compared to ROT-only control, co-injecting ROT with NBQX/AP5 significantly reduced the ATP decline in the RGC population (Fig4e-f).

Next, we confirmed the activity-dependence of ROT-induced ATP decline by interrupting our imaging, and thus pausing physiologic retinal circuit stimulation, in the presence of ROT. For RGCs that experienced ATP decline after ROT injection, 2-minute cessation of imaging caused ATP recovery toward baseline (Fig4g-i). Subsequent resumption of imaging led to rapid re-depletion. ATP measured during the minute immediately after imaging restart (“Scan restart”) was significantly recovered compared to the window prior to imaging hiatus (“ROT trough”) (Fig4j-k). Moreover, measuring acute dynamics during different timeframes, we found that ATP levels exhibited the greatest flux in the Scan restart window, reflecting rapid decline of cellular ATP upon laser scanning re-initiation (Fig4i,l-m). Importantly, after resumption of imaging, cells that again demonstrated ATP decline appeared to continue accumulating ATP at their original recovery rate (Fig4h). These dynamics indicate that blockade of ATP generation pathways may only manifest in neurons as ATP reductions when they are activated and thus consuming higher levels of ATP. Therefore, while physiologic neuronal activity was not sufficient to perturb steady-state ATP with OXPHOS intact, energy-limited conditions imposed by partial OXPHOS inhibition caused ATP stores to be sensitive to light stimulation. Together, these data show that neuronal activity is a main consumer of energy, but ATP is maintained at homeostatic levels by an intrinsic metabolic response requiring OXPHOS.

Low baseline ATP is correlated with RGC resilience to axon injury
Energetic stress can be a driver of neurodegeneration, and metabolic substrate supplementation can protect RGCs from degenerative models 35,60-62. Therefore, native differences in metabolic homeostasis could influence neuronal resiliency. Since we observed that natively well-surviving \alphaRGCs had lower homeostatic ATP levels 22 (Fig1h), we asked whether a general relationship could exist between low homeostatic ATP and RGC resilience. We performed baseline ATP measurements of RGCs, followed by optic nerve crush (ONC) axon injury and longitudinally tracked RGC survival over 2 weeks (Fig5a). Surprisingly, RGCs that survived at 14 days post ONC had lower baseline homeostatic ATP levels compared to those that died (Fig5b,c).

Lower baseline ATP among the surviving population could indicate a general relationship between low ATP and resilience, or could reflect an enrichment of low-ATP \alphaRGCs among the surviving cohort. To distinguish between these possibilities, we identified \alphaRGCs in the surviving population using SPP1 immunofluorescence. As expected, surviving RGCs identified as \alphaRGCs had lower mean baseline ATP compared to the population that did not survive (Fig5d). Interestingly, non-\alphaRGCs surviving at 14 days similarly exhibited lower baseline ATP compared to the dying population (Fig5d). This finding suggests that low baseline ATP is correlated with RGC survival independent of RGC type.

Population-wide ATP is transiently elevated days after ONC
Energetic depletion and subsequent AMPK activation have been implicated in traumatic and glaucomatous degeneration of RGCs 60,63. Thus, we examined day-by-day ATP dynamics in RGCs following ONC. Instead of expected reductions 31,60,63, we observed ATP elevations that began day 2 post ONC and peaked at day 4, before recovering toward baseline after 6 days post ONC (Fig5e). These longitudinal ATP dynamics indicate that metabolic changes occur in the days after ONC, possibly representing a protective response or sign of injury, and that cellular energetics become quiescent and stable later in the degeneration time course.

Nicotinamide treatment elevates ATP after ONC but is not protective
Metabolic supplementation with nicotinamide or pyruvate can protect RGCs in animal models of glaucoma 35,62,64. Thus, we were interested to see how supplementation may interact with cellular ATP levels to protect RGCs from ONC. To do this, we delivered 300 mg/kg nicotinamide mononucleotide (NAM) daily subcutaneously, which has previously been used to protect photoreceptors from animal models of retinitis pigmentosa, retinal detachment and ischemia reperfusion 65,66, and similar to approaches that used oral delivery of nicotinamide to protect RGCs from glaucoma 35,62. For 3 days prior to ONC, we pretreated mice with NAM imaging ATeam prior to and after this pre-treatment. This 3 day pretreatment did not lead to any measurable changes in ATP levels (\Delta zYFP/CFP = 0.002 +/- 0.78 mean +/- s.d., n = 291 RGCs from 5 retinas). After ONC, we imaged samples every 2 days for the next 14 days (Fig5f), collected retinas and stained for SPP1 to label \alphaRGCs imaged in vivo, and performed RBPMS immunostaining to quantify overall RGC survival independent of ATP measurements. Similar to our previous observations, we found that in both vehicle and NAM treated groups surviving RGCs had lower ATP levels than those that died even if \alphaRGCs were removed from the analysis (Fig5g-h). Additionally, we observed increases in ATP levels after ONC beginning by day 2-4 after crush. These increases were higher in NAM treated samples (Fig5i). Surprisingly, although NAM treatment has been found to protect RGCs from ONC and glaucoma, RGC survival as measured by RBPMS immunostaining was not found to be different between vehicle and NAM treated groups (Fig5j-k). It is possible that our lack of protection with NAM treatment is due to subcutaneous instead of oral delivery. However, we think this is unlikely since subcutaneous delivery should lead to higher NAM levels67. Furthermore, in previous experiments, NAM only protected RGCs from ONC at 3 days after injury, but was not protective from 7 days onwards 64, consistent with our findings where the difference in survival between vehicle and NAM treatments was not significant and was greatest at day 4 (NAM survival 82.8 +/- 5.0% vs. vehicle survival 76.4 +/- 4.4%, p = 0.089).

Methods
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Philip R. Williams (prwillia@wustl.edu).

Animals
All experiments involving animals follow protocols approved by IACUC at Washington University School of Medicine in St. Louis. Vglut2-Cre mice (Stock No. 028863) and C57BL/6J (Stock No. 000664) were purchased from Jackson Laboratories. Mice are housed in 12-hour light/dark cycle provided with food and water ad libitum. Male and female mice between 2-6 months of age were used for in vivo imaging, and between 2-3 months of age for ONC and quantitative immunofluorescence of mitochondrial protein expression. Sex was not considered in study design.

Intravitreal AAV injection
Bilateral intravitreal AAV injection was performed as previously described 41. Briefly, injection needles were fashioned from capillary glass micropipettes (Sutter #B150-86-10) with a pipette puller (Sutter Flaming/Brown Model P-97) and beveled to a sharp point using a custom beveling device 98. Needles were attached via mineral oil- (Fisher BP2629-1) filled tubing (McMaster Carr #1883T1, #1883T4) to a 50 \mu L Hamilton syringe (#80950). Mice were anesthetized by intraperitoneal injection of ketamine/xylazine cocktail (100 mg/kg ketamine, 10 mg/kg xylazine) and provided pre-op analgesia by subcutaneous injection of meloxicam (10 mg/kg). Sclera was punctured ~250 um posterior to the corneal limbus and ~1-2 \mu L of vitreous was withdrawn. 2 \mu L of AAV (Reagent List) was subsequently injected intravitreally. Antibiotic eye ointment was applied to the injected eye, and the animal was placed on a 37 degrees Celsius warming pad (McKesson #190147) and monitored for recovery from anesthesia. Mice were injected at least 2 weeks prior to experimentation for AAV transduction and expression.

In vivo imaging
Transpupillary in vivo 2-photon imaging was performed as previously described 40,41. Imaging was performed using a multiphoton microscope (Hyperscope, Scientifica) through a long working distance 20X 0.4 NA Plan Apo NIR air objective (Mitutoyo #378-824-5) with excitation provided by an ultrafast Ti:Sapphire laser (Mai Tai DeepSee, Spectra-Physics) tuned to 850 nm. Laser power was controlled by a Pockels cell (Conoptics #350-80-02) and capped at 45 mW at the objective. Emission signal was split into YFP and CFP channels by a 505 long pass dichroic mirror and 535/30 and 480/40 band pass filters respectively and measured using GaAsP photomultiplier tube detectors (ChromoFlex, Scientifica). Images were acquired using ScanImage software (Vidrio Technologies).

Induction of anesthesia was achieved by ketamine/xylazine (as described above) and maintained using 0.5-1% inhaled isoflurane on 0.5 L/min room air. Pre-op meloxicam (see above) analgesia was provided for imaging experiments involving intravitreal injection.

Anesthetized mice were secured to the microscope stage using a head holder (Narishige SGM-4). Body temperature was maintained using a 37 degrees Celsius rodent heating pad (Stoelting 53800M). Pupils were dilated by atropine/phenylephrine eye drops (1% w/v atropine Sigma A0132, 2.5% w/v phenylephrine Sigma P6126). Clear lubricant eye gel (GenTeal, Alcon) was placed over the eye, and a #1.5 coverslip was held perpendicular to the microscope objective and brought into close proximity to the cornea in contact with the gel and secured in place by a slide holder (ThorLabs DH1). The mouse eye was subsequently aligned with the microscope objective, and the desired imaging area and focal plane were located under epifluorescence illumination at 488 nm. Imaging area was further refined under 2-photon illumination. Images captured were timeseries of 512 x 512 pixel raster scan frames of a single focal plane, acquired at a rate of 1.07 frames/second. For injection imaging experiments, a baseline timeseries was acquired, followed by unilateral intravitreal drug injection under a dissecting microscope illuminated with white light. Injected drugs were dissolved in a vehicle of 1:1 v/v mixture of dPBS and DMSO. The imaging area was then realigned under epifluorescence and 2-photon illumination, and the post-injection timeseries was acquired. For agents that induced changes in ATP or Ca2+, alterations returned to baseline over the span of 20 to 60 minutes depending on the concentration of the drug injected, likely to due clearance from the retina or absorbance by surrounding non-retinal tissue. The irreversible CIII inhibitor Antimycin A was the only compound where recovery was not observed during the imaging period. Following imaging, mice were monitored for recovery as described above.

In vivo image processing and quantification
Acquired images were saved as TIFF files of pixel intensity values generated by ScanImage with signal from the YFP and CFP detectors interleaved sequentially. Images were read using ScanImageTiffReader (https://vidriotech.gitlab.io/scanimage-tiff-reader/) and deinterleaved into separate 3-dimensional pixel intensity arrays for YFP and CFP channels. Background levels of each channel were measured as the median of pixel intensity values across time in corner regions of the image where the scan frame is clipped by the objective lens, and subtracted from the value of each pixel in that channel. Motion correction was accomplished by registering frames within a timeseries using Suite2p 99, and multiple acquisitions were aligned to the baseline image by scale-invariant feature transform (SIFT) registration of their time- and channel- average projections and applying the transformation to each frame of the timeseries. Regions of interest (ROIs) encompassing RGC somas were generated on time- and channel- average projections using Cellpose with manual correction of incorrect segmentations. Segmented ROIs of the same cells across multiple acquisitions were matched using the LAP tracker in TrackMate 100,101. Mean pixel intensity values within each ROI were measured across time for each channel, and high frequency temporal noise was filtered using a 20-frame moving average filter. ROIs containing < 100 pixels, with average YFP+CFP intensity < 50 across time, and outside the dynamic range of the biosensor were discarded. YFP/CFP values were then calculated for each ROI to obtain the FRET ratio and filtered YFP/CFP timeseries values for both baseline and post-injection images were z-score normalized to the baseline population mean and standard deviation. Code is available at https://github.com/zelunw/RGC-ATP. For figure images, YFP and CFP were pseudocolored as magenta and green to visualize FRET ratios.

Immunofluorescence and confocal microscopy
Mice were anesthetized with intraperitoneal injection of 250 mg/kg tribromoethanol (Sigma, T48402) and sacrificed by cervical dislocation. Enucleated eyes were fixed in 4% paraformaldehyde for 30 minutes at room temperature, and retina wholemounts were dissected and placed in 30% sucrose in PBS at 4 degrees Celsius overnight. For cryosectioning, whole globes were sunk in 30% sucrose, embedded in OCT and sectioned at 20 \mu m thickness onto charged slides. Retina wholemounts were subject to 3 cycles of freeze-thaw on dry ice for antigen retrieval. Samples were washed and blocked in PBS + 0.2% TritonX-100 with 10% normal horse serum (Sigma, 158127) at room temperature for 1 hour, followed by incubation with primary antibody for 5 days or overnight for wholemounts or sections respectively, and secondary antibody for 2 days at 4 degrees Celsius or 3 hours at room temperature for wholemounts or sections respectively in the blocking buffer with 3 x 10-minute PBS washes between each step. Primary antidobies used were Guinea pig anti-RBPMS (1:2000), Raygene A008712; guinea pig anti-RBPMS (1:5000), Biomatick 1330; chicken anti-TBR2 (1:200), Millipore AB15894; Rat anti-TBR2 (1:1600), Invitrogen 14-4875-82; Goat anti-SPP1 (1:600), R&D Systems AF808; Rabbit anti-CART (1:3000), Phoenix Pharmaceuticals H-003-62; Mouse anti-NDUFB8 (1:200), Abcam AB110242; Rabbit anti-UQCRC2 (1:1000), Proteintech 14742-1; Rabbit anti-TOM20 (1:500), Proteintech 11802-2; Rabbit anti-UCP1 Millipore-Sigma U6382 (1:200); Rabbit anti-UCP2 (1:500) Proteintech 11081-1. Retina wholemounts were mounted on slides with #1.5 thickness coverglass in Vectashield (Vector Labs, H-1000-10) or Fluoroshield (Sigma #F6182) mounting medium with the ganglion cell layer facing up. Samples were imaged using a Zeiss 20X 0.8 NA Plan Apo M27 air objective on a Zeiss LSM800 or LSM980 confocal microscope. Excitation and emission wavelengths for channels imaged were as follows: Blue: Ex 405nm, Em 410-450nm; Green: Ex 488nm, Em 500-553nm; Red: Ex 561nm, Em 570-632nm; Far-Red: Ex 639nm, Em 656-700nm; Near-Infrared: Ex. 730nm, Em 755-802nm.

Immunofluorescence image analysis
For identification of RGC type, ATeam was imaged in the green channel and was used as reference for landmark-based registration of confocal and in vivo images using BigWarp 51. RGC type antibody positivity for each cell matched to an in vivo ROI was manually scored in maximum intensity z-projections of acquired confocal volumes.

For automated quantification of mitochondrial protein expression, one volumetric field of view was acquired with 0.2 \mu m z-step at a distance of ~1000 \mu m from the optic nerve head in each retina quadrant (dorsal, ventral, temporal, nasal). Using Cellpose, volumetric RGC soma masks were generated using the RBPMS-stained channel. Average fluorescence intensity of the mitochondrial proteins channel was measured in the middle 60% of z-slices of each cell ROI and z-score normalized to all ROIs measured in each volumetric field of view, and compared between RGC types. Samples using primary antibodies generated in mouse resulted in intense off-target labeling of endogenous immunoglobulins within retinal vasculature that were occasionally encompassed within ROIs; these ROIs were removed by discarding outliers with average fluorescence greater than or equal to the top quartile within the same image plus 1.5 times the interquartile range. ROIs were scored as SPP1-positive based on SPP1 channel fluorescence intensity greater than or equal to the top quartile within the same image + 1.5 times the interquartile range, measured in maximal z-projection of the image stack.

For UCP analysis, ROIs were generated manually with only the RBPMS channel displayed. Immunofluorescence intensity was measured within ROIs. Independently, SPP1 positive or negative status was recorded for each ROI without UCP channels being displayed.

Optic nerve crush
Optic nerve crush (ONC) surgery was performed as previously described 40,42. Briefly, mice were anesthetized by intraperitoneal injection of ketamine/xylazine and provided pre-op analgesia with subcutaneous meloxicam as described above. An incision was made in the lateral scleral conjunctiva, and the plane between the conjunctiva and sclera was developed by blunt dissection with Dumont #5/45 Forceps (Fine Science Tools #11251-35) and advanced toward the posterior pole of the globe. Upon visualization of the optic nerve, the tips of the forceps were positioned around the optic nerve 2 mm posterior to the globe and closed around the optic nerve for 5 seconds. For sham surgery, forceps were placed in position but no closing pressure was applied. Antibiotic eye ointment was applied over the ocular surface at the completion of surgery, and mice were placed on a heating pad and observed during recovery from anesthesia, with postoperative observation after 12 hours. Mice were killed 14 days after ONC as described above and eyes fixed with 4% paraformaldehyde for immunostaining.

NAM treatment
Nicotinamide mononucleotide (NAM) (Chem Impex #33732) stock solution (30 mg/mL) in saline was injected subcutaneously daily at a dosage of 300 mg/kg. Saline vehicle was used as a control. For injections performed on the day of in vivo imaging, injections were performed after completion of in vivo imaging procedures.

Longitudinal in vivo imaging and quantification
Prior to ONC, in vivo imaging was performed as described above for uninjured baseline. Following ONC, bilateral in vivo imaging was repeated every 2 days in the same region that was imaged prior to injury. For longitudinal cell tracking, time- and channel- averaged projections on each day were aligned by SIFT registration, and RGCs were tracked manually in TrackMate. Because caspase activity during apoptosis is known to cause cleavage of the linker region of the ATeam molecule, resulting in high fluorescence of dispersed CFP that does not correspond with cellular ATP concentration, cells with YFP/CFP ratios below 5 standard deviations of the pre-injury population mean during the course of degeneration were excluded from quantification and instead used to denote dying cells 39.

Statistical analysis
Summary ATP of individual cells over specified timeframes and conditions were quantified as the area under the curve of the ATP z-scores across time (integrated using the composite trapezoidal rule) normalized to the duration of quantification. Group results are reported as mean +/- standard deviation or standard error of the mean where indicated. Boxplot overlays denote quartiles of the data points shown in scatter plots. Statistical comparisons were made using T-test, Wilcoxon rank-sum test, one-way ANOVA with posthoc Tukey’s test, or two-sided Kruskal-Wallis test with posthoc Dunn’s test where indicated. Statistical analysis was performed using SciPy, statsmodels, scikit-posthocs, and Graphpad Prism 9.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study by Wang, Williams, and colleagues investigates the metabolic diversity among retinal ganglion cells (RGCs) and how it correlates with their resilience to degeneration. RGCs are unique neuronal cell type in both their anatomy, signaling/firing properties, and their high metabolic demands. As such, metabolic diseases typically affect RGCs (in addition to other degenerative pathology, or as the sole affected cell type in diseases such as DOA or LHON). RGCs are also structurally, metabolically, and electrically diverse - and this diversity might contribute to their resilience to disease-related stressors (e.g. >30 subtypes identified in mice). The authors use in vivo 2-photon imaging to measure ATP levels, and identified differences in energy maintenance among various RGC types. An important finding is that alpha RGCs had lower ATP levels and were more susceptible to ATP decline when ATP synthesis was inhibited. The study further explored the role of oxidative phosphorylation in maintaining ATP levels and the correlation between low ATP levels and increased survival after axon injury.\n\nThe manuscript is well written, with a clear and logical narrative, and encompasses a large volume of work. I have the following comments designed to improve the readability and citeability of current manuscript.\n\nComment 1: powering, n numbers, and heterogeneity in the data\n- Were mice injected and imaged mono- or bi- laterally? (Provide information in the methods and results.)\n- What are the number of cells per retina analyzed? (Provide a supplementary table of the number of cells for EACH individual retina imaged.)\n- P values are all very significant, most likely due to the large dataset sizes. Side-by-side boxplots should be shown using the averages of RGCs within a single retina imaged (i.e. for most of the data this will be n = 4-5). As the within mouse and across mice retina diversity is large, it is important to see that this data is not skewed by 1 or 2 outlier retinas.\n\nComment 2: RGC diversity\n- Mice have a large RGC phenotypic diversity, which is not (well) matched in humans. Although the data presented is well designed and well analyzed, it doesn't escape the fact that mice are not human. Of course, it would be unreasonable to ask the authors to repeat these experiments in NHP or human ex vivo tissue. However, this should be a major topic of discussion.\n\nComment 3: CONC\n- CONC is severe injury to the RGC, and is non-recoverable. It is unsurprising that there are changes to metabolism following such a severe injury. This isn't an issue in itself, but it would important to understand why the authors didn't pick a more moderate and clinically relevant injury such as ocular hypertension or even continuing rotenone injections to induce RGC death. This might be of relevance given the shuttling of molecules and metabolites in and out of the soma.\n- The surviving vs. dying cells is a very nice piece of data, however it is poorly described how the authors identified a cell as dying (assuming not present in the next imaging session) and whether the data is time-point or time-point or simply alive on day 0 and dead on day 14. Summary statistics of the number of alive / dead cells at each time point would be beneficial to understand the time course of disease in these authors' hands.\n\nComment 4: metabolism, mitochondria, and metabolic treatments\n- Sup Fig 1 is important to the story, and is a great figure. Consider 'promoting' to the main text.\n- Line 155 the authors say \"suggesting ... are enriched for mitochondria\". I would argue that the data is pretty strong and that the authors have used the right markers. I think it is sufficient to say that in B6J mice alpha RGCs have a high total mitochondrial volume (may be best to state this way since the authors have not done individual mitochondria analyses).\n- The authors discuss metabolic treatments for RGCs (citing nicotinamide, pyruvate as examples). Why was one of these treatments not chosen as a treatment arm? (Esp. relevant as both these treatments are progressing to Phase III clinical trials.)\n\nFigure comments:\n- Overlay text in figures is lost within the immunofluorescence, they should be placed outside.\n- In most figures the ATP level / Intensity (purple green plots) are lost on the overlay (e.g. Fig 1 B, E) - is there a way to improve these or show insets?\n\nAdditional questions for discussion points (in addition to those mentioned above):\n1. What are the specific mechanisms that cause the observed baseline ATP differences among RGC types? The study suggests that these differences are not solely due to neuronal activity, but the exact pathways remain unclear.\n2. How does mitochondrial uncoupling contribute to the energetic diversity among RGCs? Are various RGC subtypes more or less susceptible to certain OXPHOS perturbations?\n3. Beyond neuronal activity, what other cellular processes contribute significantly to ATP consumption in RGCs? The authors mention potential roles for cytoskeletal and membrane remodeling but is not fully explored or explained..\n4. What are the precise mechanisms that enable RGCs to maintain ATP homeostasis in response to rapid changes in activity? The authors appear to observe this and then move on without further explanation. What other agents could drive this? Would intravitreal ATP or NAD drive these changes rapidly?\n5. Why do RGCs with lower baseline ATP levels exhibit greater resilience to injury?\n6. What are the predicted mechanisms or causes for the transient ATP elevation observed days after optic nerve crush?", "reviewer_2": "In this study by Wang et al., the authors investigated the diversity in ATP levels that distinguished different RGC types under homeostatic conditions and during circuit activity, in vivo. They found that mitochondrial oxidative phosphorylation fuels circuit activity. They also found that low baseline ATP levels correlated with resilience to mechanical injury and observed a transient ATP elevation immediately following injury. Although interesting, the results lack substantial evidence and mechanistic details to make this study comprehensive. Please address the following major and minor concerns:\n\nMajor concerns:\n1. Fig. 2. Is it possible that the ATP reporter is highly sensitive in the low baseline ATP range in αRGC, resulting in a higher response to inhibitors than the other RGC types? Or, given that ATeam is a pH-sensitive sensor, are the low baseline ATP levels in αRGC due to lower pH in this RGC type? Please explain.\n2. Fig. 3 and Fig. 4. What is the rationale for measuring ATP levels from all RGCs instead of αRGCs? Since αRGCs have low baseline ATP levels compared to all other RGCs, how do their ATP levels change upon str/bic or NBQX/AP5 injection, and light stimulation? Show ATP level changes in αRGCs pre- and post-injection and light stimulation after immunostaining with SPP1.\n3. Fig. 5E. How does this result differ from the other RGC cell types with higher baseline ATP?\n4. Fig. 5E. Explain how the experiments are linked to AMPK. Does blocking AMPK show a decline in ATP levels? How is this mechanism connected to resilience?\n\nMinor concerns:\n5. Fig.1B-D. For the heterogeneous distribution of ATP levels across RGC populations, mention the coefficient of variation.\n6. Fig. 1B-D. What does the white color represent in the images? Most somas show white signal instead of either green or red. Describe the color gradient.\n7. Fig. 1F. Use a color code for baseline and day 7 datapoints to distinguish the respective ATP levels reflecting the heterogeneity.\n8. Fig. 1G- Mention what % of the RGCs were  RGCs, ipRGCs, and ooDSGCs in the text. Explain why the population of SPP1-labelled  RGCs is low - is it known to be low in the retina, or is it due to low SPP1 immunostaining efficacy?\n9. Fig. 1G, H. Follow the same sequence as in the text, i.e., SPP1, TBR2, and CART instead of CART, TBR2, and SPP1, or change the sequence in the text.\n10. Fig. 2F-U. Add a color legend in the figure to define each color.\n11. Fig. 2J-M. Color code data points according to RGC type.\n12. Fig. S2F. Why do the arrows point at the cells with high calcium levels for NBQX/AP5?\n13. Fig. S2A, F, K. Explain why the images look out of focus.\n14. Fig. 2. Mention the dose (in mg/kg body weight) injected for each inhibitor.\n15. Fig. 2. Since there is a recovery in ATP levels post-injection 10-15 mins, what is the clearance rate of ROT, TTFA, AA, and KCN after intraocular injection in mice? Discuss this in terms of the half-life of each of these inhibitors in mice with relevant references.\n16. Fig. 2F-I, N-Q, R-U. Define the time points of the blue, gray, and purple shaded areas in the legend.\n17. Fig. S2K. Add the time stamps to the image labels for scan onset and steady state.\n18. Fig. 3H. Pre vs 140s ATP levels were not significantly different, but 15 m ATP levels were significantly higher than pre. Explain why the ATP levels remained unchanged at 140s upon NBQX/AP5 injection when the Ca2+ levels were significantly low (Fig. S2H).\n19. Fig. 4A, C. Add time stamps in the image labels for ROT and ROT & NBQX/AP5.\n20. Fig. 4I. How do the ATP levels respond in the presence of NBQX/AP5?\n21. Fig. 5E. Summarize the data into box plots as in D to compare ATP levels for dying vs surviving at pre, 4th day, and 14th day.\n22. Fig. 1D, F, H; 2J-M; 5C-E. Mention how many animals/biological replicates in the legend.\n23. Fig. S2A, F, K; 3A, F, K; 4 A, C, F. Make image borders thicker, so the color scheme is more clearly visible.\n24. Mention the p-values for all correlation coefficients throughout the manuscript.\n25. Pg. 13, Line 272-273. Could this be measured by looking at baseline ATP in these different RGC types upon activity inhibition?\n26. Does anesthesia change the homeostatic ATP levels compared to awake animals? Explain in the discussion section.", "reviewer_3": "This interesting study investigates metabolic heterogeneity in RGCs under physiological and pathological conditions that include light stimulation, pharmacologically induced energy depletion and optic nerve injury. The approach combines ATP detection using transgenic expression of a FRET sniffer with post hoc immunostaining for aRGCs, ipRGCs and ooDSGCs RGC subtypes. ATP measurements stand out for real time single cell resolution in the intact living retina, correlation between activity (calcium) and metabolism (ATP) and state-of-the-art analyses relying on high-throughput machine learning. The authors found that the 4 RGC subtypes differ vs. baseline [ATP], with aRGCs showing the lowest levels and greatest sensitivity to ETC inhibition. That somatic ATP levels are comparable across aRGC subtypes (that include M4 cells) but different from other classes of ipRGCs is an intriguing observation. As expected, ATP levels declined in response to excessive activity while the observation that ‘physiological’ activity does not impact ATP content suggests involvement of homeostatic compensation. Perhaps unexpectedly, RGCs that survived nerve crush had lower [ATP] than the less resilient cohort.\n\nPotential weaknesses are methodological, mechanistic and conceptual. The study ignores the effect of pharmacological manipulations (ETC inhibitors) on the retina even though those effects must have been substantial as the inhibitors compromised presynaptic (excitatory and inhibitory) and inflammatory machinery and almost certainly disrupted phototransduction, neurotransmitter release, induced massive pan-retinal release of ROS together with cytokine/purine release from the glia. It is unclear if OXPHOSP inhibition differentially compromised viability or apoptosis in RGC subtypes. ATP changes in the cell body might be independent from dendritic or axonal responses and thus activity independence within the cell body could masks large activity-evoked changes in dendrites. Immunostaining for mitochondrial proteins would benefit from validation by an independent method and perhaps supplemented by direct measurements of the mitochondrial membrane potential. The study largely ignores glycolysis. Finally, impact would be strengthened if the authors tested NAD supplementation during ONC.\n\nIn summary: the study employs state-of-the-art high-throughput recordings and analyses to bring insight into RGC metabolism in the intact retina. The topic is important in part because of the extensive NAD supplementation literature which shows neuroprotection in mammalian glaucoma and ONC models. Mechanistic insight into the properties of metabolic regulation in RGCs and understanding why certain subtypes of RGC resist injury would be very useful from basic and translational perspectives. The concerns include descriptiveness, significant side effects of ETC manipulation and modest advance in mechanistic understanding.\n\nAdditional comments:\nP3 line 51 Intro. The authors state “energetic diversity across neuronal populations is much less understood” without elaborating on this key claim. What is known about the energetic diversity in neurons? What do we know about the energetic diversity among neurons in the retina?\nIbid. Why should we study energetic diversity.\nFig 1H. The authors conclude that “natively resilient aRGCs had lower homeostatic ATP levels” than ipRGCs and ODSS cells yet ipRGCs are arguably more resilient than aRGCs.\nP6. Intraocular injection of ROT, AA and other ETC drugs may have compromised the viability of photoreceptors and many RGCs, disrupted ON vs OFF signaling, induced massive oxidative stress, excitotoxicity from extracellular glutamate accumulation and inflammation. Can the authors provide evidence (TUNEL, GFAP etc) that this was/was not the case? The authors should make an explicit case for the cell-autonomy behind the stated ‘diversity’ of observed ATP effects.\nThe paradoxical observation of higher mitochondrial protein (NDUFB8, SDHA, UQCRC2, and ATP5A) expression in low-ATP aRGCs (Fig. S1) merits independent validation. Are the data congruent with subtype-specific transcriptomic evidence available from databases (Tran et al., etc)? Would changes in mitochondrial protein expression predict subtype-specific changes in the mitochondrial membrane potential, calcium transport and dehydrogenase activity?\nThe assumption that glycolysis does not play a role in activity appears reasonable even though nerve crush is known to promote glycolytic activity. However, it would be interesting to confirm the assumption with 2-deoxyglucose or iodoacetate.\nP11 line 233. “Since we observed that natively resilient aRGCs had lower homeostatic ATP levels”…. Please define resilience. Many studies (Della Santina et al.,; El Danaf et al.; Feng et al., Zapadka et al; Guttenplan et al) demonstrated that light responses, dendritic/synaptic architecture etc in aRGCs are impacted by short exposures to modest IOP elevations.\nMight activity-independence of baseline [ATP] be compartment-specific? Light-evoked ATP changes in dendritic, prelaminar and (glial) laminar regions could be missed by somatic recordings, and compartmentalization might be more pronounced for cells with large dendritic trees (eg, aRGCs).\nDiscussion. The protective effect of NAD+ supplementation suggests that IOP- and ONC-stressed RGCs experience energy deficits that are compensated by energy supplementation. Hence, the authors observation that high-ATP cells seem to be preferentially killed by ONC that leads them to suggest that “low homeostatic ATP translates to increased resilience’ seems counterintuitive and could be tested with NAD supplementation.\nMinor:\nRecommend Figures be introduced by one-sentence summary.", "reviewer_4": "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_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 have clarified where experiments were performed mono- or bi- laterally in the Methods and Results.\n- What are the number of cells per retina analyzed? (Provide a supplementary table of the number of cells for EACH individual retina imaged.)\nWe have added a supplementary table that includes all retinas used for in vivo imaging experiments and the number of cells derived from them. It should be noted that some experiments have more manipulations or repeated imaging time points, so sample quality is not the only variable affecting the number of viable cells.\n- P values are all very significant, most likely due to the large dataset sizes. Side-by-side boxplots should be shown using the averages of RGCs within a single retina imaged (i.e. for most of the data this will be n = 4-5). As the within mouse and across mice retina diversity is large, it is important to see that this data is not skewed by 1 or 2 outlier retinas.\nWe have added in Sample mean graphs and statistics to all figures. In most cases, experiments remained statistically significant. In some instances, sample numbers were small which limited power. Also, variability of drug injections from sample to sample drove more of the differences than other measured variables like RGC type. This was shown in previous Figure 2 J-M, which has been moved to Supplementary Figure 2.\nComment 2: RGC diversity\n- Mice have a large RGC phenotypic diversity, which is not (well) matched in humans. Although the data presented is well designed and well analyzed, it doesn't escape the fact that mice are not human. Of course, it would be unreasonable to ask the authors to repeat these experiments in NHP or human ex vivo tissue. However, this should be a major topic of discussion.\nWe have added a section to the Discussion elaborating on this topic.\nComment 3: CONC\n- CONC is severe injury to the RGC, and is non-recoverable. It is unsurprising that there are changes to metabolism following such a severe injury. This isn't an issue in itself, but it would important to understand why the authors didn't pick a more moderate and clinically relevant injury such as ocular hypertension or even continuing rotenone injections to induce RGC death. This might be of relevance given the shuttling of molecules and metabolites in and out of the soma.\nWe agree with Reviewer 1 that CONC is a severe and perhaps less clinically relevant model of RGC degeneration. However, CONC has many advantages as an experimental model. Primarily, for time-lapse in vivo imaging experiments, CONC ensures that all RGCs are effected by the degenerative stimulus simultaneously. In contrast, intraocular pressure or genetic models of glaucoma are not time-locked and cellular responses would be quite difficult to quantify and interpret. For example, we identified a transient elevation of ATP in the early time points after CONC. We believe that this would be difficult to observe in a slow and staggered degeneration context. Indeed, microbead models of glaucoma have identified reduced ATP levels in some RGCs at chronic degeneration time points (Quintero et al., 2022). Taken together, while we agree that it would be exciting to examine intrinsic metabolism in RGC during other models of degeneration, we feel it is beyond the scope of this individual study.\n- The surviving vs. dying cells is a very nice piece of data, however it is poorly described how the authors identified a cell as dying (assuming not present in the next imaging session) and whether the data is time-point or time-point or simply alive on day 0 and dead on day 14. Summary statistics of the number of alive / dead cells at each time point would be beneficial to understand the time course of disease in these authors' hands.\nThanks for the suggestion. We have added the number of RGCs surviving at each time point to Figure 5 and clarified the labels of dying or surviving RGCs in the Figure caption.\nComment 4: metabolism, mitochondria, and metabolic treatments\n- Sup Fig 1 is important to the story, and is a great figure. Consider 'promoting' to the main text.\nWe appreciate the suggestion, and are happy to promote this figure. However, given this was not suggested by other reviewers, we have left this as supplementary data.\n- Line 155 the authors say \"suggesting ... are enriched for mitochondria\". I would argue that the data is pretty strong and that the authors have used the right markers. I think it is sufficient to say that in B6J mice alpha RGCs have a high total mitochondrial volume (may be best to state this way since the authors have not done individual mitochondria analyses).\nWe have rephrased this section to make the statement stronger, and to appropriately indicate higher mitochondrial volume in RGCs and not mitochondrial number.\n- The authors discuss metabolic treatments for RGCs (citing nicotinamide, pyruvate as examples). Why was one of these treatments not chosen as a treatment arm? (Esp. relevant as both these treatments are progressing to Phase III clinical trials.)\nWe agree with Reviewer 1 that it would be of interest to examine how our observations of homeostatic differences in surviving ATP might interact with treatments targeting metabolic deficiencies. To do this, we delivered 300 mg/kg nicotinamide mononucleotide (NAM) daily subcutaneously, which has previously been used to protect photoreceptors from animal models of retinitis pigmentosa, retinal detachment and ischemia reperfusion (Bai and Sheline, 2013; Chen et al., 2020). This dosage is also similar to experiments that protected RGCs from optic nerve crush using nicotinamide riboside, except we dosed daily instead of every three days (Zhang et al., 2021). In models of glaucoma, although here nicotinamide was delivered orally in drinking water (Williams et al., 2017). For three days prior to optic nerve crush, we pretreated mice with NAM for three days imaging ATeam prior to and after this pre-treatment. This pretreatment did not lead to measurable changes in ATP levels ( zYFP/CFP = 0.002 +/- 0.78 mean +/- s.d. n = 291 RGCs from 5 retinas). After optic nerve crush, we imaged samples every 2 days for the next 14 days, and collected retinas and stained for SPP1 to label RGCs imaged in vivo, and performed Rbpms staining to quantify overall RGC survival independent of ATP measurements. Similar to our previous observations in Figure 5, we found that in both vehicle and NAM treated groups surviving RGCs had lower ATP levels than those that died even if RGCs were removed from the analysis. Additionally, we observed increases in ATP levels after optic nerve crush beginning by day 2 after crush. These increases were higher in NAM treated samples. Surprisingly, although NAM treatment has been found to protect RGCs from optic nerve crush and glaucoma, we did not observe increased RGC survival following NAM treatment, as measured by Rbpms immunostaining. However, in previous experiments nicotinamide only protected RGCs from optic nerve crush at 3 days after injury, but was not protective from 7 days onwards (Zhang et al., 2021). We have added these experiments to the Results section and Figure 5.\nFigure comments:\n- Overlay text in figures is lost within the immunofluorescence, they should be placed outside.\nWe have placed black boxes behind the labels to improve clarity.\n- In most figures the ATP level / Intensity (purple green plots) are lost on the overlay (e.g. Fig 1 B, E) - is there a way to improve these or show insets?\nWe have increased the weight of the yellow lines to improve their legibility.\nAdditional questions for discussion points (in addition to those mentioned above):\n1. What are the specific mechanisms that cause the observed baseline ATP differences among RGC types?\nThe study suggests that these differences are not solely due to neuronal activity, but the exact pathways remain unclear.\nWe discuss this topic in more detail below addressing questions 2 and 3. Essentially, we find some evidence for more UCP1&2 expression in alpha RGCs. Additionally, maintenance of the plasma membrane and cytoskeleton may represent other factors. We have added these points to the Discussion section of the manuscript and as Supplementary Figure 6.\n2. How does mitochondrial uncoupling contribute to the energetic diversity among RGCs? Are various RGC subtypes more or less susceptible to certain OXPHOS perturbations?\nIndeed, in Figure 2 we show that different RGC subtypes are more or less susceptible to certain OXPHOS perturbations. Generally, RGCs were showed reduced ATP to more mild inhibitions of either Complex I or Complex II, whereas ooDSGCs showed lower ATP levels in response to more complete mitochondrial inhibition caused by potassium cyanide.\nTo examine the role of mitochondrial uncoupling in RGC ATP diversity, we immunostained retina sections for UCP1 or UCP2 along with the general RGC marker RBPMS, or the RGC marker SPP1. We found that RGCs had slightly but significantly higher expression of UCP1 (non-RGC z-score -0.03 +/- 1.0 vs. RGC z-score 0.29 +/- 0.91, p = 0.02, n = 848 RGCs from 3 retinas), but had higher expression of UCP2 that was visually obvious and significantly higher when quantified (non-RGC z-score -0.09 +/- 0.94 vs. RGC z-score 1.01 +/- 1.07, p < 0.001, n = 966 RGCs from 3 retinas). We have added this data to the manuscript as Supplementary Figure 6.\n3. Beyond neuronal activity, what other cellular processes contribute significantly to ATP consumption in RGCs? The authors mention potential roles for cytoskeletal and membrane remodeling but is not fully explored or explained..\nWe have expanded this portion of the Discussion section to further elaborate on possible sources of homeostatic ATP consumption.\n4. What are the precise mechanisms that enable RGCs to maintain ATP homeostasis in response to rapid changes in activity? The authors appear to observe this and then move on without further explanation. What other agents could drive this? Would intravitreal ATP or NAD drive these changes rapidly?\nThere are many adaptations that neurons likely use to maintain ATP homeostasis in response to changes in activity. Over rapid time scales, low ATP levels induce conversion of built-up ADP to AMP and ATP, mitochondrial Ca2+ uptake from elevated cytoplasmic Ca2+ levels stimulates the citric acid cycle and OXPHOS activity, glucose transporters in vesicles are delivered to the plasma membrane, and the malate-aspartate shuttle is activated (Ashrafi et al., 2017; Baeza-Lehnert et al., 2019; Connolly et al., 2014; Denton, 2009; Diaz-Garcia et al., 2021; Gellerich et al., 2013; Glancy and Balaban, 2012; Llorente-Folch et al., 2013; Perez-Liebana et al., 2022). Over somewhat longer timescales, AMPK signaling can compensate for reduced ATP levels, and transcription factors that include NRF-1, NRF-2, PGC-1A, SP1, and SP4 can help neurons to compensate for longer term deficiencies in ATP (Johar et al., 2014; Priya et al., 2013).\nWe believe that further experiments are beyond the scope of this study, but we have added these topics to the Discussion.\nRegarding intravitreal delivery of ATP or NAD, intravitreal delivery of ATP would likely induce multiple off-target effects not related too boosting internal ATP levels since ATP is a potent signaling molecule that mediates RGC death in models of glaucoma (Reigada et al., 2008; Sakamoto et al., 2015). While we have not injected NAD into the eye directly, in our experiments added to Figure 5, we treated mice with subcutaneous NAM daily. This treatment did not chronically alter ATP levels in healthy retina, but did cause further elevations in ATP after optic nerve crush. While it is possible that higher and acute dosage with NAD into the vitreous may boost ATP levels, we feel it is beyond the scope of this study and will not add significantly to our overall findings.\n5. Why do RGCs with lower baseline ATP levels exhibit greater resilience to injury?\nWe think that there are two most likely contributors to the lower baseline ATP level being linked to RGC survival. For one, OXPHOS generates a significant amount of toxic byproducts including ROS species. Mitochondria undergoing more ATP production may also be more stressed and have a higher turnover rate. Thus, maintaining lower homeostatic ATP levels could be protective by reducing cellular stress. Another possible factor is the idea of hermetic conditioning. Constant lower ATP levels could precondition neurons to metabolic stress. However, this seems somewhat less likely given that the lower ATP levels we observe are still within normal physiological ranges, and should not represent a stressful condition. Furthermore, we actually observed an overabundance of ATP after optic nerve crush suggesting that chronic lack of ATP is not present in RGCs after axon injury. We have expanded the Discussion section to expand on these points.\n6. What are the predicted mechanisms or causes for the transient ATP elevation observed days after optic nerve crush?\nWe agree that this observation is curious, and we are confident in their accuracy given that our additional experiments with NAM treatments also noted ATP elevations after optic nerve crush, but our explanations for these ATP elevations are largely speculative. We hypothesize that two major signaling pathways likely contribute to the elevations of ATP seen in the days after optic nerve crush. Although, not reported after optic nerve crush, glaucoma models have established that AMPK activation precedes and drive RGC degeneration (Belforte et al., 2021). We expect that AMPK is similarly activated by optic nerve crush induced degeneration, although our attempts to immunostain for activated AMPK were unsuccessful (not shown). AMPK activation would lead to enzymatic activation of pathways to acutely produce more ATP via substrate import and enhanced glycolytic activity (Ashrafi et al., 2017; Kishton et al., 2016). Over longer time scales, AMPK signaling can boost mitochondrial biogenesis which would boost ATP production (Bergeron et al., 2001). mTOR signaling is decreased by optic nerve crush, which in turn reduces cellular biogenesis and could make ATP more abundant (Park et al., 2008). Taken together, we believe that AMPK activity and mTOR inhibition would be the most likely contributors to our observed ATP elevations after optic nerve crush. While we believe that further experiments would be beyond the scope of our study, we have added this to the Discussion section.\nInvited to review: 2025-03-04\nAccepted review: 2025-03-04\nReturned review: 2025-03-07\nCongratulations on the excellent study.\nReviewer #2 (Remarks to the Author):\nIn this study by Wang et al., the authors investigated the diversity in ATP levels that distinguished different RGC types under homeostatic conditions and during circuit activity, in vivo. They found that mitochondrial oxidative phosphorylation fuels circuit activity. They also found that low baseline ATP levels correlated with resilience to mechanical injury and observed a transient ATP elevation immediately following injury. Although interesting, the results lack substantial evidence and mechanistic details to make this study comprehensive.\nPlease address the following major and minor concerns:\nMajor concerns:\n1. Fig. 2. Is it possible that the ATP reporter is highly sensitive in the low baseline ATP range in αRGC, resulting in a higher response to inhibitors than the other RGC types? Or, given that ATeam is a pH-sensitive sensor, are the low baseline ATP levels in αRGC due to lower pH in this RGC type? Please explain.\nAteam1.03 is built on a mseCFP and mVenus FRET pair, which are relatively insensitive to physiological pH levels (3.5 and 5.3 pKa respectively). Estimates of intracellular pH in active neurons vary, but most observations do not drop below 6.5. Additionally, the ATeam1.03 biosensor has been applied to RGC degeneration in culture and in vivo and did not observe issues with pH sensitivity (Quintero et al., 2022). Lastly, we also used the FRET biosensor Twitch2b, which is built with the more pH sensitive Citrine (pKa 5.7), and in this case YFP/CFP ratios increased with increasing activity (Previous Supplementary Figure 2). And our previous work showed that Twitch2b ratios are higher in RGCs than other RGCs in contrast to ATeam ratios (McCracken et al., 2023).\n2. Fig. 3 and Fig. 4. What is the rationale for measuring ATP levels from all RGCs instead of αRGCs? Since αRGCs have low baseline ATP levels compared to all other RGCs, how do their ATP levels change upon str/bic or NBQX/AP5 injection, and light stimulation? Show ATP level changes in αRGCs pre- and post-injection and light stimulation after immunostaining with SPP1.\nWe did not perform type Immunostaining for these experiments because ATP changes resulting from activity manipulations were relatively small (or absent) and quite consistent across the RGC population (Fig 3D,E,I,J,N and O). This contrasts both with the Ca2+ response to activity manipulations, which were large inconsistent and in some case bidirectional across RGCs (Supp Fig 2D,E,I and J). And also contrast with pharmacological inhibition of electron transport chain function, where cause much stronger and varied changes in ATP level (Fig 2 J-M). For these reasons, we do not think it is necessary to perform RGC type analysis of circuit activity manipulations.\n3. Fig. 5E. How does this result differ from the other RGC cell types with higher baseline ATP?\nWe are a bit unclear as to the question here. We have split the RGCs into those that were observed to die during the time lapse imaging experiment and those that survived the time lapse after ONC. This includes all RGC types since we can only include RGC typing after the time lapse has completed and cannot identify the types of dead RGCs.\n4. Fig. 5E. Explain how the experiments are linked to AMPK. Does blocking AMPK show a decline in ATP levels? How is this mechanism connected to resilience?\nSince similar elevations in ATP levels were observed in both dying and surviving RGC cohorts, we do not believe that this is a feature that is related to resilience.\nMinor concerns:\n5. Fig.1B-D. For the heterogeneous distribution of ATP levels across RGC populations, mention the coefficient of variation.\nAcross these samples it was 18.9 +/- 4.5% (mean +/- s.d.), which represents about 13% of the total dynamic range of ATeam1.03 and 0.5 mM ATP if assuming these differences operate over the linear range of the biosensor (Imamura et al., 2009). We have added the coefficient of variation to the Results section.\n6. Fig. 1B-D. What does the white color represent in the images? Most somas show white signal instead of either green or red. Describe the color gradient.\nAs described in the Figure captions, we psuedocolored the CFP channel green and the YFP channel magenta. A white hue indicates a relatively equal balance between the two channels. However, if cells are saturated in an image they can also appear completely white. This is indicated in the look up table for each figure. We have done our best to represent the data while avoiding saturated RGCs wherever possible. However, since we are only using linear adjustments of minimum and maximum to alter the signal intensity, some saturated RGCs will be unavoidable. Generally, these saturated cells will have a white center with a hued halo. We can provide a more detailed description of this in the text.\nTo avoid confusion, we have added a brief description of the above in the Results and Methods sections.\n7. Fig. 1F. Use a color code for baseline and day 7 datapoints to distinguish the respective ATP levels reflecting the heterogeneity.\nThis is a scatterplot of baseline on the y axis and 7 days on the x axis, so each point is graphing both values. If there is a relationship between repeat images, then the slope should equal about 1.\n8. Fig. 1G- Mention what % of the RGCs were RGCs, ipRGCs, and ooDSGCs in the text. Explain why the population of SPP1-labelled RGCs is low - is it known to be low in the retina, or is it due to low SPP1 immunostaining efficacy?\nWe have added the proportions of each immunostained subtype to the Results section and compared them to numbers reported in the literature. Our analyzed RGCs were comprised of 29.2 +/- 4.8% CART+ RGCs, 7.6 +/- 2.4% TBR2+ RGCs, 9.3 +/- 5.7% SPP1+ RGCs, consistent with previously reported native distributions by immunostaining (Chen et al., 2021; Duan et al., 2015; Kay et al., 2011; Mao et al., 2014; Ruff et al., 2021). These were all within the same range, with ipRGCs perhaps being a bit underrepresented. This could be due to a number of factors including AAV2 tropism, promoter efficiency, or cell soma size.\n9. Fig. 1G, H. Follow the same sequence as in the text, i.e., SPP1, TBR2, and CART instead of CART, TBR2, and SPP1, or change the sequence in the text.\nWe have adjusted the text to match the sequencing of the figure.\n10. Fig. 2F-U. Add a color legend in the figure to define each color.\nWe have added this legend to Figure 2.\n11. Fig. 2J-M. Color code data points according to RGC type.\nWe have added pre vs. post scatterplots with points colored by RGC type to Figure 1. To make space for this addition, we have moved the sample coded plots to the Supplementary Figures. We feel this information is important as it shows that drug responses were variable in their intensity across different samples/injections.\n12. Fig. S2F. Why do the arrows point at the cells with high calcium levels for NBQX/AP5?\nWe thought the OFF response was interesting, but agree this distracts from the purpose of the experiment. We have removed the arrows.\n13. Fig. S2A, F, K. Explain why the images look out of focus.\nTranspupillary imaging leads to rather strong aberrations, so most imaging with this approach is less crisp than histological imaging for example. We don’t feel that these images are any less crisp than our previous images, but this might be in part due to the difference in look up tables. We think it is possible that the prevalent cyan in the Twitch2b images versus the prominent magenta in the ATeam images causes a different perception. Another possible difference is that, in our experience, Twitch2b is overall brighter than ATeam1.03. This would lead to higher density of labelled RGCs along with their dendrites and axons, which leads to more out of focus signal as compared to lower density labeling. We show in the Supplementary Table requested by Reviewer 1 that indeed more RGCs were observable in the Twitch2b samples than the ATeam samples.\n14. Fig. 2. Mention the dose (in mg/kg body weight) injected for each inhibitor.\nWe have added the volume and dosage of each inhibitor to the Results and Methods section.\n15. Fig. 2. Since there is a recovery in ATP levels post-injection 10-15 mins, what is the clearance rate of ROT, TTFA, AA, and KCN after intraocular injection in mice? Discuss this in terms of the half-life of each of these inhibitors in mice with relevant references.\nFrom our previous experience with intraocular pharmacological injections (McCracken et al., 2023), and work in this paper, we feel that the rates of recovery are much more likely to be an issue of clearance than one of half-life because most modulators (whether related to circuit activity or metabolism) recover over the course of 10 to 30 minutes. This is much faster than the half-life of these agents at body temperature. Additionally, the affective dosage is also important as we have observed longer duration of inhibition from higher concentration injections (not shown). We feel like our data is rather difficult to relate to other publications of half-life in relation to systemic delivery and would likely be more confusing than helpful as a point of discussion.\n16. Fig. 2F-I, N-Q, R-U. Define the time points of the blue, gray, and purple shaded areas in the legend.\nThanks for catching this. We have updated the figure legend accordingly.\n17. Fig. S2K. Add the time stamps to the image labels for scan onset and steady state.\nWe have added time stamps to these image panels.\n18. Fig. 3H. Pre vs 140s ATP levels were not significantly different, but 15 m ATP levels were significantly higher than pre. Explain why the ATP levels remained unchanged at 140s upon NBQX/AP5 injection when the Ca2+ levels were significantly low (Fig. S2H).\nCa2+ levels are further depressed at 15min (Fig. S2H). We believe this delayed ATP elevation is due to an accumulated effect of circuit inactivity. Thus, the RGCs are homeostatically producing a set amount of ATP to meet the expected demands that would exist even in a ‘quiet’ circuit. As these demands are lowered over a prolonged period, ATP then builds up.\n19. Fig. 4A, C. Add time stamps in the image labels for ROT and ROT & NBQX/AP5.\nThese images are average intensity projections over variable amounts of time. The time frames line up with the shaded regions on the line trace graphs. We have made this more clear in in the Figure Captions.\n20. Fig. 4I. How do the ATP levels respond in the presence of NBQX/AP5?\nWe are unclear what the question is here. If it is a suggestion to combine ROT with NBQX/AP5, we feel that this would be rather unnecessary. NBQX/AP5 blocked most circuit activity (Previous Supp Fig 2), so it is unlikely that we would expect much interaction between ROT and NBQX/AP5.\n21. Fig. 5E. Summarize the data into box plots as in D to compare ATP levels for dying vs surviving at pre, 4th day, and 14th day.\nWe are unclear why specifically pre, 4th and 14th days are requested here. We have graphed pre values in Figure 5D, and by definition there will be no dying cells on the 14th day. Thus, we feel that the sample mean and variance distribution plotted in Figure 5E is sufficient to represent this data. Our additional ATP measurements in vehicle and NAM treated samples further confirm and define the ATP dynamics after nerve crush.\n22. Fig. 1D, F, H; 2J-M; 5C-E. Mention how many animals/biological replicates in the legend.\nThis information is already present in the Figure Legends.\n23. Fig. S2A, F, K; 3A, F, K; 4 A, C, F. Make image borders thicker, so the color scheme is more clearly visible.\nWe have doubled the line thickness on image borders.\n24. Mention the p-values for all correlation coefficients throughout the manuscript.\nWe have added p-values for correlation coefficients throughout the manuscript.\n25. Pg. 13, Line 272-273. Could this be measured by looking at baseline ATP in these different RGC types upon activity inhibition?\nAfter activity inhibition, ATP was subtly shifted by a relatively uniform degree across the RGC population after quite a long delay (Figure 3H-J). Thus, there is not evidence for differential alterations in ATP level as a result of activity inhibition, and we do not believe RGC types would be different here.\n26. Does anesthesia change the homeostatic ATP levels compared to awake animals? Explain in the discussion section.\nAnesthesia is well documented to suppress neuronal activity in response to stimulation. Thus it is possible that without anesthesia, circuit activity possibly being stronger could lead to observable differences in ATP in response to light. However, awake behaving fundus imaging of neuronal activity or energetic state is unprecedented. We are also not aware of any studies comparing homeostatic ATP levels in other neuronal populations comparing awake vs. anesthetized states. We have added the possible influence of anesthesia on our observations as a point of Discussion.\nReviewer #3 (Remarks to the Author):\nThis interesting study investigates metabolic heterogeneity in RGCs under physiological and pathological conditions that include light stimulation, pharmacologically induced energy depletion and optic nerve injury. The approach combines ATP detection using transgenic expression of a FRET sniffer with post hoc immunostaining for aRGCs, ipRGCs and ooDSGCs RGC subtypes. ATP measurements stand out for real time single cell resolution in the intact living retina, correlation between activity (calcium) and metabolism (ATP) and state-of-the-art analyses relying on high-throughput machine learning. The authors found that the 4 RGC subtypes differ vs. baseline [ATP], with aRGCs showing the lowest levels and greatest sensitivity to ETC inhibition. That somatic ATP levels are comparable across aRGC subtypes (that include M4 cells) but different from other classes of ipRGCs is an intriguing observation. As expected, ATP levels declined in response to excessive activity while the observation that ‘physiological’ activity does not impact ATP content suggests involvement of homeostatic compensation. Perhaps unexpectedly, RGCs that survived nerve crush had lower [ATP] than the less resilient cohort.\nPotential weaknesses are methodological, mechanistic and conceptual. The study ignores the effect of pharmacological manipulations (ETC inhibitors) on the retina even though those effects must have been substantial\nWhile we agree with Reviewer 3 that it is impossible to dissect out the complex interactions between the effects of ETC inhibition on the environment from direct actions on RGCs themselves, we strongly believe that in vivo observations of these pathways are critical to perform. More isolated systems where only RGCs are present (e.g. in primary culture) are highly artificial, represent cells already under extreme stress from dissection and isolation, and do not contain the full diversity of RGC types since many do not survive purification. The fact that we observe reproducible RGC type differences in response to ETC inhibition is most simply explained by cell intrinsic differences since there is little logic to the fact that the milieu differs in a type specific manner.\nTo further demonstrate that our ETC observations are reproducible, we chose a second Complex I inhibitor, etomoxir, and delivered it by intravitreal injection. We found that, similar to rotenone, etomoxir led to ATP reductions ATP that were more significant in RGCs, and weak to non-existent in other RGC types. We believe this strongly supports our interpretation that the primary reason that RGC ATP levels are altered by ETC inhibition is a direct effect on these cells. This new data has been added to the Results as Supplementary Figure 1.\nas the inhibitors compromised presynaptic (excitatory and inhibitory)\nWe performed ATP measurements in conditions of excitatory or inhibitory receptor blockade (Figure 3) and noticed mild effects as compared to mitochondrial inhibition (Figure 2). Additionally, combination of Complex I inhibition with excitatory receptor inhibition reduced the phenotype indicating that presynaptic excitatory drive is still active under rotenone delivery (Figure 4). These observations demonstrate that possible circuit alterations caused by ETC inhibitors are not likely to be a major contributing factor to downstream RGC ATP levels during inhibition.\nand inflammatory machinery and almost certainly disrupted phototransduction, neurotransmitter release, induced massive pan-retinal release of ROS together with cytokine/purine release from the glia. It is unclear if OXPHOSP inhibition differentially compromised viability or apoptosis in RGC subtypes.\nWe have examined glial, inflammatory and degenerative responses to injection of our strongest ETC inhibitor, potassium cyanide (KCN), by performing histology of the microglial labeled transgenic line CX3CR1-GFP immunostained for GFAP, interleukin 6 (IL-6), CD68, and cleaved caspase-3. As a positive control, we included samples from excitotoxic lesions that we treated with intravitreal injection of 50mM NMDA and collected 2 days later. We show below in Reviewer Figure 1 that in all cases, retinas from KCN injected eyes were not noticeably different from vehicle injected negative controls. In contrast, NMDA treated positive controls showed microglial reactivity in that GFP+ microglia were swollen, ramified in the nuclear layers, and positive for CD68. KCN injected eyes also lacked Mueller Glia fibrosis as assayed by GFAP staining, lacked apoptotic RGCs as indicated by cleaved caspase-3, and did not show upregulation of the cytokine IL-6. Taken together, although degenerative cascades may eventually result from ETC inhibition, we did not notice any evidence that they are active during the time window acutely after injection during which we are performing measurements. Thus, they are unlikely to be a critical contributor to the measurements of RGC ATP levels.\nATP changes in the cell body might be independent from dendritic or axonal responses and thus activity independence within the cell body could masks large activity-evoked changes in dendrites.\nWe agree that cell compartments could definitely respond differently to activity or ETC inhibitors, and that this would be very interesting to examine. However, we would suggest that these proposed experiments are beyond the scope of this study.\nImmunostaining for mitochondrial proteins would benefit from validation by an independent method and perhaps supplemented by direct measurements of the mitochondrial membrane potential.\nAs suggested we pulled out the transcriptomic data for the mitochondrial proteins immunostained for in our previous Supplemental Figure 1, shown below as Reviewer Figure 2.\nThis analysis would suggest that the ON subset of RGCs may be critical for driving the observed differences in mitochondrial protein expression by the RGC cohort.\nThe study largely ignores glycolysis.\nWe agree with Reviewer 3 that we focused on ETC inhibitors, in part because with glycolysis feeding pyruvate into ETC it is possible that disrupting glycolysis would reduce ATP indirectly by disrupting mitochondrial pyruvate availability. However, we examined the importance of glycolysis in maintaining ATP levels by intravitreal injection of 2-deoxyglucose (2-DG). After injection of 2-DG, we did not observe any measurable reduction in RGC ATP levels (pre ATP -0.19 +/- 0.47, post 2-DG -0.39 +/- 0.18; mean +/- s.d.; p = 0.20). We have added these experiments to the Results as Supplementary Figure 3.\nFinally, impact would be strengthened if the authors tested NAD supplementation during ONC.\nWe agree with Reviewer 3 that it would be of interest to examine how our observations of homeostatic differences in surviving ATP might interact with treatments targeting metabolic deficiencies. To do this, we delivered 300 mg/kg nicotinamide mononucleotide (NAM) daily subcutaneously, which has previously been used to protect photoreceptors from animal models of retinitis pigmentosa, retinal detachment and ischemia reperfusion (Bai and Sheline, 2013; Chen et al., 2020). This dosage is also similar to experiments that protected RGCs from optic nerve crush using nicotinamide riboside, except we dosed daily instead of every three days (Zhang et al., 2021). In models of glaucoma, although here nicotinamide was delivered orally in drinking water (Williams et al., 2017). For three days prior to optic nerve crush, we pretreated mice with NAM for three days imaging ATeam prior to and after this pre-treatment. This pretreatment did not lead to measurable changes in ATP levels (D zYFP/CFP = 0.002 +/- 0.78 mean +/- s.d. n = 291 RGCs from 5 retinas). After optic nerve crush, we imaged samples every 2 days for the next 14 days, and collected retinas and stained for SPP1 to label aRGCs imaged in vivo, and performed Rbpms staining to quantify overall RGC survival independent of ATP measurements. Similar to our previous observations in Figure 5, we found that in both vehicle and NAM treated groups surviving RGCs had lower ATP levels than those that died even if aRGCs were removed from the analysis. Additionally, we observed increases in ATP levels after optic nerve crush beginning by day 2 after crush. These increases were higher in NAM treated samples. Surprisingly, although NAM treatment has been found to protect RGCs from optic nerve crush and glaucoma, we did not observe increased RGC survival following NAM treatment, as measured by Rbpms immunostaining. However, in previous experiments nicotinamide only protected RGCs from optic nerve crush at 3 days after injury, but was not protective from 7 days onwards (Zhang et al., 2021). We have added these experiments to the Results section and Figure 5.\nIn summary: the study employs state-of-the-art high-throughput recordings and analyses to bring insight into RGC metabolism in the intact retina. The topic is important in part because of the extensive NAD supplementation literature which shows neuroprotection in mammalian glaucoma and ONC models. Mechanistic insight into the properties of metabolic regulation in RGCs and understanding why certain subtypes of RGC resist injury would be very useful from basic and translational perspectives. The concerns include descriptiveness, significant side effects of ETC manipulation and modest advance in mechanistic understanding.\nAdditional comments:\nP3 line 51 Intro. The authors state “energetic diversity across neuronal populations is much less understood” without elaborating on this key claim. What is known about the energetic diversity in neurons?\nThe vast majority of studies examining neuronal metabolism either use techniques with mesoscale non-cellular resolution, focus on the interactions between neurons and glia, and/or use a single neuronal type as a representative model system. For the most part, these foundational studies do not examine diversity across the neuronal population. Where diversity has been examined has generally been in the context of outlier populations. For example, photoreceptors in the retina where aerobic glycolysis supported by extremely dense vascular networks and two specialized support cells, the retina pigment epithelium and Mueller glia cells. Photoreceptor metabolism is very well characterized and is atypical compared to other neuronal types studied since photoreceptors primarily rely on aerobic glycolysis for their ATP production (Hurley, 2017; Hurley et al., 2015; Warburg, 1925). Other examples would be cells with dense mitochondrial contributions that can be identified by cytochrome oxidase labeling either following stimulation or at rest, like the whisker barrel cortex or RGCs in cats that can be identified by cytochrome oxidase staining and are likely enriched for mitochondria and have high reliance on mitochondrial ATP generation (Kageyama and Wong-Riley, 1984; Wong-Riley and Welt, 1980).\nStudies directly examining metabolic diversity across a broader neuronal population are less common. A large scale profile of mitochondrial DNA and respiratory chain activities found brain region specific signatures that were more correlated with their network partners than non-CNS tissues; however, analysis was not carried out at the level of individual cell or neuron types (Rosenberg et al., 2023). A recent preprint examined lactate dynamics in cultured Drosophila glutamatergic neurons using the laconic biosensor and demonstrated an anticorrelation between mitochondrial density and lactate consumption (Price et al., 2025).\nWe have added these points to the Introduction and Discussion sections.\nWhat do we know about the energetic diversity among neurons in the retina?\nAs mentioned above, photoreceptors are by far the most studied retinal neurons in regards to their metabolic strategies. Photoreceptors largely rely on aerobic glycolysis to produce ATP, and were the first cell type identified to do so under normal conditions by Warburg (Warburg, 1925). Rod photoreceptors do this by highly expressing the enzyme isoforms PKM2 and LDHA, which favor the production of lactate during the final steps of glycolysis (Lindsay et al., 2014; Rueda et al., 2016). It is currently thought that photoreceptors maintain this reliance on aerobic glycolysis because of their massive biosynthetic requirements in the form of constantly shedding and reforming outer segments, placing photoreceptors in a state of constant cell growth (Viegas and Neuhauss, 2021). Broad histological screens in mice have been used to examine differences in metabolic function across major retinal cell types finding that beyond photoreceptor metabolism discussed above, inner retinal neurons have lower capacities for glycolysis; cone photoreceptors, horizontal cells and amacrine cells possessed higher levels of adenylate kinase and creatine kinase to allow for ATP buffering; and that Mueller glia likely rely on anaplerosis and cataplerosis to generate GTP and maintain ATP levels (Rueda et al., 2016).\nSome diversity in RGCs has been noted mostly related to their abundance of mitochondrial capacity. In mice, COX histological activity is differential across the RGC population (Rueda et al., 2016). In monkeys, cats and rodents cytochrome C staining, a feature of highly active neurons classically described in the whisker barrel cortex, strongly labels RGCs (Kageyama and Wong-Riley, 1984) suggesting a greater dependence on OXPHOS for their ATP demands. At the subcellular level, mitochondria cluster at RGC postsynaptic sites in their dendrites during development and likely support the energy demands of synaptic machinery (Faits et al., 2016). Interestingly, cone photoreceptor mitochondria may play a role beyond ATP generation as they form microlenses critical for image formation (Ball et al., 2022).\nWhile we feel that some of these concepts are important, details of retina specific neuronal metabolism may be a bit niche for the broader readership of Nature Communications. Thus we have added a portion of this information to the Introduction and Discussion sections of the manuscript.\nIbid. Why should we study energetic diversity.\nAt the level of basic neuronal function, electrophysiological and modeling studies have demonstrated that the great diversity of neuronal electrophysiological phenotypes is heavily driven by efficiencies in metabolic consumption (Hasenstaub et al., 2010; Moujahid et al., 2014). Unique neuronal electrophysiological properties are the building blocks of circuit function diversity and are the result of the expression of specific subtypes of voltage- and ligand-gated ion channels. Focusing on Na+ channel types and density to model energy consumption during activity across different configurations and comparing this to the properties fast spiking interneurons found that neurons maximize their channel properties to carry out their signaling with the minimal energy expenditure (Hasenstaub et al., 2010). Similar concepts emerge for retinal ganglion cells, where OFF and ON pathway converge at a point of negative contrast, which ultimately leads to a maximized energetic efficiency of feature encoding (Soto et al., 2020). Given that the fundamental properties that govern signaling in neurons are established to maximize energetic efficiency, and that clear examples of energetic specializations are present in specific well-studied neuronal types, it stands to reason that more subtle optimizations of energy production and consumption are a critical feature of neuronal type diversity.\nAdditionally, metabolism is a key factor in neuronal degeneration. Diseases like amyotrophic lateral sclerosis and Parkinson’s disease are directly driven by mitochondrial defects (Singh et al., 2021; Xiao et al., 2022). Altered metabolic states are present in most neurodegenerative conditions (Henn et al., 2022; Procaccini et al., 2016; Quintero et al., 2022). For example, in neurons derived from Alzheimer’s Disease patient iPSCs, a switch from PKM1 to PKM2 expression pushed neurons into a Warburg-like metabolic phenotype, which drove neurodegeneration that could be prevented with PKM2 inhibition (Traxler et al., 2022). For retinal ganglion cell degeneration specifically, models of glaucoma have found reduced ATP levels and mitochondria transport rates (Quintero et al., 2022). RGC mitochondrial transport is also reduced after optic nerve crush (Cartoni et al., 2016). Treatments to boost mitochondrial transport protect RGCs from degeneration in both degeneration models, restore ATP levels in glaucoma, and promote axon regeneration after nerve crush (Cartoni et al., 2016; Quintero et al., 2022). Taken together, the significant connection between metabolism and neurodegeneration justifies the study of metabolic diversity across neuronal populations, especially given the clear difference in susceptibility to neurodegeneration.\nFig 1H. The authors conclude that “natively resilient aRGCs had lower homeostatic ATP levels” than ipRGCs and ODSS cells yet ipRGCs are arguably more resilient than aRGCs.\nWe agree this phrasing is misleading. We have removed “natively resilient” from this sentence.\nP6. Intraocular injection of ROT, AA and other ETC drugs may have compromised the viability of photoreceptors and many RGCs, disrupted ON vs OFF signaling, induced massive oxidative stress, excitotoxicity from extracellular glutamate accumulation and inflammation. Can the authors provide evidence (TUNEL, GFAP etc) that this was/was not the case? The authors should make an explicit case for the cell-autonomy behind the stated ‘diversity’ of observed ATP effects.\nAs shown in Reviewer Figure 1 above, we did not notice cell death, glial reactivity via GFAP, or microglial reactivity by morphology or CD68 immunostaining due to ETC drug inhibition over the time course from which we recorded ATP responses.\nThe paradoxical observation of higher mitochondrial protein (NDUFB8, SDHA, UQCRC2, and ATP5A) expression in low-ATP aRGCs (Fig. S1) merits independent validation. Are the data congruent with subtype-specific transcriptomic evidence available from databases (Tran et al., etc)? Would changes in mitochondrial protein expression predict subtype-specific changes in the mitochondrial membrane potential, calcium transport and dehydrogenase activity?\nAs discussed above, we have cross-referenced the transcriptomic measurements for the listed genes from the Tran 2019 dataset in Reviewer Figure 1.\nRegarding how mitochondrial expression might predict type-specific diversity in mitochondrial traits like mitochondrial membrane potential, calcium transport and dehydrogenase activity, we do not think that our small immunostaining panel meant to examine representative ETC components is scale enough to infer this wide range of properties. We agree that it would be of interest to examine these traits across neuronal types, we believe that doing so would be beyond the scope of the current study.\nThe assumption that glycolysis does not play a role in activity appears reasonable even though nerve crush is known to promote glycolytic activity. However, it would be interesting to confirm the assumption with 2-deoxyglucose or iodoacetate.\nWe examined the importance of glycolysis in maintaining ATP levels by intravitreal injection of 2-deoxyglucose (2-DG). After injection of 2-DG, we did not observe any measurable reduction in RGC ATP levels (pre ATP -0.19 +/- 0.47, post 2-DG -0.39 +/- 0.18; mean +/- s.d.; p = 0.20). We have added these experiments to the Results as Supplementary Figure X.\nP11 line 233. “Since we observed that natively resilient aRGCs had lower homeostatic ATP levels”…. Please define resilience. Many studies (Della Santina et al.,; El Danaf et al.; Feng et al., Zapadka et al; Guttenplan et al) demonstrated that light responses, dendritic/synaptic architecture etc in aRGCs are impacted by short exposures to modest IOP elevations.\nWe agree that resilient is a vague descriptor and have changed the phrasing to “well-surviving” throughout the manuscript.\nMight activity-independence of baseline [ATP] be compartment-specific? Light-evoked ATP changes in dendritic, prelaminar and (glial) laminar regions could be missed by somatic recordings, and compartmentalization might be more pronounced for cells with large dendritic trees (eg, aRGCs).\nWe agree that cell compartments could definitely respond differently to alterations in circuit activity, and that this would be interesting to examine. However, our current methodology is not able to resolve dendritic or axonal compartments. We would also suggest that these proposed experiments are beyond the scope of this study.\nDiscussion. The protective effect of NAD+ supplementation suggests that IOP- and ONC-stressed RGCs experience energy deficits that are compensated by energy supplementation. Hence, the authors observation that high-ATP cells seem to be preferentially killed by ONC that leads them to suggest that “low homeostatic ATP translates to increased resilience’ seems counterintuitive and could be tested with NAD supplementation.\nAs discussed above, we have added NAM supplementation experiments to the manuscript. In accordance with previous publications (Zhang et al., 2021), we did not see protective effects of supplementation by 14 days after optic nerve crush. These data have been added to Figure 5. It thus seems that the metabolic contributions to RGC degeneration may be different between glaucoma and traumatic sources of RGC degeneration.\nMinor:\nRecommend Figures be introduced by one-sentence summary.\nWe have adjusted the Figure Titles to be more descriptive of the data contained within them."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you for clarifying the points made in the revision. One final point to make in the Discussion is regarding NAM and lack of neuroprotection - it needs to be pointed out clearly that these experiments were done subcutaneously (whereas oral has been shown as therapeutically viable for RGCs and oral is what has progressed into the Phase III clinical trials) and at a lower dose than has shown to be neuroprotective in glaucoma models (500-2000 mg/kg/d).\n\n(Remarks on code availability)\nI confirm that I can access the code, but I have not tried to run it.", "reviewer_2": "Thanks to the authors for addressing most of my comments. However, some of the major concerns and a few of minor concerns are still not addressed. Please see below.\n\nMajor concerns:\n1. Fig. 2. Is it possible that the ATP reporter is highly sensitive in the low baseline ATP range in αRGC, resulting in a higher response to inhibitors than the other RGC types? Or, given that ATeam is a pH-sensitive sensor, are the low baseline ATP levels in αRGC due to lower pH in this RGC type? Please explain.", "reviewer_4": "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.\n\n(Remarks on code availability)", "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.\n\n(Remarks on code availability)"}, "author_response": "While we pointed out this difference in NAM administration route in our Results, we agree with Reviewer #1 that this point should be emphasized when discussing the difference in outcome and have reinforced this difference in the Results section. Importantly, we do not believe that the route of administration should be the driver of this difference since subcutaneous NAM should be more bioavailable than oral (Yoshino et al., 2018). We instead believe that this is a difference in trauma induced neurodegeneration vs. glaucoma.\n\nReviewer #1 (Remarks on code availability):\nI confirm that I can access the code, but I have not tried to run it.\nReviewer #2 (Remarks to the Author):\nThanks to the authors for addressing most of my comments. However, some of the major concerns and a few of minor concerns are still not addressed. Please see below.\nMajor concerns:\n1. Fig. 2. Is it possible that the ATP reporter is highly sensitive in the low baseline ATP range in αRGC, resulting in a higher response to inhibitors than the other RGC types? Or, given that ATeam is a pH-sensitive sensor, are the low baseline ATP levels in αRGC due to lower pH in this RGC type? Please explain.\nAuthors’ response: Ateam1.03 is built on a mseCFP and mVenus FRET pair, which are relatively insensitive to physiological pH levels (3.5 and 5.3 pKa respectively). Estimates of intracellular pH in active neurons vary, but most observations do not drop below 6.5. Additionally, the ATeam1.03 biosensor has been applied to RGC degeneration in culture and in vivo and did not observe issues with pH sensitivity (Quintero et al., 2022). Lastly, we also used the FRET biosensor Twitch2b, which is built with the more pH sensitive Citrine (pKa 5.7), and in this case YFP/CFP ratios increased with increasing activity (Previous Supplementary Figure 2). And our previous work showed that Twitch2b ratios are higher in RGCs than other RGCs in contrast to ATeam ratios (McCracken et al., 2023).\nReviewer’s response: If a pH-sensitive ATP reporter (Twitch2b) is showing a different relationship, i.e, αRGCs have a higher baseline than other RGCs, contradicting what they measured with ATeam, it does mean that the baseline pH is different between the RGC types. Please clarify. Add an explanation clarifying the pH sensitivity of Ateam1.03 to the results section, including references for the pH levels (greater than 6.5) in RGCs.\nTwitch2b is not an ATP reporter, it is the Ca2+ reporter that we used in our supplementary experiments (Figure S5) to demonstrate our manipulations of circuit activity were functional, and that responses to scanning light and circuit activity changes were diverse across the RGC population.\nThe pH sensitivity of the ATeam1.03 reporter (mVenus pKa = 5.5 (Kremers et al., 2006)) is roughly a log10 less than the pH observed in active neurons (minimum pH 6.5). More specifically, intracellular pH has been studied extensively in different neuron types. Most report slightly basic pH levels (an incomplete summary: 7.29 +/- 0.01 rat hippocampus (Smith et al., 1998), 7.08 +- 0.37 rat hippocampus (Bevensee et al., 1996), 7.23 +/- 0.03 fetal rat hippocampus (Baxter and Church, 1996), 7.76 +/- 0.12 adult rat hippocampus (Raley-Susman et al., 1993), 7.17 +/- 0.03 fetal rat hippocampus (Raley-Susman et al., 1991), 6.81 +/- 0.02 rat hippocampus (Schwiening and Boron, 1994), 6.85 – 7.05 frog motoneuron (Endres et al., 1986), 7.2 – 7.3 turtle cerebellum (Chen and Chesler, 1992)). Tetanic or strong pharmacological glutamate receptor simulation induces slight acidification (ranging from 0.05 to 0.20 pH; turtle cerebellum (Chen and Chesler, 1992), rat hippocampus (Kaila et al., 1992), frog motoneuron (Endres et al., 1986)). Thus, it is extremely unlikely that RGCs under mild stimulation from 2-photon scanning should have intracellular pH anywhere near the sensitivity range of ATeam1.03.\nWe have added a brief explanation of the ATeam1.03 pH sensitivities with references to observed pH levels in active neurons to the Results section.\nThe authors did not address the question, “Is it possible that the ATP reporter is highly sensitive in the low baseline ATP range in αRGC, resulting in a higher response to inhibitors than the other RGC types?” Please explain.\nNo, we do not believe this explains our observations that RGCs are more susceptible to mitochondrial inhibition.\nFirst, since we can observe increases in RGC population ATP levels minutes after silencing circuit activity with NBQX/AP5 injection, this indicates that we are likely recording ATP levels within the linear function of ATeam1.03 response. Additionally, our baseline ATP levels reported in Figure 1 are normally and symmetrically distributed. If we were recording from the exponential upper bound of ATeam FRET ratios, we would expect a long tail of lower ratios with a pile up of higher ratios at the upper bound. Thus, if we are within the linear range of the ATeam biosensor, there is no reason for the initial ratio to impact the change in magnitude of the ATP change observed in our population.\nAdditionally, RGCs are not perfectly or exclusively low ATP RGCs. There are a number of non-RGCs with lower baseline ATP observed in our experiments that do not respond to rotenone. If an artifact of baseline ATeam ratios is driving the magnitude of response to mitochondrial inhibition, we should see very strong correlations between baseline ATP and post inhibition ATP. We instead show a mild relationship (e.g. rotenone R2 = 0.24).\nWe have added this point to the Results section.\n2. Fig. 3 and Fig. 4. What is the rationale for measuring ATP levels from all RGCs instead of αRGCs? Since αRGCs have low baseline ATP levels compared to all other RGCs, how do their ATP levels change upon str/bic or NBQX/AP5 injection, and light stimulation? Show ATP level changes in αRGCs pre- and post-injection and light stimulation after immunostaining with SPP1.\nAuthors’ response: We did not perform type Immunostaining for these experiments because ATP changes resulting from activity manipulations were relatively small (or absent) and quite consistent across the RGC population (Fig 3D,E,I,J,N and O). This contrasts both with the Ca2+ response to activity manipulations, which were large inconsistent and in some case bidirectional across RGCs (Supp Fig 2D,E,I and J). And also contrast with pharmacological inhibition of electron transport chain function, where cause much stronger and varied changes in ATP level (Fig 2 J-M). For these reasons, we do not think it is necessary to perform RGC type analysis of circuit activity manipulations.\nReviewer’s response: Explanation does not answer the question. Individual traces of single RGCs in an example retina in Fig. 3B (+Str/Bic), 4B (+ROT), and 4H (+ROT + dark adaptation) show a wide range of pre-ATP levels. It would be good to confirm whether the traces with lower pre-ATP levels correspond to αRGCs (due to their lower baseline ATP levels) and then measure how the ATP levels of these αRGCs are affected by Str/Bic, ROT, and ROT + dark adaptation. Since the paper characterizes the ATP heterogeneity in the RGC population, identifies type-specific ATP differences with αRGCs having low baseline ATP levels, and shows that αRGCs (Fig. 5D, Surviving +SPP1) are more resilient to injury, the authors are recommended to perform experiments measuring ATP levels specifically in αRGCs under the same conditions as in Fig. 3B, 4B, and 4H (currently not done in the paper) to show how αRGCs with low baseline ATP levels respond to the above conditions.\nWe are unclear what is meant by pointing out that the pre-ATP levels show a wide range. We previously address the pre-levels (baseline) in Figure 1 and show that the lower ratios are enriched for RGCs, while other RGC types tend to be distributed more evenly across the pre-distribution. We have already demonstrated in both Figure 1, and Figure 2 repeatedly, that low pre-levels are enriched for RGCs.\nWe demonstrate in Figure 3B and 3H that the ATP levels are shifting as a population, or in 4D not at all. This can be clearly observed both in our heatmap RGC traces that indicate little if any mixing of high and low pre-ATP level RGCs after injection, and in scatter plots in 3E,F and 3K,L that show a very strong relationship between pre and post injection ratios. For examples of what non-uniform traces look like, please refer to Figure 2, and Figure S5. This argues that RGCs are shifting their ATP levels uniformly in response to changes in circuit activation, RGCs included.\nFinally, the proposed experiments of imaging ATP responses to circuit activation in RGCs specifically using KCNG4-Cre transgenic mice are extremely unlikely to demonstrate anything we do not already show in Figures 3B, 4B or 4H. In Figure 3B all RGCs shifted downward by a similar amount (see quantification in 4E), indicating RGCs would shift downward by a similar amount. RGC responses to rotenone shown in 4B were already presented in Figure 2B-V where we demonstrated that ATP decrease as a result of rotenone injection was largely a feature of RGCs. Figure 4H is the same model as Figure 4B and Figure 2B-V, which would give the same results. If anything, experiments in KCNG4-Cre mice would be harder to interpret since comparison to other RGC types would not be possible.\n4. Fig. 5E. Explain how the experiments are linked to AMPK. Does blocking AMPK show a decline in ATP levels? How is this mechanism connected to resilience?\nAuthors’ response: Since similar elevations in ATP levels were observed in both dying and surviving RGC cohorts, we do not believe that this is a feature that is related to resilience.\nReviewer’s response: Line 275 – 276 mentions: “Energetic depletion and subsequent AMPK activation have been implicated in traumatic and glaucomatous degeneration of RGCs”. Line 426 – 427 mentions: “RGC survival and morphology can be improved by inhibiting AMPK signaling”. Additionally, the link between post-injury AMPK activation, as implicated in other studies, is mentioned in the results and discussion sections. Given these references and the observation in Fig. 5E that there is a transient increase in ATP levels post-injury, this paper does not show any experiments with AMPK manipulations, such as blocking AMPK followed by injury, to confirm that AMPK is responsible for this transient elevation in ATP levels post-injury or the resilience of surviving RGCs. How is the AMPK pathway linked to the results in Fig. 5E? Add the explanation to the results/discussion section or clarify that the observed elevation does not relate to AMPK signaling.\nAMPK is activated when cells build up AMP, specifically it reads out the AMP/ATP ratio. This can occur in conditions of cell stress and has been reported in models of RGC degeneration. The downstream effects of AMPK signaling are to correct the AMP/ATP ratio by both producing more ATP and consuming less ATP. We believe our results in 5E could represent an overcorrection of the metabolic state by AMPK signaling triggered by the stress of axon injury. We can include more detail to make our interpretation clearer in the Discussion.\nMinor concerns:\n15. Fig. 2. Since there is a recovery in ATP levels post-injection 10-15 mins, what is the clearance rate of ROT, TTFA, AA, and KCN after intraocular injection in mice? Discuss this in terms of the half-life of each of these inhibitors in mice with relevant references.\nAuthors’ response: From our previous experience with intraocular pharmacological injections (McCracken et al., 2023), and work in this paper, we feel that the rates of recovery are much more likely to be an issue of clearance than one of half-life because most modulators (whether related to circuit activity or metabolism) recover over the course of 10 to 30 minutes. This is much faster than the half-life of these agents at body temperature. Additionally, the affective dosage is also important as we have observed longer duration of inhibition from higher concentration injections (not shown). We feel like our data is rather difficult to relate to other publications of half-life in relation to systemic delivery and would likely be more confusing than helpful as a point of discussion.\nReviewer’s response: Clarify if this recovery in ATP levels post-injection is due to the clearance of the inhibitors. Add references for the clearance times of each inhibitor. Add this explanation to the results section.\nWe can add this description of drug clearance to our manuscript. There are no references for the clearance time of these inhibitors following intravitreal injection.\n18. Fig. 3H. Pre vs 140s ATP levels were not significantly different, but 15 m ATP levels were significantly higher than pre. Explain why the ATP levels remained unchanged at 140s upon NBQX/AP5 injection when the Ca2+ levels were significantly low (Fig. S2H).\nAuthors’ response: Ca2+ levels are further depressed at 15min (Fig. S2H). We believe this delayed ATP elevation is due to an accumulated effect of circuit inactivity. Thus, the RGCs are homeostatically producing a set amount of ATP to meet the expected demands that would exist even in a ‘quiet’ circuit. As these demands are lowered over a prolonged period, ATP then builds up.\nReviewer’s response: Add this explanation to the results/discussion section.\nWe have added this information to the manuscript.\n22. Fig. 1D, F, H; 2J-M; 5C-E. Mention how many animals/biological replicates in the legend.\nAuthors’ response: This information is already present in the Figure Legends.\nReviewer’s response: n = __ RGCs from __ retinas mentioned, but the number of animals/biological replications is not mentioned in any of the legends. Add this information.\nOur mistake, while most of our experiments were one retina from one mouse, there were some instances where both retinas from the same mouse were used. Effected experiments are:\nRepeat imaging (Figure 1F): n = 7 retinas from 5 mice\nTTFA (Figure 2KOS): n = 4 retinas from 3 mice\nAntimycin A (Figure 2LPT): n = 4 retinas from 3 mice\nNBQX/AP5 (Figure 3I-L): n = 4 retinas from 3 mice\nStimulation (Figure 3O): n = 4 retinas from 3 mice\nSurvival (Figure 5C-E): n = 5 retinas from 4 mice\nCa2+ Str/Bic (Figure S5C-F): n = 6 retinas from 5 mice\nCa2+ Stimulation (Figure S5NO): n = 13 retinas from 7 mice\nWe have corrected the n’s throughout.\nREFERENCES\nBaxter, K.A., and Church, J. (1996). Characterization of acid extrusion mechanisms in cultured fetal rat hippocampal neurones. J Physiol 493 ( Pt 2), 457-470.\nBevensee, M.O., Cummins, T.R., Haddad, G.G., Boron, W.F., and Boyarsky, G. (1996). pH regulation in single CA1 neurons acutely isolated from the hippocampi of immature and mature rats. J Physiol 494 ( Pt 2), 315-328.\nChen, J.C., and Chesler, M. (1992). pH transients evoked by excitatory synaptic transmission are increased by inhibition of extracellular carbonic anhydrase. Proc Natl Acad Sci U S A 89, 7786-7790.\nEndres, W., Ballanyi, K., Serve, G., and Grafe, P. (1986). Excitatory amino acids and intracellular pH in motoneurons of the isolated frog spinal cord. Neurosci Lett 72, 54-58.\nKaila, K., Paalasmaa, P., Taira, T., and Voipio, J. (1992). pH transients due to monosynaptic activation of GABAA receptors in rat hippocampal slices. Neuroreport 3, 105-108.\nKremers, G.J., Goedhart, J., van Munster, E.B., and Gadella, T.W., Jr. (2006). Cyan and yellow super fluorescent proteins with improved brightness, protein folding, and FRET Forster radius. Biochemistry 45, 6570-6580.\nRaley-Susman, K.M., Cragoe, E.J., Jr., Sapolsky, R.M., and Kopito, R.R. (1991). Regulation of intracellular pH in cultured hippocampal neurons by an amiloride-insensitive Na+/H+ exchanger. J Biol Chem 266, 2739-2745.\nRaley-Susman, K.M., Sapolsky, R.M., and Kopito, R.R. (1993). Cl-/HCO3- exchange function differs in adult and fetal rat hippocampal neurons. Brain Res 614, 308-314.\nSchwiening, C.J., and Boron, W.F. (1994). Regulation of intracellular pH in pyramidal neurones from the rat hippocampus by Na(+)-dependent Cl(-)-HCO3- exchange. J Physiol 475, 59-67.\nSmith, G.A., Brett, C.L., and Church, J. (1998). Effects of noradrenaline on intracellular pH in acutely dissociated adult rat hippocampal CA1 neurones. J Physiol 512 ( Pt 2), 487-505.\nYoshino, J., Baur, J.A., and Imai, S.I. (2018). NAD(+) Intermediates: The Biology and Therapeutic Potential of NMN and NR. Cell Metab 27, 513-528."}, {"round": 3, "reviewer_comments": {"reviewer_2": "All the comments are now addressed in this second revision. Please incorporate the revised text in the appropriate Results and Discussion sections.\n\n(Remarks on code availability)", "reviewer_4": "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.\n\n(Remarks on code availability)", "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.\n\n(Remarks on code availability)"}, "author_response": ""}]

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