Spatial transcriptomics reveals metabolic changes underly age-dependent declines in digit regeneration

  1. Robert J Tower  Is a corresponding author
  2. Emily Busse
  3. Josue Jaramillo
  4. Michelle Lacey
  5. Kevin Hoffseth
  6. Anyonya R Guntur
  7. Jennifer Simkin
  8. Mimi C Sammarco  Is a corresponding author
  1. Department of Orthopaedics, Johns Hopkins University, United States
  2. Department of Surgery, Tulane School of Medicine, United States
  3. Department of Mathematics, Tulane University, United States
  4. Department of Biological & Agricultural Engineering, Louisiana State University, United States
  5. Center for Molecular Medicine, Maine Medical Center Research Institute, United States
  6. Department of Orthopaedic Surgery, Louisiana State University Health Sciences Center, United States

Abstract

De novo limb regeneration after amputation is restricted in mammals to the distal digit tip. Central to this regenerative process is the blastema, a heterogeneous population of lineage-restricted, dedifferentiated cells that ultimately orchestrates regeneration of the amputated bone and surrounding soft tissue. To investigate skeletal regeneration, we made use of spatial transcriptomics to characterize the transcriptional profile specifically within the blastema. Using this technique, we generated a gene signature with high specificity for the blastema in both our spatial data, as well as other previously published single-cell RNA-sequencing transcriptomic studies. To elucidate potential mechanisms distinguishing regenerative from non-regenerative healing, we applied spatial transcriptomics to an aging model. Consistent with other forms of repair, our digit amputation mouse model showed a significant impairment in regeneration in aged mice. Contrasting young and aged mice, spatial analysis revealed a metabolic shift in aged blastema associated with an increased bioenergetic requirement. This enhanced metabolic turnover was associated with increased hypoxia and angiogenic signaling, leading to excessive vascularization and altered regenerated bone architecture in aged mice. Administration of the metabolite oxaloacetate decreased the oxygen consumption rate of the aged blastema and increased WNT signaling, leading to enhanced in vivo bone regeneration. Thus, targeting cell metabolism may be a promising strategy to mitigate aging-induced declines in tissue regeneration.

Editor's evaluation

This paper will be of interest to a broad range of scientists in the regeneration field as it builds on and complements several recent mouse digit tip regeneration single-cell RNAseq data sets. This study applies the emerging field of spatial transcriptomics to overlay gene expression information on the spatial arrangement of regenerating cells over time. The authors use their data to address several important questions related to regeneration such as "can we define a molecular signature for regenerating cells?" and "why does regenerative ability decline with age?

https://doi.org/10.7554/eLife.71542.sa0

Introduction

The ability to regenerate lost or damaged limb structures de novo, where new growth replaces both the amputated bone and surrounding soft tissue, is a highly desirable biological process that varies widely in vertebrates. While wound repair is highly conserved in all organisms, the ability to regenerate complex limb structures is limited and, in mammals, restricted to the distal one-third of the third phalangeal element (P3). This regenerative response is specific to the level of amputation: amputation of the distal one-third of the terminal phalangeal element (P3) can regenerate, whereas an amputation through the middle phalangeal element (P2) forms a hypertrophic callus of the bone. This regenerative response has been well documented in rodents, monkeys, and humans (Bryant et al., 2002; Brockes and Kumar, 2005; Han et al., 2008; Fernando et al., 2011; Simkin et al., 2013). Central to this process is the dedifferentiated tissue structure called the blastema that will ultimately give rise to the regenerated structure. Regeneration follows a defined sequence, including (i) initial injury and inflammatory response (degradation and epidermal closure days 7–10), (ii) anabolic cell dedifferentiation, recruitment, and proliferation during formation of the critical blastema (day 10), and (iii) differentiation (day 14 and later) in which the bone is regenerated via direct intramembranous ossification (Han et al., 2008; Fernando et al., 2011; Simkin et al., 2013). These distinct and independent phases of regeneration lead to bone produced via direct ossification and provide a controlled model to study bone regeneration within the context of aging.

Energetic demands are affected by cell type and function, local environment, and a cell’s progression along the differentiation pathway. As a result, proliferating skeletal progenitors and differentiated osteoblasts exhibit different levels and types of cell metabolism (van Gastel and Carmeliet, 2021). Proliferating progenitors require ATP for energy and carbon for biomass (Lunt and Vander Heiden, 2011) and are highly glycolytic (Salazar-Noratto et al., 2020). This glycolytic dependency increases in differentiated osteoblasts due to the high metabolic demand of matrix and amino acids synthesis (van Gastel and Carmeliet, 2021; Capulli et al., 2014; Bonewald, 2011; Lee et al., 2020). The aging process disrupts cell metabolism by decreasing NAD+ levels (Neri and Borzì, 2020; Song et al., 2019) and mitochondrial function and content (Yu et al., 2018; Bellantuono et al., 2009), leading to a loss of osteogenic potential. Prior research such as Seahorse analysis has allowed us to analyze mitochondrial respiration and glycolysis in live cells in vitro as it relates to osteoblast function. However, a better understanding of how cell metabolism regulates gene expression during aging, and how these changes can be modulated to benefit potential regenerative strategies in the future, is needed.

Digit regeneration via direct ossification taps into an endogenous bone regrowth process that, once dissected, has the potential to be impactful in many bone disease and injury models. Significant effort has been placed on dissecting out the essential regenerative signaling pathways for their application in non-regenerative models and tissue engineering-based approaches. Up until recently, this has proven technically challenging owing to an inability to accurately interrogate the transcriptional profile of the blastema. To this end, recent papers have made use of single-cell RNA sequencing (scRNAseq) to generate a blastema-specific gene signature (Storer et al., 2020; Johnson et al., 2020). While these published works were able to provide incredible insight into the cellular heterogeneity of the blastema, scRNAseq is unable to provide spatial context to these identified transcriptional profiles. Identifying the spatial localization of the signals that drive limb regeneration is a critical piece in discerning the mechanisms that drive limb patterning and the timing of differentiation during this multi-tissue process (Bassat and Tanaka, 2021; Storer and Miller, 2020; Johnson and Lehoczky, 2022).

Here, for the first time, we make use of spatial transcriptomics to investigate the spatially defined transcriptional profile of the regenerating blastema. Using this approach, we generated a gene signature for the blastema with high specificity and fidelity across published transcriptomic data sets (Storer et al., 2020; Johnson et al., 2020). To further characterize the blastema, we utilized aging as a model of impaired regeneration. Comparative spatial analysis revealed multiple age-dependent changes in pathway activation transcripts, including changes in proliferation and cell metabolism. Hypothesizing that age-dependent impaired regeneration was a function of dysregulated cell metabolism, we sought to modulate this response using exogenous metabolites. Administration of the metabolite oxaloacetate (OAA) reversed bone thickness and volume deficits observed in aged mice. Spatial transcriptomic analysis suggests that this rescue may be mediated through increased expression of bioenergetic transcripts and WNT pathway activation. These findings suggest that cell metabolism underpins many aspects of tissue regeneration and underscores the potential of metabolite intermediates to enhance and modify regenerative capacity.

Results

Spatial transcriptomics defines the regenerating blastema

Traditionally, identifying molecular markers specific to the blastema has been difficult because of the heterogeneity of cell types present in the blastema, as well as the shared expression of previously identified markers (i.e. developmental genes such as Msx1, stem-like genes such as Cxcl12) with other dermal cells (Storer and Miller, 2020). Instead, the blastema is defined histologically within the new growing tissue area (Storer and Miller, 2020). In light of this, we amputated the distal one-third of the third phalangeal element (P3), allowed formation of the regenerative blastema (D10), then made use of spatial transcriptomics to provide a whole transcriptome characterization of the blastema as a complete structure within the context of its surrounding tissue in situ. This technique makes use of gene expression slides coated in poly-T primers and encoded with a unique spatial barcode. Fresh frozen sections are placed on a tissue optimization slide and subjected to varying times of enzymatic permeabilization to release cellular mRNA. Captured mRNA is visualized through incorporation of fluorescent nucleotides during on-slide cDNA synthesis (Figure 1—figure supplement 1A). To assess gene expression, a similar protocol is followed using pre-determined optimal digestion times. Following library preparation and sequencing, reads are aligned, and the unique spatial barcode is used to determine the spatial location of origin for each transcript, registered to the original hematoxylin and eosin (H&E) stained image (Figure 1—figure supplement 1B). This technique yielded ~4500 spatial ‘spots’ containing ~1900 unique genes (nFeature) and ~5500 unique transcripts (nCount) per spot (Figure 1—figure supplement 1C) at a spatial resolution of 55 µm, corresponding to ~5–10 cells per spatial spot, depending on the relative location within the blastema. Spatially restricted gene expression and image registration was confirmed by using positive marker genes for the epithelium (Krt14) and bone (Bglap2), by expression of previously proposed blastema markers Pdgfra (Storer et al., 2020) and Mest (Johnson et al., 2020; Figure 1A). Conversely, although hematopoietic cells infiltrate the digit at relatively high numbers after injury (Simkin et al., 2017), these cells are present in a more diffuse pattern, surrounded by other cell types. This resulted in minimal detection of the hematopoietic cell marker CD45 (encoded by Ptprc) (Figure 1A). Owing to the registration of transcriptomic data to histological images, spatial transcriptomics allows us to spatially define the area of the blastema relative to the rest of the digit. This allowed for the manual segmentation and identification of the blastema, boundary (spots overlaying both the blastema and surrounding connective tissue), and the remaining digit structure using morphological parameters (Figure 1B). Differential gene expression was used to determine those genes showing enriched expression within the spatially defined blastema, boundary, and remaining digit regions (Supplementary files 1-3). These results identified 829 differentially expressed genes (DEGs) enriched in the blastema, 146 of which were also enriched within the 337 boundary spot DEGs, and no overlap with the 214 DEGs identifying the remaining digit (Figure 1C). Interestingly, pathway analysis on blastema DEGs showed enrichment in gene ontology terms linked to cellular bioenergetics, in addition to major signaling pathways such as transforming growth factor beta (TGFβ), PI3K-Akt, and MAPK (Figure 1D).

Figure 1 with 3 supplements see all
Transcriptional profiling of the blastema using spatial transcriptomics.

(A) Representative spatial gene expression of epithelial (Krt14), bone (Bglap2), blastema (Pdgfra, Mest), and hematopoietic cell (CD45, encoded by Ptprc) transcripts. (B) Manual segmentation of spatial data into blastema (red), boundary (green), and remaining digit spots (blue) based on histological guidance. (C) Differentially expressed genes (DEGs) showing enriched expression in the blastema, boundary spots, or remaining digit. (D) Pathway analysis of genes showing enriched expression in the blastema. Number at end of bar denotes the number of significantly regulated genes assigned to each indicated KEGG term. (E) Module scoring of top 100 blastema genes derived from spatial transcriptomics or single-cell RNA sequencing (scRNAseq) (Storer et al., 2020; Johnson et al., 2020). (F) Blastema module scoring based on decreasing numbers of genes as indicated. Dotted lines denote a blastema modular score of 0 with higher values indicative of enrichment for the blastema fingerprint.

From our blastema DEG list, we generated a 100 gene ‘blastema signature’ using genes enriched in the blastema (Supplementary file 4, listed in descending order of blastema specificity). Spatial transcriptomics blastema signature genes were selected based first on the regional specificity for the blastema, then on log fold change in gene expression between the blastema and surrounding tissue, and finally for adjusted statistical significance (see Materials and methods for details). Module scoring, reflecting the average expression of a selected gene list relative to random gene list controls, and spatial localization of our blastema signature showed a high degree of specificity for the regenerating blastema and minimal labeling of the remaining digit tissue (Figure 1E). Recently, two scRNAseq studies (Storer et al., 2020; Johnson et al., 2020) have proposed genetic signatures of the blastema from micro-dissected tissue. To validate our spatially derived blastema signature, we cross-compared our blastema signature with (Storer et al., 2020) and (Johnson et al., 2020) scRNAseq-defined blastema signatures (Supplementary file 4). Briefly, we used the same selection criteria that was applied to our spatial transcriptomics blastema signature, which excluded genes from cluster 8 enriching for proliferative genes (Figure 1—figure supplement 2), to identify a blastema gene signature from each original data set. Genes from Storer et al. were selected from category 1 identified in Table S2 of the original publication (Storer et al., 2020), and genes from Johnson et al., 2020 were selected from fibroblast-enriched genes identified in Table S1A. All blastema signatures showed preferential enrichment, to varying extents, within our blastema region relative to other regions of the digit, further validating this spatial transcriptomics technique (Figure 1E).

To determine the widespread feasibility of our spatial blastema signature to label blastema fibroblasts, we next conducted the reciprocal analysis, applying our blastema gene signature derived from spatial back to the two original sets of scRNAseq data (Figure 1—figure supplement 3). ScRNAseq data sets derived from Storer et al. (D10/14) (Figure 1—figure supplement 3A) and Johnson et al. (D11) (Figure 1—figure supplement 3B) were reanalyzed to generate UMAP similar to those presented within the original publications. Clusters circled in red denote fibroblast-like clusters used to define the blastema by the original authors. Module scoring confirmed the spatial transcriptomics-derived blastema gene signature had a high specificity for the blastema in both scRNAseq data sets, distinguishing the blastema fibroblasts from other cell types, and specifically excluded more mature osteogenic clusters (cluster 6 from Storer et al. data and cluster 5 from Johnson et al. data). Following validation of our blastema gene signature, we next applied reductive iterations to our spatial blastema gene list in order to identify a threshold of signature genes required to successfully label the blastema (Figure 1F). These results demonstrate that, although no single gene was able to show efficient labeling of the entire blastema, specific labeling of the blastema could be achieved using a reduced gene list of ~5 genes (Edil3, Mmp14, Chrdl1, Ptx3, and Bmp5). Taken together, these data demonstrate that spatial transcriptomics can successfully delineate gene expression in the blastema, and illustrate the widespread applicability of spatially derived data to other published transcriptomic data sets.

Aged mice show a reduced regenerative potential associated with altered blastema bioenergetics

Having established the ability of spatial to successfully delineate the transcriptional profile of the blastema, we next sought to apply this technique in a model of impaired regeneration to understand potential regulatory mechanisms essential for successful digit regeneration. Owing to the well-established, age-dependent impediments in bone and wound repair (Gurtner et al., 2008; Coffman et al., 2016), we investigated the effects of aging on digit regeneration. We amputated the distal one-third of P3 in young (6 months of age) and aged (18 months of age) mice and monitored over time using micro-computed tomography (micro-CT) and histological assessments (Figure 2A and B). These data revealed that, while aged mice showed no difference in the timing of the histolytic event (D7), a marked delay in the transition from blastema to anabolic bone formation was observed. While new bone was observed at D14 in young mice, new bone wasn’t observed in aged mice until D21. This delay in mineralization was associated with a reduction in trabecular bone volume and thickness, and a concordant increase in trabecular spacing (Figure 2C). These data confirmed that, similar to previous results in wound and bone regeneration (Seifert and Voss, 2013), aging results in a significant reduction in digit regeneration following amputation.

Aged mice show impaired regeneration in the digit tip amputation model.

(A) Radiographic imaging from day 0 to day 42 (D0 to D42) in young and aged mice following amputation of the distal P3. (B) Masson’s trichrome staining of young and aged mice. (C) Micro-computed tomography (micro-CT) quantification of bone volume (relative to D0), trabecular thickness (Tb. Th.), and trabecular separation (Tb. S.) of regenerated bone over time. Graphs represent average values ± SD. n=4–14 digits/time point/age. *p<0.05.

To determine the potential mechanisms underlying this impaired regeneration in aged mice, we conducted spatial transcriptomics on the aged blastema as described above. We used comparative analysis to identify genes showing differential expression between the blastema of young and aged regenerating digits (Supplementary file 5). Pathway analysis of DEGs showing differential expression in the young or old blastema identified several potentially important factors altered in our impaired regeneration model (Figure 3A). First, we identified enrichment in several pathways linked to cell proliferation in the aged blastema. Using CellCycleScoring frequently employed in scRNAseq analysis (Nestorowa et al., 2016), our data demonstrated a significant increase in S-phase and G2M-phase scoring in the aged blastema (Figure 3B), resulting in a significant redistribution of blastema and boundary spots into a proliferative phase (Figure 3C). This increased proliferation in aged blastema was confirmed histologically by staining for the proliferation marker PCNA (Figure 3D). This increased proliferative gene signature in aged mice could be the result of repeated cell cycling, or from a delay in progression of cells through the cell cycle. Blastema from young and old digits were further interrogated for genes linked to cell cycle activity, including genes encoding cyclins, as well as components of the anaphase promoting complex (Figure 3E). These data show a predominant increase in genes linked to both entry into, and progression through, the cell cycle in aged mice, further supporting an increased cell proliferation in the regenerating blastema of aged mice.

Figure 3 with 1 supplement see all
Spatial transcriptomics reveals altered cellular metabolism in aged mice.

(A) Pathway analysis of blastema genes showing differential expression between the young and aged blastema. Number at end of bar denotes the number of significantly regulated genes assigned to each indicated gene ontology (GO term). (B) Spatial and violin plots showing S-phase and G2M-phase cell cycle scoring in the blastemas of young and aged mice. (C) Cumulative distribution of indicated cell cycle phase assignment for each region of the digit. (D) PCNA immunofluorescence in young and aged blastemas. n=3–4. Numbers below image represent the ratio of PCNA-positive nuclei ± SD in aged blastemas relative to young, with values over 1 indicating increased proliferation in aged blastema. Scale bar, 200 µm. (E) Dot plot showing relative expression of cell cycle progression genes in young and aged spatial blastema. Values represent average values ± SD. ***p<0.001, *p<0.05.

To further interrogate which pathways are alternately regulated in young vs. aged mice, and the spatial patterning of the pathway expression, we conducted a modified SpatialTime analysis, a technique recently developed for analysis to look at signaling gradients within spatial transcriptomic data (Tower et al., 2021) (see Materials and methods) (Figure 3—figure supplement 1A). Briefly, a reference line is generated along the residual P3 bone stump. Next, the minimum distance between each blastema spatial spot and this reference line is calculated. Finally, the distance of each spot from the reference line is scaled from values of 0 (most proximal) to 1 (most distal). To determine the level of pathway activation, we generated gene lists from curated KEGG pathways, followed by module scoring. Whole blastema analysis shows notable reductions in the Hippo, WNT, and TGFβ pathways in aged mice, with SpatialTime analyses suggesting that both Hippo and WNT signaling are preferentially decreased distally in aged mice, while TGFβ signaling was decreased proximally, but had comparable expression distally (Figure 3—figure supplement 1B).

Beyond morphogenetic pathways, analysis of the aged blastema also showed enrichment for DEGs linked to cellular bioenergetics, and that these metabolic pathways were divergently regulated in young and aged mice (Figure 3A). Owing to the important role of cellular metabolism in cell proliferation, fate determination and function (Lee et al., 2020; Loeffler et al., 2018; Lee et al., 2017; Guntur et al., 2014; Riddle and Clemens, 2017), as well as the known role of aging in cellular metabolism (Lesnefsky and Hoppel, 2006; Hadjiargyrou and O’Keefe, 2014), we focused our attention on differential metabolic programming between young and aged mice. Metabolic analysis showed increased glycolysis and oxidative phosphorylation (OxPhos) in the blastema of aged mice, resulting in an increased overall energetic response (cumulative glycolysis and OxPhos scoring) (Figure 4A). This increased bioenergetic signature was specific for glucose metabolism, as scoring of fatty acid metabolism failed to show any significant increase. A more detailed analysis of respiratory energy metabolism suggests that aging upregulates the TCA cycle and complexes IV and V of the electron transport chain, with no significant change in mitophagy or mitochondrial biogenesis (Figure 4—figure supplement 1). Applying our SpatialTime technique, we observed that glycolysis is preferentially upregulated within the proximal blastema of aged digits and OxPhos increased in the distal blastema (Figure 4B).

Figure 4 with 1 supplement see all
Aged blastemas have increased bioenergetics.

(A) Module scoring for cell metabolism pathways within young and aged blastemas. Combined is the cumulative module scoring values of glycolysis and oxidative phosphorylation (OxPhos). Values below images indicate difference in spatial spot module score within pooled blastema of aged vs. young mice ± SD, with values above 0 indicating increased activation in aged blastema. (B) Violin (left) and SpatialTime plot showing metabolic module score. Prox, proximal; Dist, distal. (C) Seahorse Mito Stress Test (oxygen consumption rate, OCR; extracellular acidification rate, ECAR) and Glycolytic Rate Assay (glycolygic proton efflux rate, GlycoPER) of dissected blastema from young and aged mice (Oligo, oligomycin; R/AA, rotenone/antimycin A). n=8–9 mice. Graphs represent average values ± SEM. *p<0.05, ***p<0.001.

To validate differences in cell metabolism, we analyzed excised blastemas from young and aged digits and evaluated cell metabolism using Seahorse analysis (Figure 4C). Oxygen consumption rates (OCR) associated with basal respiration (p<0.05), ATP (p<0.05), maximal respiration (p<0.05), and non-mitochondrial respiration (p<0.05) were higher in the aged blastema-derived cells. The extracellular acidification rate (ECAR) (Mito Stress Test) was also significantly higher after each stressor injection, suggesting increased glycolytic rates. To address potential mitochondrial-driven CO2 acidification in the ECAR analysis, we also evaluated the glycolysis-mediated protein efflux rate. The Glycolytic Rate Assay (Seahorse analysis) confirmed a significant increase in the capacity for aged blastema cells to respond to glucose by upregulating compensatory glycolysis (p<0.05). Combined, these data support an elevated level of cell metabolism within the blastema-derived cells of aged mice.

Aged blastemas demonstrate prolonged intracellular hypoxia and increased vascularization

We have previously shown that intracellular hypoxia is an integral component of digit regeneration (Sammarco et al., 2014; Simkin et al., 2015). Owing to our observed increase in bioenergetic transcripts in vivo and increased oxygen consumption in vitro, we next sought to determine the potential effects of increased metabolism on hypoxia within the regenerating digit. Spatial transcriptomics module scoring (Figure 5A), as well as confirmatory histological labeling using hypoxyprobe (Figure 5B), both confirm elevated levels of hypoxia within the blastema of aged mice. One prominent pathway highly regulated by hypoxia is vascular endothelial growth factor (VEGF) signaling (Shweiki et al., 1992), a pathway also known to heavily influence bone development and repair (Hu and Olsen, 2016; Schipani et al., 2009). To determine whether this shift in hypoxic signaling was associated with shifts in downstream levels of VEGF signaling, and subsequent vascularization, we quantified each of these parameters in our spatial data using module scoring. Both VEGF signaling (Figure 5C) and vascular markers (Figure 5D) showed significant elevations within the blastema of aged mice. SpatialTime analysis confirmed that aged mice demonstrated increased gene expression for vessel markers concentrated predominantly in the proximal portion of the blastema (Figure 5E). This is in contrast to blastemas from young mice that showed minimal expression of vessel markers concentrated mostly in the distal tip (Figure 5E). Histological analyses confirmed that while large, CD31+ vessels were predominantly seen in the distal tip of the blastema in young mice, additional small and/or immature vessels were detected throughout the blastemas of aged mice (Figure 5F). Interestingly, this increased in intracellular hypoxia and altered vessel patterning persisted throughout the healing process in aged mice, with significantly greater hypoxyprobe labeling and CD31+ blood vessels present throughout the regenerating digit at D42 (Figure 5G). These changes in vascularization were accompanied by altered regenerative bone architecture characterized by multidirectional and disorganized void space in the aged mice and an overall increased inner void fraction (Figure 5H).

Increased intracellular hypoxia and vascularization in aged mice.

(A) Spatial module scoring of hypoxia-related transcripts. (B) Hypoxyprobe staining in young and aged blastema. Scale bar, 100 µm. Dotted lines indicate the P3 cortical bone stump. (C) Spatial module scoring of vascular endothelial growth factor (VEGF) signaling. (D) Spatial module scoring of vascular markers. Values below images indicate difference in spatial spot module score or hypoxyprobe fluorescent intensity within pooled blastema of aged minus young mice ± SD, with values above 0 indicating increased activation in aged blastema. (E) SpatialTime analysis of the vascular module score from the proximal (residual bone stump) to distal blastema tip in young and aged mice. (F) Hematoxylin and eosin (H&E) staining and immunofluorescent imaging of CD31 showing increased vascularization within the blastema of aged mice at day 10. Scale bar, 100 µm. Arrow heads denote immature vessels. (G) Hypoxyprobe and CD31 staining of digits at day 42. Scale bar, 250 µm. (H) Micro-computed tomography (micro-CT)-based inner void calculations within the regenerated digit. Graphs represent average values ± SD. n=4–6 samples/condition. *p<0.05, **p<0.01, ***p<0.001.

The metabolite OAA enhances skeletal regeneration in aged mice

Our data show that there is an increase in both glycolytic and oxidative metabolism pathways in the aged blastema, suggesting an increased energy demand. We hypothesized that unmet energy needs may underpin age-dependent metabolic increases, and that supplementing these cells with a substrate to off-set these increased requirements would enhance regeneration. Lee et al. recently demonstrated that active glycolysis in the mature osteoblast relies heavily on the malate-aspartate shuttle (Lee et al., 2020). This shuttle serves to move OAA into the mitochondria by first converting it to malate. Once inside the mitochondrial membrane malate is oxidized back to OAA again, generating NADH directly in the mitochondria for use in the electron transport chain (Lee et al., 2020). We hypothesized that supplementing with intermediates from the malate-aspartate shuttle would support this metabolic pathway and help meet the additional bioenergetic demand demonstrated by both spatial and Seahorse data. To this end we utilized the anapleurotic metabolite OAA, which has been shown to promote brain mitochondrial biogenesis and increase ATP production in nerves via systemic application (Wilkins et al., 2016; Wilkins et al., 2014).

In vitro, Seahorse analysis on aged blastemas using the Mito Stress Test revealed a significant decrease in maximal respiration after the addition of OAA, with no significant change in ECAR or glycolytic flux (Figure 6A). To determine whether this OAA-driven decrease in maximal respiration within the blastema translates into changes in cell function, we administered OAA daily to aged mice starting at the blastema phase (D10). 3D micro-CT image analysis of skeletal regeneration after OAA treatment revealed that while only slight increases in regenerative bone parameters were observed by day 21, significant increases in regenerated bone volume and thickness were observed in response to daily OAA treatment from D10 to D28 (Figure 6B and C). 2D analysis of the regenerated bone at day 28 post amputation, oriented along the axis of healing from the proximal to distal end, demonstrated that this increase in bone is predominantly within the first 0.4 mm of the bone stump, rather than the distal end of the bone (Figure 6D). These data indicate that treatment with the metabolite OAA can enhance regeneration in aged mice.

Treatment with oxaloacetate (OAA) enhances regeneration in aged mice.

(A) Seahorse Mito Stress Test (oxygen consumption rate, OCR; extracellular acidification rate, ECAR) and Glycolytic Rate Assay (glycolygic proton efflux rate, GlycoPER) of dissected blastema from aged mice treated with oxaloacetate (OAA) (Oligo, oligomycin; R/AA, rotenone/antimycin A). (B) Micro-computed tomography (micro-CT) quantification of trabecular bone volume (Tb. BV.), trabecular thickness (Tb. Th.), and trabecular separation (Tb. S.) of regenerated OAA treated bone at day 21 and day 28 post amputation, relative to time-matched, saline-treated controls. n=11–19 digits/group. Graphs represent average values ± SEM. *p<0.05, **p<0.01, ***p<0.001. (C) Representative whole (top) or bisected (bottom) day 28 digits from saline control or OAA-treated mice. (D) 2D micro-CT analysis of regenerated bone from the remaining P3 cortical bone stump extending distally from aged saline control and OAA-treated mice.

OAA-induced regeneration is associated with enhanced proliferation, bioenergetics, and BMP/WNT signaling

To determine OAA’s mechanism of action, we conducted spatial transcriptomics on aged samples treated with OAA. To identify OAA-dependent bone morphogenetic and cell function pathways, we evaluated aged samples during early bone formation phase (D21), treating with OAA daily from day 10 to 21, segmenting the area into a fibroblast and bone region (Figure 7A). Cell cycle analysis revealed that OAA further increased the number of cells in S-phase and G2M-phase scoring in both the fibroblast and bone areas of the regenerating digit (Figure 7B). This increase in proliferation was confirmed histologically by staining for the proliferation marker PCNA (Figure 7C). Given that administration of OAA enhances bone volume and thickness, but not spacing, we investigated the impact of OAA on vascularity and hypoxia-driven pathways. Spatial transcriptomics module scoring (Figure 7D), as well as confirmatory histological labeling using hypoxyprobe (Figure 7E), both showed that OAA treatment increased hypoxia expression levels in the fibroblast area and reduced hypoxia expression levels in bone area of the D21 aged digits. These values were inversely correlated with VEGF signaling, while vascular markers were found to be decreased in both regions (Figure 7F). Treatment with OAA was also found to affect cell metabolism, with OAA administration resulting in increased glycolysis in both the fibroblast and bone areas, while OxPhos was found to be increased only within the fibroblast region (Figure 7G).

Oxaloacetate (OAA) treatment promotes proliferation and WNT signaling.

(A) Aged mice treated daily with OAA from D10 were sacrificed at D21 and subjected to spatial transcriptomics and the regenerated digit divided into fibroblast (fibro) and bone areas. (B) S- and G2M-phase scoring of aged mice treated with OAA or saline controls (Cont). (C) PCNA immunofluorescence in OAA-treated aged mice vs. saline controls (D21). n=3. Numbers below image represent the ratio of PCNA-positive nuclei ± SD in aged mice treated with OAA relative to saline control, with values over 1 indicating increased proliferation in aged blastema. Scale bar, 200 µm. (D) Spatial and dot plots showing module scoring of hypoxia pathway activation. (E) Hypoxia as assessed by Hypoxyprobe immunofluorescence (D21). Values below images indicate difference in Hypoxyprobe fluorescent intensity within the regenerated digits of aged mice treated with OAA or saline control ± SD, with values below 0 indicating decreased activation in OAA-treated digits. Scale bar, 200 µm. (F) Spatial and dot plots showing module scoring of vascular endothelial growth factor (VEGF) signaling activation and vessel markers. (G) Spatial and dot plots showing module scoring of glycolysis and oxidative phosphorylation (OxPhos) activation. (H) Dot plots showing module scoring of transforming growth factor beta (TGFβ), BMP, and WNT pathway activation. n=4–7 digits per treatment group.

Pathway analysis on both the fibroblast and bone DEGs (Supplementary file 6) indicated that TGFβ, BMP, and WNT are potentially regulated in response to OAA treatment. To determine the effects of OAA or morphogenetic signaling, we assessed modular scoring of pathway activation (Figure 7H). WNT signaling was found to be upregulated in both the fibroblast and bone regions, while BMP signaling was increased in the fibroblast region and decreased in the bone region. Together, these data suggest that administration of OAA enhances regeneration by increasing proliferation and promoting differentiation through regulation of morphogenetic pathways.

Discussion

In this study, we report an important role for cell metabolism in regulating blastema function and regenerative potential. To identify potential discerning pathways differentially regulated between regenerating and non-regenerating tissue, we applied spatial transcriptomic analysis in an aged mouse model. Our results show impaired digit regeneration in aged mice, associated with increased proliferation and decreased bone building, as well as differential proximal-distal patterning of glycolysis and OxPhos. These increased metabolic demands in aged mice are associated with increased intracellular hypoxia, VEGF signaling, and vascularization. Finally, we demonstrate that administration of the metabolite OAA can partially rescue this age-dependent decline in regeneration.

Age-dependent delays in regeneration

While extensive work has been conducted to characterize the blastema in multiple animal models and regenerating conditions, it was only recently that a comprehensive transcriptional profile was available for blastema fibroblasts (Storer et al., 2020; Johnson et al., 2020). Using scRNAseq, these studies made significant contributions to our understanding of blastema cell subtypes and their transition throughout the regenerative process. Single-cell analysis, however, is unable to provide spatial information, making it difficult to place the blastema in the context of its neighboring tissues. Spatial transcriptomics, shown here for the first time in digit regeneration, allows transcriptional insight into the histologically defined blastema in the context of its spatial position within the regenerating digit. Using this technique, we identified a blastema transcriptional profile delineating this structure in not only our own spatial data, but also within previously published scRNAseq studies (Storer et al., 2020; Johnson et al., 2020). The strong correlation between these data sets suggests a high degree of conservation between spatial transcriptomic and scRNAseq data, and thus, opens up exciting new avenues for future research across platforms.

Even for highly regenerative animals like the Mexican axolotl (McCusker et al., 2015), age is associated with a decline in regenerative capacity (Seifert and Voss, 2013; Vieira et al., 2020; McCusker and Gardiner, 2011). Similarly, in humans, while regenerative capacity of the digit tips persists into adulthood, fidelity of regeneration appears to decline with age (Muneoka et al., 2008). We show here that age is associated with a delay in the switch from bone catabolism to bone anabolism, and that the final regenerate has significantly increased trabecular spaces, reduced volume, and overall impaired bone patterning, suggesting a defect in bone anabolism in aged mice. This delay is accompanied by increased proliferation, which may indicate that delays in bone regeneration may be linked to delays in cell cycle progression in the aged blastema. Increased proliferation in the aged blastema may also explain the increases in cell metabolism, since proliferation would require both energy and biosynthesis. Most studies agree that formation of the blastema relies on, at least in part, the dedifferentiation of terminal cells within the remaining digit (Storer et al., 2020; Johnson et al., 2020; Lehoczky et al., 2011; Rinkevich et al., 2011). In contrast, proliferation is thought to play a relatively less critical role in blastema formation (Storer et al., 2020; Johnson et al., 2020). While our studies are not able to determine the relative contribution of dedifferentiation to blastema formation in young and aged mice due to the lack of reliable markers, this increased proliferation may point to a potential loss of plasticity, and the subsequent loss of dedifferentiation within aged cells. Another possible explanation for the delay is that the increased intracellular hypoxia may be suppressing osteoblast differentiation, a concept demonstrated in both osteoblasts (Utting et al., 2006) and in the blastema (Sammarco et al., 2014). However, this runs counter to our OAA-treated mice which showed increased bone formation without altering intracellular hypoxia.

Mechanistic changes in digit regeneration during aging

VEGF expression is intimately linked to bone repair and regeneration (Hu and Olsen, 2016). Our initial analysis of the aged blastema showed increased hypoxia, VEGF signaling, and vascular markers. Intramembranous ossification relies on hypoxia-induced VEGF expression (Wang et al., 2007) to increase formation of vasculature and mobilize bone progenitors (Hu and Olsen, 2016; Matsubara et al., 2012). However, excess VEGF is also known to inhibit the function of pericytes (Greenberg et al., 2008), leading to formation of immature blood vessels and uncoupling of angiogenesis and osteogenesis. In addition, excess VEGF signaling has also been shown to inhibit digit regeneration in neonates (Yu et al., 2014). Together, these data suggest that excess VEGF production driven by hypoxia in the blastema of aged mice may contribute to altered bone architecture and impede regeneration in aged mice.

Despite the increased vascularization, the regenerating digits from aged mice still demonstrated increased intracellular hypoxia. However, in vitro studies (Kurokawa et al., 2015; Prior et al., 2014) suggest that this hypoxia may be directly driven by oxygen consumption by the electron transfer chain in mitochondria (Friedman and Nunnari, 2014) and continues to be an ongoing area of research (Agathocleous and Harris, 2013). A recent study by Yao et al., 2019, confirmed this same phenomenon in bone in vivo, noting that intracellular hypoxia modulates epigenetic modifications (Batie et al., 2019; Chakraborty et al., 2019; Gallipoli and Huntly, 2019), as well as gene and protein expression (Semenza, 2017). Functionally, Yao et al. demonstrate that the use of non-oxidative glycolysis protects hypoxic tissues from anoxia by regulating intracellular hypoxia during development. Lee et al. fortify this finding by showing that reduced oxygen consumption is a hallmark of osteoblasts (Lee et al., 2020). Guided by our results, and in concert with these published findings, we propose that upregulation of OxPhos during skeletal regeneration in aged mice facilitates hypoxia-driven vasculature that ultimately disrupts bone architecture.

Bioenergetics were some of the major pathways altered during aging regeneration. Our spatial transcriptomic data and Seahorse data show increased glycolysis and OxPhos in aged D10 blastema fibroblasts when compared to the young blastema. We sought to address the unmet energy needs of the aged blastema with OAA, given that mature osteoblasts rely heavily on the malate-aspartate shuttle (Lee et al., 2020), and that OAA provides a source of both NADH and biomass (Stephanopoulos et al., 1998). Phenotypically, OAA partially resolved the aged phenotype by increasing bone volume and thickness, but did not decrease trabecular spacing.

Closer investigation of the transcriptomic changes at the early bone formation stage (D21) revealed that OAA had differential effects on the fibroblasts and bone regions at D21. Within the fibroblast region, OAA increased glycolysis and OxPhos, proliferation, and hypoxia, and reduced VEGF signaling and vascular markers. Within the bone region, OAA demonstrated increased glycolysis, but not OxPhos, and was accompanied by an increase in proliferation and VEGF signaling, but not vascular markers or hypoxia. These OAA-dependent changes were accompanied by increases in WNT pathway expression in both fibroblasts and bone, which may be driving these anabolic bone processes. The regulatory axis between WNT and cell metabolism has only recently begun to be explored in bone, and while evidence has grown showing the impact of WNT on cell metabolism (Karner and Long, 2017; Karner et al., 2015; Kobayashi et al., 2016), data is scarce in understanding the converse relationship where cell metabolism modulates WNT signaling (Costa et al., 2019; Delgado-Deida et al., 2020; Risha et al., 2021; Zhang et al., 2019). WNT is a central regulator of bone repair and regeneration, and is known to differentially affect various cell lineages (Houschyar et al., 2018), which may partially explain the divergent effects in fibroblasts and bone. Further work is required to determine whether OAA-mediated rescue requires WNT signaling activation, and whether WNT is directly or indirectly regulated by OAA-induced cell metabolism.

While OAA-dependent increases in glycolysis and OxPhos could be attributed to the differential effects of WNT signaling, it’s also important to consider the critical signaling role of OxPhos-mediated ROS generation in limb regeneration. ROS production after tail amputation in Xenopus is necessary for regeneration, possibly through WNT signaling and proliferation (Love et al., 2013), and leptin and melanocortin 4 receptor, key molecules regulating energy balance, are critical in tadpole tail regeneration (Love et al., 2011) and digit regeneration (Kang et al., 2016; Zhang et al., 2018). While we chose OAA partially because it supplemented the elevated cell metabolism levels seen at D10, these increased metabolism levels may be an inherent and essential part of limb regeneration, with OAA potentially providing building blocks for cell growth and expansion, further increasing cell metabolism. Thus, it may be misguided to think of promoting regeneration by reversing a molecular phenotype, and may, instead, be an exercise in determining what is needed metabolically and providing that substrate within the existing framework. Future studies with treatment of OAA or other metabolites, combined with the ability to spatially analyze signaling pathways and cell metabolism, may identify molecular targets to better promote skeletal regeneration and bone repair and further refine the timing of these treatments.

Considerations for future studies

While spatial transcriptomics offers the unique ability to discern gene patterning in tissue across the entire transcriptome, it is important to note the limitations of this approach. Optimal permeabilization is a balance between RNA release and excess diffusion that results in loss of spatial specificity. This balance does not promote the complete recovery of mRNA within the overlaid tissue section, restricting the capture efficiency of the transcriptome. Analytically, while our results suggest that the level of transcriptional information obtained per spatial spot is consistent with typical drop-seq-based single-cell experiments, the latter relies on a large sample size to help computationally ‘fill in’ dropout transcriptional information with that of its co-clustering neighbors. Our spatial information likely reflects a similar loss of transcriptional information but without the availability of thousands of spots to compensate. As such, individual gene signatures must be considered with this limitation in mind. To overcome this drawback, we made use of module scoring, which assess the expression distribution of a list of genes (relative to a random background list). Another limitation is the low resolution, with each spatial spot representing the transcriptome from multiple cells. As such, we are unable to distinguish cell heterogeneity within close proximity. This is evident by a lack of Ptprc expression within the regenerating digit, in contrast to previous works which have demonstrated the essential presence of, for instance, macrophages in the regenerative process (Simkin et al., 2017). This lack of Ptprc expression likely reflects the fact that immune cells are scattered among other cell types, resulting in ‘masking’ of their individual transcriptome. In addition, we make use of recently developed SpatialTime analysis (Tower et al., 2021) which allows concatenation of multiple samples to reflect gene expression changes across an ‘average’ blastema. These tools significantly mitigate inherent limitations within each individual spatial spot.

This study addresses a direct comparison of young and aged digit regeneration where our data support an age-dependent delay in bone regeneration. To address differences in gene expression, we analyze both the aged and young blastema 10 days after amputation. While time course studies suggest regeneration delays occur at the level of osteogenic commitment and mineralization, using day 10 for both ages may capture differences that are merely representative of a chronological shift. Our studies in hypoxia and proliferation suggest that these gene expression differences are not simply a delay, however it is important to take this possibility into consideration. These data pave the way for additional studies that will hopefully illuminate the temporal activation pathways throughout the entire regenerative process.

While our data demonstrate that OAA can reverse aging-induced declines in regeneration, the exact mechanism of rescue remains to be elucidated. In our studies, OAA was administered throughout the blastema and bone formation phases. While pro-osteogenic transcriptional changes observed at D21, prior to any significant changes in bone architecture, suggests that OAA-mediated rescue, at least in part, functions upstream of the mineralization process to promote cell fate determination, we cannot rule out the possibility that OAA may act at the level of promoting osteoblast mineralization during bone formation and late-stage remodeling. In light of the differential effects of OAA on the fibroblast and bone areas of the digit, future work is needed to address the specific effects of OAA on the blastema and bone formation phases separately.

Conclusion

This study provides spatial registration of blastema gene expression differences in aged and young mice, and confirms prior expression profiles of the blastema (Storer et al., 2020; Johnson et al., 2020). Our data suggest that cell metabolism is altered during aging, resulting in impaired regeneration, and that while administration of the metabolite OAA is able to enhance bone formation, it is only able to partially rescue the phenotype. Furthermore, our data show that OAA-dependent regeneration is driven, at least in part, by upregulation of canonical WNT signaling. Future studies will focus on further dissecting the molecular mechanisms link metabolite signaling to these pathways.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Mus musculus)CD1Charles River LaboratoriesStrain 022Female and male, 6–22 months of age
RRID:IMSR_CRL:022
Chemical compound, drugOxaloacetateSigma07753Oxaloacetic acid
OtherZ-FixAnatech5701ZFFixative
OtherDecal ISurgipathDecalcifier
OtherBlocking solutionThermo27515Blocking solution
Commercial assay, kitMasson’s Trichrome KitPoly ScientificK037Stain kit
Commercial assay, kitTyramide signal amplificationInvitrogenT20924
Commercial assay, kitHypoxyprobe Plus KitHypoxyprobeHP2Stain kit
Commercial assay, kitMito Stress Test KitAgilent Seahorse103015-100
Commercial assay, kitGlycolytic Rate Assay KitAgilent Seahorse103344-100
Software, algorithmGEN5IPRIME V3.05.11Biotek
Software, algorithmGraphPad Prism 9GraphPad
Software, algorithmCTAnBruker
Software, algorithmNRECONBruker
Software, algorithmCTVoxBruker
Antibody(Rabbit Monoclonal) Anti-CD31 antibodyAbcamab182981(1:100)
Antibody(Mouse Monoclonal) Anti-PCNA antibodyAbcamab29RRID:AB_303394
(1:200)
Software, algorithmCellRangerVersion 610× Genomics
Software, algorithmSeuratVersion 3R packageStuart et al., 2019
Software, algorithmggpubrVersion 0.4.0R packageKassambara, STHDA July 2016

Experimental model and subject details

Mice

All experiments were performed in accordance with the standard operating procedures approved by the Institutional Animal Care and Use Committee of Tulane University Health Sciences Center (Protocol #1483). In all cases mice had free access to low-fluorescence rodent chow and water in a 12 hr dark-light cycle room. For all studies, mice of either sex were used and mice were randomly allocated to experimental groups. The ages of mice used for experiments ranged from 6 to 22 months. Experimental endpoints are noted in the figure legend. Wild-type CD1 mice were purchased from Charles River Laboratories (strain 022). Four digits from four separate mice were used for spatial transcriptomics for each age group (eight total hindlimb digits per group).

Method details

Amputations and OAA treatments

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Adult young (6–7 months) and aged (18–22 months) female and male CD1 wild-type (002) mice were purchased from Charles River Laboratories. Mice were anesthetized with 1–5% isoflurane gas with continuous inhalation. The second and fourth digits of both hindlimbs were amputated at the P3 distal level as described previously (Sammarco et al., 2015; Busse et al., 2019) and regenerating digits were collected at days 0, 7, 10, 14, 21, 28, and 42 for analysis. The third digit was used as an unamputated control. OAA (Sigma, 07753) was dissolved in phosphate-buffered saline (PBS) and pH-adjusted with NaOH to 7.0 to allow the OAA solution to drift toward alkaline levels as described by Wilkins et al., 2016. Mice were treated daily with 0.5 g/kg OAA in 200 µL via i.p. injection based on our own studies that showed aged mice routinely survived repeated doses at 0.5 g/kg but not 2 g/kg as described in the previous paper (Wilkins et al., 2016). The vehicle-treated group received PBS. Mice were dosed from day 10 until day 28.

Tissue collection, fluorescence, and histology

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Digits were fixed overnight in zinc-buffered formalin (Z-fix; Anatech). Bone was decalcified for 48 hr in a formic-acid-based decalcifier (Decal I, Surgipath). Once decalcified, all samples were processed for paraffin embedding. Sections were stained with Masson’s trichrome (Poly Scientific, K037-16OZ) and mounted using permanent mounting medium (Thermo, SP15-100). Immunofluorescent staining was performed on deparaffinized and rehydrated sections. Antigen retrieval was performed using heated antigen retrieval solution (Vector, H-3300) and allowed to cool to room temperature prior to blocking with blocking solution (Thermo, 37515). Sections were incubated with primary antibodies overnight at 4°C and subsequently incubated with fluorescently labeled secondary antibodies (1:500) for 1 hr at room temperature. Primary antibodies: Hypoxyprobe-1 Plus (Hypoxyprobe, 4.3.11.3); PCNA (Abcam, ab29); CD31 (Abcam, ab182981). For hypoxyprobe primary signal was amplified with either Alexa Fluor secondary antibodies (Invitrogen) or a tyramide signal amplification kit (Invitrogen, T20924) per manufacturer’s instructions. Administration of Hypoxyprobe-1 was as previously described (Sammarco et al., 2014). For immunofluorescence quantification, ImageJ was used to quantify images. Image subtraction was used to isolate fluorescence within the DAPI+ nuclei. For Hypoxyprobe signal, the fluorescent intensity of Hypoxyprobe was measured, adjusted for background signal outside of the digit. For PCNA quantification, PCNA fluorescence was thresholded and normalized to the number of positive nuclei.

Cell culture and metabolic assays

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For Seahorse analysis with blastema, a single blastema was dissected from the digit at day 10, removing all nail, skin, bone, and soft tissue and placed directly into an Agilent Seahorse culture plate containing blastema media (Sammarco et al., 2014). Cells were allowed to expand in the well and were assayed after 2 weeks. Complete seahorse medium was prepared from Agilent Seahorse XF Base Medium (Agilent, 102353) to contain 10 mM glucose (Agilent, 103577-100), 2 mM glutamine (Agilent, 103579-100), and 1 mM pyruvate (Agilent, 103578-100), adjusted to pH 7.4. Cells were incubated in 180 µL complete seahorse medium at 37°C for 1 hr before measurements in a Seahorse XFe24 analyzer. Stressor concentrations were as follows: 2 µM oligomycin, 0.75 µM FCCP, 1 µM rotenone, and 1 µM antimycin A. Cells were evaluated using the Seahorse Bioscience Mito Stress Test (Agilent, Santa Clara, CA) using the standard protocol after optimization of both cell seeding density and FCCP concentration as previously reported (Busse et al., 2019). OCR and ECAR were normalized to total protein. For the Glycolytic Rate Assay, complete seahorse medium was prepared from Agilent Seahorse XF Base Medium to contain 11 mM glucose, 1 mM sodium pyruvate (Gibco, A24940-01), 2 mM glutamine (Agilent, 103579-100), and 5 mM HEPES (Gibco, 103337-100) adjusted to pH 7.4. Blastemas were treated with 2 mM OAA for 24 hr prior to Seahorse analysis (Sigma, 07753).

Micro-computed tomography

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Digits were scanned on a Bruker SkyScan 1172 at a pixel size of 4 µm with 0.2 rotation angle and 5 frame averaging using a 0.25 mm aluminum filter. The X-ray source used was 50 kV, 201 µA, and 10 W. The raw scans were reconstructed using NRecon. Each scan was reconstructed with a beam hardening correction of 24%, no smoothing correction, and a dynamic range of 0.00–0.339. Reconstructed output files were in 8-bit BMP format. Digits were rotated three dimensionally in DataViewer during which the regenerated woven bone was separated from the cortical stump. Whole bone and trabecular bone data sets were binarized in CTAn. Global thresholds were used for all data sets with minimum threshold value of 0 and maximum threshold value of 74. P3 was then cropped away from P2 in three dimensions using the 3D Viewer plugin in ImageJ2. Bone volume for whole bone and trabecular bone was generated using the volume function in the BoneJ plugin for ImageJ2. Trabecular and cortical thickness values were generated using the thickness function in the BoneJ plugin. Trabecular spacing stacks were generated from the trabecular bone data sets using the background subtract function in ImageJ2. A rolling ball value of 29 was chosen such that the marrow cavity space was excluded but all trabecular spaces were included during the background subtract function. The despeckle function was then run to remove artificially small spaces on the exterior of the bone. Trabecular spacing values were generated using the thickness function in BoneJ. For 2D analysis, scans were realigned along the proximal-distal axis and an ROI was selected, beginning at the most proximal slice free of bone from the residual amputated stump. Images were thresholded as above and 2D measurements of bone area were generated in CTan (Bruker). Inner void fraction and 3D translucent images were quantified as described previously (Hoffseth et al., 2021). For OAA-treated micro-CT analysis, digits were aligned so that image stacks were aligned along the proximal-distal axis. The regenerated region was isolated based on the increased porosity and decreased cortical bone thickness associated with the transition from residual bone stump to newly deposited bone. 2D and 3D analyses for these digits were conducted in CTan.

Spatial transcriptomics

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Spatial transcriptomics was conducted using the Visium Spatial Gene Expression System (10× Genomics, Pleasanton, CA). Digits 10 days after amputation were harvested, arrayed in a 2 × 2 grid, fresh frozen in OCT, then stored at –80°C. Samples were cut at –26°C at a thickness of 12 µm. Optimization and gene expression assays were carried out according to manufacturer’s protocol. Briefly, slides were fixed in –20°C methanol, dried with isopropanol, and stained with H&E. A tile scan image of all reaction areas was generated using a Leica DM6 B microscope (Leica Microsystems Inc). For tissue optimization, enzymatic permeabilization was conducted for 0–40 min, followed by first-strand cDNA synthesis with fluorescent nucleotides. The slide was reimaged using standard Texas Red filter cube. An optimal permeabilization time of 13 min was determined by visual inspection to maximize mRNA recovery while at the same time minimizing diffusion. For gene expression, initial workflow was consistent with optimization. Library preparation, clean-up, and indexing were conducted using standard procedures. Samples were subjected to pair-ended sequencing using an Illumina HiSeq generating ~400 M reads. Alignment and demultiplexing were conducted using the SpaceRanger pipeline with subsequent analyses conducted using Seurat in R. Pathway analysis was conducted using DAVID. Pathway scoring was determined using the AddModuleScore function in Seurat using gene lists obtained from validated KEGG pathways and published literature. Spatial feature plots represent a representative image, while all quantitative measures represent the cumulative data of multiple digits (5 young digits, 7 aged digits; 4 aged controls, 7 OAA-treated aged). To determine significance between genes/modules in spatial, the Wilcox method of stat_compare_means function from the R package ggpubr was used. For SpatialTime analysis, a reference line was manually drawn along the residual bone stump. Minimum 2D geometric distances were calculated between each spatial spot within the defined blastema and this reference line using the spatial coordinates and individually scaled to values between 0 (spatial spot closes to the bone stump) and 1 (furthest spot from the bone stump) for each blastema. Once the SpatialTime values were calculated for each blastema, samples are concatenated to increase statistical power and visualize gene expression across an ‘average’ blastema.

ScRNAseq and ‘blastema gene signature’

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Previously published scRNAseq data sets were downloaded from the Gene Expression Omnibus repository and analyzed using default settings in Seurat to replicate original publication as closely as possible. For data from Storer et al., day 10 and 14 data sets (GSE135985) were used. For data from Johnson et al., day 11 data (GSE143888) was used. For the blastema signatures from our spatial data, blastema-specific DEGs (Supplementary file 1), the top 200 genes were highlighted after sorting for first the adjusted p-value, then the log fold change, and finally for specificity (defined as the ratio between spatial spots which expressed each gene within the blastema relative to spots expressing the gene outside the blastema). Genes highlighted in each of the three selection criteria were chosen first, with subsequent genes highlighted in two of the three criteria (weighted with specificity >log fold change >adjusted p-value) later selected to reach a list of 100 genes. Similar selection criteria were applied to Table S1A from Johnson et al. for genes enriched in fibroblast clusters. Following initial selection, genes derived from fibroblast cluster 8 from Johnson et al. were excluded to generate a final blastema gene list. To generate a blastema signature from Storer et al., the top 100 gene entries were selected from category 1 genes in Table S2 from the original publication. As described above, module scoring was used to show enrichment of blastema gene signatures in each of the individual scRNAseq data sets.

Statistical analysis

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Statistical analysis was performed using SPSS (v.26, IBM) and Graphs were compiled using GraphPad Prism (version 7) (GraphPad, San Diego, CA).

Micro-CT data: Eight digits were precluded from the analysis due to breakage, as determined by visual inspection of 3D digit renderings. Unless otherwise stated, outliers were removed where an outlier was defined as having a studentized residual >3.0 or <–3.0, or an unstandardized residual greater or less than three times the group interquartile range. Normality of each group was tested using the Shapiro-Wilk test. Homogeneity of variances was tested using Levene’s test of equality of variances. For all tests, p ≤ 0.05 was considered significant. The data are presented as the means  ± SD. For unamputated digit volume, a one-way ANOVA with Welch’s test was performed, N=11 (6 months) and 14 (18 months). Differences in day 0-normalized P3 bone volume were determined by two-way ANOVA, N=63 (18 months) and 86 (6 months). Following the omnibus two-way ANOVA, individual differences between age groups at each time point were determined by post hoc comparisons using Bonferroni’s adjustment for multiple comparisons.

Seahorse data: The statistical significance of the differences in the means of the investigated cell lines was determined by unpaired Student’s t test. For all tests, p ≤ 0.05 was considered significant. The data are presented as the means ± SD.

Hypoxyprobe and IHC: Age-associated differences were assessed using a one-way random effects ANOVA to account for repeated measures at the mouse level. Measurements were log-transformed to reduce skewness and results are reported on the logarithmic scale.

Data availability

Spatial transcriptomic data from day 10 young and aged mice, as well as day 21 aged mice treated with OAA or saline control have been deposited in the Gene Expression Omnibus (GEO) under the accession number GSE180682.

The following data sets were generated
    1. Sammarco MC
    2. Tower RJ
    (2022) NCBI Gene Expression Omnibus
    ID GSE180682. Spatial transcriptomics reveals increased energetic requirements underpinning age-dependent declines in digit regeneration rescued through administration of OAA.
The following previously published data sets were used
    1. Storer MA
    (2019) NCBI Gene Expression Omnibus
    ID GSE135985. Acquisition of a unique mesenchymal precursor-like blastema state underlies successful adult mammalian digit tip regeneration.
    1. Lehoczky JA
    (2020) NCBI Gene Expression Omnibus
    ID GSE143888. Single cell RNA sequencing of adult regenerating mouse digit tips.

References

    1. Bonewald LF
    (2011) The amazing osteocyte
    Journal of Bone and Mineral Research 26:229–238.
    https://doi.org/10.1002/jbmr.320
    1. Bryant SV
    2. Endo T
    3. Gardiner DM
    (2002)
    Vertebrate limb regeneration and the origin of limb stem cells
    The International Journal of Developmental Biology 46:887–896.
    1. Stephanopoulos G
    2. Aristidou A
    3. Nielsen J
    (1998)
    Metabolic Engineering
    285–308, Metabolic Pathways Synthesis, Metabolic Engineering, Lyngby, Denmark, Technical University of Denmark.

Decision letter

  1. Cheryl Ackert-Bicknell
    Reviewing Editor; University of Colorado, United States
  2. Kathryn Song Eng Cheah
    Senior Editor; University of Hong Kong, Hong Kong

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "Spatial transcriptomics reveals increased energetic requirements underpinning age-dependent declines in digit regeneration rescued through administration of OAA" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Kathryn Cheah as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. The introduction is very abbreviated, uses a large number of field-specific terms, and several places could be expanded. For example, the description of different phases of digit regeneration should be expanded as this is critical information to guide non-digit regeneration aficionados through the rest of the manuscript's results. Likewise, for a large portion of the text, information is contained in parentheses when it should just be expanded into its own sentence. This would help with the flow of information in the manuscript.

2. There is no mention in the text or methods as to the X/Y/Z resolution that is achieved via spatial transcriptomics. Some approximation should be made to the number of cells per barcoded spot.

3. The PCNA staining between young and aged digits should be quantified with consideration of total cell number and density. It is not overwhelmingly obvious that there is a difference.

To distinguish whether there is in fact increased percentage of proliferating cells and/or a lengthening of the aged digit cell cycle, the authors should consider a BrdU/EdU pulse-chase that can give kinetics of the cell cycle stages between young and aged regenerating digits.

4. Despite the impressive analysis presented, the manuscript would benefit from a discussion of the limitations of the spatial transcriptomics technique as the low read depth in single cell transcriptomes are known to limit the detection of important genes that have low expression values, and this could be further compounded by the slide-based barcoding technique. An example of such a gene would be PTPRC where the authors show in Figure 1A a lack of spatial detection of a gene carried by all immune cells. In conflict with this, the authors previous work had already identified a large number of PTPRC+ cells within the mouse digit at this time point (Development. 2017 Nov 1; 144(21): 3907-3916. doi: 10.1242/dev.150086).

5. As aged digits are slower to enter the anabolic phase of bone growth it is possible that the "spatial time" data obtained at a single timepoint may simply reflect that delay. The authors should acknowledge this limitation in interpretation. If there would be a delay in the progression of blastema and bone formation between young and aged mice, as shown in Figure 2A, would experimental samples at the same days post amputation be equivalent samples between aged and young digits? Perhaps the authors have done this analysis of heterochronic timepoint comparison, but this is not immediately apparent in the methods or text. The reviewers had a difficult time understanding how comparisons were made between young and aged cohorts.

6. One of the primary differences uncovered by the authors is the increase in the number of cells expressing the active cell cycle marker PCNA (although this is not quantified) as well as an increase in expression of S/G2/M phase genes in aged digits relative to young regenerating digits. The interpretation by the authors is that there is increased proliferation in young versus aged regenerating digits. This was interpreted to mean that a greater percentage of all cells are actively moving through the cell cycle as opposed to remaining quiescent. An alternative explanation is that there is a lengthening of the S/G2/M phases in aged cells resulting in a higher proportion of cells at a given static timepoint being in these phases. These two interpretations are not mutually exclusive, but the authors do not consider a lengthening of the cell cycle and the impact that might have on the overall process of regeneration. A slower cell cycle could potentially explain lags in blastema/bone formation. The conclusions regarding cell cycle therefore must be tempered and other possible conclusions acknowledged.

7. The authors postulate in the discussion that there could be an effect on cell dedifferentiation and redifferentiation that correlates to the lag in bone anabolism. "Most studies agree that formation of the blastema relies, at least in part, on the dedifferentiation of terminal cells within the remaining digit (6, 7, 28, 29). In contrast, proliferation is thought to play a relatively less critical contribution to blastema formation (6,7)." Can the authors easily support this claim and perhaps demonstrate deficiencies in aged digits by ratiometric comparison of spatial expression of genes that would relate to dedifferentiation (or lack of differentiation) relative to proliferation and cell cycle markers?

8. The reviewers wondered if authors had considered that the observed hypoxia might be influencing osteoblast differentiation, which is leading to a lag in bone anabolism? There is literature suggesting that hypoxia can influence differentiation of osteogenic cells, although studies seem mixed as to whether hypoxia is beneficial or suppresses differentiation (one example is PubMed ID: 16529738). This seems like a question that the authors can easily address using their spatial datasets and immunohistochemistry in addition to their already generated in vitro assays.

9. The authors claim that the increased energetic demand drives intracellular hypoxia and increased blood vessel formation providing both spatial transcriptomic data, "hypoxyprobe" data and blood vessel immunofluorescent staining to support these claims. This data would be more convincing and reliable if the relative levels were quantified between old and young digits and if it was made clear where the vascular changes occurred relative to the spatial transcriptomics.

10. Figure 3B: Reviewers were confused about which regions of the spatial transcriptomics profile went into the identification of differentially expressed genes between young and aged mice. In Figure 1B, the authors have highlighted a specific portion of the image as the blastemal (pink color). In the text describing Figure 3, the authors state, "…Pathway analysis of DEGs showing differential expression in the young or old blastemal identified…(Figure 3A) " and then later state "…Metabolic analysis showed increased glycolysis and oxidative phosphorylation (OxPhos) in the blastema of aged mice… (Figure 3B)". It is confusing, therefore, that the images shown in Panel 3B show expression patterns in regions of the tissue that are not necessarily localized to the blastema. For example, OxPhos signatures for the young mice seem to be concentrated below the blastema in the digit itself; this raises the question of whether the authors segmented their gene lists to the blastema only while analyzing pathways, or whether the whole section was considered? Perhaps the authors could outline the region of the blastema with a dashed line, to emphasize where their pathway analyses were focused? If otherwise, please clarify. Notably, a similar concern could be raised about Figure 4B and 4C and should be likewise addressed in these areas.

11. The concern was raised that the data showing in Figure 4A to be somewhat unnecessary for the paper. While the Figure 3 cell energetics data are quite relevant as they are from young and aged blastema, the data in Figure 4A are only provided from an osteogenic cell line (which has limited relevance to this study overall). Presumably these data are provided to link the exciting cell energetics data in Figure 3 to the hypoxia data in Figure 4. The reviewers recommended that the data be removed from the paper, moved to a supplementary figure as they detract from (rather than add to) the nice narrative the authors have otherwise put together. The reviewers requested that these experiments be repeated with blastema tissue rather than osteogenic cells, which would provide a much better linkage between the story told in Figure 3 and the continued story in Figure 4. A similar concern could be raised about Figure 5A-B as was raised above for Figure 4. The authors have clearly demonstrated the ability to perform these assays with blastema tissue (Figure 3D and S4C) and the justification for moving to a cell line is thin and unsupported. The reviewers were concerned because the relative importance of the Seahorse assays must be assumed to translate from osteogenic cells to blastema, rather than being directly supported with experimental evidence.

12. In Figure 3D, the authors state that OCR associated with ATP, maximal, and non-mitochondrial respiration were significantly higher in aged vs. young blastemas. However, these data are not shown anywhere, and statistical analyses are not presented in Panel D. Please add these graphs and the related statistical analyses.

All of the reviewers struggled with the presentation and interpretation of the results of the OAA study. Overall, there are so much confusion about what OAA is doing and what these data mean, that it became difficult for the reviewers to reconcile this OAA study with the hypotheses and premise of the rest of the paper. The following two comments address this part of the paper and are considered essential issues that must be resolved.

13. The choice of intervention (treatment with the anapleurotic metabolite oxaloacetate (OAA)) is hard to follow and poorly justified. The authors showed that the aged mice already had elevated OCR already vs. young mice (Figure 3B,C,D), as well as enhanced proliferation (Figure S4C). The data they provide in Figure 5 show that OAA increases OCR and proliferation. So, why is this drug a good choice when it produces the same phenotype already seen in the aged mice? This part of the story, accordingly, becomes hard to follow. As written, the rationale is unclear and should be clarified for the reader. This is particularly important because OAA did not rescue the hypoxia phenotype seen in the aged mice, despite providing a modest rescue of bone regeneration endpoints. While it is recognized that the authors likely used the MC3T3 osteoblast cell line to restrict analysis to bone producing cells to match the in vivo enhanced bone anabolism phenotype, performing the OAA treatments ex vivo, in primary culture of aged blastema tissue using procedures they have already documented (Figure 3) is a necessary addition for their conclusions to be supported.

14. The failure of OAA to alter trabecular separation and a full rescue of the aged phenotype data broadly support this hypothesis although a discussion of alternative hypotheses are not discussed (i.e., cell specific effects of OAA on cell phenotype, alternative metabolic pathway activation with excess OAA, mineralization specific gene activation etc..?). It appears that OAA acted so in a way that is apparently largely outside of the pathways identified in the spatial transcriptomics analyses. The documentation of timeline for any changes in the initiation of bone anabolism (outgrowth) upon OAA treatment are strangely missing and would be important for understanding if metabolic energy deficit is responsible for the delay or if some other pathway is involved. The actual mechanism of action of OAA is never defined but the addition of OAA seems to have decoupled bone anabolism from hypoxia, so this may not be the case. Perhaps there are non-osteogenic cells that are primarily affected by hypoxia and these cells are not assayed in vitro? Do the authors have the spatial resolution to distinguish osteogenic cell gene expression from other non-osteogenic cell types?

15. There is something counterintuitive and unexplained in the authors data related to the aged regenerative phenotype. They first observe an increased proliferation in aged blastemas and increased expression of metabolic pathways. Postulating that cells are compensating for a metabolic deficit, they add an OxPhos metabolite OAA. in vitro, this metabolite increases proliferation and oxidative phosphorylation. It is not well explained what is being achieved through the rescue experiment relative to the baseline aged phenotype other than an increased mineralization. Is it because these assays were done with the MC3T3 cell line and not primary digit explants? This is not explained sufficiently, and the in vitro assays using cell lines may not be an adequate proxy for aged tissue cells.

Please address any comments and issues raised in addition to the essential reviewer comments listed above.

Reviewer #1:

The authors set out to apply the new technology of spatial transcriptomics to the context of digit tip regeneration and identify possible targets regulating the age-related decline in regeneration quality. The application of spatial transcriptomics to this model was extremely convincing as they were able to incorporate several published datasets into their pipeline. During their analysis they were able to spatially identify a blastemal region and a border region with different gene signatures to the rest of the digit.

The authors then confirm the morphological changes in aged vs young mice using rigorous bone measurements reporting the main defects in aged mice occurring which is in agreement with a previous study by Brunauer et al., characterizing the bone volume changes in young vs aged mice. This current study adds trabecular thickness and trabecular separation measurements which can add to the understanding of regeneration quality in aged mice.

The manuscript then focuses on metabolism as a major instigator for disruption of regenerative quality. The spatial transcriptomics approach is used to reveal changes in metabolism location in the proximo-distal axis in aged digits. This analysis could be really important for understanding the dynamic spatial nature of how cells communicate within a blastema. Additional ex vivo seahorse data is supplied showing changes in metabolic parameters of aged digit cells that supports the hypothesis that changes in the aged digits metabolism may be important in age related decline in regeneration quality. The authors claim that the increased energetic demand drives intracellular hypoxia.

Next the authors perform in vitro analysis on a cultured osteoblast cell line treated with oxaloacetate (OAA) demonstrating changes in cell energy use parameters, proliferation and mineralization. They then perform studies examining OAA treatment in vivo and show data on aged digits 28 days post-amputation measuring increased bone volume and thickness. The authors also demonstrate that OAA fails to modulate hypoxia in the aged digits and put forward the hypothesis that hypoxia driven angiogenesis results in the failure of OAA to alter trabecular separation and a full rescue of the aged phenotype. Overall, the data broadly support this hypothesis, however the question of if the metabolic energy deficit is responsible for the delay or if some other pathway is involved remains unresolved.

The work presented in this manuscript is of high interest to regeneration and developmental biology disciplines. One key problem in these specialties is the difficulty in obtaining spatial information of gene expression for a large number of genes simultaneously. One can use modern in situ hybridization techniques such as RNAscope or HCR fluorescent probe (biased) analyses to identify the location of a particular gene on a tissue section however this is limited to a small number of genes at a time. The non-biased spatial transcriptomics approach has many advantages and will advance the field significantly. The authors do a good job at demonstrating this advanced technique at a single point in the regeneration timeline and add some interesting insights into the role of metabolism in aged-related decline in regeneration quality.

Reviewer #2:

Spatial transcriptomics is a fairly newly emerging field where the transcriptome from a section of tissue can be ascertained while also retaining information regarding those transcripts' spatial location in the tissue – preserving important relational information which can increase the utility of the resulting molecular data. While this technique is becoming more widely used in certain model systems (e.g., zebrafish) and tissues / organ systems (e.g., brain), it had not yet been described in the context of regenerating musculoskeletal tissue. A major strength of the manuscript, which does exactly that, is that the authors perform these analyses and then compare their results against two other recent datasets – collected using single cell RNA sequencing, another technique increasing in popularity – and compare / contrast against their own findings. Many similarities exist between the datasets, which highlights the likelihood that both spatial transcriptomics and single cell RNA-seq techniques are providing an accurate description of the molecular events taking place in digit regeneration, but notable differences exist that highlight the particular strength of spatial transcriptomics technology in this circumstance.

Another strength of the manuscript is that it does not simply stop at proving the utility of spatial transcriptomics for tracking the molecular signature of regenerating tissue – the authors go further to apply their new technology to a known instance of impaired regeneration (aging), and then use spatial transcriptomics to define the molecular events that lead to that expected outcome. This provides novel data to the literature, in that the authors have identified (using their technology) a molecular signature of impaired cellular bioenergetics, which was then pursued with other experiments (e.g., Seahorse, hypoxia endpoints, etc.) to test how and why aging tissue fails to regenerate like younger tissue.

A weakness of the manuscript is that the choice of intervention (treatment with the anapleurotic metabolite oxaloacetate (OAA)) as this drug induced, rather than blunted, the same metabolic effects seen in the aged animals that the authors hypothesized would be causative for their phenotype. This drug also produced a partial rescue of the impaired bone regeneration phenotype but did so in a way that is apparently largely outside of the pathways identified as differentially regulated between aged and young mice identified in the spatial transcriptomics analyses.

Reviewer #3:

In the manuscript by Tower et al., the authors address an important consideration related to regeneration which is the complexity and dynamicity of the process is somewhat lost in bulk and single cell transcriptomic approaches. Using a spatial transcriptomic approach, the authors can compartmentalize the regeneration process to the stump tissue, the regenerative blastema, and the boundary zone. To highlight the utility of this approach, the authors re-analyze two recent scRNAseq datasets of digit regeneration to identify consensus gene sets that predict spatial proximity within the blastema, in contrast to clustering by cell type which is the norm for scRNAseq datasets. This is well done and provides good justification for why high throughput spatial gene expression studies are necessary. The authors then shift focus to look at a primary biological question related to regeneration which is why regenerative ability declines with age. Using young and aged cohorts of mice, the authors find a decline in the speed of regeneration in aged mice, specifically in the creation of new bone and its patterning relative to young regenerating or uninjured digits. Transcriptomic analysis suggested that three potentially interconnected processes were changed – cell proliferation, hypoxia, and increased glycolytic and OxPhos cell metabolism – all increased in aged digits. The authors posit that a metabolic deficit could be underlying changes to these processes and use a metabolite that enhances oxidative metabolism to see if they can rescue aged digit regeneration. This treatment is able to somewhat rescue bone growth but cannot rescue the observed hypoxia and bone density which seems to be due to the increased vascularization that is a consequence of hypoxia.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Spatial transcriptomics reveals increased energetic requirements underpinning age-dependent declines in digit regeneration rescued through administration of OAA" for further consideration by eLife. Your revised article has been evaluated by Kathryn Cheah (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that needs to be addressed, as outlined below:

In Figure 2, the Day O, 7, 10, 12, 14, 21, 28 42 MicroCT time course is used to show changes in age related delayed bone anabolism. The reviewer and editor found it unfortunate that a OAA treated vs vehicle control aged mouse time course was not supplied to demonstrate this enhanced anabolism. Measurement of bone volume, trabecular thickness, trabecular number, and trabecular separation, of regenerated control and OAA treated bone at a single Day 28 timepoint does not tell the scientific community where the OAA is acting between 10 and 28 DPA. If these MicroCT measurements can be added reasonably quickly and would certainly help with the interpretation of the OAA effects on partially rescuing the aged bone anabolic defect. For example, if the OAA only altered bone volume between D21- D28 or if the OAA was acting earlier to advance the start the anabolism (D12-D14). If these measures cannot be collected quickly as the samples are not in hand, please expand the discussion to address this limitation of the paper. This will need to be done quickly to ensure the timely publication of this paper.

Reviewer #1:

The authors have made significant improvements to the clarity and presentation of the paper. They have addressed most of the comments by adding additional valuable data.

Although documentation of the timeline for any changes in the initiation of bone anabolism (outgrowth) upon OAA treatment are still strangely missing which would have been very informative for understanding if metabolic energy deficit is responsible for the delay or if some other pathway is involved. Despite this, the additional day 28 spatial transcriptomics data is a valuable addition to the manuscript that adds a deeper demonstration of the utility of this technique.

Overall, this is now a clear and informative manuscript of high value to the community.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Spatial transcriptomics reveals increased energetic requirements underpinning age-dependent declines in digit regeneration rescued through administration of OAA" for further consideration by eLife. Your revised article has been evaluated by Kathryn Cheah (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there is one remaining issue that must be addressed, as outlined below:

The Age-related defect in P3 digit tip regeneration is most apparent in the timing of anabolism onset occurring between D12-D14 post-amputation. At D21 this manifests as a significant difference in Bone Volume. The Tb. thickness and Tb. spacing differences are not significant at D21 (in figure 2) and are only significant a D28. Taking this into consideration, the OAA treatment data presented (in figure 6) show no differences in bone volume, Tb. thickness and Tb. spacing at D21 and OAA effects are only really observed in volume and thickness at D28 with no differences in spacing. This could be interpreted as the effect of OAA not affecting the onset of anabolism at all, but rather providing support for late-stage remodeling phase. The authors MUST discuss this, as this interpretation contradicts the statement given by the authors that "This data fortifies our findings in spatial transcriptomics (D21) that indeed OAA acts in advance to the start of anabolism."

https://doi.org/10.7554/eLife.71542.sa1

Author response

Essential revisions:

1. The introduction is very abbreviated, uses a large number of field-specific terms, and several places could be expanded. For example, the description of different phases of digit regeneration should be expanded as this is critical information to guide non-digit regeneration aficionados through the rest of the manuscript's results. Likewise, for a large portion of the text, information is contained in parentheses when it should just be expanded into its own sentence. This would help with the flow of information in the manuscript.

We appreciate this recommendation. We have expanded the introduction to address the different phases of digit regeneration, included information in parentheses, and included explanations of field-specific terms. We hope this improves the flow of information.

2. There is no mention in the text or methods as to the X/Y/Z resolution that is achieved via spatial transcriptomics. Some approximation should be made to the number of cells per barcoded spot.

Additional text has been added to clarify the spatial resolution of this technique (55 µm), as well the thickness of the section used for library construction (12 µm). To calculate the approximate number of cells present within each spatial spot, DAPI stained blastema at D10 following amputation were segmented into representative 55um circular ROIs and the number of nuclei determined. These estimations suggest each spatial spot likely corresponds to ~5-10 cells depending on the region of the blastema analyzed. This information has now been included in the manuscript.

3. The PCNA staining between young and aged digits should be quantified with consideration of total cell number and density. It is not overwhelmingly obvious that there is a difference.

To distinguish whether there is in fact increased percentage of proliferating cells and/or a lengthening of the aged digit cell cycle, the authors should consider a BrdU/EdU pulse-chase that can give kinetics of the cell cycle stages between young and aged regenerating digits.

We appreciate this recommendation and have quantified the PCNA staining (Figure 3D) with consideration of total cell number. This number represents the ratio of PCNA positive nuclei ± SD in aged blastemas relative to young, with values over 1 indicating increased proliferation in aged blastema. Additionally, we have included spatial transcriptomic analysis of genes linked to cell cycle activity, including cyclins and components of the anaphase promoting complex (Figure 3E) to further support that genes linked to entry into, and progression through, the cell cycle is elevated in the aged blastema.

4. Despite the impressive analysis presented, the manuscript would benefit from a discussion of the limitations of the spatial transcriptomics technique as the low read depth in single cell transcriptomes are known to limit the detection of important genes that have low expression values, and this could be further compounded by the slide-based barcoding technique. An example of such a gene would be PTPRC where the authors show in Figure 1A a lack of spatial detection of a gene carried by all immune cells. In conflict with this, the authors previous work had already identified a large number of PTPRC+ cells within the mouse digit at this time point (Development. 2017 Nov 1; 144(21): 3907-3916. doi: 10.1242/dev.150086).

Additional text has now been added to the discussion to clearly state the limitations associated with the low efficiency conversion of the transcriptome into cDNA for sequencing. This limitation has led us to primarily focus on overall comparisons between blastemas, the use of module scoring (which relies on a list of genes rather than an individual marker) and the use of SpatialTime which allows the concatenation of samples to display an ‘average’ blastema across space with increased statistical power.

5. As aged digits are slower to enter the anabolic phase of bone growth it is possible that the "spatial time" data obtained at a single timepoint may simply reflect that delay. The authors should acknowledge this limitation in interpretation. If there would be a delay in the progression of blastema and bone formation between young and aged mice, as shown in Figure 2A, would experimental samples at the same days post amputation be equivalent samples between aged and young digits? Perhaps the authors have done this analysis of heterochronic timepoint comparison, but this is not immediately apparent in the methods or text. The reviewers had a difficult time understanding how comparisons were made between young and aged cohorts.

To address differences in gene expression we analyze both the aged and young blastema 10 days after amputation. As stated by the reviewer, this approach may capture differences that are merely representative of a similar, but delayed, gene expression pattern in the aged sample relative to young controls. However, our studies in hypoxia and proliferation suggest that these gene expression differences are not simply a delay, but an actual shift in cell function that sustains throughout regeneration. We have added discussion of this limitation to the text of the manuscript.

6. One of the primary differences uncovered by the authors is the increase in the number of cells expressing the active cell cycle marker PCNA (although this is not quantified) as well as an increase in expression of S/G2/M phase genes in aged digits relative to young regenerating digits. The interpretation by the authors is that there is increased proliferation in young versus aged regenerating digits. This was interpreted to mean that a greater percentage of all cells are actively moving through the cell cycle as opposed to remaining quiescent. An alternative explanation is that there is a lengthening of the S/G2/M phases in aged cells resulting in a higher proportion of cells at a given static timepoint being in these phases. These two interpretations are not mutually exclusive, but the authors do not consider a lengthening of the cell cycle and the impact that might have on the overall process of regeneration. A slower cell cycle could potentially explain lags in blastema/bone formation. The conclusions regarding cell cycle therefore must be tempered and other possible conclusions acknowledged.

To address this concern, we looked at the expression of genes linked to cell cycle progression and cyclin destruction (Figure 3E). These new data suggests not only high levels of cyclin transcripts, but also several genes linked to cyclin destruction and cell cycle progression. While analysis of transcripts does not provide definitive evidence to distinguish these two possibilities, these new data supports active progression through the cell cycle over lengthening or delays of progression. Text has been added to the results to reflect these new data, as well as in the discussion to temper our original conclusion.

7. The authors postulate in the discussion that there could be an effect on cell dedifferentiation and redifferentiation that correlates to the lag in bone anabolism. "Most studies agree that formation of the blastema relies, at least in part, on the dedifferentiation of terminal cells within the remaining digit (6, 7, 28, 29). In contrast, proliferation is thought to play a relatively less critical contribution to blastema formation (6,7)." Can the authors easily support this claim and perhaps demonstrate deficiencies in aged digits by ratiometric comparison of spatial expression of genes that would relate to dedifferentiation (or lack of differentiation) relative to proliferation and cell cycle markers?

We agree with this comment that showing reduced dedifferentiation in aging mice would be ideally suited in our model. However, we were unable to find a clear transcriptional based approach to quantify dedifferentiation in young or aged blastemas. Many pathways, such as Notch and Pi3K-Akt pathways have been linked to dedifferentiation, but also in proliferation and differentiation. As such, we feel presenting these results as dedifferentiation markers may be misleading. Further, many additional markers previously proposed were not found to be expressed at meaningful levels within the blastema, likely owing to their low expression and the relatively low coverage of the whole transcriptome provided by spatial (this limitation is now stated in the discussion). In addition, we probed several genes linked to osteogenic differentiation. Seen in Author response image 1, our results show that while several genes were significantly downregulated (Runx2, Spp1), no strong evidence was obtained that osteogenesis was impaired in aged mice. This may be the result of the fact that our samples were obtained at day 10, while mineralization typically does not initiate until approximately day 14.

Author response image 1
No notable shift in osteogenic transcript expression is observed by spatial transcriptomics.

Relative expression of osteogenic genes in the blastema of young and aged mice 10 days post amputation. This osteoscore is comprised of the following genes: Alpl, Bglap, Bglap2, Col1a1, Col1a2, Dmp1, Ibsp, Mef2c, Postn, Runx2, Sp7, Sparc, Phex, Satb2, Pth1r, Ostn, Car3.

8. The reviewers wondered if authors had considered that the observed hypoxia might be influencing osteoblast differentiation, which is leading to a lag in bone anabolism? There is literature suggesting that hypoxia can influence differentiation of osteogenic cells, although studies seem mixed as to whether hypoxia is beneficial or suppresses differentiation (one example is PubMed ID: 16529738). This seems like a question that the authors can easily address using their spatial datasets and immunohistochemistry in addition to their already generated in vitro assays.

The hypoxia data from the Arnett Lab indeed provides an excellent foundation for what is the relationship between hypoxia and osteoblasts, and provided the motivation for some of our earlier studies in oxygen. Interestingly, we found that hypoxia delayed bone mineralization in the blastema (PMID: 24753124) during digit regeneration and noted this deviation from the Arnett lab findings. While spatial would be an excellent approach to further study this relationship, particularly in the blastema, our new data showing that OAA increases bone volume and thickness without decreasing hypoxia (comparably to the untreated mice) certainly adds a layer of complexity to the relationship between hypoxia and regeneration, at least in the digit model. Additionally, Figure R1 (above, #7 response) shows no strong evidence that osteogenesis was impaired in aged mice. As with dedifferentiation, this may be a result of the fact that our samples were obtained at day 10, prior to bone formation. We find our data intriguing and hope to further explore this relationship with multiple timepoints across regeneration in the future with more timepoints.

9. The authors claim that the increased energetic demand drives intracellular hypoxia and increased blood vessel formation providing both spatial transcriptomic data, "hypoxyprobe" data and blood vessel immunofluorescent staining to support these claims. This data would be more convincing and reliable if the relative levels were quantified between old and young digits and if it was made clear where the vascular changes occurred relative to the spatial transcriptomics.

We apologize for the lack of clarity when describing the data. Numbers below the spatial (Figure 5A) and hypoxyprobe images (Figure 5B) denote quantification of gene expression and average fluorescent intensity, respectively, within the blastema of aged mice, normalized to young (values greater than 0 indicating higher levels of transcripts/fluorescence in aged blastemas). Text has been added to clarify this quantification. We have also added additional analysis to show the expression level of our vessel scoring relative to SpatialTime (Figure 5E) to show that not only is vascularization increased in the aged blastema, but that these vessels are present throughout the blastema.

10. Figure 3B: Reviewers were confused about which regions of the spatial transcriptomics profile went into the identification of differentially expressed genes between young and aged mice. In Figure 1B, the authors have highlighted a specific portion of the image as the blastemal (pink color). In the text describing Figure 3, the authors state, "…Pathway analysis of DEGs showing differential expression in the young or old blastemal identified…(Figure 3A) " and then later state "…Metabolic analysis showed increased glycolysis and oxidative phosphorylation (OxPhos) in the blastema of aged mice… (Figure 3B)". It is confusing, therefore, that the images shown in Panel 3B show expression patterns in regions of the tissue that are not necessarily localized to the blastema. For example, OxPhos signatures for the young mice seem to be concentrated below the blastema in the digit itself; this raises the question of whether the authors segmented their gene lists to the blastema only while analyzing pathways, or whether the whole section was considered? Perhaps the authors could outline the region of the blastema with a dashed line, to emphasize where their pathway analyses were focused? If otherwise, please clarify. Notably, a similar concern could be raised about Figure 4B and 4C and should be likewise addressed in these areas.

We apologize for the confusion. All analyses, unless otherwise stated, were confined to the blastema as defined by Figure 1B. To better demonstrate this point, figures 3B, 4, 5, 7, S4 and S5 have been updated to reflect signal within the blastema only. Hopefully the reviewers will find these new representative images more precise and easier to interpret with regard to the corresponding quantitation.

11. The concern was raised that the data showing in Figure 4A to be somewhat unnecessary for the paper. While the Figure 3 cell energetics data are quite relevant as they are from young and aged blastema, the data in Figure 4A are only provided from an osteogenic cell line (which has limited relevance to this study overall). Presumably these data are provided to link the exciting cell energetics data in Figure 3 to the hypoxia data in Figure 4. The reviewers recommended that the data be removed from the paper, moved to a supplementary figure as they detract from (rather than add to) the nice narrative the authors have otherwise put together. The reviewers requested that these experiments be repeated with blastema tissue rather than osteogenic cells, which would provide a much better linkage between the story told in Figure 3 and the continued story in Figure 4. A similar concern could be raised about Figure 5A-B as was raised above for Figure 4. The authors have clearly demonstrated the ability to perform these assays with blastema tissue (Figure 3D and S4C) and the justification for moving to a cell line is thin and unsupported. The reviewers were concerned because the relative importance of the Seahorse assays must be assumed to translate from osteogenic cells to blastema, rather than being directly supported with experimental evidence.

Consistent with the reviewer’s request, we have removed the MC3T3 cell and now present Seahorse data from blastema cells treated with OAA (Figure 6A). In addition, to further characterize the effects of OAA in vivo, new spatial transcriptomic experiments were conducted (Figure 7) to better unravel the mechanism of action through which OAA rescues our impaired regeneration phenotype.

12. In Figure 3D, the authors state that OCR associated with ATP, maximal, and non-mitochondrial respiration were significantly higher in aged vs. young blastemas. However, these data are not shown anywhere, and statistical analyses are not presented in Panel D. Please add these graphs and the related statistical analyses.

We apologize for not including the statistical analyses relevant to the kinetic data. We have edited figures 4 and 6 to indicate specific time-point analysis and statistical significance for the third read after start and the third read after each stressor injection as our team has previously shown for OCR associated with basal, ATP, maximal, and non-mitochondrial respiration (PMID: 30953371).

All of the reviewers struggled with the presentation and interpretation of the results of the OAA study. Overall, there are so much confusion about what OAA is doing and what these data mean, that it became difficult for the reviewers to reconcile this OAA study with the hypotheses and premise of the rest of the paper. The following two comments address this part of the paper and are considered essential issues that must be resolved.

13. The choice of intervention (treatment with the anapleurotic metabolite oxaloacetate (OAA)) is hard to follow and poorly justified. The authors showed that the aged mice already had elevated OCR already vs. young mice (Figure 3B,C,D), as well as enhanced proliferation (Figure S4C). The data they provide in Figure 5 show that OAA increases OCR and proliferation. So, why is this drug a good choice when it produces the same phenotype already seen in the aged mice? This part of the story, accordingly, becomes hard to follow. As written, the rationale is unclear and should be clarified for the reader. This is particularly important because OAA did not rescue the hypoxia phenotype seen in the aged mice, despite providing a modest rescue of bone regeneration endpoints. While it is recognized that the authors likely used the MC3T3 osteoblast cell line to restrict analysis to bone producing cells to match the in vivo enhanced bone anabolism phenotype, performing the OAA treatments ex vivo, in primary culture of aged blastema tissue using procedures they have already documented (Figure 3) is a necessary addition for their conclusions to be supported.

In the manuscript, we have better explained and justified our choice of OAA for the reviewers. The reviewer is correct that our data propose an increase in both glycolytic and oxidative metabolism pathways in the aged blastema as well as enhanced proliferation, suggesting an increased energy demand. Because of these data, we hypothesized that unmet energy needs may underpin age-dependent metabolic increases. Therefore, we tested if supplementing these cells with a substrate to off-set these increased energy requirements would enhance regeneration. OAA is able to directly generate NADH in the mitochondria. As such, we use this intervention as a way to test our hypothesis. We have modified the text that OAA rescue is likely mediated through helping to meet the new metabolic requirements of aged cells, rather than attempting to rescue and revert these aged cells back to a “young” phenotype. Additionally, we have added Seahorse analysis of OAA treated blastemas from aged mice. Results from this analysis show that administration of OAA reduced the maximal respiration of aged blastemas (D10) supporting a role of OAA in reducing energy demand by aged blastema cells.

14. The failure of OAA to alter trabecular separation and a full rescue of the aged phenotype data broadly support this hypothesis although a discussion of alternative hypotheses are not discussed (i.e., cell specific effects of OAA on cell phenotype, alternative metabolic pathway activation with excess OAA, mineralization specific gene activation etc..?). It appears that OAA acted so in a way that is apparently largely outside of the pathways identified in the spatial transcriptomics analyses. The documentation of timeline for any changes in the initiation of bone anabolism (outgrowth) upon OAA treatment are strangely missing and would be important for understanding if metabolic energy deficit is responsible for the delay or if some other pathway is involved. The actual mechanism of action of OAA is never defined but the addition of OAA seems to have decoupled bone anabolism from hypoxia, so this may not be the case. Perhaps there are non-osteogenic cells that are primarily affected by hypoxia and these cells are not assayed in vitro? Do the authors have the spatial resolution to distinguish osteogenic cell gene expression from other non-osteogenic cell types?

We appreciate this comment from the reviewers. To better understand the mode of action of OAA, we have included new data on OAA-treated mice using spatial transcriptomics. These data point to a divergent effect of OAA on differentiated and undifferentiated cells. Additional analyses indicate OAA promotes proliferation and suggest this rescue may be due in part through re-activation of the WNT pathway in aged digits. We have expanded the discussion to include OAA’s ability to only partially resolve the phenotype. While OAA was not able to resolve the intracellular hypoxia seen in aged mice, this outcome is consistent with our belief that OAA helps meet the energetic needs of highly metabolically active aged cells, rather than reversing metabolism back to a young-like state. We have additionally added language to describe the lack of rescue in trabecular spacing, likely due to the inability to resolve the VEGF signaling at day 21. It is interesting to note that OAA enhanced bone deposition with minimal changes in overall hypoxia signaling (Figure 7D,E), supporting the reviewer’s thoughts that OAA treatment seems to affect osteogenesis through mechanisms outside of hypoxia.

We have also placed greater focus on the idea that while signaling pathways such as WNT are known to modulate cell metabolism, here we show the converse where cell metabolism is altering signaling pathways (Figure 7), a mechanism that warrants further investigation in future studies. As a result, we have garnered new insight into the OAA-driven mechanisms that played a role in this. While the resolution does not allow us to distinguish which individual cell types express elevated transcripts linked to hypoxia, our new data support upregulation of WNT signaling as a potential mechanism through which OAA may rescue aging-impaired regeneration.

15. There is something counterintuitive and unexplained in the authors data related to the aged regenerative phenotype. They first observe an increased proliferation in aged blastemas and increased expression of metabolic pathways. Postulating that cells are compensating for a metabolic deficit, they add an OxPhos metabolite OAA. in vitro, this metabolite increases proliferation and oxidative phosphorylation. It is not well explained what is being achieved through the rescue experiment relative to the baseline aged phenotype other than an increased mineralization. Is it because these assays were done with the MC3T3 cell line and not primary digit explants? This is not explained sufficiently, and the in vitro assays using cell lines may not be an adequate proxy for aged tissue cells.

We appreciate this comment from the reviewers. To help explain, we have included language to support our choice of OAA, including its participation in the malate-aspartate shuttle and its ability to directly generate NADH in the mitochondria, hypothesizing that OAA would help meet the unmet bioenergetic need of the aged blastema. We have also included the data that drove this choice, showing that administration of OAA reduced the maximal respiration of aged blastemas (D10) (Figure 6A). Our additional spatial transcriptomics data analyzing newly formed bone in aged mice treated with OAA show that OAA in fact increases bioenergetics and proliferation in newly regenerated bone (D21) (Figure 7). We have built in language to better illustrate that this finding does not necessarily run counter to our objectives in this study. Our metabolic approach does not seek to reverse the aged phenotype so much as to provide the building blocks that are needed to fuel the increased bioenergetics in the aged blastema. We have also included language in the discussion highlighting the critical signaling of OxPhos-mediated ROS in limb regeneration and emphasizing the possibility that increased bioenergetics may be a critical piece of limb regeneration.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that needs to be addressed, as outlined below:

In Figure 2, the Day O, 7, 10, 12, 14, 21, 28 42 MicroCT time course is used to show changes in age related delayed bone anabolism. The reviewer and editor found it unfortunate that a OAA treated vs vehicle control aged mouse time course was not supplied to demonstrate this enhanced anabolism. Measurement of bone volume, trabecular thickness, trabecular number, and trabecular separation, of regenerated control and OAA treated bone at a single Day 28 timepoint does not tell the scientific community where the OAA is acting between 10 and 28 DPA. If these MicroCT measurements can be added reasonably quickly and would certainly help with the interpretation of the OAA effects on partially rescuing the aged bone anabolic defect. For example, if the OAA only altered bone volume between D21- D28 or if the OAA was acting earlier to advance the start the anabolism (D12-D14). If these measures cannot be collected quickly as the samples are not in hand, please expand the discussion to address this limitation of the paper. This will need to be done quickly to ensure the timely publication of this paper.

We appreciate this observation. OAA is administered to the mice between day 10 and 28. We have included microCT analysis for trabecular volume, thickness and spacing for day 21 and show that while volume and thickness appear to be increasing, this is not yet statistically significant relevant to the control group. This data fortifies our findings in spatial transcriptomics (D21) that indeed OAA acts in advance to the start of anabolism.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there is one remaining issue that must be addressed, as outlined below:

The Age-related defect in P3 digit tip regeneration is most apparent in the timing of anabolism onset occurring between D12-D14 post-amputation. At D21 this manifests as a significant difference in Bone Volume. The Tb. thickness and Tb. spacing differences are not significant at D21 (in figure 2) and are only significant a D28. Taking this into consideration, the OAA treatment data presented (in figure 6) show no differences in bone volume, Tb. thickness and Tb. spacing at D21 and OAA effects are only really observed in volume and thickness at D28 with no differences in spacing. This could be interpreted as the effect of OAA not affecting the onset of anabolism at all, but rather providing support for late-stage remodeling phase. The authors MUST discuss this, as this interpretation contradicts the statement given by the authors that "This data fortifies our findings in spatial transcriptomics (D21) that indeed OAA acts in advance to the start of anabolism."

We apologize for misunderstanding the requested revisions in our last submission. We are in complete agreement with the editors that we cannot completely discount the role of OAA in late-stage remodeling. Since the mouse was dosed with OAA through the blastema, early bone, and late bone stages, the OAA-dependent effect could come from OAA influence at any or all of these stages. Additional work is required to determine the exact mechanism through which OAA promotes regeneration. While we cannot rule out the role of OAA on promoting osteoblast mineralization during the late-stage remodeling phase, our transcriptional changes observed at D21 prior to any significant changes in microCT parameters, suggest that OAA, at least in part, effects changes during the early stages of osteogenic differentiation. We hope we have made these considerations more clear in our discussion, while at the same time acknowledging the limitations of the studies presented.

https://doi.org/10.7554/eLife.71542.sa2

Article and author information

Author details

  1. Robert J Tower

    Department of Orthopaedics, Johns Hopkins University, Baltimore, United States
    Present address
    Department of Surgery, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review and editing
    For correspondence
    robert.tower@utsouthwestern.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5856-5758
  2. Emily Busse

    Department of Surgery, Tulane School of Medicine, New Orleans, United States
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Josue Jaramillo

    Department of Surgery, Tulane School of Medicine, New Orleans, United States
    Contribution
    Data curation, Formal analysis, Investigation, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Michelle Lacey

    Department of Mathematics, Tulane University, New Orleans, United States
    Contribution
    Data curation, Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Kevin Hoffseth

    Department of Biological & Agricultural Engineering, Louisiana State University, Baton Rouge, United States
    Contribution
    Formal analysis, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Anyonya R Guntur

    Center for Molecular Medicine, Maine Medical Center Research Institute, Scarborough, United States
    Contribution
    Conceptualization, Formal analysis, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Jennifer Simkin

    Department of Orthopaedic Surgery, Louisiana State University Health Sciences Center, New Orleans, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Mimi C Sammarco

    Department of Surgery, Tulane School of Medicine, New Orleans, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing
    For correspondence
    msammarc@tulane.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9491-5735

Funding

National Institute of General Medical Sciences (P20GM103629)

  • Mimi C Sammarco

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by a research grant from the National Institute of General Medical Sciences P20GM103629 (Sammarco).

Ethics

All experiments were performed in accordance with the standard operating procedures approved by the Institutional Animal Care and Use Committee of Tulane University Health Sciences Center.

Senior Editor

  1. Kathryn Song Eng Cheah, University of Hong Kong, Hong Kong

Reviewing Editor

  1. Cheryl Ackert-Bicknell, University of Colorado, United States

Publication history

  1. Received: June 22, 2021
  2. Accepted: May 18, 2022
  3. Version of Record published: May 26, 2022 (version 1)

Copyright

© 2022, Tower et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Robert J Tower
  2. Emily Busse
  3. Josue Jaramillo
  4. Michelle Lacey
  5. Kevin Hoffseth
  6. Anyonya R Guntur
  7. Jennifer Simkin
  8. Mimi C Sammarco
(2022)
Spatial transcriptomics reveals metabolic changes underly age-dependent declines in digit regeneration
eLife 11:e71542.
https://doi.org/10.7554/eLife.71542

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