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Editor’s summary

Some vertebrates can regenerate limbs, whereas others cannot. By comparing regenerating frog tadpoles and nonregenerating mouse embryonic limbs, Tsissios et al. found that species-specific oxygen sensing determines whether amputation triggers limb regeneration (see the Perspective by Paoli and Whited). Frog tadpoles exhibited reduced oxygen sensing associated with diminished regulation of hypoxia-inducible factor 1A (HIF1A), enabling robust regeneration by promoting biomechanical, epigenetic, and metabolic states conducive to tissue regrowth. By contrast, mouse limbs displayed heightened sensitivity to oxygen, which destabilizes HIF1A and prevents regeneration. Lowering environmental oxygen levels or stabilizing HIF1A allowed mouse limbs to initiate regeneration. Mui et al. used a mouse digit amputation model to investigate why some injuries regenerate while others scar. They found that the extracellular matrix, the network of proteins and sugars surrounding cells, was crucial to regeneration. Regenerating tissue is soft, fluid, and rich in hyaluronic acid, whereas nonregenerating tissue is stiff and collagen heavy. Depleting hyaluronic acid halted regeneration and triggered scarring, whereas stabilizing it improved bone regrowth. —Stella M. Hurtley

Structured Abstract

INTRODUCTION

Although regenerating complex tissues is a feature of many lower vertebrates, most mammals have traded this capacity for rapid wound healing and fibrotic scarring. In adult humans, this “regenerative window” is almost entirely closed, with the distal digit tip remaining as one of few tissues capable of complete multitissue regeneration that restores its original structure; more proximal amputations scar. Historically, research into this phenomenon has prioritized molecular signaling pathways and cellular origins. However, the role of extrinsic cues—specifically, the physical and mechanical signals from the surrounding microenvironment—has remained poorly understood in the context of whole-tissue regeneration.

RATIONALE

The extracellular matrix (ECM) is far more than a passive structural scaffold; it acts as a dynamic reservoir and hub for mechanochemical signals that regulate tissue function. Although it is well-established that ECM stiffness influences cell fate decisions, such as migration and proliferation during development, its role as a “switch” between regeneration and fibrosis has not been defined. We hypothesized that the specific composition and mechanical properties of the ECM, such as its viscoelasticity, determine whether an injury site initiates a regenerative program or a scar. By using the mouse digit tip as a comparative model for both successful regeneration and fibrotic failure, we sought to identify the precise ECM components that dictate these divergent outcomes.

RESULTS

Working in mouse digit amputation models, we found that the decisive factor between postinjury fibrosis and regeneration was the balance between the collagen matrix and hyaluronic acid (HA). Specifically, scarring regions were dominated by a stiff, collagen-heavy matrix. By contrast, regenerative regions were dominated by an HA-rich matrix, which creates a softer, more viscous (viscoelastic) tissue microenvironment. We discovered that the presence of HA actively inhibited collagen assembly, preventing the onset of stiff fibrosis. These substrate mechanics directly influenced cellular responsiveness to signaling pathways essential for bone regrowth. Cells in a soft, regenerative-mimicking environment showed robust activation of downstream effectors, whereas those in “stiff” environments exhibited a diminished response. Most notably, we showed that modulating a single ECM component, the cross-linker hyaluronan and proteoglycan link protein 1 (HAPLN1), to stabilize HA networks was sufficient to trigger bone regeneration in injuries that would otherwise result in permanent scarring.

CONCLUSION

Our findings identify ECM composition and mechanics as a primary mechanism governing mammalian regeneration. By demonstrating that the ECM acts as a mechanochemical signalling hub, we show that “retuning” the physical environment from a stiff, collagenous state to a soft, HA-rich state can unlock regenerative potential. This shift in perspective—from targeting cells to engineering the matrix—offers a promising new toolkit for regenerative medicine and the treatment of fibrotic disease. In demonstrating the role of the ECM in tissue function, this works supports ECM-focused approaches as a means to promote regenerative outcomes in mammalian tissue.
The ECM and tissue mechanics direct wound healing outcomes after digit amputations.
Mouse digit amputations provide side-by-side models of multitissue fibrosis and regeneration depending on the amputation level. In proximal amputations, collagenous ECM accumulation and stiff tissue mechanics drive scarring. Distal amputations regenerate through the actions of HA–rich, soft, and fluid ECM. Targeting the scarring ECM and its mechanical properties restores the digit. [Figure created with BioRender.com]

Abstract

Tissue regeneration is rare in mammals, but the digit tip can regrow after amputation, whereas injuries beyond the nail do not. How the microenvironment drives divergent outcomes remains unclear. In this study, we found that the extracellular matrix (ECM) and tissue mechanics govern the amputation response in mouse digits. Nonregenerative regions were stiffer and contained dense, organized collagen, whereas regenerative regions were soft and enriched in hyaluronic acid (HA). Depleting HA inhibited regeneration and promoted fibrosis, demonstrating that the HA-collagen balance shaped tissue mechanics and repair signaling. Stabilization of HA with hyaluronan and proteoglycan link protein 1 (HAPLN1) after nonregenerative amputations tuned ECM mechanics, reduced scarring, and enhanced bone repair. Thus, ECM composition and mechanics influence cell behavior and ECM-targeted strategies could help unlock mammalian regeneration.
Although normal wound healing relies upon the synthesis of extracellular matrix (ECM) as provisional scaffolding, excessive accumulation of ECM, known as fibrosis, disrupts tissue architecture and leads to substantial disease burden (1). Developing effective treatments to halt or reverse fibrosis in favor of regeneration hinges on our understanding of wound healing (2). To that end, amputation of mouse digits presents an opportunity to examine mammalian regeneration and fibrosis in parallel (3). Amputation of the tip of a digit’s third phalanx (P3) results in complete, multitissue restoration in rodents and humans (3). Central to this process is the blastema (4), a transient structure harboring heterogeneous progenitors (5, 6) that restores lost tissues (7). More proximal amputations, such as those that sever the second phalanx (P2), fail to regenerate (3). Instead, they exhibit minimal regrowth, and scar tissue overlies the injured bone stump (3). In this study, we investigated how the ECM may drive regeneration instead of fibrosis in the adult digit amputation model.
The ECM provides essential chemical and mechanical information within various biological contexts, including tissue morphogenesis (8), homeostasis (9), and aging (10). The ECM can impact regeneration (11) and fibrosis (12). Studies of scarless fetal cutaneous injuries implicated a protective role for the ECM component, hyaluronic acid (HA), a linear, nonsulfated polymer of N-acetyl-glucosamine and glucuronic acid (13). Indeed, fetal wounds heal with minimal collagen deposition while sustaining high levels of HA, whereas adult wounds show extensive deposition of collagen concurrent with rapid degradation of HA (1416). Following forelimb amputations in salamanders, HA-rich matrices that emerge during blastema formation instruct myotube migration and dedifferentiation (17), and in Xenopus, disruption of HA signaling impairs tail regeneration (18). How HA and ECM mechanics jointly govern regenerative versus fibrotic outcomes in adult mammals remains poorly understood.
In fibrotic wound repair, the ECM typically stiffens (19), which mechanically activates fibroblasts (20), the main cellular regulators of the ECM. Fibroblasts further remodel their extracellular environment (21) by way of changes to ECM composition (22) or organization (23), which alter overall tissue mechanics and establish an iterative feedback loop. The blastema’s matrix-derived mechanical cues regulate regenerative cell behavior, such as proliferation or differentiation (24, 25). Later, processes such as apoptosis (26) of fibroblasts restore tissue homeostasis. However, fibrosis progression bypasses these pathways, leading to cumulative disruption of tissue architecture. Given that fibroblasts are heterogeneous (27), some evidence suggests an intrinsic propensity for fibrosis by specific subtypes of fibroblasts (28), which may be mostly absent from regenerative tissues. It is also possible that fibrosing and regenerating ECM niches are distinct and enable opposing repair mechanisms.

The niche diverges during digit nonregeneration and regeneration

Fibrous tissue accumulates after nonregenerative amputations (3). We used second harmonic generation (SHG) microscopy to investigate the collagen matrix after digit amputations. Nonregenerative wounds contained dense, fibrosis-like collagen, which was largely absent from the blastema (Fig. 1A and fig. S1, A and B). We confirmed the collagen’s architectural features by transmission electron microscopy, which similarly showed more organized fiber bundles in nonregeneration (Fig. 1B and fig. S1C). To investigate what ECM factors underlie the difference in the collagen responses, we performed single-cell transcriptomic analysis of nonregenerative wounds and integrated these datasets with previously published blastema and nonregenerative datasets (5, 6) (Fig. 1C). To identify the primary cell type responsible for the ECM among the seven distinct populations detected (fig. S1, D and E), we assigned an ECM score to each cell based on its expression of core matrisome genes (29) (Fig. 1D). The Pdgfrα-expressing cluster showed the highest ECM activity, outpacing all other clusters (Fig. 1D). Gene set enrichment analysis revealed that this cluster up-regulated ECM-related pathways, including “Collagen Fibril Organization” and “Extracellular Matrix Organization” (fig. S1F). We deduced that fibroblasts constituted most of the Pdgfrα-expressing cluster and were the main cell type involved in establishing the extracellular milieu.
Fig. 1. The niche discriminates regeneration from fibrosis after digit tip amputation.
(A) (Left) SHG microscopy showing collagen fibers (white) in nonregenerative and regenerative digits 14 DPA. Dashed line, border of the phalanx bone. (Right) Quantification of collage fiber number in the nonregenerative (NR) wound versus regenerative (R) blastema. n = 5 mice per condition. Scale bars, 250 μm (top) and 100 μm (bottom). (B) Transmission electron microscopy (TEM) of NR and R wounds. Scale bars, 1 μm. (C) scRNA-seq strategy for characterizing NR wound and blastema cells 14 DPA. (D) UMAP of all digit cells after amputation scored by their expression of core matrisome genes. (E) (Left) Subtype analysis of Pdgfrα-expressing cells. (Right) Dot plot of top DEGs and pie charts showing their relative proportion in NR and blastema conditions. (F to H) Immunofluorescence showing TNX (white) (F), THBS4 (white) (G), and HABP (magenta) (H) 14 DPA. Green arrowheads, TNX and THSB4 in the blastema. [(F) and (G)] Scale bars, 250 and 50 μm (insets); (H) 200 μm (left) and 35 μm (right). (I) Quantification of total HA per miligram of NR versus R tissue. n = 3 replicates per condition (4 mice pooled per replicate). (J) Diagram of HA-HAPLN1-ACAN complex. (K and L) Immunofluorescence showing HAPLN1 (white) (K) and ACAN (white) (L) 14 DPA. Scale bars, 10 μm (K) and 50 μm (L). (M) (Left) AFM stiffness and fluidity maps of injured digits. Magenta arrowhead, NR wound; green, blastema. Scale bars, 500 μm. (Right) Quantification of the stiffness and fluidity in NR versus R. n = 4 mice per condition. Data are mean ± SEM or median and quartiles (AFM data). Data are representative of at least three independent experiments. Statistical significance was determined by two-tailed unpaired Student’s t test [(A) and (I)] or Mann-Whitney test (M). Additional details on statistics and reproducibility are in the materials and methods. See figs. S1 to S3 for additional supporting experiments. [Schematic in (J) created with BioRender.com]
Because fibroblasts are heterogeneous, we hypothesized that nonregenerative and regenerative digit ECMs diverge owing to underlying differences in cellular composition. Subsetting Pdgfrα-expressing cells revealed eight transcriptional signatures of which Fibroblast 1 and 2 subtypes were preferentially enriched in nonregeneration (Fig. 1E and fig. S1, G to I). With no notable transcriptional distinction in fibroblast subtypes based on wound conditions (fig. S1J), we hypothesized that the predominance and activity of Fibroblast 1 and 2 cells accounted for the fibrogenic response. All Pdgfrα-expressing clusters expressed fibrillar collagens to a similar degree (fig. S1K). However, we identified elevated collagen modeling factors in the nonregenerative wound, such as tenascin X (TNX) expressed by PDGFRα+CD34+CD31 Fibroblast 1 cells (Fig. 1, E and F, and fig. S2, A and B) and thrombospondin-4 (THBS4) expressed by SULF2+ Fibroblast 2 cells (Fig. 1, E and G, and fig. S2C). Of note, Fibroblast 3 cells, the major blastema subtype, highly expressed markers associated with limb regeneration, such as Msx1 and Mdk, but did not differentially express major collagen modeling genes (Fig. 1E). Taken together, Fibroblast 1 and 2 cells and their up-regulation of TNX and THBS4, respectively, were specific to nonregeneration, suggesting that collagen maintenance and/or modeling drive the fibrotic response.
Given that fibrosis is the antithesis of regeneration, we hypothesized that the blastema is enriched with cell subtypes responsible for synthesizing regeneration-specific ECM. Our cell subtype correlation and gene expression analysis demonstrated that Fibroblast 1 and 2 cells were most disparate from osteo-lineage (OL) cells (figs. S1J and S2D), which were approximately three times more prevalent in the blastema (fig. S1H). Furthermore, OL cells had the highest expression of collagens and proteoglycans (fig. S2E), which supports the role of OL cells as the blastema’s main ECM modulator. ECM-related gene ontology terms describing OL cells included “Glycosaminoglycan Binding,” “Proteoglycan Binding,” and “Hyaluronic Acid Binding,” suggesting an OL microenvironment in which noncollagenous matrix components, particularly hyaluronic acid (HA), appear prominently in the blastema (fig. S2F). OL cells highly expressed cartilage-associated genes Acan, Col2a1, and Hapln1 as well as related transcription factors Sox6 and Sox9 (fig. S2F). Indeed, at the tissue level, OL cells were more prevalent in regeneration and colocalized with accumulations of HA (fig. S2G), reminiscent of pericellular coats or a glycocalyx. We confirmed higher total HA in the blastema compared with that in the nonregenerative wound (Fig. 1I). Because HA binds proteoglycans through link proteins (30) (Fig. 1J), we assayed the distribution of hyaluronan and the proteoglycan link protein 1 (HAPLN1) and aggrecan (ACAN), finding a stronger presence of both in the blastema (Fig. 1, K and L). Thus, the blastema’s OL cells synthesize copious matrices composed of HA, HAPLN1, and ACAN in the absence of major collagen matrix, forming a distinct ECM niche in regeneration.
Given the importance of ECM composition, particularly collagen, in tissue mechanics (31), we used atomic force microscopy (AFM) and force-clamp force mapping to investigate the physical properties of wounded digits. We detected differences between nonregenerative wounds and the blastema, including greater stiffness and lower fluidity in the former (Fig. 1M and fig. S2H), suggesting that digits undergoing nonregenerative and regenerative responses exhibit distinct mechanical microenvironments. To explore whether the divergent niches are a product of preexisting differences between P2 and P3 cells or arise during wound healing, we performed single-cell analysis of uninjured P2 and P3 digits and integrated these datasets with previously published uninjured and injured datasets (fig. S3A). At baseline, anatomical location strongly segregated Pdgfrα-expressing cells (fig. S3, B and C). Upon injury, both nonregenerative and regenerative cells up-regulated matrisome genes and were transcriptionally more similar to each other than to their uninjured counterparts (fig. S3D). However, only regenerative digits up-regulated HA by 10 days postamputation (DPA), whereas HA remained scarce at earlier timepoints and in nonregenerative digits (fig. S3E). Thus, both intrinsic cellular properties and injury-induced ECM remodeling shape the distinct regenerative and nonregenerative niches.

Digit regeneration requires hyaluronic acid

Given the abundance of hyaluronic acid (HA) in the blastema, we asked whether digit regeneration depended upon HA. To answer this question, we degraded HA enzymatically by serial injections of hyaluronidase into the digit following amputations of the tip of the third phalanx (P3) (Fig. 2A). At 14 DPA, digits were markedly decreased in length, and the area was often smaller (Fig. 2A and fig. S4A). RUNX2+ OL cells and pericellular HA were also diminished after hyaluronidase treatment (Fig. 2B), corroborating the relationship between OL cells and HA. For continuous HA depletion, we incorporated 4-methylumbelliferone (4-MU) into the mouse diet to reduce levels of UDP-glucuronic acid, one of two substrates required for HA synthesis (32) (Fig. 2C). With reduction of extracellular HA (fig. S4, B and C), 4-MU digits at 14 DPA were reduced in length and area (fig. S4, D and E). By 28 DPA, control digits regenerated, but 4-MU digits remained significantly smaller (Fig. 2D and fig. S4E). We then examined the state of the blastema in 4-MU digits, hypothesizing that the digits’ defects were a result of compromised blastema formation. With 4-MU, digits globally down-regulated their expression of the blastema marker arylsulfatase I (ARSI) (6) (Fig. 2E). Another feature of the blastema is the prevalence of proliferative cells (33). The blastema contained high numbers of Ki67+ proliferating cells, particularly OL cells, which were substantially reduced with 4-MU treatment (Fig. 2F and fig. S4F). The fate of the blastema involves differentiation into mature tissue (7). Using microcomputed tomography, we showed that 14-DPA regenerating digits exhibited histolysis—the expulsion of the distal bone fragment resulting in bone shortening—and new bone formation (fig. S4, G and I). 4-MU delayed histolysis-induced bone shortening, and we detected no new bone (fig. S4, G and I). By 28 DPA, when regeneration was expected to be largely complete, 4-MU digit bone volume, surface area, and length remained diminished compared with those of controls (Fig. 2G and fig. S4, H and I). The regenerated digit contained an abundant population of SP7+ osteoblasts, whereas 4-MU reduced osteoblast numbers (Fig. 2H and fig. S4J). Additionally, fibrosis-like collagen was deposited in 4-MU digits, indicating a switch to a more nonregenerative ECM (Fig. 2H and fig. S4J). Thus, depletion of HA interfered with digit restoration after distal P3 amputation, likely owing to a failure of blastema formation and differentiation, which suggests an important role for HA in digit tip regeneration.
Fig. 2. HA is necessary for successful digit regeneration.
(A) (Left) Strategy for degrading HA using hyaluronidase after distal tip (P3) amputations. (Right) Gross images of control or hyaluronidase-treated digits 14 DPA, with the nail outlined in green. Scale bars, 100 μm. (B) (Left) Immunofluorescence showing HABP (magenta) and RUNX2 (white) at 14 DPA in control and hyaluronidase-treated digits. Scale bars, 50 μm. (Right) Quantification of the percentage of RUNX2+ cells, HABP signal intensity, and RUNX2 signal intensity after hyaluronidase treatment compared with those of the controls. n = 3 mice per condition. (C) Strategy for continuously depleting HA using 4-MU after distal P3 amputations. (D) Gross images of a control and 4-MU digit 28 DPA, with the nail outlined in black. Scale bars, 100 μm. (E and F) Immunofluorescence showing ARSI (white) (E) and Ki67 (magenta) (F) 14 DPA. The dashed line, the border of the phalanx bone. Scale bars, 250 and 50 μm (insets). (Right) Quantification of the number of Ki67+ and RUNX2+Ki67+ cells with 4-MU treatment versus control. n = 3 mice per condition. (G) Microcomputed tomography (μCT) analysis of skeletal morphologies of control and 4-MU digits 28 DPA. Yellow arrowhead, bone elongation; green arrowhead, bone stump. Scale bars, 500 μm. (H) Immunofluorescence showing SP7 (magenta) and SHG microscopy showing collagen fibers (white) in control and 4-MU digits 28 DPA. Orange arrowheads, SP7+ nuclei. Data are mean ± SEM and are representative of at least three independent experiments. Statistical significance was determined by two-tailed unpaired Student’s t test [(B) and (F)]. Additional details on statistics and reproducibility are in the materials and methods. See fig. S4 for additional supporting experiments. A.U., arbitrary units; FOV, field of view.

HA-collagen balance determines digit repair trajectory

Given that HA and collagen were inversely correlated in wounded digits and that HA depletion elicited a fibrosis-like ECM, we asked whether HA matrices abrogate fibrotic collagen assembly. We first compared the collagen matrix of hyaluronidase-treated versus control digits. Hyaluronidase treatment increased collagen content as well as fibrotic architectural features (Fig. 3A and fig. S5A). We observed similar attributes after HA depletion using 4-MU (Fig. 3B and fig. S5B). Overall, disruption of HA produced fibrotic collagen matrix. Because collagen content impacts tissue mechanics, we hypothesized that the fibrotic collagen emerging with HA depletion would alter the tissue’s mechanical properties. To test this idea, we perturbed HA using 4-MU prior to distal third phalanx (P3) amputations and performed stiffness and fluidity measurements. 4-MU–treated digits were stiffer and less fluid compared with controls (Fig. 3C and fig. S5C). Thus, the viscoelasticity of 4-MU–treated digits closely resembled that of nonregenerating wounds (fig. S5C), highlighting how HA-collagen interactions impact the mechanical microenvironment.
Fig. 3. The collagen-HA balance determines the switch between fibrosis and regeneration.
(A and B) (Left) Strategy to deplete HA using hyaluronidase (A) or 4-MU (B) after distal tip (P3) amputations. (Middle) SHG microscopy of collagen fibers (white) 14 dDPA, with quantification (right). Dashed line, border of the phalanx bone. Scale bars, 100 μm. n = 3 mice per condition. (C) (Left) AFM stiffness and fluidity maps of 4-MU digits 14 DPA, with quantification (right). Scale bars, 500 μm. n = 4 mice per condition. (D) (Top) UMAP of Pdgfrα-expressing cells from the 4-MU and control datasets. Prior 14-DPA blastema datasets were also integrated only to strengthen dimensionality reduction plotting; all downstream analyses were performed comparing 4-MU versus control samples only. (Bottom) Distinct subtypes detected among Pdgfrα-expressing cells. (Right) Top DEGs for each Pdgfrα-expressing subtype, along with pie charts representing their relative proportion among all cells. Fibro 3, Fibroblast 3 cells. (E) Volcano plot of DEGs in Pdgfrα-expressing cells with 4-MU treatment compared with those in controls. (F) (Left) Immunofluorescence showing pSMAD1/5/8 in 4-MU digits compared with that of controls 14 and 28 DPA, with quantification (right). Scale bars, 100 and 10 μm (magnified views). Ctrl, control. n = 3 mice per condition. Data are mean ± SEM or median and quartiles (AFM data). Data are representative of at least three independent experiments. Statistical significance was determined by two-tailed unpaired Student’s t tests [(A) and (B)], a Mann-Whitney test (C), the two-part generalized linear model MAST with a joint test summing likelihood ratio or Wald test statistics (E), or two-way ANOVA with Tukey’s multiple comparisons test (F). Additional details on statistics and reproducibility are in the materials and methods. See fig. S5 for additional supporting experiments.
To probe how tissue mechanics arising from HA depletion affected cell behavior, we performed single-cell transcriptomic analysis of distal P3–amputated digits after 4-MU treatment versus control. Unsupervised clustering and integration of the datasets resulted in nine major cell types (fig. S5D). Hyaluronic acid depletion demonstrably reduced the relative proportion of Pdgfrα-expressing cells (fig. S5D), indicating a suppression of their typical injury-induced expansion. Analyzing the Pdgfrα-expressing cells, we showed a diminished OL proportion after 4-MU treatment (Fig. 3D and fig. S5E). Comparing all Pdgfrα-expressing cells between conditions underscored the down-regulation of OL-related genes Ibsp, Alpl, and Sox6 and its target gene Acan (Fig. 3E). These results highlighted a disturbance in osteogenic differentiation and the down-regulation of cartilage-associated matrix elements. Notably, 4-MU–treated Pdgfra-expressing cells up-regulated their expression of S100a4 and Tnc (Fig. 3E), both of which are associated with increasing ECM stiffness (12, 34). These genes can also be expressed by tenocytes; however, the absence of a tenocyte-associated gene signature with 4-MU suggested that this transcriptional shift does not reflect tenogenic differentiation (fig. S5F). Indeed, the few Pdgfra-expressing cells that remained also up-regulated blastema-associated genes, but their limited abundance indicates a failure of expansion rather than loss of blastema transcriptional identity (fig. S5G).
Given that HA depletion resulted in skeletal defects and disruption of osteogenic differentiation following distal P3 amputations, we explored the relationship between HA, tissue mechanics, and the bone morphogenic protein (BMP) pathway. Unlike previous reports linking HA with Wnt signaling (18), we did not observe changes in Wnt activation in the blastema after HA depletion (fig. S5H). Consequently, we assayed the primary effector of BMP signaling, pSMAD1/5/8, within the tissue after HA depletion with 4-MU. pSMAD1/5/8 levels were elevated and increased as regeneration progressed in control digits (Fig. 3F). By contrast, 4-MU persistently suppressed pSMAD1/5/8 levels (Fig. 3F). Thus, OL cells not only help establish the ECM environment in regeneration but are also acutely sensitive to HA and changes in tissue mechanics.

Substrate stiffness mediates cell-ECM feedback

To dissect the interplay of mechanical cues and soluble signaling, we created stiff and soft StemBond hydrogels (35) to model the mechanical microenvironment of the nonregenerative wound and blastema, respectively (Fig. 4A). Simultaneously, we administered blastema-associated growth factors to examine their combined effects on cellular responses (Fig. 4A). Back skin fibroblasts on soft substrates exhibited enhanced nuclear pSMAD1/5/8 signal after BMP-7 treatment (Fig. 4A and fig. S6A). Immunoblotting further corroborated these stiffness-dependent patterns in pSMAD1/5/8 levels (fig. S6B). We also profiled changes in the gene expression of Inhibitors of DNA Binding/Differentiation (Id), to assess early downstream effectors of pSMADs. Id1 expression responded to substrate stiffness, with fibroblasts on soft substrates up-regulating the highest levels of Id1 (Fig. 4B and fig. S6C). We found similar trends among freshly isolated cells from the second and third phalanx, which up-regulated their expression of Id1 most when stimulated under soft conditions (Fig. 4C). Given no baseline differences in the ECM in our hydrogel experiments (fig. S6, D and E), our data indicated that substrate stiffness tunes responsiveness to BMP ligands.
Fig. 4. Substrate stiffness regulates feedback between ECM synthesis and response to injury signals.
(A) (Left) Strategy to test the effects of substrate stiffness on BMP signaling using StemBond hydrogels. Back skin dermal fibroblasts were used in all hydrogel experiments unless indicated otherwise. Stiff, 50 kPa; soft, 0.7 kPa. (Middle) Immunofluorescence showing pSMAD1/5/8 (white) and F-actin (magenta) in fibroblasts cultured on stiff and soft hydrogels. Arrowheads, nuclear pSMAD1/5/8. Scale bars, 20 μm. (Right) Quantification of pSMAD1/5/8 fluorescence intensity. Ctrl, control. n = 3 independent experiments. (B) qPCR analysis of Id1 gene expression. n = 3 independent experiments. (C) qPCR analysis of Id1 gene expression in P2 or P3 cells. n = 9 independent experiments, P2 cells; 7 independent experiments, P3 cells. (D) (Left) Strategy to test the effects of substrate stiffness on PDGF-BB signaling. (Middle) qPCR analysis of Hapln1 gene expression. n = 3 independent experiments. (Right) Immunofluorescence showing HAPLN1 (green), F-actin (yellow), and HABP (magenta) in cultured fibroblasts. Scale bars, 25 and 10 μm (magnified views). (E) Quantification of HABP area. n = 3 independent experiments. (F) (Left) Strategy to test the effects of substrate stiffness on collagen fibrillogenesis using TGF-β1 and ascorbic acid. (Right) Immunofluorescence of COLI (magenta), F-actin (yellow), and THBS4 (magenta). Arrowheads, regions of COLI and THBS4. Scale bars, 50 μm. (G) Working model of the feedback between ECM, tissue mechanics, and Pdgfrα-expressing cells. Data are mean ± SEM and are representative of at least three independent experiments. For all gene expression data, plots are shown as log2FC (FC, fold change), with statistical analyses performed on −ΔΔCT values. Statistical significance was determined by two-way [(A), (B), (D), and (E)] or three-way (C) ANOVA with Tukey’s multiple comparisons test. Additional details on statistics and reproducibility are in the materials and methods. See fig. S6 for additional supporting experiments. [Schematics in (A), (D), (F), and (G) created with BioRender.com]
Next, we used platelet-derived growth factor BB (PDGF-BB), a blastema injury signal (36) with potent HA-synthesizing activity (37), to test how substrate stiffness influences HA (Fig. 4D). In addition to Hapln1 and Acan, we considered the three isoforms of hyaluronan synthases. Substrate stiffness had little effect on expression levels of Has1-3, and, under these experimental conditions, Acan was not responsive to either PDGF-BB or substrate stiffness (fig. S6, F to H). By contrast, fibroblasts cultured on soft hydrogels with PDGF-BB treatment significantly up-regulated Hapln1 (Fig. 4D). Reasoning that HAPLN1 confers structural integrity to the HA matrix, we asked whether fibroblasts assemble pericellular HA matrix more robustly under soft conditions. Following PDGF-BB treatment, fibroblasts produced more aggregates of HA and HAPLN1 at the cell surface on soft versus stiff substrates (Fig. 4, D and E, and fig. S6I). Because soft mechanical cues enhance proregenerative ECM synthesis, we investigated whether a stiff environment augments nonregenerative ECM and used transforming growth factor (TGF)–β1 and ascorbic acid to induce collagen synthesis (38) (Fig. 4F). After treatment, fibroblasts on stiff substrates produced fibrillar collagen matrix that largely colocalized with THBS4 (Fig. 4F and fig. S6J), both of which typified nonregenerative healing (Fig. 1, A, B, and G). Conversely, in soft conditions that mimic the blastema, collagen I–THBS4 network formation remained undetectable, irrespective of ascorbic acid and TGF-β1 addition (Fig. 4F and fig. S6J). Thus, the softness imbued by HA may initiate positive feedback mechanisms to enhance the production of HA matrix in contrast to stiff substrate-induced collagen fibrillogenesis (Fig. 4G).

HAPLN1 facilitates repair of nonregenerative amputations

Next, we investigated whether HAPLN1 promotes pericellular HA and impacts the collagen matrix. Not only was HAPLN1 abundant alongside HA in the blastema (Fig. 1, H and K), but blastema-like substrate softness also propagated the synthesis of both (Fig. 4, D and E, and fig. S6I). Analysis of uninjured digits showed that the third phalanx (P3) region contained wide swaths of HA- and HAPLN1-rich regions, which appeared only in specific areas of the second phalanx (P2), such as the periosteum (fig. S7A). We corroborated regional differences in HAPLN1 by quantitative polymerase chain reaction (qPCR) analysis of P2 and P3 cells (fig. S7B). These findings showcased strong associations between HAPLN1 and HA in injury and homeostasis, leading us to hypothesize that HAPLN1 is a key factor mediating the presence of HA. To test this hypothesis, we overexpressed Hapln1 (Hapln1OE) or a scrambled sequence as the control (mCherry Control) in back skin fibroblasts (fig. S7, C to E). We cultured transduced cells on stiff hydrogels to simulate the fibrotic mechanical environment, with or without the presence of high–molecular weight HA to encourage pericellular HA accumulation (Fig. 5A and fig. S7, F and G). Despite HA supplementation, mCherry Control fibroblasts exhibited sparse and limited distribution of HA across their cell surface (Fig. 5A and fig. S7, F and G). Meanwhile, Hapln1OE fibroblasts synthesized large quantities of HAPLN1 that corresponded with a significant increase in pericellular HA (Fig. 5A and fig. S7, F and G). Next, we tested whether stabilizing HA matrix using HAPLN1 could inhibit stiffness-induced collagen fibrillogenesis. To mimic the fibrogenic milieu, we cultured transduced fibroblasts on stiff hydrogels and used ascorbic acid to induce collagen fibrillogenesis (Fig. 5B). Hapln1OE fibroblasts accumulated robust pericellular HA that coincided with fewer and shorter collagen fibrils compared with that of mCherry Control fibroblasts (Fig. 5B and fig. S7H). Thus, HAPLN1 increased the deposition of HA and restrained collagen fibrillogenesis, signifying HAPLN1’s potential to promote a regenerative ECM in a nonregenerative context.
Fig. 5. HA accumulation facilitates digit repair after nonregenerative amputations.
(A) (Left) Strategy to test Hapln1 overexpression (Hapln1OE) in a stiff environment. (Right) HAPLN1 immunoblot of mCherry Control and Hapln1OE back skin dermal fibroblasts, with quantification of HABP coverage per FOV. n = 3 independent experiments. (B) (Left) Strategy to assess Hapln1OE effects on collagen fibrillogenesis. (Middle) Immunofluorescence of HABP (magenta) and COLI (white) with collagen fiber quantification (right). Arrowheads, COLI and HABP staining. Scale bars, 50 μm. n = 3 independent experiments. (C) (Top) Strategy to induce restorative repair of nonregenerative digits. (D) (Left) Microcomputed tomography of digits 28 DPA with blinded segmentation (red, middle) and quantification (right). Scale bars, 1 mm (left), 200 μm (middle), and 250 μm (right). n = 18 digits per condition. (E) (Left) SHG microscopy imaging of collagen fibers (white) in digits 28 DPA with quantification (right). Dashed lines, preexisting cortical bone (green) or new bone formation (magenta). Scale bars, 200 μm. n = 3 mice per condition. (F) (Left) AFM stiffness and fluidity maps of Hapln1OE versus mCherry Control digits 14 DPA with quantification (right). Dashed lines, P2 bone. Scale bars, 500 μm. n = 4 mice per condition. (G) (Left) Immunofluorescence of digits 28 DPA stained for RUNX2 (yellow) and K14 (magenta). Dashed lines, P1 (cyan) and P2 (green). Red line, plane of amputation. Scale bars, 250 μm (left) and 150 μm (right). (Right) Quantification of RUNX2+ cells and pSMAD/1/5/8 fluorescence intensity. n = 3 mice per condition. Data are mean ± SEM or median and quartiles (AFM data) and representative of at least three independent experiments. Statistical significance was determined by one-way ANOVA with Tukey’s multiple comparisons test (A), two-tailed unpaired Student’s t test [(B), (D), (E), and (G)], or Mann-Whitney test (F). Additional details on statistics and reproducibility are shown in the materials and methods. See figs. S7 to S10 for additional supporting experiments. [Schematics in (A) and (B) created with BioRender.com]
Because regeneration requires HA, and HAPLN1 stabilizes the HA matrix, we hypothesized that overexpressing Hapln1 in nonregenerating wounds would initiate restorative repair. To test this hypothesis, we performed nonregenerative amputations on adult immunocompromised mice and transplanted mCherry Control or Hapln1OE fibroblasts into the digit tip (Fig. 5C and fig. S8A). Notably, we observed one incidence of nail regrowth at 28 DPA with Hapln1OE fibroblast transplantation (fig. S8A), which does not occur under nonregenerative conditions. Using microcomputed tomography, we showed enhanced bone repair in digits injected with Hapln1OE fibroblasts, including greater bone elongation and growth (Fig. 5D and fig. S8B). Regenerating bone tissue in Hapln1OE digits extended beyond the initial amputation plane, in contrast to minimal new tissue formation in controls (Fig. 5D and fig. S8B). To confirm that these regenerative effects were independent of cell transplantation, we injected Hapln1-overexpressing lentivirus into the digit, which similarly resulted in bone elongation and increased callus size (fig. S9A). Hapln1OE fibroblasts decreased the presence of fibrosis-like collagen structures (Fig. 5E and fig. S8, C to E) and promoted HA and HAPLN1 accumulation (fig. S8, F and G). Because HA and collagen influence tissue mechanics, we hypothesized that HAPLN1-driven ECM remodeling promotes regeneration by altering the mechanical properties of the microenvironment. Using AFM, we found a decrease in stiffness and an increase in fluidity at the amputation site of Hapln1OE digits compared with those of controls (Fig. 5F and fig. S8H).
Lastly, we explored how ECM modulation by HAPLN1 affects cellular activity. Because Sox9 was elevated in the blastema (fig. S2F), we assayed SOX9 distribution as a marker for early chondro-osteogenic commitment. Digits with mCherry Control fibroblasts exhibited low SOX9+ cell numbers, whereas clusters of SOX9+ cells localized distally from the plane of amputation in digits with Hapln1OE fibroblasts (fig. S8I). We also assayed for more differentiated RUNX2+ OL cells. In digits transplanted with mCherry Control fibroblasts, we observed RUNX2+ OL cells only immediately adjacent to the preexisting cortical bone at 14 and 28 DPA (Fig. 5G and fig. S8, J and K). However, Hapln1OE fibroblasts increased the presence and distal localization of RUNX2+ OL cells at 28 DPA, consistent with bone regrowth (Fig. 5G and fig. S8K). Furthermore, given that soft substrates enhanced responsiveness to BMP signaling in vitro, we assayed pSMAD/1/5/8 levels and found elevated signal with Hapln1OE compared with that with controls (Fig. 5G and fig. S8L). Thus, HAPLN1 triggers restorative repair after nonregenerative amputations, likely by mediating the HA-collagen matrices and softening the tissue microenvironment.
To explore whether the HA-collagen dichotomy emerges in other regenerative settings, we examined transcriptomic datasets spanning three models of regeneration: ear pinna injury, myocardial infarction, and fracture healing (fig. S10, A to D). Although the specific HA-associated enzymes, cross-linkers, and receptors varied between models, the regenerative transcriptional programs converged on the up-regulation of HA-related genes and the down-regulation of fibrillar collagen and cross-linking genes that typify fibrogenesis. Thus, different organs may synthesize and remodel the ECM through tissue-specific mechanisms; however, an HA and collagen dichotomy is a shared feature of regenerative versus fibrotic healing.

Discussion

Our understanding of fibrotic wound healing has grown rapidly in recent years (2). However, factors that orchestrate complex tissue regeneration in vertebrates remain elusive. Part of the challenge lies in the few instances of accessible, robust tissue regeneration models, particularly in adult mammals. By investigating wound healing after adult mouse digit amputations, we examined the relationship between the tissue’s physical environment and regenerative versus fibrotic outcomes. We identified the ECM and resultant tissue mechanics as key properties distinguishing nonregenerative from regenerative responses. Notably, we found large amounts of HA in the blastema, which was necessary for regeneration; its absence triggered a switch toward fibrotic ECM and tissue mechanics in the amputated digit. Substrate mechanics, in turn, impacted cellular responses to injury signals, which we propose reinforce wound healing trajectories through feedback loops. Our experiments also demonstrated partial digit restoration after nonregenerative amputations by targeting the ECM, supporting these conclusions.
Fibroblasts are the principal architects of the ECM and wound healing (20). For example, a distinct fibroblast arises in infarcted hearts, and blocking their activation by immune cells reduces scar formation (39). Similarly, mechanical activation of Engrailed-1 in specific fibroblasts drives scar formation in skin wounds, and inhibiting this activation promotes regeneration (28, 40). Our study builds on this body of work by examining both the cells and physical niche that distinguish nonregenerative wounds from the regenerative blastema. In the former condition, two distinct Pdgfrα-expressing fibroblasts predominated, promoting a stiff collagen matrix through the production of collagen-modeling factors. By contrast, OL cells contributed to the blastema’s soft, more fluid extracellular milieu by synthesizing HA and HA-associated components, such as ACAN and HAPLN1. These findings highlight the divergence of both cell and extracellular factors during regenerative and nonregenerative processes as well as the strong interdependence between the two. Moreover, these observations underscore the importance of considering the role of the physical environment as well as fibroblast diversity in determining wound healing outcomes.
By targeting HA through perturbation and rescue experiments, our study provides evidence that HA is essential for regeneration. We posit that HA facilitates wound repair by modulating collagen fibrillogenesis and tissue mechanics. Although this phenomenon has been documented in mammalian fetal wounds (1416, 41) and in other species (25), our findings in adult mice show that HA-collagen interdependency is conserved in injuries across developmental stages and may extend to other organ systems. However, the exact mechanism by which HA mediates collagen assembly remains to be elucidated. One possibility is that bulky pericellular HA matrices regulate integrin accessibility to collagen (42). Although not directly examined in our study, integrin-mediated binding to collagen is a major way by which cells sense and transmit mechanical forces (43) and regulate the presentation of soluble ligands (44); together, these mechanisms govern collagen remodeling. Simultaneously, HA matrices may physically interfere with the self-assembly and organization of collagen polymers (45), possibly through steric hindrance or by limiting diffusion of procollagen.
Lastly, we showed that the ECM and substrate mechanics regulate both fibrosis and regeneration. Prior studies have also related extrinsic physical forces to cellular rejuvenation and regeneration. For example, brain stiffening with age impairs oligodendrocyte precursor cell function (46). Inhibiting the mechanosensitive ion channel Piezo1 mitigated age-related changes in these cells, enhancing their regenerative capacity after demyelinating injuries (46). Similarly, soft substrates enhanced regeneration-associated machinery. To modify the nonregenerative wound’s extracellular environment to resemble that of the blastema, we used HAPLN1 to promote HA deposition, reduce scarring, and enhance skeletal repair. Given that link proteins stabilize aggregates of HA and proteoglycans (30, 47), we propose that HAPLN1 protects HA against fragmentation, which is commonly associated with inflammation and fibrosis (48, 49). Thus, higher-order structural organization of HA matrix is an important player in tissue mechanics and biological function. Previous studies have emphasized biochemical cues or nutritional supplements (5052) in tissue regeneration. Our data show that modulating the physical microenvironment through HA stability and tissue mechanics can also improve regenerative outcomes. Thus, combining physical and biochemical interventions may be an effective approach to promote tissue regeneration in mammals.

Materials and methods

Mice

C57BL/6, NOD SCID (6-8 weeks old) and CD1 (postnatal day 2) mice (strain codes: 027, 394, and 022, respectively) were obtained from Charles River Laboratories. The animals were maintained in a standard facility for rodents at the University of Cambridge. They were housed in groups of up to five per cage with controlled temperature, humidity, and 12 h light/dark cycles. Enrichment items provided in the cages included red tubes, shredded paper, and a loft. Mice were fed autoclaved maintenance diet (SAFE R105) and water ad libitum. Animals were randomly divided into control and treatment groups, and both male and female mice were used, and data from both sexes were pooled for final analyses. Strategies were implemented to minimize potential confounders including randomizing the order of treatments, cage location and researchers handling the animals. This research was regulated under the Animals (Scientific Procedures) Act 1986 Amendment Regulations 2012 following ethical review by the University of Cambridge Animal Welfare and Ethical Review Body (AWERB). All procedures were conducted strictly following the relevant protocols defined in Home Office Project License PP2274970 and reported according to the ARRIVE guidelines.

Digit amputation model

Digit amputations were performed on 6- to 8-week-old mice as described previously (6, 53). Briefly, mice were orally administered meloxicam (0.5 mg/ml) for analgesia and anesthetized with isoflurane. Then, using sterile scalpels, the distal one-third of terminal phalanges 2, 3, and 4 of the hindlimb were amputated to induce a regenerative response. For non-regenerative amputations, the distal one-third of second phalanges were amputated.

Second harmonic generation microscopy and collagen fiber analysis

SHG microscopy with the Zeiss LSM 880 NLO and excitation wavelength of 920 nm was used to visualize collagen fibers in post-amputated digits. The collagen fibers were segmented in batch using curvelet transform (CT)–fiber extraction (FIRE) (54). Briefly, CT denoises the SHG images and enhances the edges of collagen fibers. A FIRE algorithm segments collagen fibres by finding fiber centres through a distance transform and ridge detection, linking nearby fragments to construct full, branching fibers. Then, the width, length, number and angle of these segmented fibers were quantified. To quantify the number of fibers oriented around 30°, a custom R script was written. The most common fiber angle for a sample was designated as 0°, around which all other fiber angles were oriented. The total number of fibers oriented ± 30° were quantified and averaged for analysis between conditions. Two sections per digit were analyzed, and these counts were averaged and considered as a single biological replicate. A minimum of three separate animals were analyzed per condition, with each digit sourced from a separate animal. For experiments involving direct injections, each digit was considered a biological replicate. For collagen analysis in hydrogel experiments, a minimum of three independent experiments were performed, and each hydrogel was imaged at 5 different locations, which were averaged as a single biological replicate. For collagen analysis in Hapln1OE experiments, three digits per condition were randomly selected.

Transmission electron microscopy

Digits were fixed in 2% paraformaldehyde (PFA) and 2.5% glutaraldehyde in 0.1 M cacodylate buffer (CaCo), pH = 7.2 at 37°C for 1 h and stored in fixative at 4°C. Vibratome sections (300 μm) of the fixed digits were washed twice in 0.1 M CaCo for 30 min, post-fixed with 1% OsO4 in 0.1 M CaCo for 1 h at 4°C and washed twice in 0.05 M sodium maleate buffer (SoMa), pH = 5.2 for 30 min. Samples were en bloc stained with 0.5% uranyl acetate in 0.05 M SoMa for 1 h at 4°C and again washed twice with 0.05 M SoMa for 30 min. Samples were dehydrated in increasing ethanol concentrations (50%, 70%, 90%, 2x 100%, 10 min each) and infused with propylene oxide (2x 20 min), 50/50 mix of epoxy resin (TAAB, overnight) and 100% resin (2 days). Samples were positioned in flat bottom beem capsules and cured for 48 h at 60°C. Ultrathin sections were cut at 60 nm (Leica EM UC7 ultramicrotome, DiATOME Ultra 35° knife), transferred to formvar coated copper grids and post-stained for 1 min with UA-Zero (Agar Scientific) and 30 sec with lead citrate. Collagen rich regions were identified in 3x3 overview scans at 700x and then 5-7 images per digit were taken (Hitachi HT7800 TEM, 80 kV, 6000x magnification, EMSIS Xarosa camera, 5120x3840 pix, 2.4833 nm/pix). To quantify collagen distribution, images were opened in FIJI/ImageJ, collagen rich ROIs were manually labelled with a touch-screen marker pen (Wacom) and categorized by fiber orientation.

Single-cell library preparation, sequencing, and alignment

For single-cell RNA sequencing (scRNA-seq), tissues from 14 DPA non-regenerative (n = 2, 4 mice pooled per replicate), 4-methylumbelliferone (4-MU) control (n = 2, 5 mice pooled per replicate), and 4-MU treated (n = 2; 5 mice pooled per replicate) digits were freshly dissected, and cells were isolated from the wounded regions. The cells were resuspended at 372 cells/µl in 2% FBS in PBS. Libraries were prepared by using Chromium Next GEM Single Cell 3′mRNA v3.1 (10X Genomics, PN-120233) as per the company’s protocol and sequenced on a NovaSeq 6000 at the Cancer Research UK Institute (CRUK, Cambridge, UK). Raw 10X FASTQ files were aligned and quantified using the Cell Ranger Single-Cell Software Suite (v8.0.0, 10X Genomics). The mouse reference used was the mm10 reference genome refdata-gex-mm10-2020A, available at:https://cf.10xgenomics.com/supp/cell-exp/refdata-gex-mm10-2020-A.tar.gz.
All raw expression matrices generated are available in the Gene Expression Omnibus (GEO) under accession number GSE274858.

Single-cell transcriptomics data analysis

The 10X Genomics scRNA-seq datasets were processed in Python v3.10 using Scanpy v1.9.6 (55). Publicly available blastema (n = 3) and non-regenerative datasets (n = 2) were acquired from Gene Expression Omnibus (GEO) under the accession numbers GSE135985 (6) and GSE143888 (5). Analyses of other regenerative models were performed by acquiring datasets from GSE182141 (56), GSE130699 (57), and GSE234451 (58). Datasets that contained a high degree of cell-free (ambient) RNA molecules were cleaned with SoupX v1.6.2 (59) or CellBender v0.3.2 (60) before downstream pre-processing. Quality control for each dataset involved filtering based on fixed and batch-specific filtering thresholds. Fixed filtering thresholds included removing genes expressed in fewer than 3 cells and cells in which over 10% of the unique molecular identifiers arose from the mitochondrial genome. Batch-specific thresholds included removing cells beyond the upper and lower boundaries for number of genes and number of counts per cell. Doublets were identified using Scrublet v0.2.3 (61), with a threshold doublet score set at 0.2 to 0.25 or DoubletFinder (62). The details of the filtering paraments are listed in table S1.
During batch pre-processing, size factors for each batch were calculated for data normalization using the computeSumFactors() in R v.4.3. The size factor normalized data were then subjected to logarithmization (scanpy.pp.log1p), and highly variable genes were detected (scanpy.pp.highly_variable_genes). Principal component analysis was performed by scanpy.pp.pca and the first 30 principal components were used to calculate neighbors (scanpy.pp.neighbors) and perform uniform manifold approximation and projection (UMAP) (scanpy.tl.umap) constructions. Leiden graph-based clustering (scanpy.tl.leiden) at default settings was used for broad cell type classification with manual annotation. For integration, the top 2,000 highly variable genes were subsetted from the concatenated datasets. scVI v1.0.4 (63) was used to integrate the datasets, with parameters set to n_latent = 30, n_layers = 2, and gene likelihood = “nb”. Training was conducted for a maximum of 800 epochs or stopped early based on elbo_validation. The latent representation obtained from the model was used for computing neighbors. The final clustering of cell populations was performed by selecting the most conservative resolution that 1) yielded distinct populations and appropriate heterogeneity based on the UMAPs and 2) retained high and differential expression of canonical cell type-specific markers. Pdgfrα-expressing clusters were subsetted from the global integrated datasets for further analyses, and PCAs and UMAPs were re-calculated using scVI modeling and Leiden clustering to reveal sub-populations. The clustering resolution was determined as described above. Cell types were defined by finding marker genes with Scanpy (scanpy.tl.rank_genes_groups) using the Wilcoxon method and corrected by the Benjamini-Hochberg method.
Cell type proportion analyses were conducted with Propellor (Speckle v1.2.0) (64), and p < 0.05 designated statistically significant differences between two groups. To account for technical variability across datasets, differential gene expression was performed using MAST v.1.8.2 (65). MAST analyses were used to compare the Fibroblast 1 and 2 clusters against all other cell types, as well as against OL cells. For the 4-MU experiment, MAST analysis was performed on all Pdgfrα-expressing clusters between control and 4-MU datasets. Differentially expressed genes (DEGs) were determined as log2FC ≥ 0.5 and q-value < 0.05. The resulting DEGs were input for ClusterProfiler v4.10.1 (66) for gene ontology functional enrichment analysis. For complete Matrisome and collagen and proteoglycan scoring, ECM-related genes were taken from mus musculus complete matrisome list from the Matrisome Project (29), and gene list scores were calculated using sc.tl.score_genes. Table S2 lists the genes comprising each module.

Tissue preparation, immunostaining, and microscopy

Mouse tissues were harvested and fixed with 4% PFA at 4°C overnight and decalcified using 0.5 M pH 7.2 for 14 days. Tissues were cryo-protected overnight in 30% sucrose, embedded in OCT, and rapidly frozen. For deep imaging of digits, samples were processed as previously described (67). Briefly, tissues were sectioned to the mid-sagittal plane and blocked overnight in PBS with 5% bovine serum albumin (BSA), 10% dimethyl sulfoxide, and 0.5% Triton X-100 in PBS. Primary antibodies were diluted in blocking buffer and incubated with samples for 3 days. Samples were washed twice in PBS with 0.1% Triton X-100 (PBS-T) for 1 h per wash, and the third wash was performed overnight. Secondary antibodies were also diluted in blocking buffer, and samples were incubated for 3 days. Samples were washed as before, and Hoechst was used as a counterstain in the final 1 h wash. On the final day, samples were dehydrated and cleared using either benzyl alcohol:benzyl benzoate at a 1:2 ratio for 1 h or 50:50 CUBIC R+(M):PBS overnight at RT (TCI Chemicals, T3741), followed by 100% CUBIC R+M for 1 day. Lastly, samples were mounted on to glass bottom dishes for imaging. For thin-tissue IHC, samples were sectioned sagittally at 14 µm thickness. Prior to staining, sections were heated for 10 min at 37°C. Subsequently, slides were soaked in PBS with 0.5% Triton X-100 for 30 min and blocked for 1 h with 5% BSA in PBS-T. If primary antibodies were from goat or sheep hosts, then 10% donkey serum in PBS-T was used. Primary antibodies were diluted in blocking buffer, and slides were incubated with antibodies overnight at 4°C. The next day, slides were washed 4 times in PBS-T, 5 min each. Appropriate fluorophore-conjugated secondary antibodies were diluted in blocking buffer and applied to samples for 2 h with Hoechst. Slides were washed 4 times in PBS-T and finally mounted in ProLong Gold Antifade Mountant (Thermo Fisher Scientific, P36930). The list of primary antibodies can be found in Table S3.
For visualization of HA using hyaluronic acid binding protein (HABP, Merck, 385911-50UG), sections were incubated with streptavidin-peroxidase from Streptomyces avidinii (Merck, S5512-.1MG), followed by chromogenic development using the Pierce DAB Substrate Kit, as per the manufacturer’s instructions (Thermo Fisher Scientific, 34002). For fluorescence microscopy, streptavidin secondaries were used. For pSMAD1/5/8 immunostaining, antigen retrieval (10 mM sodium citrate dihydrate with 0.1% Triton X-100 at pH 6.0) was performed on cryo-sectioned, formalin-fixed tissues for 2 h at 70°C in a water bath. All remaining steps were performed using staining procedures described above, except PBS was replaced with TBS. For immunocytochemistry, cells were fixed with 4% PFA for 15 min and permeabilized in 0.5% Triton X-100 in PBS for 30 min. Except for pSMAD staining, which required TBS, staining procedures were the same as described above. For actin visualization, phalloidin (Proteintech, PF00001) staining was performed for 30 min. The hydrogels were inverted onto glass bottom dishes for imaging.
To visualize samples, the following confocal microscopes were used: Zeiss LSM 980 Airyscan2 and the Leica Stellaris 8. For image analysis of tissues, two sections per digit were analyzed, and these values were averaged and considered a single biological replicate. A minimum of three separate animals were analyzed per condition, with each digit sourced from a separate animal. For image analysis of cells, a minimum of three independent experiments were performed, and values for at least 10 separate cells at five different locations were averaged as a single biological replicate. The ImageJ (68) software was used for all image analysis.

HA extraction and enzyme-linked immunosorbent assay

To isolate HA from tissues, non-regenerating wounds and blastemas were micro-dissected, weighed, and incubated with digestion buffer containing 0.1 M Tris, 0.15 M NaCl, 0.01 M CaCl2, 5 mM deferoxamine mesylate, and 0.5 mg/ml Proteinase K (Merck, P6556-5MG) at pH 8.3 for 2 h with vortexing every 30 min. Samples were boiled at 100°C for 20 min to heat-inactivate Proteinase K and chilled on ice. 1 µl of Benzonase (Merck, E1014-5KU) was added to each sample and incubated for 1 h to degrade DNA and RNA. Then, samples were centrifuged for 15 min at 21,000 g at 4°C, and the supernatant was collected into a separate tube. An equal volume of phenol:chloroform:isoamyl alcohol (Merck, 77617-100ML) was added, and after vortexing, the samples were centrifuged for 15 min at 14,000 g and 4°C to separate the aqueous components from the other organics. This step was repeated using pure chloroform to remove residual phenol from the aqueous phase. HA was precipitated using 100% ethanol and spun at 10,000 g for 5 min and washed with an additional volume of ethanol. The samples of extracted HA were resuspended in water. An enzyme-linked immunosorbent assay to quantify the amount of HA in digit tissues was performed according to the manufacturer’s instructions (Biotechne, DHYAL0). Digits from four mice were pooled for a single biological replicate, with three replicates per condition.

Atomic force microscopy

Freshly isolated, post-amputated digits were cut mid-sagittally using a sharp scalpel and partially embedded in 4% agarose to stabilize the tissues on glass-bottom dishes. After the agarose had set, the samples were submerged in low-glucose DMEM without phenol red (Thermo Fisher Scientific, 11880036) and maintained at 34°C. AFM indentation measurements were performed using the setup previously described (69). Briefly, a petri dish containing the mounted digits was placed on the stage of an inverted microscope, and an AxioZoom V16 stereomicroscope (Zeiss) was mounted above the setup and used to acquire brightfield images through the AFM head. These images were used to designate a rectangular grid containing 2D spatial information upon which indentation measurements were performed. The exposed tissues were probed with 37.28 µm diameter polystyrene beads glued to Arrow-TL1 cantilevers (nominal spring constant = 0.01 N/m, NanoWorld, Arrow TL1). During measurements, the cantilever was approached at 20 µm/s until an indentation force of 10 nN was reached. This force was maintained for 3 sec and the resulting creep response (i.e. deformation over time) recorded.
The reduced apparent elastic modulus K=E01v2 value was extracted by a custom-written MATLAB script fitting the Hertz model to the force-distance data of the initial indentation (69). A custom-written Python script implementing the Power Law Rheology (PLR) model (70) was used to obtain fluidity (β) values. β can have values ranging from 0 (for an elastic solid) to 1 (for a viscous fluid). For AFM experiments, a minimum of four separate digits were measured per condition, with each digit sourced from a separate animal.

Depletion of HA matrix in mice

For intermittent degradation of HA, mice that underwent distal third phalanx (P3) amputations were administered 0.4-1 U per digit of hyaluronidase from bovine testes (Merck, H3506-100MG) reconstituted in 0.1% BSA. Control mice received 0.1% BSA alone. Administration of substances was performed at 8, 10, and 12 DPA using a 33G Hamilton syringes (Hamilton Company, 7635-01). Digits were harvested at 14DPA for processing and further analyses. Each digit was considered a biological replicate, with three replicates per condition.
For continuous knockdown of HA, 4-MU was incorporated into chocolate-flavored chow (ssniff Spezialdiäten) at 50 g/kg, resulting in a daily uptake of 250 mg of 4-MU per mouse. Control mice received the same chocolate-flavored chow without 4-MU. To acclimatize the mice to the modified diet and prevent weight loss, they were first fed maintenance chow:modified diet at a 50:50 ratio for the first seven days. Then, mice were maintained on the modified diet until the experimental endpoint. After acclimatization, mice underwent regenerative amputations, and tissues were collected at 14 and 28 DPA for further analyses. Each mouse was considered a biological replicate, with at least three replicates per condition.

Quantification of digit length and area

Fixed digits were imaged on a Leica stereomicroscope, and images were analyzed in ImageJ, where the region of the nail was manually outlined using the Polygon Selection tool, and the nail length was traced using the Straight Line tool. For the 4-MU experiment, each mouse was considered a biological replicate, and for experiments involving direct injections of substances, each digit was considered a biological replicate.

Microcomputed tomography

To assess the skeletal morphology of mouse digits, 4% PFA-fixed samples were scanned at either 14 or 28 DPA using a Nikon XTEK 225 Micro CT Scanner. Scan settings were the following: 2.9-4.9 µm resolution, energy at 75-90 kV and 30-48 µA, no filtration, 708-1000 ms exposure, 1080 projections, and two frames per projection. Image processing was done using CT Pro 3D and CT Agent (Nikon). The Materialise Mimics software was used to model samples in 3D and calculate volume and dimension measurements. For the 4-MU experiment, each mouse was considered a biological replicate, and for experiments involving direct injections of substances, each digit was considered a biological replicate. All experiments contained at least three biological replicates per condition.

Digit cell and dermal fibroblast isolation and culture

To isolate uninjured or post-amputated P3 cells, the nails from 6- to 8-week-old C57BL/6 mouse digits of both sexes were first removed to expose the underlying P3 bone and surrounding tissues. These tissues were digested in 0.25 mg/ml Liberase TH (Scientific Laboratory Supplies, 5401135001) in PBS for 1 h at 37°C. The dissociated cells were treated with 20 U/ml DNase I (Merck, 4536282001) for 5 min at 37°C. Isolation of P2 cells was performed similarly as above. Briefly, P2 tissues were separated from P3 at the distal interphalangeal joint, and the skin was peeled from the P2 bone and minced. Together, the skin and the bone were incubated in cell dissociation buffer for 1 h at 37°C. Cells from both sexes were pooled since we did not observe differences in digit wound healing between males and females. They were maintained in standard fibroblast expansion medium, which contained 10% fetal bovine serum (FBS, Gibco, 10270106), 1% Penicillin/Streptomycin (Merck, P0781), and 10 ng/ml fibroblast growth factor 2 (FGF-2) (Peprotech, 100-18B) in low-glucose DMEM with pyruvate and HEPES (Thermo Fisher Scientific, 12320032). Cells were seeded at 25,000 cells/cm2 and expanded in 20% O2 and 5% CO2 in a 37 °C humidified incubator.
To isolate dermal fibroblasts, the back skin of CD1 mice at postnatal day 2 of both sexes were dissected, and the tissues were placed in 0.25% trypsin overnight at 4°C. The next day, the dermis was separated from the epidermis using forceps and minced with scissors. The minced tissues were incubated in 1 mg/ml Collagenase P (Merck, 11213857001) and 2 mg/ml Dispase II (Merck, D4693-1G) and reconstituted in DMEM containing 2% FBS for 1 h at 37°C. DNA was digested using 20 U/ml DNase I for 5 min at 37°C. Fibroblast expansion medium was added to halt the digestion. Cells were spun for 10 min at 300 x g and resuspended in fibroblast expansion medium.

Fabrication of polyacrylamide gels of different stiffnesses

StemBond hydrogels were fabricated as previously described (35). Briefly, support coverslips were treated with 0.2 M sodium hydroxide for 35 min, cleaned, and functionalized with 3-(Trimethoxysilyl)propyl methacrylate (Merck, M6514-25ML) for 2 h. Top coverslips were treated with dichlorodimethylsilane (Merck, 440272-100ML) for 5 min. Hydrogel solutions were prepared according to Table S3 with the inclusion of 6-acrylamidohexanoic acid (Tokyo Chemical Industry, A1896) and degassed in a vacuum chamber for 20 min. To polymerize the hydrogels, TEMED (Merck, T22500-5ML) and APS (Merck, A3678-25G) were added, and the hydrogels were sandwiched between the support and top coverslips for 35 mins. The hydrogels were hydrated overnight in 1% Penicillin-Streptomycin in PBS. The next day, top coverslips were detached from the hydrogels under sterile conditions and equilibrated with MES buffer (0.1 M MES hydrate (Merck, M2933-25G), 0.1 M NaCl, pH 6.1). Hydrogels were activated by a 30 min treatment with 0.2 M EDAC (Scientific Laboratory Supplies, 03450-25G) and 0.5 M NHS (Thermo Fisher Scientific, 157272500) in MES buffer with constant rocking. After, hydrogels were coated with poly-d-lysine hydrobromide (Merck, P0899-10MG) at 100 µg/ml reconstituted in 0.05 M HEPES (Merck H3375-25G) pH 8.5. Coating was performed on a rocker for 2 h. Coating solution was aspirated, and the hydrogels were blocked with 0.5 M ethanolamine.
To test how substrate stiffness influences cell behavior, cells were cultured on stiff (50 kPa) or soft (0.7 kPa) hydrogels to mimic the mechanical microenvironment of the blastema and fibrotic tissue, respectively. Cells of no more than passage 4 were attached to the hydrogels for 24 h before treatment with PDGF-BB (50 ng/ml, Peprotech, 100-14B) or BMP-7 (200 ng/ml, Peprotech, 120-03P) in DMEM containing 1% FBS for experiments lasting less than or equal to 24 h. For longer experiments, DMEM with 10% FBS was used to maintain cell viability. To induce collagen synthesis and fibrillogenesis, 25 µg/ml 2-phospho-L-ascorbic acid trisodium salt (Merck, 49752-10G) and/or TGF-β1 was supplemented into the medium and replenished every other day until the experimental endpoint. For all hydrogel experiments, at least 3 independent experiments were performed.

Reverse transcription quantitative polymerase chain reaction

To measure gene expression levels, cells were first lysed by direct addition of TRIzol (Thermo Fisher Scientific, 15596026) for 5 min. Further RNA processing and purification was performed using Direct-zol RNA Microprep Kits (Zymo Research, R2060) according to the manufacturer’s instructions. Briefly, an equal volume of 100% ethanol to TRIzol was added to samples and spun in columns for two rounds of washing before elution in DNase/RNase-free water. Next, samples were treated with RQ1 RNase-free DNase (Promega, M6101) for 30 min at 37°C, followed by termination of the reaction using the supplied Stop Solution for 10 min at 65°C. RNA quality and concentration was measured using a Nanodrop Spectrophotometer (Thermo Fisher Scientific). Complementary DNA (cDNA) synthesis was carried out by first heating the RNA-primer mix containing 2.5 µM random hexamers (Thermo Fisher Scientific, N8080127), 0.5 mM dNTP (Thermo Fisher Scientific, 18427013), and 400 ng total RNA for 5 min at 65°C and chilling on ice for 1 min. Next, a reverse transcriptase mix containing 1x SuperScript IV, 5 mM DTT (Thermo Fisher Scientific, 18090010), and 2.0 U/µl RNaseOUT RNase Inhibitor (Thermo Fisher Scientific, 10777019) was added to each tube, which was incubated at 23°C for 10 min, 55°C for 10 min, and finally 80°C for 10 min. cDNA was diluted 1:200, and each PCR reaction contained 2 ng of cDNA, PowerTrack SYBR Green Master Mix (Thermo Fisher Scientific, A46109), and 800 nM of primer pairs. Reactions were carried out using the QuantStudio Real-Time PCR machine and software (Thermo Fisher Scientific, Waltham, MA). For all qPCR experiments, at least three independent experiments were performed. Statistical analyses were performed on −ΔΔCT values, but expression graphs were depicted as fold change (2−ΔΔCT). A list of the primers can be found in table S4.

Western blotting

For immunoblotting, adherent cells on hydrogels were inverted onto Parafilm with RIPA buffer (Thermo Fisher Scientific, 89900) containing Halt Protease and Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific, 78440). After 5 min, the cell lysate was collected into a microcentrifuge tube and agitated for 30 min at 4°C. Then, the lysate was centrifuged for 5 min at 14,000 x g and 4°C. The supernatant was aspirated, and the total protein was quantified using the BCA method (Thermo Fisher Scientific, 23225). Samples were boiled in Laemmli sample buffer (5x, 250 mM Tris base, 5% SDS), 50% glycerol, and 0.1% bromophenol blue with 2.5% 2-mercaptoethanol at 100°C for 5 min, and 10 µg of total protein per lane, along with a Precision Plus Protein Kaleidoscope Prestained Protein Standards ladder (Bio-Rad Laboratories, 1610375), was separated by gel electrophoresis. Transfer of proteins to PVDF membranes was performed using the tank transfer method with Towbin buffer (10x, 0.25 M Tris base, 1.92 M glycine). The membranes were blocked with 5% BSA in TBS with 0.1% Tween-20 (TBS-T) for 1 h with constant agitation, followed by immunostaining with primary antibodies in blocking buffer overnight at 4°C. Six 5-min washes with TBS-T were performed, followed by application of secondary antibodies for 1 h in blocking buffer. After another six 5-min TBS-T washes, protein was detected using SuperSignal West Pico PLUS Chemiluminescent Substrate (Thermo Fisher Scientific, 34577) according to the manufacturer’s instructions. Visualization was performed using the G:Box chemi XRQ machine (Syngene). Quantitative analysis of the immunoblots were performed using ImageJ. Densitometry analysis was performed by calculating background-subtracted integrated densities of pSMAD1/5/8 or HAPLN1 normalized to the loading control GAPDH (Abcam, ab9485, 1:10,000). For all western blots, at least three independent experiments were performed.

Overexpression of Hapln1 in dermal fibroblasts

pLV[Exp]-EF1A-mHapln1-mCherry (Hapln1OE) and pLV[Exp]-EF1A-Scramble-mCherry (mCherry control) transfer plasmids were cloned and transformed in Escherichia coli (VB UltraStable™ Chemically Competent Cells #UC001-010) by VectorBuilder (VectorBuilder, Chicago, IL). 3rd generation lentivirus plasmids pMDLg/pRRE, pmD2.G, and pRSV-Rev (Addgene plasmids #12251, #12259, and #12253, respectively) were used to generate lentivirus containing the Hapln1OE or mCherry Control transfer plasmid. HEK293T cells (ATCC, 293T-CRL-3216) of passage less than 20 were seeded at a density of 4x106 cells per 10-cm dish in high-glucose DMEM containing HEPES (Thermo Fisher Scientific, 10564011) and 10% FBS and allowed to attach overnight. The next day, fresh medium was supplied to the cells, which were subsequently transfected with the transfer, envelope, and packaging plasmids at a ratio of 4:2:1:1 by size (transfer:pMD2.G:pMDLg/pRRE:pRSV-Rev). The transfectant reagent:DNA complexes were made by combining TransIT-VirusGEN (Mirus Bio, MIR 6704) with plasmids in Opti-MEM I (Thermo Fisher Scientific, 31985062) and incubating for 30 mins to form complexes. The TransIT-VirusGEN:DNA complexes were added drop-wise to different areas of the dish. Lentivirus was collected at 48 and 72 h post-transfection and passed through a 0.45 µm filter. The lentivirus was then incubated 2:1 with Lenti-XTM Concentrator (Takara, 631231) for 1h and concentrated by centrifugation at 1500 g for 45 min at 4°C. The supernatant was discarded, and the virus was resuspended in 100 µl PBS per 10-cm dish.
Lentivirus multiplicity of infection and titer calculation was performed using a dilution series. The day prior to transduction, back skin dermal fibroblasts were plated at a density of 15,000 cells/cm2 in a 24-well plate. A serial dilution of lentivirus was added to the wells with the addition of 4 µg/ml of hexadimethrine bromide (Merck, H9268-10G). The plate was spun at 1,000 g for 2 h at 32°C, after which the medium was replaced with fresh fibroblast expansion medium, whose components were described above. After 48 h, the percentage of reporter-positive cells was calculated. The volume of virus needed to achieve at least 95% infection efficiency was used to transduce fibroblasts for all experiments using the steps described above.

Flow cytometry analysis

To confirm lentivirus transduction efficiency, infected cells were harvested and passed through a 40 µm filter. Then, cells were incubated with ready-to-use DAPI (Miltenyi Biotec, 130-111-570) for 5 min in 2% FBS and 2.5 mM EDTA in PBS. Unstained fibroblasts, fibroblasts stained with DAPI, and mCherry-positive cells were used as single-stained controls for compensation and gating. Typically, 100,000 events were recorded for each sample. Data were acquired on a BD Fortessa flow cytometer, and data analysis was performed in the FlowJo v10.1 software. Three independent experiments were performed for quantification of transduction efficiency.

Transplantation of genetically modified dermal fibroblasts into mice

Genetically modified fibroblasts were expanded, and cells of passage no later than six were used for transplantation. Briefly, detached cells were washed in PBS and resuspended at a concentration of 150,000 cells/µl in 33% EncapGel (Merck, 922412-1EA). NOD SCID mice, having undergone non-regenerative amputations, were administered 1 µl of cells or vehicle in the non-regenerative stump using a 32G Neuros Syringe (Hamilton, 65458-01) at 6 and 12 DPA. Mice digits were harvested at 14 and 28 DPA for further processing. Each digit was considered a biological replicate, with 18 digits per condition.

Injection of lentivirus into the mouse digits

Hapln1OE and mCherry control lentiviruses were generated as described above. A volume of 1µl of the Hapln1OE or mCherry Control lentiviruses (5 x 106 IU/mL) was directly injected into the P2 digits of C57BL/6 mice using the Microliter Neuros Syringe (Hamilton, 65458-01). One week after the injection, the digits were amputated at the non-regenerative P2 level, and an additional injection into the stump was performed at 6 DPA. Mice digits were harvested at 14 and 28 DPA for further processing.

Statistical analysis

Statistical analysis was performed using GraphPad Prism 10. The Shapiro-Wilk test was used to assess normality, and outliers were identified by ROUT tests (for multiple outliers, Q = 1%). A two-tailed student’s t test was used for pairwise comparisons, and a two- or three-way analysis of variance (ANOVA) followed by Tukey’s test was used for multiple comparisons. Data were presented as mean ± SEM. P < 0.05 was considered statistically significant. AFM data were shown as box-and-whisker plots, and a non-parametric Mann-Whitney test was used for statistical analysis. For all the figures, the number of mice and independent experiments are indicated in the figure legend. For mouse studies, sample size was determined by POWER calculations. Data were analyzed in a blinded manner, and wherever possible, investigators involved in treatment administration were not involved in data analyses or outcome assessment.

Acknowledgments

We thank the members of the Storer lab and K. To and L. Fei for comments and suggestions; the CRUK genomics core facility; D. Clements in the imaging core facilities at CSCI; J. Beck and R. McGinn for help with AFM analyses; and O. Ogundele and A. Raffaelli for helpful experimental and technical advice.

Funding:

Wellcome Trust Career Development Award G117552; 226520/Z/22/Z (M.A.S.); European Research Council Consolidator Grant 772798 (K.J.C.); European Research Council Grants 772426 and 101119729 (K.F.); Alexander von Humboldt Professorship (K.F.); National Institutes of Health, Department of Intramural Research, National Institutes of Dental and Craniofacial Research grant 1ZIADE000380 (P.G.R.); Wellcome Trust Studentship 226928/Z/23/Z (L.C.); Cambridge Trust Studentship (J.H.W.); UKRI Medical Research Council MR/Z506011/1 (N.A.B.); UKRI Medical Research Council MR/Y014537/1 (S.T., R.T.K.); Wellcome Trust 203151/Z/16/Z, 203151/A/16/Z, and 226795/Z/22/Z; UKRI Medical Research Council MC_PC_17230.

Author contributions:

Conceptualization: B.W.H.M., J.J.Y.W., C.E.D., P.G.R., K.J.C., M.A.S.; Methodology: B.W.H.M., J.J.Y.W., C.E.D., T.B., K.H., A.W., N.B., S.T., R.T.K., K.F., K.J.C., M.A.S.; Investigation: B.W.H.M., J.J.Y.W., C.E.D., T.B., K.H., L.C., J.H.W., N.A.B., S.T., E.S., A.W., M.A.S.; Visualization: B.W.H.M., J.J.Y.W., C.E.D., E.S., A.W., M.A.S.; Funding acquisition: M.A.S.; Project administration: B.W.H.M., C.E.D., T.B., K.J.C., M.A.S.; Supervision: P.G.R., K.J.C., M.A.S. Writing – original draft: B.W.H.M., K.J.C., M.A.S.; Writing – review & editing: B.W.H.M., K.J.C., M.A.S.

Competing interests:

K.J.C. is a cofounder of StemBond Technologies and cofounder and CSO of Cyclana Bio.

Data, code, and materials availability:

All raw scRNA-seq expression matrices generated from this project are available in the NCBI Gene Expression Omnibus under the accession number GSE274858. Datasets include single-cell transcriptomic profiles of nonregenerative digits after amputation, with or without inhibition of HA. This study also analyzed publicly available datasets, which are described in the supplementary materials. Code is available at Github (https://github.com/JosephJYW/HA_TissueMechanics_Science2026) and has been deposited on Zenodo (71). All other data are in the main paper or supplementary materials. The lentiviral vector used to overexpress HAPLN1 in our study, pLV[Exp]-EF1A-mHapln1-mCherry, and the scrambled control vector, pLV[Exp]-EF1A-Scramble-mCherry, were constructed and packaged by VectorBuilder. The vector IDs are VB230522-1759vcu and VB010000-9390nka and are available upon request.

License information:

Copyright © 2026 the authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original US government works. https://www.science.org/about/science-licenses-journal-article-reuse. This research was funded in whole or in part by the Wellcome Trust (Career Development Award G117552; 226520/Z/22/Z; Studentship 226928/Z/23/Z; and 203151/Z/16/Z, 203151/A/16/Z, and 226795/Z/22/Z) and the UKRI Medical Research Council (MR/Z506011/1, MR/Y014537/1, and MC_PC_17230), cOAlition S organizations, and by the European Research Council (consolidator grant 772798 and grants 772426 and 101119729 ); as required, the author will make the Author Accepted Manuscript (AAM) version available under a CC BY public copyright license.

Supplementary Materials

The PDF file includes:

Materials and Methods
Figs. S1 to S10
Tables S1 to S7

Other Supplementary Material for this manuscript includes the following:

MDAR Reproducibility Checklist

References and Notes

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