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. 2025 Mar 27;14:e105802. doi: 10.7554/eLife.105802

Transcriptome profiling of tendon fibroblasts at the onset of embryonic muscle contraction reveals novel force-responsive genes

Pavan K Nayak 1,, Arul Subramanian 1,, Thomas F Schilling 1,
Editors: Jeffrey A Farrell2, Didier YR Stainier3
PMCID: PMC12040314  PMID: 40145570

Abstract

Mechanical forces play a critical role in tendon development and function, influencing cell behavior through mechanotransduction signaling pathways and subsequent extracellular matrix (ECM) remodeling. Here, we investigate the molecular mechanisms by which tenocytes in developing zebrafish embryos respond to muscle contraction forces during the onset of swimming and cranial muscle activity. Using genome-wide bulk RNA sequencing of FAC-sorted tenocytes we identify novel tenocyte markers and genes involved in tendon mechanotransduction. Embryonic tendons show dramatic changes in expression of matrix remodeling associated 5b (mxra5b), matrilin 1 (matn1), and the transcription factor kruppel-like factor 2a (klf2a), as muscles start to contract. Using embryos paralyzed either by loss of muscle contractility or neuromuscular stimulation we confirm that muscle contractile forces influence the spatial and temporal expression patterns of all three genes. Quantification of these gene expression changes across tenocytes at multiple tendon entheses and myotendinous junctions reveals that their responses depend on force intensity, duration, and tissue stiffness. These force-dependent feedback mechanisms in tendons, particularly in the ECM, have important implications for improved treatments of tendon injuries and atrophy.

Research organism: Zebrafish

Introduction

All cells experience mechanical forces from their environments, from adhesive interactions between adjacent epithelial cells to structural interactions with the surrounding extracellular matrix (ECM). A key question is how cells adapt and respond to force by modifying their local microenvironment. Force-responsive cellular mechanisms have been implicated in cell differentiation (D’Angelo et al., 2011), morphogenesis (Hamada, 2015; Keller et al., 2008), tissue maintenance and repair (Riley et al., 2022; Zhang et al., 2022). However, these mechanisms remain understudied in vivo, particularly those that involve cell–ECM interactions. Dramatic examples include tendons and ligaments of the musculoskeletal system. Tendons experience a broad range of contractile forces from muscles, such as extreme stretching forces on the human Achilles tendon during exercise, and their constitutive fibroblast populations (called tenocytes) constantly remodel the surrounding ECM to adapt (Subramanian and Schilling, 2015; Wang, 2006). Tendon injuries and atrophy with aging are very common, and a better understanding of the roles of force in tendon development will aid in developing effective treatments.

Tendons are ECM-rich structures that connect muscles to cartilages and bones as well as to softer tissues. The events leading to the proper formation of their attachments relies largely upon cell–ECM interactions (Schweitzer et al., 2010; Subramanian and Schilling, 2015). For example, in the embryonic zebrafish trunk, myotendinous junctions (MTJs) at the vertical myosepta (VMS) of developing somites form via distinct tendon-independent and -dependent stages (Subramanian and Schilling, 2015). In the tendon-independent phase, myofibers differentiate and secrete ECM proteins such as Thbs4b that localize to the pre-tendon ECM and mediate initial fiber attachment. This coincides with tendon progenitor cell (TPC) migration into the MTJ. Later, in response to muscle contraction, TPCs differentiate into mature tenocytes and extend long microtubule-rich processes laterally into the surrounding ECM of the VMS, with which they regulate ECM composition locally in response to force (McNeilly et al., 1996; Pingel et al., 2014; Subramanian et al., 2018). Contractile forces acting on these MTJs activate transforming growth factor β (TGF-β) signaling in TPCs (Berthet et al., 2013; Pryce et al., 2009; Subramanian et al., 2018). Although not necessary for TPC specification, TGF-β induces expression of the transcription factors Scleraxis (Scx) and Mohawk (Mkx), which drive tenocyte fate by directly promoting transcription of collagens (i.e. Col1a1, Col1a2, Col12a1, and Col14) enriched in tendon ECM (Berthet et al., 2013; Maeda et al., 2011).

Cell type and ECM composition differ along the length of many tendons to aid in load bearing and force transmission. For example, the enthesis region where a tendon attaches to cartilage or bone is structurally graded in stiffness with fibrocartilage closer to the bone. This helps buffer mechanical stress between the elastic tendon tissue and rigid bony matrix (Lu and Thomopoulos, 2013). Fibrocartilage cells co-express Scx and Sox9, both direct transcriptional regulators of collagens, and muscle activity regulates the ratio of their expression levels (Blitz et al., 2013; Subramanian et al., 2023; Zelzer et al., 2014). This changes collagen levels, fibril size, and organization during injury or repair, as has been shown both in vitro and ex vivo (Ireland et al., 2001; Pingel et al., 2014). We have also shown that muscle contraction is required for embryonic tenocyte maturation, morphogenesis and ECM production in zebrafish tendons in vivo (Subramanian et al., 2018; Subramanian and Schilling, 2014).

To identify genes regulated by muscle contraction in tendons we have performed genome-wide bulk RNA-sequencing (RNA-seq) on FAC-sorted tenocytes of zebrafish embryos during the onset of muscle contractions and active swimming behavior. In addition to upregulation of known tenocyte markers, we find several other genes up- or downregulated as tendons differentiate that have not been implicated in tenocyte development or mechanotransduction. These include genes encoding two ECM proteins, Matrix Remodeling Associated 5b (mxra5b) and Matrilin 1 (matn1), as well as the transcription factor Kruppel-like factor 2a (klf2a). We confirm that muscle contraction regulates their transcription in tenocytes at later stages, after the onset of cranial muscle activity, by comparing wild-type and paralyzed embryos. Using genetic and physiological perturbations of muscle contraction in vivo, we show gene expression changes both in whole embryos and sorted tenocytes. Quantitative in situ methods show that their expression is contained within embryonic tendon entheses and MTJs and that their transcriptional responses to force vary depending on the strength and continuity of muscle contraction. These findings provide insights into tendon attachment specific and force-dependent feedback mechanisms in tendons during development in vivo, which have important implications for improved treatments for tendon disease, injury, and atrophy.

Results

Onset of active muscle contraction alters tenocyte gene expression

We previously showed that trunk tenocytes in zebrafish undergo dramatic morphological transformations when muscle contractions begin (Subramanian et al., 2018; Subramanian et al., 2023). These occur when embryos transition from twitching (36 hr post-fertilization, hpf) to free-swimming behaviors (48 hpf), as well as between sporadic jaw contractions at 60 hpf, and free-feeding behavior at 72 hpf (Figure 1A–D). These morphological changes likely reflect force-induced transcriptional changes in tenocytes, in addition to changes driving differentiation. To identify potential force-responsive factors, we conducted RNA-seq with FAC-sorted populations of Tg(scxa:mCherry)-positive tenocytes isolated from dissociated twitching (36 hpf) or free-swimming embryos (48 hpf). The Tg(scxa:mCherry) line predominantly labels both embryonic trunk and cranial tenocytes. We FACS-sorted mCherry+ cells using WT stage-matched non-fluorescent embryos as negative controls (Figure 1—figure supplement 1). Differential expression analysis revealed 2788 differentially expressed genes (DEGs) between twitching and free-swimming stages with p-value <0.05 (Figure 1E; Supplementary file 1). These included known tenocyte markers such as tnmd, mkxa, and egr1 upregulated at swimming (Figure 1E, F), confirming that many of the sorted mCherry+ cells were tenocytes or TPCs. Principle components associated with biological replicates segregated according to experimental condition (36 vs. 48 hpf), validating library preparation (Figure 1G; Figure 1—figure supplement 1). GO analysis for Biological Process terms associated with the top DEGs showed significant enrichment for ‘skeletal system development’ and ‘ECM organization’ (Figure 1H). Surprisingly, these included col2a1a and col9a1a, which are typically associated with cartilage development and morphogenesis (Figure 1F) suggesting that an early subset of scxa+ cells in embryonic tendons are specified as developing enthesis cells (Subramanian et al., 2023). Dual-expressing scxa/sox9a+ cells localize to cartilage attachment sites of cranial muscles at 48 hpf, prior to the onset of jaw movements (Figure 1A–D), consistent with specification of enthesis progenitors before the tendons or their skeletal muscle attachments become functional. These results also fit with recent single-cell sequencing studies of enthesis lineage trajectories in mice (Fang et al., 2022).

Figure 1. Onset of embryonic muscle contraction regulates transcription in tenocytes.

(A–D) Diagrams depicting changes in tenocyte distribution and morphology during onset of trunk and cranial muscle contractions, (A) 36 hpf when twitching movements are sporadic and (B) 48 hpf when embryos become free swimming. Lateral views of 36 (A) and 48 hpf embryos (B). Insets show lateral and transverse views of migrating tenocyte progenitors (A) and differentiated tenocytes at somite boundaries with polarized, branched projections (B). Ventral views of the embryonic head in 60 hpf (C) and 72 hpf (D) embryos just prior to and during the onset of jaw movements. Cartilage (green), tenocytes (magenta), and muscles (cyan) showing tenocyte elongation, particularly in the sternohyoid tendon (sht) and condensation, as well as the mandibulohyoid junction (mhj). (E) Heatmaps from bulk RNA-sequencing (RNA-seq) showing the top 1000 differentially expressed genes (DEGs) between 36 and 48 hpf. p < 0.05. (F) Elevated expression of tenocyte marker genes mkxa, tnmd, and egr1 and extracellular matrix (ECM) genes col2a1a, col9a1a in RNA-seq experiments at 48 hpf. Datapoints represent normalized read counts of single biological replicates at each color-coded timepoint (n = 7 for 36 hpf, n = 4 for 48 hpf). (G) Elevated expression of cartilage marker genes col2a1a and col9a1a in 48 hpf samples. (H) PCA of individual replicates showing separation of experimental conditions by timepoint. (I) GO analysis using Biological Process (BP) terms of top 2788 DEGs by adjusted p-value.

Figure 1.

Figure 1—figure supplement 1. FACS gating thresholds for mCherry+ cells.

Figure 1—figure supplement 1.

(Left to right for each) Forward Scatter A (FSC-A) versus Side Scatter A (SSC-A) shows P1 threshold. FSC-A versus Forward Scatter H (FSC-H) shows P2 threshold to select for single cells. FSC-A versus mCherry-A shows P3 fluorescence gating for mCherry+ cells. (A) P3 selection gating allowed selection of cells with strong mCherry signal based on Negative control AB (WT) sample versus mCherry expressing tenocytes (48 hpf) FACS gating. Established P3 gating selected for mCherry-positive cells in all 36 and 48 hpf Tg(scxa:mCherry) samples. (B) Thresholds used in 36 and 48 hpf Tg(scxa:mCherry) samples for FACS prior to bulk RNA-sequencing (RNA-seq).

To identify cell signaling pathways implicated in force responses during embryonic tendon development, we analyzed our DEG list using ShinyGO (Ge et al., 2020; Supplementary file 2) and DAVID (Supplementary file 3), both of which interrogate Gene Ontology and KEGG pathway databases (Huang et al., 2009). ShinyGO identified DEGs associated with 52 different pathways with FDR <0.05, including TGF-β, MAPK, Wnt, and Notch signaling, along with cell–cell adhesion and cell–ECM adhesion (Supplementary file 2). DAVID identified many of the same pathways as well as DEGs involved in RA metabolism, an emerging pathway of interest in tendon development (McGurk et al., 2017; Supplementary file 3).

Because our RNA-seq datasets were obtained from tenocytes during the onset of muscle contractions and swimming we also searched for DEGs associated with mechanosensitive pathways. Three genes of particular interest, matn1, klf2a, and mxra5b, stood out based on their force-dependent regulation in other biological contexts or regulation by TGF-β, a well-known force-responsive signal (Maeda et al., 2011; Subramanian and Schilling, 2015). The top-most upregulated gene was matn1, which encodes an ECM protein highly enriched in cartilage; Matn1 enhances chondrogenesis of synovial fibroblasts treated with TGF-β (Pei et al., 2008). The transcription factor klf2a was also strongly upregulated; Klf2 and Klf4 have been implicated in enthesis development in mammalian tendons. Klf proteins also repress TGF-β signaling in endothelial cells (Boon et al., 2007; Li et al., 2021) and klf2a expression is mechanosensitive during heart valve development (Steed et al., 2016). The third DEG of particular interest was mxra5b, which encodes an ECM protein expressed in both tendons and ligaments during chick development (Robins and Capehart, 2018) and regulated by TGF-β in cultured human kidney epithelial cells (Poveda et al., 2017). Though other potentially mechanosensitive genes were present in our bulk RNA-seq dataset, we focused on matn1, klf2a, and mxra5b for further analysis based on evidence implicating them in mechanotransduction in other tissue contexts.

matn1, klf2a, and mxra5b are expressed in cranial and trunk tenocytes in vivo

To verify tenocyte-specific expression of matn1, klf2a and mxra5b, we performed in situ hybridization (ISH). Conventional chromogenic ISH for matn1 detected no expression at 36 hpf but very strong expression at 48 and 60 hpf in developing craniofacial and pectoral fin cartilages (Figure 2—figure supplement 1A–C). Differential expression of matn1 in our tendon dataset could reflect expression in developing fibrocartilage enthesis progenitors closely associated with cartilages. To test this idea, we conducted fluorescent in situ Hybridization Chain Reaction (isHCR) for scxa and matn1 at 51 hpf, slightly later than our RNA-seq samples to allow better visualization of differentiated chondrocytes, and 72 hpf after the onset of jaw movements. scxa/matn1 co-expressing cells localized to the intermandibularis anterior tendon (ima) and sternohyoid tendon (sht), specifically in the entheses that attach to meckels, anterior edge of the ceratohyal cartilages, and the posterior enthesis of the ceratohyal (ch-pqt), at 72 hpf (Figure 2A–F, Figure 2—figure supplement 2A–D).

Figure 2. Expression of matn1, klf2a, and mxra5b with scxa in cranial and trunk tenocytes.

Ventral cranial (A–F, K–V) and lateral trunk (G–J) views of 72 hpf (A–F, K–V) and 48 hpf (G–J) embryos showing isHCR of matn1 (A, C–F), klf2a (G, I–K, M–P), and mxra5b (Q, S–V) in combination with scxa (B–F, H–J, L–P, R–V). (D–F, J, N–P, T–V) Higher magnification views of tenocyte nuclei in marked ROI. (C, D, M, N, S, T) ROI and panels outlined in magenta show magnified views of 3D volumes of tenocytes associated with imt. (I, J) ROI and panels outlined in cyan show magnified views of 3D volume of VMS tenocytes. (C, E, M, O, S, V) ROI and panels outlined in royal blue show magnified views of 3D volume of tenocytes associated with sht enthesis. (C, F, M, P) ROI and panels outlined in yellow show magnified views of 3D volumes of tenocytes associated with ch-pqt. (S, U) ROI and panels outlined in green show magnified views of 3D volumes of tenocytes associated with mhj. Each magnified view of ROI displays a translucent outline of the nuclear 3D volume with white puncta representing voxel colocalizations of isHCR as depicted by the colocalization function in Imaris (see Methods). mc – Meckel’s cartilage, pq – palatoquadrate, ch – ceratohyal, bh – basihyal cartilage, ima – intermadibularis anterior tendon, mhj – mandibulohyoid junction, sht – sternohyoideus tendon, ch-pqt – ceratohyal-palatoquadrate tendon, sb – somite boundary. Scale bars = 20 µm.

Figure 2.

Figure 2—figure supplement 1. matn1, klf2a, and mxra5b are expressed in musculoskeletal tissues of developing embryos.

Figure 2—figure supplement 1.

Lateral (A, B, D–I) and ventral (C) views of embryos showing expression of matn1 (A–C), klf2a (D–F), and mxra5b (G, H). (A–C) 48 hpf embryos show matn1 expression in cartilage progenitors at 48 hpf and in differentiated cartilages (and associated tenocytes) at 60 hpf (B, C). Lateral views of 36 hpf (D), 48 hpf (E), and 60 hpf (F) embryos show klf2a expression in pharyngeal mesenchyme (D), skeletal progenitors and in tenocytes along VMS (E, F). Lateral views of 36 hpf (G), 48 hpf (H), and 60 hpf (I) embryos show mxra5b expression in tenocytes along horizontal myosepta (HMS) along the notochord and VMS. Scale bars = 100 μm. Abbreviations: abc = anterior basicranial commissure, ch = ceratohyal cartilage, ep = ethmoid plate, hs = hyosymplectic cartilage, mc = meckel’s cartilage, nc = notochord, pf = pectoral fin, pq = palatoquadrate cartilage, sb = somite boundaries, t = trabeculae cartilage.
Figure 2—figure supplement 2. matn1 is expressed in differentiating cranial tenocytes.

Figure 2—figure supplement 2.

Ventral view of the developing mandibular arch in a 51 hpf embryo showing in situ Hybridization Chain Reaction (isHCR) of matn1 (A, C, D) and scxa (B–D). (D) magnified view of yellow ROI (C) shows outline of tenocyte nuclear 3D volume with white puncta representing voxel colocalizations of matn1 and scxa as depicted by colocalization using Imaris (see methods). ep = ethmoid plate cartilage, ch = ceratohyal cartilage, mc = meckel’s cartilage, pq = palatoquadrate cartilage, ch-pqt = ceratohyal-palatoquadrate tendon, ima = intermandibularis anterior tendon, mhj = mandibulohyoid junction tendon, sht = sternohyoideus tendon. Scale bars = 20 µm.

For klf2a, chromogenic ISH revealed expression at VMS (somite boundaries) in the trunk at 48 hpf as well as developing pharyngeal arches and pectoral fins at 48 and 60 hpf (Figure 2—figure supplement 1D–F). This was confirmed by double isHCR of klf2a and scxa showing overlapping expression in tenocytes at VMS at 48 hpf (Figure 2G–J). klf2a expression was also detected in multiple cranial tendons at 72 hpf, most prominantly in the entheses of the ima, sht, and ch-pqt (Figure 2K–P). This provides the first evidence for klf2a as an enthesis marker in craniofacial tendons, similar to Klf2 expression in developing mouse limb entheses (Kult et al., 2021; Lu and Thomopoulos, 2013; Zelzer et al., 2014).

mxra5b expression was first detected by chromogenic ISH at VMS near the horizontal myoseptum (HMS), which separates dorsal and ventral somites at 36 hpf, as well as in the notochord and cranial mesenchyme at 48 hpf (Figure 2—figure supplement 1G, H). Expression increased and extended along the VMS by 60 hpf (Figure 2—figure supplement 1). Double isHCR for scxa and mxra5b, detected mxra5b expression in cranial entheses (including ima, mhj, and sht – as well as others not shown), and in the mandibulohyoid junction tendon (mhj) in embryos at 72 hpf (Figure 2Q–V). Similar to klf2a, mxra5b expression has not been described in cranial connective tissues previously.

Tenocyte-specific gene expression of matn1, klf2a, and mxra5b is regulated by muscle contraction

Since matn1, klf2a, and mxra5b were among the top DEGs in tenocytes at the onset of active swimming and persistent muscle activity, we reasoned that mechanical force regulates their expression. To test this, we performed Real Time Quantitative-PCR (RT-qPCR) in genetically paralyzed embryos. Relative expression of each gene was compared between wild-type (WT) embryos and homozygous mutants lacking the function of the voltage-dependent L-type calcium channel subtype beta-1 (cacnb1−/−), which blocks muscle contraction (Subramanian et al., 2018; Zhou et al., 2006). At 48 hpf, all three genes were downregulated in cacnb1−/− mutants versus WT (Figure 3—figure supplement 1A). In contrast, at 72 hpf once jaw movements had begun, only matn1 and mxra5b remained downregulated in cacnb1−/− embryos while klf2a expression increased (Figure 3—figure supplement 1B).

To confirm that loss of muscle contraction caused these transcriptional changes in tenocytes we injected Tg(scxa:mCherry) embryos at the 1-cell stage with full-length alpha-bungarotoxin mRNA (aBTX), which paralyzes embryos by irreversibly binding to acetylcholine receptors at neuromuscular synapses. Bulk RNA-seq of sorted mCherry+ cells from whole aBTX-injected embryos at 48 hpf compared with WT uninjected controls (Figure 3A) identified 1450 DEGs. PC analysis clearly separated WT and aBTX biological replicates (Figure 3B, Supplementary file 4). 280 DEGs overlapped between both bulk RNA-seq runs (Figure 3C, Supplementary file 5). GO term analysis, using shinyGO (Ge et al., 2020), identified many of the same pathways downregulated in cacnb1−/− embryos, as well as others not previously implicated in tendon mechanotransduction. Seveal of these mapped to terms such as ‘Focal Adhesion’, including rhoab, rock2a (both part of Rho-ROCK signaling), and col9a1a (Figure 3E, Supplementary file 6) further implicating these as force dependent in tendons.

Figure 3. Paralysis regulates tenocyte gene expression in developing musculoskeletal system.

(A) Heatmap of differentially expressed genes (DEGs) from bulk RNA-sequencing (RNA-seq) between WT and aBTX-injected (aBTX-inj) paralyzed 48 hpf embryos (force perturbed). (B) PCA of individual replicates WT versus aBTX-inj embryos’ RNA-seq separate by experimental condition. (C) Venn diagram shows overlap of genes between developmental time-point and force perturbed RNA-seq experiments. (D) Comparison of normalized read counts between replicates of matn1, klf2a, and mxra5b in 36 versus 48 hpf and WT versus aBTX RNA-seq experiments. (E) KEGG pathway analysis plot shows enrichment of overlapping genes from (C). ns = not significant, *p < 0.05, ***p < 0.001.

Figure 3.

Figure 3—figure supplement 1. Paralysis regulates gene expression of matn1, klf2a, and mxra5b in developing embryos.

Figure 3—figure supplement 1.

Bar plots showing global changes in relative expression from RT-qPCR of matn1, klf2a, and mxra5b genes between WT and cacnb1−/− mutant embryos at 48 hpf (A) and 72 hpf (B). Bar plots show global changes in relative expression of matn1, klf2a, and mxra5b between 48 hpf uninjected WT controls (blue) and aBTX-injected paralyzed (green) embryos (C), aBTX-injected paralyzed (green) and aBTX-injected ‘Twitching’ (partially recovered, magenta) embryos (D), and between aBTX-injected paralyzed (green) and aBTX-injected, ‘Recovered’ (cyan) embryos (E) at 48 hpf (right). ns = not significant, *p < 0.05, **p < 0.01, ***p < 0.001.

Comparisons of matn1, klf2a, and mxra5b expression between aBTX and WT versus our original 36 hpf versus free swimming 48 hpf RNA-seq experiment, revealed similar trends in expression. This suggests that the expression changes seen at embryonic stages (36 vs. 48 hpf) reflect tenocyte responsiveness to muscle contraction (Figure 3D). Further, comparing the 48 hpf WT versus cacnb1−/− mutant RT-qPCR with both bulk RNA-seq experiments, matn1 and mxra5b expression were both consistently downregulated by paralysis, while klf2a expression was more variable across experiments (Figure 3A, D).

Having shown reproducible changes in their expression between bulk RNA-seq results, we next asked if variable recovery of muscle contractile forces differentially affects changes in matn1, klf2a, and mxra5b expression caused by paralysis. To test this, we used 90 ng/µl of aBTX , a concentration optimized to paralyze embryos only for the first 2 days of embryogenesis after which they gradually recover. Nearly all aBTX-injected embryos regained muscle contractions and were swimming at 48 hpf. We performed RT-qPCR on cDNA derived from these embryos and compared them to aBTX paralyzed (aBTX-P) and uninjected controls. We separated 48 hpf recovered embryos into two subgroups based on the extent of muscle contraction: (1) partially recovered (Twitching or aBTX-T), in which embryos showed sporadic contractions of the trunk and pectoral fin muscles, similar to twitching 36 hpf embryos and (2) fully recovered (Recovered, or aBTX-R), in which embryos swam freely. At 48 hpf, RT-qPCR revealed significant global downregulation of matn1 and mxra5b in αBTX paralyzed embryos compared to WT uninjected siblings, like cacnb1−/− mutant embryos (Figure 3—figure supplement 1C) and were upregulated in aBTX-T and aBTX-R embryos (Figure 3—figure supplement 1D). In contrast, klf2a was upregulated in paralyzed embryos, though this increase was also not statistically significant from WT controls (Figure 3—figure supplement 1E). These results, combined with those from RNA-seq, suggest that matn1, klf2a, and mxra5b transcription during development are regulated by muscle contraction.

To verify that these transcriptional changes occur specifically in tenocytes in response to force, we examined matn1, klf2a, and mxra5b expression in scxa-positive cells by isHCR with mCherry antibody staining of Tg(scxa:mCherry) fish using our αBTX paralysis-recovery experimental protocol (Figure 4, Figure 5, Figure 6). Additionally, we quantified expression at multiple attachment regions across different tendons for each gene to determine if responses differed between spatially distinct tendons and by attachment type (e.g. enthesis vs. MTJ).

Figure 4. Mechanical force differentially regulates expression of matn1 in ima enthesis tenocytes.

Ventral views of Meckel’s cartilage and associated tenocytes showing in situ Hybridization Chain Reaction (isHCR) of matn1 (green) and anti-mCherry immunofluorescence (magenta) marking the tenocytes in Tg(scxa:mCherry) embryos at 72 hpf in WT uninjected (WT) (A–E), aBTX-inj (Paralyzed) (F–J), partially recovered aBTX-inj (Twitching) (K–O), and completely recovered aBTX-inj (Full Recovery) (P–T) conditions at ima enthesis. (D, I, N, S) Grayscale images showing nuclei stained with DAPI with ROIs showing isolated 3D volumes of chondrocytes (green) and enthesis tenocytes (magenta) based on DAPI signal. (E, J, O, T) Insets showing magnified views of the 3D volumes of tenocytes associated with ima enthesis depicting expression of matn1 and stained for mCherry. (U) Violin plot showing changes in mean fluorescence intensity of matn1 in ima enthesis tenocyte nuclei between WT (n = 8), Paralyzed (n = 8), Twitching (n = 6), and Full Recovery (n = 7) with ~8 nuclei measured per embryo. p-value calculated with linear mixed effects model with Tukey post hoc test. *p < 0.05, **p < 0.01, ***p < 0.001. Scale bars = 20 µm.

Figure 4—source data 1. Measurements of matn1 isHCR signal intensity in ima enthesis tenocytes.

Figure 4.

Figure 4—figure supplement 1. Mechanical force regulates expression of matn1 and klf2a in sht enthesis tenocytes.

Figure 4—figure supplement 1.

Ventral views of ceratohyal (ch) cartilage and associated tenocytes showing in situ Hybridization Chain Reaction (isHCR) of matn1 (green) (A–L) and klf2a (green) (N–Y) and anti-mCherry immunofluorescence (magenta) marking the tenocytes in Tg(scxa:mCherry) embryos at 72 hpf in WT uninjected (WT) (A–C, N–P), aBTX-inj (Paralyzed) (D–F, Q–S), partially recovered aBTX-inj (Twitching) (G–I, T–V), and completely recovered aBTX-inj (Full Recovery) (J–L, W–Y) conditions at sht enthesis. (B, E, H, K, O, R, U, X) Grayscale images showing nuclei stained with DAPI with ROIs showing isolated 3D volumes of chondrocytes (green) and sternohyoideus-ceratohyal (sht) enthesis tenocytes (magenta) based on DAPI signal. (C, F, I, L, P, S, V, Y) Insets showing magnified views of the 3D volumes of tenocytes associated with sht enthesis depicting expression of matn1 and stained for mCherry. (M) Violin plot showing changes in mean fluorescence intensity of matn1 in sht enthesis tenocyte nuclei between WT (n = 8), Paralyzed (n = 8), Twitching (n = 6), and Full Recovery (n = 7) with ~8 nuclei measured per embryo. (Z) Violin plot showing changes in mean fluorescence intensity of klf2a in sht enthesis tenocyte nuclei between WT (n = 15), Paralyzed (n = 16), Twitching (n = 14), and Full Recovery (n = 11) with ~8 nuclei measured per embryo. p-values calculated with linear mixed effects model with Tukey post hoc test. ***p < 0.001. Scale bars = 20 µm.
Figure 4—figure supplement 1—source data 1. Measurements of matn1 and klf2a isHCR signal intensity in sht enthesis tenocytes.

Figure 5. Mechanical force differentially regulates expression of mxra5b in sht enthesis tenocytes.

Ventral views of ceratohyal (ch) cartilage and associated tenocytes showing in situ Hybridization Chain Reaction (isHCR) of mxra5b (green) and anti-mCherry immunofluorescence (magenta) marking the tenocytes in Tg(scxa:mCherry) embryos at 72 hpf in WT uninjected (WT) (A–E), aBTX-inj paralyzed (F–J), partially recovered aBTX-inj (Twitching) (K–O), and completely recovered aBTX-inj (Full Recovery) (P–T) conditions at sht enthesis. (D, I, N, S) Grayscale images showing nuclei stained with DAPI with ROIs showing isolated 3D volumes of chondrocytes (green) and sht enthesis tenocytes (magenta) based on DAPI signal. (E, J, O, T) Insets showing magnified views of the 3D volumes of tenocytes associated with sht enthesis depicting expression of mxra5b and stained for mCherry. (U) Violin plot showing changes in mean fluorescence intensity of mxra5b in sht enthesis tenocyte nuclei between WT (n = 7), Paralyzed (n = 8), Twitching (n = 8), and Full Recovery (n = 4) with ~8 nuclei measured per embryo. p-value calculated with linear mixed effects model with Tukey post hoc test. *p < 0.05, **p < 0.01. Scale bars = 20 µm.

Figure 5—source data 1. Measurements of mxra5b isHCR signal intensity in sht enthesis tenocytes.

Figure 5.

Figure 5—figure supplement 1. Mechanical force differentially regulates expression of mxra5b and klf2a in ima enthesis tenocytes.

Figure 5—figure supplement 1.

Ventral views of Meckels cartilage and associated tenocytes showing in situ Hybridization Chain Reaction (isHCR) of klf2a (green) (A–L) and mxra5b (green) (N–Y) and anti-mCherry immunofluorescence (magenta) marking the tenocytes in Tg(scxa:mCherry) embryos at 72 hpf in WT uninjected (WT) (A–C, N–P), aBTX-inj (Paralyzed) (D–F, Q–S), partially recovered aBTX-inj (Twitching) (G–I, T–V), and completely recovered aBTX-inj (Full Recovery) (J–L, W–Y) conditions at ima enthesis. (B, E, H, K, O, R, U, X) Grayscale images showing nuclei stained with DAPI with ROIs showing isolated 3D volumes of chondrocytes (green) and ima enthesis tenocytes (magenta) based on DAPI signal. (C, F, I, L, P, S, V, Y) Insets showing magnified views of the 3D volumes of tenocytes associated with ima enthesis depicting expression of mxra5b and klf2a and stained for mCherry. (M) Violin plot showing changes in mean fluorescence intensity of klf2a in ima enthesis tenocyte nuclei between WT (n = 15), Paralyzed (n = 16), Twitching (n = 14), and Full Recovery (n = 11) with ~8 nuclei measured per embryo. (Z) Violin plot showing changes in mean fluorescence intensity of mxra5b in ima enthesis tenocyte nuclei between WT (n = 7), Paralyzed (n = 8), Twitching (n = 8), and Full Recovery (n = 4) with ~8 nuclei measured per embryo. p-values calculated with linear mixed effects model with Tukey post hoc test. ***p < 0.001. Scale bars = 20 µm.
Figure 5—figure supplement 1—source data 1. Measurements of klf2a and mxra5b isHCR signal intensity in ima enthesis tenocytes.
Figure 5—figure supplement 2. Mechanical force regulates expression of mxra5b in mhj myotendinous junction tenocytes.

Figure 5—figure supplement 2.

Ventral views of mandibulohyoid (mhj) myotendinous junction (MTJ) associated tenocytes showing in situ Hybridization Chain Reaction (isHCR) of mxra5b (green) and anti-mCherry immunofluorescence (magenta) marking the tenocytes in Tg(scxa:mCherry) embryos at 72 hpf in WT uninjected (WT) (A–C), aBTX-inj (Paralyzed) (D–F), partially recovered aBTX-inj (Twitching) (G–I), and completely recovered aBTX-inj (Full Recovery) (J–L) conditions. (B, E, H, K) Grayscale images showing nuclei stained with DAPI with ROIs showing isolated 3D volumes of mhj tenocytes (magenta) based on DAPI signal. (C, F, I, L) Insets showing magnified views of the 3D volumes of tenocytes associated with mhj MTJ depicting expression of mxra5b and stained for mCherry. (M) Violin plot showing changes in mean fluorescence intensity of mxra5b in mhj MTJ tenocyte nuclei between WT (n = 7), Paralyzed (n = 8), Twitching (n = 8), and Full Recovery (n = 4) with ~10 nuclei measured per embryo. p-value calculated with linear mixed effects model with Tukey post hoc test. ***p < 0.001. Scale bars = 20 µm.
Figure 5—figure supplement 2—source data 1. Measurements of mxra5b isHCR signal intensity in mhj MTJ tenocytes.
Figure 5—figure supplement 3. Mechanical force differentially regulates expression of mxra5b and klf2a in sht myotendinous junction tenocytes.

Figure 5—figure supplement 3.

Ventral views of sht and associated myotendinous junction (MTJ) Tenocytes showing in situ Hybridization Chain Reaction (isHCR) of mxra5b (green) (A–L) and klf2a (green) (N–Y) and anti-mCherry immunofluorescence (magenta) marking the tenocytes in Tg(scxa:mCherry) embryos at 72 hpf in WT uninjected (WT) (A–C, N–P), aBTX-inj (Paralyzed) (D–F, Q–S), partially recovered aBTX-inj (Twitching) (G–I, T–V), and completely recovered aBTX-inj (Full Recovery) (J–L, W–Y) conditions at sht MTJ. (B, E, H, K, O, R, U, X) Grayscale images showing nuclei stained with DAPI with ROIs showing isolated 3D volumes of ima enthesis tenocytes (magenta) based on DAPI signal. (C, F, I, L, P, S, V, Y) Insets showing magnified views of the 3D volumes of tenocytes associated with sht enthesis depicting expression of mxra5b and klf2a and stained for mCherry. (M) Violin plot showing changes in mean fluorescence intensity of mxra5b in sht MTJ tenocyte nuclei between WT (n = 7), Paralyzed (n = 8), Twitching (n = 8), and Full Recovery (n = 4) with ~4 nuclei measured per embryo. (Z) Violin plot showing changes in mean fluorescence intensity of klf2a in ima enthesis tenocyte nuclei between WT (n = 15), Paralyzed (n = 16), Twitching (n = 14), and Full Recovery (n = 11) with ~4 nuclei measured per embryo. p-values calculated with linear mixed effects model with Tukey post hoc test. *p < 0.05, **p < 0.01. Scale bars = 20 µm.
Figure 5—figure supplement 3—source data 1. Measurements of mxra5b and klf2a isHCR signal intensity in sht MTJ tenocytes.

Figure 6. Mechanical force regulates expression of klf2a in mhj myotendinous junction tenocytes.

Figure 6.

Ventral views of mandibulohyoid junction (mhj), myotendinous junction (MTJ) associated tenocytes showing in situ Hybridization Chain Reaction (isHCR) of klf2a (green) and anti-mCherry immunofluorescence (magenta) marking the tenocytes in Tg(scxa:mCherry) embryos at 72 hpf in WT uninjected (WT) (A–E), aBTX-inj (Paralyzed) (F–J), partially recovered aBTX-inj (Twitching) (K–O), and completely recovered aBTX-inj (Full Recovery) (P–T) conditions. (D, I, N, S) Grayscale images showing nuclei stained with DAPI with ROIs showing isolated 3D volumes of mhj tenocytes (magenta) based on DAPI signal. (E, J, O, T) Insets showing magnified views of the 3D volumes of tenocytes associated with mhj MTJ depicting expression of klf2a and stained for mCherry. (U) Violin plot showing changes in mean fluorescence intensity of klf2a in mhj MTJ tenocyte nuclei between WT (n = 17), Paralyzed (n = 15), Twitching (n = 14), and Full Recovery (n = 11) with ~10 nuclei measured per embryo. p-value calculated with linear mixed effects model with Tukey post hoc test. *p < 0.05, ***p < 0.001. Scale bars = 20 µm.

Figure 6—source data 1. Measurements of klf2a isHCR signal intensity in mhj MTJ tenocytes.

For matn1, we quantified expression by measuring its fluorescence intensity in individual tenocytes in 3D at the intermandibularis anterior (ima) enthesis where the ima attaches to meckel’s (mc) cartilage and the sht enthesis at the anterior end of the ch cartilage (Figure 4A–T, Figure 4—figure supplement 1A–L; Subramanian et al., 2023). Cells were selected for quantification by their co-expression of matn1 and Scxa and positions near chondrocytes expressing matn1 alone and tenocytes expressing Scxa alone, as described previously (Subramanian et al., 2023). In these ima tenocytes, we found no significant difference in matn1 expression between WT and paralyzed embryos, but increased expression in fully recovered (aBTX-R) embryos relative to WT, Paralyzed, and Twitching (aBTX-T) embryos (Figure 4U). Conversely, tenocytes of the sht enthesis showed no significant difference in expression across any of the conditions (Figure 4—figure supplement 1M).

We also examined fluorescence intensity in scxa/mxra5b or scxa/klf2a double positive tenocytes located at ima and sht entheses, as well as mhj and sht MTJs. mxra5b expression in the ima enthesis was significantly reduced in paralyzed, aBTX-T twitching, and remained low in aBTX-R fully recovered embryos compared to WT (Figure 5—figure supplement 1Z). However, in tenocytes of all other measured attachment sites (sht enthesis, mhj MTJ, sht MTJ), mxra5b expression returned to WT levels upon full recovery (Figure 5, Figure 5—figure supplement 2M, Figure 5—figure supplement 3M). klf2a expression in ima and sht entheses was significantly increased in paralyzed and aBTX-T embryos compared to WT and further increased upon full recovery (Figure 4—figure supplement 1Z, Figure 5—figure supplement 1M). However, unlike entheses, klf2a expression in sht MTJ tenocytes only increased significantly from twitching to full recovery, and in mhj MTJ tenocytes the pattern was much more variable, increasing upon paralysis, decreasing to WT levels at twitching, and re-increasing beyond WT levels at full recovery (Figure 5—figure supplement 3, Figure 6U).

To address functions of matn1, klf2a, and mxra5b in tenocytes we used multiplex CRISPR/Cas9 mutagenesis (Wu et al., 2018) to generate F0 CRISPants for matn1, klf2a, and mxra5b. While we did not observe obvious phenotypic defects in matn1 and klf2a CRISPants, possibly due to genetic redundancy with other similar proteins, Tg(scxa:mCherry) embryos injected with four mxra5b gRNAs had qualitatively fewer trunk tenocytes when compared to uninjected controls (Figure 7A). Additionally, trunk VMS in mxra5b CRISPR-injected embryos displayed a wider sb angle (Figure 7B), although this may reflect a role for mxra5b in the notochord, where it is also expressed (Figure 2—figure supplement 2G–I). These results suggest that mxra5b may be required for embryonic axial tenocyte migration and/or differentiation.

Figure 7. Loss of mxra5b function affects somite boundary structure.

Figure 7.

(A) Lateral views of WT and mxra5b multiplex CRISPants at 48 and 72 hpf Tg(scx:mCherry) embryos stained with anti-mCherry to show tenocytes at the somite boundary (SB). (B) Quantification of somite boundary angle measurements of 48 hpf WT or mxra5b multiplex CRISPant embryos. p-value calculated with Watson’s U2 test. *p < 0.05.

Discussion

Previous studies of mechanotransduction in tenocytes, particularly at the transcriptional level, have largely been limited to adult tendons or in vitro assays using mesenchymal stem cells. Few have addressed how functional differences in tendons are established during embryonic development. We report the first genome-wide survey of embryonic mechanoresponsive genes and transcriptional responses across multiple tendon types. We identify three genes induced at the onset of muscle attraction and later maintained by contractile force (Figure 8A). Paralysis of zebrafish embryos alters expression of two ECM proteins in tenocytes, matn1 and mxra5b, as well as the transcription factor klf2a. All three are expressed in cranial entheses, while mxra5b and klf2a are also expressed in trunk MTJs (Figure 2, Figure 8B, Figure 2—figure supplement 1, Figure 2—figure supplement 2). Our previous studies have shown that in both tissues embryonic tenocytes in zebrafish acquire specialized morphologies and gene expression profiles as muscles first form functional attachments (Subramanian et al., 2018; Subramanian et al., 2023; Subramanian and Schilling, 2015). In contrast to classical studies of mature tendons these results suggest that cells with distinct enthesis or MTJ signatures arise in the embryo to fine-tune the ECM to match the functional demands of and forces exerted by individual muscles.

Figure 8. Model depicting role of mechanical force in regulating expression of genes in tenocytes during onset of active muscle contraction.

Figure 8.

(A) Cartoon showing role of force in regulating tenocyte morphogenesis and gene expression in tenocytes between 36 and 48 hpf stages correlating with onset of active swimming The variability in gene expression is related to increase in both magnitude and persistence of muscle contraction force. (B) Representative model summarizing the multifaceted role of muscle contractile force on expression dynamics of matn1, klf2a, and mxra5b genes in cranial tendon attachments. (C) Force-responsive gene expression is more nuanced than a binary on/off control.

Classically tendon types and subdomains are distinguished by their collagen composition, and many collagens are direct Scx or Mkx transcriptional targets (Bobzin et al., 2021; Felsenthal and Zelzer, 2017; Subramanian and Schilling, 2015). This helps explain the gradient of stiffness and corresponding Scx/Sox9 expression within an enthesis (Blitz et al., 2013; Lu and Thomopoulos, 2013; Subramanian et al., 2023; Zelzer et al., 2014). Our results highlight additional genes implicated in cartilage (i.e. matn1) and fibrocartilage (i.e. KLF) in entheseal tenocytes and their force responses. Though typically thought of as cartilage-specific, matn1 and its relatives have been reported in single-cell RNA-seq (scRNA-seq) analyses of adult tenocytes and fibrocartilage (Kaji et al., 2021). We find that zebrafish matn1 regulation differs between entheses that form at different stages (Figure 4, Figure 4—figure supplement 1). Whereas paralyzed embryos at both twitching and swimming stages show reduced tenocyte matn1 expression (Figure 3D), our isHCR data reveal that expression only rebounds after full recovery of muscle contraction in the ima enthesis (Figure 8B, Figure 4—figure supplement 1; Figure 4). These spatial and temporal differences support our hypothesis that these are bona fide embryonic entheseal tenocytes specified at the edges of cartilages as muscles first attach (Subramanian et al., 2023). They are also consistent with studies showing that matn1 transcription is upregulated upon mechanical load in cultured chondrocytes (Chen et al., 2016). Chondrocyte ECM becomes disorganized in Matn1−/− mutant mice exposed to mechanical loads after medial meniscus destabilization surgery (Chen et al., 2016; Li et al., 2020). Our data implicate matn1 in tendon/fibrocartilage mechanotransduction and in the initial establishment of ECM stiffness gradients at entheses during embryogenesis (Figure 4, Figure 4—figure supplement 1; Lu and Thomopoulos, 2013).

Mxra5 (also known as adlican) encodes a secreted proteoglycan implicated in cell–cell adhesion and ECM remodeling, mainly in the context of colorectal and other cancers (He et al., 2015; Wang et al., 2013). Mxra5 is expressed in tendons and other connective tissues of developing chick embryos as well as human fibroblasts (Chondrogianni et al., 2004; Robins and Capehart, 2018). We find that zebrafish mxra5b expression is downregulated in all tenocytes at the onset of embryonic muscle contraction, unlike matn1 (Figures 3 and 5, Figure 5—figure supplements 13). Consistent with a force-responsive gene, MXRA5 is inhibited by TGF-β1 (Poveda et al., 2017), and associated with migration of dental pulp stem cells (Yoshida et al., 2023). Our results provide the first evidence for regulation of mxra5b transcription in tenocytes by mechanotransduction. However, despite reductions in mxra5b levels overall with loss of active muscle contraction, our isHCR results suggest that these changes differ between distinct tendons and force conditions (Figure 5). For example, in the ima enthesis, paralysis downregulates mxra5b expression, with little rebound after recovery (Figure 8B; Figure 5—figure supplement 1). In contrast, at other entheses and MTJs mxra5b expression returns to WT levels upon full recovery after paralysis (Figures 5 and 8, Figure 5—figure supplements 2 and 3). mxra5b expression may require continuous mechanical activation, levels of which differ between tendons as well as entheses or MTJs (Figure 8B). This heterogeneity may help explain differences between our RNA-seq results for mxra5b and isHCR expression data, since the RNA-seq experiments were performed on FAC-sorted tenocytes of all tendons (Figure 3).

Similar to matn1 and mxra5b, (1) zebrafish klf2a expression localizes to embryonic cranial entheses, (2) its transcription increases in tenocytes at the onset of muscle contraction, and (3) these responses vary between spatially distinct tendons and tendon subdomains (Figure 8B, Figure 3, Figure 4—figure supplement 1, Figure 5—figure supplements 1 and 3, Figure 6). Mammalian Klf2 and Klf4 have been implicated in cell differentiation at tendon-bone entheses (Kult et al., 2021). Cranial tenocytes in zebrafish upregulate klf2a upon recovery from paralysis (Figures 3 and 6, Figure 4—figure supplement 1, Figure 5—figure supplements 1 and 3), though there are discrepancies between isHCR, bulk RNA-seq, and RT-qPCR measurements. These may reflect the fact that klf2a is also expressed in other tissues, such as embryonic vascular and endocardial cells (Figure 2; Goddard et al., 2017) or differences in expression between trunk and cranial tenocyte populations. The isHCR data show distinct entheseal klf2a and MTJ expression patterns (Figure 4—figure supplement 1, Figure 5—figure supplements 1 and 3, Figure 6 and Figure 8B). Klf2-binding sites have been identified upstream of ECM genes such as Col5 in sorted entheseal tenocytes (Kult et al., 2021). Klf2 expression is also upregulated by fluid forces in endocardial cells leading to fibronectin synthesis (Boselli et al., 2015; Lee et al., 2006; Steed et al., 2016). Thus, force-dependent klf2a expression may be critical for tissue-specific ECM remodeling in many contexts.

Together, our bulk RNA-seq analysis of embryonic zebrafish tenocytes and their transcriptional responses to muscle contraction: (1) identifies new regulators of tenocyte–ECM, going beyond the better studied collagens, and (2) highlights the importance of considering developmental events that specify the mechanical properties of tendons as they form. Genes such as matn1, mxra5b, and klf2a show unique expression profiles and changes due to perturbation of muscle contraction, both during normal embryonic development and in response to paralysis (Figure 8B). The presence of these genes in embryonic tendons and their responses to force during normal development versus recovery from paralysis raises questions as to whether the mechanisms that initially establish these structures differ from those that control their maintenance (Figure 8B). Though cell–ECM feedback mechanisms have been studied in controlled 3D microenvironments in vitro, extrapolating these mechanisms into an understanding of in vivo biological processes like development and tissue homeostasis is necessary (Saraswathibhatla et al., 2023). Given the large variation of cell–ECM feedback mechanisms throughout embryonic development, understanding specific tenocyte–ECM interactions will require novel approaches to measuring the effect of varying (1) ECM microenvironment protein compositions, or local ‘matrisomes’, on tenocyte gene expression and (2) intrinsic gene expression patterns of heterogeneous tenocyte populations spatially and functionally. Single-cell approaches (e.g. scRNA-seq) at different developmental stages and in the presence or absence of force, will provide a clearer understanding of how individual spatially and functionally distinct tenocyte populations respond to force in development. Integrating such knowledge of the basic biology of tenocytes at multiple scales will be essential for developing a better picture of tenocyte–ECM interactions at individual tendons, paving the path to advance personalized translational therapies for tendon injuries.

Materials and methods

Key resources table.

Reagent type (species) or resource Designation Source or reference Identifiers Additional information
Strain (Danio rerio) AB Schilling lab RRID:NCBITaxon_7955
Genetic reagent (Danio rerio) Tg(scxa:mCherry) Galloway lab fb301Tg; RRID:ZFIN_ZDB-GENO-180925-6 scx BAC transgenic in AB background
Genetic reagent (Danio rerio) cacnb1−/−; Tg(scxa:mCherry) Schilling lab Ir1092;fb301; RRID:ZFIN_ZDB-ALT-191023-1 cacnb1 mutant in Tg(scx:mCherry) background
Sequence-based reagent T7 sequence-tagged primers This paper Supplementary file 7 2 mM final concentration
Commercial assay or kit Protoscript II first strand cDNA synthesis kit New England Biolabs Cat # E6560
Commercial assay or kit T7 RNA polymerase Millipore Sigma (Roche) Cat # 10881767001
Commercial assay or kit Monarch Total RNA Miniprep kit New England Biolabs Cat # T2010S
Commercial assay or kit DIG RNA labeling mix Millipore Sigma (Roche) Cat # 11277073910
Commercial assay or kit MEGAshortscript T7 transcription kit Thermo Fisher Scientific (Invitrogen) Cat # AM1354
Commercial assay or kit Luna Universal qPCR master mix New England Biolabs Cat # M3003S
Commercial assay or kit Zirconium beads Benchmark Scientific Cat # D1032-10
Commercial assay or kit RNEasy Micro Kit QIAGEN Cat # 74004
Commercial assay or kit 40 µm filter Pluriselect-USA Cat # 43-10040-50
Commercial assay or kit HCR Buffers (v3.0) Molecular Instruments Hybridization buffer, Wash buffer, Amplifier buffer
Antibody Anti-Digoxigenin-AP, Fab fragments Millipore Sigma (Roche) Cat # 11093274910 RRID:AB_514497 1:2000
Antibody Rat monoclonal anti-mCherry antibody Invitrogen (Thermo Fisher Scientific) Cat # M11217 RRID:AB_2536611 1:500
Antibody Chicken polyclonal anti-GFP antibody abcam Cat # ab13970 RRID:AB_300798 1:1000
Antibody Mouse monoclonal anti-myosin heavy chain antibody Developmental Studies Hybridoma Bank (DHSB) Cat # A4.1025 RRID:AB_528356 1:200
Antibody Alexa Fluor 594 AffiniPure F(ab’)2 Fragment Donkey polyclonal anti-Rat IgG (H+L) Jackson ImmunoResearch Laboratories Cat # 712-586-153 RRID:AB_2340691 1:1000
Antibody Alexa Fluor 488 AffiniPure F(ab’)2 Fragment Donkey polyclonal anti-Chicken IgY IgG (H+L) Jackson ImmunoResearch Laboratories Cat # 703-546-155 RRID:AB_2340376 1:1000
Antibody Alexa Fluor 647 AffiniPure F(ab’)2 Fragment Donkey polyclonal anti-Mouse IgG (H+L) Jackson ImmunoResearch Laboratories Cat # 715-606-151 RRID:AB_2340866 1:1000
Chemical compound, drug Nitro Blue Tetrazolium chloride solution (NBT) Millipore Sigma (Roche) Cat # 11383213001
PubChem CID: 9281
Chemical compound, drug 5-Bromo-4-chloro-3-indolyl phosphate solution Millipore Sigma (Roche) Cat # 11383221001
PubChem CID: 81059
Chemical compound, drug Ethylenediaminetetraacetic acid disodium salt Millipore Sigma (Roche) Cat # E5134
PubChem CID: 8759
Chemical compound, drug Calcium chloride hexahydrate Millipore Sigma (Roche) Cat # 21108
PubChem CID: 6093252
Chemical compound, drug Dulbecco’s phosphate-buffered saline (DPBS) 1× Thermo Fisher Scientific (Gibco) Cat # 14190144
Chemical compound, drug Agarose low gelling temperature Millipore Sigma (Sigma-Aldrich) Cat # A9414
Chemical compound, drug SSC buffer 20× Millipore Sigma (Sigma-Aldrich) Cat # S6639-1L
Chemical compound, drug DAPI Millipore Sigma (Sigma-Aldrich) Cat # D9542
PubChem CID: 2954
Sequence-based reagent matn1-B1 Molecular instruments NM_001099740.2 20 probe set
Sequence-based reagent mxra5b-B1 Molecular instruments XM_017357865.2 20 probe set
Sequence-based reagent mxra5b-B1 Molecular instruments XM_017357865.2 20 probe set
Sequence-based reagent klf2a-B3 Molecular instruments NM_131856.3 20 probe set
Sequence-based reagent scxa-B2 Molecular instruments NM_001083069 18 probe set
Sequence-based reagent B1-h1&h2- Alexa Fluor 488 amplifier hairpins Molecular instruments HCR RNA-FISH (v3.0)
Sequence-based reagent B2-h1&h2- Alexa Fluor 546 amplifier hairpins Molecular instruments HCR RNA-FISH (v3.0)
Sequence-based reagent B3-h1&h2- Alexa Fluor 647 amplifier hairpins Molecular instruments HCR RNA-FISH (v3.0)
Other BD FACSAria II Cell Sorter Becton, Dickinson and Company RRID:SCR_018934
Other Bioanalyzer 2100 instrument Agilent RRID:SCR_018043
Other HiSeq 4000 sequencing system Illumina RRID:SCR_016386
Other NextSeq 550 system Illumina RRID:SCR_016381
Other LightCycler 480 Real Time PCR System Roche RRID:SCR_018626
Other SP8 Lightning Confocal microscope Leica RRID:SCR_018169
Other Zeiss Axioplan 2 imaging system Zeiss RRID:SCR_020918
Other MicroPublisher color RTV-5.0 CCD camera QImaging
Other BeadBug 3 microtube homogenizer Benchmark Scientific Cat # D1030
Peptide, recombinant protein Protease (Subtilisin Carlsberg) from Bacillus licheniformis Millipore Sigma (Sigma-Aldrich) Cat # P5380
UniProtKB: P00780.SUBC_BACLI
Peptide, recombinant protein Collagenase Type IV from Hathewaya histolytica (Clostridium histolyticum) Thermo Fisher Scientific (Gibco Life technologies) Cat # 17104019
Peptide, recombinant protein Deoxyribonuclease I (DNase I) from bovine pancreas Millipore Sigma (Roche) Cat # 10104159001 UniProtKB: P00639.DNAS1_BOVIN
Peptide, recombinant protein Bovine serum albumin stock solution (10%) Miltenyi Biotec Cat # 130-091-376
Recombinant DNA reagent pmtb-t7-alpha-bungarotoxin Addgene (Megason lab) Cat # 69542
RRID:Addgene_69542
Software, algorithm Spliced Transcripts Alignment to a Reference (STAR) v2.5.2a Dobin lab RRID:SCR_004463
Software, algorithm Smart-seq2 single sample pipeline Broad Institute RRID:SCR_021228
Software, algorithm RSEM v1.2.31 Dewey lab RRID:SCR_000262
Software, algorithm DESeq2 v1.30.1 Anders lab RRID:SCR_015687
Software, algorithm ClustVis Vilo lab RRID:SCR_017133
Software, algorithm ClusterProfiler R package Qing-Yu lab RRID:SCR_016884
Software, algorithm ShinyGO Ge lab RRID:SCR_019213
Software, algorithm VennDiagram v1.7.3 Boutros lab RRID:SCR_002414
Software, algorithm GeneOverlap v1.26.0 Shen lab RRID:SCR_018419
Software, algorithm LightCycler Software Roche RRID:SCR_012155
Software, algorithm Zeiss Zen Microscopy software Zeiss RRID:SCR_013672
Software, algorithm Leica Application Suite X Leica RRID:SCR_013673
Software, algorithm Imaris Bitplane RRID:SCR_007370
Other Optical Biology Core at UCI Department of Developmental Biology, UCI RRID:SCR_026614 Core facility
Other Genomics Research and Technology Hub Core at UCI Department of Biological Chemistry, UCI RRID:SCR_026615 Core facility
Other Flow Cytometry Core at UCI Stem Cell Research Center, UCI RRID:SCR_026616 Core facility

Zebrafish embryos, transgenics, and mutants

WT zebrafish (AB strain; RRID:NCBITaxon_7955), TgBAC(scxa:mCherry)fb301 transgenics referred to as Tg(scxa:mCherry) (RRID:ZFIN_ZDB-GENO-180925-6), or cacnb1ir1092/ir109;fb301Tg (referred to as cacnb1−/− mutants; RRID:ZFIN_ZDB-ALT-191023-1) embryos were raised in embryo medium at 28.5°C (Westerfield, 2000) and staged as described (Kimmel et al., 1995). Craniofacial musculoskeletal structures were identified and annotated as described previously (Schilling and Kimmel, 1997; Subramanian et al., 2023). All protocols performed on embryos and adult zebrafish in this study had prior approval from the IACUC at UC Irvine (protocol # AUP-23-099).

In situ hybridization

Digoxigenin-labeled antisense RNA probes for matn1, klf2a, and mxra5b were generated using T7 sequence-tagged primers (Supplementary file 7). Total embryo RNA was extracted from 72 hpf WT embryos using Trizol (Invitrogen 15596026) and a Monarch Total RNA Miniprep kit (New England Biolabs (NEB) T2010S). cDNA was synthesized using oligo dT primers and a ProtoScript II First Strand cDNA Synthesis Kit (NEB E6560) and used as a template to synthesize RNA probes using T7 RNA polymerase (Roche, 10881767001) and DIG RNA labeling mix (Roche, 11277073910). Whole-mount ISH was performed with anti-DIG-AP fragments (Roche, 11093274910) at 1:2000 dilution, as described in Thisse and Thisse, 2008.

In situ hybridization chain reaction (isHCR) and immunohistochemistry

isHCR probes were designed by Molecular Instruments Inc (Los Angeles, CA) and whole mount isHCR was performed with amplifiers/probes obtained from Molecular Instruments according to the isHCR v3.0 protocol as described (Choi et al., 2014; Subramanian et al., 2023; Trivedi et al., 2018). Probes/amplifier combinations used were: matn1 (NCBI ref # NM_001099740.2); mxra5b (NCBI ref # XM_017357865.2) in B1 with B1 Alexa Fluor 488, scxa (NCBI ref # NM_001083069) in B2 with B2 Alexa Fluor 546, klf2a (NCBI ref # NM_131856.3) in B3 with B3 Alexa Fluor 647.

Whole embryo immunohistochemistry was performed as described in Subramanian et al., 2018. Primary antibodies used: rat monoclonal anti-mCherry (Molecular Probes − 1:500 dilution, M11217, RRID:AB_2536611), chicken anti-GFP (Abcam – 1:1000 dilution, ab13970, RRID:AB_300798), mouse anti-myosin heavy chain (Developmental Hybridoma – 1:250, A1025, RRID:AB_528356). Secondary antibodies used: Alexa Fluor 594 conjugated donkey anti-rat IgG (Jackson ImmunoResearch – 1:1000 dilution, 712-586-153, RRID:AB_2340691), Alexa Fluor 488 conjugated donkey anti-chicken IgY (Jackson Immunoresearch, 1:1000 dilution, 703-546-155, RRID:AB_2340376), Alexa Fluor 647 conjugated donkey anti-mouse IgG (Jackson Immunoresearch, 1:1000 dilution, 715-606-151, RRID:AB_2340866).

Embryo dissociation and FAC sorting

For WT 36–48 hpf bulk RNA-sequencing (bulk RNA-seq), transgenic Tg(scxa:mCherry) zebrafish embryos were dissociated using collagenase IV (Gibco, 17104019) at a concentration of 6.25 mg/ml without trypsin addition at a temperature of 28°C for roughly 40 min, homogenizing every 5 min using a P1000 pipette as described in Barske et al., 2016. Cells were then filtered through a 40-μm filter (Pluriselect-usa, 43-10040-50). Dissociated cell suspensions were sorted on a BD FACS Aria II cell sorter (RRID:SCR_018934) at the Flow Cytometry Core facility (RRID:SCR_026616). mCherry-positive cells were gated and sorted for those expressing at high levels.

For aBTX-injected 48 hpf bulk RNA-seq, transgenic Tg(scxa:mCherry) embryos, aBTX- or uninjected siblings, were dissociated using Subtilisin A cold-active protease in a stock solution consisting of: 5 µl of 1 M CaCl2 (Sigma 21108; PubChem CID: 6093252), 100 µl of protease stock solution (100 mg of Bacillus licheniformis protease (Sigma P5380; UniProtKB: P00780.SUBC_BACLI) solubilized in 1 ml of Ca and Mg free PBS), 889 µl of 1× DPBS (Thermo Fisher 14190144), 1 µl of 0.5 M EDTA (Sigma E5134; PubChem CID: 8759), and 5 µl of DNAse I (Roche 10104159001; UniProtKB: P00639.DNAS1_BOVIN) stock (25 U/µl in PBS, stored at –80°C) adapted from O’Flanagan et al., 2019. Embryos were triturated once every 2 min for 15 s using a wide bore 1 ml pipette. Every 15 min, the tissue suspension was checked under a dissecting scope to verify dissociation. Full dissociation took ~30 min per sample, and samples were subsequently run through a 40-μm filter to separate dissociated cells from clumps of aggregated undissociated tissue/ECM and washed with 10 ml of PBS/BSA (0.01% BSA in PBS, made fresh on the day of dissociation) and transferred to a 15 ml conical tube. Cells were centrifuged at 600 × g for 5 min at 4°C, supernatant discarded, and cells were resuspended in 1 ml of ice-cold PBS/BSA before being placed on ice (Subramanian et al., 2025). Cells expressing high levels of mCherry+ cells were gated and sorted on a BD FACS Aria II cell sorter.

Bulk RNA-seq library preparation and sequencing

For comparing 36–48 hpf bulk RNA-seq samples an RNEasy Micro Kit (QIAGEN, 74004) was used for RNA extraction of cell lysates from FAC-sorted cells. RNA quality was checked at the UC Irvine Genomics High Throughput Facility (GHTF; RRID:SCR_026615) using a Bioanalyzer 2100 (Agilent; RRID:SCR_018043). The Smart-seq2 protocol (RRID:SCR_021228) was utilized for cDNA library construction (Picelli et al., 2014). Libraries were sequenced at the GHTF using a HiSeq 4000 sequencer (Illumina; RRID:SCR_016386) at a read depth of ~35 M reads per replicate. From 11 total biological replicates (7 for 36 hpf, 4 for 48 hpf) we obtained approximately 10,000 cells per sample replicate.

For 48 hpf bulk RNA-seq experiments, library preparations from aBTX-injected and uninjected siblings were performed by the UCI GHTF. Libraries were sequenced at GHTF on a NextSeq 550 sequencer (Illumina; RRID:SCR_016381) at a read depth of ~35 M reads per replicate.

Bulk RNA-seq data analysis

Bulk RNA-seq reads were mapped to the zebrafish genome version GRCz10 and quantified using STAR v2.5.2a (RRID:SCR_004463) (Dobin et al., 2013) and RSEM v1.2.31 (RRID:SCR_000262) (Li and Dewey, 2011). Differential gene expression analysis and PCA were performed using R package DESeq2 v1.30.1 (RRID:SCR_015687) (Love et al., 2014). Pairwise comparisons were performed between 36 and 48 hpf sorted tenocytes, and a Benjamini–Hochberg FDR adjusted p-value <0.05 was used as a threshold for considering significant differences in gene expression levels. PCA was performed on normalized count data which underwent variance-stabilization-transformation using DESeq2. Heatmaps were generated using ClustVis (RRID:SCR_017133) (Metsalu and Vilo, 2015). GO term enrichment analysis was performed using the ClusterProfiler R package (RRID:SCR_016884) (Wu et al., 2021) and ShinyGO (RRID:SCR_019213) (Ge et al., 2020).

aBTX injections

aBTX mRNA was synthesized from the pmtb-t7-alpha-bungarotoxin vector (Megason lab, Addgene, 69542; RRID:Addgene_69542) as described in Subramanian et al., 2018; Subramanian and Schilling, 2014 and injected into embryos at the 1-cell stage at a volume of 500 picoliters per embryo. aBTX mRNA was injected at a concentration of 90 ng/μl (45 pg/embryo)to paralyze embryos that were collected for analysis at 48 hpf and 150 ng/μl (90 pg/embryo) to paralyze embryos that were collected for analysis at 72 hpf.

RT-qPCR

WT, cacnb1−/−, aBTX-paralyzed, twitching, and recovered embryos were collected at respective timepoints, homogenized in Trizol with prefilled tube kits using high impact zirconium beads (Benchmark Scientific, D1032-10) using a BeadBug 3 Microtube Homogenizer D1030 (Benchmark Scientific), and RNA was extracted as described previously (Subramanian et al., 2018). cDNA was prepared according to the standard oligo-dT primer protocol using the ProtoScript II First Strand cDNA Synthesis Kit (NEB E6560). cDNA was diluted 1:25 in water and used as template for RT-qPCR using the Luna Universal qPCR master mix (NEB M3003S). Primers used are listed inSupplementary file 7. Primer efficiencies were calculated with the formula PCR-efficiency = 10(−1/slope) from a linear regression of Cp/ln(DNA) using a serial dilution of each primer with 72 hpf embryo cDNA as described in Pfaffl, 2001. PCR reactions were performed on a LightCycler 480 II Real Time PCR Instrument (Roche; RRID:SCR_018626) and analyzed using LightCycler 480 Software (Roche; RRID:SCR_012155). Each RT-qPCR experiment was repeated in triplicate for each biological replicate, and at least two biological replicates were used for each analysis. p-values were calculated using a two-tailed Student’s t-test with α = 0.05 in Microsoft Excel. Bar charts in Figure 3 present mean ± standard error. Venn diagram was created using the VennDiagram v1.7.3 (RRID:SCR_002414) R package with the gene list overlap tested with the Fisher’s exact test from the GeneOverlap v1.26.0 (RRID:SCR_018419) R package (Li Shen, 2017).

Imaging and isHCR quantification

Whole embryos imaged for chromogenic ISH were mounted on slides in 80% glycerol and imaged using a Zeiss Axioplan 2 compound microscope (RRID:SCR_020918) utilizing an AxioCam 305 Color Micropublisher 5.0 RTV camera with Zeiss Zen 3.1 (blue edition; RRID:SCR_013672) software. Embryos imaged for isHCR were embedded in 1% low melting point agarose/5× SSC and imaged on a Leica SP8 confocal microscope (RRID:SCR_018169) using the PL APO CS2 40×/1.10 W objective. Whole embryos imaged for isHCR were mounted in slide dishes in 1% low melt agarose with either 5× SSC (if only isHCR was performed) or 1× PBT (if isHCR combined with immunofluorescence was performed) and imaged using a Leica SP8 confocal microscope with LASX software (RRID:SCR_013673). isHCR voxel colocalizations in Figure 2 and Figure 2—figure supplement 1 were performed using the ‘Coloc’ function in Imaris 10.0.1 (RRID:SCR_007370) at the Optical Biology Core (RRID:SCR_026614) as described in Subramanian et al., 2023. Voxel colocalization only shows overlap of fluorescent channels within a particular voxel which may not, in some instances, fully reflect actual colocalization of fluorescence within a particular cell due to the punctate nature of isHCR fluorescence. isHCR single-cell quantification was performed in Imaris 10.0.1 using DAPI (Sigma D9542; PubChem CID: 2954) as a nuclear marker, as described in Subramanian et al., 2023. Embryo imaging for a single experiment was performed with identical parameters across conditions. Briefly, an ROI of the DAPI-stained nucleus from each 3D stack was traced through individual z-slices and mean voxel-intensity (AU) was measured. matn1/Scxa co-expressing cells measured were located at the ima enthesis on Meckels cartilage and sht enthesis at the anterior edge of the ch cartilage. klf2a/Scxa and mxra5b/Scxa co-expressing cells measured were located at the ima enthesis and sht enthesis, mhj MTJ and sht MTJ. Experimental conditions pertaining to each embryo image were saved separately, measurements were performed, and conditions were matched to each image. All p-values were calculated using a linear mixed effects model with individual embryos set as the random variable, and cells set as the fixed variable using the lme4 and lmetest R packages. Tukey–Kramer post hoc tests for pairwise analyses were then performed (ns = not significant, *p < 0.05, **p < 0.01, ***p < 0.001).

Multiplex CRISPR–Cas9 genome editing of matn1, klf2a, and mxra5b

matn1, klf2a, and mxra5b multiplex gRNA injections were performed using the methodology described in Wu et al., 2021 using gRNA primer sequences obtained from the primer database provided. Briefly, PCR was performed with four primers (per gene) targeting coding regions with T7 and spacer sequences for template gRNA synthesis. Transcription was performed with the T7 Megashortscript kit (Invitrogen AM1354). A 500-ng/µl solution of all four gRNAs were incubated at 37°C and injected into 1-cell stage embryos at a 500-pl volume per embryo.

Acknowledgements

We would like to acknowledge Dr. Daniel Dranow for reviewing the manuscript and assistance provided for experimental design. This study was made possible in part through access to the Optical Biology Core Facility of the Developmental Biology Center, a shared resource supported by the Cancer Center Support Grant (CA-62203). This work was supported by the National Science Foundation (MCB2028424), the National Institutes of Health (R01 DE13828, R01 DE30565, and R01 AR67797 to TFS) and by a fellowship awarded to PKN from the National Science Foundation-Simons Center for Multiscale Cell Fate supported by the Simons Foundation (594598).

Funding Statement

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

Contributor Information

Thomas F Schilling, Email: tschilli@uci.edu.

Jeffrey A Farrell, Eunice Kennedy Shriver National Institute of Child Health and Human Development, United States.

Didier YR Stainier, Max Planck Institute for Heart and Lung Research, Germany.

Funding Information

This paper was supported by the following grants:

  • National Science Foundation MCB2028424 to Thomas F Schilling.

  • National Institutes of Health R01 DE13828 to Thomas F Schilling.

  • National Institutes of Health R01 DE30565 to Thomas F Schilling.

  • National Institutes of Health R01 AR67797 to Thomas F Schilling.

  • National Science Foundation- Simons Center for Multiscale Cell Fate 594598 to Pavan K Nayak.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing.

Conceptualization, Formal analysis, Validation, Visualization, Methodology, Writing – review and editing.

Resources, Supervision, Funding acquisition, Investigation, Project administration, Writing – review and editing.

Ethics

This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved Institutional Animal Care and Use Committee (IACUC) protocols (#AUP-23-099) of the University of California Irvine. The protocol was approved by the UCI IACUC Committee and ULAR (University Laboratory Animal Welfare).

Additional files

Supplementary file 1. Differentially expressed gene list of bulk RNA-seq of sorted mCherry+ tenocytes from 36 hpf vs 48 hpf embryos.
elife-105802-supp1.csv (282.7KB, csv)
Supplementary file 2. ShinyGO analysis of 36 vs. 48 hpf bulk RNA-seq differentially expressed genes.
elife-105802-supp2.csv (15.3KB, csv)
Supplementary file 3. DAVID analysis of 36 vs. 48 hpf bulk RNA-seq differentially expressed genes.
elife-105802-supp3.xlsx (10.6KB, xlsx)
Supplementary file 4. Differentially expressed gene list from bulk RNA-seq of sorted mCherry+ tenocytes from 48 hpf WT vs aBTX-injected embryos.
elife-105802-supp4.csv (140.6KB, csv)
Supplementary file 5. List of differentially expressed genes overlapping between 36 hpf vs 48 hpf bulk RNA-seq and 48 hpf WT vs a-BTX injected paralysis bulk RNA-seq.
elife-105802-supp5.csv (3.3KB, csv)
Supplementary file 6. ShinyGO analysis of differentially expressed genes overlapping between 36 hpf vs 48 hpf bulk RNA-seq and 48 hpf WT vs a-BTX injected paralysis bulk RNA-seq.
elife-105802-supp6.csv (12.9KB, csv)
Supplementary file 7. List of primers used for chromogenic in situ hybridizations and RT-qPCRs.
elife-105802-supp7.xlsx (10.8KB, xlsx)
MDAR checklist

Data availability

We have uploaded our datasets, software code, etc. to the GEO portal. We have received the GEO accession numbers for the datasets – GSE292682 and GSE292683. All source data (quantification data) have been uploaded with the manuscript and referred to in the figure legends, respectively.

The following datasets were generated:

Nayak PK, Subramanian A, Schilling TF. 2025. Raw reads for bulk RNAseq of FAC sorted tenocytes from 36 hpf vs. 48 hpf zebrafish embryos. NCBI Gene Expression Omnibus. GSE292682

Nayak PK, Subramanian A, Schilling TF. 2025. Raw reads of bulk RNAseq from FAC sorted tenocytes of 48 hpf WT vs. aBTX injected paralyzed zebrafish embryos. NCBI Gene Expression Omnibus. GSE292683

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Editor's evaluation

Jeffrey A Farrell 1

This valuable manuscript presents solid evidence that identifies potential force-responsive gene expression responses within tenocytes and developing tendons by comparing unperturbed animals to those with paralyzed muscles. A handful of these force-responsive genes are then validated, which reveals that force-responsive gene expression differs between individual tendons or local biophysical environments and shows a phenotype in mutants for a force-responsive gene expressed during tendon development. Future work that further explores how these particular examples relate to broader force-responsive gene expression programs and that identifies stronger phenotypes when force-responsive gene expression is disrupted will strengthen its conclusions. This work is of interest to the fields of developmental biology, mechanobiology, muscle and tendon biology.

Decision letter

Editor: Jeffrey A Farrell1
Reviewed by: Clarissa Henry2

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 the paper "Transcriptome profiling of tendon fibroblasts at the onset of embryonic muscle contraction reveals novel force-responsive genes" for consideration by eLife. Your article has been reviewed by 3 peer reviewers who have opted to remain anonymous.

Comments to the Authors:

We are sorry to say that the reviewers have decided that this work will not be considered further for publication by eLife. The manuscript has potential but our view is that substantial and possibly repeated revisions would be needed before it could be accepted.

Reviewer #1 (Recommendations for the authors):

The authors exploit an important transition in zebrafish development to understand force response in tenocytes. They identify over 1000 genes that respond to force (by muscle contraction), providing an extensive list of genes to evaluate for roles in mechanoresponse and tendon maturation. While the experiments presented in this manuscript initiate an exciting research program that will advance our understanding of tendon biology, the limited focus on three genes with distinct expression profiles and responses to force limit the interpretation of the presented data. The observation of discrete expression profiles for tendons across the skeleton is interesting, but more work will be necessary to establish the principles of general and local tenocyte response to force. This work continues an exciting direction of study for the authors that will likely reveal tendon-specific and general mechanisms of force response.

The basis of the manuscript centers on changes driven by the muscle contraction that initiates between 36 hpf and 48 hpf, an event that the authors have previously demonstrated drives a range of interesting morphological changes in tendon cells. The authors capture several genes that change gene expression as muscle contraction initiates, including three factors (klf2a, matn1, and mxra5b) that they focus on based on previous literature implicating them with known mechano-responsive pathways. However, whether these genes are direct responders to force or further downstream responders that control the morphological changes previously described is unclear. Despite possible issues with redundancy, the authors should provide a characterization of stable mutant lines from the crispants described in the discussion to strengthen the manuscript.

While the authors discuss possible pathways upstream of candidate genes (ie TGF-β upstream of matn1), the authors do not test the responsiveness of these genes to the predicted pathways in zebrafish, which reduces the impact of relating published studies.

The discrepancies between the experiments that attempt to validate gene expression changes captured in their RNA-seq experiment make the ultimate conclusions of this paper hard to interpret. While the authors suggest interesting ideas about the reasoning for each specific inconsistency, the discrepancies make it unclear which experiment reflects the biological processes described. For example, although not explicitly stated, Figure 4C suggests mxra5b is down-regulated between 36 and 48 hpf, suggesting it is down-regulated as the muscles contract. This is in contrast with the results from the RT-qPCR experiments and in situ-based experiments, confounding conclusions. The authors' results with klf2a are especially hard to interpret, as each experiment shows a different result. Unless the authors provide additional genes with similar responses, the data weakens the manuscript.

Given that their experiments are focused on purified tendon cells, the authors should consider removing all whole animal RT-qPCR experiments from the manuscript. In exchange, fluorescent-based experiments using tendon markers should be repeated in cacnb1-/- fish.

In addition, because the authors only focus on three genes that happen to have different expression patterns with tendons and different responses to force, the hypothesized model for the diverse local responses is poorly supported. Expanding their study to include genes that share similar gene expression profiles and responses to force will strengthen the study.

The authors should consider including RNA-seq data from cacnb1-/- and aBTX-P treated fish to expand their list of relevant force-responsive genes with the patterns captured from their analysis. Additionally, the authors suggest divergent force-responsive features between the trunk and craniofacial tendons. To support this beyond the description of three genes, the authors should consider performing RNA-seq experiments that compare force response between craniofacial and trunk tendons.

Reviewer #2 (Recommendations for the authors):

This manuscript addresses the useful question of how movement impacts tendon development by taking the approach of transcriptomic analysis of tendon precursor cells before and after swimming. The authors claim that they identified novel tendon-associated genes, three of which were investigated further by conducting paralysis/rescue studies to show that expression is force-responsive.

One weakness of this manuscript is that it is not well grounded in extant literature regarding the development and differentiation of cartilage/tendons, but more importantly with previous deep sequencing of scx-expressing cells (mESC-derived, mammalian, porcine, and meta-analyses have been conducted – for example, PMIDs PMC8270956, PMC5037390, PMC7810463, PMC5751986). The manuscript is quite thin, and one approach to filling the manuscript out could be to significantly increase the integration and analysis of their data that focuses on changes occurring during development.

The authors rest a lot of the significance of the study on the novelty of the genes identified, but this is also somewhat confusing, "By paralyzing and restoring muscle contractions in embryos in vivo, we show that three of these novel genes….). For example, mxra5b does not appear to be novel (identified in avian development as expressed in tendons/ligaments (PMID 29877573) and in adult pig tendons (PMC5751986). Similarly, kruppel-like factors have been identified in both being force responsive and as regulators of bi-fated tendon-to-bone attachment cells.

The data methodology/interpretation is either confusing or this reviewer is confused, but the data do not necessarily seem to agree with the stated conclusions. In general, this is a potentially exciting manuscript but, in its current form, is not presented as rigorous or compelling enough.

Example 1 Figure 3 – sxca ish is a little bit difficult to believe as the red dots look like they could be background as well, could the authors cite previous clear data showing that the unimpressive red dots are real? Also, could the authors discuss the other attachment sites (presumably there are some) where co-expression was not observed, and discuss the implications of these data? This figure is very difficult for the reader to scan – it would be helpful to include more labels/graphics, etc. Also – is this a one-off or was this observed in multiple embryos? And, if swimming has already started by 72hpf, was ish done prior to this to show co-expression? It seems like showing expression after swimming is contrary to the point note the figure says 72hpf the text says 51hpf but the figure looks more like the older fish could look in figure 2 – a bit difficult because orientation seems to be different.

Also, co-expression by ish of kfl2a and scxa is difficult in this figure because it is not clear what the white outlines correspond to – they don't clearly overlap with dapi, thus how are they drawn? How frequently was this observed – in all somite boundaries? Similar questions abound with mxra5b – this seems to have such low expression, is it all co-localized with scxa, and what happens later in development?

Example 2 Figure 4 – could the authors please speak to why matn1 is not significantly downregulated at attachment sites in paralyzed embryos versus wild-type in panel I but it is significantly downregulated in whole embryos in panel E? Does this data suggest that matn1 is not force-responsive at attachment sites? Can the authors please explain how the swimming force in the trunk impacts cranial cartilage development? That is one confusing aspect of this manuscript, how head cartilage development relates to swimming. Perhaps showing images of how cartilage and trunk tendons are affected in paralyzed embryos would be helpful.

Reviewer #3 (Recommendations for the authors):

In this work, the authors aim to delineate a model in which mechanical force during embryonic muscle development regulates novel genes in tendon fibroblasts. The role of mechanical forces in regulating gene programs is an exciting area of development and is leading to many unexpected findings across fields.

There are additional experiments that could significantly strengthen the appended manuscript:

1) It would help the reader to understand how clean the FACS-sorted cell population used for sequencing analysis is. In brief, it is hard to fully digest the genomics data without having an understanding of this tendon fibroblast population is pure.

2) Are there phenotypic effects on tendon development from loss of function of any of the identified genes? This remains a large open-ended question of this study.

3) There are instances where the authors use the word 'specific' for the expression patterns of these genes. This is a bit of a misnomer, as genes such as Klf2 are expressed in the vasculature and mxra5b in the notochord. This expression in alternative tissues speaks to why readers need to fully understand how clean and specific the isolated cell population used for sequencing was.

4) The downstream qPCR analysis from this study is done globally on whole fish embryos. Given that these genes are not specific, it is difficult to relate changes in gene expression in whole fish to specific cell types.

5) The authors' use of fluorescent in situ hybridization to increase resolution and assess the overlap of their genes of interest with known genes expressed in tendon fibroblasts is commendable. Unfortunately, most of the expression overlap of interest is on the order of pixel-level resolution and is difficult to interpret for a broad audience.

The authors should be commended for an attempt to address this critical area of biology, however, the manuscript in its current form is lacking the data and resolution to fully support their conclusions.

While the concept presented in this manuscript is extremely interesting and could be of high value to the field, as things currently stand, there isn't enough supporting evidence to support the publication of this work.

Specific suggestions that would need to be addressed:

1) the isolated cell population used for sequencing needs to be validated. The authors need to confirm that there are no contaminating cells, such as endothelial cells or notochord cells, in their preps. Without this, much of the data could be artifactual. For instance, in Figure 3F, almost the entirety of klf2a expression is in the vasculature. While the authors highlight potential areas of interest, it is impossible to tell from confocal stacks if this is a true overlap of expression or just expression in different planes that appear overlapping because the images are Z-projected. In general, the images are not particularly compelling to hinge on the entirety of the manuscript on.

2) Given comment 1, the paper could be strengthened by demonstrating the functional effect of these genes in tendon fibroblasts. What happens if they aren't there? Are there consequences for tendon development? Without this, it is hard to fully realize the implications of the authors' data.

3) While Figure 4 shows that there could be a link between mechanics and expression of these genes, the model used to test this relies on an ion channel loss of function setting. It is nearly impossible to decouple the change in gene expression from the loss of mechanics in this model from the loss of the gating current of the channel. For this reason, additional orthogonal models should be used to bolster and support the authors' claims.

These points should be addressed before a deeper review of the conclusions from this manuscript could be fully addressed.

eLife. 2025 Mar 27;14:e105802. doi: 10.7554/eLife.105802.sa2

Author response


We thank the reviewers for their constructive comments. Here we list our point-by-point responses and have revised the manuscript accordingly.

Multiple reviewers points.

1. All 3 reviewers asked for a better explanation of the changes in matn1, klf2a, and mxra5b expression in the bulk RNA-seq experiments with cacnb1-/- mutants to clarify if they were due to paralysis or instead to gene expression changes reflecting embryonic tendon maturation. To address this, we conducted an additional bulk RNA-seq experiment at 48 hpf (hours postfertilization) on sorted mCherry+ tenocytes from α bungarotoxin (aBTX) injected Tg(scxa:mCherry) paralyzed embryos and uninjected controls. Changes in matn1, klf2a, and mxra5b expression showed a similar trend to our bulk RNA-seq results at 36-48 hpf (Figure 3G), supporting the hypothesis that they are caused by changes muscle contractile forces. We have also added discussion of how these gene expression changes differ from those associated with tenocyte maturation.

2. All 3 reviewers were concerned about contamination of sorted tenocytes with other cell types, in particular because the RT-qPCR results with sorted cells differed from those found by bulk RNA-seq. To address this we provide data showing the stringency of our FACS gating thresholds for the mCherry+ signal (Figure 1—figure supplement 1). In addition, tenocyte-specific expression profiles of matn1, klf2a, and mxra5b were quantified using IMARIS 3D imaging software to select individual tenocyte nuclei from HCRish’s at two different tendon attachments for matn1 and four different attachments for klf2a, and mxra5b, across four different contractile force conditions (Figures 2, 4, 5, 6 and all supplements for these figures). These data reveal not only temporal but also spatial heterogeneity in expression of matn1, klf2a, and mxra5b in tenocyte subpopulations in response to force.

3. Several reviewers criticized figure quality, particularly for our HCRish analyses. Former figures 2 and 3 (current figures 2, 4, 5, 6 and all associated supplements) have now been completely revised to include high resolution images of HCRishs for matn1, mxra5b, and klf2a. These show co-localization with scxa using IMARIS software. The original chromogenic ISH images have been moved to the supplements (See Figure 2—figure supplement 1).

4. Several reviewers suggested we remove the RT-qPCR results. However, these were used initially to validate genes with mechanosensitive activation in our bulk RNA-seq experiments. Global expression changes occur in tissues other than tenocytes (e.g. matn1 in cartilage, klf2a in vasculature) in these embryos. Going by the concerns of the reviewers, we have decided to move the RT-qPCR figure to supplementary data (Figure 3—figure supplement 1). Our HCRish measurements show spatially restricted expression to tenocytes and how they are affected by varying force.

Reviewer #1 (Recommendations for the authors):

The authors exploit an important transition in zebrafish development to understand force response in tenocytes. They identify over 1000 genes that respond to force (by muscle contraction), providing an extensive list of genes to evaluate for roles in mechanoresponse and tendon maturation. While the experiments presented in this manuscript initiate an exciting research program that will advance our understanding of tendon biology, the limited focus on three genes with distinct expression profiles and responses to force limit the interpretation of the presented data. The observation of discrete expression profiles for tendons across the skeleton is interesting, but more work will be necessary to establish the principles of general and local tenocyte response to force. This work continues an exciting direction of study for the authors that will likely reveal tendon-specific and general mechanisms of force response.

See response to multiple reviewers #1 above

1. The basis of the manuscript centers on changes driven by the muscle contraction that initiates between 36 hpf and 48 hpf, an event that the authors have previously demonstrated drives a range of interesting morphological changes in tendon cells. The authors capture several genes that change gene expression as muscle contraction initiates, including three factors (klf2a, matn1, and mxra5b) that they focus on based on previous literature implicating them with known mechano-responsive pathways. However, whether these genes are direct responders to force or further downstream responders that control the morphological changes previously described is unclear. Despite possible issues with redundancy, the authors should provide a characterization of stable mutant lines from the crispants described in the discussion to strengthen the manuscript.

All 3 reviewers asked for a better explanation of the changes in matn1, klf2a, and mxra5b expression in the bulk RNA-seq experiments with cacnb1-/- mutants to clarify if they were due to paralysis or instead to gene expression changes reflecting embryonic tendon maturation. To address this, we conducted an additional bulk RNA-seq experiment at 48 hpf (hours postfertilization) on sorted mCherry+ tenocytes from α bungarotoxin (aBTX) injected Tg(scxa:mCherry) paralyzed embryos and uninjected controls. Changes in matn1, klf2a, and mxra5b expression showed a similar trend to our bulk RNA-seq results at 36-48 hpf (Figure 3G), supporting the hypothesis that they are caused by changes muscle contractile forces. We have also added discussion of how these gene expression changes differ from those associated with tenocyte maturation.

2. While the authors discuss possible pathways upstream of candidate genes (ie TGF-β upstream of matn1), the authors do not test the responsiveness of these genes to the predicted pathways in zebrafish, which reduces the impact of relating published studies.

Our study focuses on force responses of klf2a, matn1, and mxra5b, depending on the tendon type and interface (e.g. MTJ or enthesis). From published literature (including work from our lab) we know that TGFb and other mechanotransduction pathways regulate these responses in tendon tissue. Our work focuses on the mechanosensitive activation of genes during a key developmental stage marking the transition from sporadic movements to free-swimming behavior. Comprehensive analysis of various mechanotransduction pathways regulating each gene is beyond the scope of this paper.

3. The discrepancies between the experiments that attempt to validate gene expression changes captured in their RNA-seq experiment make the ultimate conclusions of this paper hard to interpret. While the authors suggest interesting ideas about the reasoning for each specific inconsistency, the discrepancies make it unclear which experiment reflects the biological processes described. For example, although not explicitly stated, Figure 4C suggests mxra5b is down-regulated between 36 and 48 hpf, suggesting it is down-regulated as the muscles contract. This is in contrast with the results from the RT-qPCR experiments and in situ-based experiments, confounding conclusions. The authors' results with klf2a are especially hard to interpret, as each experiment shows a different result. Unless the authors provide additional genes with similar responses, the data weakens the manuscript.

See also the response to multiple reviewers #1 above. We now include spatial expression data in addition to the temporal gene expression data (See Figure 2, 4, 5, 6, and all associated supplements). We propose a model in which matn1, klf2a, and mxra5b respond to force differently depending on tissue attachment type (i.e. hard vs. soft; enthesis vs MTJ), force intensity, and persistence (paralyzed vs twitching vs fully recovered) (See Figure 8).

4. Given that their experiments are focused on purified tendon cells, the authors should consider removing all whole animal RT-qPCR experiments from the manuscript. In exchange, fluorescent-based experiments using tendon markers should be repeated in cacnb1-/- fish.

See response to multiple reviewers #4 above. Our spatial analyses of gene expression now include HCRish in different tendons, entheses and MTJs in aBTX-injected embryos rather than cacnb1-/-. In previous work in our lab, we know that both aBTX and cacnb1-/- show similar phenotypes and effects on tenocyte development (Subramanian et al. 2018, 2023).

6. In addition, because the authors only focus on three genes that happen to have different expression patterns with tendons and different responses to force, the hypothesized model for the diverse local responses is poorly supported. Expanding their study to include genes that share similar gene expression profiles and responses to force will strengthen the study.

We focus on genes differentially expressed between two key developmental time points during which there is a dramatic change in intensity and persistence of force from muscle activity. We chose matn1, mxra5b, and klf2a based on previous studies supporting a role for force in their expression in other tissues and little evidence for their roles for mechanotransduction in tendons. We utilized the in-depth analysis of these genes to establish a new paradigm of mechanosensitive activation (i.e. that it is tissue specific and not occurring necessarily in a binary on/off modality). Including other genes into this study may show different variability of expression, which is beyond the scope of this paper.

6. The authors should consider including RNA-seq data from cacnb1-/- and aBTX-P treated fish to expand their list of relevant force-responsive genes with the patterns captured from their analysis. Additionally, the authors suggest divergent force-responsive features between the trunk and craniofacial tendons. To support this beyond the description of three genes, the authors should consider performing RNA-seq experiments that compare force response between craniofacial and trunk tendons.

See also our response to point #1 above. Paralysis induced by cacnb1-/- or via injection of aBTX have similar effects on tenocyte morphogenesis (Subramanian et al. 2018) and gene expression (Subramanian et al. 2023). Many of the force-responsive genes identified overlap between the two RNA-seq experiments (See Figure 3E) and we find similar temporal and spatial changes in matn1, mxra5b, and klf2a expression in both datasets (See Figure 3D).

Reviewer #2 (Recommendations for the authors):

1. This manuscript addresses the useful question of how movement impacts tendon development by taking the approach of transcriptomic analysis of tendon precursor cells before and after swimming. The authors claim that they identified novel tendon-associated genes, three of which were investigated further by conducting paralysis/rescue studies to show that expression is force-responsive.

One weakness of this manuscript is that it is not well grounded in extant literature regarding the development and differentiation of cartilage/tendons, but more importantly with previous deep sequencing of scx-expressing cells (mESC-derived, mammalian, porcine, and meta-analyses have been conducted – for example, PMIDs PMC8270956, PMC5037390, PMC7810463, PMC5751986). The manuscript is quite thin, and one approach to filling the manuscript out could be to significantly increase the integration and analysis of their data that focuses on changes occurring during development.

We thank the reviewer for pointing out these studies, which are largely in vitro using mammalian cells and difficult to compare with our in vivo approaches in zebrafish development. PMC7810463 shows expression of KLF2 during murine enthesis development, but does not investigate effects of force on KLF2 expression. PMC5751986 compares KLF2 expression between tendon and enthesis but again not in the context of force.

2. The authors rest a lot of the significance of the study on the novelty of the genes identified, but this is also somewhat confusing, "By paralyzsing and restoring muscle contractions in embryos in vivo, we show that three of these novel genes….). For example, mxra5b does not appear to be novel (identified in avian development as expressed in tendons/ligaments (PMID 29877573) and in adult pig tendons (PMC5751986). Similarly, kruppel-like factors have been identified in both being force responsive and as regulators of bi-fated tendon-to-bone attachment cells.

Our study shows the first evidence of a role for mechanical force from muscle contraction on mxra5b, klf2a or matn1 expression. Since some previous work has implicated these genes in force responses and/or in tendons, we have largely avoided use of “novel” in the manuscript when referring to matn1, klf2a, and mxra5b.

3. The data methodology/interpretation is either confusing or this reviewer is confused, but the data do not necessarily seem to agree with the stated conclusions. In general, this is a potentially exciting manuscript but, in its current form, is not presented as rigorous or compelling enough.

Example 1 Figure 3 – sxca ish is a little bit difficult to believe as the red dots look like they could be background as well, could the authors cite previous clear data showing that the unimpressive red dots are real? Also, could the authors discuss the other attachment sites (presumably there are some) where co-expression was not observed, and discuss the implications of these data? This figure is very difficult for the reader to scan – it would be helpful to include more labels/graphics, etc. Also – is this a one-off or was this observed in multiple embryos? And, if swimming has already started by 72hpf, was ish done prior to this to show co-expression? It seems like showing expression after swimming is contrary to the point note the figure says 72hpf the text says 51hpf but the figure looks more like the older fish could look in figure 2 – a bit difficult because orientation seems to be different.

Please see our response to multiple reviewers #1 and #3. Revised figures show HCRish images of matn1, klf2a, and mxra5b expression, including close-up views of selected dual scxa expressing nuclei, colocalizing voxels with IMARIS software.

1. Figure 1 now illustrates the embryonic zebrafish axial and cranial musculoskeletal systems including the relevant tendons in this study, also labeled in other figures.

2. Revised HCRish data were reanalyzed in IMARIS for colocalization with scxa. It is true that some attachment sites lack scxa tenocytes, (1) e.g. matn1 expressing tenocytes in the mandibulohyoid junction (mhj) tendon as this is a muscle-muscle attachment, as well as the sternyhoideus (sh) MTJ since there is no adjacent cartilage. matn1 is primarily expressed in cartilages and entheses as we show in the new Figure 2, Figure 4, and associated supplements.

3. Co-localization with scxa was performed and quantified for matn1, klf2a, and mxra5b in HCRish with multiple embryos, but we include one representative image for each (See Figure 2, 4, 5, 6 and all associated supplements). We have also expanded the Methods section describing in detail numbers of embryos imaged and tenocytes quantified for each tendon. Including expression changes after onset of swimming behavior is relevant, especially for matn1 and klf2a, since they are upregulated upon initiation of muscle contraction, as we showed in the original submission with bulk RNA-seq.

As described in our response to multiple reviewers #1, to confirm roles for force in regulation of matn1, klf2a, and mxra5b expression we have repeated paralysis experiments using aBTX-injection, with similar results. Over 200 genes are shared between the two datasets suggesting that onset of force rather than simply tendon maturation account for these results.

4. Also, co-expression by ish of kfl2a and scxa is difficult in this figure because it is not clear what the white outlines correspond to – they don't clearly overlap with dapi, thus how are they drawn? How frequently was this observed – in all somite boundaries? Similar questions abound with mxra5b – this seems to have such low expression, is it all co-localized with scxa, and what happens later in development?

We have used IMARIS to improve the HCRish images dramatically. We have revised the images and now present the co-expression of klf2a and scxa in both the trunk at 48 hpf and the head at 72 hpf along with magnified views of individual nuclei which contain colocalization of klf2a and scxa fluorescently labelled transcripts using the voxel colocalization tool available in IMARIS software (See Figure 2G-P). Co-localization in the trunk is observed at most somite boundaries. mxra5b expression in the trunk does not obviously colocalize with scxa, though it localizes to somite boundaries (See Figure 2G-I). Cranial mxra5b and scxa clearly co-localize in cranial tenocytes (See Figure 2Q-V).

5. Example 2 Figure 4 – could the authors please speak to why matn1 is not significantly downregulated at attachment sites in paralyzed embryos versus wild-type in panel I but it is significantly downregulated in whole embryos in panel E? Does this data suggest that matn1 is not force-responsive at attachment sites? Can the authors please explain how the swimming force in the trunk impacts cranial cartilage development? That is one confusing aspect of this manuscript, how head cartilage development relates to swimming. Perhaps showing images of how cartilage and trunk tendons are affected in paralyzed embryos would be helpful.

Please see our responses to multiple reviewers #1 and #3. We have revised Figure 3 and moved RT-qPCR results to Figure 3- supplementary figure 1. Differences between the whole embryo RT-qPCR and HCRish results can be explained by the nature of the measurements. matn1 expression shows no expression in the trunk tenocytes, but shows changes in expression between 36 hpf and 48 hpf, and between WT and paralyzed embryos at 48hpf in the bulk RNAseq. This can be because: 1) matn1 expression is also localized to the pec fin buds (which are not part of the head tendons), which are contracting during the onset of swimming behavior and 2) at 48 hpf, matn1 expression also localizes to the developing jaw cartilages and tendons (See Figure 1C and Figure 2. Supplement 2), which lie ventrally closer to the trunk muscles and heart before extending anteriorly by 60 hpf. Therefore it is possible that the forces from contractions of the trunk musculature and pec fins have an effect on matn1 expression in the cranial tissues as well indeed, we see mechanosensitive transcriptional patterns of matn1 expression in the RT-qPCRs, and new bulk RNAseq involving aBTX injected paralyzed embryos at 48 hpf, see Figure 3A and G).

Reviewer #3 (Recommendations for the authors):

In this work, the authors aim to delineate a model in which mechanical force during embryonic muscle development regulates novel genes in tendon fibroblasts. The role of mechanical forces in regulating gene programs is an exciting area of development and is leading to many unexpected findings across fields.

There are additional experiments that could significantly strengthen the appended manuscript:

1) It would help the reader to understand how clean the FACS-sorted cell population used for sequencing analysis is. In brief, it is hard to fully digest the genomics data without having an understanding of this tendon fibroblast population is pure.

Please see our response to multiple reviewers #2.

2) Are there phenotypic effects on tendon development from loss of function of any of the identified genes? This remains a large open-ended question of this study.

We performed multiplex CRISPR injections and saw no noticeable embryonic zebrafish phenotypes for matn1 and klf2a. mxra5b-/- CRISPant embryos showed disruptions of somite boundary morphology as well as fewer scxa+ trunk tenocytes, which we have included as Figure 7. matn1-/- knockout mice show either no (Li et al. 2020), or very mild skeletal abnormalities and reduced collagen 2 fibril thickness and collagen 2 synthesis (Huang et al. 1999, Chen et al. 2016). We created a stable matn1 mutant line but mutants were viable and fertile with no obvious phenotype, though we will further pursue the possibility that these fish have mild cartilage/skeletal phenotypes as adults.

3) There are instances where the authors use the word 'specific' for the expression patterns of these genes. This is a bit of a misnomer, as genes such as Klf2 are expressed in the vasculature and mxra5b in the notochord. This expression in alternative tissues speaks to why readers need to fully understand how clean and specific the isolated cell population used for sequencing was.

Please see our response to multiple reviewers #2 and #3. New bulk-RNA-seq experiments combined with spatial gene expression data should alleviate concerns about tissue or cell-type specificity.

4) The downstream qPCR analysis from this study is done globally on whole fish embryos. Given that these genes are not specific, it is difficult to relate changes in gene expression in whole fish to specific cell types.

Please see our response to multiple reviewers #4 above

5) The authors' use of fluorescent in situ hybridization to increase resolution and assess the overlap of their genes of interest with known genes expressed in tendon fibroblasts is commendable. Unfortunately, most of the expression overlap of interest is on the order of pixel-level resolution and is difficult to interpret for a broad audience.

Please see our response to multiple reviewers #3. We have completely revised the HCRish images and quantification of the spatial expression data.

While Figure 4 shows that there could be a link between mechanics and expression of these genes, the model used to test this relies on an ion channel loss of function setting. It is nearly impossible to decouple the change in gene expression from the loss of mechanics in this model from the loss of the gating current of the channel. For this reason, additional orthogonal models should be used to bolster and support the authors' claims.

Please see our response to multiple reviewers #1. Both cacnb1-/- and a-BTX injection cause paralysis by different mechanisms. Both have been extensively used in zebrafish to cause paralysis and cause no obvious changes in embryogenesis or cranial musculoskeletal development until 5 dpf (Subramanian et al. 2018, Subramanian et al. 2023) and have been used extensively in other systems (Sohal et al. 1979).

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Citations

    1. Nayak PK, Subramanian A, Schilling TF. 2025. Raw reads for bulk RNAseq of FAC sorted tenocytes from 36 hpf vs. 48 hpf zebrafish embryos. NCBI Gene Expression Omnibus. GSE292682
    2. Nayak PK, Subramanian A, Schilling TF. 2025. Raw reads of bulk RNAseq from FAC sorted tenocytes of 48 hpf WT vs. aBTX injected paralyzed zebrafish embryos. NCBI Gene Expression Omnibus. GSE292683

    Supplementary Materials

    Figure 4—source data 1. Measurements of matn1 isHCR signal intensity in ima enthesis tenocytes.
    Figure 4—figure supplement 1—source data 1. Measurements of matn1 and klf2a isHCR signal intensity in sht enthesis tenocytes.
    Figure 5—source data 1. Measurements of mxra5b isHCR signal intensity in sht enthesis tenocytes.
    Figure 5—figure supplement 1—source data 1. Measurements of klf2a and mxra5b isHCR signal intensity in ima enthesis tenocytes.
    Figure 5—figure supplement 2—source data 1. Measurements of mxra5b isHCR signal intensity in mhj MTJ tenocytes.
    Figure 5—figure supplement 3—source data 1. Measurements of mxra5b and klf2a isHCR signal intensity in sht MTJ tenocytes.
    Figure 6—source data 1. Measurements of klf2a isHCR signal intensity in mhj MTJ tenocytes.
    Supplementary file 1. Differentially expressed gene list of bulk RNA-seq of sorted mCherry+ tenocytes from 36 hpf vs 48 hpf embryos.
    elife-105802-supp1.csv (282.7KB, csv)
    Supplementary file 2. ShinyGO analysis of 36 vs. 48 hpf bulk RNA-seq differentially expressed genes.
    elife-105802-supp2.csv (15.3KB, csv)
    Supplementary file 3. DAVID analysis of 36 vs. 48 hpf bulk RNA-seq differentially expressed genes.
    elife-105802-supp3.xlsx (10.6KB, xlsx)
    Supplementary file 4. Differentially expressed gene list from bulk RNA-seq of sorted mCherry+ tenocytes from 48 hpf WT vs aBTX-injected embryos.
    elife-105802-supp4.csv (140.6KB, csv)
    Supplementary file 5. List of differentially expressed genes overlapping between 36 hpf vs 48 hpf bulk RNA-seq and 48 hpf WT vs a-BTX injected paralysis bulk RNA-seq.
    elife-105802-supp5.csv (3.3KB, csv)
    Supplementary file 6. ShinyGO analysis of differentially expressed genes overlapping between 36 hpf vs 48 hpf bulk RNA-seq and 48 hpf WT vs a-BTX injected paralysis bulk RNA-seq.
    elife-105802-supp6.csv (12.9KB, csv)
    Supplementary file 7. List of primers used for chromogenic in situ hybridizations and RT-qPCRs.
    elife-105802-supp7.xlsx (10.8KB, xlsx)
    MDAR checklist

    Data Availability Statement

    We have uploaded our datasets, software code, etc. to the GEO portal. We have received the GEO accession numbers for the datasets – GSE292682 and GSE292683. All source data (quantification data) have been uploaded with the manuscript and referred to in the figure legends, respectively.

    The following datasets were generated:

    Nayak PK, Subramanian A, Schilling TF. 2025. Raw reads for bulk RNAseq of FAC sorted tenocytes from 36 hpf vs. 48 hpf zebrafish embryos. NCBI Gene Expression Omnibus. GSE292682

    Nayak PK, Subramanian A, Schilling TF. 2025. Raw reads of bulk RNAseq from FAC sorted tenocytes of 48 hpf WT vs. aBTX injected paralyzed zebrafish embryos. NCBI Gene Expression Omnibus. GSE292683


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