IntroductionTendons are necessary to support body movement by transferring forces between muscle and bone. Unfortunately, tendon injuries are quite prevalent in athletes and in the aging population, comprising 45% of musculoskeletal consultations in the U.S. alone (Yang et al., 2013). Tendons are structurally complex tissues, and their low vascularity and cellularity are contributing factors to their poor regenerative capacity (Liu et al., 2017). The current standard of treatments for severe tendon injury includes conservative approaches and/or surgical intervention (Yang et al., 2013). However, the resulting prolonged rehabilitation, muscle weakness, and high re-injury rates dramatically limit patient outcomes (Webster et al., 2014). Development of a stem cell-mediated solution could improve patient outcomes, yet current approaches fall short in terms of tissue biomechanical performance and tissue organization (Puetzer et al., 2021; Cai et al., 2020; Ho et al., 2014).
Cell-based therapies have shown potential for tendon repair (Abbah et al., 2014). Autologous tendon progenitors and mesenchymal stromal cells (MSCs) have been explored as potential cell sources; however, their limitations for clinical application are associated with their heterogeneity and the need for in vitro expansion for obtaining clinically relevant cell numbers. Cell expansion in vitro can result in phenotypic drift and subsequent functional loss, in addition to low proliferative ability (Jo et al., 2019). Utilizing tenocytes differentiated from induced pluripotent stem cells (iPSCs) holds promise due to their unparalleled developmental plasticity, unlimited self-renewal capacity, and the potential scalability for an off-the-shelf cell source application. Well-established protocols have been developed for the differentiation from pluripotent stem cells to different musculoskeletal cell types, including chondrocytes and osteoblasts (Wang et al., 2013; Kanke et al., 2014; Yamashita et al., 2015; Wu et al., 2021). Recent studies have successfully differentiated tenocytes using mouse iPSCs and embryonic stem cells (Komura et al., 2020; Nakajima and Ikeya, 2021; Kaji et al., 2021; Yoshimoto et al., 2022). Though, recent work has shown a divergence in developmental processes between mouse and human embryos and subsequent differences in developmental cues on tenogenic differentiation between species (Donderwinkel et al., 2022; Brown et al., 2015; Havis et al., 2014; Havis et al., 2016). This highlights the importance of researching the developmental cues of human cells. Furthermore, other studies have either not reported induction efficiency or showed limited syndetome-like/tenocyte induction (Komura et al., 2020; Dale et al., 2018). Deriving a homogenous population of tenocytes from iPSCs with high efficiency continues to be a challenge, largely in part to the limited understanding of their developmental origins and differentiation path from multipotential precursors.
In vertebrate embryogenesis, tendons arise from mesodermal cells of different origin. Axial, limb, and cranial tendons originate from paraxial mesoderm, lateral plate mesoderm, or neural crest (NC), respectively. Despite differential cell and tissue interactions between the three regions, the major molecular regulators of tendon differentiation are shared between all three (Schweitzer et al., 2010; Huang et al., 2015). BMP, TGFβ, Activin/Nodal, FGF, and WNT signaling pathways regulate mesoderm induction from pluripotent cells. Along the mediolateral axis of the embryo, the balance between BMP and WNT signaling gradients specifies mesodermal subtypes (Tani et al., 2020; Loh et al., 2016). The paraxial or presomitic mesoderm (PSM) is derived from neuromesodermal progenitors (NMPs) that are bipotential and able to differentiate into ectodermal and mesodermal lineages (Tani et al., 2020). Recent in vitro studies demonstrated that during mesoderm specification, WNT controls the allocation to PSM and represses lateral plate mesoderm (Loh et al., 2016; Nakajima et al., 2018). Somites (SM) are derived from the anterior PSM following dynamic morphogenetic cyclic signaling, involving Notch, WNT, and FGF (Tani et al., 2020). Somite progenitors are multipotential and are further specified by signaling molecules from the surrounding tissues. Sonic hedgehog (SHH) is secreted from the notochord and the neural tube, and it has been demonstrated as a crucial signaling modulator in sclerotome (SCL) specification of somites combined with low levels of WNT and BMP (Tani et al., 2020; Loh et al., 2016). Murine and avian embryonic development studies concluded that combined SHH activation and BMP inhibition may be sufficient for SCL induction (Tani et al., 2020). However, it was later shown in vitro that WNT directly antagonizes SHH and induces somites to dermomyotome (Loh et al., 2016). Since SCL is in contact with different cell populations and thus diverging signals, it gives rise to different cell populations along the three major patterning axes. SCL develops dorsally into the syndetome (SYN), the precursor of tendons of the body trunk, which is marked by the expression of the transcription factor scleraxis (SCX) (Tani et al., 2020). Recent studies have indicated that FGF, BMP, and TGFβ are orchestrating syndetome differentiation from sclerotome (Kaji et al., 2021; Yoshimoto et al., 2022; Nakajima et al., 2021). A few groups have differentiated mouse iPSCs (Kaji et al., 2021; Yoshimoto et al., 2022; Nakajima et al., 2021) and human iPSCs (Nakajima et al., 2021) to SYN through SCL and have pinpointed the primary role of BMP and TGFβ signaling. However, the nuances of WNT signaling in SYN specification have yet to be explored.
In this study, we sought to fine-tune induction to SYN by further elucidating the signaling pathways involved with tenocyte formation using single-cell transcriptomics. SYN was induced from GMP-ready human iPSCs by stepwise differentiation through PSM-derived SM cells using chemically defined media. At every stage of the induction, we monitored known markers for each developmental step and conducted further characterization with single-cell RNA sequencing (scRNA-seq) and immunostaining. ScRNA-seq analysis revealed off-target differentiation toward a neural phenotype, with WNT family members being identified as crucial to the generation of neural by-products. We hypothesized that WNT signaling inhibition would block cell fate bifurcation to ‘unwanted’ states and drive induction toward a single path. Informed by single-cell transcriptomics, we added the WNT inhibitor Wnt-C59 to the later stages of the differentiation, which improved final induction efficiency of the differentiated tendon progenitor population.
DiscussionIn this study, informed by ontogenesis and recent reports modeling PSC differentiation in vitro, we successfully differentiated human iPSCs to syndetome-like cells in a stepwise manner using a GMP-ready line and chemically defined serum-free media through balancing the BMP, TGFβ, Activin/Nodal, FGF, and WNT signaling pathways. iPSCs were differentiated to PSM, somite, sclerotome, and syndetome-like cells (Figure 1A, B). Gene expression analysis, IF, and scRNA-seq analysis validated the stepwise differentiation using classical markers that define each developmental step. However, gene regulatory network analysis revealed that the major off-target cell populations were highly associated with WNT signaling activation beginning from the SCL stage and into the SYN stage. Using scRNA-seq, we confirmed our hypothesis that WNT inhibition with the WNT signaling inhibitor (WNTi) Wnt-C59 blocked cell fate bifurcation to the ‘unwanted’ neural fates and drove induction toward tenogenesis.
Gene expression analysis showed significant upregulation of stage-specific markers (Figure 1C–G). PSM induction was assessed via the Notch receptor ligand Delta-like protein-1 (DLL1) gene and protein expression. Somites form from the epithelization and segmentation of paraxial mesoderm along the rostro-caudal (RC) axis of the embryo in the process of mesenchymal-to-epithelial transition (MET). Studies in several animal models have shown that somite RC polarity is established prior to SM formation and it is orchestrated by NOTCH signaling (Tani et al., 2020). Many DLL-family proteins, which are NOTCH receptor ligands, are central to this process, with DLL1 shown as the most critical during PSM formation (Hofmann et al., 2004; Teppner et al., 2007). Here, we found that the initial seeding density of iPSCs impacted DLL1 expression and thus induction efficiency. Higher starting densities resulted in decreased DLL1+ cell ratios assessed by flow cytometry (Figure 1—figure supplement 1). This is in line with several reports that have identified starting PSC seeding density as an additional factor influencing cell differentiation in animal models and human cells (LeBlanc et al., 2022). This could be attributed to auto/paracrine factors and cell-cell contact (Wu et al., 2015). Unfortunately, iPSC seeding density is often not reported in published protocols and it may be contributing to discrepancies and reproducibility concerns between different laboratories. Taken together, our data indicate that starting seeding density is a crucial component of chemical induction which could influence reproducibility and translatability of a given approach.
Somitogenesis induction was achieved by TGFβ inhibition (SB431542) and moderate WNT activation (CHIR99021) similar to previous reports (Nakajima et al., 2018). Combined SHH signaling activation (SAG) and BMP inhibition (LDN193189) resulted in induction of SM to SCL. Last, stepwise FGF8 followed by BMP and TGFβ administration induced SYN formation. Early tendon-associated markers were significantly upregulated at the final timepoint of SYN induction (Figure 1G). FGF ligands including FGF8 are secreted by the myotome region of the somite, located next to the sclerotome, inducing scleraxis (SCX) expression, which in turn drives syndetome specification (Tani et al., 2020). Protein expression at each stage was verified with IF (Figure 2). However, at the end of differentiation we noticed gradual cell attrition in the cultures (Figures 1B and 2I). This could be attributed to the heterogeneity of the cell population at SCL stage, which was confirmed by scRNA-seq (Figure 3A).
In the current study, scRNA-seq analysis showed that mesodermal cells persisted until SYN induction (Figure 3A–C). DLL1 expression was significantly upregulated at PSM and downregulated at SM stage. At PSM, 96.2% of the cell population was DLL1+ (Figure 1—figure supplement 1). However, dot plots showed some DLL1 and DLL3 expression in all stages and clusters (Figure 3C, Figure 3—figure supplement 1). Consequently, using a single marker may not be sufficient to assess lineage differentiation efficiency. Trajectory analysis showed two distinctive bifurcations at SM and SCL stages. The first branchpoint was observed at the SM stage, when a Mes (DLL1+DLL3+PARAXIS+) and a transient NMP (PAX3+NRP2+COLEC12+) cell population were identified. NMPs were present at SM and disappeared thereafter, while at SCL two ‘side-arm’ clusters evolved (Figure 3A). The NL cluster was enriched for several neural-associated markers (NRN1+NNAT+CRABP1+ONECUT2+NPTX2+). In the NMP-C side-arm cluster the top DEGs were a combination of neural-associated markers (NOTCH1+CRABP1+GREB1+ID4+), cranial mesodermal markers (DLL1+DLL3+), and ECM genes including collagens and FN1. The emergence of these two off-target cell populations became quite prominent at SCL, while they were further amplified at SYN (Figure 3A).
Sclerotome is derived from the ventromedial part of somites and its induction is orchestrated by SHH (Tani et al., 2020). It is established that SHH and NOG produced in the notochord and neural tube stimulate MET, which produces both NC cells from the ectoderm as well as SCL at the ventromedial aspect of somites (Kahane and Kalcheim, 2020). Different groups have achieved sclerotome induction of iPSCs with varying approaches. For instance, Loh et al., 2016 identified that WNT and SHH acted antagonistically in SCL specification. The NMP-C off-target cell population that was first observed in the SCL and SYN groups could have arisen from NMPs (Figure 3A). NC, which are a transient cell population of ectodermal origin, are derived from bipotential NMPs from the primitive streak in the presence of FGF, WNT, and likely medium BMP activity (Piacentino and Bronner, 2018; Wymeersch et al., 2021). However, the timing and levels of BMP signaling required for NC differentiation are still not well understood. (Wu et al., 2021) observed NC off-target populations during stepwise iPSC to somite to chondroprogenitor differentiation. In that study, the authors first observed NC at the chondroprogenitor stage, and hypothesized that they emerged due to BMP treatment for 3 days at the SCL stage (Wu et al., 2021). In the present study, we observed the emergence of NC cells earlier, and even though we applied a BMP inhibitor during the SM to SCL differentiation, it may not have been sufficient to modulate BMP activation. Lastly, the NC off-target population could have been derived from NMP/NC cells or from NMPs present at SM. In vivo somites give rise to the dermatome and myotome dorsally and the SCL ventromedially (Tani et al., 2020). The dermatome sits next to the ectoderm and can differentiate into the meningotome, which can form spinal meninges as well as fibroblasts of the meninges and arachnoids (Christ et al., 2004). The off-target neural cluster in the present study showed increased expression of markers associated with NC as well as neural markers. Thus, it could be posited that it was derived from the NC off-target subpopulation identified at SCL.
Next, DEG analysis using the IPA platform revealed the interaction between WNT and BMP signaling at the cornerstone between the SYN cluster and the NL and NC off-target ‘side-arm’ clusters (Figure 3J–L). More specifically, upregulation of genes associated with WNT signaling activation in both off-target clusters was observed. Therefore, a WNT pathway inhibitor was employed during SCL and SYN differentiation, which resulted in significant downregulation of selected neural markers, assessed by RT-qPCR and IF (Figure 4).
Treatment with the WNTi following the SM stage resulted in the expansion of the SYN clusters (Figure 5A) as well as complete elimination of the NL off-target cluster (Figure 5A). Further, a new cell population was observed only in the WNTi-treated group, expressing some chondrogenic markers (COL2A1+SOX9+FN1+BGN+COL1A1+), tenogenic genes, and Klf transcription factors, which were recently shown to be associated with bipotential tendon-to-bone attachment cells (Kult et al., 2021). In the same study, these cells were shown to activate a combination of tendon and chondrogenic transcriptomes. Fibrocartilage is found in the enthesis of tendons and shares the same progenitors as the syndetome (Blitz et al., 2013; Sugimoto et al., 2013). Intriguingly, the FC cluster was not observed in the second cell line we differentiated (Figure 3—figure supplement 2), and it was the only observed difference between the two lines. This could have also been related to lineage bias, since the 007i line had been derived from PBMCs while the 83i line has been derived from fibroblasts (Kim et al., 2024). Fibrocartilage progenitors express SCX; however, divergence of SCX+ cells toward tendon or fibrocartilage has yet to be fully understood. In this work, WNT inhibition at the SM stage resulted in increased numbers of SCX+ derivatives compared to SYN while eliminating the NL population. However, the NC cell population was not diminished, indicating that these cells might have been generated due to ‘contaminating’ multipotential NMPs still present at SM and SCL. IPA network analysis revealed WNT pathway activation in mesodermal and NC clusters and not the final products of the induction, that is, NL, FC, and SYN clusters (Figure 5G). This further confirms our hypothesis regarding its activation following SM specification and suggests that it plays an important role in specifying the fate of NMPs toward neural lineages. Further studies are needed to delineate the level of BMP activity required to either induce or block bifurcation of NMPs to NC fates. Interestingly, SCX expression peaked in the middle of SYN induction and decreased at the end (Figure 5—figure supplement 2), when TNMD, COLA1A, and other tenogenic markers (collagens, DCN, BGN1, FN1) were upregulated. This suggests that SYN induction could be further optimized and shortened. Further, WNT inhibition during the entirety of the SYN induction might potentially negatively impact SYN maturation, and it should be further fine-tuned. For instance, WNT signaling has been shown to upregulate TNMD expression in equine BM-MSCs (Miyabara et al., 2014), while it was shown to suppress TNMD and other tenogenic genes in tendon-derived cells in vitro (Kishimoto et al., 2017).
Previous studies investigating iPSC to SYN differentiation reported varying yields at the end of induction. Nakajima et al., 2018 reported 68% SCX + cells using flow cytometry. In a follow-up study, the same research group reported 91.6% SCX+, 90.4% MKX+, 79.9% COL1A1+and 77.5% COL1A2+ protein expression using IF quantification (Nakajima and Ikeya, 2021). However, although informative, it could be noted that IF is only a semi-quantitative assessment burdened with operator bias and lower sensitivity compared to flow cytometry or scRNA-seq, unless performed in a more automated manner (Lara et al., 2021). Nevertheless, despite those caveats, the high individual expression of all four markers (77.5–91.6%) with IF supports a relatively efficient SYN differentiation overall. Kaji et al., 2021 differentiated mESCs from SCX-GFP reporter mice to SYN and reported 90% efficiency assessed by flow cytometry for SCX, while Sugimoto et al. showed 66% efficiency utilizing a similar Scx-GFP mouse model combined with flow cytometry and scRNA-seq assessments (Kaji et al., 2021; Yoshimoto et al., 2022). In the current study, SYN clusters, characterized by scRNA-seq analyses, greatly increased in the SYNWNTi group, from 47.6% prior to WNTi application to 67–78% of total cells after treatment (Figure 5F, Figure 3—figure supplement 2). In conclusion, we showed that WNT inhibition was able to successfully modulate the later stages of differentiation from SM to SYN.
This study is not without limitations. The IPA network analysis is a knowledge-based and hypothesis-driven platform. We have specifically targeted known pathways to be involved in syndetome differentiation. However, WNT signaling stood out with very specific affinity to the off-target populations, and we have verified our findings with experiments proving this hypothesis. In the current study, IF was used solely as a qualitative assessment of protein presence and localization to support our main quantitative findings that were used to optimize the induction (gene expression, flow cytometry, single-cell transcriptomics). However, IF could also be used quantitatively if a more unbiased quantitative approach is employed as described earlier. Further, the SCL to SYN induction, maturation, and expansion in culture was considerably long and resulted in decreased expression of SCX (Figure 5—figure supplement 2), suggesting that it could be shortened. The objective of this study was to test and optimize the induction protocol in two different lines: one that has been widely tested and published (83i derived from fibroblasts) and a GMP-ready line (007i derived from PBMCs). The difference between the two lines could be further explored, since it could be the result of lineage bias. Even though following WNTi treatment, induction efficiency increased considerably, there were still other populations present, including NC and undifferentiated cells. Cell sorting could be used to isolate a more homogeneous cell population. Further, removal of undifferentiated cells is crucial for in vivo preclinical studies. Future work is warranted to assess the functionality of the cells in vivo.
ConclusionsTendons have poor innate healing capacity, and novel approaches are urgently needed. Tendon cell therapy offers potential for regeneration of injured tissues. In this study, we successfully differentiated a GMP-ready human iPSC line to syndetome-like cells in a stepwise manner using fully defined media. However, scRNA-seq trajectory analysis revealed off-target differentiation toward a neural phenotype, resulting in heterogeneous differentiation. DEG and regulatory network analyses demonstrated WNT-associated effectors implicated in the off-target groups, which prompted us to apply a WNT inhibitor at the somite stage. Taken together, our data provide evidence that by manipulating WNT signaling, we can achieve a more specific and robust differentiation of iPSCs to tendon progenitors. Elucidating the mechanism of the WNT signaling pathway using a development-inspired protocol can lead to the development of more powerful and specific differentiation protocols for cell therapy applications. iPSC-derived tendon progenitors can be an off-the-shelf cell source for tendon and ligament injuries that are currently untreatable or have poor surgical outcomes.
10.7554/eLife.89652.3.sa4Author responsePapalamprouAngelaAuthorCedars-Sinai Medical CenterLos AngelesUnited StatesYuVictoriaAuthorCedars-Sinai Medical CenterLos AngelesUnited StatesJiangWensenAuthorCedars-Sinai Medical CenterLos AngelesUnited StatesSheynJuliaAuthorCedars-Sinai Medical CenterLos AngelesUnited StatesStefanovicTinaAuthorCedars-Sinai Medical CenterLos AngelesUnited StatesChenAngelAuthorCedars-Sinai Medical CenterLos AngelesUnited StatesCastanedaChloeAuthorCedars-Sinai Medical CenterLos AngelesUnited StatesChavezMelissaAuthorCedars-Sinai Medical CenterLos AngelesUnited StatesSheynDmitriyAuthorCedars-Sinai Medical CenterLos AngelesUnited StatesThe following is the authors’ response to the original reviews.
Reviewer 1:
Comment 1: IPA analysis was performed after scRNA-seq. Although it is knowledge-based software with convenient graphic utilities, it is questionable whether an unbiased genome-level analysis was performed. Therefore, it is not convincing if WNT is the only and best signal for the branching-off marker. Perhaps independent approaches, such as GO, pathway, or module analyses, should be performed to validate the finding.
Thanks for your comment. We agree with the reviewer that IPA is a knowledge-based and a hypothesis-driven method. Our hypothesis was that WNT/BMP pathways, among others, are heavily involved in the development of mesenchymal tissues in general and differentiation of tendons specifically. Therefore, we have looked at differentially expressed genes between clusters from a broad array of pathways featured in IPA that could point us towards molecular function that could make a difference. We further corroborated this hypothesis by using WNT inhibitors in subsequent experiments. To address this point, we have supplemented the discussion section with the following remark:
“This study is not without limitations. The IPA network analysis is a knowledge-based and hypothesis driven platform. We have specifically targeted known pathways to be involved in syndetome differentiation. However, WNT signaling stood out with very specific affinity to the off-target populations and we have verified our findings with experiments proving this hypothesis.”
Per the reviewer’s suggestion, we also performed a non-biased GO analysis (Supp. Fig. 6). Multiple pathways were detected in the three clusters of interest (Supp. Fig. 6A-C), including integrin-related and TGFβ-related pathways. However, in these three clusters of interest, WNT signaling was also detected as a prominent pathway. Therefore, we could conclude that it plays a pivotal role in the differentiation process. This hypothesis was later corroborated with WNT inhibitor experiments.
Comment 2: According to the method section, two iPSC lines were used for the study. However, throughout the manuscript, it is not clearly described which line was used for which experiment. Did they show similar efficiency in differentiation and in responses to WNTi? It is also worrisome if using only two lines is the norm in the stem cell field. Please provide a rationale for using only two lines, which will restrict the observation of individual-specific differential responses throughout the study.
Thanks for your comment. This proof-of-concept study is the first investigation that compares data of an in vitro tenogenic induction protocol that has been tested in more than one human iPSC lines. We agree that line-specific phenomena are difficult to interpret and reproduce. Therefore, it is critical to provide data supporting that the findings can be reproduced in more than one line. Some early studies used one line as proof of concept, however now we realize the need to show that the protocol works in at least one additional line.
Here we used the GMP-ready iPSC line CS0007iCTR-n5 for all optimization experiments. This newer low passage feeder-free line was generated from PBMCs and was designated as GMP-ready in the manuscript because it has been derived and cultured using cGMP xeno-free components (mTESR plus medium and rhLaminin-521 matrix substrate instead of Matrigel). We then wanted to confirm the application of the optimized protocol using the reference control line CS83iCTR-22n1 which has already been more widely used by our group1-5 and others.6 This line has been derived from fibroblasts and has been grown and expanded using MatrigelTM and mTESR1, followed by mTESR plus media.
The question of number of lines needed is stage-dependent. In our opinion at the proof-of-concept level, two lines, one of which has been generated in GMP-like conditions is sufficient. Confirmation with multiple lines becomes more pertinent as we move towards scale-up/manufacturing, where considerations regarding robustness and consistency are raised. However, at this stage, it is crucial to understand the developmental processes that are involved in cell differentiation to ensure a more robust protocol can be modified and adapted later. In future studies, as we move towards clinical translation, it is warranted that the approach presented in this work will be further optimized and subsequently evaluated using at least 3 different cell lines that have been generated from various sources.
Comment 3: How similar are syndetome cells with or without WNTi? It would be interesting to check if there are major DEGs that differentiate these two groups of cells.
Thanks for your comment. Single cell RNAseq analysis revealed that treatment with WNTi upregulated tenogenic markers. In SYNWNTi, the expression levels of stage-specific markers COL1A1, COL3A1, SCX, MKX, DCN, BGN, FN1, and TNMD were higher compared to the untreated SYN group, as shown in Figure 5C. Density plots depicted an increase in the number of cells expressing COL1A1, COL3A1, SCX and TNMD in SYNWNTi compared to the SYN group, as illustrated in Figure 5D. Trajectory analysis of the WNTi-treated group revealed the absence of bifurcations observed in the untreated group (Fig. 5E). Therefore, it can be conjured that syndetome cells with and without WNTi are different.
Comment 4: Please discuss the improvement of the current study compared to previous ones (e.g., PMID 36203346 my study, 35083031- Tsutsumi, 35372337- Yoshimoto).
Thanks for your comment. In Papalamprou et al (2023)3, we differentiated iPSCs to mesenchymal stromal-like cells (iMSCs), which were then cultured into a 2D dynamic bioreactor for 7 days. In that study, we examined the impact of simultaneous overexpression of the tendon transcription factor Scleraxis (SCX) using a lentiviral vector and mechanical stimulation on the process of tenogenic differentiation. Following 7 days of uniaxial cyclic loading, we observed notable modifications in the morphology and cytoskeleton organization of iPSC-derived MSCs (iMSCs) overexpressing SCX. Additionally, there was an increase in extracellular matrix (ECM) deposition and alignment, along with upregulation of early and late tendon markers. This proof-of-concept study showed that iPSC-derived MSCs could be a viable cell candidate for cell therapy applications and that mechanical stimulation is contributing to the differentiation of iMSCs towards the tenogenic lineage.
Similarly, Tsutsumi et al7 overexpressed the tendon transcription factor Mohawk (MKX) stably in iPSC-derived MSCs using lentiviral vectors. These cells were then used to seed collagen hydrogels which were mechanically stimulated in a cyclic stretch 3D culture bioreactor for 15 days to create artificial tendon-like tissues, which the authors termed “bio-tendons”. Bio-tendons were then decellularized to remove cellular remnants from the xenogeneic human iPSC-derived cells and were subsequently transplanted in an in vivo Achilles tendon rupture mouse model. The authors reported improved histological and biomechanical properties in the Mkx-bio-tendon mice vs. the GFP-bio-tendon controls, providing another proof-of-concept study in favor of the utilization of iPSC-derived MSCs for tendon cell therapies, while also addressing the immunogenicity of cells of allogeneic/xenogeneic origin. Therefore, the above two studies used tendon transcription factor overexpression and mechanical loading either in 2D or 3D to differentiate MSCs towards the tendon/ligament lineage.
Yoshimoto et al8 optimized a stepwise iPSC to tenocyte induction protocol using a SCX-GFP transgenic mouse iPSC line, by monitoring GFP expression over time. The group performed scRNA-seq to characterize the induction of mesodermal progenitors towards the tenogenic lineage and to shed light into their developmental trajectory. That study unveiled that Retinoic Acid (RA) signaling activation enhanced chondrogenic differentiation, which was in contrast to the study of Kaji et al (2021), which also used a SCX-GFP mouse iPSC line. Kaji et al inhibited TGF and BMP signaling during the process of mesodermal induction and reported that RA signaling eliminated SCX induction entirely and promoted a switch to neural fate. Yoshimoto et al suggested that variations in mesodermal cell identity could be due to the different methods used for mesodermal differentiation. In contrast to the Kaji et al study, Yoshimoto et al opted to stimulate WNT and block the Hedgehog pathway during mesoderm induction. Loh et al (2016) identified the branchpoint from the primitive streak to either the paraxial mesoderm (PSM) or the lateral plate mesoderm (LPM) as the result of two mutually exclusive signaling conditions. Specifically, they reported that induction of PSM was achieved through BMP suppression and WNT stimulation, while the specification of lateral mesoderm was accomplished by BMP stimulation and WNT suppression, all with concurrent TGFβ suppression/FGF stimulation. Lastly, a similar approach towards PSM induction from primitive streak (TGF off/BMP off/ WNT on/FGF on) has been used by many subsequent studies Matsuda et al (2020),9 Wu et al (2021)10 and Nakajima et al (2021).11 The diversity of the above-mentioned approaches points to the plasticity of mesodermal progenitors and the need for additional studies to better understand mesodermal specification and subsequent induction towards sclerotome and syndetome.
In the current study we optimized a stepwise differentiation protocol using xeno-free cGMP ready media and two different cell lines, one of which was cGMP-ready. We used scRNA-seq to characterize the differentiation, which led us to identify off-target cells that were closer to a neural phenotype. We performed pathway analyses and hypothesized that WNT signaling activity might have contributed to the emergence of the off-target cells. To test this, we used a WNT inhibitor (PORCN) to block WNT activity at the SCL stage and at the SYN stage. We found that blockade of WNT signaling at the end of the SM stage and during SCL and SYN induction resulted in a more homogeneous population, while eliminating the neural-like cell cluster. This is the first study that utilized scRNA-seq to shed light into the developmental trajectory of stepwise iPSC to tendon differentiation of human iPSCs and provided a proof-of-concept for the generation of a more homogeneous syndetome population. Further studies are needed to further fine-tune both the process and the final product, as well as elucidate the functionality of iPSC-derived syndetome cells in vitro and in vivo.
Reviewer 2:
General concerns: The authors demonstrated the efficiency of syndetome induction solely by scRNA-seq data analysis before and after pathway inhibition, without using e.g. FACS analysis or immunofluorescence (IF)-staining based assessment. A functional assessment and validation of the induced cells is also completely missing.
We appreciate and agree with the reviewer’s critique regarding further analyses of differentiated iPSC-derived syndetome-like cells, including functional assessment of the differentiated cells. Immunofluorescence was used at all timepoints of induction for phenotype confirmation (Fig. 2,4). Flow cytometry for DLL1 was utilized to benchmark efficient differentiation to PSM Loh et al,12 Nakajima et al11. Specifically, DLL1 expression was assessed with flow cytometry after 4 days of induction, and was used to optimize the parameter of initial iPSC aggregate seeding density, which has been previously found to be crucial for in vitro differentiation protocols (Loh et al12). Unfortunately, this parameter is usually not reported although it could be critical to establish protocol replication between different lines.
The function of tendon progenitors is usually reported as response to mechanical cues and the ability to regenerate tendon injuries. In future studies we intend to assess the functionality of the generated syndetome and tendon progenitors and their response to in vitro biomechanical stimulation as previously reported to iMSCSCX+ cells3, 13 and in vivo in a critical tendon defect similarly to what has been previously reported.2
Comment 1: Notably, in Figure 1D, certain PSM markers (TBXT, MSGN1, WNT3A) show higher expression on day 3. If the authors initiate SM induction on day 3 instead of day 4, could this potentially enhance the efficiency of syndetome-like cell induction?
Thanks for your comment. In the current work, we initially optimized differentiation to PSM via expression of DLL1, whose gene expression peaked at d4. We found that this was influenced by the initial iPSC aggregate seeding density. We wanted to generate a homogeneous DLL1+ population which we assessed via gene expression, flow cytometry, IF and scRNA-seq (Fig. 1D, 2C, 3C and Suppl. Fig.1). Given the fact that different lines might display a diverse developmental timeline, we also confirmed reproducibility of the protocol with a second cell line. We appreciate the reviewer’s suggestion to investigate additional protocol iterations, such as the proposed one at the PSM stage, as we move towards a better understanding of key developmental events during in vitro induction.
Comment 2: In the third paragraph of the result section the authors note, "Interestingly, SCX, a prominent tenogenic transcription factor, was significantly downregulated at the SCL stage compared to iPSC, but upregulated during the differentiation from SCL to SYN." Despite this increase, the expression level of SCX in SYN remains lower than that in iPSCs in Fig.1G and Fig.3C. Can the authors provide an explanation for this? Can the authors provide IF data using iPSCs and compare it with in vitro-induced SYN cells? Can the authors provide e.g. additional scRNA-seq data which could support this statement?
Thank you for your comment. In Fig. 1G, SCX expression in SYN was upregulated compared to SCL, however, it was shown to be similar to iPSCs. This suggests a baseline stochastic expression of SCX possibly stemming from spontaneous differentiation of iPSCs in culture (Fig. 3C). Previous research has shown that tenogenic marker gene expression tends to reduce during postnatal tendon maturation Yin et al., 2016b14 Grinstein et al., 2019.15 Yoshimoto et al (2022) utilized a transgenic mouse iPSC-SCX-GFP line to track SCX expression. It was shown that SCX expression peaked after 7d of tenogenic induction and was then decreased at day 14, which marked the end of tenogenic induction. The authors postulated that this pattern of gene expression could either indicate further maturation of tenocytes at subsequent time points, or that the number of non-tenogenic cells increased from T7 to T14.
In the present work, we showed SCX gene expression upregulation in SYN compared to SCL, as well as significant upregulation of TNMD, EGR1, COL1A1 and COL3A1 (Fig.1G). Supp. Fig.8 has been added to show feature plots of SCX and TNMD expression from SCL, SYN and SYNWNTi. The significant upregulation of later markers of tenogenic differentiation suggests that the 21 days of tenogenic induction might have matured the cells. Since gene expression analysis only conveys a snapshot of the transcriptional profile of a cell population, it is likely that we might have missed the peak of SCX upregulation (Supp. Fig. 5). Following treatment with the WNT inhibitor, the SYNWNTi group displayed increased SCX expression (% cells expressing SCX) compared to SYN, which might also be due to a more homogeneous population of syndetome-like cells following treatment with WNTi. In the SYNWNTi group, TNMD was shown to be expressed in the SYN cluster, whereas SCX was mostly found in the cluster that was labelled as fibrocartilage (FC) cluster based on the expression of COL2A1/SOX9/FN1/BGN/COL1A1 markers. Due to the fact that SCX+/SOX9+ progenitor cells are able to give rise to both tendon and cartilage (Sugimoto 2013)16, it could be postulated that this cluster contains tendon progenitors. Interestingly, the FC cluster was not observed in the second iPSC line that we tested, which resulted in a more homogeneous induction to syndetome (78.5% vs. 66.9% SYN cells, Supp. Table 1 & Supp. Fig.3). This slight discrepancy between the two lines and more specifically the presence of the FC cluster only in the 007i line, warrants further investigation. Taken together, these data indicate that the tenogenic induction duration could likely be shortened. Further work to assess the time course of SCX expression over the entire tenogenic induction could be used to further optimize the in vitro induction. For instance, a human edited iPSCSCX-GFP+ line could be generated and used to track SCX expression during the entire induction.
Comment 3: In the fourth paragraph of the result section the authors state, "SM markers (MEOX1, PAX3) and SCL markers (PAX1, PAX9, NKX3.2, SOX9) were upregulated in a stepwise manner." However, the data for MEOX1 and NKX3.2 seems to be missing from Figure 3B-C. The authors should provide this data and/or additional support for their claim.
Thanks for your comment. Feature plots for MEOX1 and NKX3.2 have been added to the Supplemental information (Supp. Fig. 9).
Comment 4: In Figures 2B and 2E, the background of the red channel seems extremely high. Are there better images available, particularly for MEOX1? Given the expected high expression of MEOX1 in SM cells, the authors should observe a strong signal in the nucleus of the stained somitic mesoderm-like cells, but that is not the case in the shown figure. The authors should provide separate channel images instead of merged ones for clarity. The antibody which the authors used might not be specific. Can the authors provide images using an antibody which has been shown to work previously e.g. antibody by ATLAS (Cat#: HPA045214)?
As requested by the reviewer, we have provided separate channels for those images in the Supplement (Supp. Fig. 7). The images show relatively high expression of these markers in SM cells.
Comment 5: In Fig. 2C and Supplementary Fig. 1, the authors present data from immunofluorescence (IF) staining and FACS analysis using a DLL1 antibody. While FACS analysis indicates an efficiency of 96.2% for DLL1+ cells, this was not clearly observed in their IF data. How can the authors explain this discrepancy? Could the authors quantify their IF data and compare it with the corresponding FACS data?
Thanks for your comment. We performed flow cytometric analysis of DLL1 expression to optimize cell seeding density using the 007i line. In the present study, we used IF only in a qualitative manner, that is to confirm protein expression of selected markers. It could be noted that the use of poly-lysine coated coverslips, which are needed for IF, might have slightly altered the density of the cells on the coverslip vs. the plate. Lastly, it cannot be ruled out that the different substrate could have influenced their phenotype differentially through matrix interactions and signaling. On the other hand, flow cytometry by nature is a quantitative and single cell approach, whereas IF staining is qualitative. Therefore, for the purpose of this proof-of-concept work, we tend to trust the quantitative data from the flow cytometry results more than semi-quantitative confirmation achieved through IF staining using coverslips.
Comment 6: In Fig. 2G, PAX9 is expected to be expressed in the nucleus, but the shown IF staining does not appear to be localized to the nucleus. Could the authors provide improved or alternative images to clarify this? The authors should use antibodies shown to work with high specificity as already reported by other groups.
Thanks for your comment. Indeed, the staining seems to be mostly cytoplasmic. We have used antibodies that were previously reported3 and repeated the staining, however, the same results were replicated. We can speculate that this transcription factor has additional role in the iPSC-derived cells and might be traveling to the cytoplasm. Unfortunately, we have no evidence to this phenomenon.
Comment 7: Why did the authors choose to display day 10 data for SYN induction in Fig. 4A? Could they provide information about the endpoint of their culture at day 21?
Thank you for your comment. In Fig. 1G we provided gene expression analyses results for several selected early and later tendon markers for the endpoint of our culture, that is day 21. Following scRNA-seq at each stage of the differentiation (iPSC at d0, PSM at d4, SM at d8, SCL at d11 and the endpoint day 32 for SYN), we performed DEG analysis using the IPA platform. We identified activation of genes associated with the WNT signaling pathway in the off-target clusters. We hypothesized that WNT pathway inhibition might block the formation of unwanted fates and induce a more homogeneous differentiation outcome. We thus tested a WNT inhibitor and compared the inhibitor-treated group with a non-treated group. We then assessed selected neural markers during the course of the inhibitor application. In Fig. 4A we presented gene expression of key selected markers at day 21 using qPCR, which was approximately in the middle of the syndetome induction. Since we observed that the inhibitor downregulated the selected neural markers, we then applied the inhibitor until the endpoint of the initial induction and proceeded to analyze the results using scRNA-seq (Fig. 5). Lastly, it should be acknowledged that this was a proof-of-concept study, and additional optimizations are needed regarding the application of the inhibitor (timing, duration, concentration, etc).
Comment 8: In Supplementary Fig. 5, the authors depicted the expression level of SCX, a SYN marker, which peaked at day 14 and then decreased. By day 21, it reached a level comparable to that of iPSCs. Given this observation, could the authors provide a characterization of the cells at day 21 during SYN induction using IF? What was the rationale behind selecting 21 days for SYN induction? The authors also need to show 'n numbers'; how many times were the experiments repeated independently (independent experiments)?
Thanks for your comment. During the optimization process, we initially used RT-qPCR to track gene expression of selected tenogenic markers using the 007i line. We found that after 21 days of tenogenic induction there was upregulation of the few established tendon markers, that is COL1A1, COL3A1, EGR1 and quite importantly, the more definitive later tendon marker, TNMD. Thus, we decided to proceed with this protocol prior to testing other compounds including the WNT inhibitor WNT-C59. However, as has been discussed in the manuscript, this extended tenogenic induction resulted in cell attrition without the application of the WNT inhibitor. This phenomenon was ameliorated following WNT inhibition. Thus, it could be postulated that the protocol could be further optimized by shortening tenogenic induction to less than 21 days.
The experiments that were conducted to optimize the differentiation process were repeated independently at least n=3 times using qPCR and IF using two lines, that is the 007i and the 83i line as described in the manuscript. The scRNAseq analysis represents a population of cells from in vitro differentiation that originated from the same donor line, therefore it was performed on n=1 sample at each stage. However, the effects of inhibitor application (sample SYNWNTi) were also confirmed using a second cell line (83i), thus a total of n=2 independent samples were analyzed.
Comment 9: Overall the shown immunofluorescence (IF) data does not appear convincing. Could the authors please provide clearer images, including separate channel images, a bright field image, and magnified views of each staining?
Thanks for your comment. The separate channels images were added to the supplemental data (Supp. Fig. 7). We agree with the reviewer regarding the limitations of IF staining, especially with the added confounding factor of using poly-lysine coated coverslips. We would like to point out, that in the current work IF staining is not the main finding or the primary outcome measure, and that it is only used to further support the differentiation by providing a qualitative assessment of protein presence and localization. We describe in this paper our thesis regarding the limitations of IF and the need for more high-throughput unbiased approaches to quantification when using IF staining. For instance, spatial transcriptomics combined with mass cytometry or flow cytometry could be used for a more unbiased approach. Thus, in the present manuscript we based our conclusion on the quantitative gene expression, single cell sequencing and flow cytometry.
Comment 10: As stated by the authors in the manuscript, another research group performed FACS analysis to assess the efficiency of syndetome induction using SCX antibody, and/or quantification of immunofluorescence (IF) with SCX, MKX, COL1A1, or COL2A1 antibodies. Could the authors conduct a comparative analysis of syndetome induction efficiency both before and after protocol optimization, utilizing FACS analysis in conjunction with an SCX reporter line or antibody staining, e.g. quantifying induction efficiency via immunofluorescence (IF) staining with syndetome-specific marker genes?
Thank you for your comment. As discussed in a previous comment, we agree with the reviewer that the generation of a human iPSC-SCX-GFP line would shed light into SCX expression over the entire course of induction. In the current work we used IF as qualitative confirmation of specific marker expression and we showed the presence of SCX, MKX, COL1 and COL3 in SYNWNTi as well as the absence of neuronal markers. As we also pointed it out in the present manuscript, IF can only be considered as a semi-quantitative assessment burdened with several technical limitations as well as operator bias and lower sensitivity and accuracy compared to flow cytometry or scRNA-seq, unless performed in a more unbiased manner. To further clarify this point, firstly, using poly-lysine coated coverslips for IF staining, results in a different substrate environment compared to the Geltrex-coated plates that were used for the induction. Additionally, we noticed that cells grew overconfluent at the edges of the coverslips. This is an important point, since as we have observed in this work, seeding density is critical for the reproducibility of the protocol. It could further be postulated that a different cell substrate stiffness might also have an effect on this process. In our opinion, in this context IF should rather be used qualitatively and a combination of flow cytometry with scRNAseq should be utilized to draw quantitative conclusions such as induction efficiencies of a certain cell type. Since we also observed inconsistencies with the SCX antibodies we tested, the generation of edited human iPSC lines (such as SCX-GFP, MKX-GFP and TNMD-GFP) would be the preferred approach to further explore the efficiency of differentiation.
Comment 11: To enhance the paper's significance, the authors should conduct functional validation experiments and proper assessment of their induced syndetome-like cells. They could perform e.g. xeno-transplantation experiments with syndetome cells into SCID-mice or injury models. They could also assess whether the in vitro induced cells could be applied for in vitro tendon/ligament formation.
Thanks for your comment. For the purpose of this proof-of-concept in vitro study, our primary goal was to initially evaluate a stepwise tenogenic induction protocol using GMP-ready cell lines and chemically defined media. Then, we wanted to utilize the analytical power of scRNA-seq in order to characterize and optimize the protocol, thus focusing on one developmental stage that is not well understood, that of syndetome specification from sclerotome, and hypothesized that by fine-tuning the WNT pathway we would be able to generate a more homogeneous syndetome cell population. We fully agree with the reviewer that the warranted next steps should be to conduct several functional validation experiments, such as in vitro 2D/3D tendon/ligament formation and in vivo transplantation in allogeneic or xenogeneic injury models.
Comment 12: The authors should also compare their scRNA-seq data with actual human embryo data sets, something which could be done given the recent increase in available human embryo scRNA-seq data sets.
This is a great idea and intriguing study. Unfortunately, not all data sets are available at the moment and specifically embryonic and MSK scRNA-seq data is very scarce, although growing. We have no access to data sets from human tendon development, and thus will have to leave this comparison for future studies.
Reviewer 3:
Comment 1: The data outlining the differences between the differentiation outcome of the two tested iPSCs is intriguing, but the authors fail to comment on potential differences between the two iPSC lines that could result in drastically different cell outputs from the same differentiation protocol. This is a critically important point, as the majority of the SCX+ cells generated from the 007i cells using their WNTi protocol were found in the FC subpopulation that failed to form from the 83i line under the same protocol. From the analysis of only these 2 cell lines in vitro, it is difficult to assess whether this WNTi protocol can be broadly used to generate tenogenic cells.
Thanks for your comment. This proof-of-concept study is the first investigation that compares data of an in vitro tenogenic induction protocol that has been tested into more than one cell lines. Using unsupervised clustering we identified 11 clusters, which were classified into 6 cell subpopulations. The only observed difference between the two lines was a small subset that was labeled as fibrocartilage (FC), which displayed expression of both tenogenic and chondrogenic markers. This subpopulation was observed in 007i line but not in the 83i line at the end of the SYN induction. Importantly, DEG analysis also showed that it was enriched for SCX. It has been shown that SCX+/SOX9+ progenitors are a distinct multipotent cell group, responsible for the development of SCX−/SOX9+ chondrocytes and SCX+/SOX9− tenocytes/ligamentocytes (Sugimoto 2013)16. As noted in a previous comment (Comment 2 from Reviewer 1), we might have missed SCX upregulation during the 21-day syndetome induction. This can be further supported by Fig. 5E trajectory analysis which shows that this subpopulation (FC) precedes the SYN cell subpopulation. The fact that this subpopulation was present in one line but not the other, might indicate that 83i line resulted in a more mature tendon population. Therefore, we would rather posit that in the case of 83i line, it might not be that the FC subpopulation failed to form, but rather that it was missed in our scRNAseq endpoint analysis which showed that a more homogeneous SYN population was formed (8.7 % in 007i vs. 0.26 % in 83i, Supp. Table 1 & Supp. Fig. 3B). Future studies are warranted to characterize the SYN induction timeline as it pertains to SCX expression followed up by maturation from tenogenic progenitor to tenocytes.
Comment 2: The authors make claims to changes in protein expression but fail to quantify either fluorescence intensity or percent cell expression from their immunofluorescence analyses to substantiate these claims. These claims are not fully supported by the data as presented as it is unclear whether there is increased expression of tendon markers at the protein level or more cells surviving the protocol. Additionally, in images where 3 channels are merged, it would be helpful to show individual channels where genes are shown in similar spectra (ie. Fig 2I SCX/MKX). Furthermore, the current layout and labelling scheme of Figure 4 makes it very difficult to compare conditions between SYN and SYNWNTi protocols.
Thanks for your comment. Protein expression at each stage was verified with immunofluorescence cytochemistry whereby cells were cultured onto poly-lysine coated coverslips, which were then fixed, stained and imaged (Fig. 2). However, prior to WNT inhibitor application, we noticed gradual cell attrition in the cultures at the end of differentiation (Fig. 1B, 2I). The images show qualitative differences with and without the WNT inhibitor. This could be attributed to the heterogeneity of the cell population at SCL stage, which was confirmed by scRNA-seq (Fig. 3A). As it has been discussed previously (Reviewer 2 comments 5 & 9), in the current paper we didn’t provide any IF quantitative analysis because of the qualitative nature of the staining technique. In future work another high-resolution imaging modality will be considered like single cell proteomics and flow cytometry or mass cytometry in order to perform a more unbiased quantitative single cell analysis across different stages and samples. Furthermore, we have added single channel images in the supplemental information.
Comment 3: Individual data points should also be presented for all qPCR experiments (ie. Fig 4A). Biological replicate information is missing from several experiments, particularly the immunofluorescence data, and it is unclear whether the qPCR data was generated from technical or biological replicates.
Thanks for your comment. We have added additional information regarding replicates in each figure legend. We have also changed Fig. 4A.
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