Biology subjects

Cahan, P.

Publications and source records attributed to Cahan, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Anatomically matched spatial comparisons identify Trps1 as a regulator of knee interzone formation

How the interzone forms and gives rise to articular cartilage, ligaments, and synovium is incompletely understood. Using our previously generated spatial and single-cell transcriptomic atlas of the mouse hindlimb, we compared the knee interzone with neighboring tissues and with the ankle interzone at matched positions, and identified Trps1 as a knee interzone-enriched candidate. Pseudotime analysis placed Trps1-high cells in a transitional state between chondrogenic progenitors and matrix-producing chondrocytes, with higher chondrogenic and lower fibroblastic program scores. Targeted Trps1 knockdown in hindlimb explants disrupted joint formation, permitted Col2a1 expression within the interzone, and reduced adjacent cartilage formation. The associated chondrogenic program was conserved in the human embryonic knee, whereas TRPS1 expression in osteoarthritic cartilage was associated with a fibroblastic matrix program. These results support a role for Trps1 in knee interzone development and show that anatomically matched spatial comparison with explant perturbation can nominate and test joint regulators.

developmental biology↗

bioRxiv2026

A spatial and single-cell transcriptomic atlas of the embryonic synovial joint and its explant model

Synovial joint formation requires the coordinated differentiation of multiple cell types while balancing opposing migratory cues that recruit joint progenitors and exclude other populations. More specifically, a subset of limb bud mesenchyme is recruited into the interzone while neighboring chondrocytes and endothelial cells are excluded. Existing single-cell references sample this process sparsely, without position, or without the surrounding mesenchyme. Here, we present an atlas of the mouse hindlimb from E11.5 to E15.5 that pairs 228,703 single-cell transcriptomes with 47 spatial transcriptomic sections, annotates 56 cell types by their location, and extends the same profiling to limb explant culture. Explants preserve the lineages and axial patterning of the limb for three days, with delayed chondrocyte hypertrophy. Using the atlas, we found that Sema3c is co-expressed with Sox9 and Gdf5 during condensation and is required for chondrogenesis and joint formation in culture; that interzone cells carry a chondrogenic signature early and a tenogenic signature late; and that a core interzone signature includes eleven previously unreported markers, one of which, Igfbp5, is required for knee formation in explants.

developmental biology↗

bioRxiv2026

CMAC: A deep learning framework for absolute cardiomyocyte transcriptional age estimation

Cardiomyocyte maturation spans embryogenesis through postnatal life and involves coordinated transcriptional transitions that are disrupted in disease and incompletely recapitulated by stem cell-derived cardiomyocytes. However, no unified framework exists for assigning absolute transcriptional maturation age across datasets, platforms, and experimental conditions. Here, we developed CMAC (Cardiomyocyte Maturation Age Clock), integrating 18 murine single-cell RNA-sequencing datasets spanning embryonic and postnatal timepoints, into a batch-invariant probabilistic atlas using single cell variational inference (scVI) with a 100-dimensional latent representation. Analysis of the developmental trajectory revealed highly non-uniform transcriptional change, with 63.9% of the total transcriptional journey completed by birth and the neonatal transition representing the largest single transcriptional step. An XGBoost regressor trained on the CMAC latent space predicted absolute chronological age with a leave-one-dataset-out mean absolute error (MAE) of 4.09 days (median 3.27 days), Pearson r = 0.903, and Spearman {rho} = 0.908 across 18 datasets and six sequencing platforms, outperforming linear regression (MAE = 6.18 days) and PCA-based representations (MAE = 6.35 days). In an independent P14 cohort comprising 98,163 cardiomyocytes from six biological replicates, CMAC achieved a mouse-level MAE of 0.33 days, with all six mice predicted within 1.1 days of chronological age. Application of CMAC to a previously published PGC1/{beta} double-knockout model with established delayed cardiomyocyte maturation independently recapitulated this phenotype, quantifying maturation deficits of 4.77 to 8.60 days across P7 to P28, with the largest deficit at P14. Together, these results establish CMAC as a quantitative, deep learning framework for assigning absolute transcriptional maturation age and quantifying deviations from normal cardiomyocyte development.

genomics↗

bioRxiv2026

DDX3 Regulates the Innate Immune Response to Bone Sarcomas

Osteosarcoma (OS) and Ewing sarcoma (EWS) are the most common malignant bone tumors in children and adolescents, with survival rates around 25% in metastatic disease and few advances in treatment in decades. High DDX3 expression has been reported across various sarcoma subtypes. Depending on the context, DDX3 appears to have opposing roles in regulating the tumor immune microenvironment. Within macrophages, DDX3 promotes inflammatory cytokine expression and supports immune cell function. In contrast, in tumor cells DDX3 suppresses a pro-inflammatory state by unwinding dsRNAs, preventing a Type I interferon response. We show that inhibiting DDX3 with RK-33 leads to dsRNA accumulation, inducing a Type I interferon response and broader inflammatory gene expression changes across multiple sarcoma models, shifting macrophage polarization toward a pro-inflammatory M1-like phenotype. To evaluate whether this innate immune microenvironmental remodeling could translate into clinical benefit, we assessed the therapeutic efficacy of RK-33 alone or in combination with mifamurtide, an immunostimulant, in immune competent mouse models of osteosarcoma, with metastatic burden as the primary outcome. We found that in the absence of MYC over-expression, the combination treatment significantly reduced metastatic spread. These findings support targeting DDX3 as a novel innate immune based therapeutic strategy and highlight that the tumors molecular landscape critically influences therapeutic responsiveness.

cancer biology↗

bioRxiv2026

Prediction of parsimonious and temporally sensitive sets of cell fate engineering transcription factors with IMCell

Transcription factor (TF) cocktails used in cell identity reprogramming protocols have largely been developed from experimental approaches. A handful of computational approaches have been reported, though have not been widely adopted by the scientific community. To standardize their use and assess their performance, we built CompForce, a platform that integrates these tools. Using CompForce, we found that existing computational methods offer modest improvements over differential expression on both synthetic and literature-curated data, and that their lackluster and inconsistent performance could be attributed to a reliance on local centrality metrics. To improve upon these methods, we developed IMCell, a prediction method that is inspired by the influence maximization problem. Unlike existing tools, IMCell returns optimized TF sets rather than ranked TF lists. We demonstrate that IMCell vastly out-performs existing tools, and further extend it to dynamic, stepwise contexts. The tools presented here are available in the R packages CompForce and IMCell.

bioinformatics↗

bioRxiv2025

VIDEO - Visual Integration of Drosophila Enhancer Organization: A tool for integrating and visualizing chromatin accessibility, in vivo transcription factor binding and motif occurrence in tissue-specific differentially expressed genes

AO_SCPLOWBSTRACTC_SCPLOWDissecting gene regulation today relies on many genomic assays - including transcriptional output from RNA-seq, chromatin accessibility from ATAC-seq, and transcription factor (TF) binding from ChIP-seq. Whereas numerous tools exist for each modality and some integrate data across modalities, few allow researchers to interactively explore and visualize how TF binding motifs intersect with transcriptional activity and chromatin accessibility in a tissue-specific context. Here we introduce VIDEO (Visual Integration of Drosophila Enhancer Organization), a web-based analysis tool that enables visualization of conserved TF binding motifs within proximal promoters of genes differentially expressed in specific tissues. Starting with gene lists derived from in situ hybridization, microarray, and/or scRNA-seq studies of WT or mutant samples, one can identify the TFs expressed in each tissue and learn if and where the consensus binding motifs for those TFs are found within the proximal enhancers of a custom gene set. This pipeline also allows for coincident visualization of active chromatin, as determined from ATAC-seq data, and for the visualization of DNA binding data from ChIP-seq datasets for specific TFs. To demonstrate its utility, we apply VIDEO to the well-characterized regulatory system of CrebA and the secretory pathway in the Drosophila salivary gland. We also explore a lesser-known system in the embryonic hindgut to show how utilization of this tool can serve to generate hypotheses regarding regulatory interactions.

genomics↗

bioRxiv2025

CrebA regulation of secretory capacity: Genome-wide transcription profiling coupled with in vivo DNA binding studies

DNA binding assays, expression analyses, and binding site mutagenesis revealed that the Drosophila CrebA transcription factor (TF) boosts secretory capacity in the embryonic salivary gland (SG) through direct regulation of secretory pathway component genes (SPCGs). The mammalian orthologues of CrebA, the Creb3L-family of leucine zipper TFs, not only activate SPCG expression in a variety of mammalian tissues but can also activate SPCG expression in Drosophila embryos, suggesting a highly conserved role for this family of proteins in boosting secretory capacity. However, in vivo assays reveal that CrebA binds far more genes than it regulates, and it remains unclear what distinguishes functional binding. It is also unclear if CrebA is the major factor driving SPCG gene expression in all Drosophila embryonic tissues and/or if CrebA also regulates other tissue-specific functions. Thus, we did single cell RNA sequencing (scRNA-seq) of wild-type (WT) and CrebA null embryos to explore the relationship between CrebA binding and gene regulation. We find that CrebA binds the proximal promoters of its targets, that SPCGs are the major class of genes regulated by CrebA across tissues, and that CrebA is sufficient to activate SPCG expression even in cells that do not normally express the protein. A comparison of scRNA-Seq to other methods for capturing regulated transcripts reveals that the different methodologies identify overlapping but distinct sets of CrebA targets.

genomics↗

bioRxiv2024

Inhibition of DKK-1 by WAY262611 Inhibits Osteosarcoma Metastasis

Osteosarcoma (OS) is the most common primary malignant bone tumor in childhood. Patients who present with metastatic disease at diagnosis or relapse have a very poor prognosis, and this has not changed over the past four decades. The Wnt signaling pathway plays a role in regulating osteogenesis and is implicated in OS pathogenesis. DKK-1 inhibits the canonical Wnt signaling pathway, causing inhibition of osteoblast differentiation and disordered bone repair. Our lab previously demonstrated that a monoclonal antibody against DKK-1 prevented metastatic disease in a mouse model. This study expands upon those findings by demonstrating similar results with a small molecule inhibitor of DKK-1, WAY262611, both in vitro and in vivo. WAY262611 was evaluated in vitro on osteosarcoma cell lines, including proliferation, caspase activation, cell cycle analysis, and signaling pathway activation. We utilized our orthotopic implantation-amputation model of osteosarcoma metastasis in vivo to determine the impact of WAY262611 on primary tumor progression and metastatic outgrowth of disseminated tumor cells. Differentiation status was determined using single cell RNA sequencing. We show here that WAY262611 activates canonical Wnt signaling, enhances nuclear localization and transcriptional activity of beta-catenin, and slows proliferation of OS cell lines. We also show that WAY262611 induces osteoblastic differentiation of an OS patient-derived xenograft in vivo, as well as inhibiting metastasis. This work credentials DKK-1 as a therapeutic target in OS, allowing for manipulation of the Wnt signaling pathway and providing preclinical justification for the development of new biologics for prevention of osteosarcoma metastasis.

cancer biology↗

bioRxiv2024

DNA methylation memory of pancreatic acinar-ductal metaplasia transition state altering Kras-downstream PI3K and Rho GTPase signaling in the absence of Kras mutation

A critical area of recent cancer research is the emergence of transition states between normal and cancer that exhibit increased cell plasticity which underlies tumor cell heterogeneity. Pancreatic ductal adenocarcinoma (PDAC) can arise from the combination of a transition state termed acinar-to-ductal metaplasia (ADM) and a gain-of-function mutation in the proto-oncogene KRAS. During ADM, digestive enzyme-producing acinar cells acquire a transient ductal epithelium-like phenotype while maintaining their geographical acinar organization. One route of ADM initiation is the overexpression of the Kruppel-like factor 4 gene (KLF4) in the absence of oncogenic driver mutations. Here, we asked to what extent cells acquire and retain an epigenetic memory of the ADM transition state in the absence of oncogene mutation. We identified differential DNA methylation at Kras-downstream PI3K and Rho/Rac/Cdc42 GTPase pathway genes during ADM, as well as a corresponding gene expression increase in these pathways. Importantly, differential methylation persisted after gene expression returned to normal. Caerulein exposure, which induces widespread digestive system changes in addition to ADM, showed similar changes in DNA methylation in ADM cells. Regions of differential methylation were enriched for motifs of KLF and AP-1 family transcription factors, as were those of human pancreatic intraepithelial neoplasia (PanIN) samples, demonstrating the relevance of this epigenetic transition state memory in human carcinogenesis. Finally, single-cell spatial transcriptomics revealed that these ADM transition cells were enriched for PI3K pathway and AP1 family members, linking epigenetic memory to cancer cell plasticity even in the absence of oncogene mutation.

cancer biology↗

bioRxiv2024

Cell cycle expression heterogeneity predicts degree of differentiation

Methods that predict fate potential or degree of differentiation from transcriptomic data have identified rare progenitor populations and uncovered developmental regulatory mechanisms. However, some state-of-the-art methods are too computationally burdensome for emerging large-scale data and all methods make inaccurate predictions in certain biological systems. We developed a method in R (stemFinder) that predicts single cell differentiation time based on heterogeneity in cell cycle gene expression. Our method is computationally tractable and is as good as or superior to competitors. As part of our benchmarking, we implemented four different performance metrics to assist potential users in selecting the tool that is most apt for their application. Finally, we explore the relationship between differentiation time and cell fate potential by analyzing a lineage tracing dataset with clonally labelled hematopoietic cells, revealing that metrics of differentiation time are correlated with the number of downstream lineages.

bioinformatics↗

bioRxiv2024

Polyamine Depletion by D, L-alpha-difluoromethylornithine Inhibits Ewing Sarcoma Metastasis by Inducing Ferroptosis

AO_SCPLOWBSTRACTC_SCPLOWPolyamine metabolism and signaling play important roles in multiple cancers but have not previously been studied in Ewing sarcoma. Here, we show that blocking polyamine synthesis with D, L-alpha-difluoromethylornithine (DFMO) causes a G1 cell cycle arrest, dose-dependent decreases in sarcosphere formation from Ewing sarcoma cell lines growing in non-adherent conditions and a decrease in clonogenic growth in soft agar. Further, we utilized our orthotopic implantation/amputation model of Ewing sarcoma metastasis to demonstrate that DFMO slowed primary tumor growth in addition to limiting metastasis. RNA sequencing demonstrated gene expression patterns consistent with induction of ferroptosis caused by polyamine depletion. Induction of ferroptosis was validated in vitro by demonstrating that ferrostatin-1, an inhibitor of ferroptosis, allows sphere formation even in the presence of DFMO. Collectively, these results reveal a novel mechanism by which DFMO prevents metastasis - induction of ferroptosis due to polyamine depletion. Our results provide preclinical justification to test the ability of DFMO to prevent metastatic recurrence in Ewing sarcoma patients at high risk for relapse.

cancer biology↗

bioRxiv2024

Mapping Somatosensory Afferent Circuitry to Bone Identifies Neurotrophic Signals Required for Fracture Healing

The profound pain accompanying bone fracture is mediated by somatosensory neurons, which also appear to be required to initiate bone regeneration following fracture. Surprisingly, the precise neuroanatomical circuitry mediating skeletal nociception and regeneration remains incompletely understood. Here, we characterized somatosensory dorsal root ganglia (DRG) afferent neurons innervating murine long bones before and after experimental long bone fracture in mice. Retrograde labeling of DRG neurons by an adeno-associated virus with peripheral nerve tropism showed AAV-tdT signal. Single cell transcriptomic profiling of 6,648 DRG neurons showed highest labeling across CGRP+ neuron clusters (6.9-17.2%) belonging to unmyelinated C fibers, thinly myelinated A{delta} fibers and A{beta}-Field LTMR (9.2%). Gene expression profiles of retrograde labeled DRG neurons over multiple timepoints following experimental stress fracture revealed dynamic changes in gene expression corresponding to the acute inflammatory (S100a8, S100a9) and mechanical force (Piezo2). Reparative phase after fracture included morphogens such as Tgfb1, Fgf9 and Fgf18. Two methods to surgically or genetically denervate fractured bones were used in combination with scRNA-seq to implicate defective mesenchymal cell proliferation and osteodifferentiation as underlying the poor bone repair capacity in the presence of attenuated innervation. Finally, multi-tissue scRNA-seq and interactome analyses implicated neuron-derived FGF9 as a potent regulator of fracture repair, a finding compatible with in vitro assessments of neuron-to-skeletal mesenchyme interactions.

cell biology↗

bioRxiv2024

OneSC: A computational platform for recapitulating cell state transitions

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high- resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico synthetic cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.

systems biology↗

bioRxiv2024

The Hippo-YAP/TAZ-TEAD axis promotes multipotency in bone marrow stromal cells

The mechanisms by which bone marrow stromal cells (BMSCs) maintain multilineage potency in vitro remain elusive. To identify the transcriptional regulatory circuits that contribute to BMSC multipotency, we performed paired single-nucleus multiomics of the expansion of freshly isolated BMSCs and of BMSCs undergoing tri-lineage differentiation. By computationally reconstructing the regulatory programs associated with initial stages of differentiation and early expansion, we identified the TEAD family of transcription factors, which is inhibited by Hippo signaling, as highly active in the BMSC in vitro multipotent state. Pharmacological inhibition of TEAD enhanced BMSC osteogenic and adipogenic differentiation, whereas its activation maintained BMSCs in an undifferentiated state, supporting a model whereby isolation of BMSCs coincides with a TEAD-controlled transcriptional state linked to multipotency. Our study highlights the Hippo pathway as a pivotal regulator of BMSC multipotency, and our regulatory network inferences are a reservoir of testable hypotheses that link transcription factors and their regulons to specific aspects of BMSC behavior.

genomics↗

bioRxiv2023

Model-X knockoffs reveal data-dependent limits on regulatory network identification

Computational biologists have long sought to automatically infer transcriptional regulatory networks (TRNs) from gene expression data, but such approaches notoriously suffer from false positives. Two points of failure could yield false positives: faulty hypothesis testing, or erroneous assumption of a classic criterion called causal sufficiency. We show that a recent statistical development, model-X knockoffs, can effectively control false positives in tests of conditional independence in mouse and E. coli data, which rules out faulty hypothesis tests. Yet, benchmarking against ChIP and other gold standards reveals highly inflated false discovery rates. This identifies the causal sufficiency assumption as a key limiting factor in TRN inference.

bioinformatics↗

bioRxiv2022

Platform-Agnostic CellNet (PACNet) enables cross-study meta-analysis of cell fate engineering protocols

The optimization of cell fate engineering protocols requires evaluating their fidelity, efficiency, or both. We previously adopted CellNet, a computational tool to quantitatively assess the transcriptional fidelity of engineered cells and tissues as compared to their in vivo counterparts based on bulk RNA-Seq. However, this platform and other similar approaches are sensitive to experimental and analytical aspects of transcriptomics methodologies. This makes it challenging to capitalizing on the expansive, publicly available sets of transcriptomic data that reflect the diversity of cell fate engineering protocols. Here, we present Platform-Agnostic CellNet (PACNet), which extends the functionality of CellNet by enabling the assessment of transcriptional profiles in a platform-agnostic manner, and by enabling the comparison of user-supplied data to panels of engineered cell types from state-of-the-art protocols. To demonstrate the utility of PACNet, we evaluated a range of cell fate engineering protocols for cardiomyocytes and hepatocytes. Through this analysis, we identified the best-performing methods, characterized the extent of intra-protocol and inter-lab variation, and identified common off-target signatures, including a surprising neural and neuroendocrine signature in primary liver-derived organoids. Finally, we made our tool accessible as a user-friendly web application that allows users to upload their own transcriptional profiles and assess their protocols relative to our database of reference engineered samples. Highlights* The development of Platform-Agnostic CellNet (PACNet) that classifies engineered cell populations from transcriptome data regardless of profiling method or transcript abundance estimation method * PACNet enables cross-study comparisons of cell fate engineering protocols * Comparison of cardiomyocyte engineering protocols emphasizes metabolic selection as a key step in achieving a strong cardiomyocyte fate. * PACNet identifies an unexpected off-target neural and neuroendocrine signature in primary liver-derived organoids. eTOC BlurbCahan and colleagues created a computational resource, PACNet, which evaluates the fidelity of cell engineering expression profiles in a platform-agnostic manner to facilitate cross-protocol benchmarking. Examining state-of-the-field cardiomyocyte and hepatocyte derivation protocols, they identified that two techniques in cardiomyocyte engineering best increase cardiac identity and that an off-target neural/neuroendocrine signature in primary liver-derived organoids may reflect a cholangiopathic signature. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/506886v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@e4e985org.highwire.dtl.DTLVardef@9f485borg.highwire.dtl.DTLVardef@1475d0borg.highwire.dtl.DTLVardef@2d8c31_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2021

Gene regulation network inference using k-nearest neighbor-based mutual information estimation- Revisiting an old DREAM

BackgroundA cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past twenty years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization. ResultsIn this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov-Stoogbauer-Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods. ConclusionsUsing three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction - which combines CMIA, and the KSG-MI estimator - achieves an improvement of 20-35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or choose gene candidates for experimental validations.

bioinformatics↗

bioRxiv2021

Signaling and transcriptional networks governing late synovial joint development

BackgroundDuring synovial joint development, cavitation marks the end of the emergence of new cell types and the onset of the consolidation of cell type specific programs. However, the transcriptional programs that regulate this crucial stage prior to joint maturation are incompletely characterized. Gdf5-lineage cells give rise to the majority of joint constituents such as articular cartilage, meniscus, ligament, and tendon. Therefore, to explore pre-maturation of the synovial joint, we performed single cell RNA-Seq analysis of 1,306 Gdf5-lineage cells from the murine knee joint at E17.5. ResultsUsing computational analytics and in situ hybridization, we identified nine sub-states contributing to articular cartilage, meniscus, cruciate ligament, synovium, lining, and surrounding fibrous tissue. We identified a common progenitor population that is predicted to give rise to ligamentaocytes, articular chondrocytes, and lining cells. We further found that while a large number of signaling pathways orchestrate the differentiation of this progenitor to either ligamentocytes or to lining cells, only continued FGF signaling guides these cells to a default chondrocyte fate. ConclusionsOur single cell transcriptional atlas is a resource that can be used to better understand and further study synovial joint development and the reactivation of embryonic programs in diseases such as osteoarthritis.

developmental biology↗