Musculoskeletal Network

Musculoskeletal Network - Overview

Using high-precision transcriptomics to systematically map the atlas of human intervertebral disc (IVD) at single-cell resolution. Consequently, we found remarkable cellular diversity in the human IVD and identified a set of signature markers to recognize the cell types spatially. Furthermore, we deciphered a biological classification of chondrocyte subclusters with distinct role in the ECM homeostasis.Notably, the critical clues were also discovered for progenitor cells with bi-lineage differentiation trajectories in the nucleus pulposus, which discriminatively marked by the ancestry molecules PDGFRA and PROCR and highly enriched PDGF network. Finally, we uncovered the potential vital factors maintaining the IVD homeostasis from the intercellular crosstalk based on the signaling network landscape of the IVD microenvironment. Overall design: Single-cell sequencing of 91,295 IVD cells from 3 nucleus pulposus, 2 cartilage endplate and 2 annulus fibrosus in IVD using the 10X genomics Kit.

A Single-cell Transcriptomic Atlas of Human Intervertebral Disc

Human limbs emerge during the fourth post-conception week as mesenchymal buds, which develop into fully formed limbs over the subsequent months. This process is orchestrated by numerous temporally and spatially restricted gene expression programmes, making congenital alterations in phenotype common. Decades of work with model organisms have defined the fundamental mechanisms underlying vertebrate limb development, but an in-depth characterization of this process in humans has yet to be performed. Here we detail human embryonic limb development across space and time using single-cell and spatial transcriptomics. We demonstrate extensive diversification of cells from a few multipotent progenitors to myriad differentiated cell states, including several novel cell populations. We uncover two waves of human muscle development, each characterized by different cell states regulated by separate gene expression programmes, and identify musculin (MSC) as a key transcriptional repressor maintaining muscle stem cell identity. Through assembly of multiple anatomically continuous spatial transcriptomic samples using VisiumStitcher, we map cells across a sagittal section of a whole fetal hindlimb. We reveal a clear anatomical segregation between genes linked to brachydactyly and polysyndactyly, and uncover transcriptionally and spatially distinct populations of the mesenchyme in the autopod. Finally, we perform single-cell RNA sequencing on mouse embryonic limbs to facilitate cross-species developmental comparison, finding substantial homology between the two species.

A human embryonic limb cell atlas resolved in space and time

Throughout postnatal life, haematopoiesis in the bone marrow (BM) maintains blood and immune cell production. Haematopoiesis first emerges in human BM at 11-12 post conception weeks while fetal liver (FL) haematopoiesis is still expanding. Yet, almost nothing is known about how fetal BM evolves to meet the highly specialised needs of the fetus and newborn infant. Here, we detail the development of fetal BM including stroma using single cell RNA-sequencing. We find that the full blood and immune cell repertoire is established in fetal BM in a short time window of 6-7 weeks early in the second trimester. Fetal BM promotes rapid and extensive diversification of myeloid cells, with granulocytes, eosinophils and dendritic cell subsets emerging for the first time. B-lymphocytes expansion occurs, in contrast with erythroid predominance in FL at the same gestational age. We identify transcriptional and functional differences that underlie tissue-specific identity and cellular diversification in fetal BM and FL. Finally, we reveal selective disruption of B-lymphocyte, erythroid and myeloid development due to cell intrinsic differentiation bias as well as extrinsic regulation through an altered microenvironment in the fetal BM from constitutional chromosome anomaly Down syndrome during this crucial developmental time window. 

There are no restrictions on data availability for new data presented in this study. FASTQ and raw count matrices for droplet-based scRNA-seq data for FBM from fetuses with Down syndrome and fetuses without Down syndrome have been deposited at EMBL-EBI ArrayExpress and at the European Nucleotide Archive (ENA), with accession codes as follows: E-MTAB-9389 (FBM from fetuses with Down syndrome and FBM from fetuses without Down syndrome), E-MTAB-10042 (FBM from fetuses with Down syndrome) and ERP125305 (FBM from fetuses without Down syndrome). FASTQ and raw count matrices for all other new data in this study have been deposited at EMBL-EBI ArrayExpress and at the Gene Expression Omnibus (GEO) with accession codes E-MTAB-9801 (FBM Smart-seq2 scRNA-seq); E-MTAB-9389 (BCR- or TCR-enriched VDJ FBM scRNA-seq; FASTQs only); GSE166895 (CD34+ FBM, fetal liver and cord blood CITE-seq) and GSE166895 (FBM total CITE-seq). The following data are also available to download as Scanpy h5ad objects with transformed counts through our interactive webportal: https://fbm.cellatlas.io/: (i) FBM scRNA-seq (fetuses with Down syndrome); (ii) FBM scRNA-seq (fetuses without Down syndrome); (iii) CD34+ FBM, fetal liver and cord blood CITE-seq; and (iv) FBM total CITE-seq. All source data are available in the accompanying source data file, unless manuscript or figure legend refers to a Supplementary Table. External datasets incorporated into this study include: (i) human fetal liver and yolk sac scRNA-seq data4 (E-MTAB-7407); (ii) human blood monocyte–DC scRNA-seq data10 (GSE94820); (iii) mouse BM scRNA-seq data19 (GSE122467); (iv) fetal and paediatric thymus scRNA-seq data3 (E-MTAB-8581); and (v) adult BM and cord blood scRNA-seq data from the Human Cell Atlas Data Coordination Portal ‘Census of Immune Cells’ project (https://data.humancellatlas.org/explore/projects/cc95ff89-2e68-4a08-a234-480eca21ce79). At the time of submission, there are no known accessibility restrictions on these external datasets. Source data are provided with the paper.

Cell count:
Total post-QC is 164,709:
- 119,971 for 10x scRNA-seq (103,228 from fetal bone marrow; 16,743 from Down syndrome fetal bone marrow) 
- 486 for Smart-seq2 scRNA-seq (from fetal bone marrow)
- 44,252 for CITE-seq (35,273 from CD34+ fetal bone marrow, fetal liver, and cord blood; 8,978 from non-FACs sorted fetal bone marrow)

Blood and immune development in human fetal bone marrow and Down syndrome

Multiple myeloma (MM) is characterized by the uncontrolled proliferation of plasma cells. Despite recent treatment advances, it is still incurable as disease progression is not fully understood. To investigate MM and its immune environment, we apply single cell RNA and linked-read whole genome sequencing to profile 29 longitudinal samples at different disease stages from 14 patients. Here, we collect 17,267 plasma cells and 57,719 immune cells, discovering patient-specific plasma cell profiles and immune cell expression changes. Patients with the same genetic alterations tend to have both plasma cells and immune cells clustered together. By integrating bulk genomics and single cell mapping, we track plasma cell subpopulations across disease stages and find three patterns: stability (from precancer to diagnosis), and gain or loss (from diagnosis to relapse). In multiple patients, we detect "B cell-featured" plasma cell subpopulations that cluster closely with B cells, implicating their cell of origin. We validate AP-1 complex differential expression (JUN and FOS) in plasma cell subpopulations using CyTOF-based protein assays, and integrated analysis of single-cell RNA and CyTOF data reveals AP-1 downstream targets (IL6 and IL1B) potentially leading to inflammation regulation. Our work represents a longitudinal investigation for tumor and microenvironment during MM progression and paves the way for expanding treatment options.

Co-evolution of tumor and immune cells during progression of multiple myeloma.

Cellular heterogeneity can emerge from the expression of only one parental allele, however it has remained unknown to what degree patterns of random monoallelic expression of autosomal genes (aRME) are mitotically inherited (clonal) or stochastic (dynamic) in somatic cells. Here, we resolved this by applying allele-sensitive single-cell RNA-seq on primary mouse fibroblasts and in vivo human T-cells to simultaneously investigate clonal and dynamic aRME. Dynamic aRME affected a considerable portion of the transcriptome, with levels dependent on the cell’s transcriptional activity, but clonal aRME was surprisingly scarce (<1% of genes) and affected mainly lowly expressed genes. Consequently, the overwhelming portion of aRME occurs transiently and is scattered throughout somatic populations rather than, as previously hypothesized, confined to spatially restricted patches of clonally related cells.

Dissecting the clonal nature of allelic expression in somatic cells by single-cell RNA-seq

Unbiased whole-genome methylome was studied in the wound-edge (WE) tissue of chronic wound patients. Methylation status of proximal promoter (1Kb) was calculated using MethylCap-Seq and PrEMeR-CG data analyses. A total of 4689 differentially methylated regions (DMRs) were identified in chronic WE compared to unwounded (UW) human skin. Hypermethylation was more frequently observed (3661 DMRs) in the chronic WE compared to hypomethylation (1028 DMRs). Twenty-six hypermethylated DMRs were involved in epithelial to mesenchymal transition (EMT). Bisulfite sequencing validated hypermethylation of a predicted specific upstream regulator TP53. Whole genome RNA sequencing analysis was performed to qualify findings from methylome analysis. Hierarchical clustering analyses identified a large set of genes, the expression of which were significantly downregulated in chronic WE compared to UW skin. Analysis of the downregulated genes identified the TP53 signaling pathway, including P63, P73, FOXO3, SIRT1 and HDAC1, as being significantly silenced. Direct comparison of hypermethylation and downregulated gene expression identified four genes, ADAM17, NOTCH, TWIST1 and SMURF1, that were common to both data sets and functionally represented the EMT pathway. Single cell RNA sequencing studies identified that these effects on gene expression were limited to the keratinocyte cell compartment. Overall design: In this work, scRNA-seq analyses of human chronic WE characterized its heterogenous cellular composition as compared to unwounded skin.

Genome-Wide DNA Hypermethylation in the Wound-Edge of Chronic Wound Patients Opposes Closure by Impairing Epithelial to Mesenchymal Transition

Synovial inflammation is associated with pain severity in patients with knee osteoarthritis (OA). The aim here was to determine in a population with knee OA, whether synovial tissue from areas associated with pain exhibited different synovial fibroblast subsets, compared to synovial tissue from sites not associated with pain. A further aim was to compare differences between early and end-stage disease synovial fibroblast subsets. Parapatellar synovitis was significantly associated with the pattern of patient-reported pain in knee OA patients. Synovial tissue from sites of patient-reported pain exhibited a differential transcriptomic phenotype, with distinct synovial fibroblast subsets in early OA and end-stage OA. Functional pathway analysis revealed that synovial tissue and fibroblast subsets from painful sites promoted fibrosis, inflammation and the growth and activity of neurons. The secretome of fibroblasts from early OA painful sites induced neurite outgrowth in dorsal root ganglion neurons. Sites of patient-reported pain in knee OA is associated with a different synovial tissue phenotype and distinct synovial fibroblast subsets. Further interrogation of these fibroblast pathotypes will increase our understanding of the role of synovitis in OA joint pain and provide a rationale for the therapeutic targeting of fibroblast subsets to alleviate pain in patients. Overall design: Patients with early knee OA (n=29) and end-stage knee OA (n=22) were recruited. Patient reported pain was recorded by questionnaire and using an anatomical knee pain map. Proton density fat suppressed MRI axial and sagittal sequences were analysed and scored for synovitis. Synovial tissue was obtained from the medial and lateral parapatellar and suprapatellar sites. Fibroblast single cell RNA sequencing was performed using Chromium 10X and analysed using Seurat. Transcriptomes were functionally characterised using Ingenuity Pathway Analysis and the effect of fibroblast secretome on neuronal growth assessed using rat DRG.

Identification of distinct synovial fibroblast subsets associated with pain and progression of knee osteoarthritis using single-cell RNA sequencing

Single-cell expression profiling by RNA-Seq promises to exploit cell-to-cell variation in gene expression to reveal regulatory circuitry governing cell differentiation and other biological processes. Here, we describe Monocle, a novel unsupervised algorithm for ordering cells by progress through differentiation that dramatically increases temporal resolution of expression measurements. This reordering unmasks switch-like changes in expression of key regulatory factors, reveals sequentially organized waves of gene regulation, and exposes regulators of cell differentiation. A functional screen confirms that a number of these regulators dramatically alter the efficiency of myoblast differentiation, demonstrating that single-cell expression analysis with Monocle can uncover new regulators even in well-studied systems. Overall design: We selected primary human myoblasts as a model system of cell differentiation to investigate whether ordering cells by progress revealed new regulators of the process. We sequenced RNA-Seq libraries from each of several hundred cells taken over a time-course of serum-induced differentiation. Please note that this dataset is a single-cell RNA-Seq data set, and each cell comes from a capture plate. Thus, each well of the plate was scored and flagged with several QC criteria prior to library construction, which are provided as sample characteristics; CONTROL indicates that this library is a off-chip tube control library constructed from RNA of approximately 250 cells and 'DEBRIS' indicates that the well contained visible debris (and may or may not include a cell). Libraries marked DEBRIS thus cannot be confirmed to come from a single cell.

Pseudo-temporal ordering of individual cells reveals regulators of differentiation

Developmental processes underlying normal tissue regeneration have been implicated in cancer, but the degree of their enactment during tumor progression and under the selective pressures of immune surveillance, remain unknown. Here we show that human primary lung adenocarcinomas are characterized by the emergence of regenerative cell types, typically seen in response to lung injury, and by striking infidelity among transcription factors specifying most alveolar and bronchial epithelial lineages. In contrast, metastases are enriched for key endoderm and lung-specifying transcription factors, SOX2 and SOX9, and recapitulate more primitive transcriptional programs spanning stem-like to regenerative pulmonary epithelial progenitor states. This developmental continuum mirrors the progressive stages of spontaneous outbreak from metastatic dormancy in a mouse model and exhibits SOX9-dependent resistance to natural killer cells. Loss of developmental stage-specific constraint in macrometastases triggered by natural killer cell depletion suggests a dynamic interplay between developmental plasticity and immune-mediated pruning during metastasis.

Regenerative lineages and immune-mediated pruning in lung cancer metastasis

The developmental trajectory of human skeletal myogenesis and the transition between progenitor and stem cell states are unclear. To address this, we employed single cell RNA-sequencing to profile human skeletal muscle tissues from embryonic, fetal and postnatal stages. In silico, we identified myogenic as well as other cell types and constructed a “roadmap” of human skeletal muscle ontogeny across development. In a similar fashion, we also profiled the heterogeneous cell cultures generated from multiple human pluripotent stem cell (hPSC) myogenic differentiation protocols, and mapped hPSC-derived myogenic progenitors to an embryonic-to-fetal transition period. Additionally, we found differentially enriched biological processes and discovered co-regulated gene networks and transcription factors present at distinct myogenic stages. In summary, this work serves as a resource for advancing our knowledge of human myogenesis. It also provides a tool for better characterization and understanding of hPSC-derived myogenic progenitors for translational applications in skeletal muscle based regenerative medicine.

Single Cell RNA-Sequencing of Human Limb Skeletal Muscle across Development and Myogenic Culture from Pluripotent Stem Cells

Self-renewal of tendons is rare since the vascular formation inside is extremely poor, thus reconstructive surgery using autologous tendons has been often taken place in the case of severe injury. However, the rate of re-injury after surgery is relatively high, and collection of autologous tendons leads muscle weakness which results in prolonged rehabilitation. Here, we introduce the induced pluripotent stem cells (iPSCs)-based technology aiming at developing a new therapeutic option after tendon injury. We derived tenocytes from human iPSCs by recapitulating the normal progression of step-wise narrowing fate decisions in the vertebrate embryo. We used single-cell RNA sequencing to analyze the developmental trajectory of iPSCs-derived tenocytes. Overall design: Single cell rnaseq analysis of the developmental trajectory of iPSC-derived tenocytes

Single cell RNAseq analysis of the developmental trajectory of iPSC-derived tenocytes

The homeostasis of bone metabolism depends on the coupling and precise regulation of various types of cells in bone tissue. However, the communication and interaction between bone tissue cells at the single-cell level remains poorly understood. Thus, we performed single-cell RNA sequencing (scRNA-seq) on the primary human femoral head tissue cells (FHTCs). Nine cell types were identified in 26,574 primary human (FHTCs), including granulocytes, T cells, monocytes, B cells, red blood cells, osteoblastic lineage cells, endothelial cells, endothelial progenitor cells (EPCs) and plasmacytoid dendritic cells. We identified serine protease 23 (PRSS23) and matrix remodeling associated protein 8 (MXRA8) as novel bone metabolism-related genes. Additionally, we found that several subtypes of monocytes, T cells and B cells were related to bone metabolism. Cell-cell communication analysis showed that collagen, chemokine, transforming growth factor and their ligands have significant roles in the crosstalks between FHTCs. In particular, EPCs communicated with osteoblastic lineage cells closely via the "COL2A1-ITGB1" pair. Collectively, this study provided an initial characterization of the cellular composition of the human FHTCs and the complex crosstalks between them at the single-cell level. It is a unique starting resource for in-depth insights into bone metabolism. Overall design: Applied scRNA-seq technology to characterize cellular heterogeneity at single-cell level in freshly isolated human femoral head tissue cells.

Single-cell RNA sequencing of human femoral head in vivo

Skeletal muscle is a complex heterogeneous tissue comprised of diverse muscle fiber and non-fiber cell types that, in addition to movement, influences other systems such as immunity, metabolism and cognition. We investigated gene expression patterns of resident human skeletal muscle cells using single-cell RNA-seq of dissections from vastus lateralis. We generate transcriptome profiles of 11 mononuclear human skeletal muscle mononuclear cell types, including immune, endothelial, pericyte and satellite cells.  We delineate two fibro-adipogenic progenitor cell subtypes that may contribute to heterotopic ossification and muscular dystrophy fibrosis under pathological conditions. An important application of cell type signatures is for computational deconvolution of cell type specific changes using data from bulk transcriptome experiments. Analysis of transcriptome data from a 12 week resistance training study using the human skeletal muscle cell-type signatures revealed significant changes in specific mononuclear cell-type proportions related to age, sex, acute exercise and training. This characterization of human skeletal muscle cell subtypes will resolve cell type specific changes in large-scale physical activity muscle transcriptome studies and can further the understanding of the diverse effects of exercise and the pathophysiology of muscle disease. This project contains 3 datasets: Single cell RNA sequencing was conducted on four samples of mononuclear muscle cells isolated from a single vastus lateralis biopsy; Muscle fibers that were isolated from biopsies, where MHC content of each fiber was determined by SDS-PAGE, and fibers of each fiber-type (I vs. IIa) that were pooled for sequencing; Single cell RNA sequencing that was conducted on samples of cells isolated from limb muscle (Quadriceps) and respiratory muscle (Diaphragm) in mouse..

Single-cell transcriptional profiles in human and mouse skeletal muscle

Neuroblastoma is a pediatric tumor of the developing sympathetic nervous system. However, the cellular origin of neuroblastoma has yet to be defined. Here we studied the single-cell transcriptomes of neuroblastomas and normal human developing adrenal glands at various stages of embryonic and fetal development. We defined normal differentiation trajectories from Schwann cell precursors over intermediate states to neuroblasts or chromaffin cells and showed that neuroblastomas transcriptionally resemble normal fetal adrenal neuroblasts. Importantly, neuroblastomas with varying clinical phenotypes matched different temporal states along normal neuroblast differentiation trajectories, with the degree of differentiation corresponding to clinical prognosis. Our work highlights the roles of oncogenic MYCN and loss of TFAP2B in blocking differentiation and may provide the basis for designing therapeutic interventions to overcome differentiation blocks.

Single-cell transcriptomic analyses provide insights into the developmental origins of neuroblastoma.

Aging is accompanied by a loss of muscle mass and function, termed sarcopenia, causing numerous morbidities and economic burdens in human populations. Mechanisms implicated in age-related sarcopenia include inflammation, muscle stem cell depletion, mitochondrial dysfunction and loss of motor neurons, but whether there are key drivers of sarcopenia is not yet known. To gain deeper insights into age-related sarcopenia, we performed transcriptome profiling on lower limb muscle biopsies from 72 young, old and sarcopenic subjects using bulk RNA-seq (N = 72) and single-nuclei RNA-seq (N = 17). Using this combined approach, we discovered novel changes in gene expression that occur with age and sarcopenia in multiple cell types comprising mature skeletal muscle. Notably, we found increased expression of the genes MYH8 and PDK4, and decreased expression of the gene IGFN1, in old muscle. We also identified a small population of nuclei that express CDKN1A, present only in aged samples, consistent with P21CIP1 senescence in this subpopulation. Our findings identify unique cellular sub-populations populations in aged and sarcopenic skeletal muscle, which will facilitate the development of new therapeutic strategies to combat age-related sarcopenia. Overall design: We performed transcriptome profiling on lower limb muscle biopsies from 72 young, old and sarcopenic subjects using bulk RNA-seq (N = 72) and single-nuclei RNA-seq (N = 17).

Single-nuclei profiling of aged skeletal muscle

Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.

Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function.

We have created a compendium of single cell transcriptome data from the model
organism Mus musculus comprising more than 100,000 cells from 20 organs and
tissues. These data represent a new resource for cell biology, revealing gene
expression in poorly characterized cell populations and allowing for direct and
controlled comparison of gene expression in cell types shared between tissues, such
as T-lymphocytes and endothelial cells from distinct anatomical locations. Two
distinct technical approaches were used for most tissues: one approach, microfluidic
droplet-based 3’-end counting, enabled the survey of thousands of cells at relatively
low coverage, while the other, FACS-based full length transcript analysis, enabled
characterization of cell types with high sensitivity and coverage. The cumulative
data provide the foundation for an atlas of transcriptomic cell biology.

Tabula Muris: Transcriptomic characterization of 20 organs and tissues from Mus musculus at single cell resolution

Our current knowledge of the different immune cells in neuroblastoma is based on in vitro and in vivo studies mainly focusing on a single cell type. Importantly, different studies have conveyed conflicting results. Moreover, a comprehensive immune cell overview at the single-cell level is still missing and understanding the complete immune cell composition of neuroblastoma will be crucial for the development of novel immunotherapeutics against the disease. In this study, we performed single-cell RNA-sequencing on nineteen human neuroblastoma samples coupled with multiplex immunohistochemistry and survival analysis using additional datasets to provide a comprehensive cellular and molecular immune cell landscape of human neuroblastoma. Further, we contrasted our data with single-cell RNA-sequencing data from normal fetal adrenal gland to characterize cell-state changes from normal tissue to cancerous neuroblastoma. Our analysis revealed 27 immune cell subtypes including distinct subpopulations of myeloid, NK, B and T cells not identified in neuroblastoma before. Several immune cell subtypes demonstrated a survival benefit such as inflammatory monocytes, tumor associated macrophages, various T cell populations, and Active NK cells. Furthermore, in contrast to adult cancers and previous neuroblastoma studies, we demonstrated an increase in inflammatory monocyte cell-state when contrasting normal and tumor tissue, while we do not observe differences in cytotoxicity and exhaustion score for cytotoxic T cells, nor in Treg activity. Finally, we performed a systemic receptor-ligand interaction analysis between tumor, stroma and immune cells, where we showed the neuroblastoma tumor microenvironment is highly complex and strongly correlated to survival. In addition, we highlighted several interactions that we suggest to be tested in future studies as a therapeutic option in human neuroblastoma. Taken together, our study significantly adds to the in depth understanding of the immune cell landscape, the complexity of the tumor microenvironment and it provides a resource for the development of novel immunotherapeutics for neuroblastoma.

The immune cell atlas of human neuroblastoma

Rhabdomyosarcoma (RMS) is the most soft tissue sarcoma of childhood and histologically mimics developing skeletal muscle. To better understand RMS, we performed single-cell RNA-seq of one embryonal subtype RMS (ERMS) and one alveolar subtype RMS (ARMS). We expanded our observations by performing single-nucleus RNA-seq of 18 patient tumors (12 ERMS, 6 ARMS). All 18 of these tumors were used to generate orthotopic patient-derived xenografts, and we performed single-cell RNA-seq of all 18 O-PDXs to compare heterogeneity between the original tumor and the patient-derived model. 15 of the O-PDXs underwent lentiviral barcoding, and lineage could be tracked using a separate dial-out PCR step during the single-cell RNA-seq workflow. We performed single-cell RNA-seq of an ex vivo organoid, showing that we can preserve heterogeneity in this model. We expanded our study to the epigenetic level by using single-cell ATAC-sequencing of 7 O-PDXs. Finally, we longitudinally tracked shifts in heterogeneity in an O-PDX model treated with chemotherapy. Overall design: We performed single-cell RNA-seq from 2 patient RMS tumors, 18 O-PDXs, and 1 organoid. Additionally, we performed single-nucleus RNA-seq from 18 patient RMS tumors and single-cell ATAC-seq from 7 O-PDXs. We also provide dialout PCR data from 15 RMS O-PDX that were used for lentiviral barcode experiments to track clonal lineages within O-PDXs.

Musculoskeletal Network

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Musculoskeletal Network atlases are still under development.

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