skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


This content will become publicly available on December 1, 2025

Title: Transcriptional profiling of human cartilage endplate cells identifies novel genes and cell clusters underlying degenerated and non-degenerated phenotypes
Abstract BackgroundLow back pain is a leading cause of disability worldwide and is frequently attributed to intervertebral disc (IVD) degeneration. Though the contributions of the adjacent cartilage endplates (CEP) to IVD degeneration are well documented, the phenotype and functions of the resident CEP cells are critically understudied. To better characterize CEP cell phenotype and possible mechanisms of CEP degeneration, bulk and single-cell RNA sequencing of non-degenerated and degenerated CEP cells were performed. MethodsHuman lumbar CEP cells from degenerated (Thompson grade ≥ 4) and non-degenerated (Thompson grade ≤ 2) discs were expanded for bulk (N=4 non-degenerated,N=4 degenerated) and single-cell (N=1 non-degenerated,N=1 degenerated) RNA sequencing. Genes identified from bulk RNA sequencing were categorized by function and their expression in non-degenerated and degenerated CEP cells were compared. A PubMed literature review was also performed to determine which genes were previously identified and studied in the CEP, IVD, and other cartilaginous tissues. For single-cell RNA sequencing, different cell clusters were resolved using unsupervised clustering and functional annotation. Differential gene expression analysis and Gene Ontology, respectively, were used to compare gene expression and functional enrichment between cell clusters, as well as between non-degenerated and degenerated CEP samples. ResultsBulk RNA sequencing revealed 38 genes were significantly upregulated and 15 genes were significantly downregulated in degenerated CEP cells relative to non-degenerated cells (|fold change| ≥ 1.5). Of these, only 2 genes were previously studied in CEP cells, and 31 were previously studied in the IVD and other cartilaginous tissues. Single-cell RNA sequencing revealed 11 unique cell clusters, including multiple chondrocyte and progenitor subpopulations with distinct gene expression and functional profiles. Analysis of genes in the bulk RNA sequencing dataset showed that progenitor cell clusters from both samples were enriched in “non-degenerated” genes but not “degenerated” genes. For both bulk- and single-cell analyses, gene expression and pathway enrichment analyses highlighted several pathways that may regulate CEP degeneration, including transcriptional regulation, translational regulation, intracellular transport, and mitochondrial dysfunction. ConclusionsThis thorough analysis using RNA sequencing methods highlighted numerous differences between non-degenerated and degenerated CEP cells, the phenotypic heterogeneity of CEP cells, and several pathways of interest that may be relevant in CEP degeneration.  more » « less
Award ID(s):
2143123
PAR ID:
10558881
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Publisher / Repository:
BIOMED CENTRAL (BMC)
Date Published:
Journal Name:
Arthritis Research & Therapy
Volume:
26
Issue:
1
ISSN:
1478-6362
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract BackgroundCollective cell migration underlies many essential processes, including sculpting organs during embryogenesis, wound healing in the adult, and metastasis of cancer cells. At mid-oogenesis,Drosophilaborder cells undergo collective migration. Border cells round up into a small group at the pre-migration stage, detach from the epithelium and undergo a dynamic and highly regulated migration at the mid-migration stage, and stop at the oocyte, their final destination, at the post-migration stage. While specific genes that promote cell signaling, polarization of the cluster, formation of protrusions, and cell-cell adhesion are known to regulate border cell migration, there may be additional genes that promote these distinct active phases of border cell migration. Therefore, we sought to identify genes whose expression patterns changed during border cell migration. ResultsWe performed RNA-sequencing on border cells isolated at pre-, mid-, and post-migration stages. We report that 1,729 transcripts, in nine co-expression gene clusters, are temporally and differentially expressed across the three migration stages. Gene ontology analyses and constructed protein-protein interaction networks identified genes expected to function in collective migration, such as regulators of the cytoskeleton, adhesion, and tissue morphogenesis, but also uncovered a notable enrichment of genes involved in immune signaling, ribosome biogenesis, and stress responses. Finally, we validated the in vivo expression and function of a subset of identified genes in border cells. ConclusionsOverall, our results identified differentially and temporally expressed genetic networks that may facilitate the efficient development and migration of border cells. The genes identified here represent a wealth of new candidates to investigate the molecular nature of dynamic collective cell migrations in developing tissues. 
    more » « less
  2. In NSCLC, there is a pressing need for immunotherapy predictive biomarkers. The processes underlying B-cell dysfunction, as well as their prognostic importance in NSCLC, are unknown. Tumor-specific B-cell gene co-expression networks were constructed by comparing the Boolean implication modeling of single-cell RNA sequencing of NSCLC tumor B cells and normal B cells. Proliferation genes were selected from the networks using in vitro CRISPR-Cas9/RNA interfering (RNAi) screening data in more than 92 human NSCLC epithelial cell lines. The prognostic and predictive evaluation was performed using public NSCLC transcriptome and proteome profiles. A B cell proliferation and prognostic gene co-expression network was present only in normal lung B cells and missing in NSCLC tumor B cells. A nine-gene signature was identified from this B cell network that provided accurate prognostic stratification using bulk NSCLC tumor transcriptome (n = 1313) and proteome profiles (n = 103). Multiple genes (HLA-DRA, HLA-DRB1, OAS1, and CD74) differentially expressed in NSCLC B cells, peripheral blood lymphocytes, and tumor T cells had concordant prognostic indications at the mRNA and protein expression levels. The selected genes were associated with drug sensitivity/resistance to 10 commonly used NSCLC therapeutic regimens. Lestaurtinib was discovered as a potential repositioning drug for treating NSCLC. 
    more » « less
  3. IntroductionConsidering the significant role played by both intrinsic and extrinsic electric fields in the growth and maturation of the central nervous system, the impact of short exposure to external electric fields on the development and differentiation of retinal organoids was investigated. MethodsRetinal organoids derived from human embryonic stem cells were used at day 80, a key stage in their differentiation. A single 60-minute exposure to a biphasic electrical field was administered to assess its influence on retinal cell populations and maturation markers. Immunohistochemistry, qPCR, and RNA sequencing were employed to evaluate cell type development and gene expression changes. ResultsElectrical stimulation significantly enhanced neuronal development and increased the population of photoreceptors within the organoids. RNA sequencing data showed upregulated expression of genes related to rod photoreceptors, Müller cells, horizontal cells, and amacrine cells, while genes associated with retinal pigment epithelium and retinal ganglion cells were downregulated. Variations in development and maturation were observed depending on the specific parameters of the applied electric field. DiscussionThese findings highlight the significant impact of extrinsic electrical fields on early retinal development and suggest that optimizing electrical field parameters could effectively address certain limitations in retinal organoid technology, potentially reducing the reliance on chemicals and small molecules. 
    more » « less
  4. Abstract BackgroundSingle-cell RNA-sequencing (scRNA-seq) technologies allow for the study of gene expression in individual cells. Often, it is of interest to understand how transcriptional activity is associated with cell-specific covariates, such as cell type, genotype, or measures of cell health. Traditional approaches for this type of association mapping assume independence between the outcome variables (or genes), and perform a separate regression for each. However, these methods are computationally costly and ignore the substantial correlation structure of gene expression. Furthermore, count-based scRNA-seq data pose challenges for traditional models based on Gaussian assumptions. ResultsWe aim to resolve these issues by developing a reduced-rank regression model that identifies low-dimensional linear associations between a large number of cell-specific covariates and high-dimensional gene expression readouts. Our probabilistic model uses a Poisson likelihood in order to account for the unique structure of scRNA-seq counts. We demonstrate the performance of our model using simulations, and we apply our model to a scRNA-seq dataset, a spatial gene expression dataset, and a bulk RNA-seq dataset to show its behavior in three distinct analyses. ConclusionWe show that our statistical modeling approach, which is based on reduced-rank regression, captures associations between gene expression and cell- and sample-specific covariates by leveraging low-dimensional representations of transcriptional states. 
    more » « less
  5. Abstract Clustered regularly interspaced short palindromic repeats (CRISPR) screening coupled with single-cell RNA sequencing has emerged as a powerful tool to characterize the effects of genetic perturbations on the whole transcriptome at a single-cell level. However, due to its sparsity and complex structure, analysis of single-cell CRISPR screening data is challenging. In particular, standard differential expression analysis methods are often underpowered to detect genes affected by CRISPR perturbations. We developed a statistical method for such data, called guided sparse factor analysis (GSFA). GSFA infers latent factors that represent coregulated genes or gene modules; by borrowing information from these factors, it infers the effects of genetic perturbations on individual genes. We demonstrated through extensive simulation studies that GSFA detects perturbation effects with much higher power than state-of-the-art methods. Using single-cell CRISPR data from human CD8+T cells and neural progenitor cells, we showed that GSFA identified biologically relevant gene modules and specific genes affected by CRISPR perturbations, many of which were missed by existing methods, providing new insights into the functions of genes involved in T cell activation and neurodevelopment. 
    more » « less