Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.10.29.762608

Integrated transcriptomic and epigenomic profiling of human cartilage reveals coordinated changes in fibronectin expression, splicing, and receptor signaling during osteoarthritis progression

Abstract

Osteoarthritis (OA) is defined by progressive loss of articular cartilage, but how chondrocytes change as the disease advances is unclear because most studies have focused on end-stage tissue. We profiled human knee articular cartilage across the full range of macroscopic OA grade, from young healthy to aged severe OA donors, by bulk RNA sequencing of 92 knees, single-nucleus RNA sequencing of 108,600 chondrocyte nuclei from 30 donors and single-nucleus multiome sequencing of 24 of these donors, treating grade as a continuous variable. With increasing grade, cartilage increased extracellular matrix (ECM) synthesis and remodeling genes, lost glucocorticoid-responsive, lipid-metabolic and antioxidant programs, and changed the splicing of a small set of ECM transcripts, led by the extra domain B (EDB) exon of fibronectin (FN1). Nine chondrocyte clusters were present in every disease stage and formed a continuum ordered by cartilage zone and hypertrophic differentiation, with PRG4+ superficial and COL10A1+ hypertrophic chondrocytes as the only distinct cell types at its ends and a pre-hypertrophic state between them. With grade, all clusters shared a response in which ECM production rose and homeostatic programs fell, and they differed in which of its components was strongest: ECM production in the superficial cells, the secretory pathway in the hypertrophic and CYTL1+ cells, and inflammatory signaling in the ANXA1+ and CRISPLD2+ cells. Inferred communication between chondrocytes was remodeled with stage. FN1 was induced in every cluster in excess of its fibril-assembly machinery, together with the fibronectin-cleaving protease HTRA1, and FN1 signaling shifted in receptor use from integrin 5{beta}1 toward CD44 and, less robustly, v{beta}8, accompanied by a rise in TGF-{beta}/SMAD response genes. In four early-OA donors, tenascin-C was already largely induced and the FN1 receptor shift from 5{beta}1 toward CD44 was partly present, whereas the rise of FN1 outside superficial chondrocytes, of FGF1 and of developmental and ossification programs came with progression. Chromatin accessibility changed in the same direction across clusters, with regions linked to FN1 opening and regions linked to ITGA5 closing. OA progression in human cartilage therefore appears to be a change of chondrocyte state that differs in emphasis along a zonal and hypertrophic continuum and includes changes in fibronectin regulation, production, processing, and receptor engagement.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lotz, M., Swahn, H., Sakamoto, T., Kenvisay, C., Olmer, M., Uchida, Y., Asahara, H., Miller, R. E., Malfait, A.-M., Orange, D. E., Mehta, B., Otero, M., Bugbee, W. D.. 2026-11-03. Integrated transcriptomic and epigenomic profiling of human cartilage reveals coordinated changes in fibronectin expression, splicing, and receptor signaling during osteoarthritis progression. https://doi.org/10.64898/2026.10.29.762608

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

Olfaction-based biosensors stand to have profound impacts in fields such as medical diagnostics, national security, and drug discovery. A promising mode of vapor phase biodetection mimics volatile organic compound-olfactory receptor (VOC-OR) interactions with target-specific protein or peptide probes; however, lack of experimental coverage remains a core bottleneck. Current computational approaches aim to improve prediction of VOC-OR binding to reduce experimental burden, and current machine learning approaches fail to demonstrate a level of generalization that addresses current engineering challenges. Here, using the Molecular to Olfactory Dataset (M2OR) and Insect Olfactory Binding Protein Database (iOBPdb) datasets, data partitioning methods and their performance are evaluated on a neural network and contrastive learning framework on such VOC-OR binding datasets, varying: model training, architecture, and structure of the input data distribution. It was found that holding out certain chemical or proteomic motifs from the training data diminished model performance with a two layer neural network model. The model was able to generalize to state of the art performance (0.88 AUCROC) on every data split by applying a contrastive learning objective defined by shared molecular constituents and a stochastic sampler. This suggests the contrastive objective provides a strong inductive bias for neural networks to predict novel VOC-OR pairs. These findings highlight that model performance on pairwise biological interaction datasets can be inflated through identity leakage from constituent overlap, and emphasize the need for more robust evaluation protocols. Furthermore, we show contrastive regularization mitigates dataset degeneracy through enforcement of compatibility in the models latent geometry that may extend to other binder target-datasets where identity leakage confounds evaluation.

bioinformatics↗

Plate design confounds donor variation and model evaluation in single-cell perturbation screens

Single-cell screens of immune cells from many donors are used to study why individuals respond differently and to benchmark response models. Each donor-perturbation combination typically occupies one well, controls are grouped, and donors share one layout, so technical effects can look biological. Using a 90-cytokine screen, two compound screens, four cohorts and simulations, we show that well effects explain most replicate-control variation in abundant cell types, controls carry a shared offset, and identical control wells differ by hundreds of differentially expressed genes at a standard threshold, about as many as two receptor-negative proteins do. Negative controls, design-aware estimators and perturbation-specific evaluation change the interpretation of four recent analyses. A deep-learning model that scores below a perturbation-agnostic predictor under standard evaluation outperforms a simple baseline on perturbation-specific differences in two of three cell types (significantly in one with stricter controls), but predicts less well the component shared with inert wells.

bioinformatics↗

LinearRaccess: linear-time approximation of RNA accessibility without base-pair span limits

Motivation: RNA accessibility is the probability that every nucleotide in a contiguous interval is simultaneously unpaired; it is widely used to predict binding sites for RNA-binding proteins and other RNA-RNA interactions, often in the form of the opening energy, an equivalent measure on a logarithmic scale. Existing tools restrict the maximum base-pair span, discarding long-range base pairs that matter biologically. Others sample structures, introducing statistical variance, or compute accessibility exactly without a span limit, at cubic time cost. No existing method computes the accessibility of windows longer than one nucleotide deterministically in linear time while preserving long-range base pairs. We present LinearRaccess, which computes accessibility directly in linear time via beam-pruned inside-outside dynamic programming with a difference-array aggregation step, without restricting base-pair span. Results: For a sequence of length N and a fixed beam width b, LinearRaccess runs in O(N) time and O(Nb) memory. Against exact accessibility on 12,872 bpRNA-1m(90) sequences of 100-1,000 nt, its median opening-energy error is below 0.001 kcal/mol (0.14 kcal/mol for 700-1,000 nt), and it distinguishes windows of very small accessibility, including 35-nt windows, below the resolution of sampling-based estimates. LinearRaccess analyzes the full 29,903-nt SARS-CoV-2 genome in 419 seconds. Conclusion: LinearRaccess is the first method to compute the accessibility of windows longer than one nucleotide deterministically (without sampling) in linear time without restricting base-pair span. Availability: https://github.com/TakumiOtagaki/LinearRaccess, archived at Zenodo (https://doi.org/10.5281/zenodo.23598502).

bioinformatics↗