Dataset Information

Deep Learning-Based Multimodal Clustering Model for Endotyping and Post-Arthroplasty Response Classification using Knee Osteoarthritis Subject-Matched Multi-Omic Data

ABSTRACT: Deep Learning-Based Multimodal Clustering Model for Endotyping and Post-Arthroplasty Response Classification using Knee Osteoarthritis Subject-Matched Multi-Omic Data

PROVIDER: PRJNA924328 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR23094200.fastq.gz	Fastqsanger.gz
	SRR23094300.fastq.gz	Fastqsanger.gz
	SRR23094400.fastq.gz	Fastqsanger.gz
	SRR23094500.fastq.gz	Fastqsanger.gz
	SRR23094600.fastq.gz	Fastqsanger.gz

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Project description:Background: Primary knee osteoarthritis (KOA) is a heterogeneous disease with clinical and molecular contributors. Biofluids contain microRNAs and metabolites that can be measured by omic technologies. Deep learning captures complex non-linear associations within multimodal data but, to date, has not been used for multi-omic-based endotyping of KOA patients. We developed a novel multimodal deep learning framework for clustering of multi-omic data from three subject-matched biofluids to identify distinct KOA endotypes and classify one-year post-total knee arthroplasty (TKA) pain/function responses. Materials and Methods: In 414 KOA patients, subject-matched plasma, synovial fluid and urine were analyzed by microRNA sequencing or metabolomics. Integrating 4 high-dimensional datasets comprising metabolites from plasma (n=151 features), along with microRNAs from plasma (n=421), synovial fluid (n=930), or urine (n=1225), a multimodal deep learning variational autoencoder architecture with K-means clustering was employed. Features influencing cluster assignment were identified and pathway analyses conducted. An integrative machine learning framework combining 4 molecular domains and a clinical domain was then used to classify WOMAC pain/function responses post-TKA within each cluster. Findings: Multimodal deep learning-based clustering of subjects across 4 domains yielded 3 distinct patient clusters. Feature signatures comprising microRNAs and metabolites across biofluids included 30, 16, and 24 features associated with Clusters 1-3, respectively. Pathway analyses revealed distinct pathways associated with each cluster. Integration of 4 multi-omic domains along with clinical data improved response classification performance, with Cluster 3 achieving AUC=0·879 for subject pain response classification and Cluster 2 reaching AUC=0·808 for subject function response, surpassing individual domain classifications by 12% and 15% respectively. Interpretation: We have developed a deep learning-based multimodal clustering model capable of integrating complex multi-fluid, multi-omic data to assist in KOA patient endotyping and test outcome response to TKA surgery.

Project description:Arthrofibrosis is characterized by excessive extracellular matrix (ECM) deposition that results in restricted joint motion after total knee arthroplasty (TKA). Current surgical and pharmacologic treatment options are limited. Therefore, an in vitro model for joint myofibroblastogenesis is valuable to investigate the arthrofibrotic process and identify diagnostic biomarkers and treatment options. In this study, we obtained intraoperative posterior capsule (PC), quadriceps tendon (QT), and suprapatellar pouch (SP) tissue from knees of four patients undergoing primary TKA for osteoarthritis and characterized primary outgrowth cells from these tissues in the absence and presence of transforming growth factor beta 1 (TGFβ1), a pro-myofibroblastic cytokine. Light microscopy of knee outgrowth cells revealed spindle-shaped cells while immunofluorescence (IF) established staining for the fibroblast-specific antigen TE-7 and Vimentin, which are characteristics of fibroblasts. These fibroblasts differentiate readily into myofibroblasts as highlighted by enhanced alpha smooth muscle actin (ACTA2) mRNA and protein expression and increased collagen mRNA (i.e., collagen type 1 (COL1A1) and collagen type 3 (COL3A1)) expression and collagenous matrix deposition in the presence of TGFβ1. Of note, these studies also revealed that knee-derived fibroblasts are more sensitive to TGFβ1-mediated myofibroblastogenesis than adipose-derived mesenchymal stem cells. Importantly, while outgrowth fibroblasts isolated from four patients and three anatomical regions exhibit similar gene expression profiles, these knee fibroblasts form a unique gene expression cluster within the fibroblast niche as revealed by RNA-sequencing analysis. In conclusion, our study provides a fibroblast/myofibroblast model of outgrowth knee cells derived from patients undergoing primary TKA that can be employed to assess myofibroblast-related processes and test novel pharmacological strategies in vitro for arthrofibrosis.

Dataset Information

Deep Learning-Based Multimodal Clustering Model for Endotyping and Post-Arthroplasty Response Classification using Knee Osteoarthritis Subject-Matched Multi-Omic Data

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