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Biomedical subjects

Hongbo Wang

Publications and source records attributed to Hongbo Wang.

2 recordsLinked to original sources

PASTA: versatile tyramide-oligonucleotide amplification for multimodal spatial biology.

Spatial proteomics is limited by detection sensitivity, multiplexing and multimodal integration, leaving a gap between discovery and clinical assays. Here we present protein and nucleic acid serial tyramide amplification (PASTA), using horseradish peroxidase-mediated oligonucleotide deposition and cyclical imaging for high-plex, multimodal spatial profiling. Compatible with conjugated antibodies and in situ hybridization, PASTA enables simultaneous protein and RNA codetection from formalin-fixed, paraffin-embedded samples, providing a cost-effective bridge from discovery to clinical validation.

Tyramine

Machine learning algorithm-based biomarker exploration and validation of mitochondria-related diagnostic genes in osteoarthritis.

The role of mitochondria in the pathogenesis of osteoarthritis (OA) is significant. In this study, we aimed to identify diagnostic signature genes associated with OA from a set of mitochondria-related genes (MRGs). First, the gene expression profiles of OA cartilage GSE114007 and GSE57218 were obtained from the Gene Expression Omnibus. And the limma method was used to detect differentially expressed genes (DEGs). Second, the biological functions of the DEGs in OA were investigated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Wayne plots were employed to visualize the differentially expressed mitochondrial genes (MDEGs) in OA. Subsequently, the LASSO and SVM-RFE algorithms were employed to elucidate potential OA signature genes within the set of MDEGs. As a result, GRPEL and MTFP1 were identified as signature genes. Notably, GRPEL1 exhibited low expression levels in OA samples from both experimental and test group datasets, demonstrating high diagnostic efficacy. Furthermore, RT-qPCR analysis confirmed the reduced expression of Grpel1 in an in vitro OA model. Lastly, ssGSEA analysis revealed alterations in the infiltration abundance of several immune cells in OA cartilage tissue, which exhibited correlation with GRPEL1 expression. Altogether, this study has revealed that GRPEL1 functions as a novel and significant diagnostic indicator for OA by employing two machine learning methodologies. Furthermore, these findings provide fresh perspectives on potential targeted therapeutic interventions in the future.

Humans