Table 1 Omics approach
From: Recent advances in omics and the integration of multi-omics in osteoarthritis research
Omics Approach | Technology | Methodology | Advantages | Limitations |
|---|---|---|---|---|
Genomics | Genome-wide association studies (GWAS) | Identifies genetic variants associated with traits/diseases by comparing SNP frequencies in large populations | Identifies genetic risk factors; applicable to large cohorts | Cannot directly identify causal genes; most risk variants are in non-coding regions |
Transcriptomics | Bulk RNA-seq | Measures gene expression levels across all cell types in a sample | High sensitivity for detecting differential gene expression | Lacks cellular resolution; signal is averaged across cell populations |
| Â | Single-cell RNA-seq (scRNA-seq) | Measures gene expression at the single-cell level | Reveals cellular heterogeneity; identifies rare cell types | High dropout rates; limited detection of low-abundance transcripts |
| Â | Spatial transcriptomics | Maps gene expression onto tissue sections while preserving spatial context | Links gene expression to tissue architecture | Lower sequencing depth than scRNA-seq; requires specialized imaging platforms |
Epigenomics | Chromatin Immunoprecipitation Sequencing (ChIP-seq); Cut&Run; Cut&Tag | Captures DNA–protein interactions by immunoprecipitating specific histone modifications or transcription factors, followed by sequencing | Identifies histone modification and transcription factor binding sites | Requires high input DNA; limited resolution; requires optimized protocols; cannot distinguish allele-specific binding |
| Â | Chromatin accessibility (ATAC-seq) | Maps open chromatin regions to identify active regulatory elements | Identifies active enhancers and promoters; links non-coding variants to regulatory activity | Prefers fresh tissue; cannot directly measure transcription; requires prediction of regulatory elements |
| Â | DNA Methylation (Bisulfite Sequencing, WGBS, RRBS) | Measures DNA methylation at cytosine residues within CpG dinucleotides, linking DNA methylation with gene silencing or activation | Provides insights into gene silencing and activation via methylation patterns | Data is correlative; does not establish causality; limited to CpG methylation |
| Â | Single-cell chromatin accessibility (scATAC-seq) | Measures open chromatin at single-cell resolution | Identifies cell-type-specific regulatory elements | Sparse data; higher sequencing cost |
| Â | Spatial epigenomics (Spatial-CUT&RUN) | Profiles histone modifications or transcription factors in intact tissue sections while preserving spatial context | Resolves epigenetic landscapes in specific tissue structures | Lower throughput than bulk ChIP-seq; requires optimized protocols |
Proteomics | Mass spectrometry-based proteomics | Identifies and quantifies proteins based on their mass-to-charge ratio | Directly measures functional molecules (proteins) | Limited by dynamic range; post-translational modifications require specialized analysis |
Metabolomics | Liquid chromatography-mass spectrometry (LC-MS) | Detects and quantifies metabolites in biological samples | Identifies metabolic pathways altered in disease | High variability; metabolite identification is challenging |