https://orcid.org/0000-0002-4479-6448
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ABSTRACT
Technologies are transforming the understanding of adipose tissue as a complex and dynamic tissue that plays a critical role in energy homoeostasis and metabolic health. This mini-review provides a brief overview of the potential impact of novel technologies in biomedical research and aims to identify areas where these technologies can make the most significant contribution to adipose tissue research. It discusses the impact of cutting-edge technologies such as single-cell sequencing, multi-omics analyses, spatial transcriptomics, live imaging, 3D tissue engineering, microbiome analysis, in vivo imaging, and artificial intelligence/machine learning. As these technologies continue to evolve, we can expect them to play an increasingly important role in advancing our understanding of adipose tissue and improving the treatment of related diseases.
List of abbreviations
| 3D | = | 3-dimentional |
| AI | = | Artificial intelligence |
| AHR | = | Aryl hydrocarbon receptor |
| ANN | = | Artificial neural networks |
| APEX | = | Ascorbate peroxidase |
| ASC | = | Adipose-derived mesenchymal stem cells |
| AT | = | Adipose tissue |
| BAT | = | Brown adipose tissue |
| CT | = | Computed tomography |
| DEXA | = | Dual-energy X-ray absorptiometry |
| ExSeq | = | Expansion sequencing |
| FISSEQ | = | Fluorescent in situ sequencing |
| Geo-seq | = | Geospatial transcriptomics sequencing |
| GF | = | Germ-free |
| HFD | = | High-fat diet |
| HDST | = | High-definition spatial transcriptomics |
| IL-6 | = | Interleukin 6 |
| iRFP | = | Near-infrared fluorescent protein |
| ISH | = | In situ hybridization |
| ISS | = | In situ sequencing |
| LPS | = | Lipopolysaccharides |
| MERFISH | = | Multiplexed error-robust fluorescence in situ hybridization |
| ML | = | Machine learning |
| MRI | = | Magnetic resonance imaging |
| MSC | = | Mesenchymal stem cell |
| MSOT-US | = | Multispectral optoacoustic imaging technology with ultrasound tomography |
| NGS | = | Next-generation sequencing |
| NIR | = | Near-infrared (fluorescence imaging) |
| NF-κB | = | Nuclear factor kappa-light-chain-enhancer of activated B cells |
| PET | = | Positron emission tomography |
| PET/CT | = | Positron emission tomography/computed tomography |
| PPARα | = | Peroxisome proliferator-activated receptor alpha |
| PTM | = | Post-translational modification |
| PVAT | = | Perivascular adipose tissue |
| SAT | = | Subcutaneous adipose tissue |
| SBL | = | Sequencing by ligation |
| scATAC-seq | = | Single-cell chromatin accessibility profiling |
| scDNA-seq | = | Single-cell DNA sequencing |
| SCFA | = | Short-chain fatty acid |
| scProteomics | = | Single-cell proteomics |
| scRNA-seq | = | Single-cell RNA sequencing |
| sc-seq | = | Single-cell sequencing |
| seqFISH+ | = | An advanced version of sequential fluorescence in situ hybridization |
| Slide-seqV2 | = | An improved version of Slide-seq |
| snRNA-seq | = | Single-nuclei RNA sequencing |
| T2DM | = | Type 2 diabetes mellitus |
| TLR4 | = | Toll-like receptor 4 |
| TNFα | = | Tumor necrosis factor alpha |
| VAT | = | Visceral adipose tissue |
| WAT | = | White adipose tissue |
Disclosure statement
No potential conflict of interest was reported by the author(s).