Nano pom-poms prepared exosomes enable highly specific cancer biomarker detection.
Nan HeSirisha ThippabhotlaCuncong ZhongZachary GreenbergLiang XuZiyan PessettoAndrew K GodwinYong ZengMei HePublished in: Communications biology (2022)
Extracellular vesicles (EVs), particularly nano-sized small EV exosomes, are emerging biomarker sources. However, due to heterogeneous populations secreted from diverse cell types, mapping exosome multi-omic molecular information specifically to their pathogenesis origin for cancer biomarker identification is still extraordinarily challenging. Herein, we introduced a novel 3D-structured nanographene immunomagnetic particles (NanoPoms) with unique flower pom-poms morphology and photo-click chemistry for specific marker-defined capture and release of intact exosome. This specific exosome isolation approach leads to the expanded identification of targetable cancer biomarkers with enhanced specificity and sensitivity, as demonstrated by multi-omic exosome analysis of bladder cancer patient tissue fluids using the next generation sequencing of somatic DNA mutations, miRNAs, and the global proteome (Data are available via ProteomeXchange with identifier PXD034454). The NanoPoms prepared exosomes also exhibit distinctive in vivo biodistribution patterns, highlighting the highly viable and integral quality. The developed method is simple and straightforward, which is applicable to nearly all types of biological fluids and amenable for enrichment, scale up, and high-throughput exosome isolation.
Keyphrases
- papillary thyroid
- mesenchymal stem cells
- high throughput
- stem cells
- squamous cell
- high resolution
- single molecule
- healthcare
- squamous cell carcinoma
- lymph node metastasis
- copy number
- cell therapy
- gene expression
- mass spectrometry
- cell free
- case report
- childhood cancer
- pet imaging
- quality improvement
- machine learning
- computed tomography
- big data
- deep learning
- high density
- quantum dots
- circulating tumor cells
- pet ct
- sensitive detection
- genome wide
- data analysis