Sui, Z. | Search bioRxiv

bioRxiv2025

Functional interaction of hybrid extracellular vesicle-liposome nanoparticles with target cells: absence of toxicity

Building on the success of COVID-19 vaccine development, lipid nanoparticles (LNPs) have emerged as leading vehicles for mRNA delivery in a range of therapeutic applications. Naturally-occurring extracellular vesicles (EVs), which share similar physical properties with LNPs, present a promising alternative platform because of their relative stability and lower immunogenicity. A key challenge common to both EVs and LNPs is enabling efficient vesicle - cell interactions and establishing a polarized permeability pathway required for effective cargo transfer. Membrane recognition and intercalation are essential for the function and delivery capacity of both systems, regardless of their complexity. In this study, we leveraged recent advances to create hybrid extracellular vesicles (HEVs) by using LNPs to load mRNA into EVs. We characterized HEV formation using Forster resonance energy transfer (FRET), cryo-electron microscopy (Cryo-EM), and super-resolution microscopy, and demonstrated their ability to deliver mRNA to recipient cells. In both, in vitro and in vivo models, HEVs exhibited superior transfection efficiency compared to conventional LNPs composed of synthetic lipids, while significantly reducing LNPs cytotoxicity - a not-well-recognized limitation of synthetic lipid-based systems. These results highlight HEVs as a safer and more effective alternative for mRNA and small molecule delivery. Future therapeutic strategies could involve isolating EVs from patients, hybridizing them with synthetic lipid carriers loaded with therapeutic cargo, and reintroducing them for personalized treatment.

cell biology↗

bioRxiv2024

Exploit Spatially Resolved Transcriptomic Data to Infer Cellular Features from Pathology Imaging Data}

Digital pathology is a rapidly advancing field where deep learning methods can be employed to extract meaningful imaging features. However, the efficacy of training deep learning models is often hindered by the scarcity of annotated pathology images, particularly images with detailed annotations for small image patches or tiles. To overcome this challenge, we propose an innovative approach that leverages paired spatially resolved transcriptomic data to annotate pathology images. We demonstrate the feasibility of this approach and introduce a novel transfer-learning neural network model, STpath (Spatial Transcriptomics and pathology images), designed to predict cell type proportions or classify tumor microenvironments. Our findings reveal that the features from pre-trained deep learning models are associated with cell type identities in pathology image patches. Evaluating STpath using three distinct breast cancer datasets, we observe its promising performance despite the limited training data. STpath excels in samples with variable cell type proportions and high-resolution pathology images. As the influx of spatially resolved transcriptomic data continues, we anticipate ongoing updates to STpath, evolving it into an invaluable AI tool for assisting pathologists in various diagnostic tasks.

cancer biology↗