Proteomics analysis of serum small extracellular vesicles for the longitudinal study of a glioblastoma multiforme...

Area of Science:

• Biochemistry
• Proteomics
• Extracellular Vesicle Research

Background:

• Longitudinal studies of disease models are crucial for understanding molecular changes during disease progression and therapeutic responses.
• Analyzing the proteome of small extracellular vesicles (sEVs) from low serum volumes in mice presents a significant technical challenge.
• Existing methods are insufficient for proteomic analysis of sEVs from the small serum volumes (approx. 75 µl) obtainable from mice every 14 days.

Purpose of the Study:

• To develop and validate a novel method for the proteomic analysis of sEVs isolated from small volumes (50 µl) of mouse serum.
• To compare the efficacy of precipitation-based purification (PPT) versus size exclusion chromatography (SEC) for sEV isolation.
• To apply the optimized method to a longitudinal study of serum-sEV proteome changes in a glioblastoma multiforme (GBM) mouse model.

Main Methods:

• Comparison of two sEV isolation techniques: precipitation-based purification (PPT) and size exclusion chromatography (SEC).
• Optimization of the sEV isolation procedure and proteolytic digestion for low serum volumes.
• Application of the optimized method to analyze serum-sEV proteomes from a GBM mouse model at multiple longitudinal time points.

Main Results:

• Size exclusion chromatography (SEC) yielded purer sEVs compared to PPT, based on size and protein identification.
• The developed method enabled the identification and quantification of 274 protein groups from 50 µl of serum.
• Longitudinal analysis revealed 25 deregulated proteins in GBM serum sEVs, including known GBM progression and metastasis markers (e.g., Myh9, Tln-1, Angpt1, Thbs1).

Conclusions:

• A robust method for serum-sEV proteomic analysis from low mouse blood volumes has been established.
• The SEC method is superior for isolating high-purity sEVs for proteomic studies.
• This methodology facilitates longitudinal proteomic profiling of sEVs in disease models, offering insights into disease mechanisms and biomarker discovery for GBM.