Evaluation of AAV Serotype Adaptability Using Dielectrophoresis

Adeno-associated viruses (AAVs) are pivotal delivery vectors for gene therapy due to their broad tissue tropism, low pathogenicity, and ability to support long-term gene expression. During AAV manufacturing, however, full capsids containing the therapeutic 4.7 kb single-stranded DNA genome are produced alongside empty and partially filled capsids. The later can reduce therapeutic efficacy.

Current analytical methods for the determination and separation of AAV populations often require large sample volumes or high particle concentrations, may compromise capsid integrity, and can exhibit limited adaptability across the different serotypes. Consequently, there is a need for adaptable, non-invasive techniques capable of distinguishing capsid populations across a broad range of AAV serotypes.

In our current work, we have demonstrated that dielectrophoresis (DEP) can be used as an alternative to the manipulation and differentiation of loaded and unloaded AAV1 capsids, exploiting the differences in electrical properties resulting from the loading. We are now looking for an internship student to evaluate how this approach can be extended across multiple AAV serotypes and to determine the underlying factors influencing serotype-dependent behavior. The results will contribute to a scientific publication.

Objective

The primary objective of this internship is to investigate the adaptability of DEP-based AAV characterization across different serotypes and to assess its potential as a universal analytical tool for capsid differentiation.

What You Will Do

• Investigate the influence of capsid loading state (empty versus full) on DEP behavior across serotypes.
• Evaluate the effect of labeling on particle manipulation and detection.
• Analyze DEP response data to identify serotype-dependent trends and distinguish electrical characteristics.

What you will learn:

• Fundamentals of DEP manipulation and characterization of AAV particles.
• Hands-on experience with fluorescence imaging, microfabrication techniques and cleanroom processing.
• Practical skills in Python for image processing and data analysis.
• Experience in experimental design, data interpretation, and scientific communication.
  
   

Requirements:

• Master's student in (Bio)chemistry, (Bio)physics, (Bio)nanoscience/technology, or a related field.
• Hands-on experience in laboratory-based experiments.
• Basic programming skills.
• Analytical mindset and problem-solving skills.
• Ability to work independently and in a team environment.
• Good communication skills in English.