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Bioengineered iPSC-Derived Neural Networks on High-Density Microelectrode Arrays for Studying Pathological Changes in Alzheimer’s Disease
Are you interested in uncovering how Alzheimer’s disease disrupts communication in the brain — and exploring new ways to study and possibly intervene in this process?
In this project, you will use cutting-edge microfluidic platforms to construct bioengineered neural networks that better mimic the structure and function of brain microcircuits. These networks, established from human iPSC-derived neurons, will be studied throughout their development using high-density microelectrode arrays (HD-MEAs), enabling detailed tracking of their electrical activity at high spatiotemporal resolution.
You will introduce Alzheimer’s disease-related pathology into the networks and investigate how it alters connectivity, signaling patterns, and neural responses to stimulation over time.
The project offers a unique opportunity to combine experimental work in cellular neuroscience with computational analysis of neural network function. Depending on your background and interests, your work can be directed more toward wet-lab techniques (e.g., cell culturing, immunostaining, confocal imaging, electrophysiology) or toward data analysis and modeling (e.g., signal processing, graph theory, information theory).
Neurodegenerative diseases, such as Alzheimer’s disease (AD), are among the most severe and life-altering conditions in medicine. These disorders are marked by progressive neuronal degeneration, which gradually diminishes patients' cognitive functions, physical abilities, and independence. Many neurodegenerative diseases involve the prion-like spread of misfolded proteins between cells, which is linked to circuit-level dysfunction and network remodeling as the brain attempts to maintain function. The complexity of pathological changes, along with the intricate connectivity of affected brain regions, makes understanding and treating neurodegenerative diseases particularly challenging.
Despite significant advances, key challenges remain in developing treatments for these disorders, including understanding the causes of the diseases, how pathology spreads within neural networks, and how these networks adapt as the disease progresses. Addressing these challenges requires new approaches to study the changes occurring in vulnerable brain regions during the early stages of the disease. Recent advances in microfluidic technologies enable the construction of modular bioengineered neural networks in well-controlled microenvironments. By separating neural populations into chambers connected by microtunnels only permissible to their neurites, these platforms allow detailed investigation of how neurons connect and communicate across defined network architectures. Combining the platforms with high-density microelectrode arrays (HD-MEAs) also facilitate high-resolution recordings of the networks´ electrophysiological activity. This makes it possible to track changes in network function under healthy and diseased conditions, providing valuable insights into disease mechanisms and responses to therapeutic interventions.
Neurodegenerative diseases, such as Alzheimer’s disease (AD), are among the most severe and life-altering conditions in medicine. These disorders are marked by progressive neuronal degeneration, which gradually diminishes patients' cognitive functions, physical abilities, and independence. Many neurodegenerative diseases involve the prion-like spread of misfolded proteins between cells, which is linked to circuit-level dysfunction and network remodeling as the brain attempts to maintain function. The complexity of pathological changes, along with the intricate connectivity of affected brain regions, makes understanding and treating neurodegenerative diseases particularly challenging.
Despite significant advances, key challenges remain in developing treatments for these disorders, including understanding the causes of the diseases, how pathology spreads within neural networks, and how these networks adapt as the disease progresses. Addressing these challenges requires new approaches to study the changes occurring in vulnerable brain regions during the early stages of the disease. Recent advances in microfluidic technologies enable the construction of modular bioengineered neural networks in well-controlled microenvironments. By separating neural populations into chambers connected by microtunnels only permissible to their neurites, these platforms allow detailed investigation of how neurons connect and communicate across defined network architectures. Combining the platforms with high-density microelectrode arrays (HD-MEAs) also facilitate high-resolution recordings of the networks´ electrophysiological activity. This makes it possible to track changes in network function under healthy and diseased conditions, providing valuable insights into disease mechanisms and responses to therapeutic interventions.
Key research questions we aim to explore:
- How can we bioengineer neural microcircuits that better replicate the structure and activity of brain regions affected in Alzheimer’s disease?
- How do disease-related factors alter communication within and between neural populations?
- How does Alzheimer’s disease-related pathology spread through neural networks?
- Can electrophysiological or chemical interventions limit or reverse the spread of pathology in these networks?
If you're interested, please email your CV, transcript of records, and a brief (1/2 page) statement of motivation. Feel free to include any relevant experience as well. We can then arrange an in-person or Zoom interview to discuss further and tailor the specific goals to your expertise.
**Relevant backgrounds / Requirements:**
Relevant study backgrounds include, but are not limited to: biomedical engineering, neuroscience, biophysics, biotechnology, and nano- or microtechnology.
Experience with some of the following techniques and concepts is advantageous, but not a requirement:
- Aseptic cell handling techniques
- Electrophysiology
- Molecular cell biology techniques
- Programming in Matlab and/or Python
- Linear algebra and statistics
The most important requirements are a genuine interest in the project, a willingness to learn, and a self-driven attitude. If you are passionate about exploring the workings of the brain and contributing to the understanding of neurodegenerative diseases, I look forward to hearing from you!
Key research questions we aim to explore:
- How can we bioengineer neural microcircuits that better replicate the structure and activity of brain regions affected in Alzheimer’s disease?
- How do disease-related factors alter communication within and between neural populations?
- How does Alzheimer’s disease-related pathology spread through neural networks?
- Can electrophysiological or chemical interventions limit or reverse the spread of pathology in these networks?
If you're interested, please email your CV, transcript of records, and a brief (1/2 page) statement of motivation. Feel free to include any relevant experience as well. We can then arrange an in-person or Zoom interview to discuss further and tailor the specific goals to your expertise.
**Relevant backgrounds / Requirements:**
Relevant study backgrounds include, but are not limited to: biomedical engineering, neuroscience, biophysics, biotechnology, and nano- or microtechnology.
Experience with some of the following techniques and concepts is advantageous, but not a requirement:
- Aseptic cell handling techniques
- Electrophysiology
- Molecular cell biology techniques
- Programming in Matlab and/or Python
- Linear algebra and statistics
The most important requirements are a genuine interest in the project, a willingness to learn, and a self-driven attitude. If you are passionate about exploring the workings of the brain and contributing to the understanding of neurodegenerative diseases, I look forward to hearing from you!
Nicolai Winter-Hjelm
nwinte@ethz.ch
ETH Zürich
Institute for Biomedical Engineering
GLC F18
https://lbb.ethz.ch/the-group/post-doc/winter-hjelm--nicolai.html
