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Microfluidic and Computational Tools for Neurodegeneration Studies.

Kin Gomez¹, Victoria R Yarmey^1,2, Hrishikesh Mane¹

¹Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA;

Annual Review of Chemical and Biomolecular Engineering

|January 15, 2025

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Microfluidic technology and advanced computational tools accelerate neurodegenerative disease (ND) research by enhancing biomarker analysis and data interpretation for better diagnostics and treatments.

Area of Science:

Neuroscience
Biotechnology
Computational Biology

Background:

Neurodegenerative diseases (NDs) present complex pathologies challenging conventional research methods.
Limitations in current analytical techniques hinder the development of accurate diagnostics and effective therapies for NDs.

Purpose of the Study:

To explore the role of microfluidic technology in advancing neurodegenerative disease research.
To highlight the necessity of sophisticated computational tools for analyzing complex biological data.

Main Methods:

Utilizing microfluidic devices for improved biomarker quantification, brain organoid culture, and small animal model manipulation.
Applying advanced analytical algorithms and machine learning platforms to process and analyze data from microfluidic systems and other biological datasets (genomic, proteomic, anatomical, cognitive).

Keywords:

biomarkers brain-on-a-chip computational tools disease modeling microfluidics neurodegeneration

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Co-culture of Glutamatergic Neurons and Pediatric High-Grade Glioma Cells Into Microfluidic Devices to Assess Electrical Interactions

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Main Results:

Microfluidic technology enhances experimental throughput and the number of measurable metrics.
Computational tools are essential for managing and interpreting the large, complex datasets generated by microfluidic systems and other high-throughput methods.
These integrated approaches show potential for discerning patterns across diverse data types.

Conclusions:

Microfluidics and advanced computation offer powerful synergistic approaches to accelerate discoveries in neurodegenerative disease research.
These technologies can significantly improve the characterization, diagnosis, and treatment platforms for neurodegenerative diseases, leading to better clinical outcomes.