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We created an integrated microfluidic cell separation system that incorporates hydrophoresis and dielectrophoresis modules to facilitate high-throughput continuous cell separation. The hydrophoresis module consists of a serpentine channel with ridges and trenches to generate a diverging fluid flow that focuses cells into two streams along the channel edges. The dielectrophoresis module is composed of a chevron-shaped electrode array. Separation in the dielectrophoresis module is driven by inherent cell electrophysiological properties and does not require cell-type-specific labels. The chevron shape of the electrode array couples with fluid flow in the channel to enable continuous sorting of cells to increase throughput. We tested the new system with mouse neural stem cells since their electrophysiological properties reflect their differentiation capacity (e.g., whether they will differentiate into astrocytes or neurons). The goal of our experiments was to enrich astrocyte-biased cells. Sorting parameters were optimized for each batch of neural stem cells to ensure effective and consistent separations. The continuous sorting design of the device significantly improved sorting throughput and reproducibility. Sorting yielded two cell fractions, and we found that astrocyte-biased cells were enriched in one fraction and depleted from the other. This is an advantage of the new continuous sorting device over traditional dielectrophoresis-based sorting platforms that target a subset of cells for enrichment but do not provide a corresponding depleted population. The new microfluidic dielectrophoresis cell separation system improves label-free cell sorting by increasing throughput and delivering enriched and depleted cell subpopulations in a single sort.
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High-Content Screening and Analysis of Stem Cell-Derived Neural Interfaces Using a Combinatorial Nanotechnology and Machine Learning Approach
A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-based therapies. Nevertheless, high-throughput investigation of the cell-type-specific biophysical cues associated with stem cell-derived neural interfaces continues to be a significant obstacle to overcome. To this end, we developed a combinatorial nanoarray-based method for high-throughput investigation of neural interface micro-/nanostructures (physical cues comprising geometrical, topographical, and mechanical aspects) and the effects of these complex physical cues on stem cell fate decisions. Furthermore, by applying a machine learning (ML)-based analytical approach to a large number of stem cell-derived neural interfaces, we comprehensively mapped stem cell adhesion, differentiation, and proliferation, which allowed for the cell-type-specific design of biomaterials for neural interfacing, including both adult and human-induced pluripotent stem cells (hiPSCs) with varying genetic backgrounds. In short, we successfully demonstrated how an innovative combinatorial nanoarray and ML-based platform technology can aid with the rational design of stem cell-derived neural interfaces, potentially facilitating precision, and personalized tissue engineering applications.
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- Award ID(s):
- 1803517
- PAR ID:
- 10466948
- Publisher / Repository:
- American Association for the Advancement of Science
- Date Published:
- Journal Name:
- Research
- Volume:
- 2022
- ISSN:
- 2639-5274
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.34133/2022/9784273
