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- Langer, Krzysztof, et al.
(author)
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Rapid Production and Recovery of Cell Spheroids by Automated Droplet Microfluidics
- 2020
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In: SLAS TECHNOLOGY. - : Elsevier BV. - 2472-6303 .- 2472-6311. ; 25:2, s. 111-122
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Journal article (peer-reviewed)abstract
- The future of the life sciences is linked to automation and microfluidics. As robots start working side by side with scientists, robotic automation of microfluidics in general, and droplet microfluidics in particular, will significantly extend and accelerate the life sciences. Here, we demonstrate the automation of droplet microfluidics using an inexpensive liquid-handling robot to produce human scaffold-free cell spheroids at high throughput. We use pipette actuation and interface the pipetting tip with a droplet-generating microfluidic device. In this device, we produce highly monodisperse droplets with a diameter coefficient of variation (CV) lower than 2%. By encapsulating cells in these droplets, we produce cell spheroids in droplets and recover them to standard labware containers at a throughput of 85,000 spheroids per microfluidic circuit per hour. The viability of the cells in spheroids remains high throughout the process and decreases by >10% (depending on the cell line used) after a 16 h incubation period in nanoliter droplets and automated recovery. Scaffold-free cell spheroids and 3D tissue constructs recapitulate many aspects of functional human tissue more accurately than 2D or single-cell cultures, but assembly methods for spheroids (e.g., hanging drop microplates) have limited throughput. The increased throughput and decreased cost of our method enable spheroid production at the scale needed for lead discovery drug screening, and approach the cost at which these microtissues could be used as building blocks for organ-scale regenerative medicine.
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| 2. |
- Trossbach, Martin, et al.
(author)
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High-throughput cell spheroid production and assembly analysis by microfluidics and deep learning
- 2023
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In: SLAS TECHNOLOGY. - : Elsevier BV. - 2472-6303 .- 2472-6311. ; 28:6, s. 423-432
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Journal article (peer-reviewed)abstract
- 3D cell culture models are important tools in translational research but have been out of reach for high-throughput screening due to complexity, requirement of large cell numbers and inadequate standardization. Microfluidics and culture model miniaturization technologies could overcome these challenges. Here, we present a high throughput workflow to produce and characterize the formation of miniaturized spheroids using deep learning. We train a convolutional neural network (CNN) for cell ensemble morphology classification for droplet microfluidic minispheroid production, benchmark it against more conventional image analysis, and characterize minispheroid assembly determining optimal surfactant concentrations and incubation times for minispheroid production for three cell lines with different spheroid formation properties. Notably, this format is compatible with large-scale spheroid production and screening. The presented workflow and CNN offer a template for large scale minispheroid production and analysis and can be extended and re-trained to characterize morphological responses in spheroids to additives, culture conditions and large drug libraries.
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