Lipidomic profiling has been linked to the detection of cancers as dysregulation of lipid metabolism is closely associated with many disease states. Current work on chip‐based profiling has been limited and is largely hindered by issues associated with the chip's sophisticated fabrication processes. We report here the design and fabrication of a highly efficient microfluidic mixer/extractor by using three‐dimensional (3D) printing technology for on‐chip cell lysis/enrichment for lipidomic profiling with matrix‐assisted laser desorption/ionization mass spectrometry (MALDI‐MS). The platform consists of a micropillar mixer for flow‐through lysis and an on‐chip reservoir to separate phases where the lipid‐enriched layer was collected for subsequent MS analysis. The mass transfer between the two phases was simulated by a computational fluid dynamics study, and the efficiency in cell lysis in different extraction solvent systems was characterized by fluorescence microscopy. Results showed increased performance in extraction with the micropillar mixer as compared with the standard Bligh‐Dyer method. For lipid profiling of
Accelerating Simulation of Particle Trajectories in Microfluidic Devices by Constructing a Cloud Database
Microfluidic cell sorters have shown great potential to revolutionize the current technique of enriching rare cells. In the past decades, different microfluidic cell sorters have been developed by researchers for separating circulating tumor cells, T-cells, and other biological markers from blood samples. However, it typically takes months or even years to design these microfluidic cell sorters by hand. Thus, researchers tend to use computer simulation (usually finite element analysis) to verify their designs before fabrication and experimental testing. Despite this, conducting precision finite element analysis of microfluidic devices is computationally expensive and labor-intensive. To address this issue, we recently presented a microfluidic simulation method that can simulate the behavior of fluids and particles in some typical microfluidic chips instantaneously. Our method decomposes the chip into channels and intersections. The behavior of fluid in each channel is determined by leveraging analogies with electronic circuits, and the behavior of fluid and particles in each intersection is determined by querying a database containing 92,934 pre-simulated channel intersections. While this approach successfully predicts the behavior of complex microfluidic chips in a fraction of the time required by existing techniques, we nonetheless identified three major limitations with this method: (1) the library of pre-simulated channel intersections is unnecessarily large (only 2,072 of 92,934 were used); (2) the library contains only cross-shaped intersections (and no other intersection geometries); and (3) the range of fluid flow rates in the library is limited to 0 to 2 cm/s. To address these deficiencies, in this work we present an improved method for instantaneously simulating the trajectories of particles in microfluidic chips. Firstly, inspired by dynamic programming, our new method optimizes the generation of pre-simulated intersection units and avoids generating unnecessary simulations. Secondly, we constructed a cloud database (http://cloud.microfluidics.cc) to share our pre-simulated results and to let users become contributors and upload their simulation results into the cloud database as a benefit to the whole microfluidic simulation community. Lastly, we investigated the impact of different channel angles and different fluid flow rates on predicting the trajectories of particles. We found a wide range of device geometries and flow rates over which our existing simulation results can be extended without having to perform additional simulations. Our method should accelerate the simulation of particles in microfluidic chips and enable researchers to design new microfluidic cell sorter chips more efficiently.
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- NSF-PAR ID:
- 10067824
- Date Published:
- Journal Name:
- 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
- Page Range / eLocation ID:
- 666 to 671
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract C. reinhardtii cells by MALDI‐MS, over 65 lipid species from the monogalactosyldiacylglycerol, digalactosyldiacylglycerol, diacylglyceryltrimethylhomo‐Ser, and triacylglycerol lipid families have been identified. The effect of organic solvents on extraction and lipid profiles was also investigated, and the results indicated that the extractant formula has a diverse impact on the collection of certain types of lipid species, presenting useful guidance for the system to be applied to targeted enrichment of lipids with specific cells. The microfluidic chips by the 3D printing technique reported here offer new platforms potentially for clinical lipidomics and can provide a novel avenue in disease diagnosis. -
Focusing particles into a tight stream is critical for many microfluidic particle-handling devices such as flow cytometers and particle sorters. This work presents a fundamental study of the passive focusing of polystyrene particles in ratchet microchannels via direct current dielectrophoresis (DC DEP). We demonstrate using both experiments and simulation that particles achieve better focusing in a symmetric ratchet microchannel than in an asymmetric one, regardless of the particle movement direction in the latter. The particle focusing ratio, which is defined as the microchannel width over the particle stream width, is found to increase with an increase in particle size or electric field in the symmetric ratchet microchannel. Moreover, it exhibits an almost linear correlation with the number of ratchets, which can be explained by a theoretical formula that is obtained from a scaling analysis. In addition, we have demonstrated a DC dielectrophoretic focusing of yeast cells in the symmetric ratchet microchannel with minimal impact on the cell viability.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISVLSI.2018.00126
