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An acoustofluidic trap and transfer approach for organizing a high density single cell array.

ABSTRACT: We demonstrate a hybrid microfluidic system that combines fluidic trapping and acoustic switching to organize an array of single cells at high density. The fluidic trapping step is achieved by balancing the hydrodynamic resistances of three parallel channel segments forming a microfluidic trifurcation, the purpose of which was to capture single cells in a high-density array. Next, the cells were transferred into adjacent larger compartments by generating an array of streaming micro-vortices to move the cells to the desired streamlines in a massively parallel format. This approach can compartmentalize single cells with efficiencies of ≈67% in compartments that have diameters on the order of ∼100 um, which is an appropriate size for single cell proliferation studies and other single cell biochemical measurements.

SUBMITTER: Ohiri KA

PROVIDER: S-EPMC6078799 | biostudies-other | 2018 Jul

REPOSITORIES: biostudies-other

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An acoustofluidic trap and transfer approach for organizing a high density single cell array.

Ohiri Korine A KA Kelly Sean T ST Motschman Jeffrey D JD Lin Kevin H KH Wood Kris C KC Yellen Benjamin B BB

Lab on a chip 20180701 14

We demonstrate a hybrid microfluidic system that combines fluidic trapping and acoustic switching to organize an array of single cells at high density. The fluidic trapping step is achieved by balancing the hydrodynamic resistances of three parallel channel segments forming a microfluidic trifurcation, the purpose of which was to capture single cells in a high-density array. Next, the cells were transferred into adjacent larger compartments by generating an array of streaming micro-vortices to m ...[more]

PMID: 29931016

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Project description:Spatial capture-recapture (SCR) models have advanced our ability to estimate population density for wide ranging animals by explicitly incorporating individual movement. Though these models are more robust to various spatial sampling designs, few studies have empirically tested different large-scale trap configurations using SCR models. We investigated how extent of trap coverage and trap spacing affects precision and accuracy of SCR parameters, implementing models using the R package secr. We tested two trapping scenarios, one spatially extensive and one intensive, using black bear (Ursus americanus) DNA data from hair snare arrays in south-central Missouri, USA. We also examined the influence that adding a second, lower barbed-wire strand to snares had on quantity and spatial distribution of detections. We simulated trapping data to test bias in density estimates of each configuration under a range of density and detection parameter values. Field data showed that using multiple arrays with intensive snare coverage produced more detections of more individuals than extensive coverage. Consequently, density and detection parameters were more precise for the intensive design. Density was estimated as 1.7 bears per 100 km2 and was 5.5 times greater than that under extensive sampling. Abundance was 279 (95% CI?=?193-406) bears in the 16,812 km2 study area. Excluding detections from the lower strand resulted in the loss of 35 detections, 14 unique bears, and the largest recorded movement between snares. All simulations showed low bias for density under both configurations. Results demonstrated that in low density populations with non-uniform distribution of population density, optimizing the tradeoff among snare spacing, coverage, and sample size is of critical importance to estimating parameters with high precision and accuracy. With limited resources, allocating available traps to multiple arrays with intensive trap spacing increased the amount of information needed to inform parameters with high precision.

Dataset Information

An acoustofluidic trap and transfer approach for organizing a high density single cell array.

Publications

An acoustofluidic trap and transfer approach for organizing a high density single cell array.

Similar Datasets

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