Research Papers

Liquid Handling Optimization in High-Throughput Biodosimetry Tool

[+] Author and Article Information
Dakai Bian

Department of Mechanical Engineering,
Columbia University,
New York, NY 10027
e-mail: db2875@columbia.edu

Jason C. Tsui, Y. Lawrence Yao

Department of Mechanical Engineering,
Columbia University,
New York, NY 10027

Mikhail Repin, Guy Garty, Helen Turner, David J. Brenner

Center for Radiological Research,
Columbia University Medical Center,
New York, NY 10032

1Corresponding author.

Manuscript received September 1, 2015; final manuscript received April 13, 2016; published online August 31, 2016. Assoc. Editor: Rafael V. Davalos.

J. Med. Devices 10(4), 041007 (Aug 31, 2016) (10 pages) Paper No: MED-15-1250; doi: 10.1115/1.4033600 History: Received September 01, 2015; Revised April 13, 2016

Due to the need of high-speed and efficient biodosimetric assays for triage and therapy in the event of radiological or nuclear attack, a robotically based automated biodosimetry tool (RABiT) has been developed over the past few years. Adapting the micronucleus assay from filter plates to V-shaped plates presented challenges in the liquid handling, namely, cell splashing out of the V-shaped well plate during the cell harvesting, poor cell distribution on the bottom of the image plate during the dispensing, and cell loss from the image plate during the aspiration in the liquid handling process. Experimental and numerical investigations were carried out to better understand the phenomena and mitigate the problems. Surface tension and contact angle among the fluids and the plate wall were accounted for in the discrete and multiphase numerical models. Experimental conditions were optimized based on the numerical results showing the relationship between nozzle speed and amount of splashed liquid, and the relationship between aspiration speed and number of escaped cells. Using these optimized parameters, numbers of micronuclei in binucleated cells showed the same dose dependence in the RABiT-prepared samples as those in the manually prepared ones. Micronucleus assay protocol was fully realized on RABiT.

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Fig. 1

Optical microscopy of binuclei with micronuclei stained with DNA-specific fluorescent dye DAPI. Lymphocytes are cultured to division: lymphocytes with chromosome damage form binuclei with one or more micronuclei containing chromosomal fragments.

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Fig. 2

(a) Layout of the RABiT. The system is composed of eight modules: input stage, service robot, centrifuge, cell harvesting, liquid handling, incubation, control system, and imaging system module. (b) General process steps of the system.

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Fig. 3

Three-dimensional drawing of (a) filter plate contains a polycarbonate membrane on the bottom of the well and a recessed underdrain beneath the membrane. (b) V-shaped well plate has a conic bottom which is made of polystyrene. (c) Imaging plate has a rectangular well and a transparent thin film sealing the bottom of the plate. (a) is the well plate used before, (b) and (c) are the well plates currently in use.

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Fig. 4

(a) Image of the capillary after centrifuging. Due to the difference of density, red blood cells are at the bottom of the capillary, separation media is in the middle, and lymphocytes band is located above the separation media and below plasma. Laser cutting position is 10 mm below the lymphocyte band. (b) Three-dimensional drawing of capillary gripper. Each capillary is picked up due to the friction force by the O rings in the collet. Plunger in the shaft is used to partially seal the capillary tip not to lose cells during transportation. Compressed air from the nozzle comes into the shaft from the top, pushing the liquid out of the capillary [3].

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Fig. 5

Well plate geometries used in the numerical modeling. (a) 2D cross section geometry of the filter plate, the bottom of which is made of polycarbonate membrane. (b) 2D cross section geometry of the V-shaped well plate with conic bottom.

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Fig. 6

Imaging well plate geometries used in the numerical modeling. (a) 2D cross section geometry of the imaging well plate, bottom of which is a transparent thin film. (b) 3D geometry of the imaging plate; the cannula is placed 1 mm above the bottom and the arrows indicate the velocity field of the liquid region.

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Fig. 7

Numerical modeling of splashing phenomenon in (a) in the filter plate, the inlet velocity from the capillary (black solid block) was 1.4 m/s. The bottom of the well is a porous membrane filter. The contact angle between filter bottom and liquid was 30 deg. No splashing from the simulation matched the experimental observations; (b) in the V-shaped well plate, the inlet velocity was the same as that of (a). The wettability of the V-shaped well plate was set as a contact angle of 150 deg. Due to the differences of geometry and wettability, with the same inlet speed, liquid splashed in the V-shaped well plate; (c) The inlet velocity reduced from 1.4 m/s to 0.65 m/s and the same amount of liquid was collected from the capillary. Under this condition, no cell splashing occurred.

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Fig. 8

The relationship between percent of liquid splashed out of the V-shaped well and inlet velocity obtained from the numerical modeling. Typical image cases, no splashing at low velocity (left), and splashing at high velocity (right) are superposed for qualitative comparison. Dye is used to enhance the contrast, and in real process, the separation medium is colorless.

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Fig. 9

Optical microscopy of poor cell distribution at different positions in the imaging plate: (a) at corners and edges and (b) in the middle of the well. (a) Huge amount of cells gathered and created clusters at corners and along the edge. (b) Only a few of cells were in the middle area. The density of cells in the middle was much less than that at the corner and along the edge, which indicated the poor cell distribution.

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Fig. 10

Numerical modeling of poor cell distribution during liquid dispensing in the imaging plate. Cells were considered as sphere particles with diameter of 10 μm and density of 1.6 g/cm3. The dispensing speed is 0.05 m/s. The color contour showed the distribution of the cells on the bottom surface of imaging plate; the bar represented the density of the cells at any location. It clearly showed that the cells gathered along the edges and much more cells were found near the edge than in the central area.

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Fig. 11

Numerical modeling of improved cell distribution during liquid dispensing in the imaging plate. The dispensing speed is 0.03 m/s. Much more evenly distributed cells were found under this condition: the cells located throughout the entire surface with little variation in cell density.

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Fig. 12

Optical microscopy of improved cell distribution at different positions in the imaging plate: (a) at corners and edges and (b) in the middle. (a) Plenty of cells were observed but the density of cells was slightly lower than that in Fig. 9(a); (b) evenly distributed cells were observed in the center with higher cell density than that in Fig. 9(b).

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Fig. 13

Numerical modeling of cell loss during liquid aspiration in the imaging plate with (a) high aspiration speed (0.05 m/s) and (b) low aspiration speed (0.03 m/s). The cells were modeled by 1371 spherical particles evenly distributed on the bottom of the well plate initially. The simulation used in (a) had an aspiration speed high enough that cells began to escape from the bottom of the well through the cannula tip. Model (b) used a lower aspiration speed. The particles on the bottom of the well slightly changed their positions and moved closer to the central area when the liquid was being aspirated, but they were not able to overcome their own gravity and escape from the well.

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Fig. 14

The relationship between the aspiration speed and the number of escaped cells in the numerical modeling. Typical images that show the cell loss at low (left) and high (right) aspiration speeds in the plate are superposed for qualitative comparison.

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Fig. 15

Chart of the detected level of micronuclei with increasing radiation by two prepared methods from the same donor with standard error bars. Both manually prepared samples and RABiT prepared samples show the same trend: the curves increase monotonically.




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