Top PDF Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

flow rates above 20 µL/min (Figure S4). These results demonstrate that devices with a height of 60 µm do not fulfill the necessary requirements for sample flow focusing within 10 µm from the coverslip. To eliminate the problem of sample backflow towards the inlet we next tested devices with a height of 120 µm. An increase in the area of the channel cross-section is expected to alleviate backflow since channel pressure is expected to drop. The logical way to achieve this would be to increase channel height, as opposed to channel width, as we would otherwise lose horizontal focusing. Similar to the previous device, the fluorescein flow rate was kept at 0.25 µL/min and sheath flow rate was slowly increased from 0.25 µL/min (0.43 mm/s) to 200 µL/min (347.22 mm/s). Again, to evaluate fluorescein confinement we used confocal microscopy and the results are shown in Figure 4a. We found that for a 1:1 sheath-to-sample flow rate ratio, fluorescein occupies a large fraction of the channel volume, also shown in red in the image overlay in Figure 4b. With increasing sheath flow rates, we observe continuous fluorescein confinement, seen in the figures as a reduction in the width and height of the fluorescein cone. Quantification of the fluorescein cone height from the images revealed a confinement within a distance of 10 µm from the cover slip at flow rates over 100 µL/min (173.61 mm/s), shown as the shaded area in the graph in Figure 4c. The distance between the microscope slide and the tip of the fluorescein cone decreased further to a minimum of ~5µm when the highest tested flow rate (200 µL/min) was used. At such high sheath flow rates, sample flow begins to become unstable, which results in a change in the observed fluorescein shape, as seen in Figure 4a. Importantly, due to the larger cross-section of this device, flow equilibrium was maintained, and no fluorescein backflow was present even at very high sheath-to-sample flow rate ratios. As seen in the graph, experimentally measured fluorescein heights correspond well with the equivalent simulated heights, and confinement below ~5 µm was experimentally reproduced. To better visualize confinement, we used COMSOL Multiphysics ® to generate animations that show sample flow confinement with increasing sheath flow rates in 2D and 3D, see Supplementary Animation S1 and S2. In summary, devices with a height of 120 µm can robustly confine fluorescein as close as ~5 µm from the microscope slide.
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A microfluidic platform for the characterisation of CNS active compounds

A microfluidic platform for the characterisation of CNS active compounds

Importantly, interchangeable perfusion of different solutions through a single culture chamber was achieved without disturbance in the flow. This was obtained by rapidly replacing the liquid volume inside the inlet open well of the perfusion chamber, selecting flow rate values that were much greater than that created in the perfused chamber. The open well system, in combination with carefully selected values of the flow rates (equations 1–2), generated a self-adjusting hydrostatic pressure difference between inlet and outlet wells of the perfused chamber that maintained a constant flow rate in the perfused chambers for the duration of the experiment. If the flow rate Figure 5. Repeated glutamate application induces increased neuronal activity in synaptically connected hippocampal cultures. (a) Ca 2+ imaging traces representative of activity obtained from individual neurons in
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A Microfluidic Platform for Single Cell Fluorometric Granzyme B Profiling

A Microfluidic Platform for Single Cell Fluorometric Granzyme B Profiling

traps. With the same flow rate, excess cells were flushed out by rinsing the chip with 50 µL of buffer medium. Afterwards, the pneumatic valves were pressurized with 0.07 MPa to seal the microchambers and isolate each trapped cell. After flushing the chip with Granzyme B recognition substrate (Ac-IEPD-AFC) in buffer, the pneumatic valves were briefly opened and closed to allow the substrate to enter the chamber and interact with the cell. Thirty minutes incubation is then performed. The non-fluorescent substrate is hydrolysed by the expressed Granzyme B in an enzyme-catalyzed reaction which resulted in the release of AFC proportional to the enzymatic activity present. Both an inverted microscope (IX70, Olympus) with photodetector (Femtowatt Silicon Photoreceiver, New Focus Sigma Koki), λ = 405 nm excitation laser (LAVIOS Laser Module, excitation power = 1.7 mW), and inverted microscope (IX83, Olympus) with Confocal scanner unit (CSU-W1, Yokogawa), CCD camera (Zyla sCMOS, ANDOR Tech) were used to observe and measure the fluorescence in the microchambers. However, using the former took longer time to measure more than a hundred chambers, thus imaging using the confocal microscope was performed. On the other hand, it shows the flexibility of the platform to be used in different microscope systems. The acquired data was then analyzed and plot using ImageJ image analysis software. Figure 3 shows the experimental set-up and the flowchart of the experiment protocol. To compare with conventional methods, the GrB activity assay was also performed on a 96 well plate for bulk samples. The standard curves obtained using the microfluidic device for single cell measurement and a 96-well plate for bulk sample measurement can be found in the supplementary pages (Figure S4).
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Engineered 3D vascular and neuronal networks in a microfluidic platform

Engineered 3D vascular and neuronal networks in a microfluidic platform

ImageJ. Statistical and image analysis. The neurite morphological profiles in 3D culture were quantified using Imaris and ImageJ. First, the neurite structures were identified from fluorescent images stained with Tuj1 using the plugin of filament tracer with spine in the Imaris software, then the rendering images were saved as TIF files. The TIF files were then loaded in Image J and neurite length and spine density were quantified by the Image J plugin, Neuron J. Fluctuation amplitudes of the Ca 2+ indicator were also quantified by ImageJ. Time-lapse images captured by fluorescence microscopy were converted to grayscale (16 bit) and ROIs were calculated by the change of fluorescent intensity of Fluo-8 in circular regions in cells using ImageJ. Based on these ROI values, graphs of intensity changes were generated. To quantify the vascular structure in the microfluidic device, 3D images captured by confocal microscopy were converted to maximum projection intensity images and binarized. Then, area coverage, effective diameter, branch number and branch length of the microvascular networks were quan- tified using the ImageJ plugin AnalyzeSkeleton (http://fiji.sc/AnalyzeSkeleton). The data are expressed as mean values ± SD, and one-way or two-way ANOVA tests were performed. If significant, the data were tested with multiple comparison tests (Tukey-Kramer post hoc tests, n = 10; **p < 0.05, *p < 0.01). The tests were performed using MATLAB software (Math Works, Worcester, MA).
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Manufacturing Microfluidic Flow Focusing Devices For Stimuli Responsive Alginate Microsphere Generation And Cell Encapsulation

Manufacturing Microfluidic Flow Focusing Devices For Stimuli Responsive Alginate Microsphere Generation And Cell Encapsulation

9 1.3. Microfluidics Microfluidics is an expanding field due to precise control capabilities afforded by using small controlled portions of material. Microfluidic flow focusing devices (MFDs) allow multiple experiments to be performed on small scales with accurate metering capability. The field of microfluidics applies directly to chemical synthesis, biological analysis, optics, and information technology [30]. Microfluidic flows typically exhibit the properties of low Reynolds numbers and high surface tension [31]. When dissimilar and immiscible fluids meet at a designed junction, the dispersion phase fluid extends until a droplet breaks off due to Rayleigh instability [32]. A narrowing section of the junction creates a point of high shear. The continuous phase fluid extends and pinches off the dispersion phase fluid forming uniform droplets. The sizes of the droplets decrease by increasing the flow rate of the continuous phase. Alternatively, an increase in dispersion phase flowrate also increases the frequency of droplet generation [33]. Polydimethylsiloxane (PDMS) is a silicone based organic polymer that has desirable properties for micro channel creation. PDMS is inexpensive, rapidly produces prototypes, and is biocompatible, allowing the MFDs made of it to be economic and safe [34].
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Capillary Phenomena in Textiles with Continuous Microfluidic Flow.

Capillary Phenomena in Textiles with Continuous Microfluidic Flow.

Several equations have been used in many textile studies to analyze wicking performance in quantitative ways (Table 3-1). Usually, these precedent studies have proceeded with typical liquid moisture management property tests of textiles, and then, tried to investigate data through the various equations, shown in Table 3-1. However, those wicking-related equations were not designed for the wicking performance of fabrics under the human sweating circumstance but generally for a straight capillary channel with a constant circular cross-section in contact with an infinite reservoir. However, fabrics have complex capillary channels due to their own structure such as knit and woven. Specifically, the twist level and the cross-sectional area of a yarn within a fabric keep changing over the locations within the fabric. For example, Lucas-Washburn equation has been frequently used for the vertical wicking test with a fabric strip because this test is conducted with infinite reservoir. Even though the average effective capillary radius of a substrate can be calculated through Lucas-Washburn equation and wicking experimental data, this radius does not have any particular significance for the textile substrate. Even though the capillary channel from the calculated radius is assumed as a circular shape in the equations, the actual liquid in the capillary channels of a textile substrate exists in an effective medium space between fibers of arbitrary shape and with arbitrary arrangements, shown in Fig. 5-6. Additionally, the typical wicking test methods may not represent human sweating because those methods are conducted with either infinite reservoirs or limited, but large amounts of liquid (Table 3-2). However, because human sweat is generated from each sweat gland with a continuous flow, continuous liquid flow should be used for the wicking performance test of apparel.
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Development of Impedance Base Microfluidic Flow Sensor

Development of Impedance Base Microfluidic Flow Sensor

microfluidic flow and speed sensor devices which can detect a microfluidic droplet or wetting, study were carried out with available commercial material such as acrylic, copper board, double sided tape and glue. However, the aim of the project is to develop electronic device that can detect very small volume of fluid, therefore, device development consists of channel and geometry parameters that model and analyze while further development incorporate microcontroller that allow device to read and display result on LCD and excel datasheet. The model become more easy and in- expensive as less circuit is required for simple electronics model, also device can easily be implemented because device fabrication do not really require lab-on-chip process.
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Volumetric flow rate in simulations of microfluidic devices

Volumetric flow rate in simulations of microfluidic devices

Abstract. In this work, we examine the volumetric flow rate of microfluidic devices. The volumetric flow rate is a parameter which is necessary to correctly set up a simulation of a real device and to check the conformity of a simulation and a laboratory experiments [1]. Instead of defining the volumetric rate at the beginning as a simulation parameter, a parameter of external force is set. The proposed hypothesis is that for a fixed set of other parameters (topology, viscosity of the liquid, …) the volumetric flow rate is linearly dependent on external force in typical ranges of fluid velocity used in our simulations. To confirm this linearity hypothesis and to find numerical limits of this approach, we test several values of the external force parameter. The tests are designed for three different topologies of simulation box and for various haematocrits. The topologies of the microfluidic devices are inspired by existing laboratory experiments [3 - 6]. The linear relationship between the external force and the volumetric flow rate is verified in orders of magnitudes similar to the values obtained from laboratory experiments.
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Microphysiological systems and low-cost microfluidic platform with analytics

Microphysiological systems and low-cost microfluidic platform with analytics

Hickman’s group has developed a defi ned base media system that enables culture of a wide range of cell types for many months, and removes a major variable (serum) from the system. Hickman’s group published the fi rst serum-free, defi ned culture system for neurons in 1995 [2] and has since then advanced this work from the use of rodent cells to human cells. In most cases, the cultured cells have been shown to maintain functionality for at least 2 to 3 months in this system. Th is common, defi ned media system utilization has been expanded to include human neurons [3,4], glia [5,6], muscle [3] and cardio- myocytes [7] from adult, fetal and embryonic stem cell sources. Hickman’s group has progressed in the creation of a functional neuromuscular junction model for rat–rat [8] to rat–human [4] to human–human [3] using the same basic serum-free culture system.
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Scanning electrochemical cell microscopy platform for ultrasensitive photoelectrochemical imaging

Scanning electrochemical cell microscopy platform for ultrasensitive photoelectrochemical imaging

An SECCM platform has been described that enables the investigation of (photo)electrochemical systems at the micro to nanoscale with high sensitivity. The high spatial resolution achieved with SECCM (photo)electrochemical imaging has been coupled with ultrasensitive measurements at the tens of fA range, opening up prospects for accessing a wide range of (photo)electrochemical phenomena, with future applications, such as assessing materials for solar energy conversion, water treatment, bio-sensing and photosynthesis.

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Continuous flow separations in microfluidic devices

Continuous flow separations in microfluidic devices

minutes, with throughputs of 100s pg h 21 . Free-flow electrophoresis and free-flow isoelectric focussing Free-flow electrophoresis (FFE) is performed in a shallow chamber, as shown in Fig. 8a. Buffer and sample solutions are continuously pumped into this chamber through numerous inlet channels and collected via outlet channels. Perpendicular to the direction of flow, a homogeneous electric field is applied. Charged molecules are thus subjected to two flow vectors: the hydrodynamic flow in the y-direction and the electrophoretically induced flow in the x-direction. The observed flow path is the sum of these two vectors. The direction of deflection depends on whether the molecule is an anion or a cation; the extent of deflection depends on the charge to size ratio. Molecules with zero net charge are not deflected and flow straight through the chamber. Free-flow electrophoresis was initially developed on the larger scale. 34,35 Miniaturisation of the separation chamber, typically to a width and length of several mm and a depth of a few mm, brings similar advantages to those for capillary electrophoresis: Joule heating can dissipate quickly due to the larger surface to volume ratio, hence, high electric fields can be applied, and thus separation efficiency and speed are improved.
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Investigation of the heat shock response in yeast: quantitative modeling and single-cell microfluidic studies

Investigation of the heat shock response in yeast: quantitative modeling and single-cell microfluidic studies

3·8 A) FACS measurement data of the WT and a panel of mutants be- fore and after heat shock at 39 ◦ C. In each strain a number of ser- ines/thereonines are mutated to investigate the effect of phosphoryla- tion in activation of Hsf1. The most important strain is 152 A in which 152 out of 153 possible phosphorylation sites are mutated to alanine. This strain induces the heat shock response to 75% of the wild type. B) i to vi: single cell microfluidic heat shock data of WT and the panel of mutants. The data corresponds well with the FACS measurements and validates our technology. C) The transcriptional reporter construct in the cells used to report HSF1 activation as YFP signal. . . . . . . . . 50
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A surface acoustic wave pumped
lensless microfluidic imaging system for flowing cell detection and counting

A surface acoustic wave pumped lensless microfluidic imaging system for flowing cell detection and counting

The PDMS microfluidic channel was fabricated by soft- photolithography [38] and replica molding method [39] using a 5-inch silicon. The channel features were first designed in AutoCAD (Autodesk, San Rafael, CA) and then written to a film mask. Negative photoresist SU-8 (SU-8 3050, Microchem, MA) was spin-coated on a glass slide and subsequently through soft baking. After UV light exposure, the developing and silanization were performed with post-baking. Then the SU-8 master was fabricated on a glass slide. Next, a volumetric ratio of 10:1 mixture of PDMS (Sylgard 184, Dow Corning, MI) and curing agent are poured onto the SU-8 master. After degassing and curing, the PDMS replica was peeled off from the master and punched on top for inlet and outlet. To fully use the active pixel area, the channel length was selected as 4.5mm. The channel width was 1mm so that cells can flow in a straight and predictable manner in the channel. The height of the sensor was 30μm as it is just larger than the normal cell diameter. The surfaces of the microfluidic channel and the LiNO 3 substrate
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The entrapment and characterization of
single spermatozoa in a microfluidic system

The entrapment and characterization of single spermatozoa in a microfluidic system

With the PDMS chip set-up 2 (Figure 4.1b), the liquid was not pushed in, but drawn through the channel by suction. Inlet A was made using a 3 mm pen; this large diameter was chosen to prevent the formation of air bubbles when pipetting the liquid in the inlet. Inlet 3 was closed, while inlets 1 and 2 were both connected to a syringe to provide suction. Exchange of liquids in inlet A was performed by using a fiberless tissue to suck up the remaining liquid and a pipette, to add the new liquid to the inlet. Because the second chip set-up used suction, introduction of PDMS debris was prevented. Furthermore, closing inlet 3 was not necessary, because a large flowrate from the syringe connected to inlet 2 was enough to provide a sufficient pressure difference and to create a fluid flow between the main channels. A disadvantage of the second design is the fact that a droplet, which was placed on inlet 3, would flow over the PDMS into inlet A. The connection of the droplets caused mixing of the liquids, introducing cells in the other channel.
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Multistrategy self-organizing map learning for classification problems

Multistrategy self-organizing map learning for classification problems

Early studies have shown that the multistrategy learning of PSO-SOM approach was first introduced by Shi and Eberhart [8] with modified particle swarm optimizer. Subse- quently, Xiao et al. [9, 10] used hybrid SOM-PSO approach to produce better clustering of gene datasets. The authors used SOM learning and PSO to optimise the weights of SOM. However, the merit for combination of SOM-PSO without conscience factor was poor than SOM alone. This is because this factor is valuable as a competitive learning technique, but it reduces the number of epochs necessary to produce a ro- bust solution. In 2006, O’neill and Brabazon [11] adopted PSO as unsupervised SOM algorithm. The authors suggested using di ff erent distance metric in calculating the distance be- tween input vectors and each member of the swarm to pro- duce competitive result for data classification. However, in this study, types of SOM lattice structure were not con- sidered.
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Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers.

Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers.

vial), and seeded in a PDMS 200-μm multi-well mold. HepG2 spheroids were formed in DMEM culture medium with 10% heat-inactivated fetal bovine serum (FBS), and 1 μg/mL rat tail collagen gel type I obtained from Life Technologies. Spheroid formation media for primary hepatocyte consisted of Hepatocyte Maintenance Medium (Triangle Research Labs) with 1 μg/mL rat tail collagen gel type I. To maintain high cell viability, the culture medium was replaced every 48 hours. At Day 5 the spheroids were harvested from microwells, made of PDMS, and mixed with a hydrogel prepolymer solution including 10% (w/v) custom made gelatin methacryloyl (GelMA) and 0.5% (w/v) 1-[4-(2-hydroxyethoxy)-phenyl]-2-hydroxy-2-methyl-1-propanone (Ciba Specialty Chemicals, Tarrytown, NY, USA) used as photoinitiator (PI). GelMA droplets with approximately 1 mm in size were bio- printed in the microfluidic bioreactor using an Organovo NovoGen MMX bioprinter (San Diego, CA, USA) to form a 5 × 4 microarray (20 drops with 18 average spheroid number). After 17 s of UV light exposure at 850 mW at a distance of 8.5 cm, the GelMA drops were crosslinked to the glass at the bottom of the bioreactor. The biore- actor setup was operated by a peristaltic pump and included a reservoir holding 4 mL basal medium (Figs 1c and S1). In additional, the bioreactor held about 1 mL culture medium and the flow rate was maintained at 200 μL/hr. For off-chip and on-chip biomarker measurements, media supernatant was collected from the bioreactor on Days 1, 3, and 5 of culture. For hepatotoxicity assessment studies, the liver bioreactor was directly integrated with the automated microfluidic bead-based sensing system and the effect of 5 mM and 10 mM APAP were continuously monitored for 5 days and the secreted biomarkers were measured on Days 1, 3, and 5 of culture.
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A stochastic transcriptional switch model for single cell imaging data

A stochastic transcriptional switch model for single cell imaging data

Gene expression is made up of inherently stochastic processes within single cells and can be modeled through stochastic reaction networks (SRNs). In particular, SRNs capture the features of intrinsic vari- ability arising from intracellular biochemical processes. We extend current models for gene expression to allow the transcriptional process within an SRN to follow a random step or switch function which may be estimated using reversible jump Markov chain Monte Carlo (MCMC). This stochastic switch model provides a generic framework to capture many different dynamic features observed in single cell gene expression. Inference for such SRNs is challenging due to the intractability of the transition densities. We derive a model-specific birth–death approximation and study its use for inference in comparison with the linear noise approximation where both approximations are considered within the unifying framework of state-space models. The methodology is applied to synthetic as well as experimental single cell imaging data measuring expression of the human prolactin gene in pituitary cells.
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Classification of companies with theassistance of self learning neural networks

Classification of companies with theassistance of self learning neural networks

Abstract: The article is focused on rating classification of financial situation of enterprises using self-learning artificial neural networks. This is such a situation where the sets of objects of the particular classes are not well-known. otherwise, it would be possible to use a multi-layer neural network with learning according to models. The advantage of a self-learning network is particularly the fact that its classification is not burdened by a subjective view. With reference to complexity, this sorting into groups may be very difficult even for experienced experts. The article also comprises the examples which con- firm the described method functionality and the neural network model used. A major attention is focused on the classifica- tion of agricultural companies. For this purpose, financial indicators of eighty one agricultural companies were used. Key words: artificial intelligence, neural network, Kohonen network, learning, classification
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Rapid self-assembly of DNA on a microfluidic chip

Rapid self-assembly of DNA on a microfluidic chip

In the HA method, electrophoretic conditions are chosen in order to enhance the velocity differences between the duplexes so that the process of duplex self-assembly can be used to determine the presence of a heterozygous state (hence indicating the presence of a mutation). In SSCP, isolated strands of ssDNA find near-complementary sequences on the same strand, with the result that the strand folds upon itself in a sequence dependent manner forming new conformations. This is a simplistic descrip- tion since ssDNA without self-similar sequences, and homoduplex dsDNA, may also take complex forms. Tech- niques such as HA that aim to separate homoduplex frag- ments from heteroduplex fragments often use some combination of thermally and chemically denaturing conditions to cause the partial melting of the duplex, resulting in a shift in mobility or chromatography column retention time that increases with the degree of mismatch. Many medical diagnostics could be implemented on microchips if an effective implementation of a highly sen- sitive mutation analysis method could be integrated with PCR/CE. Considerable work has been done in developing SSCP [11] and HA [2,3,12]. An excellent review of such methods has been produced by Jin et al. [13]. The main drawback is the lower sensitivity of these methods. In macroscopic work Kozlowski and Krzyzosiak [5] and Kourkine et al. [14] greatly improved their sensitivities by combining SSCP and HA methods to develop capillary- based electrophoretic techniques with sensitivities of 90– 94 % for SSCP and 75–81 % for HA. In a landmark anal- ysis, Kourkine et al. achieved 100 % sensitivity by analys- ing denatured and non-denatured fragments in tandem. Despite being highly effective, the additional sample preparation required by these methods (i.e. separately preparing both single and double stranded DNA and maintaining this strandedness) complicates their imple- mentation on microchips.
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Severity classification of Tenosynovial Giant Cell Tumours on MR imaging

Severity classification of Tenosynovial Giant Cell Tumours on MR imaging

improved overall response rate by RECIST: 39% in the pexidartinib- group (N = 61) and 0% of placebo-group (N = 59), after median six months follow-up [ 23 ]. The preliminary results with cabiralizumab (N = 22) are consistent, with radiographic response and improvement in pain and function in five out of 11 patients 28 [ 21 ]. However, long term e fficacy data have not yet been reported with these newer agents. Patient inclusion for these trials is very heterogeneous. A strict pa- tient selection is desirable, to accurately evaluate effect of these treat- ments. At present, patient selection for trial inclusion is established by preference of treating physician and might di ffer per center. Defining more aggressive TGCT subtypes and including these uniformly defined patients into trials would more adequately investigate the e ffect and toxicities of treatment [ 11 ]. In this study, we propose to include pa- tients defined with ‘severe diffuse’ TGCT subtype. Monitoring the effect of systemic therapy also benefits from clear agreements on parameters. Uniform MR descriptions are of utmost importance for clinical and research purposes. Thus far, no well-defined tumour parameters exist. Definition of unambiguous MR criteria is challenging, because of the rarity of the tumour and small number of heterogeneous cases, variety of joints involved, different disease severity as well as several treatment modalities [ 4 , 6 ]. So far, MR imaging has shown to be the best dis- criminating method to evaluate TGCT [ 10 , 16 ]. In our study, six ob- jective clinically relevant MR parameters were de fined in relation to anatomical or surgical landmarks. According to our exclusion criteria for the development of the severity classi fication, parameters cartilage covered bone invasion and neurovascular involvement showed in- adequate number of presence and were therefore not used. However, in larger case series these two parameters might correlate with more ag- gressive disease and hence a higher recurrence rate.
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