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Method Article

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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DOI:

10.3791/55311

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March 13th, 2017

 ,  , 
1School of Electrical and Computer Engineering, Georgia Institute of Technology, 2Institute of Electronics and Nanotechnology, Georgia Institute of Technology, 3Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology

In This Article

Summary

We demonstrate a microfluidic platform with an integrated surface electrode network that combines resistive pulse sensing (RPS) with code division multiple access (CDMA), to multiplex detection and sizing of particles in multiple microfluidic channels.

Abstract

Microfluidic processing of biological samples typically involves differential manipulations of suspended particles under various force fields in order to spatially fractionate the sample based on a biological property of interest. For the resultant spatial distribution to be used as the assay readout, microfluidic devices are often subjected to microscopic analysis requiring complex instrumentation with higher cost and reduced portability. To address this limitation, we have developed an integrated electronic sensing technology for multiplexed detection of particles at different locations on a microfluidic chip. Our technology, called Microfluidic CODES, combines Resistive Pulse Sensing with Code Division Multiple Access to compress 2D spatial information into a 1D electrical signal. In this paper, we present a practical demonstration of the Microfluidic CODES technology to detect and size cultured cancer cells distributed over multiple microfluidic channels. As validated by the high-speed microscopy, our technology can accurately analyze dense cell populations all electronically without the need for an external instrument. As such, the Microfluidic CODES can potentially enable low-cost integrated lab-on-a-chip devices that are well suited for the point-of-care testing of biological samples.

Introduction

Accurate detection and analysis of biological particles such as cells, bacteria or viruses suspended in liquid is of great interest for a range of applications1,2,3. Well-matched in size, microfluidic devices offer unique advantages for this purpose such as high-sensitivity, gentle sample manipulation and well-controlled microenvironment4,5,6,7. In addition, microfluidic devices can be designed to employ a combination of fluid dynamics and force fields to passively fractionate a heterogeneous population of biological particles based on various properties8,9,10,11,12. In those devices, the resultant particle distribution can be used as readout but spatial information is typically accessible only through microscopy, limiting the practical utility of the microfluidic device by tying it to a lab infrastructure. Therefore, an integrated sensor that can readily report particles' spatiotemporal mapping, as they are manipulated on a microfluidic device, can potentially enable low-cost, integrated lab-on-a-chip devices that are particularly attractive for the testing of samples in mobile, resource-limited settings.

Thin film electrodes have been used as integrated sensors in microfluidic devices for various applications13,14. Resistive Pulse Sensing (RPS) is particularly attractive for integrated sensing of small particles in microfluidic channels as it offers a robust, sensitive, and high-throughput detection mechanism directly from electrical measurements15. In RPS, the impedance modulation between a pair of electrodes, immersed in an electrolyte, is used as a means to detect a particle. When the particle passes through an aperture, sized on the order of the particle, the number and amplitude of transient pulses in the electrical current are used to count and size particles, respectively. Moreover, the sensor geometry can be designed with a photolithographic resolution to shape resistive pulse waveforms in order to enhance sensitivity16,17,18,19 or to estimate vertical position of particles in microfluidic channels20.

We have recently introduced a scalable and simple multiplexed resistive pulse sensing technology called Microfluidic Coded Orthogonal Detection by Electrical Sensing (Microfluidic CODES)21. Microfluidic CODES relies on an interconnected network of resistive pulse sensors, each consisting of an array of electrodes micromachined to modulate conduction in a unique, distinguishable manner, so as to enable multiplexing. We have specifically designed each sensor to produce orthogonal electrical signals similar to the digital codes used in code division multiple access22 (CDMA) telecommunication networks, so that individual resistive pulse sensor signal can be uniquely recovered from a single output waveform, even if signals from different sensors interfere. In this way, our technology compresses 2D spatial information of particles into a 1D electrical signal, permitting monitoring of particles at different locations on a microfluidic chip, while keeping both device- and system-level complexity to a minimum.

In this paper, we present a detailed protocol for experimental and computational methods necessary to use the Microfluidic CODES technology, as well as representative results from its use in analysis of simulated biological samples. Using the results from a prototype device with four multiplexed sensors as an example to explain the technique, we provide protocols on (1) the microfabrication process to create microfluidic devices with the Microfluidic CODES technology, (2) the description of the experimental setup including the electronic, optical, and fluidic hardware, (3) the computer algorithm for decoding interfering signals from different sensors, and (4) the results from detection and analysis of cancer cells in microfluidic channels. We believe that using the detailed protocol described here, other researchers can apply our technology for their research.

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Protocol

1. Design of Coding Electrodes

Note: Figure 1a shows the 3-D structure of the micropatterned electrodes.

  1. Design a set of four 7-bit Gold codes for encoding the microfluidic channels23.
    1. Construct two linear feedback shift-registers (LFSRs), each representing a primitive polynomial.
    2. Use the LFSRs to generate a preferred pair of 7-bit m-sequences.
    3. Cyclically shift the preferred pair of m-sequences and add them in mod 2 to generate four distinct Gold codes.
  2. Design the layout of the coding electrodes (Figure 1b).
    1. Place three electrode terminals, representing the positive, negative, and reference electrodes at three corners.
    2. Route positive and negative electrode traces on opposite sides of each microfluidic channel.
    3. Extend positive and negative electrodes into the microfluidic channels as electrode fingers, following the uniquely assigned Gold code (Figure 1c).
    4. Place the reference electrode in between the positive and negative electrode fingers.
    5. Place positive and negative electrode traces far from the outermost reference electrode fingers in order to minimize electrical conduction outside the coding region.

2. Microfabrication of Surface Electrodes

Note: Figure 2b shows the fabrication process of surface electrodes.

  1. Clean a 4-inch borosilicate glass wafer in a piranha solution (98% sulfuric acid : 30% hydrogen peroxide = 5 : 1) at 120 °C for 20 min to remove all the organic contaminants. Then place the wafer on a 200 °C hot plate for 20 min to remove residual water.
  2. Transfer the wafer to a spinner. Dispense 2 mL negative photoresist onto the wafer and spin the wafer at a speed of 3,000 rpm for 40 s to uniformly coat the wafer with a 1.5-µm photoresist layer.
  3. Place the wafer on a 150 °C hot plate and bake the spun photoresist for 1 min.
  4. Expose the photoresist to 365-nm UV light (225 mJ/cm2) through a chrome mask using a mask aligner.
  5. Place the wafer on a 100 °C hot plate and bake the exposed photoresist for 1 min.
  6. Develop the photoresist by immersing the wafer in a photoresist developer (RD6) for 15 s. Gently spray deionized (DI) water and wash the wafer. Dry by blowing compressed nitrogen.
  7. Place the wafer with patterned photoresist into an e-beam metal evaporator, and deposit a 20-nm-thick chrome film, followed by an 80-nm-thick gold film onto the wafer at a base pressure of 3×10-6 Torr with a deposition rate of 1 Å/s.
  8. Immerse the metal-coated wafer into acetone in an ultrasonic bath set at a frequency of 40 kHz with 100% amplitude for 30 min at room temperature to etch the underlying photoresist and complete the lift-off process.
  9. Dice the wafer into smaller pieces using a conventional dicing saw.

3. Fabrication of the SU-8 Mold for Microfluidic Channels

Note: Figure 2a shows the fabrication process of the mold for microfluidic channels.

  1. Clean and bake a 4-inch silicon wafer using the same procedure described in 2.1.
  2. Transfer the wafer to a spinner. Pour 4 mL photoresist onto the wafer. Coat the wafer with photoresist.
    1. Spin the wafer at 500 rpm for 15 s.
    2. Spin the wafer at 1,000 rpm for 15 s.
    3. Spin the wafer at 3,000 rpm for 60 s to obtain a uniformly coated 15-µm thick photoresist layer.
  3. Place the wafer face up on a cleanroom wipe soaked in acetone and remove the residual photoresist from the backside and edges of the wafer.
  4. Transfer the wafer onto a hot plate for soft baking. First, bake the wafer at 65 °C for 1 min. Then quickly move the wafer to a 95 °C hot plate and bake for 2 min.
  5. Expose the photoresist to 365-nm UV light (180 mJ/cm2) through a chrome mask by using a mask aligner.
  6. Bake the wafer following exposure at 65 °C for 1 min and then at 95 °C for 2 min.
  7. Immerse the wafer in developer and gently shake the container for 3 min. Then, rinse the wafer with isopropanol alcohol (IPA) and dry it by blowing compressed nitrogen. If a white-colored residue appears on the wafer, immerse it into the developer again and develop for longer time and dry.
  8. Bake the wafer on a 200 °C hot plate for 30 min to dry it completely.
  9. Measure the thickness of the patterned photoresist using a profilometer at different locations across the wafer to ensure uniformity.
  10. Silanize the mold wafer by utilizing the technique of vapor deposition. Add 200 µL of trichlorosilane in a Petri dish and place in a vacuum desiccator along with the SU-8 mold wafer for 8 h.

4. Assembly of the Microfluidic CODES Device

  1. Place the 4-inch silicon wafer with the mold in a 150-mm diameter Petri dish, and fix it by taping from its edges.
  2. Mix the polydimethylsiloxane (PDMS) pre-polymer and cross-linker at a ratio of 10:1, and pour 50 g of the mixture into the Petri dish. Place the Petri dish in a vacuum desiccator to degas the mixture for 1 h, and then cure it in an oven at 65 °C for at least 4 h (Figure 2a).
  3. Cut out the cured PDMS layer using a scalpel and peel it off the mold wafer using a tweezer. The size of the proof-of-principle device is approximately 20 mm × 7 mm. Then punch holes with a diameter of 1.5 mm through the PDMS for the inlet and outlet of the microfluidic channel using a biopsy puncher.
  4. Clean the patterned side of the PDMS part by placing it on a clean-room adhesive tape.
  5. Clean the glass substrate with surface electrodes by rinsing it with acetone, IPA, DI water and dry using compressed nitrogen.
  6. Activate the surfaces of PDMS and glass substrate in oxygen plasma for 30 s with the micromachined side of each part facing up in an RF plasma generator set at 100 mW.
  7. Align the PDMS microfluidic channel with surface electrodes on the glass substrate using an optical microscope and then bring the two plasma-activated surfaces in physical contact.
  8. Bake the device on a 70 °C hot plate for 5 min, with the glass side facing the hot plate.
  9. Connect the contact pads of the electrodes with wires by soldering.

5. Preparation of the Simulated Biological Sample

  1. Culture the HeyA8 human ovarian cancer cells in RPMI 1640 supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin in 5% CO2 atmosphere at 37 °C until they reach 80% confluence.
  2. Aspirate the media from the culture flask using a glass pipette. Dispense and then aspirate 1x phosphate buffered saline (PBS) to wash the cells.
  3. Incubate cells in 2 mL 0.05% (w/v) trypsin solution for 2 min at 37 °C to suspend adherent cells. Then, add 4 mL of the culture media to neutralize the trypsin.
  4. Centrifuge the cell suspension at 100 × g for 5 min to pellet the cells in a test tube. Then, aspirate supernatant completely.
  5. Re-suspend the cells in 1-2 mL 1x PBS by gently pipetting up and down to mechanically dissociate cell clumps.
  6. Draw a small amount of cell suspension into a pipette and count the number of cells using hemocytometer.
  7. Dilute the cell suspension with PBS to prepare a sample with final cell concentration of 105-106 cells/mL.

6. Running the Microfluidic CODES Device

Note: Figure 3 shows the experimental setup.

  1. Place the Microfluidic CODES device on the stage of an optical microscope.
  2. Apply a 400 kHz sine wave to the reference electrode on the chip using an electronic function generator.
  3. Connect positive and negative sensing electrodes to two independent trans-impedance amplifiers to convert current signals from each to voltage signals.
  4. Subtract the positive sensing electrode voltage signal from the negative sensing electrode voltage signal using a differential voltage amplifier in order to obtain a bipolar signal.
  5. Use a high-speed camera to optically record operation of the device for validation and characterization purposes.
  6. Drive the cell suspension through the Microfluidic CODES device at a constant flow rate (50-1,000 µL/h) using a syringe pump.
  7. Measure the impedance modulation signal using a lock-in amplifier.
    1. Connect the reference AC signal to the reference input of the lock-in amplifier. Connect the differential bipolar signal to the lock-in amplifier as input signal.
    2. Obtain the RMS amplitude of the differential signal from the lock-in amplifier output.
  8. Sample the lock-in amplifier output signal at 1 MHz rate into a computer through a data acquisition board for further analysis.

7. Processing of Sensor Signals

  1. Transfer recorded electrical data into MATLAB for post-processing and decoding.
  2. Filter the recorded signal in the digital domain using a Butterworth filter (MATLAB built-in function) to remove the high frequency noise (>2.5 kHz).
  3. Generate a template code library from sensor signals.
    1. Identify representative non-overlapping code signals corresponding to each sensor in the device and extract these signal blocks from the dataset as separate waveform vectors.
    2. Normalize each template code waveform vector by its power. Use the MATLAB built-in function (bandpower) to measure the signal power.
    3. Use MATLAB function (resample) to expand the template library by digitally creating versions of normalized code signals with varying durations to accommodate variations in the cell flow speed over the electrodes.
  4. Identify the signal blocks that correspond to sensor activity (threshold: SNR > 12 dB) in the filtered waveform. Waveform with SNR under the threshold would be treated as noise.
  5. Decode individual blocks of sensor activity in the recorded signal by using an iterative algorithm based on the successive interference cancellation, a technique commonly employed in multi-user CDMA communication networks24,25.
    1. Calculate cross-correlation of each signal block with all of the templates in the library using sliding dot product.
    2. Identify the template that produces the largest auto-correlation peak to determine the dominant individual sensor code signal. Record the both time and amplitude of the autocorrelation peak.
    3. Construct an estimate sensor code signal by scaling the identified code template based on the measured autocorrelation peak amplitude and timing information (determined in step 7.5.2).
    4. Subtract the estimated sensor code signal from the original data.
    5. Iterate the process from 7.5.1, until the residual signal does not resemble any signal in the template library, mathematically defined as the correlation coefficient being less than 0.5.
  6. Refine initial sensor signal estimations from step 7.5 using an optimization process.
    1. Reconstruct the signal by adding estimated individual sensor signals from each iteration.
    2. Sweep the amplitude, duration and timing of individual sensor signals around the original estimates to produce the best fit with the recorded electrical signal based on least-squares approximation26.
  7. Convert amplitudes of estimated sensor signals into cell size by calibrating electrical signals against optical images.

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Results

A Microfluidic CODES device consisting of four sensors distributed over four microfluidic channels is shown in Figure 1b. In this system, the cross-section of each microfluidic channel was designed to be close to the size of a cell so that (1) multiple cells cannot pass over the electrodes in parallel and (2) cells remain close to the electrodes increasing the sensitivity. Each sensor is designed to generate a unique 7-bit digital code. The device was then tested using a ...

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Discussion

Multiple resistive pulse sensors have previously been incorporated into microfluidic chips28,29,30,31,32. In these systems, resistive pulse sensors were either not multiplexed28,29,30,31 or they required individual sensors to be driv...

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Disclosures

The authors have nothing to disclose.

Acknowledgements

This work was supported by National Science Foundation Award No. ECCS 1610995. The authors would like to thank the Institute of Electronics and Nanotechnology and the Parker H. Petit Institute for Bioengineering and Bioscience staff for their support in using shared facilities. The authors also would like to thank Chia-Heng Chu for his help in preparing the manuscript.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
98% Sulfuric Acid   BDH ChemicalsBDH3074-3.8LP
30% Hydrogen Peroxide  BDH ChemicalsBDH7690-3
TrichlorosilaneAldrich Chemistry235725-100G
NR9-1500PY Negative PhotoresistFuruttex
Resist Developer RD6Furuttex
AcetoneBDH ChemicalsBDH1101-4LP
SU-8 2015 Negative PhotoresistMicrochemSU8-2015
SU-8 DeveloperMicrochemY010200
Polydimethylsiloxane (PDMS)Dow Corning3097358-1004Sylgard 184 Silicone Elastomer Kit
Isopropyl AlcoholBDH ChemicalsBDH1133-4LP
RPMI 1640Corning Cellgro10-040-CV
Fetal Bovine Serum (FBS)Seradigm1500-050
Penicillin-StreptomycinAmrescoK952-100ML
Phosphate-Buffered Saline (PBS)Corning Cellgro21-040-CM
PHD 22/2000 Syringe PumpHarvard Apparatus70-2001
HF2LI Lock-in AmplifierZurich Instrument
HF2TA Current AmplifierZurich Instrument
Eclipse Ti-U MicroscopeNikon Corporation
DS-Fi2 High-Definition Color Camera Nikon Corporation
v7.3 High-speed CameraPhantom
PCIe-6361 Data Acquisition Board National Instruments781050-01
BNC-2120 Shielded Connector BlockNational Instruments777960-01 
PX-250 Plasma Treatment SystemNordson MARCH 

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Microfluidic CODESResistive Pulse SensingCode Division Multiple AccessParticle DetectionMicrofluidic ChannelsCell SizingLab-on-a-ChipPoint-of-Care TestingElectronic Sensing
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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