Technical Note
pubs.acs.org/ac
On-Demand Lensless Single Cell Imaging Activated by Differential
Resistive Pulse Sensing
Jinhong Guo,†,§ Xiwei Huang,‡,§ and Ye Ai*,†
†
Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, P. R. China
‡
ABSTRACT: We present an on-demand single cell imaging
technique activated by differential resistive pulse sensing in a
portable system integrating a microfluidic differential coulter
counter and a lensless complementary metal-oxide-semiconductor (CMOS) imaging sensor. Dual parametric single
cell analysis and on-demand single cell imaging have been
demonstrated by microbeads of different sizes and a cell
mixture including red blood cells (RBCs) and tumor cell line
HepG2 cells. The on-demand imaging capability could avoid
generating useless images without cells and enable selective
imaging of single cells within a specific size range.
T
using pixel super-resolution algorithms.12,13 Current lensless
imaging systems are mainly applied for static cell counting and
differentiation.9,14,15 In a flow-through mode, continuous
imaging will generate a huge amount of images with useless
information, for example, images without biological cells. The
huge amount of images will slow down and overload the image
processing, which is the major challenge for high-throughput
imaging-based single cell analysis.
In this work, we integrate the lensless single cell imaging with
differential resistive pulse sensing in a portable microfluidic
system to address the aforementioned challenges of both
techniques for accurate and high-throughput single cell analysis.
In such an integrated system, the resistive pulse sensing and
imaging-based cytometry can be concurrently performed for
single cell analysis. Additionally, the resistive pulse sensing can
activate the lensless CMOS sensor, which enables on-demand
single cell imaging based on the cell size information.
raditional cellular assays based on a large number of cells
can only provide population level analyses. Biological cells
are intrinsically heterogeneous, and population level analysis
could be misleading. In recent years, single cell level analysis
has emerged as a useful tool to assay heterogeneity within cell
populations and identify rare cell populations.1 The coulter
counter is the first technique for single cell analysis, which can
count cells and characterize the cell size in a continuous flow.2
When a single cell translocates through an aperture with a size
comparable to the cell size, the temporal blockage of the cell
inside the aperture induces a remarkable change in the electrical
resistance that is dependent on the cell size. Combined with
microfluidics technology, the coulter counter has been
implemented as a cost-effective and compact platform to
analyze various biological cells.3−6 However, this technique can
only extract the size information, and it is very difficult for this
method to characterize the cell morphology like cell shape,
texture, and intracellular structure. In addition, it may not be
able to correctly differentiate clumps of smaller cells from larger
single cells, which is an important issue in searching rare cell
populations.
The conventional microscopic imaging technique is able to
provide a direct observation of the biological cells and thus
remains one of the most efficient methods for single cell
analysis. However, this imaging technique is typically restricted
in well-established modern laboratories due to its high
implementation cost and bulky size. The rapidly growing
market in on-site healthcare requires portable point-of-care
diagnostic tools that can bring imaging-based single cell analysis
from modern laboratories to the sites. In recent years, the
integration of microfluidic channels with lensless complementary metal-oxide-semiconductor (CMOS) imaging sensors has
developed on-chip cell imaging systems in portable devices.7−11
Although the typical size of a single pixel is beyond 1 μm, it is
still able to reconstruct images with submicrometer resolution
© 2015 American Chemical Society
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DEVICE AND EXPERIMENTAL SETUP
Device Design. Figure 1 shows the schematic structure of
the developed on-demand single cell imaging system including
a microfluidic device and a lensless CMOS imaging sensor. The
microfluidic device has two identical channels sharing the same
inlet and outlet reservoirs. The two channels have the same
aperture for resistive pulse sensing. The testing sample solution
is directly dropped into one of the reservoirs and is then
introduced to the microfluidic channel by gravity. Two AgCl
electrodes are immersed in the two reservoirs to apply a DC
bias across the two channels, which in turn generate
electroosmotic flow (EOF) to drive biological cells through
Received: April 13, 2015
Accepted: June 2, 2015
Published: June 2, 2015
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DOI: 10.1021/acs.analchem.5b01378
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Technical Note
Analytical Chemistry
Figure 1. (a) Schematic structure of the developed on-demand single cell imaging system. The microfluidic differential coulter counter is built on the
CMOS imaging sensor with white light illumination. (b) A three-dimensional illustration of the microfluidic device on the CMOS imaging sensor.
(c) Working principle of the on-demand single cell imaging system. Two electrodes immersed in the inlet and outlet reservoirs are used to apply an
electrical bias across the two sensing apertures. Two branch channels at the end of the sensing apertures are connected to a differential amplifier for
the detection of voltage modulation. FPGA is the central microcontroller for signal processing that can actively control the CMOS sensor to take
snapshots of single cells based on the voltage modulation.
conventional coulter counter with a single aperture for resistive
pulse sensing, the dual-channel configuration can enhance the
throughput of single cell analysis. Furthermore, the differential
resistive pulse sensing can achieve better signal-to-noise ratio
and better signal stability in a long-term monitoring.16
The amplified voltage pulse modulation is collected and
digitalized by an ADC chip and then transferred to a central
controller operated by a Field Programmable Gate Array
(FPGA, Altera, Cyclone II, USA). A threshold voltage
modulation is set in the FPGA controller to detect the
translocation event of cells above a certain size through the
aperture. Once a voltage pulse modulation higher than the
preset threshold value is detected, the FPGA produces a signal
to activate the CMOS sensor working in the snapshot mode to
capture the cell image. The CMOS sensor (Aptina MT9M032,
San Jose, CA, USA) has an imaging area of 3.24 mm (H) ×
2.41 mm (V) with a pixel size of 2.2 μm. The frame rate is 60
fps at a resolution of 1280 (H) × 720 (V), which can be further
increased by reducing the field-of-view (FOV). In this study,
the FOV only covers the exit of the two sensing apertures to
capture images of single cells when leaving the apertures. To
the sensing apertures assisted by gravity. At the end of each
aperture, a branch microfluidic channel is used to detect the
voltage modulation induced by single cell translocation through
the aperture. The two branch microfluidic channels are
connected to a differential amplifier. When there is no cell
translocation through either of the two sensing apertures, the
voltages at the two input ports of the differential amplifier are
nearly identical, and therefore, the measured output voltage,
Vout, is zero. Once a single cell is translocating through either of
the two sensing apertures, the voltage at the corresponding
input port changes accordingly, and the other port is used as a
reference; the voltage difference between the two input ports is
then amplified by the differential gain with a low-pass filter,
based on which cells could be counted and the size information
that could be extracted. In an extremely rare scenario that two
exactly identical cells translocate through both apertures at
exactly the same time, the output of the differential amplifier
will be zero and results in misreading. To reduce the probability
of simultaneous cell translocation through both apertures, the
sample concentration should be in a reasonable range and the
aperture length should be reasonably short. Different from the
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DOI: 10.1021/acs.analchem.5b01378
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Analytical Chemistry
optimize the image contrast, the protective layer and microlens
layer on the top of the CMOS imaging sensor are carefully
removed and the microfluidic channel is directly attached on
the CMOS imaging sensor to minimize the distance between
single cells and imaging pixels.
Device Fabrication. The microfluidic device was fabricated
using a soft lithography technique reported previously.17,18
Briefly, the master mold was fabricated by patterning a negative
photoresist SU-8 (SU-8 25, Microchem, USA) on a clean glass
slide. A polydimethylsiloxane (PDMS) mixture of prepolymer
and curing agent at a weight ratio of 10:1 was casted on the SU8 master mold after a complete degassing and then cured by
polymerization. Cured PDMS was then peeled off from the
mold and punched in the reservoir locations. The fabricated
aperture is 30 μm in length, 30 μm in width, and 30 μm in
height. Each microfluidic channel is 2 mm in length, 200 μm in
width, and 30 μm in height. The diameter of the reservoirs is 1
mm. The PDMS layer and the bare CMOS imaging sensor
were treated by plasma before being brought into contact for
bonding to form the final chip device.
Testing Sample. Two monodisperse particle suspensions
of 5 and 15.6 μm in diameter polystyrene microbeads were
prepared by diluting the original sample in a 1× PBS solution
to obtain a reasonable concentration (1 × 104 mL−1). Under
this sample concentration and the fabricated aperture length,
simultaneous particle translocation through both apertures was
hardly observed in the experiments. Bovine serum albumin
(BSA) was added in the microbead suspensions to avoid
agglomeration and adhesion onto the channel wall. A tumor
cell line, HepG2 cells (American Type Culture Collection, MD,
USA), were cultured in Dulbecco’s Modified Eagle Medium
(DMEM) supplemented with 10% fetal bovine serum (FBS),
penicillin (100 μg/mL), 1 mM sodium pyruvate, and 0.1 mM
MEM nonessential amino acids, at 37 °C under a 5% CO2 in a
T75 flask. Red blood cells (RBCs) were separated from the
whole blood using optiprepRM density gradient medium. After
centrifugation at 2000g for 15 min, RBCs were collected from
the bottom of the centrifuge tube. The two different cell
samples were washed three times with 1× PBS and then mixed
and suspended in fresh 1× PBS solution.
Figure 2. Measured differential resistive pulses induced by several
consecutive 5 μm (a) and 15.6 μm (b) polystyrene microbeads
through the sensing apertures. Each upward or downward pulse
represents one single particle translocation event. Five and two
translocation events are detected in (a) and (b), respectively. Each
pulse triggers the CMOS imaging sensor to capture an image of a
single microbead.
15.4 ± 2.2 μm, respectively. When cells are flowing in a very
close distance above the sensing pixel array, the image capturing
process is likely to digitize the original analog cell images. As
long as the size variation is larger than half the pixel size, it can
be detected. Therefore, the smallest cell size that the system can
detect is half the pixel size, which is 1.1 μm in this prototype.
Next, we used the cell mixture to demonstrate the capability
of the developed system in differentiating different cell types.
Figure 3 shows the measured voltage modulations induced by a
series of cell events through the sensing apertures. RBCs and
HepG2 cells have an average size of 5 and 18 μm, respectively.
Therefore, the two different cell types induce quite distinct
voltage modulations, based on which 7 RBCs and 4 HepG2
cells were counted and differentiated as shown in Figure 3. In
this experiment, the threshold voltage modulation is set to 0.04
mV for on-demand single cell imaging. Pixel analysis of the
captured single cell images indicates that the measured size of
RBCs and HepG2 cells are 6.6 ± 2.2 μm (pixel number = 3)
and 17.6 ± 2.2 μm (pixel number = 8), respectively. The
concurrent resistive pulse sensing and imaging-based cytometry
can both differentiate the two different cell types. When the
threshold voltage modulation is set above the maximum pulse
induced by RBCs, the on-demand imaging system only
captures the images of single HepG2 cells, demonstrating the
capability of selective single cell imaging based on the cell size.
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RESULTS AND DISCUSSION
We first used the microbead suspensions to calibrate the
developed system integrating resistive pulse sensing and
imaging-based cytometry. Figure 2a shows the measured
differential voltage modulation induced by five consecutive
translocation events of 5 μm microbeads through the sensing
apertures. When there is no particle translocation through
either of the two sensing apertures, the measured voltage at the
output port of the differential amplifier is nearly zero with a
noise of ±0.01 mV. The upward and downward voltage pulses
represent the particle translocation through different sensing
apertures. The magnitude of the voltage pulse is from 0.04 to
0.07 mV, which has been successfully applied to trigger the
CMOS imaging sensor to take snapshots of single particles.
Figure 2b shows that 15.6 μm microbeads can induce a much
larger voltage modulation (a magnitude of ∼1 mV), which
demonstrates that the voltage modulation is highly dependent
on the particle size through the sensing aperture. Resistive pulse
activated imaging was also demonstrated using the 15.6 μm
microbeads. The captured images of 5 and 15.6 μm microbeads
cover 3 and 7 pixels, respectively. Since each pixel is 2.2 μm, the
measured size of 5 and 15.6 μm microbeads are 6.6 ± 2.2 and
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Analytical Chemistry
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Figure 3. Measured differential resistive pulses induced by several
consecutive cell events, indicating the detection of 11 biological cells
(7 RBCs and 4 HepG2 cells) in total. Each upward or downward
resistive pulse activates the on-demand imaging of single cells.
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CONCLUSION
In summary, differential pulse resistive sensing and imagingbased cytometry have been integrated together in a portable
system for single cell analysis. The differential pulse resistive
sensing can actively control the lensless CMOS sensor to
implement on-demand single cell imaging. Microbeads of
different sizes, as well as a cell mixture including RBCs and
HepG2, have been used to validate the on-demand single cell
imaging. A significant advantage of this technique is that it can
avoid generating useless images without cells and remarkably
increase the efficiency of image processing. Furthermore, this
technique could enable the ability to selectively capture images
of single cells within a specific size range, which could be a
potential solution to search rare cell populations from highly
heterogeneous biological samples.
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AUTHOR INFORMATION
Corresponding Author
*E-mail: [email protected]. Tel: (+65) 6499 4553.
Author Contributions
§
Jinhong Guo and Xiwei Huang contributed equally to this
work.
Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS
This work was supported by SUTD-MIT International Design
Center IDG11300101 (Y.A.).
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