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Citation: Ma, C.; Sun, Y.; Huang, Y.;
Gao, Z.; Huang, Y.; Pandey, I.; Jia, C.;
Feng, S.; Zhao, J. On-Chip Nucleic
Acid Purification Followed by ddPCR
for SARS-CoV-2 Detection. Biosensors
2023,13, 517. https://doi.org/
10.3390/bios13050517
Received: 29 March 2023
Revised: 19 April 2023
Accepted: 21 April 2023
Published: 5 May 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
biosensors
Article
On-Chip Nucleic Acid Purification Followed by ddPCR for
SARS-CoV-2 Detection
Cong Ma 1,2,3,† , Yimeng Sun 2,3, Yuhang Huang 2,4, Zehang Gao 2 ,5 ,† , Yaru Huang 2,4, Ikshu Pandey 6,
Chunping Jia 2,3, Shilun Feng 2, 3, * and Jianlong Zhao 2,3,7,*
1School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
2State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information
Technology, Chinese Academy of Sciences, Shanghai 200050, China
3Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences,
Beijing 100049, China
4School of Life Sciences, Shanghai Normal University, Shanghai 200235, China
5Department of Clinical Laboratory, The Third Affiliated Hospital of Guangzhou Medical University,
Guangzhou 510150, China
6Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA;
ipandey1@jhu.edu
7Xiangfu Laboratory, Jiaxing 314102, China
*Correspondence: shilun.feng@mail.sim.ac.cn (S.F.); jlzhao@mail.sim.ac.cn (J.Z.);
Tel.: +86-21-62511070-8707 (S.F.); +86-21-62511070-8701 (J.Z.)
† These authors contributed equally to this work.
Abstract:
We developed a microfluidic chip integrated with nucleic acid purification and droplet-
based digital polymerase chain reaction (ddPCR) modules to realize a ‘sample-in, result-out’ infectious
virus diagnosis. The whole process involved pulling magnetic beads through drops in an oil-
enclosed environment. The purified nucleic acids were dispensed into microdroplets by a concentric-
ring, oil–water-mixing, flow-focusing droplets generator driven under negative pressure conditions.
Microdroplets were generated with good uniformity (CV = 5.8%), adjustable diameters (50–200
µ
m),
and controllable flow rates (0–0.3
µ
L/s). Further verification was provided by quantitative detection
of plasmids. We observed a linear correlation of R
2
= 0.9998 in the concentration range from 10 to 10
5
copies/
µ
L. Finally, this chip was applied to quantify the nucleic acid concentrations of the severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The measured nucleic acid recovery rate of
75
±
8.8% and detection limit of 10 copies/
µ
L proved its on-chip purification and accurate detection
abilities. This chip can potentially be a valuable tool in point-of-care testing.
Keywords: nucleic acid purification; droplet generation; ddPCR; SARS-CoV-2
1. Introduction
The COVID-19 (Coronavirus disease 2019) outbreak has been ongoing worldwide for
several years, and many diagnostic methods have been developed to detect it. The antigen
test is a widely used method for pre-diagnosis thanks to its simple operation protocols
and rapid result turnaround time. However, its disadvantages, including low sensitivity
(10
3
copies/
µ
L) and false positive results, limit its application in precision medicine [
1
].
In contrast, quantitative real-time polymerase chain reaction can determine the relative
concentration of the nucleic acids from the cycle threshold (Ct) and the standard curve
obtained from samples with known concentration. It is the gold standard in detection
methods because of its reliability [2].
Generally, nucleic acid purification is required before PCR to remove inhibitors prior
to the amplification reaction [3]. Researchers have also tried to introduce purification-free
methods to simplify the diagnosis procedures. For example, Beltran-Pavez et al. achieved
extraction-free detection of SARS-CoV-2 from saliva, but their approach encountered a
Biosensors 2023,13, 517. https://doi.org/10.3390/bios13050517 https://www.mdpi.com/journal/biosensors
Biosensors 2023,13, 517 2 of 13
high percentage of false-negative problems [
4
]. Their work emphasized the following fact:
nucleic acid purification is a prerequisite to achieve reliable and accurate detection [
5
], re-
moval of inhibitors is still required [
6
], and it increases detection reliability by avoiding false
results [
7
]. However, the present purification process is manual and time-consuming [
8
],
requiring extensive washes with pipette mixing [
9
], and therefore needs to be implemented
carefully to avoid contamination [10,11].
Some researchers have improved the detection efficiency by integrating all the purifi-
cation steps on a single microfluidic chip to improve sensitivity [
12
]. In Ali et al.’s work,
nucleic acid fragments were extracted selectively using magnetic beads and were widely
used in the purification process to construct automated point-of-care systems factors [
13
].
This method relied on the following steps: releasing nucleic acids from samples in a lysis
solution, extracting them from the contaminating solution, removing inhibitors by passing
them through an oil phase and wash buffer using the magnetic beads flowing through the
solutions and controlled by a magnet [
14
], and finally detecting the fluorescence intensities
of solutions after the thermal cycling [
15
]. Based on this method, Juang et al. developed
an oil-immersed, lossless, total-analysis system to achieve purification and detection of
nucleic acids on a chip [
16
]. The magnetic beads entered wells via a channel controlled by a
manually moved magnet under the chip. Their work proved the feasibility of microfluidic
chips to improve nucleic acid purification efficiency and their potential application in
point-of-care testing (POCT). In industry, some POCT devices have been applied in clinical
diagnosis. Cobas Liat, the first U.S. Food and Drug Administration (FDA)-authorized POCT
system, can detect SARS-CoV-2 within 20 min. However, it still suffers from false-negative
results because of poor analytical sensitivities (relative to RT-PCR) [
17
]. The accuracy of the
GeneXpert system was also lower than the Multiplex PCR assay (MPCR) in rapid diagnos-
tics because of its qualitative detection mode [
18
]. These devices, to some extent, meet the
requirements of POCT for simplicity, modularity, and pollution avoidance. However, the
deficiencies in accuracy indicated that these qualitative detection methods used by Cobas
Liat and GeneXpert were affected by PCR efficiency differences, the limitations of relative
quantitative methods, and using a single result of an experiment that lacks the ability to
reduce error [19].
Digital polymerase chain reaction (dPCR), proposed by Vogelstein and Kinzler [
20
],
is an absolute quantitative nucleic acid detection method that does not need to compare
with standard reference samples and does not rely on standard curves [
21
]. It is easy to
determine the threshold for positive and negative results without requiring reference results
because tens of thousands of reaction units containing no more than a single molecule can
simultaneously exhibit both negative and positive results. It has the advantages of high
precision, high sensitivity, and absolute quantification ability based on the Poisson distribu-
tion of nucleic acids, so it is a potential technology in early detection and monitoring [
22
].
Researchers have proven that the sensitivity of dPCR was determined to be equal to or
greater than that of RT-qPCR [
23
]. In the past few years, dPCR has demonstrated its accu-
racy and reduced false-negative results in the ultrasensitive detection of SARS-CoV-2 and
other pathogenic bacteria [
24
]. Droplet-based digital polymerase chain reaction (ddPCR)
and cavity-based digital polymerase chain reaction (cdPCR) are the two approaches used
to implement dPCR [
25
]. ddPCR has been widely used in past decades because of its
lower cost [
26
], simpler fabrication process [
27
], and less solution waste [
28
] compared
to cdPCR. The ddPCR is more suitable than RT-qPCR methods for determining the copy
number [
23
]. There are some studies that have reported that ddPCR was more accurate
than RT-qPCR in detecting and quantifying SARS-CoV-2 levels, especially in patients with
low viral loads [29].
Yin et al. demonstrated an integrated microfluidic chip consisting of a fast nucleic acid
extraction followed by a digital PCR module for POCT application [
30
], which realized
sample-in-digital-answer-out diagnosis. Hu et al. also developed a smartphone-based
digital-detection device with a rapid droplet nucleic acid isolation method for sensitive
Biosensors 2023,13, 517 3 of 13
point-of-care detection [
31
]. Their work demonstrated the feasibility of POCT digital nucleic
acid detection using magnetic-beads-based purification methods followed by dPCR.
The surface-wetting, force-driven, droplet-formation structure designed by Liu et al. [
32
],
and the easy-to-operate co-flow step-emulsion droplet device developed by Wei et al. [
28
], can
be used for droplet formation in the case of water–oil mixing after nucleic acid purification
on a chip driven under negative pressure conditions. Their work has demonstrated the
feasibility of integrating nucleic acid purification with digital PCR modules.
Based on the droplet-generation methods mentioned above, this research achieved the
following goals through a newly designed microfluidic chip:
(1)
The microfluidic chip was used to achieve fully enclosed nucleic acid purification, to
reduce pollution, to reduce the demands on detection personnel, equipment, and the
environment, and to expand the application range of nucleic acid detection;
(2)
Through the innovative design of the droplet-formation structure, the on-chip nucleic
acid purification and digital-detection unit were integrated on a closed chip to simplify
the workflow and improve the detection efficiency and accuracy;
(3)
The developed microfluidic chip was applied to detect COVID-19 nucleic acids, the
chip’s performance of the was verified, and analysis and comparison were conducted
to promote the application of the microfluidic chip in field detection.
Therefore, a nucleic acid purification and ddPCR integrated microfluidic chip was
developed. It can achieve nucleic acid purification and quantitative analysis in an oil-
enclosed environment. It uses a magnet that drives the magnetic beads to extract nucleic
acid in the purification process and distributes the nucleic acids in the PCR reaction solution;
it then generates separated microdroplets for ddPCR through the concentric-ring, oil–water-
mixing, flow-focusing structure.
In this work, the critical component of the chip, a concentric ring, oil-water-mixing,
flow-focusing droplet-generation structure, was characterized. The parameters related to
droplet generation, especially the channel width at the focus point, negative pressure, and
surfactant concentration, were optimized. The performance of this microfluidic chip has
been demonstrated in gradient-diluted N gene plasmid sample quantification experiments.
Then, the nucleic acid recovery rate was characterized by synthetic RNA fragments. Finally,
accurate quantitative concentration results were generated for the SARS-CoV-2 pseudovirus.
2. Experimental Section
2.1. Chip Microfabrication
The microfluidic chip has two main parts, as shown in Figure 1A,B. The sample-
purification part has four open wells for sample lysis, washing, and mixing. The ddPCR
part includes a concentric-ring, oil–water-mixing, flow-focusing structure and droplets-
tilting glass cavity.
Biosensors 2023, 13, x FOR PEER REVIEW 4 of 14
Figure 1. Chip structure. (A) Components and structure layers of the chip. (B) Detail of the PDMS
structures.
The channel and well paerns were designed using AutoCAD and then transferred
using a MicroWriter maskless lithography machine (ML3, Durham Magneto Optics,
Cambridge, UK) onto a 4-inch silicon wafer (MCL Electronic Materials) coated with a
photoresist layer. The microfluidic block was made via standard soft lithographic
methods using PDMS (DOWSIL, South Charleston, WV, USA, and SU8 3050 photoresist
(Microchem, Westborough, MA, USA). The channel height was 170 ± 5 µm. The PDMS
blocks were punched with four holes at locations determined by modeling structures.
After bonding onto a 1 mm thick glass slide in a plasma condition, it was heated overnight
in 120 °C ovens to recover hydrophobic features, ensuring that magnetic beads could
move fluently and droplets could generate stably. The glass cavity was built by sticking a
covere glass onto the glass slide with two pieces of 100 µm height tape. Ultraviolet curing
glue was used to seal the ends of the glass cavity, the outlet tube, and the contact surface
between the PDMS block and the glass cavity (Figure S1).
2.2. Nucleic Acid Purification
The chip’s purification and droplet generation processes shown in Figure 2A include
the following parts: (I) The preparation steps. Fill the wells and glass cavity with oil, inject
lysis reagent in well 1 (lysis well), wash solution in well 2 (wash well), rewash solutions
in well 3 (wash well), and PCR reagent in well 4 (mix well) to form aqueous drops; add
samples into the drop in the first well and wait 10 min for lysing (Table S1). (II) The
purification steps. After the nucleic acid process is completed, pull a magnet from the first
well to the fourth well under the glass slide, and introduce the magnetic beads from the
lysis well to the mix well through the channel between wells. Stir the magnetic beads in
each of the two wash wells and let them stay for one minute to achieve efficient washing.
Finally, stir the magnetic beads for one minute and let them stay for five minutes in the
mix well to elute the nucleic acid into the PCR reaction solution (Steps 1–4). (III) The
amplification reaction and droplet-generation preparation steps. After the elution process,
pull the magnetic beads back to wash well 2 (Step 5), where they are removed by pipee
from the chip to avoid interference with the droplet generation process; (IV) Droplet
generation process and amplification process (Step 6).
Figure 1.
Chip structure. (
A
) Components and structure layers of the chip. (
B
) Detail of the PDMS structures.
The channel and well patterns were designed using AutoCAD and then transferred using
a MicroWriter maskless lithography machine (ML3, Durham Magneto Optics, Cambridge, UK)
onto a 4-inch silicon wafer (MCL Electronic Materials) coated with a photoresist layer. The
Biosensors 2023,13, 517 4 of 13
microfluidic block was made via standard soft lithographic methods using PDMS (DOWSIL,
South Charleston, WV, USA, and SU8 3050 photoresist (Microchem, Westborough, MA, USA).
The channel height was 170
±
5
µ
m. The PDMS blocks were punched with four holes at
locations determined by modeling structures. After bonding onto a 1 mm thick glass slide in
a plasma condition, it was heated overnight in 120
◦
C ovens to recover hydrophobic features,
ensuring that magnetic beads could move fluently and droplets could generate stably. The
glass cavity was built by sticking a covere glass onto the glass slide with two pieces of 100
µ
m
height tape. Ultraviolet curing glue was used to seal the ends of the glass cavity, the outlet
tube, and the contact surface between the PDMS block and the glass cavity (Figure S1).
2.2. Nucleic Acid Purification
The chip’s purification and droplet generation processes shown in Figure 2A include
the following parts: (I) The preparation steps. Fill the wells and glass cavity with oil,
inject lysis reagent in well 1 (lysis well), wash solution in well 2 (wash well), rewash
solutions in well 3 (wash well), and PCR reagent in well 4 (mix well) to form aqueous
drops; add samples into the drop in the first well and wait 10 min for lysing (Table S1).
(II) The purification steps. After the nucleic acid process is completed, pull a magnet
from the first well to the fourth well under the glass slide, and introduce the magnetic
beads from the lysis well to the mix well through the channel between wells. Stir the
magnetic beads in each of the two wash wells and let them stay for one minute to achieve
efficient washing. Finally, stir the magnetic beads for one minute and let them stay for five
minutes in the mix well to elute the nucleic acid into the PCR reaction solution (Steps 1–4).
(III) The amplification reaction and droplet-generation preparation steps. After the elution
process, pull the magnetic beads back to wash well 2 (Step 5), where they are removed by
pipette from the chip to avoid interference with the droplet generation process; (IV) Droplet
generation process and amplification process (Step 6).
To further characterize the purification and detection performance, we used a sample
release reagent (Sangon Biotech, Shanghai, China) to release the nucleic acid in the mix well
without purificatio. We compared it with the results after the whole process on the chip
(Figure S4 and Table S3). Here we detected 10
3
copies/
µ
L DNA template with saliva and
nasal mucus to simulate actual nasopharyngeal swab samples. The results without and with
purification are shown in Figure 2C and indicate that lysing the sample directly in the PCR
reaction reagent brought inhibitors in an amplification reaction, which caused a lower end-
point fluorescence intensity for the drop. This experiment proved that the purification step
improved fluorescence intensity by 26%, improving the fluorescence difference between
positive and negative results. In addition, the purified results reduced the margin of error
by 52% compared to the unpurified results. This inhibitor phenomenon was also reported
by N. Vasudevan et al. [
33
]. The fact that absolute quantification measurement results in
droplets can reduce this distraction was also pointed out in their work.
2.3. Droplet Generation
Prior to droplet generation in the chip, some preparation is required. Hydrophobicity
is crucial for droplet generation and avoiding droplet rupture. The contact angles of
slides increased clearly in the hydrophobicity of the bottom glass slide and cover glass
after treatments. Sigmacoat (Sigma-Aldrich, St. Louis, MO, USA) was selected as the
hydrophobic treatment reagent because its coat is formed by chemical reaction and shows
good durability throughout the experiment. Surfactants also play an important role in
avoiding droplet fusion (Figure S2). Mineral oil (Sigma-Aldrich, USA) was homogenized
by an ultrasonic bath after adding EM90 (Degussa, Frankfurt, Germany) 3.59%, Triton X100
(Sigma-Aldrich, USA) 0.13%, and Span 80 (Sigma-Aldrich, USA) 0.79% in mass fraction to
make the droplet-generation oil.
In the droplet-generation experiment, a negative pressure, set using an air pressure
valve range from
−
25 kPa to
−
40 kPa, was found suitable to generate pure water droplets.
Pressure lower than
−
5 kPa cannot pull the solution drop from the last well to the focusing
Biosensors 2023,13, 517 5 of 13
point in the channel. On the other hand, negative pressure exceeding a certain threshold
will lead to a continuous flow because the higher-flow-rate aqueous phase cannot be
discretized by oil flow.
Biosensors 2023, 13, x FOR PEER REVIEW 5 of 14
Figure 2. Workflow of on-chip purification processes. (A) The detection process, from sample
loading to elution is shown as a side view in the sequence of lysis, wash 1, wash 2, and mixing in
the wells from left to right. (B) Wash efficiency equals to the fluorescence intensity of the last drop
containing the magnetic beads extracted from 20 µM FITC fluorescent dye solution in the lysis well.
The scale bar represents 1 mm. (C) Fluorescence intensity results of the sample with interference
comparison between purification on-chip and direct amplification after nucleic acid release. Error
bars in the above figures represent the standard deviation based on at least three replicates of each
experiment.
To further characterize the purification and detection performance, we used a sample
release reagent (Sangon Biotech, Shanghai, China) to release the nucleic acid in the mix
well without purificatio. We compared it with the results after the whole process on the
chip (Figure S4 and Table S3). Here we detected 10
3
copies/µL DNA template with saliva
and nasal mucus to simulate actual nasopharyngeal swab samples. The results without
and with purification are shown in Figure 2C and indicate that lysing the sample directly
in the PCR reaction reagent brought inhibitors in an amplification reaction, which caused
a lower end-point fluorescence intensity for the drop. This experiment proved that the
purification step improved fluorescence intensity by 26%, improving the fluorescence
difference between positive and negative results. In addition, the purified results reduced
the margin of error by 52% compared to the unpurified results. This inhibitor
phenomenon was also reported by N. Vasudevan et al. [33]. The fact that absolute
quantification measurement results in droplets can reduce this distraction was also
pointed out in their work.
Figure 2.
Workflow of on-chip purification processes. (
A
) The detection process, from sample loading
to elution is shown as a side view in the sequence of lysis, wash 1, wash 2, and mixing in the wells
from left to right. (
B
) Wash efficiency equals to the fluorescence intensity of the last drop containing
the magnetic beads extracted from 20
µ
M FITC fluorescent dye solution in the lysis well. The scale
bar represents 1 mm. (
C
) Fluorescence intensity results of the sample with interference comparison
between purification on-chip and direct amplification after nucleic acid release. Error bars in the
above figures represent the standard deviation based on at least three replicates of each experiment.
In the following experiments, the width of the aqueous phase channel at the focusing
point, the surfactant concentration, and the negative pressure value were the main factors
determining whether the droplets could be formed successfully. At first, chips with different
widths of channels at the flow-focusing point were used to generate droplets. The results,
shown in Figure 3C, revealed the linear relationship between the channel width and the
diameter of the droplet. These results proved that this concentric-ring, oil–water-mixing,
flow-focusing structure could generate different size droplets by simply setting the channel
width of the aqueous phase at the focus point.
The surfactant is an essential material in the reagent for avoiding droplet fusion [
34
].
The related factors, including the driven negative pressure and surfactant concentration,
were characterized to find the droplet-generation condition (Figure S3). The results revealed
that the existence of surfactant reduced the required driven negative pressure by shifting the
pressure from a high range to a range lower than
−
25 kPa. However, excessive surfactant
concentration in the solution led to a failure to generate droplets (Figure 4A), which was
Biosensors 2023,13, 517 6 of 13
because the surfactant decreased the solution’s surface tension and increased the aqueous
phase flow, resulting in insufficient shear force being provided by a fixed oil flow, which
then cannot separate the aqueous flow into discrete droplets.
Biosensors 2023, 13, x FOR PEER REVIEW 7 of 14
Figure 3. Dropletgeneration performance test. (A) Photograph of the microfluidic chip filled with a
millimeter-scale drop in four wells and generated micrometer-scale droplets in a glass cavity. (B)
Blue-dye-solution droplet-generation process in the concentric-ring, oil-water-mixing, flow-
focusing structure. (C) The linear correlation between the droplet diameter and the width in 165 µm
channel-height chip driven by −25 kPa pressure. The insert images show that the droplet diameter
increases with increases in width. Scale bar = 500 µm. (D) The variation in diameter of 1% Tween 20
solution droplets during the whole droplet-generation process in the chip with 80 µm width driven
under different negative pressures. Error bars in the above figures represent the standard deviation
based on at least 3 replicates of each experiment.
Figure 4. Droplet formation test. (A) Negative pressure and surfactant concentration effects on
droplet generation. The numbers in the table are the mean value of the droplet diameter. ’x’
represents droplet formation failed, and ’√’ represents droplet formation succeed. (B) Droplet-
generation change with the negative-pressure change for 1% Tween 20 solutions driven under −5
kPa pressure. The insert images show the droplet-generation process under the corresponding
pressure range. Scale bar = 500 µm.
Figure 3.
Dropletgeneration performance test. (
A
) Photograph of the microfluidic chip filled with
a millimeter-scale drop in four wells and generated micrometer-scale droplets in a glass cavity.
(
B
) Blue-dye-solution droplet-generation process in the concentric-ring, oil-water-mixing, flow-
focusing structure. (
C
) The linear correlation between the droplet diameter and the width in 165
µ
m
channel-height chip driven by
−
25 kPa pressure. The insert images show that the droplet diameter
increases with increases in width. Scale bar = 500
µ
m. (
D
) The variation in diameter of 1% Tween 20
solution droplets during the whole droplet-generation process in the chip with 80
µ
m width driven
under different negative pressures. Error bars in the above figures represent the standard deviation
based on at least 3 replicates of each experiment.
The droplet-generation experiment was performed using the optimized chip in Figure 3A.
The statistical analysis results, as shown in Figure 3B,C, indicated that the linear correlation
between the diameter of pure water droplets and slit width was y = 1.1791x + 2.3416 in the
chip with 165
µ
m channel height under the condition of being driven by
−
25 kPa pressure.
This result showed that droplets of different sizes can be obtained simply by changing the
width of the water channel.
The results of droplets’ diameter change over time during the generation process are
shown in Figure 3D, and these indicated the following features for the solution with 1%
Tween 20 (Sigma-Aldrich, USA) surfactant: (1) The droplet generation at the midpoint
of the entire process is more stable than at the start and end points. Especially at the
endpoint, the drop will pass through the flow-focusing point fluently without splitting
because the size of the remaining drop is too small to be restricted in the mix well. (2) The
droplet-generation process driven under pressures ranging from
−
5 to
−
15 kPa is more
stable than when driven with lower and higher pressures. Drivenunder pressure ranging
from 0 to
−
5 kPa, the aqueous-solution drop only intermittently reached the flow-focusing
point due to surface tension pulling the deformed drop tip back. This occurred when the
Biosensors 2023,13, 517 7 of 13
aqueous solution was drawn to the flow-focusing point, and the split of the aqueous phase
with increasing flow rate became difficult due to a limited shearing force of oil. (3) The
diameter of the droplet was about 100
µ
m, controlled by the 80
µ
m width channel and
driven under
−
5 and
−
10 kPa pressure at a stable state, but increased to about 200
µ
m at
the beginning. The drop movement could lead to this phenomenon, which was generated
from the flow-focusing point to the converging channel in the flow direction.
Biosensors 2023, 13, x FOR PEER REVIEW 7 of 14
Figure 3. Dropletgeneration performance test. (A) Photograph of the microfluidic chip filled with a
millimeter-scale drop in four wells and generated micrometer-scale droplets in a glass cavity. (B)
Blue-dye-solution droplet-generation process in the concentric-ring, oil-water-mixing, flow-
focusing structure. (C) The linear correlation between the droplet diameter and the width in 165 µm
channel-height chip driven by −25 kPa pressure. The insert images show that the droplet diameter
increases with increases in width. Scale bar = 500 µm. (D) The variation in diameter of 1% Tween 20
solution droplets during the whole droplet-generation process in the chip with 80 µm width driven
under different negative pressures. Error bars in the above figures represent the standard deviation
based on at least 3 replicates of each experiment.
Figure 4. Droplet formation test. (A) Negative pressure and surfactant concentration effects on
droplet generation. The numbers in the table are the mean value of the droplet diameter. ’x’
represents droplet formation failed, and ’√’ represents droplet formation succeed. (B) Droplet-
generation change with the negative-pressure change for 1% Tween 20 solutions driven under −5
kPa pressure. The insert images show the droplet-generation process under the corresponding
pressure range. Scale bar = 500 µm.
Figure 4.
Droplet formation test. (
A
) Negative pressure and surfactant concentration effects on droplet
generation. The numbers in the table are the mean value of the droplet diameter. ’x’ represents
droplet formation failed, and ’
√
’ represents droplet formation succeed. (
B
) Droplet-generation
change with the negative-pressure change for 1% Tween 20 solutions driven under
−
5 kPa pressure.
The insert images show the droplet-generation process under the corresponding pressure range.
Scale bar = 500 µm.
The effect of surfactants on droplet generation indicated that the chip may have com-
patibility problems when PCR reagents contain surfactants with different concentrations.
The following three approaches were used in our experiment to solve this problem: (1) Re-
duce the surfactant concentration in the oil or use pure mineral oil. (2) Set a stable PCR
mix reagent and add different upstream primers, downstream primers, and probe reagents.
The small volume fraction of the primers and probes for other target genes have little effect
on the overall PCR reagent, so they ensure this chip can be used in different detection
applications. (3) Reduce the mix well diameter from 5 mm to 2 mm to maintain the drop
formation of a high-surfactant-concentration solution, which avoids having the solution
flow out of control at the bottom of the well.
The fluctuation of droplet diameters during the droplet generation process reduces the
accuracy of the quantification results. However, the number of these nonuniform droplets
will not exceed 1%, so this fluctuation has only a limited impact on the quantitative results.
This will also ensure the qualitative results, even though the detection concentration will
be lower than the real concentration if the uniform droplets contain more than one nucleic
acid. However, the quantitative results of dPCR for low-concentration nucleic acid have a
large margin of error originally due to the very small number of positive droplets.
In summary, this chip offered good droplet-generation performance under suitable
pressures. It can dispense a 5
µ
L solution drop into 10,000 monodispersed 100
µ
m diameter
droplets, with a flow rate ranging from 0 to 0.3
µ
L/s when driven under negative pres-
sure. Its corresponding advantages include eliminating the two channels in the traditional
positive-drive-pressure flow-focusing structure in favor of merely one, and avoiding chan-
nel absorption for the sample solution. It also exhibits robustness when encountering the
risk of being blocked by impurities like dust because the length of the micrometer-scale
narrow channel in the whole chip is smaller than 2 mm. In such a case, the droplets can
still be generated stably even if one of the oil channels is blocked. Moreover, this method
avoids bubbles due to the oil phase and the aqueous phase arriving at the flow-focusing
point at different times by prefilling the oil.
Biosensors 2023,13, 517 8 of 13
2.4. Detection Verification
The chip’s ability to quantify the plasmids with the SARS-CoV-2 gene was verified by
adding only PCR reaction solution to the fourth well (Table S2). The PUC57 plasmid with N gene
sequence was procured from Sangon Biotechnology (Shanghai, China). After the droplet was
generated, the chip was put on PCR thermocycling amplifiers (Eppendorf, Hamburg, Germany),
with which wet gloves and metal containers were required to avoid droplet fusion (Table S6).
Then, an IX51 microscope with a DP80 camera (Olympus, Tokyo, Japan) was used to take
pictures of the droplet’s storage cavity after the amplification reaction. ImageJ software was
used to analyze all fluorescence images to separate positive droplets from negative droplets.
In the following experiments, about 10
5
copies/
µ
L N gene DNA templates were
diluted in five gradients to verify the detection accuracy of the chip. Figure 5A,B shows
the fluorescence picture analysis. It shows that the droplet’s variable coefficient size
distribution was 5.8%. These results indicate good stability during droplet generation and
heating, which determines the precision of the quantification results. Qualification was
also implemented simultaneously in this chip by leaving some reagent in the mix well
during droplet generation. The larger drop sizes in the mix well can be identified using
optical detection devices, making it suitable for different applications and instruments. The
performance of the qualitative identification of millimeter-scale drops is shown in Figure 5C
using fluorescence images and the mean gray value of drops after 45 thermal cycles. The
mean gray values of drops were used to distinguish negative and positive samples. These
results confirmed that the qualitative result had a clear difference between positive and
negative samples but also emphasized that it is difficult to quantify the concentration of
nucleic molecules due to the deviation caused by many factors such as drop deformation,
amplification efficiency, thermocycler number, etc. Results in Figure 5D show excellent
agreement with the dilution factors in the 10
−5
~10 range for about 10
5
copies/
µ
L nucleic
acid in all reactions with a linear fit curve (R
2
= 0.9998). These good quantification results
emphasized the role of ddPCR in precise diagnosis (Table S4).
2.5. SARS-CoV-2 Diagnosis
The pseudovirus was processed and detected in this chip to simulate the SARS-CoV-2
diagnosis. After the purification process of SARS-CoV-2 on a chip, as mentioned above, the
outlet of the chip was connected to a negative pressure system, and the pressure regulator
was set at a suitable value. The PCR mixture solution drop was cut into microdroplets by
flowing oil when it passed through the flow-focusing structure when driven under negative
pressure (Figure 6A). The droplets were stored in the cavity for 45 thermal cycles (Table S7)
and the following detection.
The images shown in Figure 6only represent the part of the positive droplets found in
the microscope field because low-concentration positive results cannot show in gradient
in images with hundreds of droplets. The final quantitative results in this work were
calculated by statistical results from more images than are shown in these figures (Table S5).
The micron-sized, microdroplet-based quantitative diagnosis method compared with the
millimeter-sized-drops qualitative diagnosis method is shown in Figure 6B. It confirms the
reliability and accuracy of this ddPCR chip in detecting 10 copies/
µ
L (100% repeatability).
The chip also exhibited the potential of ddPCR for 1 copy/
µ
L sample diagnosis with an
80% probability, and 60% probability of single-drop qualitative detection in the mix well.
These results indicated the following advantages of droplet digital quantitative detection:
higher repeatability in low-concentration detection, absolute count and calculation without
comparing to negative or standard reference, and ease of distinguishing the positive results
from surrounding negative droplets.
Biosensors 2023,13, 517 9 of 13
Biosensors 2023, 13, x FOR PEER REVIEW 9 of 14
amplification reaction. ImageJ software was used to analyze all fluorescence images to
separate positive droplets from negative droplets.
In the following experiments, about 10
5
copies/µL N gene DNA templates were
diluted in five gradients to verify the detection accuracy of the chip. Figure 5A, and B
shows the fluorescence picture analysis. It shows that the droplet’s variable coefficient size
distribution was 5.8%. These results indicate good stability during droplet generation and
heating, which determines the precision of the quantification results. Qualification was
also implemented simultaneously in this chip by leaving some reagent in the mix well
during droplet generation. The larger drop sizes in the mix well can be identified using
optical detection devices, making it suitable for different applications and instruments.
The performance of the qualitative identification of millimeter-scale drops is shown in
Figure 5C using fluorescence images and the mean gray value of drops after 45 thermal
cycles. The mean gray values of drops were used to distinguish negative and positive
samples. These results confirmed that the qualitative result had a clear difference between
positive and negative samples but also emphasized that it is difficult to quantify the
concentration of nucleic molecules due to the deviation caused by many factors such as
drop deformation, amplification efficiency, thermocycler number, etc. Results in Figure
5D show excellent agreement with the dilution factors in the 10
−5
~10 range for about 10
5
copies/µL nucleic acid in all reactions with a linear fit curve (R
2
= 0.9998). These good
quantification results emphasized the role of ddPCR in precise diagnosis (Table S4).
Figure 5. Chip verification. (A) Left: work mode without purification process; right: fluorescence
images of the droplets for 100,000 copies/µL after PCR. (B) The diameter distribution of droplets.
(C) Qualitative gradient dilution results of 100,000 copies/µL N gene DNA template by measuring
the fluorescence intensity of drop in the last well. (D) The linear correlation (blue dashed line)
between the measured concentrations and five tenfold gradients dilution factor of about 100,000
copies/µL N gene DNA molecules. The data are expressed as mean and standard deviation based
on at least three replicates of each experiment. All scale bars represent 500 µm.
2.5. SARS-CoV-2 Diagnosis
Figure 5.
Chip verification. (
A
) Left: work mode without purification process; right: fluorescence
images of the droplets for 100,000 copies/
µ
L after PCR. (
B
) The diameter distribution of droplets.
(
C
) Qualitative gradient dilution results of 100,000 copies/
µ
L N gene DNA template by measuring the
fluorescence intensity of drop in the last well. (
D
) The linear correlation (blue dashed line) between
the measured concentrations and five tenfold gradients dilution factor of about 100,000 copies/
µ
L N
gene DNA molecules. The data are expressed as mean and standard deviation based on at least three
replicates of each experiment. All scale bars represent 500 µm.
Biosensors 2023, 13, x FOR PEER REVIEW 10 of 14
The pseudovirus was processed and detected in this chip to simulate the SARS-CoV-
2 diagnosis. After the purification process of SARS-CoV-2 on a chip, as mentioned above,
the outlet of the chip was connected to a negative pressure system, and the pressure
regulator was set at a suitable value. The PCR mixture solution drop was cut into
microdroplets by flowing oil when it passed through the flow-focusing structure when
driven under negative pressure (Figure 6A). The droplets were stored in the cavity for 45
thermal cycles (Table S7) and the following detection.
Figure 6. SARS-CoV-2 on-chip diagnosis. (A) On-chip workflow from nucleic acid purification to
digital detection is shown as the top view. (B) The detection-limit test of 1 copy/µL of SARS-CoV-2
pseudovirus shows qualitative results from the millimeter-scale drop and quantitative results from
microdroplets. All scale bars represent 500 µm.
The images shown in Figure 6 only represent the part of the positive droplets found
in the microscope field because low-concentration positive results cannot show in
gradient in images with hundreds of droplets. The final quantitative results in this work
were calculated by statistical results from more images than are shown in these figures
(Table S5). The micron-sized, microdroplet-based quantitative diagnosis method
compared with the millimeter-sized-drops qualitative diagnosis method is shown in
Figure 6B. It confirms the reliability and accuracy of this ddPCR chip in detecting 10
copies/µL (100% repeatability). The chip also exhibited the potential of ddPCR for 1
copy/µL sample diagnosis with an 80% probability, and 60% probability of single-drop
qualitative detection in the mix well. These results indicated the following advantages of
droplet digital quantitative detection: higher repeatability in low-concentration detection,
absolute count and calculation without comparing to negative or standard reference, and
ease of distinguishing the positive results from surrounding negative droplets.
With the help of the quantification ability of the ddPCR, the nucleic acid recovery
rate of the purification process was measured by comparing the measured nucleic acid
concentration result for RNA fragments (RNA with and without purification). Then, 200
copies/µL of SARS-CoV-2 pseudovirus (Bangdesheng Biotechnology, Guangzhou,
China), the highest-concentration pseudovirus in the laboratory, were quantified with
nucleic acid release and detection kits (Shengxiang Biotechnology, Shanghai, China). The
Figure 6.
SARS-CoV-2 on-chip diagnosis. (
A
) On-chip workflow from nucleic acid purification to
digital detection is shown as the top view. (
B
) The detection-limit test of 1 copy/
µ
L of SARS-CoV-2
pseudovirus shows qualitative results from the millimeter-scale drop and quantitative results from
microdroplets. All scale bars represent 500 µm.
Biosensors 2023,13, 517 10 of 13
With the help of the quantification ability of the ddPCR, the nucleic acid recovery
rate of the purification process was measured by comparing the measured nucleic acid
concentration result for RNA fragments (RNA with and without purification). Then,
200 copies/
µ
L of SARS-CoV-2 pseudovirus (Bangdesheng Biotechnology, Guangzhou,
China), the highest-concentration pseudovirus in the laboratory, were quantified with
nucleic acid release and detection kits (Shengxiang Biotechnology, Shanghai, China). The
results in Table 1indicate that magnetic beads could extract 75
±
8.8% nucleic acid from the
solution in the first well. This efficiency improved upon previous work by other researchers,
such as Hu et al. [
31
] (recovery = 75%) and Carvalho et al. [
35
] (recovery = 42%). Other
nucleic molecules may be lost during the capture, wash, and elution process. This recovery
rate can calculate a more accurate concentration of nucleic molecules in the primary sample.
However, a pseudovirus nucleic acid recovery rate of 66
±
3.8% was also measured in our
exper. This which could have been due to non-ideal lysis efficiency, which is limited by
lysis time and reagent.
Table 1. RNA quantification results.
Sample Type Primary Concentration (Copies/µL) Concentration (Copies/µL) Recovery (%)
Synthetic RNA fragments 279 ±16 226 ±14 75 ±8.8
Pseudovirus 200 133 ±7 66 ±3.8
10 4 ±1 40 ±25.0
3. Conclusions
A nucleic acid purification and ddPCR integrated microfluidic chip was developed
in this work. Its primary component, a concentric-ring, oil-water-mixing, flow-focusing
structure for microdroplets generation, was designed and optimized. This structure can
generate many picoliter-sized, uniform, monodispersed droplets from micrometer-scale
drops in oil when driven under negative pressure. This structure highlights the feasibility
of the whole ddPCR process being integrated with a sample-purification module on a
single chip. This structure also provides a novel droplet-generation method for digital
detection after oil-enclosed purification, which avoids needing to extract the solution drop
to implement PCR in another chip. It transforms the endpoint single-drop qualitative
analysis into absolute digital quantification. The chip’s advantages include being sim-
ple and easy to operate, no reagent waste, high integration level, prevention of aerosol
contamination, and high sensitivity. It achieved higher-accuracy detection by replacing
the qualitative detection of the currently used on-chip purification and detection chip,
and it improved the integration level of nucleic acid detection by integrating purification
ddPCR detection. On this basis, a 66
±
3.8% recovery rate of nucleic acid and an available
detection limit of 10 copies/
µ
L in SARS-CoV-2 pseudovirus detection were characterized.
When used for 1 copy/
µ
L sample detection, this chip has an 80% detection probability for
positive samples.
Compared with other works using continuous flow PCR microfluidic chips [
36
] and
digital microfluidic platforms with magnetic beads [
37
], our chip takes longer to work,
but it is easier and less costly to process because it does not include electrodes. It is also
more accurate because of the quantitative results. Yin et al. reported a rapid nucleic acid
purification and digital-detection method for on-site, real-time detection. Still, it used a
microcavity as the reaction unitwhich is difficult to separate the water phase in oil to fill
the microcavity. Our chip showed the advantages of convenience and efficiency, simple
processing, and high integration [
38
]. The results obtained in this study show higher
sensitivity than that of RT-PCR methods and a lower than the 0.1–1 copies/
µ
L average
detection limit of dPCR methods of previous studies [
39
]. The unsatisfactory detection limit
may be due to the unsatisfactory purification effect on the chip compared to professional
nucleic acid purification equipment. The results in this experiment have higher accuracy
and better linear correlation compared with the multi-biomarker colorimetric detection
Biosensors 2023,13, 517 11 of 13
on-chip RT-LAMP method [
40
]. However, miniaturized equipment for droplet detection
needs to be further studied to meet POCT detection requirements, among which the most
important is to reduce the volume of optical detection equipment. Optical fibers [
41
] and a
lensless microscopic imaging technique [42] could be a potential miniaturization solution.
After the abovementioned experimental verification and comparison of results, we
believe that this chip could be a powerful supplement to the present ddPCR and oil-based
nucleic acid purification microfluidic chips because of the following advantages: (1) the
potential to apply automation pressure with a magnetically driven method; (2) minimal
cross-contamination in oil-sealed conditions; (3) the high accuracy and sensitivity of digital
quantification; and (4) the ease of expelling the droplets from the chip to determine the
amplification product. It could be a valuable digital microfluidic chip for contamination-
free, accurate, automated detection in POCT applications.
4. Discussion and Outlook
The remaining challenges and opportunities for improvement for this chip are as
follows: (1) integrating multi-chips into the system for high-throughput detection, which
could be achieved with multi-channels design in the microfluidic chip; coupling the magnet-
ically controlled motion system to reduce manpower requirements; replacing the manually
controlled pressure regulator with automated, programmed pressure systems to improve
the automation level; (2) optimizing the droplet-generation structure to be more stable
in the whole droplet-generation process; applying rapid droplet digital PCR reactions to
reduce the time required; (3) implementing an injection-molding fabrication technique to
reduce the cost and complexity of present chip production; (4) improving upon the glass
cavity because it contains sparsely distributed droplets and takes up substantial space;
designing a dual-layer chip in which the glass cavity under the purification module will
comprise more than half the chip area; and (5) further optimizing the structure to avoid
small-size satellite droplets being generated accidentally along with normal droplets, as
shown in Figure S3.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/bios13050517/s1, Figure S1: CAD design drawing of lithography
mask and fabrication process of the chip; Figure S2: Comparison of hydrophobic performance of the
glass slides after different hydrophobic treatments, and two kinds of reagent solution hydrophobic
performance on the glass slide; Figure S3: Droplet formation of aqueous reagents with different
surfactants; Table S1: The reagents composition; Table S2: Primer and MGB probe sequence for
the ORF1ab, N, and E genes; Figure S4: Fluorescence intensity drops compared to amplification
after lysis and amplification after purification on the chip; Table S3: Fluorescence intensity of drops
comparison after different process; Table S4: Quantitative concentration of plasmid with N gene;
Table S5: Quantitative concentration of synthetic RNA and pseudovirus; Table S6: PCR amplification
procedure of DNA plasmid; Table S7: PCR amplification procedure of Synthetic RNA fragment.
Author Contributions:
C.M.: Conceptualization, Investigation, Validation, Analysis, Writing—
original draft. Z.G.: Conceptualization, Methodology, Investigation, Review & Editing. Y.S. and Y.H.
(Yuhang Huang): Methodology, Data Curation. Y.H. (Yaru Huang): Resources, Review, Formal analy-
sis. I.P.: Review & Editing—original draft. C.J. and J.Z.: Supervision, Review & Editing—original
draft, Funding acquisition. S.F.: Project administration, Review & Editing—original draft, Funding
acquisition. All authors have read and agreed to the published version of the manuscript.
Funding:
This work was supported by grants from the equipment research and development projects
of the Chinese Academy of Sciences (GJJSTD20210006, YJKYYQ20210049), China Postdoctoral Science
Foundation (2022M720918), the Strategic Priority Research Program of the Chinese Academy of
Sciences (Nos. XDA16021200), the Science and Technology Commission of Shanghai Municipality
Project (22xtcx00100) and Shanghai Pujiang Program (21PJ1415000).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Biosensors 2023,13, 517 12 of 13
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Rodriguez-Mateos, P.; Ngamsom, B.; Walter, C.; Dyer, C.E.; Gitaka, J.; Iles, A.; Pamme, N. A lab-on-a-chip platform for integrated
extraction and detection of SARS-CoV-2 RNA in resource-limited settings. Anal. Chim. Acta
2021
,1177, 338758. [CrossRef]
[PubMed]
2.
Yin, H.; Wu, Z.; Shi, N.; Qi, Y.; Jian, X.; Zhou, L.; Tong, Y.; Cheng, Z.; Zhao, J.; Mao, H. Ultrafast multiplexed detection of
SARS-CoV-2 RNA using a rapid droplet digital PCR system. Biosens. Bioelectron. 2021,188, 113282. [CrossRef] [PubMed]
3.
Xu, D.; Jiang, X.; Zou, T.; Miao, G.; Fu, Q.; Xiang, F.; Feng, L.; Ye, X.; Zhang, L.; Qiu, X. A microfluidic system for rapid nucleic acid
analysis based on real-time convective PCR at point-of-care testing. Microfluid. Nanofluidics 2022,26, 69. [CrossRef] [PubMed]
4.
Beltrán-Pavez, C.; Alonso-Palomares, L.A.; Valiente-Echeverría, F.; Gaggero, A.; Soto-Rifo, R.; Barriga, G.P. Accuracy of a RT-qPCR
SARS-CoV-2 detection assay without prior RNA extraction. J. Virol. Methods 2021,287, 113969. [CrossRef]
5.
He, H.; Li, R.; Chen, Y.; Pan, P.; Tong, W.; Dong, X.; Chen, Y.; Yu, D. Integrated DNA and RNA extraction using magnetic beads
from viral pathogens causing acute respiratory infections. Sci. Rep. 2017,7, 45199. [CrossRef]
6.
Lalli, M.A.; Langmade, J.S.; Chen, X.; Fronick, C.C.; Sawyer, C.S.; Burcea, L.C.; Wilkinson, M.N.; Fulton, R.S.; Heinz, M.; Buchser,
W.J.; et al. Rapid and Extraction-Free Detection of SARS-CoV-2 from Saliva by Colorimetric Reverse-Transcription Loop-Mediated
Isothermal Amplification. Clin. Chem. 2021,67, 415–424. [CrossRef]
7.
Sur, K.; McFall, S.M.; Yeh, E.T.; Jangam, S.R.; Hayden, M.A.; Stroupe, S.D.; Kelso, D.M. Immiscible Phase Nucleic Acid Purification
Eliminates PCR Inhibitors with a Single Pass of Paramagnetic Particles through a Hydrophobic Liquid. J. Mol. Diagn.
2010
,
12, 620–628. [CrossRef]
8.
Boom, R.; Sol, C.J.; Salimans, M.M.; Jansen, C.L.; Dillen, P.M.W.-v.; Noordaa, J.v.d. Rapid and simple method for purification of
nucleic acids. J. Clin. Microbiol. 1990,28, 495–503. [CrossRef]
9.
Schneider, L.; Cui, F.; Tripathi, A. Isolation of target DNA using synergistic magnetic bead transport and electrokinetic flow.
Biomicrofluidics 2021,15, 024104. [CrossRef]
10.
Borst, A.; Box, A.T.A.; Fluit, A.C. False-Positive Results and Contamination in Nucleic Acid Amplification Assays: Suggestions
for a Prevent and Destroy Strategy. Eur. J. Clin. Microbiol. Infect. Dis. 2004,23, 289–299. [CrossRef]
11.
Berry, S.M.; Alarid, E.T.; Beebe, D.J. One-step purification of nucleic acid for gene expression analysis via Immiscible Filtration
Assisted by Surface Tension (IFAST). Lab Chip 2011,11, 1747. [CrossRef] [PubMed]
12.
Uehara, M.; Hanamura, M.; Yamada, K.; Yamaguchi, A.; Murayama, T.; Saito, Y.; Idegami, K.; Honda, T. A Rapid and Automated
Device for Purifying Nucleic Acids. Anal. Sci. 2016,32, 371–374. [CrossRef]
13.
Ali, N.; Rampazzo, R.D.C.P.; Costa, A.D.T.; Krieger, M.A. Current Nucleic Acid Extraction Methods and Their Implications to
Point-of-Care Diagnostics. BioMed Res. Int. 2017,2017, 1–13. [CrossRef] [PubMed]
14.
Hawkins, T.L.; O‘Connor-Morin, T.; Roy, A.; Santillan, C. DNA purification and isolation using a solid-phase. Nucleic Acids Res.
1994,22, 4543–4544. [CrossRef] [PubMed]
15.
Strotman, L.; O’Connell, R.; Casavant, B.P.; Berry, S.M.; Sperger, J.M.; Lang, J.M.; Beebe, D.J. Selective Nucleic Acid Removal via
Exclusion (SNARE): Capturing mRNA and DNA from a Single Sample. Anal. Chem. 2013,85, 9764–9770. [CrossRef] [PubMed]
16.
Juang, D.S.; Juang, T.D.; Dudley, D.M.; Newman, C.M.; Accola, M.A.; Rehrauer, W.M.; Friedrich, T.C.; O’Connor, D.H.; Beebe, D.J.
Oil immersed lossless total analysis system for integrated RNA extraction and detection of SARS-CoV-2. Nat. Commun.
2021
,
12, 4317. [CrossRef] [PubMed]
17.
Hansen, G.; Marino, J.; Wang, Z.-X.; Beavis, K.G.; Rodrigo, J.; Labog, K.; Westblade, L.F.; Jin, R.; Love, N.; Ding, K.; et al. Clinical
Performance of the Point-of-Care cobas Liat for Detection of SARS-CoV-2 in 20 Minutes: A Multicenter Study. J. Clin. Microbiol.
2021,59. [CrossRef]
18.
Sah, A.K.; Joshi, B.; Khadka, D.K.; Gupta, B.P.; Adhikari, A.; Singh, S.K.; Rai, G.; Vaidya, G.S.; Rajbhandari, R.; Pant, B.; et al.
Comparative Study of GeneXpert MTB/RIF Assay and Multiplex PCR Assay for Direct Detection of Mycobacterium tuberculosis
in Suspected Pulmonary Tuberculosis Patients. Curr. Microbiol. 2017,74, 1026–1032. [CrossRef]
19.
Svec, D.; Tichopad, A.; Novosadova, V.; Pfaffl, M.W.; Kubista, M. How good is a PCR efficiency estimate: Recommendations for
precise and robust qPCR efficiency assessments. Biomol. Detect. Quantif. 2015,3, 9–16. [CrossRef]
20. Vogelstein, B.; Kinzler, K.W. Digital PCR. Proc. Natl. Acad. Sci. USA 1999,96, 9236–9241. [CrossRef]
21.
Suo, T.; Liu, X.; Feng, J.; Guo, M.; Hu, W.; Guo, D.; Ullah, H.; Yang, Y.; Zhang, Q.; Wang, X.; et al. ddPCR: A more accurate tool for
SARS-CoV-2 detection in low viral load specimens. Emerg. Microbes Infect. 2020,9, 1259–1268. [CrossRef] [PubMed]
22.
Minato, T.; Ito, S.; Li, B.; Fujimori, H.; Mochizuki, M.; Yamaguchi, K.; Tamai, K.; Shimada, M.; Tokunaga, H.; Shigeta, S.; et al.
Liquid biopsy with droplet digital PCR targeted to specific mutations in plasma cell-free tumor DNA can detect ovarian cancer
recurrence earlier than CA125. Gynecol. Oncol. Rep. 2021,38, 100847. [CrossRef] [PubMed]
23.
Park, C.; Lee, J.; Hassan, Z.U.; Ku, K.B.; Kim, S.J.; Kim, H.G.; Park, E.C.; Park, G.S.; Park, D.; Baek, S.H.; et al. Comparison
of Digital PCR and Quantitative PCR with Various SARS-CoV-2 Primer-Probe Sets. J. Microbiol. Biotechnol.
2021
,31, 358–367.
[CrossRef]
Biosensors 2023,13, 517 13 of 13
24.
Song, W.; Zhang, T.; Lin, H.; Yang, Y.; Zhao, G.; Huang, X. Conventional and Microfluidic Methods for the Detection of Nucleic
Acid of SARS-CoV-2. Micromachines 2022,13, 636. [CrossRef]
25.
Mao, X.; Liu, C.; Tong, H.; Chen, Y.; Liu, K. Principles of digital PCR and its applications in current obstetrical and gynecological
diseases. Am. J. Transl. Res. 2019,11, 7209–7222. [PubMed]
26.
Sánchez Barea, J.; Lee, J.; Kang, D.-K. Recent Advances in Droplet-based Microfluidic Technologies for Biochemistry and Molecular
Biology. Micromachines 2019,10, 412. [CrossRef]
27.
Wood, S.A.; Pochon, X.; Laroche, O.; Ammon, U.; Adamson, J.; Zaiko, A. A comparison of droplet digital polymerase chain
reaction (PCR), quantitative PCR and metabarcoding for species-specific detection in environmental DNA. Mol. Ecol. Resour.
2019,19, 1407–1419. [CrossRef]
28.
Wei, C.; Yu, C.; Li, S.; Meng, J.; Li, T.; Cheng, J.; Pan, F.; Li, J. Easy-to-Operate Co-flow Step Emulsification Device for Droplet
Digital Polymerase Chain Reaction. Anal. Chem. 2022,94, 3939–3947. [CrossRef]
29.
Ishak, A.; AlRawashdeh, M.M.; Esagian, S.M.; Nikas, I.P. Diagnostic, Prognostic, and Therapeutic Value of Droplet Digital PCR
(ddPCR) in COVID-19 Patients: A Systematic Review. J. Clin. Med. 2021,10. [CrossRef]
30.
Yin, J.; Hu, J.; Sun, J.; Wang, B.; Mu, Y. A fast nucleic acid extraction system for point-of-care and integration of digital PCR.
Analyst 2019,144, 7032–7040. [CrossRef]
31.
Hu, F.; Li, J.; Zhang, Z.; Li, M.; Zhao, S.; Li, Z.; Peng, N. Smartphone-Based Droplet Digital LAMP Device with Rapid Nucleic
Acid Isolation for Highly Sensitive Point-of-Care Detection. Anal. Chem. 2020,92, 2258–2265. [CrossRef] [PubMed]
32.
Liu, F.; Xu, T.; Liu, W.; Zheng, X.; Xu, J.; Ma, B. Spontaneous droplet generation via surface wetting. Lab Chip
2020
,20, 3544–3551.
[CrossRef] [PubMed]
33.
Vasudevan, H.N.; Xu, P.; Servellita, V.; Miller, S.; Liu, L.; Gopez, A.; Chiu, C.Y.; Abate, A.R. Digital droplet PCR accurately
quantifies SARS-CoV-2 viral load from crude lysate without nucleic acid purification. Sci. Rep. 2021,11. [CrossRef] [PubMed]
34. Baret, J.-C. Surfactants in droplet-based microfluidics. Lab Chip 2012,12, 422–433. [CrossRef] [PubMed]
35.
Carvalho, J.; Puertas, G.; Gaspar, J.; Azinheiro, S.; Diéguez, L.; Garrido-Maestu, A.; Vázquez, M.; Barros-Velázquez, J.; Cardoso, S.;
Prado, M. Highly efficient DNA extraction and purification from olive oil on a washable and reusable miniaturized device. Anal.
Chim. Acta 2018,1020, 30–40. [CrossRef]
36.
Hsieh, H.-Y.; Chang, R.; Huang, Y.-Y.; Juan, P.-H.; Tahara, H.; Lee, K.-Y.; Vo, D.N.K.; Tsai, M.-H.; Wei, P.-K.; Sheen, H.-J.; et al.
Continuous polymerase chain reaction microfluidics integrated with a gold-capped nanoslit sensing chip for Epstein-Barr virus
detection. Biosens. Bioelectron. 2022,195, 113672. [CrossRef]
37.
Hung, P.-Y.; Jiang, P.-S.; Lee, E.-F.; Fan, S.-K.; Lu, Y.-W. Genomic DNA extraction from whole blood using a digital microfluidic
(DMF) platform with magnetic beads. Microsyst. Technol. 2017,23, 313–320. [CrossRef]
38.
Yin, J.; Zou, Z.; Hu, Z.; Zhang, S.; Zhang, F.; Wang, B.; Lv, S.; Mu, Y. A “sample-in-multiplex-digital-answer-out” chip for fast
detection of pathogens. Lab Chip 2020,20, 979–986. [CrossRef]
39.
Gupta, N.; Augustine, S.; Narayan, T.; O’Riordan, A.; Das, A.; Kumar, D.; Luong, J.H.T.; Malhotra, B.D. Point-of-Care PCR Assays
for COVID-19 Detection. Biosensors 2021,11, 141. [CrossRef]
40.
Wang, L.-X.; Fu, J.-J.; Zhou, Y.; Chen, G.; Fang, C.; Lu, Z.S.; Yu, L. On-chip RT-LAMP and colorimetric detection of the prostate
cancer 3 biomarker with an integrated thermal and imaging box. Talanta 2020,208, 120407. [CrossRef]
41.
Hengoju, S.; Shvydkiv, O.; Tovar, M.; Roth, M.; Rosenbaum, M.A. Advantages of optical fibers for facile and enhanced detection
in droplet microfluidics. Biosens. Bioelectron. 2022,200, 113910. [CrossRef] [PubMed]
42.
Fang, Y.; Yu, N.; Jiang, Y. Super-Resolution Lensless Imaging of Cells Using Brownian Motion. Appl. Sci.
2019
,9, 2080. [CrossRef]
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