3. in such system, reporter molecules with high-density would generate local-
ized high signal intensity on the individual beads that can “illuminate” the
single-bead microenvironment under excitation, resulting in superior sensitiv-
ity. Such “ultrabright” beads can be easily detected using conventional micro-
scope with low-magnification even mobile phone-based microscope. Differ-
ent feasible algorithms can then be employed for beads digital counting,
which can be converted to the target analyte concentration using appropriate
algorithm. The screened biomarker can be used for toward separation of
different groups in a cohort study to permit disease early diagnostics, stratifi-
cation, therapy monitoring, etc.53-56
So far, plasmonic- and nonplasmonic-
materials have been widely used as optical labels in cooperation with
confinement strategy for ultrasensitive assay development, which will be
discussed in the following sections.
Nanoplasmonics based reporter
When considering the direct signal output, plasmonic objects with size
larger than ~30 nm can be visualized under dark-field microscopy (DFM)
due to their strong scattering capabilities. This arises from the localized
surface plasmon resonance (LSPR) effect. When the size of the plasmonic
particles exceeds a certain threshold—yet remains smaller than the wave-
length of light—there is a significant enhancement in light scattering.48,57-60
On the other hand, plasmon can also enhance fluorescence through surface
plasmon interactions by mediating the distance (e.g. adjusting the spacer
thickness) between the plasmonic materials and the fluorophore. These
interactions amplify the local electromagnetic field. This, in turn, increases the
excitation rate and the quantum yield of the fluorophores, resulting in ulti-
mately enhanced fluorescence signals.61,62
These intrinsic advantages permit
plasmonics-related detection with achievable ultrasensitivity. The most
widely explored formats are plasmonic label by its alone and surface plas-
mon-enhanced fluorescence.
Nanoplasmonics alone. The surface plasmon effect refers to the collec-
tive oscillation of electrons at the interface
between a metal and a dielectric material, lead-
ing to unique optical phenomena.49
Dark-field
spectroscopy (DFS) is a typical plasmonic parti-
cle spectroscopy technique that employs
microscopy in combination with a spectrometer
to observe plasmonic particles.63,64
Lee group65
developed a plasmonic exosome assay using
transmission surface plasmon resonance with
antibodies functionalized nanohole arrays to
identify ovarian cancer exosomal surface
biomarkers (Figure 2A). They demonstrated
improved signal amplification through
secondary labeling with nanoprobes such as
gold nanostars of variable sizes and dimen-
sions for detection of 12 surface markers. The
sensitivity enhancement factors were 104
-fold
and 102
-fold compared to Western blot and
chemiluminescence ELISA, respectively. The
plasmonic structure sensing mechanism was
confirmed through Three-Dimensional Finite-
Difference Time-Domain simulations (3D-FDTD)
simulations, showing enhancement with the
immune-sandwich format on a glass substrate.
By using this technique, exosomal EpCAM and
CD24 were stably probed, and were demon-
strated the ability to differentiate ovarian cancer
patients (n=20) from non-cancer patients
(n=10).
In another work, Hu and co-workers66
built a
nanoplasmon-enhanced scattering (nPES)
assay to directly probe the membrane antigens from tumour-derived EVs, in
which the detection antibody-conjugated gold nanospheres (50 nm) and
nanorods (25×60 nm) were bind to EVs, which were captured by antibodies
pre-decorated glass chip to form the “sandwich” format, producing a local
plasmon effect that enhances tumour-derived EV detection sensitivity and
specificity (Figure 2B). The unique plasmonic color of gold nanoparticles
(AuNPs) and gold nanorods (AuNRs) allows the duplexed profiling of EV
membrane marker, and their coupling caused red shift and color change can
indicate the co-existence of the membrane antigens. They leveraged the
Image J to analyze batches of DFM images. The method can automatically
selected image areas with a brightness value of 255. After identifying these
areas, the software calculated the ratios between the selected areas and the
total areas of the images. These ratios were used as a metric for the specific
nPES EV signal. In their study, the authors demonstrated that nanoplasmon-
enhanced scattering assay could effectively distinguish early-stage pancre-
atic cancer from pancreatitis and controls in a cohort of 155 subjects.
However, the assay capacity for high-throughput surface marker profiling is
limited considering the proximity of surface antigens and the steric hindrance
of AuNPs and AuNRs during recognition and binding to the exosomal
surface.
Gooding group67
developed a core-satellite formation of Au nanoparticles
(i.e., 67 nm-AuNP-analyte-enzyme coated 10 nm-AuNP) for the detection of
interleukin 6 (IL-6). They utilized a CMOS-equipped digital camera and a dark-
field microscope to analyze thousands of dropcasted-gold nanospheres
within seconds using Image J (Figure 2C). The research team developed a
MATLAB script to transform images from the RGB color space into the HSV
(i.e. Hue, Saturation, Value) color space, facilitating the separation of intensity
and color variables. Following this conversion, a bandpass filter was applied
to remove noisy pixels and enhance the image quality. The next step involved
the identification of single nanoparticles based on their distinct morphology
and intensity of the point-spread-functions. Each identified nanoparticle was
Medicine
Figure 1. Schematic illustration of confinement
based ultrasensitive optical detection
REVIEW
The Innovation Medicine 1(2): 100023, September 21, 2023 3
4. then labeled, and its color data (hue) is extracted using an intensity-weighted
averaging method. This process entails creating a mask around the pixel with
the highest intensity within a 3-pixel radius. By employing this robust
process, the research team can independently count nanoparticles of various
colors, enabling a comprehensive assessment of the nanoparticle population
in the image. Compared to the LoD of ELISA and Western blot (~0.5 ng/mL),
this dark-field imaging approach shows a 50-fold decrease in LoD due to the
plasmon coupling. This method can circumvent the influence of non-specific
adsorption due to the localized surface plasmon resonance (LSPR) sandwich
assay, which is tolerant to non-specific binding effects, a major problem in
the field of biosensing.
Wang group68
designed another plasmonic nanobiosensor based on
Au@Ag core-shell structure for detecting microRNA 21 (miR-21) (Figure 2D).
After capturing miR-21, an average localized surface plasmon resonance
(LSPR) scattering wavelength shift of about 0.4 nm can be obtained. In this
system, the “pyramidal” DNA tetrahedral structure was utilized as the recog-
nition probe for miR-21 capped on noble-metal nanoparticles (e.g., gold or
silver nanoparticles) by employing the shifts of the LSPR scattering spec-
trum peak (λmax) as the output signal, permitting real-time detection of miR-
21 with sensitivity at the aM level within a large dynamic range of 1 aM ‒ 1
nM. However, the DNA-microarray platform could only give rise to tiny LSPR
scattering spectral wavelength shift (~0.4 nm) on this nanobiosensor, which
may not be enough to be captured intuitively by a common instrument.
Plasmon coupled with fluorescence. Noble metal-based plasmonic parti-
cles can not only provide signal output by themselves, but also have the
unique ability to couple with other fluorescent emission luminogens or quan-
tum dots to enhance emission properties, permitting dark-filed and fluores-
cence dual-channel signal output. The physicochemical properties of plas-
monic nanoparticles are specifically designed to facilitate plasmon-enhanced
fluorescence (PEF) pathway,49
which can significantly enhance the fluores-
cence of nearby fluorophore zone using spacers like SiO2 or polymers with
appropriate manipulation for the avoidance of quenching. While conventional
fluorescent molecules and optical nanoparticles suffer from low brightness,
poor photostability, and photobleaching, plasmon-enhanced fluorescence
offers improved performance in terms of higher sensitivity and long-term
photo-stability. Quantum dots (QDs) are a promising class of fluorescent
probes for biological imaging owing to their unique stokes shift, powerful
stability, and relatively long fluorescence lifetime. However, the fluorescence
intensity of a single quantum dot is not sufficient for observation under a
conventional optical microscope. Wang group51
presented bioorthogonal
nanoparticle detection (BOND) technology in a PEF-based suspension
microarray using QDs as a signal reporter. The spherical gold-coated
magnetic beads and flat gold film-coated glass were compared and served
as solid support. They were decorated with anti-PSA (prostate-specific anti-
gen) capture antibodies, followed by recognition and binding of the target
analyte and the QDs-modified detection antibody, generating the final sand-
wich immunocomplex. After collection of fluorescence images of the plas-
monic slide, the imagines were analyzed by software Genepix 6.1. This soft-
ware can automatically identify the features and report the fluorescence
intensities of each spot with background correction. After collecting the
intensity data for each point, the mean fluorescence intensity was calculated
for each set of spots, which can be converted to original concentration using
Poisson equation. The designed platform utilizes BOND- and PEF- amplifica-
tion strategies for ultrasensitive detection of proteins with detection sensitiv-
ity (1 fM) and dynamic range (5 orders of magnitude) was achieved for PSA
detection (Figure 3A). They demonstrated that both spherical and flat plas-
monic surfaces can enhance the fluorescence of QDs, and the immunocom-
plex served as an excellent spacer.
To ensure multiplexed profiling, Shao group62
developed a templated plas-
monics for exosomes (TPEX) platform for multiparametric exosome analysis.
This advanced method has been employed to analyze samples from 20
subjects (12 with colorectal cancer and 8 with gastric cancer), and has
demonstrated its potential in predicting patient prognosis using exosomal
proteins (MUC1, EpCAM, CD24, CD63) (Figure 3B). Exosomes were incubated
Figure 2. Ultrasensitive plasmonic detection strategies (A) Detection of tumor-derived extracellular vesicles near a metallic array, with FDTD simulations highlighting confined
electromagnetic fields near nanopores.65
(Nature Biotechnology, 2014). (B) Nanoplasmon-enhanced scattering (nPES) assay for EVs detection.66
(copyright Springer Nature,
2017). (C) Core-satellite immunoassay for the detection of IL-6.67
(copyright Elsevier Ltd., 2018). (D) Smart nanobiosensor for single molecule analysis using tsDNA17-modified
Au@Ag nanocubes on an ITO glass to capture miR-21.68
(copyright American Chemical Society, 2018).
REVIEW
4 The Innovation Medicine 1(2): 100023, September 21, 2023 www.the-innovation.org/medicine
5. with fluorescent molecular probes and AuNPs. The exosome-bound AuNP
can develop into a gold nanoshell-coated exosomes by seeding method, and
the authors demonstrated that a large red shift in its plasmonic resonance
can effectively quench the fluorescence signal of probes attached to the
same vesicle. Besides, all the experimental steps were integrated into the
miniaturized microfluidic chip that facilitated TPEX measurements of target in
the complex clinical biofluids. A custom-designed smartphone-based optical
detector can then be used for the image-based data acquisition and analysis,
which can be completed within 15 min.
Recently, Singamaneni and co-authors52
showed that a plasmonic
nanoscale construct based on bovine serum albumin (BSA)-fluorophore
decorated AuNRs which served as an “add-on” label for a broad range of
bioassays with improved signal-to-noise ratio and dynamic range without
altering their workflow and readout devices (Figure 3C). In this work, BSA was
used as a scaffold in the design of nanoconstructs, acting as a stabilizing
agent to prevent nanoconstruct aggregation and as a blocking agent to mini-
mize non-specific binding of the plasmonic-fluor. Compared to ELISA, the
presented plasmonic assay exhibited 189-fold lower LOD and more than two
orders of magnitude larger dynamic range. Such device was successfully
utilized to scan an array of biomarkers associated with kidney disease.
However, it should be noted that the exposed fluorophore on the AuNRs
surface may be suffered from photostability, a protection shell would be
favorable. Zheng and co-workers fabricated Ag@SiO2 core-shell structure
dopped with FITC, which are single ultrabright green particles that can be
visualized by a conventional fluorescence microscope. This system permits
long-term stability (6 months) with negligible attenuation of fluorescence
intensity due to the silica shell protection and the resistance to the dye
desorption.63
Magnetic beads and nucleic acid amplification based detection
In addition to the plasmonic reporters, there are other non-plasmonic
reporters available, which can be complementary candidates and have been
actively explored for ultrasensitive assay. In the context of using fluores-
cence probes for ultrasensitive detection, traditional method using repeated
labeling of fluorophores is often laborious and can lead to photobleaching,
false-positive signal as well as a low signal-to-noise ratio69
. Consequently,
there is a pressing need to amplify the fluorescence signal toward single-
entity detection in a robust way. In such regard, nucleic acid-labelled fluo-
rophore-based detection probe in cooperation with solid supports like
magnetic beads is a good option. Nucleic acid amplification (NAA) is a valu-
able technique for detecting pathogens or other biomolecules by amplifying
specific nucleic acid sequences.70
Numerous isothermal NAA techniques,
including the catalyzed hairpin assembly (CHA),71
hybridization chain reac-
tion (HCR),72
and rolling circle amplification (RCA),73,74
have emerged as
promising alternatives for achieving rapid and efficient signal amplification
without the need of thermocycling. The Walt group75
developed an ultrasen-
sitive single-molecule detection platform that employed rolling circle isother-
mal amplification and a sandwich structure for Brachyury detection, achiev-
ing an impressive sensitivity of 244.6 aM (Figure 4A). In this design, magnetic
microbeads (MBs) conjugated with antibodies were used to probe human
cytokines interleukin-1β (IL-1β) and IL-10. Then, single-stranded DNA
(ssDNA) with a specific detection antibody and an RCA trigger were used to
capture target exosomes. The RCA reaction enabled numerous fluorescent
probes to hybridize with the RCA products, amplifying fluorescence signals.
Beads were then placed on a slide and dried, forming monodispersed beads
for digital counting using colocalized red (MBs)-green (probe) fluorescence.
Following the similar principle, the Gao group76
introduced modifications by
using an aptamer as a recognition element instead of antibodies. They
employed an addressable DNA nanoflower (DNF) attached to the surface of
magnetic beads, which introduced a large number of fluorescence probes
onto the nanoflower through base complementary pairing. This configuration
enabled fluorescence imaging for the detection of 17β-estradiol (E2). a typi-
cal environmental estrogen, and a small molecule model target (Figure 4B).
The fluorescence image was automatically analyzed by Fiji image analysis
Medicine
Figure 3. Strategies utilizing plasmonic coupled fluorescence (A) BOND overview and its role in plasmonic suspension microarray.51
(copyright Elsevier Ltd., 2023).
(B) Schematic of plasmonic-fluor as a biolabel boosting fluorescence intensity and improving signal-to-noise in assays.62
(copyright Springer Nature, 2020). (C) Templated plas-
monics for exosomes (TPEX) platform showing labeled exosomes for multiparametric molecular profiling through SPR redshift signals.52
(copyright Science Advances, 2020).
REVIEW
The Innovation Medicine 1(2): 100023, September 21, 2023 5
6. software, simplifying the testing workflow. The surface confinement strategy,
which involves using NAA and attaching optical probes to solid support, is
promising but comes with a set of challenges. The intricate and prolonged
wash procedure during binding and polymerization stages often results in
significant product loss. Moreover, the inherent negative charge of DNA tends
to non-specifically adsorb positively charged molecules, leading to undesir-
able false positives and high background interference. There is also a notable
heterogeneity in the brightness of individual beads due to different amplifica-
tion cycles, stemming from the sensitivity of enzyme activity to environmen-
tal influences.
To tackle these issues, appropriate antifouling strategies should be
employed, such as the use of nonionic and non-protein blockers to suppress
non-specific adsorption.77-79
They can help reduce DNA-induced false posi-
tive signals, thereby pushing the detection limit further and improving the
current fluorescence threshold. Automation for capture-detection would be
another solution to overcome the multi-step manual handling caused errors.
Additionally, rational fundamental design on sequences, the recently
proposed one-pot amplification strategy might be a good alternative for its
further evolution and improvement.80,81
It is anticipated that RCA can be
further improved by using signal enhancement and noise reduction
approaches. Finally, the ability to control the efficiency of aggregation on indi-
vidual magnetic beads results in discrepancies in fluorescence intensity
among beads, which can have a substantial influence on the outcomes. It is
worth noting that reactions such as RCA, LAMP, and PCR all require
enzymes, whose activity can be easily affected by the environment, leading to
low stability and assay accuracy. Hu et al.82
ingeniously utilized size encod-
ing approach to accomplish multiplexed detection by employing microbeads
of four different sizes, followed by decoration with aptamers to target extra-
cellular vesicle (EV) surface markers (Figure 4C). They designed a microflu-
idic chip featuring an array of differently sized channels, which allowed
magnetic beads of corresponding sizes to enter separate channels to reach
their blockade with spatial resolution. This combination of size coding and
spatial coding permitted simultaneous analysis of PD-L1, EpCAM, and CD63
from intact EVs for cancer diagnosis (healthy control n=10, cancer patients
n=25). RCA amplification with hybridization of complementary DNA probe
endowed the signal amplification capability. This study demonstrated that
Figure 4. Various single-molecule detection methods based on nucleic acid amplification (A) Schematic of dropcasted single molecule assays.75
(copyright American Chemi-
cal Society, 2020). (B) Principle of single-molecule detection of E2 used by addressable DNA nanoflowers and automatic digital quantitative analysis.76
(copyright American
Chemical Society, 2022). (C) Schematic of size-coded affinity matrix microfluidic chip strategy for multiplex EV phenotyping.82
(copyright American Chemical Society, 2022).
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6 The Innovation Medicine 1(2): 100023, September 21, 2023 www.the-innovation.org/medicine
7. this tEV phenotyping method can rapidly and simultaneously detect six
different tEV phenotypes with high sensitivity (Table 1).
Visible microsphere
In the aforementioned discussion, signal amplification techniques are high-
lighted, which enables the conversion of undetectable signals into observ-
able fluorescent outputs through confinement-based signal enhancement.
This method involves directly doping reporters like fluorescent tags to micro-
spheres. The number of doped fluorophores and their intrinsic quantum yield
determines the brightness of resultant fluorescent microbeads. Alternatively,
the microbeads can be used as reporters as they can easily be visualized by
conventional microscope. Noji et al.83
introduced a sensitive wash-free detec-
tion method for PSA (Figure 5A). In their work, PSA reacted with antibody-
coated magnetic nanoparticles (~800 nm) and were then pooled into a
femtoliter-sized reactor by magnetic force. The images were analyzed using
Image J and Trackmate softwares. These tools can localize the center of
individual particles by calculating the intensity of light spots. As a result, the
number of tethered particles could directly correlate with the concentration of
the target antigen. The number of particles tethered was proportional to the
concentration of the target antigen. This method directly treated magnetic
microbeads as reporters without the need of additional dyes. However, this
method requires extensive mathematical calculations of the Brownian motion
of molecules and imaging validation through other instruments. Yiping Chen84
and his team encoded multiple targets using polystyrene microspheres with
five different particle sizes, and the unconjugated microspheres in the super-
natant after immunoreaction were recorded for subsequent visible signals
under an optical microscope (Figure 5B). The obtained images were further
analyzed using artificial intelligence computer vision technology to decode
the inherent characteristics of the polystyrene microspheres and revealed the
type and concentration of the target. This method sidesteps the complexities
of chip production and signal amplification but requires precise size control of
the microspheres. Non-specific adsorption and precipitation must also be
considered. The bright-field microscopy is straightforward but impurities or
dusts can interfere, offering opportunity for the fluorescently encoded micro-
spheres. To this end, He et al.85
directly labeled magnetic beads (~1 µm) with
fluorescent microspheres (~120 nm) to capture the target using a compo-
nents of capture DNA-bead, analyte, and detection DNA-fluorescent micro-
sphere in an order (Figure 5C). Using SARS-CoV-2 ssDNA as the model, they
achieved a 1.5 fM detection limit by counting and decoding bead-micro-
sphere pairs and analyzed the data with NIH ImageJ software.86
This study
highlights the potential of single-particle luminescence for single-molecule
detection. Noteworthy materials include aggregation-induced emission (AIE)
Medicine
Table 1. The typical examples using external-/internal- confinement strategy for ultrasensitive optical assay.
Confinement Type Analyte LOD Working Range Volume Ref.
External confinement Plasmonic alone CD63 NA NA 1 μL 62
External confinement Plasmonic alone EphA2 0.2 ng/μL 0.1–10000 ng/uL 1 μL 133
External confinement Plasmonic alone miR-21 0.1 aM 1 aM to 1 nM NA 68
External confinement Plasmonic with fluorescence ALP 5.8 μU/mL 0.06 to 2.48 mU/mL 100 μL 134
External confinement Plasmonic with fluorescence PSA 1 fM 1 fM-1 nM NA 135
External confinement Nucleic acid amplification 17β-estradiol (E2) 63.09 × 10−6
nM 0.001836 to 183.6 nM 10 μL 76
External confinement Nucleic acid amplification IFNγ and IL-2 (3×) 30 aM and 20 aM NA 2 μL 136
External confinement Nucleic acid amplification IL-6 13 pM NA 250 μL 137
External confinement Nucleic acid amplification PSA 97.2 aM 1.62 nm 2 μL 138
External confinement Nucleic acid amplification CEACAM-7 2.82 pM NA 25 μL 139
External confinement Nucleic acid amplification LwaCas13a 15.8 fM 10 cp/μL up to 106
cp/μL 2 μL 140
External confinement without amplification SARS-CoV-2 (ssDNA) 1.5 fM 0 to 1 pM 5 μL 85
External confinement without amplification CEA 1.2 pg/mL NA NA 141
External confinement without amplification Aflatoxin B1 0.17 ng/mL 3.13 to 125.00 ng/mL 15 μL 142
External confinement without amplification PSA 1.2 pg /mL 10 pg/mL and 1 ng/mL 100 μL 143
Internal confinement Droptlet(oil in water) β-gal NA 0.1 cpd-3 cpd（40-1202 fM） NA 144
Internal confinement Droptlet(oil in water) PSA 0.48 ng/mL 0.5–30 ng /mL 30 μL 145
Internal confinement Droptlet(oil in water) HPV’s target DNA 2000 copies/mL 107
copies/mL to 103
copies/mL 100 μL 92
Internal confinement Droptlet(oil in water) BβG 930 zM 1 aM to 1 fM 400 μL 146
Internal confinement Droptlet(oil in water) Exosomes 10−17
M 10-105
Exosomes/uL NA 93
Internal confinement Droptlet(hydrogel) IL-6 1.36 fM 0-50 fM 10 μL 147
Internal confinement microwell femtomolar proteins 2.84 fM 493 aM (32 fg/mL)-
120 fM (7.8 pg/mL)
2 μL 148
Internal confinement microwell Thyroid stimulating
hormone
0.0013 μIU/mL 0.02-1 μIU/mL 100 μL 100
Internal confinement microwell CD9+/CD63+ EVs NA 30-22800 ng/uL 3-8 μL 149
Internal confinement microwell serum cytokines 0.198pg/ml 0.16 pg/mL- 2.5 ng/mL <15 μL 103
ALP, alkaline phosphatase; PSA, prostate-specific antigen; β-Gal, β-galatosidase; BβG, biotinylated β-galactosidase; EphA2, Ephrin type-A receptor 2;
CEACAM-7, CEA Cell Adhesion Molecule 7
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The Innovation Medicine 1(2): 100023, September 21, 2023 7
8. molecules embedded within metal-organic frameworks (MOFs) that intensify
fluorescence emission with the restraint movement by the MOF cage, making
it favorable optical reporter. Similarly, upconversion nanoparticles (UCNPs)87
are anti-Stokes luminescent materials that transform low-energy near-
infrared (NIR) radiation into higher-energy radiation. They are favorable for
detection with reduced interference and resistance to photobleaching.
Carbon dots (CDs)88,89
are photoluminescent nanomaterials that are water-
soluble, biocompatible, and photostable, fitting the need for biological and
environmental sample detection. Ongoing research in luminescent materials
will bolster single-particle detection, broadening its applications in diagnos-
tics, environmental monitoring, and chemical sensing.
To conclude, the external confinement strategies widely employ micro-/
nano- architectures with the adhesion of optical reporters (e.g., organic dyes,
plasmonic nanoparticles, quantum dots, etc.) on to their outer surface. In
cooperation with signal amplification strategies like nucleic acid amplifica-
tion or surface plasmon-enhanced fluorescence, the signal output can be
dramatically enhanced to permit the stable probing of target at ultra-low
levels, even single-entity level. It is expected that more novel formats on the
nanoarchitecture design or signal enhancement and the cooperation with
internal confinement will be introduced to achieve a synergetic effect, which
would be the next hot spot.
INTERNAL CONFINEMENT STRATEGY
Besides the external confinement strategy, the internal strategy has also
been actively explored as a vital complementary way. In such system, the
signal output reporters, e.g., fluorophores or catalyzed photonic/colorful
product, are usually confined within a small minuscule internal compartment
of an architecture like microwell, droplet, metal-organic-frame (MOF), hydro-
gel, etc. The signal output molecules are restricted, producing localized ultra-
high concentrations with a resultant amplified signal. In the case of droplet-
based systems, soft materials like emulsions or hydrogels are used to
confine the signal molecules, restricting the signal output within the liquid
droplet boundaries. For microwell-based systems, rigid materials such as
plastics or silicon are used to form an array of small wells, limiting the signal
output within the confined space of individual wells.
Confinement within droplet
In droplet system, mineral oil wrapped aqueous phase is commonly used.
By application of positive external pressure to the inlet or negative pressure to
the outlet generated through syringe pump90
or syringe with a binder clip91
,
respectively, the stable water-in-oil droplet format can be formed. Only if the
enzyme and substrate are encapsulated in the same droplet, the enzyme can
catalyze and produce a fluorescent product thus illuminate the droplet under
Figure 5. Various single-molecule detection methods based on microspheres (A) Digital HoNon-ELISA: Detection of protein biomarkers using antibody-coated magnetic
nanoparticles.83
(copyright American Chemical Society, 2019). (B) APT Immunoassay: AI-driven assay using 2D microspheres for multi-target detection.84
(copyright American
Chemical Society, 2023). (C) Enzyme-free Assay: Detects SARS-CoV-2 ssDNA with a bead-based sandwich structure.85
(copyright Elsevier Ltd, 2023).
REVIEW
8 The Innovation Medicine 1(2): 100023, September 21, 2023 www.the-innovation.org/medicine
9. excitation. Then, digital readout (“1” or “0”) can be generated once the
number of droplets is far more than that of the target analyte.92
Zheng et al.,93
utilized the capture antibody (anti-CD63)-detection antibody (anti-GPC-1) co-
localization strategy for the single-exosome detection with an achievable
LOD down to ~10−17
M and a dynamic range of 5 log of the linear regime
(Figure 6A). Although the droplet system using enzyme-catalyzed fluores-
cence within a confined volume can generate a strong signal for ultrasensi-
tive measurement and save sample consumption, it requires complex
manual operations, e.g., the stepwise capture processes must be done
outside the chip, and it only permits singplexing using current format. In such
regard, the capability for automation, integration, and multiplexed profiling is
favorable. Moreover, to further enhance the effectiveness of the droplet-
based chip, it is crucial to develop a matched mobile platform that facilitates
fast and easy access to assay data by integrating mobile device, thus high-
lighting the importance of improved connectivity and user-friendly interfaces.
The Walt team94
has developed a platform for rapid, sub-picogram-per-
milliliter, multiplexed digital droplet detection by using color-encoded beads
and droplet microfluidics comprising of partition-incubation-detection
compartments (Figure 6B). This innovative approach employs a mobile
phone-based imaging technique and coding-based duplexed profiling, elimi-
nating the need for costly benchtop optics and ensuring a detection speed
100 times faster than conventional assays due to time domain-encoded
mobile phone imaging. The performance of this assay was characterized by
spiking recombinant proteins in fetal bovine serum (FBS) with a limit of
detection (LOD) at 4 fg/mL (i.e., 300 aM). By integrating the coding-based
duplexed profiling, they further optimized the system performance, enhanc-
ing its detection accuracy and reliability. This development will aid its
commercialization, making a user-friendly system for self-testing anytime
and anywhere. Liang’s group95
devised a microfluidic system, known as digi-
tal droplet with auto-catalytic hairpin assembly (ddaCHA), for the digital
quantification of single microRNAs (miRNA) (Figure 6C). This process
involves partitioning miRNA molecules into individual picoliter-sized droplets,
generating a digital readout in which the target molecule is classified as either
present ("positive") or absent ("negative"). The researchers successfully
implemented an enzyme-free auto-catalytic hairpin assembly (aCHA) within
the droplets, demonstrating its effectiveness in minimizing the impact of
external environmental factors and temperature fluctuations on droplet
performance. In their investigation, an automated Python program was
employed to monitor and compute the fluorescence intensities of individual
droplets when they are flowing through a high-throughput microfluidic chip.
By assigning a distinct identifier to each droplet within the flow channel, the
script ensured that each droplet was only quantified once, thereby eliminat-
ing the risk of replicated counting. The significance of automation in such
detection systems is obvious as it considerably reduces manual labor and
minimizes potential errors in the process.
However, the complexity of these systems can be daunting for untrained
end users, prompting the development of new approaches that aim to
simplify or automate the microfluidic steps. Wang group96
presented a novel
lab-on-a-particle assay that employs hydrogel particles for simultaneous
immobilization of protein molecules and emulsification templating, enabling
digital counting of β-galactosidase. Following this process, image analysis
algorithms in MATLAB are used to analyze the fluorescence signal of each
droplet by averaging signals over the particle. A signal is considered to be
positive if it exceeds 2.5 × standard deviations above the mean of the back-
ground signals (Figure 6D). Moving forward, the efforts will be paid for refin-
ing this process and measuring actual targets further using appropriate affin-
Medicine
Figure 6. Droplet-based strategies for single-molecule detection (A) Droplet digital ExoELISA for exosome quantification using magnetic beads in microdroplets for fluorescent
readout.93
(copyright Springer Nature, 2018). (B) The µMD's droplet generation and detection for mini dELISA on mobile platforms with antibody-functionalized, color-coded
beads.94
(copyright National Academy of Science, 2019). (C) Droplet-generating CFF junction design for single-molecule miRNA imaging.95
(copyright American Chemical Society,
2022). (D) Production of Hydrogel particles through step emulsification at low pH.96
(copyright Royal Society of Chemistry, 2021).
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The Innovation Medicine 1(2): 100023, September 21, 2023 9
10. ity capture probes.
Droplet-based single-molecule detection has shown great potential in vari-
ous applications, however, it presents certain drawbacks. For instance,
droplet generation and manipulation can be technically challenging, and
droplet stability may be affected by evaporation or coalescence over time.97,98
Additionally, droplet-based approaches may not be suitable for all sample
types or analytes, such as those with high viscosity or complex molecular
structures due to challenges in generating uniform droplets and potential loss
of analyte integrity. Furthermore, recovering analytes from droplets for further
analysis could be difficult. These limitations call for the need of alternative
approaches like microwell-based single particle detection, which may provide
a more robust and versatile solution for many applications.
Confinement within microwell
Microwell strategies serve as an alternative to droplet-based techniques by
confining chromatic or luminescent products within a tiny-volume microw-
ells rather than the water-in-oil droplets. The microwell system is usually
generated by nano-/micro-fabrication. Microwell-based techniques offer a
controlled and stable environment for target analysis, as they do not rely on
droplet generator and the complex manipulation. By employing microwell
arrays, researchers can effectively isolate and analyze individual particles.
Microwell confinement-based detection was originally developed by the Walt
group99
. This detection method, also referred to as Single Molecular Array
(Simoa), has been successfully commercialized. In the Simoa approach, each
femtoliter-sized reaction chamber in the array contains a single magnetic
bead, and the number of microbeads (MBs) is deliberately maintained to
significantly exceed the number of target molecules from highly diluted
sample. In essence, it utilizes the concept of transformation of on-bead
confinement to the micro-well confinement system, the enzymatically
catalyzed fluorescence can be monitored by ultrasensitive CCD camera. The
beads with the sandwich complexes are then loaded into these chambers,
with each chamber designed to accommodate one bead. Subsequently, a
fluorogenic substrate is introduced, which allows the enzyme on the detec-
tion antibody to convert the substrate into a fluorescent product. This
process results in a binary "0" or "1" signal output, corresponding to the
absence or presence of the target analyte, enabling highly sensitive detection.
However, the Simoa system highly relies on the pricey bulk instrument and
micro-well plates, which is not affordable to many remote areas and source-
limited regions. Also, the current format is mainly designed for singleplexed
profiling while multiplexing ability is still limited.
As microwell detection has evolved with continuous improvements and
refinements, researchers have explored its potential in various applications.
For instance, Karen group100
has developed a novel digital microfluidics (DMF)
chip-based method for detecting thyroid-stimulating hormone (TSH), a criti-
cal marker for assessing thyroid function. Such device requires a tiny sample
volume of 1.1 µL and offers a LOD at 1.3 nanoIU/mL (Figure 7A). Microwells
Figure 7. Microwell confinement-based detection platforms (A) Schematic illustration of the digital microfluidics (DMF) chip with microwell arrays.100
(copyright American
Chemical Society, 2022). (B) Schematic illustrations of bead trapping.101
(copyright Elsevier Ltd., 2019). (C) Schematic of the microwell-based biosensor with edge
enhancement.102
(copyright American Chemical Society, 2021). (D) Schematic illustration of the digital microfluidics (DMF) chip with microwell arrays.103
(copyright Elsevier Ltd.,
2021).
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10 The Innovation Medicine 1(2): 100023, September 21, 2023 www.the-innovation.org/medicine
11. were fabricated on a PET substrate using a UV imprinting process and resist
material through a roll-to-roll fabrication method. In the DMF chip, precise
control of electric fields between the top and bottom electrodes enables effi-
cient droplet manipulation within these microwells, and the hydrophobic-in-
hydrophobic microwells on the chip also boost a higher seeding efficiency
(97.6% ± 0.6%) due to minimized surface energy, ensuring stable droplet
confinement, reduced merging or spreading. In another study, the Sun
group101
has developed a microfluidic chip using polydimethylsiloxane
(PDMS) based microwell patterned arrays to detect tumor necrosis factor α
(TNF-α) (Figure 7B). Beads were injected into a chip and were allowed to
settle for several minutes due to gravity. Subsequently, oil was injected
through the inlet to seal an aqueous solution containing a single bead in the
well. This power-free oil-sealed water phase eliminates the need for external
pumping systems or valves, making it a cost-effective approach. Further-
more, the fabrication cost of these chips is low, and there is no need for
complex surface modifications. The method also maintains good sensitivity
with a limit of detection (LOD) of 12.62 fg/mL, which is comparable to perfor-
mance using the commercial Simoa HD-1 Analyzer (14 fg/mL). In order to
amplify optical signals and increase sensitivity, Li group102
used edge-
enhanced microwell (40 μm depth) immunoassay for detection of interferon-
γ (Figure 7C). The microwells are coated with capture antibodies, which facili-
tate the binding of IFN-γ and the following Texas conjugated detection anti-
bodies to form sandwich immune-complex. Due to larger axial resolution
along with the vertical detection, the fluorescence emission was enhaced,
then the signals of the analytes will be amplified greatly, which formed fluo-
rescent rings. The edge enhancement effect of the microwell vertical side-
wall shows a 6-fold sensitivity enhancement compared to those obtained on
a flat surface.
It is worth mentioning that all the examples presented above are focusing
on detecting single target, which may not reflect disease evolution and
progression accurately. Also, lots of diseases originate by complex multiple
factors, thus there is a need for multiplexed assays to permit multi-analyte
profiling. In conventional assays, the detection of multiple biomarkers from
one set of samples is challenging due to the complexity of encoding and
decoding of numerous signal output from different analytes, and various error
sources can lead to a decrease in the accuracy of the assay.54
Therefore,
rational design that allows distinguishable signal readouts, decent sensitivity,
and specificity is the key pathway. Recognizing the existing limitations, Sung
group103
introduced the pre-equilibrium digital ELISA (PEdELISA) microarray
platform that leverages spatial-spectral encoding and machine learning on a
microfluidic chip (Figure 7D). This allows 12-cytokine assay using only 15 μL
of serum. The 12-plex PEdELISA microarray uses a CNN-based parallel
computing algorithm, ensuring unsupervised image data analysis with high
accuracy. It autonomously classifies and segments image features at a high
throughput (1 min/analyte), achieving 8‒10 times higher accuracy than tradi-
tional GTS-based algorithms without manual corrections. The entire detec-
tion and analysis cycle can be completed within 40 min. In essence,
PEdELISA enhances multiplexing efficiency by integrating advanced
microfluidics with a machine learning image processing method.
AI APPROACH FOR OPTICAL DETECTION IN CLINICAL ANALYSIS
Indeed, with the remarkable advancements in hardware technology, there
has been an extraordinary surge in the development and implementation of
artificial intelligence (AI) tools that are specifically designed for disease diag-
nosis. These bespoke AI systems excel at processing vast volumes of data
and images sourced from a myriad of equipment. By incorporating AI models,
such as Linear Discriminant Analysis (LDA)104-106
, Support Vector Machines
(SVM)107,108
, Logistic Regression, Linear Regression, Linear Mixed Models and
Random Forest Models (RFM)109,110
, these systems significantly enhance both
the efficiency and accuracy of diagnostic procedures. Within the scope of our
specialized system, the primary objective is the automated detection and
quantification of visible particle signals as particle count can act as a surro-
gate for biomarker concentration. By accurately quantifying specific protein
biomarkers in patients, these AI-driven approaches can significantly advance
medical research and patient care, potentially leading to earlier interventions
and improved treatment outcomes. The schematic diagram of this AI appli-
cation in our review can be summarized as follows: Image acquisition →
Particle detection and counting → Biomarker concentration representation →
Data Analysis→Diagnosis (Figure 8). This progression succinctly captures
the role of AI in augmenting diagnostic procedures in healthcare.
Automation and standardization of image processing
AI technology has revolutionized optical detection by automating data
processing, boosting efficiency, and reducing human error. In image process-
Medicine
Figure 8. The general process of AI techniques for data interpretation
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The Innovation Medicine 1(2): 100023, September 21, 2023 11
12. ing, AI excels in automating particle counting through several steps:
(1) Image Preprocessing: AI algorithms enhance image quality by decreas-
ing noise and elevating contrast. Techniques like Gaussian filtering, thresh-
olding, and contrast normalization111,112
are used for clarity, facilitating better
detection and accurate particle counting. (2) Object Detection: Depending on
image complexity, methods range from connectivity analysis for simple
images to advanced techniques like edge detection and machine learning for
intricate ones. (3) Object Counting: After segmentation, objects are enumer-
ated using tools like ImageJ/FIJI, MATLAB, and Python libraries such as
OpenCV and Scikit-Image. The Chen group113
demonstrated this, differentiat-
ing polystyrene (PS) microspheres with a CV technique, achieving 93.5% to
100% accuracy, surpassing traditional methods. For example, the Chen
group113
utilized computer vision (CV) methods to differentiate and quantify
polystyrene (PS) microspheres of varying sizes. (Figure 9A) Through tech-
niques like binarization thresholding and a customized algorithm, they
achieved impressive accuracy ranging from 93.5% to 100%, surpassing tradi-
tional manual methods.
To sum up, ultra-sensitive optical detection can be realized through vari-
ous rational signal amplification strategies, translating minuscule signals into
countable particles, bypassing the need for complex AI technologies typically
required in other systems. However, when a scenario necessitates more intri-
cate analysis, such as distinguishing signals based on their intensity or corre-
lating signal strength with specific biomarker concentrations, advanced algo-
rithms and strategies become invaluable. Nevertheless, the incorporation of
AI, even within these simplified environments and small-scale clinical sample
analysis, significantly enhances the accuracy and reliability of our method-
ologies.
AI approaches to data interpretation
Analysis of collected biomarker data plays a crucial role in gaining a
comprehensive understanding and diagnosis of diseases. The abundance
and diversity of biomarkers offer valuable insights for disease diagnosis,
monitoring treatment response, tracking disease progression, and identifying
disease subtypes. With the advent of AI techniques, the analysis of biomarker
data has become more efficient and accurate, enabling the delivery of
personalized healthcare services to patients. In the following section, we will
elaborate how various AI techniques work on these critical medical areas.
In the domain of early disease diagnosis, SVM-based methods are notably
adept at dealing with high-dimensional data, like gene expression profiles.
Their proficiency in extracting complex patterns from this data allows them to
discern distinct gene expression signatures that could separate cancer cells
apart from healthy cells. This distinguishing capability is pivotal in facilitating
the early detection of cancer.
Given the prominence of high-dimensional gene expression data in cancer
research, the application of SVMs might be more prevalent in this field than in
others.114,115
However, their performance depends on the correct selection of a
kernel function and adjustment of parameters, which may require specialized
domain knowledge. Further research in this area could greatly enhance the
applicability of SVMs.
Moving on to the monitoring of treatment responses, Random Forest
models play a central role in this domain.116-119
These models are ensemble
learning methods that combine multiple decision trees. This approach could
provide an understanding of the intricate interactions between various
biomarkers and treatment outcomes. For instance, when managing chronic
diseases like diabetes, Random Forest models can effectively handle diverse
Figure 9. AI-assisted strategies for disease diagnosis (A) Schematic illustration of workflow and principles of CV-enabled image processing for simultaneous detection.113
(copyright American Chemical Society, 2023). (B) Schematic illustration of machine learning-based SERS detection by PCA-LDA.125
(copyright American Chemical Society, 2023).
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12 The Innovation Medicine 1(2): 100023, September 21, 2023 www.the-innovation.org/medicine
13. data, ranging from glucose levels and insulin doses to lifestyle factors like diet
and physical activity.120
This capability could help develop a more personal-
ized and effective treatment strategies. However, the complexity of the model
can increase as the number of features or variables grows, making it chal-
lenging to interpret the results and understand the underlying patterns effec-
tively. Therefore, future research may explore more effective ways to opti-
mize the number of decision trees in the ensemble or propose new data
dimensionality reduction techniques.
Linear Discriminant Analysis (LDA) plays an important role in tracking
disease progression.121-124
Its capability to simplify high-dimensional
biomarker data makes it instrumental in identifying longitudinal changes in
biomarker levels, paving the way for a deeper understanding of disease
trajectories over time. For instance, when monitoring heart disease progres-
sion, LDA can be applied to unravel dynamic alterations in multiple biomark-
ers, such as cholesterol levels, blood pressure readings, and inflammation
markers. This analytical power aids in understanding the fluid nature of
disease progression and enabling timely interventions. For example, Jin
group 125
employed a combined approach of principal component analysis
(PCA) and Linear Discriminant Analysis (LDA) to extract features from high-
dimensional Surface-Enhanced Raman Scattering (SERS) spectra (Figure
9B). First, they performed dimensionality reduction on the SERS spectra using
PCA, reducing them to 46 principal components that accounted for 95% of
the total variance. Next, they applied LDA to further reduce the dimensionality
of these 46 principal components to just two LDA factors. Remarkably, this
combined approach resulted in a clear separation and accurate identification
of exosomes derived from normal cells and cancerous cells.
In the field of disease subtyping, linear and logistic regression models serve
as potent tools for grouping.126,127
These models can shed light on the associ-
ations between biomarkers and specific disease subtypes. In addition, these
models can integrate an array of biomarkers, such as gene expression
profiles and metabolic markers, to estimate disease subtypes, enabling more
effective and personalized management plans.
In conclusion, AI techniques have demonstrated their effectiveness in
analyzing biomarker data, facilitating a deeper understanding and diagnosis
of diseases. These techniques contribute to early disease detection, treat-
ment response monitoring, disease subtyping, ultimately providing more
accurate and tailored healthcare services for patients. However, it is also
essential to be aware of potential challenges. AI models heavily rely on the
quality of the training data. Therefore, ensuring the accuracy, completeness,
and standardization of biomarker data is critical to yield reliable results. In
addition, these models often involve complex mathematical computations
and require domain knowledge for optimal parameter tuning, highlighting the
need for interdisciplinary collaborations between clinicians, data scientists,
and bioinformaticians. Another potential challenge in embracing AI advance-
ments is overfitting due to sample size. Overfitting occurs when a model is
excessively complex and learns noise or specific patterns from the training
data that do not generalize well to real data.
Another area that warrants attention is the interpretability of these models.
While AI models can make highly accurate predictions, the underlying
reasoning can be opaque, a phenomenon often termed as the "black box"
problem. This lack of transparency can hinder physicians' trust in the model
predictions, thereby limiting their adoption in clinical practice. Lastly, ethical
and legal considerations surrounding data privacy and security, and algorith-
mic bias should also be considered when deploying AI in healthcare. As we
move towards this future, it is imperative to address these challenges head-
on to fully harness the potential of AI in transforming patient care and
outcomes. The amalgamation of AI with medical science is truly an exciting
frontier. It promises not only advancements in disease understanding and
management but also a new era of personalized medicine. With careful navi-
gation, we stand on the cusp of a healthcare revolution.
CONCLUSION AND PERSPECTIVE
This review provides a comprehensive view of confinement-based ultra-
sensitive optical assays (Table 1), enabling the (digital) measurement of
analytes without relying on advanced imaging systems such as total internal
reflection fluorescence (TIRF)43,44
microscopy or photo-activated localization
microscopy (PALM).128,129
As the investigation of novel and uncommon
disease markers expands, along with the need to decipher multifactorial
diseases, there is a rising demand for the development of unsophisticated,
ultrasensitive, and multiplexed detection platforms to address these complex
challenges. Luminescent systems, either through self-illuminating
compounds or through the products of enzymatically induced luminescence
or color systems, can be confined to internal or external spaces. These
systems can cooperate with numerous signal amplification approaches, such
as incorporating surface plasmon enhancement and nucleic acid amplifica-
tion, to realize ultra-bright and ultra-strong signal readouts. The core aim is to
harmonize the cooperation of signal amplification with noise reduction,
reaching a balance that is crucial to achieving a distinguishable signal from
the background at a single-entity level. This balance plays a key role in
enabling early diagnosis and treatment. However, this cutting-edge detection
approach still faces significant obstacles and challenges, highlighting the
need for further investigation and resolution to fully exploit its potential in
diagnostic and therapeutic applications.
Ultrasensitive optical detection requires specific probes to avoid false
results. Enhancing probe specificity can be achieved through unique molecu-
lar design, directed evolution, high-throughput screening, multi-domain
probes, and computational modeling. These steps can substantially improve
the accuracy and reliability in applications like disease diagnosis. Various
signal amplification strategies aim for ultrasensitivity, with the reduction of
background noise being paramount. Incorporating anti-fouling techniques,
such as PEGylation and zwitterionic coatings, can minimize contaminants,
enhancing detection accuracy.130
With the rise of automation and AI, their synergy with ultrasensitive detec-
tion is gaining traction. As the detection process requires sample purification
to eliminate interference, automation offers improved efficiency and accu-
racy over traditional methods. AI is particularly adept at rapidly interpreting
images obtained through microscopy. Moreover, training AI on vast patient
datasets allows for predicting disease trajectories and crafting tailored treat-
ment plans. By fusing AI with particle-guided optical assays, we pave the way
for innovative disease diagnostics and treatments. While many of these tech-
nologies remain in their nascent stages, leveraging larger sample sizes in the
future could lead to significant breakthroughs. This evolution symbolizes the
shift from mere big data to its profound mining, optimizing the union of AI
and detection hardware.131
The intersection of biotechnology (BT) and information technology (IT) is
undeniably shaping our present and future. To achieve impactful outcomes,
strategies in confinement, signal amplification, and encoding must seam-
lessly integrate. The overarching goal is to craft miniaturized, automated, and
smart systems for monitoring evolving disease threats with validated mark-
ers, a venture that underscores the importance of interdisciplinary teamwork.
The next decade promises transformative advancements, especially in single-
entity detection technology.132
With potential applications spanning molecu-
lar biology, biomedicine, to nanotechnology, it is essential to address any
challenges and champion continued innovation for its broader acceptance.
Such progression is vital for proactive disease management, improving
human health, and realizing novel technological leaps.
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ACKNOWLEDGMENTS
The work is supported by the Major Research Plan of the National Natural Science
Foundation of China (Grant No.92259102), Dr Cheng Jiang appreciates the financial
support from the Chinese University of Hong Kong (Shenzhen) startup funding
(K10120220253), Shenzhen-Hong Kong Cooperation Zone for Technology and Innova-
tion (HZQBKCZYB-2020056), Shenzhen Science and Technology Program
(2022A1515110206), Shenzhen International Science and Technology Cooperation
Project (GJHZ20220913144002005).
AUTHOR CONTRIBUTIONS
C.J., K.G. and C.X. supervised and revised the manuscript. W.Z. and Y.L. wrote and
edited the manuscript. All authors contributed to the article and approved the submitted
version.
DECLARATION OF INTERESTS
The authors declare no competing interests.
LEAD CONTACT WEBSITE
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