Sensors 2012, 12, 15467-15499; doi:10.3390/s121115467
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Review
Porous Bead-Based Diagnostic Platforms:
Bridging the Gaps in Healthcare
Jie Chou 1, Jorge Wong 2, Nicolaos Christodoulides 1,3, Pierre N. Floriano 1,3, Ximena Sanchez 1,3
and John McDevitt 1,3,*
1
2
3
Department of Bioengineering, Rice University, 6100 Main St MS-142, Houston, TX 77005, USA;
E-Mails: jie.chou@rice.edu (J.C.); christo@rice.edu (N.C.); pfloriano@rice.edu (P.N.F.);
vsan@rice.edu (X.S.)
Department of Chemistry, University of Texas at Austin, 1 University Station A5300, Austin,
TX 78712, USA; E-Mail: jwongc@gmail.com
Department of Chemistry, Rice University, 6100 Main St MS-142, Houston, TX 77005, USA
* Author to whom correspondence should be addressed; E-Mail: mcdevitt@rice.edu;
Tel.: +1-713-348-2123; Fax: +1-713-348-2302.
Received: 1 September 2012; in revised form: 25 October 2012 / Accepted: 1 November 2012 /
Published: 9 November 2012
Abstract: Advances in lab-on-a-chip systems have strong potential for multiplexed
detection of a wide range of analytes with reduced sample and reagent volume; lower costs
and shorter analysis times. The completion of high-fidelity multiplexed and multiclass
assays remains a challenge for the medical microdevice field; as it struggles to achieve and
expand upon at the point-of-care the quality of results that are achieved now routinely in
remote laboratory settings. This review article serves to explore for the first time the key
intersection of multiplexed bead-based detection systems with integrated microfluidic
structures alongside porous capture elements together with biomarker validation studies.
These strategically important elements are evaluated here in the context of platform
generation as suitable for near-patient testing. Essential issues related to the scalability of
these modular sensor ensembles are explored as are attempts to move such multiplexed and
multiclass platforms into large-scale clinical trials. Recent efforts in these bead sensors
have shown advantages over planar microarrays in terms of their capacity to generate
multiplexed test results with shorter analysis times. Through high surface-to-volume ratios
and encoding capabilities; porous bead-based ensembles; when combined with
microfluidic elements; allow for high-throughput testing for enzymatic assays; general
chemistries; protein; antibody and oligonucleotide applications.
Sensors 2012, 12
15468
Keywords: point-of-care; beads; porous; immunoassays; microfluidics; clinical chemistry;
validation
1. Introduction
Diagnostic tools are critical to the delivery of effective healthcare treatment, yet current in vitro
diagnostic (IVD) devices to date have been shown to be incapable of keeping pace with the rapidly
increasing information content related to disease diagnosis and progression generated with advanced
“omics” methods such as genomics, proteomics, metabolomics and glycomics [1,2]. Here, despite the
thousands of biomarker discovery papers published, only 1.5 protein biomarkers per year on average
have received US FDA approval during the past 15 years [3–6]. Unfortunately, most modern clinical
analyzers are dedicated to single classes of analytes and are burdened by bulky, expensive,
laboratory-confined instrumentation preventing broad access to these assays at the point-of-care (POC).
The movement of new technologies to POC settings and the use of noninvasive sampling modalities
have important implication in terms of improvement in the efficiency of the delivery of healthcare.
Unfortunately, to date POC devices suffer in two major respects relative to their remote laboratory
counterparts. First, in general the POC devices are more expensive and, second, these portable systems
more often than not, yield performance inferior to that derived from traditional laboratory settings [7,8].
Furthermore, large sample volume requirements and lack of standard instrumentation that is responsive
to a broad range of analytes complicate clinical validation studies that need to follow the initial
discoveries and proof of principle phases.
Traditional approaches to clinical analysis involve a well-appointed centralized laboratory, three
degrees of separation from the patient. This hierarchy introduces a number of critical junctures in
which errors may be introduced and delays incurred. To simplify and offer assay results immediately,
research into devices that give results at the POC, specifically bedside, ambulance or remote location,
currently flourishes—a situation advantageous to both patients and healthcare providers [9–13]. POC
diagnostic systems have been extensively reviewed in recent years, from both the points of view of
usage [14–16] and fabrication [17–19]. The ability to process large amounts of information at the
point-of-need is common in the field of electronics, yet the ability to similarly process complex
molecular disease signatures has not yet been fully demonstrated [7]. The marriage of microelectronics
and IVD areas provides huge opportunities to healthcare industries seeking affordable and accessible
diagnostic infrastructures [7,20].
In the past few decades, significant advances in medical microdevice technologies have afforded
new sensor ensembles capable of multiplexed detection of a wide range of analytes [21–23], including
diagnostic targets, such as disease-specific proteins [15], metabolites and other small molecules [24],
nucleic acids [25–27], bacteria and bacterial spores [28–34], and human cells [19,35,36]. Diagnostic
devices for limited-resource settings, including the developing world, have seen significant
development efforts recently as this area requires new affordable technologies that can work outside of
the traditional laboratory settings [19,36–41].
Sensors 2012, 12
15469
Through the miniaturization of macro-components, micro total analysis system (μTAS) and lab on a
chip (LOC) devices have ushered in a new generation of high-throughput testing modalities that
promise new options for biomarker measurements [7,42–44]. For example, Quake’s work has
advanced the “large-scale integration” of microfluidics, analogous to the electronics field [20]. Mirkin,
Heath and Wang used nanowires, precious metal nanoparticles, and magnetic techniques, respectively,
to measure diverse sample types and create a variety of assembly types [13,45,46], while Sia has
introduced more integrated approaches via microelectromechanical systems [47]. Singh has continued
to increase integration through the use of chip-based separation and quantitation [48]. Both Singh and
Ligler have extended their integrated approaches into the rapid, multiplexed detection of toxins and
other biothreats [49]. Work by Madou and others have resulted in the LabCD, which eliminates
traditional active mechanical valves and pumps by using centrifugal and centripetal force to perform
fluid movement and control [50]. Walt’s work with electronic noses uses arrays of optical fibers as the
underlying infrastructure for biological sensing systems [51]. Finally, researchers in the Toner group
have explored a number of novel methods for the isolation and enumeration of lymphocytes,
erythrocytes, and circulating tumor cells [52,53].
There now is a strong potential to leverage these medical microdevice discoveries for a broad
impact in diagnostics for IVD and global heath applications using such chip-based approaches.
Unfortunately, to date very few complete workable POC clinical devices have emerged despite
tremendous progress in LOC, microfabrication, microfluidics, and related areas [43,54]. Indeed, while
the core of typical LOC systems is substantially smaller than that of the bench-top counterparts, most
systems still rely on a network of macroscopic laboratory-based infrastructure for sample processing,
sample introduction, analyte detection, data processing, and reagent handling, thus limiting their utility
for POC applications [7].
In addition to key work in the LOC area, which includes on-chip sample processing, significant
progress has also been made recently in the area of protein, antibody and oligonucleotide planar
microarray technologies with off-chip (i.e., lab confined) sample processing. The emergence of
high-density planar microarrays has enabled parallelized testing for clinical testing and validation.
With modest sample volume requirements, these microarrays have afforded multiplexed testing of
hundreds to thousands of analytes simultaneously for both proteomic and genomic applications [55–59].
Unfortunately, the cumbersome and time-consuming processing steps, as well as the large expense of
the microarray disposable elements, have limited their utility to sophisticated research settings. Thus,
the microarray systems that are now popular in research venues have not yet impacted significantly
routine clinical settings [60].
To overcome these limitations with respect to time course, sampling and cost, recent advancements
in the integration of bead sensors into microfluidic devices have demonstrated short timeframes for
analysis that brings promise for their use at the point-of-care [61,62]. These highly sensitive sensing
elements have attracted significant interest for the detection of biological and chemical agents for
applications ranging from cardiac and cancer health, drug screening, and environmental screening [63–66].
Compared to the gold standard, enzyme linked immunosorbent assay (ELISA), which takes 2 to 24 h
to complete, analyses with bead sensors can be completed in less than an hour [64,67]. Further, the
multi-functionality of these beads introduced many possibilities for their incorporation into
microfluidic devices for the detection of a wide range of analytes. Low nonspecific binding properties,
Sensors 2012, 12
15470
highly parallelized processes for production, and multiplexing capabilities of beads offer significant
new opportunities for use in the context of near-patient testing.
This review article examines the convergence of LOC technologies with bead-based sensor
ensembles with biomarker validation alongside the use of porous media, so as to service the
high-fidelity capture and analysis of a plurality of key biomarker systems in validated studies. While
several prior reports have summarized the advantages of solid-state bead supports [61,67–69], the use
of porous beads has not been covered previously in great detail. As summarized here, the enhanced
mass transport, tunable porosities and high binding capacities of porous beads serve as key variables
that have strong potential to lead to transformative changes in high-performance biomarker detection
using scalable detection approaches. The combination of these elements allows for the development
and deployment of high-performance platform systems that can function for the first time at the POC
with performance that rivals the traditional remote laboratory instrumentation.
2. Solid-State Bead Sensors
2.1. High Surface-To-Volume Ratio
Many modern bioscience analyte detection approaches such as ELISA utilize flat surfaces to
generate signals. These approaches are limited by the low intrinsic signaling capabilities and slow
transport characteristics afforded by these low-dimensional systems, which in most cases also rely on
time-consuming amplification strategies. When compared to flat surfaces, 3-dimensional spherical
beads offer significantly higher surface areas for immobilization of capture probes. For example,
calculations by Kawaguchi show that 1 g of microspheres with a diameter of 0.1 µm has a surface area
of 60 m2 [67]. With the same mass, further decreases in bead size would increase total surface area.
Flat surfaces, however, are constant and limited to the open surface area available on the device. In
contrast to the performance of flat surface-based immunoassays, the higher available surface area on
beads increases sensitivities and lowers limits of detection [70].
When beads are interfaced with microfluidics, convective flow replenishes the analytes that become
bound to capture probes. In contrast, assays performed with ELISA are for the most part static aside
from some modest agitation where the dominant method for transport is diffusion. Because the
diffusion distance is a few millimeters and the time to diffuse a distance is proportional to the square of
the specific distance, ELISAs typically require several hours to overnight to perform [42]. The high
surface-to-volume ratio of beads allows for timeframes of bead-based assays to be compressed relative
to these planar counterparts.
When microfluidics is integrated with bead-based capture ensembles, immunoassays can be
performed over much shorter timeframes. For example, Sato et al. revealed the surface-to-volume ratio
of 45 µm polystyrene beads was ~37 times higher than that of the flat surface in a microtiter plate [71].
Because of the high surface-to-volume ratios and the short diffusion distances afforded by the trapping
of beads at the end of a barrier, the equilibration time for the capture of human secretory
immunoglobulin A on beads was found to be 90 times less than that on flat surfaces. Likewise, the
total analysis time was reduced from 24 h to less than 1 h. Similarly, Zammatteo et al. compared the
capture of DNA probes on the surfaces of polystyrene microwells and beads [72]. When the total
Sensors 2012, 12
15471
surface area on the wells was equal to that on beads (1.4 cm2), the final signal was found to be similar.
However, the incorporation of higher amounts of beads increased the total surface area for capture. In
contrast, the total surface area for binding on microwells remained constant. By increasing the amount of
beads per test by a factor of 4, the signals on microbeads were two times more than those of microwells.
Moreover, nucleic acid hybridization kinetics performed much faster on beads than on microwells.
2.2. Plug and Play
Further, beads exhibit a high degree of practicality with respect to their production and
incorporation into microfluidic devices. The synthesis of beads can be completed in a very scalable
manner. Beads can be produced in bulk quantities on the order of millions to billions of beads per
batch. Once made, the surfaces (and interior regions in the case of porous media) of such beads allow
for the functionalization of a variety of a capture probes for both genomic and proteomic applications.
The beads produced and functionalized in these batches exhibit high reproducibility for both size and
chemistry considerations. Moreover, beads produced in such large numbers benefit from the
economics of scale with lower cost associated to each sensor. Once processed, these beads can be
stored for long durations (i.e., several years) until they are ready for use [7]. Multiple sets of beads
functionalized with different capture probes can be quickly inserted into microfluidic devices as “plug
and play” elements to address different clinical needs.
In contrast, functionalization of capture probes on a planar microarrays is done through a very
serialized, tedious, and time-consuming process [69]. For example, passive immobilization of
antibodies usually requires several hours due to the slow rate of diffusion of free probes. During the
attachment of capture probes, further quality assurance measures must take place to ensure the same
immobilization conditions on surfaces treated with the same chemistry. Because beads are
functionalized in batches, the statistical difference from bead-to-bead is very low. Further, a
modification of an array necessitates the creation of new devices and a microarray configuration. With
beads, a modification of a test panel is often as simple and an addition, subtraction, or replacement of a
bead with different functionalized probes [70,73]. Functionalized beads can be inserted into chip
structures within seconds using “pick and place” strategies adapted from the microelectronics industry.
The ability to complete quality control on a large batch of beads in a highly parallel manner once,
instead of completing similar oversight for every device that is generated by alternative spotting or
lithographic reagent deposition steps, serves as a huge potential advantage for the bead sensor
ensemble approaches and provides testament for the growing interests and popularity of the
bead-based approaches in the bioscience and clinical measurement fields [7,74,75].
2.3. High-Throughput
Often in clinical tests, the amount of sample is limited, such as is the case with neonatal testing.
Further, prospective clinical trials and animal studies serve as additional areas where the completion of
testing with minimal sample volume is critically important. Therefore, the ability to test for multiple
analytes in a single sample simultaneously allows for efficient and faster results with the use of less
expensive reagents and limited sample volumes. Microarray techniques have allowed for
high-throughput testing in this capacity. Through the spotting of different capture probes on planar
Sensors 2012, 12
15472
surfaces, multiple analytes can be detected simultaneously as mentioned above. Each location is
spatially encoded to detect a specific type of target analyte. Spatially recognizable software can decode
and quantify each test location. Delehanty et al. used a microarray printer to spot antibodies on discrete
locations within 6 channels of a glass slide. This development led to the simultaneous detection of both
protein and bacterial analytes [76].
Similarly, bead sensors can be incorporated in large quantities into microfluidic devices to allow for
highly parallelized detection of analytes and samples. The use of multiple beads to target a specific
type of analyte permits statistical redundancy for high quality analyses. The multiplexing of beads
functionalized with different capture probes can similarly be performed with minimal work [77,78].
For example, Ng et al. revealed that the incorporation of an array of polymer beads held by
micropillars allows for the spatially addressable, rapid detection of nucleotides and multiple bacterial
species [79]. This methodology afforded the capacity for DNA-based detection of 10 bacterial species
and 2 single nucleotide polymorphisms in less than 10min. In addition, Zhao et al. used encoded
photonic beads to simultaneously profile the biomarkers CA125, CA19-9, AFP, and CEA associated
with tumors including colorectal, gastric, and lung cancer [80]. The use of silica colloidal crystal beads
allowed for the identification of the four different bead types. Furthermore, the Walt group uses 3 µm,
spectrally encoded polymer beads for detection of numerous targets [81–83]. This high-throughput
platform can detect around 100 different DNA targets simultaneously with highly statistical precision
through high redundancy of each bead types.
Additionally, in the remote laboratory setting the integration of beads with suspension array
technology (SAT) allows for rapid sample processing with rates in the thousands of measurements per
second [84]. This platform decodes and measures encoded beads, typically only a few microns in
diameter, in a flow cytometer. The combination of microbeads and flow cytometry technology can
process through a 100-plex assay every 30 s [85]. Under a continuous automated process, this system
can analyze almost 300,000 assays each day. Further, Kuckuck et al. demonstrated that the rate of
processing can be further increased to 96-well plates per minute [86]. Here, the throughput limit
approached the rate of the autosampler. SATs have evolved to accept beads for both applications in
proteomics and genomics [87]. For example, SATs have allowed the high-throughput testing of
pathogenic diseases [88], cytokines [89], and nucleic acid [90].
2.4. Encoding
In multiplexed assays in planar microarrays, the identities of capture probes are determined from
their positions in the array. This positional encoding affords microarrays the ability to perform
thousands of tests simultaneously. Similarly, methods to attach a code to each bead (encode) allows for
its differentiation (decode) from other bead types and permits parallel screening of multiple analytes in
a single sample.
One of the most common methods to encode beads is to employ a fluorophore. These luminescent
dyes with different spectral characteristics and concentration values allow for a set of uniquely
distinguishable codes. These spectrally encoded beads are commonly used in flow cytometers.
Luminex Corp, one of most well-established bead-based instrument suppliers, uses three fluorophores
to encode a panel of up to 500 different 5.5 µm beads. Each bead type is matched to a specific capture
Sensors 2012, 12
15473
probe. Using a 2-laser system, beads delivered through a suspension array are quickly decoded and
their intensities are measured [91]. Similarly, BD Biosciences offers fluorescently dyed 7.5 µm beads
of different concentrations [55,65,68,92]. With a two-laser system, these beads are analyzed inside the BD
FACSArray which has multiplexed capabilities to detect up to four different spectral wavelengths [55].
A 96-well plate containing processed samples can be analyzed at a rate of 15,000 events per second.
The use of these spectrally encoded beads on these flow cytometry-based platforms have been
demonstrated for the detection of single nucleotide polymorphisms [93], cytokines [94–96], bacterial
pathogens [97,98], and infectious diseases [99]. Several studies, performed on these systems, reported
analyses times much shorter than that of ELISA with sensitivities and increased dynamic ranges that
compare or rival ELISA [85,100,101].
Similarly, Illumina developed a high-density optical fiber microwell array [81,102–104]. The tips of
these glass optical fibers are etched with hydrofluoric acid to create a 5 µm well. When bundled
together, this array contains 50,000 fibers with a diameter of 1–2 mm [102]. When immersed in a
solution of spectrally encoded beads, tens of thousands of 3 µm beads randomly disperse and assemble
onto the etched microwell array. After excess solution and microspheres are removed, an imaging
system decodes and quantifies the signal on each bead. The microarray has a test density that is
significantly higher than that on an automatically spotted planar microarray. Because of this high
density, only a small volume of sample is required to run tens of thousands of tests in a single run.
Similarly, Illumina has also developed an etched silicon chip containing a hexagonal array of
microwells, each measuring ~3 µm, that can hold randomly dispersed beads. Using a CCD camera,
individual beads are decoded and quantified. BioArray Solutions, which was purchased by Immucor, Inc.
in 2008, uses a similar technology as Illumina. In this BeadChip format, encoded beads are randomly
patterned onto a silicon chip, for the detection of complex nucleic acids and proteins [105,106]. Figure 1
showcases some of the current nonporous bead-based clinical analyzers that are used for remote
laboratory measurements.
Figure 1. Current solid-state bead-based clinical laboratory analyzers include the
(A) Luminex 100/200, (B) BD FACSArray, (C) Diasorin Liaison, and (D) Illumina
BeadXpress.
Sensors 2012, 12
15474
One limitation of these encoding schemes is the complication of possible overlap between spectral
encoders or reporters. This complication limits the potential amount of simultaneous tests that can be
performed in a single assay to about 100 different groups. The positionally-addressable identification
of bead types, analogous to location-based encoding in microarrays, may extend the limit of different
tests performed in a single assay. For example, Ng et al. used an array of polyacrylamide gel pads to
form pillars to trap different bead types. Because of the natural immobilization of beads to the
polymeric matrix, beads with similar probes are easily anchored in gaps between the micropillars. A
second set of beads with a different probe set, spotted onto different positions of the array, allow for
the differentiation of bead types. This process can be repeated to attach different beads [79,107].
Similarly, the Ikami et al. immobilized microbeads in hydrogel supports to allow for position-based,
addressable decoding [108]. Here, fluid containing microbeads with similar capture probes is
photo-polymerized with a photomask to form a hydrogel pillar. Uncured solution is washed. The
process is repeated for beads with different probe sets. This bead-hydrogel device allows for the
simultaneous detection of three proteins in about 4 min using only 0.5 µL of total sample and reagents.
Other less common approaches to encode beads include chemical, graphical, electronic, and physical
encoding [69].
3. Towards Point-of-Care
The advantages of high-throughput multiplex testing through high surface-to-volume ratios of solid
support beads have allowed for shorter analysis times with low sample and reagent requirements.
Nonetheless, the timeframes to complete these tests are often still not consistent with the POC [109,110].
For example, a typical doctor’s visit that lasts 15–30 min does not permit for a diagnostic test that
requires more than 1hr to complete. Previously, a set of guidelines for POC tests has been developed
and designated with the acronym COMMAND QUALS [75]. Likewise, clinical analyzers need to be
Cheap, Obvious, Miniaturized, Multiplexed, Automated, Nonperishable, Dependable, Quick,
Unobtusive, Adaptable, Limited (volume), and Self-contained.
Improvements in mass transport and high-efficiency signaling are crucial here to achieve the ideal
timeframes and high-fidelity analyte detection using simple instrumentation as is necessary in POC
usage. Advantages of enhanced mass transport in porous mediums, such as gel pads and hydrogels,
have demonstrated faster timeframes over planar microarrays. With the ability to functionalize a range
of different capture probes, these porous networks have the ability to capture a wide range of analytes.
The development of new microstructure concepts with engineered active transport through and within
porous reactive particles serves as a promising new method for rapid yet high-efficiency capture within
minisensor ensembles [111–114]. The ability for analytes to transport into the interior matrix and high
capacities for capture probes have allowed for shorter analysis times and higher sensitivities than those
of planar microarrays [115,116].
In the supported porous bead array area, prior efforts have led to the development of a microfluidic
bead-based platform termed the Programmable Bio-Nano-Chip (p-BNC). This p-BNC platform is
based on the concept that multiplex assays for a variety of disease applications could be achieved on a
modular sensor suite that combines an application specific cartridge in conjunction with a universal
analyzer. The approach uses a sensing platform that employs an array of 280 µm porous, agarose beads
S
Sensors
2012, 12
154775
ssituated in an
a array com
mbining inddividual flow-through wells. As shown
s
in Fiigure 2, fluiid containinng
a
analytes
of interest flow through and aroundd the porou
us bead sensors that arre situated in
i individual
f
flow-through
h containerrs. Delivereed analytes are captureed by immoobilized cappture probees within thhe
b
bead
matrix and can bee quantifiedd with a secoondary fluo
orescently laabeled reporrter. The un
nique p-BN
NC
d
design
provvides enhannced convective transpport to thee interior of
o porous bbead matricces, efficiennt
d
diffusion
disstances, andd short depletion layerrs [117]. Th
he use of poorous beadss in this preessure-driveen
d
design
resultts in increassed sensitivvities compaared to thosee of flat miccrofluidic chhannels.
Figuree 2. (A) Naanonets creaated from agarose
a
fibeers serve ass a high surrface area medium
m
onto which
w
can be attacheed a high-ddensity of biocapture agents suuch as antib
bodies,
(B) theese nanonetts comprisee the backboone of a miccrosponge (i.e.,
(
porouss bead) unitt that is
both inndexed mattched to the aqueous medium
m
as well
w as serviing as the loocation for analyte
a
capturre and repoorting, (C) the beads are arrayed
d in predefi
fined locatioons within a chip
structuure that is being
b
develloped to feature integrrated microofluidic deliivery elemeents all
supporrted withinn (D) Photoograph of an injectio
on-molded lab card ddedicated to
o CD4
countiing. Specifications witth lab cardss with identtical footpriint are beinng developeed with
laminaate approaches and customizedd to contain all off the fluidds, reagentts and
self-coontained waaste provisioons for the bead-based
b
applicationn.
Likewisee, porous beads,
b
whenn combined with new
w LOC cooncepts, affford an op
pportunity to
t
c
complete
high-perform
mance testing using simple insttrumentatioon that is compatiblee with POC
innstrumentattion. Shownn in Figure 3 are previous instrum
mentation syystems that have been developed in
i
b
both
academ
mic researchh settings as
a well as through co
ommercial partnerships
p
s to supporrt the use of
o
p
porous
beadd integratedd sensing ensembles for future use in neear-patient ttesting. Th
he laboratorry
b
benchtop
coonfigurationn used for the
t initial conceptual
c
experimentts here sum
mmarized co
onsists of an
a
a
array
of aniisotropicallyy etched microcontain
m
ners, each holding
h
indiividual poroous bead-based sensinng
e
elements
(Figure 3(A))). This microchip is held within a stainless steel flow cell (Figure 3(D)) witth
f
fluid
deliverred with extternal pumpps and contrrol systems. Previous trranslationall efforts of this
t approacch
leed to the evvolution of a membranne-based annalyzer/card system witth an instruument that was
w designeed
too serve botth membranne-based ass well as beead-based applications
a
s (Figure 3(B,C)) and the creatioon
o an injecction-moldeed cartridgee for the membrane-based system dedicaated to CD
of
D4 countinng
(Figure 3(E
E)). The p-B
BNC cartriidges, dediccated to seerve bead-bbased appliccations, aree still in thhe
d
developmen
nt stage withh efforts deedicated to optimizatio
o
n of a num
mber of micrrofluidic co
omponents of
o
thhe cartridgee format witth laminate approachess, such as seelf-containeed buffer packs, reagen
nts, and wastte
r
reservoirs
(C
CAD in Figgure 3(F)) to attain sppecification
ns that would be suitabble for inteegration witth
S
Sensors
2012, 12
154776
innstruments such as onees depicted in Figure 3(B,C) and technology transfer
t
to a manufactu
uring partneer.
F
Further,
a seeries of porttable and laab-based im
mage analysiis systems suitable
s
for quantificattion of signal
o the beadss within eachh card are allso being devveloped, tessted and valiidated. Notee that this insstrumentatioon
on
a the time of this wrriting is nott yet comm
at
mercially av
vailable. Thhe ability tto measure quickly annd
e
efficiently
m
multiple
biomarkers at the POC leends strong potential too impact cliinical laboraatory sciencce
a
after
these medical
m
microdevices move
m
througgh the appro
oval and vallidation stagges [51,118].
Figuree 3. The evolution
e
o several generationss of the p-BNC.
of
p
(A)) Starting with a
laboraatory benchttop configurration, the p-BNC
p
conssists of an array
a
of anissotropically etched
microaarrays on a silicon miccrochip helld within a stainless stteel flow ceell. These systems
s
are noow moving through riggorous cliniical testing (see below
w). (B) Prevvious transllational
effortss with com
mmercial firm
ms have deefined targeet specificaations for evvolution fro
om the
benchttop platform
m shown inn panel A into
i
an anallyzer/card system
s
prodduced initiaally for
membrane-based applicationns for celluular testing. (C) The same instrum
ment as sho
own in
panel B, was deesigned to be compaatible (hard
dware, opticcs, softwarre) with bo
oth the
membrane-based and bead--based apprroach. Micrrofluidic caartridges arre currently
y being
w specifiications con
nsistent
develooped to perrmit integraation of thee bead-baseed system with
with the
t type off instrumenttation show
wn in panels B&C. (D
D) Silicon-bbased micro
ofluidic
devicee housed in stainless steeel flow celll used in beenchtop connfiguration shown in paanel A.
(E) An
A injectionn-molded cartridge forr the mem
mbrane-basedd system ddedicated to
o CD4
countiing was prroduced foor instrumeent shown in panel B.
B (F) p-B
BNCs for porous
bead-bbased testinng modalitiees are show
wn in a CAD
D diagram and will unndergo tech
hnology
transfeer efforts at a later stagge for the insstrument sh
hown in pannel C.
Sensors 2012, 12
15477
4. Porous Bead Sensors
In contrast to a 2-dimensional planar surface of a typical microfluidic structure, high surface-to-volume
ratios of porous substrates allow for enhanced sensitivities and lowers limit of detection values for
delivered analytes [71,119]. Due to the sizes of biomolecule capture agents such as antibodies and
limited surface area for immobilization, the capacity of binding on flat surfaces is significantly less
than that for porous media [74]. The kinetics of binding of analytes to probes in highly porous media is
often described as near-solution kinetics [68]. Here, reaction kinetics between a molecular analyte and
an immobilized molecular capture probes occur as rates similar to those of two free molecules in
solution. Further, internal transport and high binding densities characteristic of sporous substrates
make these sensors suitable alternatives to current detection technologies, where rapid results are
desirable for low volumes of sample containing low concentrations of target analyte.
Likewise, the p-BNC method that utilizes the porous bead as an immunosensor meets and often
exceeds analytical characteristics, such as test dynamic range and limit of detection [7] of mature
research or commercial instrumentation for a wide variety of analyte systems, thereby allowing
dilution of the sample, if needed [24,28].
The strong analytical performance of the p-BNCs can be linked directly to the porosity and
3-dimensionality of the agarose bead capture elements. The choice of agarose is based in part on the
potential for scalability, as it is derived from inexpensive sources (i.e., seaweed). The same beads are
already made in large quantities to support immunochromatographic applications that are dedicated to
applications such as the purification of proteins. In addition to its tunable porosities (see below), this
polymerized sugar matrix exhibits ultra-low nonspecific binding characteristics and the medium is
index matched with water. The latter optical characteristics (unlike paper) make the material ideal not
only for separation, but also as an environment (i.e., a mini-cuvette) for optical detection.
Other advantages of the agarose bead sensors include (a) a capacity to be tailored so as to
accommodate the specifications (such as molecular weight, size and shape) of the targeted analytes,
(b) a capacity to be implemented for both two-site immunometric as well as competitive assays, (c) a
capacity to be mass produced for widespread clinical purposes, (d) a capacity to be stabilized so as to
withstand extreme storage conditions, (e) similar to immunochromagraphic applications, a capacity
to be recycled for successive assay runs, as needed, and (f) a capacity to support fluorescence-,
colorimetry-, and electrochemistry- based signal transduction [120].
4.1. Fibrous Network
Agarose beads are typically produced in bulk using standard emulsion polymerization methods [121].
By varying the agitation conditions, the gel temperature, the surfactant concentration and the feedstock
concentrations, it is possible to obtain agarose beads of sizes ranging from 15 µm to more than 500 µm.
Further, judicious choice of conditions can be used to isolate beads with agarose weight concentration
values that vary from 0.5% to 8%. Important to note is the fact that the balance of the bead is
composed of the background solvent and as such this medium serves as an ideal bridge between the
solution and the solid-state support. Beads of specific diameter are further selected using sieving
methods, as previous described [111]. Importantly, the control of agarose content by weight during the
S
Sensors
2012, 12
154778
pproduction of
o porous aggarose beadds enables the
t tunabilitty of mass transport
t
w
within the po
orous matrixx.
F
Figure
4 shhows the inverse
i
expponential reelationship of the porre size as a function
n of agarosse
c
concentratio
on. An agarrose concenntration of 0.5%
0
to 8%
% corresponds to a porrosity of 99.5% to 92%
%.
H
Higher
levels of porosity allow foor faster mass transpo
ort of analyttes to openn surface areeas. Becausse
9
92%
to 99.55% of the bead
b
is solvvent (i.e., water),
w
the high open surface areea on the ag
garose fiberrs
w
within
the poorous matriix with is iddeal for the capture
c
of laarge biomolecules.
Figuree 4. Controol of weightt fraction of agarose content
c
duriing production serves to
t tune
the average pore size of the beads. The SEM imag
ges, showingg (A) 0.5%, (B) 2%, (C
C) 4%,
D) 8% agaroose by weigght, reveal the decreasse in pore size
s
as the ddensity of agarose
a
and (D
fibers increases. Scale
S
bars labeled
l
(i) and
a (ii) in panel
p
A dem
monstrate m
measuremen
nts used
to deteermine averrage pore sizze of bead concentratio
c
ons from 0.55–8%. (E) T
This exponeentially
decreaasing relatioonship of pore size as a function of agarosee percentagee, calculateed from
differeent microscoopy techniqques [74].
S
Sensors
2012, 12
154779
t
limiited surfacee areas do noot
While plaanar microaarrays have the capacityy for high-throughput testing,
a
afford
the fllexibility foor planar miicroarrays to
t overcomee differencees in timefraames and differences
d
i
in
c
concentratio
on response curves for various anaalytes that need
n
to be measured
m
inn the same setting. Thhis
liimitation haas resulted in slow proogress in thee area of prrotein and antibody
a
arrrays [68]. The
T tunabilitty
o porous beads
of
b
here described, however, offers
o
the ability to test various bbiomarkers through thhe
f
flexibility
of tunable tiimeframes and responnse curves. The custom
mized assayy performan
nce has beeen
d
demonstrate
ed for differrent densitiees of capturre probes th
hat target CEA
C
and IL
L-1β, each with
w differennt
m
molecular
w
weights
[744]. As shown in Figuure 5(A), the rates of
o analyte bindings widely
w
diffe
fer
d
depending
o the binding kineticss of biomarrkers. High-affinity biooanalytes bbind earlier during masss
on
trransport insside the beaad resulting in higher inntensities att the rim of the bead. Inn contrast, analytes
a
witth
loow affinitiees diffuse further
f
into the bead, which
w
leadss to lower intensities are the rim
m of the beaad
u
under
the saame timefraames. Furthher, the tunaability of th
he agarose content
c
in pporous bead
ds allows foor
thhe control of bead sensitivity
s
and rate of
o binding. Figure 5(B
B) shows a simulatio
on of signal
d
developmen
nt for beads with differrent pore sizes. With small
s
agarose content, correspond
ding to largeer
p
pore
sizes, the
t ease off transport of
o analytes allows forr faster bindding rates. A reduction of agarosse
c
content,
from
m 8% to 0.55%, results in
i a decreasse in the hallf equilibriuum time from
m 20 min to
o 9 min.
Figuree 5. (A) Thhe effect of tunability of
o bead poro
osity from 0.5%
0
to 8%
% on the cap
pture of
4
6
biomaarkers with low (10 L/mol/s) and
a
high (10
(
L/moll/s) associaation rates (k_on)
(B) Timecourse
T
t
total bound
b
anallytes on porous beadds under varying
v
showing the
agarosse content.
Due to thhe small-scaale dimensioons of micrrofluidic chaannels, transsport typicaally exists in
n the laminaar
f
flow
regime.. The dominnation of visccous forces over inertia reduces irreegularities frrom turbulen
nt flow [1222].
A such, annalytes muust traversee long difffusion distaances beforre capture. This diffu
As
usion-limiteed
trransport to the surface immobilizeed capture probes
p
leadss to long satturation tim
mes and low sensitivitiees.
G
Gervais
et al.
a has repoorted that thhe typical capture efficciency of pllanar microoarrays, und
der a flow of
o
2
5 µL/min through a chaannel with cross-sectio
c
nal area of 50 × 500 µm
m and a deetection zon
ne of 1mm, is
o
only
7%. Likewise,
L
thhis diffusionn-limited trransport leaads to a 933% loss in analyte caapture [1233].
F
Further,
thesse losses arre even morre significannt at higherr flow rates.. On the othher hand, co
omputational
s
simulations
and relatedd control experiments
e
s have show
wn that in the p-BNC
C platform
m the capturre
Sensors 2012, 12
15480
efficiency is 23.5% at a 400 µL/min flow rate with 4% agarose bead. To secure the same 23.5% level
of capture with planar microarrays, it is necessary to slow down the flow rate and wait at least
3× longer. This extension in timeframe places some restrictions on the prospects for near-patient
testing for planar microsystems that lack alternative capture methodologies.
4.2. Fibrous Network
The hydrophilic fibers within the porous medium allow for the functionalization of a variety of
capture probes. For example, several groups have successfully demonstrated the functionalization of
hydrogel for the detection of a range of analytes that include proteins, nucleotides, and cells [124–126].
Moreover, the hydrophilic surfaces of these fibers retain better protein activity over that of planar
surfaces [127]. Scanning electron microscopy (Figure 6(A)) shows the surface morphology of a
porous, homogenous bead created using an emulsion method [111]. While at first glance, the surface
appears smooth, at higher magnification, as shown in Figure 6(C), details of the fibrous network are
revealed. The densely packed nanofibers here exhibit pore sizes of approximately 100–200 nm in
diameter. This range agrees with microscopy measurements within non-spherical, porous
medium [128,129]. Figure 6(B) shows the surface morphology of a superporous bead. These beads,
produced through double emulsion, exhibit large macropores that form interconnected tunnels within
the bead. Magnification of the non-cavity regions show similar pore sizes as the homogenous case, as
shown in Figure 6(D). Furthermore, because of the 3-dimensional geometry of a bead, the signals
acquired from the bead are typically aggregations of thousands of layers. For example, signal on a
bead is derived from a thickness that is 1,000–20,000 times larger than that from a flat monolayer on a
ELISA plate [74].
Figure 6. SEM images comparing porous agarose beads. (A) SEM images showing the
surface morphology of homogenous beads containing 4% agarose and (B) superporous
beads containing 4% agarose with ~30 µm microcavities that allow for rapid access of
fluids in the interior matrix of the bead. (C) Corresponding SEM images of fibrous
networks for homogenous bead and (D) superporous bead.
Sensors 2012, 12
15481
Transport into the bead matrix is dependent on external flow rate and the pore size of the dense
fibrous matrix [130]. Careful tuning of the pore size allows for control of the mass transport within
porous beads. Thompson et al. developed a model in an attempt to understand the effects of analyte
diffusion coefficient, flow rate, and capture probe density on the kinetics on bead surfaces [131]. Further,
the combination of confocal microscopy and computational simulations helped to define the spatial and
temporal distribution of bound biotinylated quantum dots on streptavidin-coated beads [132].
Prior work here has revealed the existence of internal convection within porous beads. The unique
design employed by the p-BNC, consisting of individual beads in flow-through microcontainers,
allows for pressure-driven convective transport of analytes within the porous matrix [117]. This
pressure-driven design, which forces fluids into the porous medium, increases analyte-antibody
interactions and allows for faster signal generation. When porous beads are employed in lateral flow
designs, however, the convective transport within the bead matrix is limited [133,134]. Instead, the
poor capture efficiency between antibody and antigen results in equilibrium saturation times of several
hours [116,134]. To overcome such limitations, Bau et al. implemented a breathing bead methodology
to expand and compress porous beads to accelerate mass transfer within such beads to increase signal
intensity by a factor of ~2.5 [135]. Within the p-BNC, the use of an intimate contact between the bead
ensemble and the porous beads appears to be essential to create a pressure gradient atop the bead that
can be used to facilitate the internal transport within the bead interior [136].
Further, modification of the pore size through control of agarose content in the beads allows for the
increase of internal transport. In contrast to lateral flow designs, the flow-through design redirects
fluids to create a high-pressure gradient that enhances internal mass transport. An adaptation of the
Koreny-Carman equation shows that fluid velocity in a porous medium is proportional to applied
pressure gradient and square of the pore diameter [137]. Previous studies have revealed that the
internal convective transport is linearly proportional to the rate of bulk fluid delivery [138]. When the
agarose concentration of the bead is increased from 0.5% to 8%, the ratio of the internal to external
flow rates decreases from 1:170 to 1:3,100, equivalent to an 18-fold decrease in internal convective
transport [117].
Superporous beads have shown promise to enhance the mass transport of analytes that include
proteins and cells into the internal bead matrix [139–142]. In addition to the fibrous network with pore
sizes between 100–800 nm, as in the case of homogenous beads, superporous beads also contain large
flow cavities with diameters of 10–30 µm [74]. These cavities of micropores allow for quicker access
of fluids into the bead core and reduce equilibrium times than those exhibited by homogenous beads
with similar sizes of micropore. For example, Larsson et al. observed intra-particle fluid velocities in
superporous beads to be as high as 17% of the interstitial velocity in a chromatography column [130].
Moreover, the use of superporous beads have been shown to reduce back pressure build-up in
microfluidic devices [143]. Large cavities with diameters of 30 µm result in craters on the surface.
Internally, these craters lead to interconnecting cavities that form long tunnels for easy access of fluids
into the interior matrix (Figure 7(B)).
S
Sensors
2012, 12
154882
C
of the cappture of C--reactive prrotein with homogenous and
Figuree 7. (A) Comparison
superpporous beadds. (B) Thee diffusion rate
r
of CRP
P into supeerporous beeads is 50× higher
than thhat for homoogenous beads.
Figure 7((A) comparres the captture of C-rreactive pro
otein (CRP)) by superpporous and homogenouus
b
beads.
At very
v
short timeframes,
t
, the rate of
o CRP cap
pture in suuperporous bbeads is ap
pproximatelly
2
2.1×
faster than
t
homoggenous beadds. To reachh the same fluorescencce level in homogenou
us beads, thhe
leength of thee assay wouuld need to be
b extendedd 5-fold. Altthough satuuration intennsity is somewhat higheer
f homogennous beads due to the increased mass
for
m
of thee medium, the
t long duuration required to reacch
s
saturation
iss not ideal for point-oof-care timee and samp
ple volume constraintss. While co
ontrolling thhe
a
agarose
conntent in hom
mogenous beads afforrds higher penetrationn of analytes into thee bead, largge
m
macropores
in superporrous beads allow for increased acccessibility to otherwisse underutillized internal
b
binding
sites [74]. As shown in Figure
F
7(B)), analyte diffusivity
d
inn superporoous beads, measured
m
b
by
thhe square penetration
p
of signal over
o
time, is 50× higher than thhe limited iinternal tran
nsport insidde
h
homogenous
s beads. The
T bottom line here is that thee enhancedd transport through av
vailability of
o
s
superpore
caavities may allow for faster
fa
assay completion
n times while assay clinnical perform
mance woulld
h
have
to be compared too that of hom
mogeneous beads.
b
4 High Caapacity for Binding
4.3.
B
The capaacity of bindding of cappture probess on porouss medium iss two to thrree orders of
o magnitudde
h
higher
than that of plannar surfaces [116]. Foor example, the use of porous hyddrogels, as substrates to
t
im
mmobilize antibodies,, show incrreased captture of anaalytes as a result of tthe higher capacity foor
a
antibody
im
mmobilizatioon [144]. As
A such, higgher capturee probe dennsities immoobilized on beads allow
w
f decreaseed limit of detections. For exampple, Zubstov
for
v et al. revealed that w
while the mean
m
bindinng
d
distances
beetween antibodies in porous
p
hydrrogels is an
n order of magnitude
m
higher than
n that of flat
s
surfaces,
fibbrous 3D suurfaces offeer capacitiees that are two
t
to threee orders off magnitudee higher thaan
thhose availaable on suurfaces [116]. Furtherr, due to internal diffusion andd faster biinding ratees,
p
porous
gel--based sennsing elements exhibbited higheer fluoresceent signalss than tho
ose of theeir
s
surface
counnterparts.
Sensors 2012, 12
15483
As revealed by simulations, Figure 8 compares the spatial distribution of bound analytes within
porous beads under low (0.04 mg/mL), medium (0.12 mg/mL), and high (2.5 mg/mL) binding
densities of anti-BSA capture probes at 30 min and 120 min of analyte delivery. At high densities of
capture probes, mass transport of free analytes to the internal core of the bead matrix is limited
(Figure 8, bottom). Here, the short residence time of the free analyte results in high signal localized at
the rim of the bead. However, as initial capture probes at the rim of the bead become bound, a moving
boundary of bound analytes develops and penetrates radially towards the center of the bead matrix.
This moving boundary continues as free analytes bind to internal capture probe sites, until the bead is
completely saturated with bound analytes. In contrast, lower capture probe densities result in lower
saturation intensities, but allow for faster analyte binding into the bead matrix (Figure 8, top). Here, the
moving boundary permeates radially towards the center of the bead at a much faster rate than that for
high capture probe densities. As shown in Figure 8, under the same timeframes, the uniform
distribution of signal develops under 0.04 mg/mL of capture antibodies while a higher signal is
localized at the rim of the bead under 2.5 mg/mL of capture antibodies. Because the percentage of
available capture probes decrease at fast rate, signal quickly reaches equilibrium at the rim of the bead
and then within the internal matrix of the bead. These results derived by simulation have also been
validated through experimental studies [117].
Figure 8. Spatial distribution of fluorescently-labeled BSA, as a function of microbead
receptor concentration and time, as predicted by finite element analysis, at the diametral
plane of the microbead under different binding densities.
Furthermore, the density of binding is nonlinear to the concentration of capturing antibody used for
conjugation. While porous medium offers high capacities for binding, no further benefits in signal
occurs as higher concentrations of probes are used for immobilization due to saturation of available
Sensors 2012, 12
15484
sites for antibody immobilization. For example, the high, medium, low densities of capture probes
shown in Figure 8, corresponding to a capture probe density of 100:4.8:1.6 ratio, result in a respective
intensity ratio of 100:59:13. For superporous beads, Yang et al. witnessed limited signal increases for
capturing antibody concentrations higher than 0.5 mg/mL [143]. While there exists a critical antibody
density that does not allow for further binding, the capability for binding resulting from the high
surface-to-volume ratio in the bead matrix exceeds that of planar surfaces and allows for higher
sensitivities. While the total surface area for binding is limited, an increase in agarose content during
the production of the beads, or higher agarose concentration, allows for more surface area for binding.
As such, the interplay between porosity and capture probe concentration allows for control of bead
sensitivities as well as assay response times.
5. Steps toward Broad-Scale Clinical Practice
Having clearly articulated the analytical performance advantages of using porous beads for
biomarker capture with microstructures, it is now essential to explore the next critical steps required to
use these mini-sensor ensembles in real-world clinical practice. Indeed, despite large investments in
translational research programs, most bioscience research efforts remain largely decoupled from
broad-scale clinical practice, both for the biomarkers as well as for the devices that measure them [7].
This general trend extends to medical microdevices and LOC systems as has been highlighted in recent
perspective articles [54,145]. The typical structure associated with the device development process,
whether from academia, national labs or the industrial sector, is a succession of lengthy steps that often
happen in a linear, sub-optimal and disjointed manner taking considerable time and draining precious
resources and momentum out of venture capital and federal funding, alike.
A case in point for the dismal rate of translation of new medical tests into real-world practice is
extracted from a recent analysis by Schully et al. [146] of the Fiscal Year 2007 extramural grant
portfolio of the National Cancer Institute (NCI), as well as cancer genetic research articles published in
2007. The group classified both funded grants and publications as follows: T0, as discovery research;
T1, as research to develop a candidate health application (e.g., device or therapy); T2 as research that
evaluates a candidate application and develops evidence-based recommendations; T3 as research that
assesses how to integrate an evidence-based recommendation into cancer care and prevention; and T4
as research that assesses health outcomes and population impact. They found that 1.8% of the grant
portfolio and 0.6% of the published literature was T2 research or beyond.
In an attempt to move the medical microdevices and porous bead sensor systems into broad-scale
clinical practice, a number of clinical trials and pilot studies have been initiated. Likewise, the
laboratory version of the p-BNC bead-based methodology described above along with a related
membrane-based system not covered here are now involved in six clinical trials and two pilot studies,
respectively, involving over 5,000 patients, including over 10 clinical sites for diseases in the areas of
cardiac heart disease, ovarian cancer, prostate cancer and drugs of abuse (See Table 1) [147–149].
High-impact diseases and conditions, such as cardiovascular disease and various cancers, including
ovarian cancer and prostate cancer are the targets of these p-BNC development and biomarker
validation trials. The ability to use validated biomarkers in a common platform affords interesting
synergies with respect to opening up new and more efficient treatment methods for management of
Sensors 2012, 12
15485
patients [7,8]. The same platform approach is also dedicated to testing for anti-epilepsy drugs, as well
as drugs of abuse, with targeted applicability in various settings, including home and the identification
of drivers under the influence of drugs, at the point of arrest.
Table 1. The bead-based p-BNC is involved in six clinical trials through several major
sponsors to target a number of diseases though the validation of a number of biomarkers.
Study
Sponsor
National Institute of
Dental and
Craniofacial
Research
(NIDCR)
Advanced Bio-NanoHome Office-Center
Chips for Saliva-Based
of Applied Science
Drug Tests at the Point of
and Technology
Arrest
(HO-CAST)
Cancer Prevention
Research Institute of
Texas
(CPRIT)
Texas Cancer Diagnostics
Pipeline Consortium
Cancer Prevention
Research Institute of
Texas
(CPRIT)
Pilot and Prospective
Studies for the
Texas Emerging
Development of the
Technology Fund
Trauma Chip
Development of p-BNCs
for the Monitoring of
John S. Dunn
Anti-Epilepsy Drugs
Foundation
Levels in Saliva
Development of A
Lab-on-a-Chip System
for Saliva-Based
Diagnostics
Area
# Of
Subjects
Clinical Site
Biomarkers
Cardiac
Disease
1,050
patients
Baylor College
of Medicine
15 proteins
Drugs of
Abuse
340
participants
Baylor College
of Medicine
12 drugs
Ovarian
Cancer
2,660
patients
MD Anderson
Cancer Clinic
4 proteins
Prostate
Cancer
1,100
patients
Acute
Kidney
Failure
120 patients
Epilepsy
100 patients
UT Health
Science
Center-San
Antonio
UT Health
Science
CenterHouston
UT Health
Science
CenterHouston
3 proteins
5 proteins
3 proteins
The capacity of the p-BNCs to multiplex, or simultaneously measure multiple analytes within a
single assay run, is not only economically beneficial, but it also allows to test for biomarkers that
collectively provide a more comprehensive look into the overall well-being of a person, while at the
same time derive a more specific look to specific stages of the disease process.
Case in point, to date there are no global methods to define in an efficient way the entire
cardiovascular health of patients. Today cardiac heart disease is the number one killer globally [150].
While there are many good approved biomarkers, it has not been cost effective to manage patient’s
care through measurement of all of these biomarkers for all patients [150]. Cost restrictions and a lack
of understanding of the disease progression have limited progress in this area. With these limitations in
mind, recent efforts target the development of customized cardiac chips that could evaluate the overall
cardiovascular health of patients.
Sensors 2012, 12
15486
For example, biomarkers for all aspects of care regarding cardiovascular disease, including
diagnosis of acute myocardial infarction, prognosis or risk for secondary cardiac events, monitoring,
risk stratification and guidance for therapeutic interventions of cardiac patients, were developed as
multiplexed panels [150]. Figure 9 details the use of porous agarose beads, as supported in the p-BNC
in this capacity. Here, four panels of multiplex biomarkers target different cardiovascular diseases that
include the risk for primary cardiac event, expanded acute myocardial infarction (AMI) diagnosis, risk
for secondary cardiac events, and congestive heart failure. Panel sizes and bead sensors are easily
swapped to serve the needs of the diagnostic application. Noted for each array is the redundancy of
bead sensors per analyte, which contributes to accurate and precise measurements. Also, noted here is
the presence of calibrator beads (Cal), which serve for the baseline calibration of the p-BNC system,
and negative control (-ve CTL) beads, coupled to antibodies irrelevant to any of the analytes targeted
in each test, which serve as indicators of the specificity of the antigen-antibody reactions that take
place within the lab card.
Figure 9. The p-BNC diagnostic applications for cardiovascular disease include (A) Risk
for primary cardiac event chip: Apolipoprotein A1 (ApoA1), Apolipoprotein B (ApoB),
C-reactive protein (CRP), Interleukin-1beta (IL-1b), Monocyte chemoattractant protein-1
(MCP-1), Tumor necrosis factor-alpha (TNF-a), Soluble CD40 ligand (sCD40L), Human
serum albumin (hsa), Interleukin-6 (IL-6) and Myeloperoxidase (MPO); (B) Expanded
AMI diagnosis chip: Cardiac troponin I (cTnI), Myoglobin (CRP), IL-1β, MPO and MYO;
(C) Risk for secondary cardiac events chip: ApoA1, ApoB and D-dimer; and (D)
Congestive heart failure chip: Brain natriuretic peptide (BNP)
Key to future successful implementation of these tests is the fact that they meet the analytical
performance requirements, as dictated by the pathophysiological levels of the various biomarkers for
healthy vs. diseased. Likewise, in order for these chip-based tests to have clinical relevance, they must
not only respond on a timeframe consistent with near-patient usage, but they must meet or exceed the
analytical, and, thereby, clinical performance of the gold standards or reference methods, that are for
the most part limited to the laboratory setting (see Table 2). With this in mind, a significant amount of
effort has now been devoted to the determination of the analytical performance of key biomarkers
using the porous bead sensor systems. These measurements are completed using human clinical
samples and thus move above and beyond the common starting place of purified antigens within
buffer solutions.
Sensors 2012, 12
15487
Table 2. Initial specifications obtained with laboratory-based p-BNC porous bead-based
approach: List of developed biomarker assays, targeted use, and device performance
characteristics in the context of real-world clinical testing.
Biomarker
Clinical Use
C-reactive protein
AMI, Risk Assessment
Soluble CD40 ligand
Cardiac Risk Assessment
Monocyte chemoattractant protein-1 Cardiac Risk Assessment
Myeloperoxidase
Cardiac Risk Assessment
Myeloperoxidase (multiplexed)
Cardiac Risk Assessment
Interleukin-1beta
Cardiac Risk Assessment
Interleukin-6
Cardiac Risk Assessment
Tumor necrosis factor-alpha
Cardiac Risk Assessment
Cardiac troponin I
AMI Diagnosis
Myoglobin
AMI Diagnosis
CK-MB
AMI Diagnosis
Apolipoprotein A1
Risk for recurrence/Prognosis
Apolipoprotein B
Risk for recurrence/Prognosis
Brain natriuretic peptide
Congestive Heart Failure
N-Terminal proBNP
Congestive Heart Failure
Human serum albumin
Cardiac Risk Assessment
Transferrin
Blood contamination in saliva
Carcinoembryonic antigen
Ovarian Cancer
Cancer antigen 125
Ovarian Cancer
Human ep growth fact Rec. 2-neu
Ovarian Cancer
Prostate-specific antigen
Prostate Cancer
Free prostate-specific antigen
Prostate Cancer
Complexed prostate-specific antigen
Prostate Cancer
Cocaine
Road Side Drug Testing
Diazepam
Road Side Drug Testing
Tetrahydrocannabinol
Road Side Drug Testing
D-Amphetamine
Road Side Drug Testing
Methamphetamine
Road Side Drug Testing
Oxazepam
Road Side Drug Testing
Nordiazepam
Road Side Drug Testing
Temazepam
Road Side Drug Testing
Morphine
Road Side Drug Testing
Methadone
Road Side Drug Testing
MDA
Road Side Drug Testing
MDMA
Road Side Drug Testing
a
all units are ng/mL unless otherwise specified.
here as U/mL.
b
Range a (ng/mL) LOD a (ng/mL) Method
0.1–10,000
0.1–1,000
0.001–20
0.05–500
1.2–500
0.001–1
0.001–1
0.01–10
0.05–50
0.1–1,000
1.7–50
1–1,000
1–1,000
0.05–10
0.1–500
1–1,000
0.05–10,000 b
0.1–100
1–400 c
0–60
0.1–100
0.1–100
0.63–100
1.3–10,000
0.14–1,000
0.22–10,000
0.22–1,000
10–8,000
1.6–100,000
0.72–100,000
1.1–100,000
0.46–1,000
1.02–10,000
7.1–1,000
0.41–1,000
0.1
0.1
0.001
0.05
1.2
0.001
0.001
0.01
0.05
0.1
1.7
1
1
0.05
0.1
1
0.05 b
0.02
1c
0.27
0.1
0.1
0.63
1.3
0.14
0.22
0.22
1
1.6
0.72
1.1
0.46
1.02
7.1
0.41
Theoretical
Practical
Practical
Practical
Theoretical
Practical
Practical
Practical
Theoretical
Theoretical
Theoretical
Practical
Practical
Theoretical
Theoretical
Practical
Theoretical
Theoretical
Theoretical
Theoretical
Theoretical
Theoretical
Theoretical
Practical
Practical
Practical
Practical
Practical
Theoretical
Theoretical
Theoretical
Theoretical
Theoretical
Theoretical
Theoretical
units are expressed here as µg/mL. c units are expressed
With this real-world clinical context in mind, the limits of detection (LODs) for the two-site
immunometric and competitive assays have been established as reported in Table 2. Here both
practical and theoretical methods for determination of LOD values have been employed [151–153].
Sensors 2012, 12
15488
The former method was used for some of the initial work and the latter for the majority of the more
recent activities. For the theoretical LOD values, a 4- or 5-parameter logistic curve fit was applied to
the dose response curve and then the intersection with the intensity level three standard deviations
above the zero calibrator mean of three trials was established to yield the concentration value that
defines the LOD. In cases where no ultra-low concentration standards were run, the practical method
was used. Here the LOD was established as the lowest concentration of antigen yielding an average
bead signal that lies at three standard deviations above (two-type immunometric) or below
(competitive) the mean value recorded for the zero analyte calibrator.
From Table 2 it can be seen that the p-BNC tests for drugs of abuse exhibit exceptional assay
performance characteristics with limits of detection (LODs) comparable to the laboratory-confined
reference method of LC-MS/MS and vastly superior (significantly lower) than their
immunochromatographic (ICS) counterpart tests. Similar to LC-MS/MS and unlike ICS tests, which
are qualitative (Yes/No) type of tests, p-BNC-based drug tests are fully quantitative. Furthermore,
unlike LC-MS/MS which requires tedious sample processing and is limited to testing for one drug at a
time, p-BNC tests once in their final format, will be amenable to the point of need, require no
extensive sample processing and offer the capacity to multiplex, or test more than one analyte (drug)
concurrently, using microliters of sample.
6. Current Bead-Based Analyzers
Table 3 showcases current bead-based analyzers and the timeframes to complete the test. Given the
timeframes to perform an analysis, most analyzers based on solid support bead sensors are confined to
testing in the clinical laboratory. For example, the Luminex xMap system, which employs a suspension
array technology to detect multiple analytes simultaneously, has a timeframe for detection that is in the
range of two hours to overnight. It is apparent that the timeframes offered would not be ideal for
point-of-care testing. As such, an increase in the sensitivity of bead-based approaches would lend to
faster analysis times that are suitable for point-of-care testing.
Table 3. List of bead-based instruments, number of different bead sensors for
multiplexing, and total time to complete a test.
Instrument
xMap
BD FACSArray
AtheNA Multi-Lyte
VeraCode BeadXpress
Liaison
p-BNC *
Source
Luminex
BD
Alere
Illumina
Diasorin
McDevitt Lab
Total Tests
500
35
26
48
144
100
Time
390 min
35 min
60 min
30 min
60 min
20 min
Setting
Laboratory
Laboratory
Laboratory
Laboratory
Laboratory
Lab & POC
* The analyzer and biochips associated with the p-BNC approach for porous beads are still in development
and are not yet commercially available. Specifications are being developed for future technology transfer
through commercial partnerships. Characteristics listed in the table are projected for ultimate use.
Sensors 2012, 12
15489
The combination of porous support beads and tailored microstructures with active transport features
allows for enhanced mass transport of analytes. Short analysis timeframes of these highly sensitive
sensing elements reduce analysis times achieving what would be consistent with the point-of-care.
7. Scalability
As mentioned above, in order for the medical microdevices area to reach their full potential, it will
be necessary for these mini-test ensembles and their associated measurement platforms to be fully
validated with results that exhibit strong performance characteristics that rival those achieved in
traditional laboratory settings [7]. Further, it will be necessary that these devices show scalability and
performance that exceeds existing laboratory capabilities. To be scalable, enhanced performance must
be achieved with reduced cost and increased efficiencies.
The pathway to scalability would be expected to occur with modular assay formats that encompass
major diagnostic classes and with the establishment of standard testing procedures [7]. It is in this
capacity that the microelectronics industry should be considered to be a key model for the diagnostics
industry to follow. While initial discoveries in the electronics area, such as the vacuum tube, offered
basic logical operations, large mainframes and electrical component failures limited the widespread
adoption. However, with the advent of the solid-state three-point transistor and photolithographic
processing that brought standards and scalability, the microelectronics industry pushed the limits of its
capabilities as components became smaller and more powerful. Furthermore, for this now dominant
industry, Moore’s law serves as a goal to produce increased computational power at reduced cost.
Unfortunately, today healthcare costs are increasing at an alarming rate with only modest increases
in the quality of patient care [154]. Today over 60%–70% of medical decisions are made with
consultation of clinical testing results, yet less than 5% of total costs in this space are associated with
the testing results [155]. Likewise, it is clear that there is good value in clinical testing and information
content here derived will play an important role for the future management of healthcare and wellness.
It might be expected, therefore, that the development of scalable high-performance diagnostic
platforms alongside Moore’s law-like goals could have potential to lead to transformative changes in
healthcare. Figure 10 provides a representative list of diagnostic instruments and shows how the
number of tests that are performed per session have evolved with time. In assembling this information,
the “Q” quotient is used, as this value designates the number of tests that are performed per person per
time [75]. With this quantity in mind, the focus is placed directly on the information content as needed
to impact clinical decision making for the individual patient. In this capacity, the time value here
designated includes transit, preparation, and analysis periods. From this graph, it is quite interesting to
note that bead-based systems and chip-based approaches are now providing the highest level of
information extraction to date. It is also interesting to note that, similar to the field of microelectronics,
there is an evolving trend with time to secure more biomarker-derived information.
For the purpose of this graph, it is assumed that all the information content obtained from
microarray discovery data is used in the clinical decision making process. However, more typically
only a small number of sequences contribute to the differential disease diagnosis and thus these
specific signatures serve as a small portion of the acquired information. In the future it might be
expected that as these devices move closer to widespread clinical practice, a stronger focus on key
Sensors 2012, 12
15490
information from the selected sequences derived from more efficient capture methods will evolve.
Integrated LOC systems with improved efficiencies in sample processing are expected to play key
roles in extracting the key information that is required to manage patient care in cost effective and
efficient ways moving forward.
Figure 10. Graph over time showing the increase in Q, the number of tests performed per
person per time, for various representative diagnostic approaches.
8. Conclusions
In many ways, the convergence of microfluidics, biomarker validation, and porous bead ensembles
serve to overcome some of the significant challenges that the medical microdevices have, to date,
faced with respect to scalability and performance. When combined with new concepts for noninvasive
sampling [156], there is now strong potential to move these sensor modalities into broad-scale clinical
practice. Before this is possible, it will be necessary to complete more thorough clinical testing and
validation. The ability to complete multiplexed testing on porous beads that can be customized for
response time allows for multiple biomarkers to be measured within one environment in ways that
would not readily be achieved with more traditional planar approaches. Further, the prospect for using
the same diagnostic core in the discovery, clinical validation and broad-scale testing serves to establish
a promising pathway that has potential to increase the pace of both new device and new biomarker
approvals [7].
Further, the power of scalability through reduced time per test could lead the way to a new
generation of diagnostic and detection tools for health care applications. These devices, with the ability
to quickly provide robust, affordable, and accurate results, could potentially diagnose diseases at early
stages, monitor health risks, manage illness and provide appropriate treatment options [37]. The ability
to detect disease early on can improve the quality of the patient life, influence life style changes, and
Sensors 2012, 12
15491
reduce overall treatment costs. As such, these advanced medical microdevices may serve as the
enabling technology base that can help to usher in long awaited transition from reactive to preventative
medicine [54].
Acknowledgements
Funding for this work was provided by the National Institutes of Health (NIH) through the National
Institute of Dental and Craniofacial Research (Award Numbers 3 U01 DE017793-02S1 and 5 U01
DE017793-2). This content is solely the responsibility of the authors and does not necessarily represent
or reflect the official views of the NIH or the US government. Other segments of this work are
supported by the United Kingdom’s Home Office Center for Applied Science and Technology as well
as by the Cancer Prevention & Research Institute of Texas.
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
Hanash, S.M.; Pitteri, S.J.; Faca, V.M. Mining the plasma proteome for cancer biomarkers.
Nature 2008, 452, 571–579.
Hunter, D.J.; Khoury, M.J.; Drazen, J.M. Letting the genome out of the bottle—Will we get our
wish? N. Engl. J. Med. 2008, 358, 105–107.
Anderson, N.L. The clinical plasma proteome: A survey of clinical assays for proteins in plasma
and serum. Clin. Chem. 2010, 56, 177–185.
Anderson, N.L.; Anderson, N.G. The human plasma proteome. Mol. Cell. Proteomics 2002, 1,
845–867.
Vitzthum, F.; Behrens, F.; Anderson, N.L.; Shaw, J.H. Proteomics: From basic research to
diagnostic application. A review of requirements & needs. J. Proteome Res. 2005, 4, 1086–1097.
Paulovich, A.G.; Whiteaker, J.R.; Hoofnagle, A.N.; Wang, P. The interface between biomarker
discovery and clinical validation: The tar pit of the protein biomarker pipeline. Proteomics Clin.
Appl. 2008, 2, 1386–1402.
Jokerst, J.V.; Jacobson, J.W.; Bhagwandin, B.D.; Floriano, P.N.; Christodoulides, N.;
McDevitt, J.T. Programmable nano-bio-chip sensors: Analytical meets clinical. Anal. Chem.
2010, 82, 1571–1579.
Floriano, P.N.; Christodoulides, N.; Miller, C.S.; Ebersole, J.L.; Spertus, J.; Rose, B.G.; Kinane, D.F.;
Novak, M.J.; Steinhubl, S.; Acosta, S.; et al. Use of saliva-based nano-biochip tests for acute
myocardial infarction at the point of care: A feasibility study. Clin. Chem. 2009, 55, 1530–1538.
Yager, P.; Edwards, T.; Fu, E.; Helton, K.; Nelson, K.; Tam, M.R.; Weigl, B.H. Microfluidic
diagnostic technologies for global public health. Nature 2006, 442, 412–418.
Soper, S.A.; Brown, K.; Ellington, A.; Frazier, B.; Garcia-Manero, G.; Gau, V.; Gutman, S.I.;
Hayes, D.F.; Korte, B.; Landers, J.L.; et al. Point-of-care biosensor systems for cancer
diagnostics/prognostics. Biosen. Bioelectron. 2006, 21, 1932–1942.
Myers, F.B.; Lee, L.P. Innovations in optical microfluidic technologies for point-of-care
diagnostics. Lab Chip 2008, 8, 2015–2031.
Sensors 2012, 12
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
15492
Ouellette, A.L.; Li, J.J.; Cooper, D.E.; Ricco, A.J.; Kovacs, G.T. Evolving point-of-care
diagnostics using up-converting phosphor bioanalytical systems. Anal. Chem. 2009, 81,
3216–3221.
Osterfeld, S.J.; Yu, H.; Gaster, R.S.; Caramuta, S.; Xu, L.; Han, S.J.; Hall, D.A.; Wilson, R.J.;
Sun, S.; White, R.L.; et al. Multiplex protein assays based on real-time magnetic nanotag
sensing. Proc. Nat. Acad. Sci. USA 2008, 105, 20637–20640.
Lewandrowski, K. Point-of-care testing: An overview and a look to the future (circa 2009,
United States). Clin. Lab Med. 2009, 29, 421–432.
Melanson, S.E.F. Literature review on point-of-care testing (August 2009–December 2010).
Point of Care 2011, 10, 63–68.
Durner, J. Clinical chemistry: Challenges for analytical chemistry and the nanosciences from
medicine. Angew. Chem. Int. Ed. 2010, 49, 1026–1051.
Gervais, L.; de Rooij, N.; Delamarche, E. Microfluidic chips for point-of-care
immunodiagnostics. Advan. Mater. 2011, 23, H151–H176.
Warsinke, A. Point-of-care testing of proteins. Anal. Bioanal. Chem. 2009, 393, 1393–1405.
Gubala, V.; Harris, L.F.; Ricco, A.J.; Tan, M.X.; Williams, D.E. Point of care diagnostics: Status
and future. Anal. Chem. 2011, 84, 487–515.
Thorsen, T.; Maerkl, S.J.; Quake, S.R. Microfluidic large-scale integration. Science 2002, 298,
580–584.
Mukhopadhyay, R. Microfluidics: On the slope of enlightenment. Anal. Chem. 2009, 81,
4169–4173.
Haeberle, S.; Zengerle, R. Microfluidic platforms for lab-on-a-chip applications. Lab Chip 2007,
7, 1094–1110.
Schulte, T.H.; Bardell, R.L.; Weigl, B.H. Microfluidic technologies in clinical diagnostics.
Clin. Chim. Acta 2002, 321, 1–10.
Luppa, P.B.; Müller, C.; Schlichtiger, A.; Schlebusch, H. Point-of-care testing (poct): Current
techniques and future perspectives. TrAC Trend. Anal. Chem. 2011, 30, 887–898.
Wu, P.; Castner, D.G.; Grainger, D.W. Diagnostic devices as biomaterials: A review of nucleic
acid and protein microarray surface performance issues. J. Biomater. Sci.-Poly. Ed. 2008, 19,
725–753.
Pabbaraju, K.; Tokaryk, K.L.; Wong, S.; Fox, J.D. Comparison of the luminex xtag respiratory
viral panel with in-house nucleic acid amplification tests for diagnosis of respiratory virus
infections. J. Clin. Microbiol. 2008, 46, 3056–3062.
Sista, R.; Hua, Z.; Thwar, P.; Sudarsan, A.; Srinivasan, V.; Eckhardt, A.; Pollack, M.; Pamula, V.
Development of a digital microfluidic platform for point of care testing. Lab Chip 2008, 8,
2091–2104.
Stewart, D.S.; Reineke, K.F.; Tortorello, M.L. Comparison of assurance gold salmonella eia, bax
for screening/salmonella, and gene-trak salmonella dlp rapid assays for detection of salmonella in
alfalfa sprouts and sprout irrigation water. J. AOAC Int. 2002, 85, 395–403.
Bourbeau, P.P.; Heiter, B.J.; Figdore, M. Use of gen-probe accuprobe group b streptococcus test
to detect group b streptococci in broth cultures of vaginal-anorectal specimens from pregnant
women: Comparison with traditional culture method. J. Clin. Microbiol. 1997, 35, 144–147.
Sensors 2012, 12
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
15493
Vickers, I.; O’Flanagan, D.; Cafferkey, M.; Humphreys, H. Multiplex pcr to determine
streptococcus pneumoniae serotypes causing otitis media in the republic of ireland with further
characterisation of antimicrobial susceptibilities and genotypes. Eur. J. Clin. Microbiol. Infect. D
2011, 30, 447–453.
Keiler, K.C.; Shapiro, L.; Williams, K.P. Tmrnas that encode proteolysis-inducing tags are found
in all known bacterial genomes: A two-piece tmrna functions in caulobacter. Proc. Nat. Acad.
Sci.USA 2000, 97, 7778–7783.
McGuinness, S.; McCabe, E.; O’Regan, E.; Dolan, A.; Duffy, G.; Burgess, C.; Fanning, S.;
Barry, T.; O’Grady, J. Development and validation of a rapid real-time pcr based method for the
specific detection of salmonella on fresh meat. Meat Sci. 2009, 83, 555–562.
O’Grady, J.; Lacey, K.; Glynn, B.; Smith, T.J.; Barry, T.; Maher, M. Tmrna—A novel
high-copy-number rna diagnostic target—Its application for staphylococcus aureus detection
using real-time nasba. FEMS Microbiol. Lett. 2009, 301, 218–223.
Dimov, I.K.; Garcia-Cordero, J.L.; O’Grady, J.; Poulsen, C.R.; Viguier, C.; Kent, L.; Daly, P.;
Lincoln, B.; Maher, M.; O’Kennedy, R.; et al. Integrated microfluidic tmrna purification and
real-time nasba device for molecular diagnostics. Lab Chip 2008, 8, 2071–2078.
Toner, M.; Irimia, D. Blood-on-a-chip. Annu. Rev. Biomed. Eng. 2005, 7, 77–103.
Chin, C.D.; Linder, V.; Sia, S.K. Lab-on-a-chip devices for global health: Past studies and future
opportunities. Lab Chip 2007, 7, 41–57.
Weigl, B.; Domingo, G.; Labarre, P.; Gerlach, J. Towards non- and minimally instrumented,
microfluidics-based diagnostic devices. Lab Chip 2008, 8, 1999–2014.
Martinez, A.W.; Phillips, S.T.; Carrilho, E.; Thomas, S.W., 3rd; Sindi, H.; Whitesides, G.M.
Simple telemedicine for developing regions: Camera phones and paper-based microfluidic
devices for real-time, off-site diagnosis. Anal. Chem. 2008, 80, 3699–3707.
Yager, P.; Domingo, G.J.; Gerdes, J. Point-of-care diagnostics for global health. Annu. Rev.
Biomed. Eng. 2008, 10, 107–144.
Lee, W.G.; Kim, Y.G.; Chung, B.G.; Demirci, U.; Khademhosseini, A. Nano/microfluidics for
diagnosis of infectious diseases in developing countries. Adv. Drug Deliv. Rev. 2010, 62,
449–457.
Fu, E.; Yager, P.; Floriano, P.N.; Christodoulides, N.; McDevitt, J.T. Perspective on diagnostics
for global health. IEEE Pulse 2011, 2, 40–50.
Sato, K.; Hibara, A.; Tokeshi, M.; Hisamoto, H.; Kitamori, T. Integration of chemical and
biochemical analysis systems into a glass microchip. Anal. Sci. 2003, 19, 15–22.
Vilkner, T.; Janasek, D.; Manz, A. Micro total analysis systems. Recent developments.
Anal. Chem. 2004, 76, 3373–3385.
Sorger, P.K. Microfluidics closes in on point-of-care assays. Nat. Biotech. 2008, 26, 1345–1346.
Goluch, E.D.; Nam, J.M.; Georganopoulou, D.G.; Chiesl, T.N.; Shaikh, K.A.; Ryu, K.S.;
Barron, A.E.; Mirkin, C.A.; Liu, C. A bio-barcode assay for on-chip attomolar-sensitivity protein
detection. Lab Chip 2006, 6, 1293–1299.
Qin, L.; Vermesh, O.; Shi, Q.; Heath, J.R. Self-powered microfluidic chips for multiplexed
protein assays from whole blood. Lab Chip 2009, 9, 2016–2020.
Sensors 2012, 12
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
15494
Sia, S.K.; Linder, V.; Parviz, B.A.; Siegel, A.; Whitesides, G.M. An integrated approach to a
portable and low-cost immunoassay for resource-poor settings. Angew. Chem. Int. Ed. Eng. 2004,
43, 498–502.
Srivastava, N.; Brennan, J.S.; Renzi, R.F.; Wu, M.; Branda, S.S.; Singh, A.K.; Herr, A.E. Fully
integrated microfluidic platform enabling automated phosphoprofiling of macrophage response.
Anal. Chem. 2009, 81, 3261–3269.
Kim, J.S.; Anderson, G.P.; Erickson, J.S.; Golden, J.P.; Nasir, M.; Ligler, F.S. Multiplexed
detection of bacteria and toxins using a microflow cytometer. Anal. Chem. 2009, 81, 5426–5432.
Madou, M.; Zoval, J.; Jia, G.; Kido, H.; Kim, J.; Kim, N. Lab on a CD. Annu. Rev. Biomed. Eng.
2006, 8, 601–628.
Walt, D.R. Chemistry. Miniature analytical methods for medical diagnostics. Science 2005, 308,
217–219.
Cheng, X.; Gupta, A.; Chen, C.; Tompkins, R.G.; Rodriguez, W.; Toner, M. Enhancing the
performance of a point-of-care cd4+ t-cell counting microchip through monocyte depletion for
hiv/aids diagnostics. Lab Chip 2009, 9, 1357–1364.
Maheswaran, S.; Sequist, L.V.; Nagrath, S.; Ulkus, L.; Brannigan, B.; Collura, C.V.; Inserra, E.;
Diederichs, S.; Iafrate, A.J.; Bell, D.W.; et al. Detection of mutations in EGFR in circulating
lung-cancer cells. N. Eng. J. Med. 2008, 359, 366–377.
Whitesides, G.M. The origins and the future of microfluidics. Nature 2006, 442, 368–373.
Hartmann, M.; Roeraade, J.; Stoll, D.; Templin, M.; Joos, T. Protein microarrays for diagnostic
assays. Anal. Bioanal. Chem. 2009, 393, 1407–1416.
Hall, D.A.; Ptacek, J.; Snyder, M. Protein microarray technology. Mech. Age. Dev. 2007, 128,
161–167.
Gresham, D.; Dunham, M.J.; Botstein, D. Comparing whole genomes using DNA microarrays.
Nat. Rev. Genet. 2008, 9, 291–302.
Tomlinson, I.M.; Holt, L.J. Protein profiling comes of age. Genome Biol. 2001,
doi:10.1186/gb-2001-2-2-reviews1004.
Venkatasubbarao, S. Microarrays—Status and prospects. Trends Biotechnol. 2004, 22, 630–637.
Rifai, N.; Gillette, M.A.; Carr, S.A. Protein biomarker discovery and validation: The long and
uncertain path to clinical utility. Nat. Biotech. 2006, 24, 971–983.
Lim, C.T.; Zhang, Y. Bead-based microfluidic immunoassays: The next generation.
Biosens. Bioelectron. 2007, 22, 1197–1204.
Verpoorte, E. Beads and chips: New recipes for analysis. Lab Chip 2003, 3, 60N–68N.
Bangs, L.B. New Developments in Particle-Based Immunoassays : Introduction. Pure Appl.
Chem. 1996, 68, 1873–1879.
Meza, M.B. Bead-based hts applications in drug discovery. Drug Discov. Today 2000, 5, 38–41.
Vignali, D.A.A. Multiplexed particle-based flow cytometric assays. J. Immunol. Meth. 2000,
243, 243–255.
Brodsky, A.S.; Johnston, A.P.; Trau, M.; Silver, P.A. Analysis of RNA-protein interactions by
flow cytometry. Curr. Opin. Mol. Ther. 2003, 5, 235–240.
Kawaguchi, H. Functional polymer microspheres. Prog. Polym. Sci. 2000, 25, 1171–1210.
Sensors 2012, 12
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
15495
Derveaux, S.; Stubbe, B.G.; Braeckmans, K.; Roelant, C.; Sato, K.; Demeester, J.;
De Smedt, S.C. Synergism between particle-based multiplexing and microfluidics technologies
may bring diagnostics closer to the patient. Anal. Bioanal. Chem. 2008, 391, 2453–2467.
Wilson, R.; Cossins, A.R.; Spiller, D.G. Encoded microcarriers for high-throughput multiplexed
detection. Angew. Chem. Int. Ed. Eng. 2006, 45, 6104–6117.
Kim, J.; Heo, J.; Crooks, R.M. Hybridization of DNA to bead-immobilized probes confined
within a microfluidic channel. Langmuir 2006, 22, 10130–10134.
Sato, K.; Tokeshi, M.; Odake, T.; Kimura, H.; Ooi, T.; Nakao, M.; Kitamori, T. Integration of an
immunosorbent assay system: Analysis of secretory human immunoglobulin a on polystyrene
beads in a microchip. Anal. Chem. 2000, 72, 1144–1147.
Zammatteo, N.; Alexandre, I.; Ernest, I.; Le, L.; Brancart, F.; Remacle, J. Comparison between
microwell and bead supports for the detection of human cytomegalovirus amplicons by sandwich
hybridization. Anal. Biochem. 1997, 253, 180–189.
Walsh, M.K.; Wang, X.; Weimer, B.C. Optimizing the immobilization of single-stranded DNA
onto glass beads. J. Biochem. Biophys. Meth. 2001, 47, 221–231.
Jokerst, J.V.; Chou, J.; Camp, J.P.; Wong, J.; Lennart, A.; Pollard, A.A.; Floriano, P.N.;
Christodoulides, N.; Simmons, G.W.; Zhou, Y.; et al. Location of biomarkers and reagents
within agarose beads of a programmable bio-nano-chip. Small 2011, 7, 613–624.
Jokerst, J.V.; McDevitt, J.T. Programmable nano-bio-chips: Multifunctional clinical tools for use
at the point-of-care. Nanomedicine 2010, 5, 143–155.
Delehanty, J.B.; Ligler, F.S. A microarray immunoassay for simultaneous detection of proteins
and bacteria. Anal. Chem. 2002, 74, 5681–5687.
Konry, T.; Hayman, R.B.; Walt, D.R. Microsphere-based rolling circle amplification microarray
for the detection of DNA and proteins in a single assay. Anal. Chem. 2009, 81, 5777–5782.
Situma, C.; Hashimoto, M.; Soper, S.A. Merging microfluidics with microarray-based bioassays.
Biomol. Eng. 2006, 23, 213–231.
Ng, J.K.; Selamat, E.S.; Liu, W.T. A spatially addressable bead-based biosensor for simple and
rapid DNA detection. Biosens. Bioelectron .2008, 23, 803–810.
Zhao, Y.; Zhao, X.; Pei, X.; Hu, J.; Zhao, W.; Chen, B.; Gu, Z. Multiplex detection of tumor
markers with photonic suspension array. Anal. Chim. Acta 2009, 633, 103–108.
Walt, D.R. Fibre optic microarrays. Chem. Soc. Rev. 2010, 39, 38–50.
Walt, D.R. Bead-based optical fiber arrays for artificial olfaction. Curr. Opin. Chem. Biol. 2010,
14, 767–770.
Walt, D.R. Techview: Molecular biology. Bead-based fiber-optic arrays. Science 2000, 287,
451–452.
Nolan, J.P.; Lauer, S.; Prossnitz, E.R.; Sklar, L.A. Flow cytometry: A versatile tool for all phases
of drug discovery. Drug Discov. Today 1999, 4, 173–180.
Nolan, J.P.; Sklar, L.A. Suspension array technology: Evolution of the flat-array paradigm.
Trends Biotechnol. 2002, 20, 9–12.
Kuckuck, F.W.; Edwards, B.S.; Sklar, L.A. High throughput flow cytometry. Cytometry 2001,
44, 83–90.
Sensors 2012, 12
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
15496
Nolan, J.P.; Mandy, F.F. Suspension array technology: New tools for gene and protein analysis.
Cell. Mol. Biol. 2001, 47, 1241–1256.
Diaz, M.R.; Fell, J.W. Use of a suspension array for rapid identification of the varieties and
genotypes of the cryptococcus neoformans species complex. J. Clin. Microbiol. 2005, 43,
3662–3672.
Khan, I.H.; Krishnan, V.V.; Ziman, M.; Janatpour, K.; Wun, T.; Luciw, P.A.; Tuscano, J.
A comparison of multiplex suspension array large-panel kits for profiling cytokines and
chemokines in rheumatoid arthritis patients. Cytometry Part B 2009, 76B, 159–168.
Dunbar, S.A. Applications of luminex xmap technology for rapid, high-throughput multiplexed
nucleic acid detection. Clin. Chim. Acta 2006, 363, 71–82.
Nolan, J.P.; Yang, L.; van der Heyde, H.C. Reagents and Instruments for Multiplexed Analysis
Using Microparticles. In Current Protocols in Cytometry; John Wiley & Sons, Inc.: Hoboken, NJ,
USA, 2001.
Fulton, J.R.; McDade, R.L.; Smith, P.L.; Kienker, L.J.; Kettman, J.R. Advanced multiplexed
analysis with the flowmetrixtm system. Clin. Chem. 1997, 43, 1749–1756.
Taylor, J.D.; Briley, D.; Nguyen, Q.; Long, K.; Iannone, M.A.; Li, M.S.; Ye, F.; Afshari, A.;
Lai, E.; Wagner, M.; et al. Flow cytometric platform for high-throughput single nucleotide
polymorphism analysis. Biotechniques 2001, 30, 661–666, 668–669.
Oliver, K.G.; Kettman, J.R.; Fulton, R.J. Multiplexed analysis of human cytokines by use of the
flowmetrix system. Clin. Chem. 1998, 44, 2057–2060.
Prabhakar, U.; Eirikis, E.; Miller, B.E.; Davis, H.M. Multiplexed cytokine sandwich
immunoassays: Clinical applications. Methods Mol. Med. 2005, 114, 223–232.
Prabhakar, U.; Eirikis, E.; Davis, H.M. Simultaneous quantification of proinflammatory
cytokines in human plasma using the labmap assay. J. Immunol. Method. 2002, 260, 207–218.
Dunbar, S.A.; Vander Zee, C.A.; Oliver, K.G.; Karem, K.L.; Jacobson, J.W. Quantitative,
multiplexed detection of bacterial pathogens: DNA and protein applications of the luminex
labmap system. J. Immunol. Method. 2003, 53, 245–252.
Dunbar, S.A.; Jacobson, J.W. Rapid screening for 31 mutations and polymorphisms in the
cystic fibrosis transmembrane conductance regulator gene by lminex xmap suspension array.
Methods Mol. Med. 2005, 114, 147–171.
Yan, X.; Zhong, W.; Tang, A.; Schielke, E.G.; Hang, W.; Nolan, J.P. Multiplexed flow
cytometric immunoassay for influenza virus detection and differentiation. Anal. Chem. 2005, 77,
7673–7678.
Dasso, J.; Lee, J.; Bach, H.; Mage, R.G. A comparison of elisa and flow microsphere-based
assays for quantification of immunoglobulins. J. Immunol. Method. 2002, 263, 23–33.
Morgan, E.; Varro, R.; Sepulveda, H.; Ember, J.A.; Apgar, J.; Wilson, J.; Lowe, L.; Chen, R.;
Shivraj, L.; Agadir, A.; et al. Cytometric bead array: A multiplexed assay platform with
applications in various areas of biology. Clin. Immunol. 2004, 110, 252–266.
Gorris, H.H.; Blicharz, T.M.; Walt, D.R. Optical-fiber bundles. FEBS J. 2007, 274, 5462–5470.
Stitzel, S.E.; Aernecke, M.J.; Walt, D.R. Artificial noses. Annu. Rev. Biomed. Eng. 2011, 13,
1–25.
Walt, D.R. Fiber optic array biosensors. Biotechniques 2006, 41, 529, 531, 533.
Sensors 2012, 12
15497
105. Stevens, T.W.; Iwaki, Y. Introduction to bioarray solutions’ beadchip technology. ASHI Quart.
2008, 12, 38–40.
106. Thompson, J.A. Microbead-Based Biosensing in Microfluidic Devices. Ph.D. Thesis, University
of Pennsylvania, Philadelphia, PA, USA, 2011.
107. Ng, J.K.; Wang, W.; Liu, W.T.; Chong, S.S. Spatially addressable bead-based biosensor for rapid
detection of beta-thalassemia mutations. Anal. Chim. Acta 2010, 658, 193–196.
108. Ikami, M.; Kawakami, A.; Kakuta, M.; Okamoto, Y.; Kaji, N.; Tokeshi, M.; Baba, Y.
Immuno-pillar chip: A new platform for rapid and easy-to-use immunoassay. Lab Chip 2010, 10,
3335–3340.
109. Peredy, T.R.; Powers, R.D. Bedside diagnostic testing of body fluids. Am. J. Emerg. Med. 1997,
15, 400–407.
110. Altieri, M.F.; Camarca, M. Point of care testing. Clin. Pediatr. Emerg. Med. 2001, 2, 275–279.
111. Christodoulides, N.; Dharshan, P.; Wong, J.; Floriano, P.N.; Neikirk, D.; McDevitt, J.T.
A microchip-based assay for interleukin-6. Methods Mol. Biol. 2007, 385, 131–144.
112. Christodoulides, N.; Mohanty, S.; Miller, C.S.; Langub, M.C.; Floriano, P.N.; Dharshan, P.;
Ali, M.F.; Bernard, B.; Romanovicz, D.; Anslyn, E.; et al. Application of microchip assay system
for the measurement of c-reactive protein in human saliva. Lab Chip 2005, 5, 261–269.
113. Christodoulides, N.; Tran, M.; Floriano, P.N.; Rodriguez, M.; Goodey, A.; Ali, M.; Neikirk, D.;
McDevitt, J.T. A microchip-based multianalyte assay system for the assessment of cardiac risk.
Anal. Chem. 2002, 74, 3030–3036.
114. Goodey, A.; Lavigne, J.J.; Savoy, S.M.; Rodriguez, M.D.; Curey, T.; Tsao, A.; Simmons, G.;
Wright, J.; Yoo, S.J.; Sohn, Y.; et al. Development of multianalyte sensor arrays composed of
chemically derivatized polymeric microspheres localized in micromachined cavities. J. Am.
Chem. Soc. 2001, 123, 2559–2570.
115. Rubina, A.Y.; Dementieva, E.I.; Stomakhin, A.A.; Darii, E.L.; Pan’kov, S.V.; Barsky, V.E.;
Ivanov, S.M.; Konovalova, E.V.; Mirzabekov, A.D. Hydrogel-based protein microchips:
Manufacturing, properties, and applications. Biotechniques 2003, 34, 1008–1014, 1016–1020, 1022.
116. Zubtsov, D.A.; Savvateeva, E.N.; Rubina, A.Y.; Pan’kov, S.V.; Konovalova, E.V.;
Moiseeva, O.V.; Chechetkin, V.R.; Zasedatelev, A.S. Comparison of surface and hydrogel-based
protein microchips. Anal. Biochem. 2007, 368, 205–213.
117. Chou, J.; Lennart, A.; Wong, J.; Ali, M.F.; Floriano, P.N.; Christodoulides, N.; Camp, J.;
McDevitt, J.T. Modeling analyte transport and capture in porous bead sensors. Anal. Chem. 2012,
84, 2569–2575.
118. Sanhai, W.R.; Sakamoto, J.H.; Canady, R.; Ferrari, M. Seven challenges for nanomedicine.
Nat. Nanotechnol. 2008, 3, 242–244.
119. Seong, G.H.; Crooks, R.M. Efficient mixing and reactions within microfluidic channels using
microbead-supported catalysts. J. Am. Chem. Soc. 2002, 124, 13360–13361.
120. Liu, C.; Schrlau, M.G.; Bau, H.H. Single bead-based electrochemical biosensor.
Biosens. Bioelectron. 2009, 25, 809–814.
121. Nazir, A.; Schroën, K.; Boom, R. Premix emulsification: A review. J. Membr. Sci. 2010, 362, 1–11.
122. De Mello, A.J.; Beard, N. Focus. Dealing with “real” samples: Sample pre-treatment in
microfluidic systems. Lab Chip 2003, 3, 11N-20N.
Sensors 2012, 12
15498
123. Gervais, T.; Jensen, K.F. Mass transport and surface reactions in microfluidic systems.
Chem. Eng. Sci. 2006, 61, 1102–1121.
124. Zhan, W.; Seong, G.H.; Crooks, R.M. Hydrogel-based microreactors as a functional component
of microfluidic systems. Anal. Chem. 2002, 74, 4647–4652.
125. Rubina, A.Y.; Pan’kov, S.V.; Dementieva, E.I.; Pan’kov, D.N.; Butygin, A.V.; Vasiliskov, V.A.;
Chudinov, A.V.; Mikheikin, A.L.; Mikhailovich, V.M.; Mirzabekov, A.D. Hydrogel drop
microchips with immobilized DNA: Properties and methods for large-scale production.
Anal. Biochem. 2004, 325, 92–106.
126. Khademhosseini, A.; Yeh, J.; Jon, S.; Eng, G.; Suh, K.Y.; Burdick, J.A.; Langer, R. Molded
polyethylene glycol microstructures for capturing cells within microfluidic channels. Lab Chip
2004, 4, 425–430.
127. Sung, W.C.; Chen, H.H.; Makamba, H.; Chen, S.H. Functionalized 3d-hydrogel plugs covalently
patterned inside hydrophilic poly(dimethylsiloxane) microchannels for flow-through
immunoassays. Anal. Chem. 2009, 81, 7967–7973.
128. Pernodet, N.; Maaloum, M.; Tinland, B. Pore size of agarose gels by atomic force microscopy.
Electrophoresis 1997, 18, 55–58.
129. Wang, R.Y.; Liu, X.Y.; Narayanan, J.; Xiong, J.Y.; Li, J.L. Architecture of fiber network: From
understanding to engineering of molecular gels. J. Phys. Chem. B 2006, 110, 25797–25802.
130. Larsson, P.O.; Gustavsson, P.E.; Axelsson, A. Direct measurement of intraparticle fluid velocity
in superporous agarose beads. J. Mol. Recognit. 1998, 11, 270–272.
131. Thompson, J.A.; Bau, H.H. Microfluidic, bead-based assay: Theory and experiments.
J. Chromatogr. B 2010, 878, 228–236.
132. Thompson, J.; Bau, H. Porous bead-based microfluidic assay: Theory and confocal microscope
imaging. Microfluid. Nanofluid. 2012, 12, 625–637.
133. Chen, L.; Chen, Z.T.; Wang, J.; Xiao, S.J.; Lu, Z.H.; Gu, Z.Z.; Kang, L.; Chen, J.; Wu, P.H.;
Tang, Y.C.; et al. Gel-pad microarrays templated by patterned porous silicon for dual-mode
detection of proteins. Lab Chip 2009, 9, 756–760.
134. Arenkov, P.; Kukhtin, A.; Gemmell, A.; Voloshchuk, S.; Chupeeva, V.; Mirzabekov, A. Protein
microchips: Use for immunoassay and enzymatic reactions. Anal. Biochem. 2000, 278, 123–131.
135. Thompson, J.A.; Bau, H.H. Pulsating bead-based assay. Anal. Chem. 2011, 83, 2858–2861.
136. Chou, J.; Kulla, E.; Simmons, G.W.; Floriano, P.N.; Christodoulides, N.; McDevitt, J.T.
Enhancement of performance in bead based microfluidics: Effects of geometry. Lab Chip 2012,
in submission.
137. Coulson, J.M.; Richardson, J.F.; Backhurst, J.R.; Harker, J.H. Chemical Engineering;
Butterworth-Heinemann: Oxford, UK, 1991; Volume 2, pp. 132–136.
138. Chou, J.; Li, L.E.; Christodoulides, N.; Floriano, P.N.; McDevitt, J.T. Effects of sample delivery
on analyte capture in porous bead sensors. Lab Chip 2012, in submission.
139. Gustavsson, P.E.; Axelsson, A.; Larsson, P.O. Direct measurements of convective fluid velocities
in superporous agarose beads. J. Chromatogr. A 1998, 795, 199–210.
140. Gustavsson, P.E.; Axelsson, A.; Larsson, P.O. Superporous agarose beads as a hydrophobic
interaction chromatography support. J. Chromatogr. A 1999, 830, 275–284.
Sensors 2012, 12
15499
141. Du, K.F.; Bai, S.; Dong, X.Y.; Sun, Y. Fabrication of superporous agarose beads for protein
adsorption: Effect of CaCO3 granules content. J. Chromatogr. A 2010, 1217, 5808–5816.
142. Gottschalk, I.; Gustavsson, P.E.; Ersson, B.; Lundahl, P. Improved lectin-mediated
immobilization of human red blood cells in superporous agarose beads. J. Chromatogr. B 2003,
784, 203–208.
143. Yang, Y.; Nam, S.-W.; Lee, N.Y.; Kim, Y.S.; Park, S. Superporous agarose beads as a solid
support for microfluidic immunoassay. Ultramicroscopy 2008, 108, 1384–1389.
144. Zubtsov, D.A.; Ivanov, S.M.; Rubina, A.Y.; Dementieva, E.I.; Chechetkin, V.R.; Zasedatelev, A.S.
Effect of mixing on reaction-diffusion kinetics for protein hydrogel-based microchips.
J. Biotechnol. 2006, 122, 16–27.
145. Sia, S.K.; Kricka, L.J. Microfluidics and point-of-care testing. Lab Chip 2008, 8, 1982–1983.
146. Schully, S.D.; Benedicto, C.B.; Gillanders, E.M.; Wang, S.S.; Khoury, M.J. Translational
research in cancer genetics: The road less traveled. Public Health Genomics 2011, 14, 1–8.
147. Weigum, S.E.; Floriano, P.N.; Christodoulides, N.; McDevitt, J.T. Cell-based sensor for analysis
of egfr biomarker expression in oral cancer. Lab Chip 2007, 7, 995–1003.
148. Weigum, S.E.; Floriano, P.N.; Redding, S.W.; Yeh, C.K.; Westbrook, S.D.; McGuff, H.S.;
Lin, A.; Miller, F.R.; Villarreal, F.; Rowan, S.D.; et al. Nano-bio-chip sensor platform for
examination of oral exfoliative cytology. Cancer Prev. Res. 2010, 3, 518–528.
149. Yeh, C.K.; Christodoulides, N.J.; Floriano, P.N.; Miller, C.S.; Ebersole, J.L.; Weigum, S.E.;
McDevitt, J.; Redding, S.W. Current development of saliva/oral fluid-based diagnostics.
Tex. Dent. J. 2010, 127, 651–661.
150. Christodoulides, N.; Floriano, P.N.; Sanchez, X.; Li, L.; Hocquard, K.; Patton, A.; Muldoon, R.;
Miller, C.S.; Ebersole, J.L.; Redding, S.; et al. Programmable bio-nanochip technology for the
diagnosis of cardiovascular disease at the point of care. Methodist DeBakey Cardiovasc. J. 2012,
8, 6–12.
151. Long, G.L.; Winefordner, J.D. Limit of detection a closer look at the iupac definition.
Anal. Chem. 1983, 55, 712A–724A.
152. MacDougall, D.; Crummett, W.B. Guidelines for data acquisition and data quality evaluation in
environmental chemistry. Anal. Chem. 1980, 52, 2242–2249.
153. McNaught, A.D.; Wilkinson, A. Compendium of Chemical Terminology, 2nd ed.; Blackwell
Science: Oxford, UK, 1997.
154. US Department of Health and Human Services. National Health Expenditures 2009 Highlights.
Avaiable online: http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trendsand-Reports/NationalHealthExpendData/downloads/tables.pdf (accessed on 10 May 2011).
155. Group, T.L. The Value of Diagnostics Innovation, Adoption and Diffusion into Health Care;
The Lewin Group: Falls Church, VA, USA, 2005.
156. Gaidos, S. A Spitting Image of Health. Avaiable online: http://www.sciencenews.org/view/
feature/id/335816/title/A_Spitting_Image_of_Health (accessed on 15 March 2012).
© 2012 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 license
(http://creativecommons.org/licenses/by/3.0/).
Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
Porous Bead-Based Diagnostic Platforms: Bridging the Gaps in Healthcare
Sensors, 2012
...Read more
Related Papers
Lab on a Chip, 2012
Download
Frontiers in Bioscience, 2010
Download
RSC Adv., 2015
Download
Procedia Engineering, 2011
Download
Download
Journal of Economy Culture and Society, 2024
Download
Mihai-Dumitru Grigore (ed.), Orthodoxy on the Move: Mobility, Networks, and Belonging between the 16th and 20th Centuries, Cluj-Napoca: Babeș-Bolyai University Press [Studia Universitatis Babes Bolyai - Theologia Orthodoxa 68:1], 2023
Download
Вестник угроведения, 2020
Download
Revue Milieu(x), 2021
Download
Download
