Biosensors and Nanotechnology E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Veröffentlichungsjahr: 2017

Beschreibung

Provides a broad range of information from basic principles to advanced applications of biosensors and nanomaterials in health care diagnostics

This book utilizes a multidisciplinary approach to provide a wide range of information on biosensors and the impact of nanotechnology on the development of biosensors for health care. It offers a solid background on biosensors, recognition receptors, biomarkers, and disease diagnostics. An overview of biosensor-based health care applications is addressed. Nanomaterial applications in biosensors and diagnostics are included, covering the application of nanoparticles, magnetic nanomaterials, quantum dots, carbon nanotubes, graphene, and molecularly imprinted nanostructures. The topic of organ-specific health care systems utilizing biosensors is also incorporated to provide deep insight into the very recent advances in disease diagnostics.

Biosensors and Nanotechnology: Applications in Health Care Diagnostics is comprised of 15 chapters that are presented in four sections and written by 33 researchers who are actively working in Germany, the United Kingdom, Italy, Turkey, Denmark, Finland, Romania, Malaysia and Brazil. It covers biomarkers in healthcare; microfluidics in medical diagnostics; SPR-based biosensor techniques; piezoelectric-based biosensor technologies; MEMS-based cell counting methods; lab-on-chip platforms; optical applications for cancer cases; and more. 

• Discusses the latest technology and advances in the field of biosensors and their applications for healthcare diagnostics
• Particular focus on biosensors for cancer
• Summarizes research of the last 30 years, relating it to state-of-the-art technologies

Biosensors and Nanotechnology: Applications in Health Care Diagnostics is an excellent book for researchers, scientists, regulators, consultants, and engineers in the field, as well as for graduate students studying the subject.

Leseprobe

Table of Contents

Cover

Title Page

List of Contributors

Preface

Acknowledgments

Section 1: Introduction to Biosensors, Recognition Elements, Biomarkers, and Nanomaterials

1 General Introduction to Biosensors and Recognition Receptors

1.1 Introduction to Biosensors

1.2 Enzyme‐Based Biosensors

1.3 DNA‐ and RNA‐Based Biosensors

1.4 Antibody‐Based Biosensors

1.5 Aptasensors

1.6 Peptide‐Based Biosensors

1.7 MIP‐Based Biosensors

1.8 Conclusions

Acknowledgment

References

2 Biomarkers in Health Care

2.1 Introduction

2.2 Biomarkers

2.3 Biological Samples and Biomarkers

2.4 Personalized Health and Point‐of‐Care Technology

2.5 Use of Biomarkers in Biosensing Technology

2.6 Biomarkers in Disease Diagnosis

2.7 Conclusions

References

3 The Use of Nanomaterials and Microfluidics in Medical Diagnostics

3.1 Introduction

3.2 Nanomaterials in Medical Diagnostics (Bottom‐Up Approach)

3.3 Application of Microfluidic Devices in Clinical Diagnostics (Top‐Down Approach)

3.4 Integration of Microfluidics with Nanomaterials

3.5 Future Perspectives of Nanomaterial and Microfluidic‐Based Diagnostics

References

Section 2: Biosensor Platforms for Disease Detection and Diagnostics

4 SPR‐Based Biosensor Technologies in Disease Detection and Diagnostics

4.1 Introduction

4.2 Basic Theoretical Principles

4.3 SPR Applications in Disease Detection and Diagnostics

4.4 Conclusions

Acknowledgment

References

5 Piezoelectric‐Based Biosensor Technologies in Disease Detection and Diagnostics

5.1 Introduction

5.2 QCM Biosensors

5.3 Disease Diagnosis Using QCM Biosensors

5.4 Conclusions

Acknowledgment

References

6 Electrochemical‐Based Biosensor Technologies in Disease Detection and Diagnostics

6.1 Introduction

6.2 Electrochemical Biosensors: Definitions, Principles, and Classifications

6.3 Biomarkers in Clinical Applications

6.4 Conclusions

References

7 MEMS‐Based Cell Counting Methods

7.1 Introduction

7.2 MEMS‐Based Cell Counting Methods

7.3 Electrical and Electrochemical Cell Counting Methods

7.4 Gravimetric Cell Counting Methods

7.5 Conclusion and Comments

References

8 Lab‐on‐a‐Chip Platforms for Disease Detection and Diagnosis

8.1 Introduction

8.2 Continuous Flow Platforms

8.3 Paper‐Based LOC Platforms

8.4 Droplet‐Based LOC Platforms

8.5 Digital Microfluidic‐Based LOC Platforms

8.6 CD‐Based LOC Platforms

8.7 Wearable LOC Platforms

8.8 Conclusion and Outlook

References

Section 3: Nanomaterial’s Applications in Biosensors and Medical Diagnosis

9 Applications of Quantum Dots in Biosensors and Diagnostics

9.1 Introduction

9.2 Quantum Dots: Optical Properties, Synthesis, and Surface Chemistry

9.3 Biosensor Applications of QDs

9.4 Other Biological Applications of QDs

9.5 Water Solubility and Cytotoxicity

9.6 Conclusion

Acknowledgment

References

10 Applications of Molecularly Imprinted Nanostructures in Biosensors and Medical Diagnosis

10.1 Introduction

10.2 Molecular Imprinted Polymers

10.3 Imprinting Approaches

10.4 Molecularly Imprinted Nanostructures

10.5 MIP Biosensors in Medical Diagnosis

10.6 Diagnostic Applications of MIP Nanostructures

10.7 Conclusions

References

11 Smart Nanomaterials

11.1 Introduction

11.2 Metal Nanoparticles

11.3 Magnetic Nanoparticles

11.4 Carbon Nanotubes

11.5 Graphene

11.6 Nanostructured Metal Oxides

11.7 Nanostructured Hydrogels

11.8 Nanostructured Conducting Polymers

11.9 Conclusions and Future Trends

Acknowledgment

References

12 Applications of Magnetic Nanomaterials in Biosensors and Diagnostics

12.1 Introduction

12.2 MNP‐Based Biosensors for Disease Detection

12.3 MNPs in Cancer Diagnosis and Therapy

12.4 Cellular Applications of MNPs in Biosensing, Imaging, and Therapy

12.5 Conclusions

Acknowledgment

References

13 Graphene Applications in Biosensors and Diagnostics

13.1 Introduction

13.2 Graphene and Biosensors

13.3 Medical Applications of Graphene

13.4 Conclusions

Acknowledgment

References

Section 4: Organ‐Specific Health Care Applications for Disease Cases Using Biosensors

14 Optical Biosensors and Applications to Drug Discovery for Cancer Cases

14.1 Introduction

14.2 Biosensor Technology and Coupling Chemistries

14.3 Optical Biosensors for Drug Discovery

14.4 Computational Simulations and New Approaches for Drug–Receptor Interactions

14.5 Conclusions

Acknowledgment

References

15 Biosensors for Detection of Anticancer Drug–DNA Interactions

15.1 Introduction

15.2 Voltammetric Techniques

15.3 Optical Techniques

15.4 Electrochemical Impedance Spectroscopy Technique

15.5 QCM Technique

15.6 Conclusions

Acknowledgments

References

Index

End User License Agreement

List of Tables

Chapter 02

Table 2.1 Main biological sample, sampling techniques, and key properties.

Table 2.2 Selected analyte tests available in POCT format that have been waived by Clinical Laboratory Improvement Amendments of 1988.

Table 2.3 Clinically important biomarkers and associated disease states.

Chapter 04

Table 4.1 Advantages and disadvantages of SPR biosensors.

Table 4.2 Detection of pathogens using biomimetic SPR sensors.

Chapter 05

Table 5.1 Literature related to QCM immunosensor and the limits of detection achieved for some pathogenic bacteria.

Chapter 06

Table 6.1 FDA‐approved serum and urine biomarkers for cancer diagnosis, prognosis, and therapy selection.

Table 6.2 Biosensor for cancer biomarkers detection.

Table 6.3 Biomarkers for cardiovascular diseases.

Table 6.4 Electrochemical biosensor for cardiac biomarkers detection.

Table 6.5 Electrochemical biosensor for autoimmune and infectious disease biomarkers detection.

Chapter 12

Table 12.1 Various applications of MNPs in biosensors for the detection of target biological compounds in different samples.

Table 12.2 Different biomolecules and coating agents utilized in the manufacturing of magnetic nanoparticles.

Table 12.3 Different general features of iron oxide‐based MNPs synthesized using different chemical techniques.

Chapter 14

Table 14.1 Key features of Biacore 8K SPR system and Biacore 4000 as potential high‐throughput drug discovery tools.

Table 14.2 Developers of label‐free optical sensor platforms.

Table 14.3 Characteristics of MASS‐1 (Sierra Sensors, Hamburg, Germany) surface plasmon resonance biosensor.

Table 14.4 Examples of SPR‐based drug discovery works from the literature.

Chapter 15

Table 15.1 Summary of the recent biosensor studies of interactions between anticancer drugs and nucleic acids.

List of Illustrations

Chapter 01

Figure 1.1 Schematic of a second‐generation biosensor.

Figure 1.2 Schematic of interstrand binding in DNA.

Figure 1.3 Schematic of an antibody‐based immunosensor.

Figure 1.4 Scheme for the systematic evolution of ligands by exponential (SELEX) enrichment process.

Figure 1.5 Schematic representation of the imprinting process.

Chapter 02

Figure 2.1 Schematic of an ideal POC diagnostic device. The ideal POC device would be able to quantitatively detect several analytes, within minutes, at ultrasensitive femtomolar sensitivity. Sample handling, preparation, detection, and electronics for displaying the diagnostic result would all be included.

Figure 2.2 Schematic of a typical biosensor.

Chapter 03

Figure 3.1 Classification of nanomaterials for use in medical diagnostics.

Chapter 04

Figure 4.1 Applications of SPR including sample targets, measurable parameters, and reactions that facilitate measurement.

Figure 4.2 Generic principle of SPR. The receptor of interest is immobilized to a polymer matrix using a well‐established surface chemistry. The analyte is then passed through a flow cell over the receptor‐derivatized matrix. Any change in mass following the interaction between the receptor and the analyte is detected as a change in the angle of the incident light needed to generate the surface plasmon resonance phenomenon at the gold polymer interface (a). This is measured as an energy or reflectance dip as a function of pixels, which translates to response units (RU) over time. The RU change is directly proportional to molecular mass change, and so binding kinetics and stoichiometry can be measured in real time without any label (b).

Figure 4.3 Interrogation modes for commercial surface plasmon resonance (SPR)‐based instruments.

Figure 4.4 Light‐scattering images and microabsorption spectra of HaCaT noncancerous cells, HOC cancerous cells, and HSC cancerous cells after incubation with unconjugated colloidal gold nanoparticles. The images display that the particles are inside the cells in the cytoplasm region but do not seem to adsorb strongly on the nuclei of the cells. The absorption spectra were measured for 25 different single cells of each kind. They show that nanoparticles have an SPR absorption maximum around 548 nm, independent of the cell type. The broad long wavelength tails in the absorption spectra suggest the presence of aggregates. It also shows that no specific difference is observed in either the scattering images or the absorption spectra of the gold nanoparticles in the cancerous and the noncancerous cells.

Figure 4.5 Schematic illustration of the AuNP‐enhanced SPR biosensor with an aptamer–antibody sandwich assay.

Figure 4.6 Synthesis of an adenovirus‐specific biomimetic receptor and its use in an SPR‐based biosensor for the real‐time detection of viruses.

Chapter 05

Figure 5.1 Experimental apparatus for a piezoelectric system.

Figure 5.2 The principle of (a) QCM and (b) a sensorgram of an interaction of antibody and antigen showing the establishment of an initial baseline, association, dissociation, and regeneration phases.

Figure 5.3 Biosensor detection principle with representation of the gold electrode surface (a); EIS measurements showing a signal change in the Nyquist plot before (i) and after (ii) PSA binding (b); QCM‐D measurements showing frequency and dissipation responses before (i) and after (ii) PSA binding (c).

Figure 5.4 Schematic representation of a flow‐type QCM immunosensor system. a, Buffer reservoir; b, micro‐dispensing pump; c, injector; d, flow‐through cell; d1, acryl holder; d2, O‐ring; d3, QCM; d4, joint; e, disposal basin; f, oscillator module; g, quartz crystal analyzer; h, PC.

Chapter 06

Figure 6.1 Schematic representation of electrochemical biosensors development: (a) catalytic biosensor; (b) affinity biosensor: (1) label‐free approaches and (2) label approach.

Figure 6.2 Schematic representation of immunosensor development for CA125 detection: direct adsorption (ISA) and covalent amine bond formation (ISB). (a) Cystamine‐modified gold electrode, (b) covalent immobilization of Au–Ag NPs, (c) covalent immobilization of

anti‐

CA125 on citrate stabilized Au–Ag NPs for immunosensor A (

ISA

), (d) amine functionalization of immobilized Au–Ag NPs, and (e) covalent immobilization of

anti

‐CA125 on amine functionalized Au–Ag NPs for immunosensor B (

ISB

).

Figure 6.3 Representation of immunosensors development and approaches for CA125 and CEA determination.

Figure 6.4 Molecular‐imprinted polymer (MIP)‐based electrochemical biosensor for myoglobin determination. (a) Formation of a polymer layer on bare gold chip with carboxylated poly(vinyl chloride) (PVC‐COOH). (b) Covalent attachment of myoglobin protein on the activated surface. (c) Imprinting of the protein template. (d) Removal of the template from polymer matrix and obtaining MIP for myoglobin detection.

Figure 6.5 Immunosensor development (a) and signal acquisition (b) for determination C‐reacted protein in serum samples.

Figure 6.6 Scheme of immunosensor development for MPO

active

(route 3a) and MPO

mass

(route 3b) detections.

Figure 6.7 Molecular beacon‐based electrochemical biosensor for hepatitis B virus determination.

Chapter 07

Figure 7.1 The fluorescence Jablonski diagram. (1) Excitation of atoms to higher energy states . (2) Dissipation of some energy as heat or other background processes. (3) Emission of photons (luminescence) and return of the atoms to the initial state

S

i

.

Figure 7.2 Schematic of the subcomponents and processes that constitute an integrated portable micro‐cytometer reader, substance packs, and disposable microfluidic chip.

Figure 7.3 Lensless shadow image (a) and fluorescence image of the fluorescent microspheres (b); lensless shadow image (c) and fluorescence image of L929 cells (d), as well the composition (e) of both type pictures; and the bright filed (f) and fluorescent microscopy images for the same L929 sample (g).

Figure 7.4 Wide field‐of‐view fluorescence imaging of the GFP cells. (a) The 3.7 mm × 3.5 mm image. (b1, c1, d1, and e1) Cropped images of typical cells in (a), including G1 (b1), G2 (c1), metaphase (d1), and anaphase (e1) (arrows) (b2, c2, d2, and e2). The same cells as imaged by a conventional microscope with a 20×/0.4 NA objective.

Figure 7.5 LUCAS images for (a) monocytes, (b) NIH‐3T3 fibroblasts, (c) red blood cells. Each image has next to it a zoomed version of a cell and its comparison with a low NA microscope image. (d) Computer‐assisted automated detection of the dynamic location of the red blood cells. A total of 41 cells are counted within a field of view of 1.4 mm × 1.4 mm.

Figure 7.6 Illustration of a Coulter counter.

Figure 7.7 Dimensions of a cell in an aperture.

Figure 7.8 (a) Schematics of the MEMS Coulter counter. (b) A magnified view of the focusing and detection electrodes, and the Coulter channel.

Figure 7.9 Effect of mass loading on cantilever.

Figure 7.10 Illustration of mass, spring, and damping system.

m

denotes the resonating mass,

B

is damping constant, and

k

is spring constant.

Figure 7.11 Generation of drive force of resonator.

Figure 7.12 Electrical model of thermal actuators.

Figure 7.13 Spring, mass, and damping system whose motion will be sensed electrostatically.

Figure 7.14 Cantilever vibrating in

z

direction.

Figure 7.15 A resonant sensor with uniform mass sensitivity.

Figure 7.16 (a) SMR system proposed in Ref. [30] is illustrated. (b) Immobilized molecules increase the mass of the channel and create a change in resonance frequency. (c) During particle flow, resonance frequency changes according to position of the particle. Exact mass of the particle can be found by the peak resonance frequency shift.

Figure 7.17 Laterally resonating structure for cell sensing.

Figure 7.18 Air trapping method.

Chapter 08

Figure 8.1 Automated on‐chip DNA purification. (a–e) The workflow summarizing the on‐chip DNA isolation protocol. (f) Picture of the parallel DNA purifier system that has a two‐layer structure for fluid flow and valve actuation. Fluid channels and control channels have widths of 100 and 200 µm, respectively.

Figure 8.2 CTC isolation chip. Whole blood mixed with antibody‐tagged magnetic beads is sent to the channel in parallel with buffer solution. Deterministic lateral displacement compartment of the chip enables the separation of white blood cells (WBCs) and CTCs from the whole blood. Then, WBCs and CTCs are brought on a single line after passing asymmetric focusing elements utilizing inertial microfluidics. Finally, magnetic force is applied over centrally aligned cells that force magnetically labeled CTCs to a different outlet, completing the isolation process.

Figure 8.3 Agilent’s capillary electrophoresis chip. (a) Sample is delivered to the junction area. (b) Small amount of sample is metered into the detection channel. (c) DNA is electrophoretically separated. (d) Separated DNA is fluorescently detected.

Figure 8.4 (a) Complete single‐stranded DNA is cut into small pieces, and every piece is bonded on a single bead for amplification to cover all surfaces of the bead where it is placed on a small pH sensitive well. (b) Nucleotides are flowed through the chip; if there is a conjugate base in the well, then it is incorporated, releasing H

+

ions. Changing pH, resulting from H

+

ions, is detected by CMOS sensor underneath the well. (c) Signals obtained from each well are encoded to obtain the DNA sequence.

Figure 8.5 Illustration of a single‐layer microfluidic paper‐based device fabricated by photolithography.

Figure 8.6 Different detection methods used in a paper‐based platform. (a) Colorimetric detection of glucose and protein.(b) Electrochemical detection of three analytes implemented on a single device.

Figure 8.7 A collection of droplet unit operations: (a) droplet generation [46], (b) mixing and generation [47], (c) fusion [48], (d) incubation, (e) storage [49], (f) detection, (g) sorting, and (h) re‐injection [46].

Figure 8.8 Detection of mutated DNA using digital PCR in picoliter microdroplets. (a) An overview of the system showing the production, mixing, and collection of droplets containing DNA, PCR reagents, and TaqMan probes. (b) Thermocycling and amplification of the emulsion. (c) Reinjection of droplets and analysis of the fluorescence signal of individual droplets.

Figure 8.9 A sample digital microfluidic platform system that is capable of working with multiple droplets without requiring any channels, pumps, or valves.

Figure 8.10 Digital microfluidic platforms: (a) Close system and (b) open system. (c) 2D schematic illustration of digital microfluidic chip illustrating dispensing, merging, splitting, and mixing units.

Figure 8.11 (a) Photograph of a CD microfluidic cartridge unit, reaction wells are filled with dye solution. (b) Schematic of the microfluidic design showing some critical components such as inlet, metering chambers, burst valves, and reaction wells.

Chapter 09

Figure 9.1 Relation of quantum dot size with emission peak using single wavelength excitation.

Figure 9.2 Quantum dots (QDs) are a heterogeneous group of materials. Biological fate and toxicity depend on QD physicochemical properties. Shape, core composition, size, and shell composition can be manipulated during QD synthesis. Post‐synthesis surface ligands are added to solubilize and target the particles. An additional coating can further protect the QD core from oxidation. Surface chemistry influences the quantum dot’s propensity to aggregate, particularly in biological solutions.

Figure 9.3 (a) Principle of target‐recycled nonenzymatic amplification and (b) schematic of QD‐labeled strip biosensor for amplification product detection.

Figure 9.4 (a) Fluorescence graphs of the immune complexes detected using different concentrations of CYRFA 21‐1, NSE, and CEA. (b) Standard curves for the detection of CYRFA 21‐1, NSE, and CEA in the multiplexed assay (using four‐parameter equation fitting). (c) The linear range of the plot in (b).

Figure 9.5 Breast tumor targeting with QD‐PEG‐P. (a) Intravital whole‐body fluorescent imaging (ventral view) of MCF10CA1a breast tumor‐bearing nude mice at 28 h after intravenous injection of PBS, QD‐PEG, or QD‐PEG‐P. Images were taken under 700 nm channel of LI‐COR Pearl Impulse small animal imaging system. White arrows show the position of MCF10CA1a tumors (circled in black). (b)

Ex vivo

CW and TG imaging of the tissues harvested from the mice in (a) after the

in vivo

imaging. B, brain; H, heart; K, kidney; Li, liver; Lu, lung; Sp, spleen; T, tumor.

Chapter 10

Figure 10.1 Schematic presentation of the molecular imprinting process.

Figure 10.2 Virus detection using molecularly imprinted nanopolymer‐functionalized SPR biosensor.

Chapter 11

Figure 11.1 Schematic of a biosensor.

Figure 11.2 Glucose biosensor containing AuNPs. (a, b) Deposition of LbL multilayers containing polyvinyl sulfonate/PAMAM‐Au. (c) After deposition of three bilayers, an ITO‐(PVS/PAMAM‐Au)

3

@CoHCF electrode was prepared by potential cycling. (d) Immobilizing enzyme using bovine serum albumin (BSA), glutaraldehyde, and glucose oxidase (GOx).

Figure 11.3 Schematics of used detection formats: direct detection (a), sandwich assays with amplification by detection antibody (b), and MNP‐dAb without (c) and with (d) applied magnetic field. Detection format consisting of preincubating MNP‐dAb with βhCG followed by sandwich assay upon applied magnetic field gradient (e).

Figure 11.4 NMR‐filter system for bacterial concentration and detection. (a) The system consists of a microcoil and a membrane filter integrated with a microfluidic channel. The microcoil is used for NMR measurements; the membrane filter concentrates bacteria inside the NMR detection chamber to achieve high detection sensitivity. (b) A prototype device with two measurement sites. The NMR detection volume was approximately 1 μL.

Figure 11.5 Schematic of (a) graphene and single‐walled carbon nanotube (SWCNT) and (b) few layer graphene and multiwalled carbon nanotube (MWCNT) structures.

Figure 11.6 (a) SEM image of the well‐aligned MWCNTs and (b) current–time response obtained on increasing the glucose concentration from 2.0 µmol L

−1

to 11 mmol L

−1

at (A) GCE and (B) MWCNT electrodes. Shown in the inset is the dependence of the current response versus the concentration of glucose at (a) GCE and (b) MWCNT electrodes.

Figure 11.7 Schematic of a label‐free electrochemical aptasensor for thrombin detection prepared on a single‐walled carbon nanotube (SWCNT)‐casted glassy carbon electrode (GCE) and the EC′ reaction mechanism. Inset: the electrocatalytic current of Ru(bpy)

3

2+

with (solid) and without (dashed) probe TBA that contains guanine.

Figure 11.8 (a) Representative Nyquist diagrams of 1.0 mmol L

−1

[Fe(CN)

6

]

3−/4−

in 0.1 mol L

−1

KCl recorded at a ssDNA/Fe

3

O

4

/CNTs/CPE and after the hybridization reaction with different concentrations of the BCR/ABL fusion gene target sequence: (

a

) ssDNA/Fe

3

O

4

/CNTs/CPE, (

b

) 1.0 × 10

−15

, (

c

) 1.0 × 10

−14

, (

d

) 1.0 × 10

−13

, (

e

) 1.0 × 10

−12

, (

f

) 1.0 × 10

−11

, (

g

) 1.0 × 10

−10

, and (

h

) 1.0 × 10

−9

 mol L

−1

. (b) The plot of ∆

R

ct

versus the logarithm of the BCR/ABL fusion gene target sequence concentrations.

Figure 11.9 (a) Cyclic voltammograms of CuO NWs/CF with or without nitrogen bubbling; (b) anti‐interference property of the CuO NWs/CF electrode with initial addition of 1.0 mmol L

−1

glucose and 0.1 mmol L

−1

ascorbic acid (AA), uric acid (UA), dopamine (DA), 0.5 mmol L

−1

cysteine, 0.1 mmol L

−1

sodium chloride (NaCl), and then again 1.0 mmol L

−1

glucose, followed by addition of 0.05 mmol L

−1

lactose, sucrose, and maltose and lastly of 1.0 mmol L

−1

glucose; (c) reproducibility of six CuO NWs/CF electrodes for detection of 0.5 mmol L

−1

glucose; the inset shows the repeatability of CuO NWs/CF electrode for detecting 0.5 mmol L

−1

glucose for eight times; (d) the stability measurement of CuO NWs/CF electrode for 15 days.

Figure 11.10 Schematic of the mechanism proposed by Marioli and Kuwana, M

*

 = Zn, Co.

Figure 11.11 (a) Schematic of the steps involved in the synthesis of the spotted nanoflower (NF) DNA bioelectrode. (b) FESEM image of low magnification revealing the flower‐like ZnO nanostructure possessing hexagonally shaped tips, which demonstrate the high crystallinity of the prepared ZnO nanowire ends. (c and d) Low‐ and high‐magnification images of spotted NFs indicate that radially oriented NFs have an average length of 2–3 µm and a diameter of approximately 100 nm.

Figure 11.12 (a) Impedance spectra of (i) spotted NF, (ii) spotted NF/p‐DNA (probe), and (iii) spotted NF/p‐DNA/t‐DNA (duplex) bioelectrode; the inset shows the Randles equivalent circuit, where the parameters

R

a

,

R

ct

, and CPE represent the bulk solution resistance, charge transfer resistance, and constant phase element, respectively. (b) Impedimetric response curve of spotted NF/p‐DNA hybridized with different concentrations of complementary target DNA (i–viii), 10 μM to 100 fm. (c) Imaginary part showing the overall impedance, which decreases, and the peak frequency, which is shifted toward the higher frequencies as the concentration of complementary DNA decreases. (d) The gain curve of spotted NF/p‐DNA hybridized at different concentrations.

Figure 11.13 Comparison of volumes of a topological gel (polyrotaxane gel) in as‐prepared (a), dried (b), and swelling equilibrium (c) states.

Figure 11.14 Classification of hydrogels based on different properties.

Figure 11.15 Schematic of the general sensing mechanism of a CPH‐based electrode platform. (a) PtNPs and enzymes were loaded onto hierarchically 3D porous PAni hydrogel matrices to form PAni hydrogel/PtNP hybrid electrodes. (b) The PtNP‐catalyzed sensing process of the biosensor based on PAni/PtNP/enzyme hybrid films.

Figure 11.16 Schematic of four strategies for selective saccharide sensing via multivalent boronic acid–saccharide interactions, exemplified by glucose sensing. (a) Synthetic diboronic acids that form 1 : 1 cyclic boronate esters with glucose, (b) boronic acid‐containing polymers that bind glucose with two of the pendant boronic acid moieties, (c) aggregation of simple boronic acids via non‐covalent interactions to allow multivalent glucose binding, and (d) boronic acid‐conjugated nanomaterials as multivalent scaffolds. Note that the aggregates shown in (c) can be aggregates or saccharide binding altered aggregates of boronic acid or saccharide binding induced aggregates of boronic acid.

Figure 11.17 (a) Fluorescence intensity changes of three different types of hydrogels, A, B, and C, with acrylamide concentration ratio of 1 : 10 : 100 in response to different titers of AIV H5N1 (2

0

, 2

2

, 2

4

, and 2

6

HAU). The means and error bars (standard deviation) were calculated based on three replicates. (b) Two different reaction mechanisms based on the size‐dependent property of the aptamer–hydrogel embedded with ssDNA

2

‐QD conjugates upon target binding for hydrogels A, B, and C.

Figure 11.18 Structures of the most common conducting polymers.

Figure 11.19 Schematic of the mechanism of the soft‐template synthesis of different conducting polymer nanostructures: (a) micelles acted as soft templates in the formation of nanotubes. Micelles were formed by the self‐assembly of dopants, and polymerization was carried out on the surface of the micelles; (b) nanowires formed by the protection of dopants. The polymerization was carried out inside the micelles; (c) monomer droplets acted as soft templates in the formation of microsphere; and (d) polymerization on the substrate producing aligned nanowire arrays. Nanowires were protected by the dopants, and polymerization was carried out on the tips of nanowires.

Figure 11.20 SEM images of (a) granular PPy . Cl (scale bar, 200 nm), (b) PPy . Cl nanoclips (scale bar, 1 µm; inset, digital picture of paper clips), (c) PAni HCl nanoclips (scale bar, 1 µm), and (d) PEDOT Cl nanoclips (scale bar, 1 µm).

Chapter 12

Figure 12.1 Schematic illustration of the strategy for dopamine (DA) detection using dithiobis(sulfosuccinimidylpropionate)‐modified gold nanoparticles (DTSSP‐AuNPs) and Fe

3

O

4

magnetic particles (MPs) (a) and the interaction between DA, Fe

3

O

4

, and DTSSP‐AuNPs (b).

Chapter 13

Figure 13.1 A visual depiction of the structure of a layered microscopic segment of graphene.

Figure 13.2 A schematic showing the conventional methods commonly used for the synthesis of graphene along with their key features and the possible applications.

Figure 13.3 Amperometric responses of modified electrode to successive additions of 0.1 mM

L

‐cysteine, 0.1 mM tyrosine, 0.1 M glucose, 0.1 mM BSA, 0.01 mM UA, and 0.01 mM AA in 0.1 M phosphate buffer of pH 7.0 at 0.7 V.

Figure 13.4 Schematic synthesis of graphene–Au hybrid nanosheets.

Figure 13.5 (a) The biosensing of cholesterol ester with enzyme‐integrated rGO–nPd‐based biosensor is illustrated. Amperometric (b) response obtained for the sensing of cholesterol ester. Corresponding calibration plot is shown in (c).

Figure 13.6 (a) DPVs of 1 mM AA and 200 μM UA mixture (black line), 1 mM AA, 200 μM UA, and 0.1 μM DA in a ternary mixture. The line with two peak points represents the data for 200 μM UA + 1 mM AA. The other line with three peak points are for 0.1 μM DA + 200 μM UA + 1 mM AA. (b) DPV responses of different concentrations of DA in PBS solution (pH = 7.0) containing 1 mM AA and 200 μM UA. The bottom line shows the data for blank. The height of the peak increases with the increased DA concentration.

Figure 13.7 Schematic representation of the Aβab‐MNG platform construction.

Figure 13.8 Schematic of synchronous electrosynthesis of PXa–ERGO for DNA EIS detection.

Figure 13.9 Schematic diagram of SPR principle. Light is directed through a prism of high RI into a surface layer with low RI (sample). At a particular angle total internal refection of the impinging light occurs. Although the light does not enter into the sample medium, the intensity at the interfacial boundary is not equal to zero. The photon energy from the light is transferred to the metal electrons, causing them to oscillate and produce surface‐bound plasmons. This produces an exponential evanescent wave that penetrates a defined distance (~100 nm) into the low index medium, resulting in a characterized decrease in reflected light intensity.

Figure 13.10 Principle of graphene‐based fiber optic SPR biosensor and experimental setup.

Chapter 14

Figure 14.1 Stages of drug discovery process.

Figure 14.2 Covalent coupling methods to immobilize receptors onto the sensor surface. (a) Water‐soluble EDC‐mediated coupling. (b) Amino‐presenting surfaces. (c) Salicylhydroxamic acid‐derivatized surfaces.

Figure 14.3 Non‐covalent coupling methods to immobilize receptors onto the sensor surface.

Chapter 15

Figure 15.1 Schematic presentation of biosensing of anticancer drug–DNA interactions.

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Biosensors and Nanotechnology

Applications in Health Care Diagnostics

Edited by Zeynep Altintas

Technical University of Berlin, Berlin, Germany

This edition first published 2018© 2018 John Wiley & Sons, Inc.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

The right of Zeynep Altintas to be identified as the editor of this work has been asserted in accordance with law.

Registered OfficeJohn Wiley & Sons, Inc, 111 River Street, Hoboken, NJ 07030, USA

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For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

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Limit of Liability/Disclaimer of WarrantyIn view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

Library of Congress Cataloging‐in‐Publication data applied for

ISBN: 9781119065012

Cover design by WileyCover image: © petersimoncik/Gettyimages

List of Contributors

Sinan AkgolDepartment of Biochemistry, Faculty of ScienceEge UniversityIzmirTurkey

Deniz Aktas‐UygunDepartment of Chemistry, Faculty of Science and ArtsAdnan Menderes UniversityAydinTurkey

Zeynep AltintasTechnical University of BerlinBerlinGermany

Adina Arvinte“Petru Poni” Institute of Macromolecular ChemistryCentre of Advanced Research in Bionanoconjugates and BiopolymersIasiRomania

Mohammad AsghariInstitute of Materials Science and Nanotechnology, National Nanotechnology Research Center (UNAM)Bilkent UniversityAnkaraTurkey

Jon AshleyDepartment of Micro‐ and NanotechnologyTechnical University of DenmarkLyngbyDenmark

Eren AydınDepartment of Electrical and Electronics EngineeringMiddle East Technical UniversityAnkaraTurkey

Frank DavisDepartment of Engineering and Applied DesignUniversity of ChichesterChichesterUK

Ece EksinDepartment of Analytical Chemistry, Faculty of PharmacyEge UniversityIzmirTurkey

Caglar ElbukenInstitute of Materials Science and Nanotechnology, National Nanotechnology Research Center (UNAM)Bilkent UniversityAnkaraTurkey

Arzum ErdemDepartment of Analytical Chemistry, Faculty of PharmacyEge UniversityIzmirTurkey

Wellington M. FakanyaAtlas Genetics LtdWiltshireUK

Furkan GökçeDepartment of Electrical and Electronics EngineeringMiddle East Technical UniversityAnkaraTurkey

Mustafa Tahsin GulerDepartment of PhysicsKirikkale UniversityKirikkaleTurkey

Ziya IsiksacanInstitute of Materials Science and Nanotechnology, National Nanotechnology Research Center (UNAM)Bilkent UniversityAnkaraTurkey

Ali KalantarifardInstitute of Materials Science and Nanotechnology, National Nanotechnology Research Center (UNAM)Bilkent UniversityAnkaraTurkey

Mustafa KangülDepartment of Electrical and Electronics EngineeringMiddle East Technical UniversityAnkaraTurkey

Ece KesiciDepartment of Analytical Chemistry, Faculty of PharmacyEge UniversityIzmirTurkey

Haluk KülahDepartment of Electrical and Electronics EngineeringMiddle East Technical UniversityAnkaraTurkey

Giovanna MarrazzaDepartment of Chemistry “Ugo Schiff”University of FlorenceFlorenceItaly

Noor Azlina MasdorCranfield UniversityCranfieldUKandMalaysian Agricultural Research and Development Institute (MARDI)Kuala LumpurMalaysia

Ebru ÖzgürDepartment of Electrical and Electronics EngineeringMiddle East Technical UniversityAnkaraTurkey

Andrea RavalliDepartment of Chemistry “Ugo Schiff”University of FlorenceFlorenceItaly

Frieder W. SchellerInstitute of Biochemistry and BiologyUniversity of PotsdamPotsdamGermany

Adama Marie SesayUnit of Measurement Technology, Kajaani University ConsortiumUniversity of OuluOuluFinland

Flavio M. ShimizuSão Carlos Institute of Physics (IFSC)University of São Paulo (USP)São CarlosBrazil

Yi SunDepartment of Micro‐ and NanotechnologyTechnical University of DenmarkLyngbyDenmark

Pirkko TervoUnit of Measurement Technology, Kajaani University ConsortiumUniversity of OuluOuluFinland

Elisa TikkanenUnit of Measurement Technology, Kajaani University ConsortiumUniversity of OuluOuluFinland

Murat UygunDepartment of Chemistry, Faculty of Science and ArtsAdnan Menderes UniversityAydinTurkey

Özge ZorluDepartment of Electrical and Electronics EngineeringMiddle East Technical UniversityAnkaraTurkey

Preface

Multifactorial diseases such as cancer, cardiovascular disorders, and infectious diseases are the leading cause of death worldwide. This is mostly due to the lack of early diagnosis, which plays a key role in successful treatment and in elimination of huge costs required for the treatment. Today, common use of biosensor technology in the field of medical diagnostics and drug discovery has resulted in cost‐effective, rapid, reliable, and easy‐to‐use sensing platforms. Biomedical sensors are analytical devices that utilize recognition elements such as antibodies, aptamers, peptides, and molecularly imprinted polymers for detection. They possess two main elements: (i) a biological recognition element (receptor) that supplies specific binding through a biochemical interaction of a target to a receptor and (ii) a signal transducer that converts this biochemical reaction into an easily measurable electrical signal. Other components of biosensors are the input/output systems to operate the sensing device and fluidics systems to handle reagents and samples necessary for the testing. In this book, we aim to describe a range of biosensor technologies for the detection of cancer, cardiac problems, and neurodegenerative and infectious diseases with the hope of helping the integration of biomedical sensors into common clinical usage.

Advancements taking place in nanotechnology, microelectronics, computational science, and biomedical engineering have led to new technologies and application‐specific devices by bringing various disciplines together. However, there is still a gap between research and clinical applications. Taking this fact into account, the objective of this book is to provide a wide range of information from basic to the advanced applications in the biosensor area and impact of nanotechnology on the development of biosensors for healthcare. A significant up‐to‐date review of various sensor platforms, their use in cancer, cardiovascular system problems, neurodegenerative disorders, infectious diseases, and drug discovery with the implementations of smart nanomaterials is also given. This project is a comprehensive approach to the medical biosensors area presenting a thorough knowledge of the subject and an effective integration of these sensors on healthcare in order to appropriately convey the state‐of‐the‐art fundamentals and applications of the most innovative technologies.

This book is comprised of 15 chapters written by 31 researchers who are actively working in Germany, the United Kingdom, Italy, Turkey, Denmark, Finland, Romania, Malaysia, and Brazil. The book covers four main sections: Section 1 describes general information on biosensors, recognition receptors, biomarkers, and disease diagnostics. Section 2 provides biosensor‐based healthcare applications through various sensing systems, and it covers all main types of biosensors including surface plasmon resonance‐, piezoelectric‐, electrochemical‐, microelectromechanical‐, and lab‐on‐a‐chip‐based sensors in disease detection and diagnostics. Applications of nanomaterials in biosensors and diagnostics follow this part as the third main section, Section 3, and it talks about the application of quantum dots, carbon nanotubes, metal nanoparticles, molecularly imprinted nanostructures, magnetic nanomaterials, and graphene with the latest trends in the field. The last section, Section 4, is dedicated to organ‐specific healthcare applications for disease cases using biosensors. In this part, optical biosensors and applications to drug discovery for cancer cases, and also DNA‐based biosensors for anticancer drug detection, are covered.

The anticipated audience is researchers, scientists, regulators, consultants, and engineers. Furthermore, graduate students will find this book very useful since it provides a wide range of knowledge on biosensors for healthcare diagnostics. The contributors of the book were also asked to use a pedagogical tone to comply with the needs of novice researchers such as doctoral students and postdoctoral scholars as well as of senior researchers seeking new pathways. All related and significant subtopics are given in one book to provide a not only comprehensive but also easily understandable handbook in the area. Educational purposes were also considered while generating this book; hence it has a great potential to be used as a textbook in universities and research institutes. The complexity and flow of the book is suitable for all related and interested students in the area.

March 2017, Berlin

Zeynep AltintasDepartment of Chemical EngineeringandDepartment of Biomolecular ModellingTechnical University of Berlin, Germany

Acknowledgments

We are very thankful to all the authors for their participation and invaluable contributions in the making of this book. I also extend my thanks to Tom Scrace and Sumathi Elangovan of John Wiley who assisted me in all stages of preparing this book for the publication. Last, but not least, I dedicate this book to my parents, Ilyas and Eva, with sincere regards and my niece, Beren, who inspired me while working on the book.

Section 1Introduction to Biosensors, Recognition Elements, Biomarkers, and Nanomaterials

1General Introduction to Biosensors and Recognition Receptors

Frank Davis1 and Zeynep Altintas2

1 Department of Engineering and Applied Design, University of Chichester, Chichester, UK

2 Technical University of Berlin, Berlin, Germany

1.1 Introduction to Biosensors

There are laboratory tests and protocols for the detection of various biomarkers, which can be used to diagnose heart attack, stroke, cancer, multiple sclerosis, or any other conditions. However, these laboratory protocols often require costly equipment, and skilled technical staff, and hospital attendance and have time constraints. Much cheaper methods can provide cost‐effective analysis at home, in a doctor’s surgery, or in an ambulance. Rapid diagnosis will also aid in the treatment of many conditions. Biosensors generically offer simplified reagentless analyses for a range of biomedical [1–8] and industrial applications [9, 10]. Due to this, biosensor technology has continued to develop into an ever‐expanding and multidisciplinary field during the last few decades.

The IUPAC definition of a biosensor is “a device that uses specific biochemical reactions mediated by isolated enzymes, immunosystems, tissues, organelles or whole cells to detect chemical compounds usually by electrical, thermal or optical signals.” From this definition, we can gain an understanding of what a biosensor requires.

Most sensors consist of three principal components:

Firstly there must be a component, which will selectively recognize the analyte of interest. Usually this requires a binding event to occur between the recognition element and target.

Secondly some form of transducing element is needed, which converts the biochemical binding event into an easily measurable signal. This can be a generation of an electrochemically measurable species such as protons or H

2

O

2

, a change in conductivity, a change in mass, or a change in optical properties such as refractive index.

Thirdly there must be some method for detecting and quantifying the physical change such as measuring an electrical current or a mass or optical change and converting this into useful information.

There exist many methods for detecting binding events such as electrochemical methods including potentiometry, amperometry, and AC impedance; optical methods such as surface plasmon resonance; and piezoelectric methods that measure mass changes such as quartz crystal microbalance (QCM) and surface acoustic wave techniques. A detailed description of these would be outside the remit of this introduction, but they are described in many reviews and elsewhere in this book. Instead this chapter focuses on introducing the recognition receptors used in biosensors.

1.2 Enzyme‐Based Biosensors

Leyland Clark coated an oxygen electrode with a film containing the enzyme glucose oxidase and a dialysis membrane to develop one of the earliest biosensors [11]. This could be used to measure levels of glucose in blood; the enzyme converted the glucose to gluconolactone and hydrogen peroxide with a concurrent consumption of oxygen. The drop in dissolved oxygen could be measured at the electrode and, with careful calibration, levels of blood glucose calculated. This led to the widespread use of enzymes in biosensors, mainly driven by the desire to provide detection of blood glucose. Diabetes is one of the major health issues in the world today and is predicted to affect an estimated 300 million people by 2045 [12]. The world market for biosensors was approximately $15–16 billion in 2016. In 2009 approximately half of the world biosensor market was for point‐of‐care applications and about 32% of the world commercial market for blood glucose monitoring [13].

Enzymes are excellent candidates for use in biosensors, for example, they have high selectivities; glucose oxidase will only interact with glucose and is unaffected by other sugars. Being highly catalytic, enzymes display rapid substrate turnovers, which is important since otherwise they could rapidly become saturated or fail to generate sufficient active species to be detected. However, they demonstrate some disadvantages: for instance, a suitable enzyme for the target of interest may simply not exist. Also enzymes can be difficult and expensive to extract in sufficient quantities and can also be unstable, rapidly denaturing, and becoming useless. They can also be subject to poisoning by a variety of species. Moreover, detection of enzyme turnover may be an issue, for instance, in the glucose oxidase reaction; it is possible to directly electrochemically detect either consumption of oxygen [11] or production of hydrogen peroxide. However in samples such as blood and saliva, there can be other electroactive substances such as ascorbate, which also undergo a redox reaction and lead to false readings. These types of biosensors are often called “first‐generation biosensors.” To address this issue of interference, a second generation of glucose biosensors was developed where a small redox‐active mediating molecule such as a ferrocene derivative was used to shuttle electrons between the enzyme and an electrode [14]. The mediator readily reacts with the enzyme, thereby avoiding competition by ambient oxygen. This allowed much lower potentials to be used in the detection of glucose, thereby reducing the problem of oxidation of interferents and increasing signal accuracy and reliability. Figure 1.1 shows a schematic of a second‐generation glucose biosensor.

Figure 1.1 Schematic of a second‐generation biosensor.

Third‐generation biosensors have also been developed where the enzyme is directly wired to the electrode, using such materials as osmium‐containing redox polymers [15] or conductive polymers such as polyaniline [16]. More recently nanostructured materials such as metal nanoparticles, carbon nanotubes, and graphene have been used to facilitate direct electron transfer between the enzyme and the electrode as described in later chapters. As an alternative to glucose oxidase, sensors based on glucose dehydrogenase have also been developed.

The techniques for glucose sensing using glucose oxidase can be applied to almost any oxidase enzymes, allowing sensors to be developed based on cholesterol oxidase, lactate oxidase, peroxidase enzymes, and many others. Sensors have also been constructed using urease, which converts urea to ammonia, causing a change in local pH that can be detected potentiometrically or optically by combining the enzyme with a suitable optical dye. Enzyme cascades have also been developed; for example, cholesterol esters can be determined using electrodes containing cholesterol esterase and cholesterol oxidase. Applications of enzyme‐containing biosensors have been widely reviewed [16–18].

1.3 DNA‐ and RNA‐Based Biosensors

DNA is contained within all living cells as a blueprint for making proteins, and it can be thought of as a molecular information storage device. RNA also has a wide number of applications in living things, including acting as a messenger between DNA and the ribosomes that synthesize proteins and as a regulator of gene expression. Both DNA and RNA are polymeric species based on a sugar–phosphate backbone with nucleic bases as side chains, in DNA, namely, adenine, cytosine, guanine, and thymine. In RNA uracil is utilized instead of thymine. It is the specific binding between base pairs, that is, guanine to cytosine or adenine to thymine (uracil), that determine the structure of these polymers, in the case of DNA leading to a double helix structure (Figure 1.2) [19].

Figure 1.2 Schematic of interstrand binding in DNA.

DNA sensors are usually of a format where one oligonucleotide chain is bound to a suitable transducer, that is, an electrode, surface plasmon resonance (SPR) chip, quartz crystal microbalance (QCM), and so on, and is exposed to a solution containing an oligonucleotide strand of interest [20]. The surface‐bound oligonucleotide is selected to be complementary to the oligonucleotide of interest, and the bound and solution strands will undergo sequence‐specific hybridization as the recognition event.

An in‐depth review of DNA sensing is outside the scope of this introduction and has been reviewed elsewhere [20–24]; however, a few examples are given here. A method based on ruthenium‐mediated guanine oxidation allowed selective electrochemical detection of messenger RNA from tumors at 500 zmol L−1 levels [25]. A sandwich‐type assay using magnetic beads and fluorescence analysis utilized a complementary nucleotide to dengue fever virus RNA to allow detection at levels as low as 50 pmol L−1 [26]. Five different probe DNAs could be immobilized onto an SPR‐imaging chip and simultaneously used to determine binding of RNA sequences found in several pathogenic bacteria such as Brucella abortus, Escherichia coli, and Staphylococcus aureus [27] for use in food safety.

1.4 Antibody‐Based Biosensors

Antibodies are natural Y‐shaped proteins produced by living systems, usually as a defense mechanism against invading bacteria or viruses. They bind to specific species (antigens) with an extremely high degree of specificity by a mixture of hydrogen bonds and other non‐covalent interactions, with the binding taking place in the cleft of the protein molecule [28]. One major advantage of antibodies is that they can be “raised” by inoculating laboratory animals with the target in question; the natural defense mechanisms of the animal are to develop antibodies to the antigen. These antibodies can then be harvested from animals. A range of animals are used including mice, rats, rabbits, and larger animals such as sheep or llamas. Therefore, it is possible to develop a selective antibody for almost any target. This high selectivity led to first the development of the Nobel prize‐winning radioimmunoassay [29] and then later the enzyme‐linked immunosorbent assay (ELISA) [30], which is commonly used today to quantify a wide range of targets in medical and environmental fields.

Once developed the antibody can be immobilized onto a transducer to develop a biosensor, shown schematically in Figure 1.3. One issue is that when antibodies bind to their antigens to form a complex, no easily measured by‐products such as electrons or redox‐active species are produced. There are several methods of addressing this drawback. For example, a sandwich immunoassay format can be used where an antibody is bound to the surface and an antigen bound to it from the solution to be analyzed. Development then occurs by exposing the sensor to a labeled secondary antibody, which binds to the antigen, and then the presence of the label is detected; this can be an enzyme or a fluorescent or electroactive species. Competitive assays where the sample is spiked with a labeled antigen and then the labeled and sample antigens compete to bind to the immobilized antibody are also used. However these require labeling of the antibody/antigen, which can be problematic, leading to loss of activity and requiring additional steps with their time and cost implications. Therefore, label‐free detection methods have been widely studied that can simply detect the binding event directly without need for labeling. These include electrochemical techniques such as AC impedance, optical techniques such as SPR, and mass‐sensitive techniques such as QCM [28].

Figure 1.3 Schematic of an antibody‐based immunosensor.

Another issue is that the strong binding between antibody and antigen means that there is no turnover of substrate; the binding is essentially irreversible. In this case, the sensors are often prone to saturation and can only be used once. Although the antibody–antigen reaction can be reversed by extremes of pH or strongly ionic solutions, these can damage the antibody, leading to permanent loss of activity. However, if costs can be brought down far enough, the possibilities of simple single‐shot tests for home use become possible. This led to the first commercially available immunoassay, the home pregnancy test, which detects the presence of human chorionic gonadotrophin (hCG). Initial tests simply detect its presence by showing a blue line, that is, pregnant or not pregnant; however later models incorporate an optical reader that measures the color intensity, thereby assessing the hCG level and giving an estimate of time since conception.

1.5 Aptasensors

Aptamers are a family of RNA/DNA‐like oligonucleotides capable of binding a wide variety of targets [31] including proteins, drugs, peptides, and cells. When they bind their targets, the binding event is usually accompanied by conformational changes in the aptamer; for example, it may fold around a small molecule. These structural changes are often easy to detect, making aptamers ideal candidates for sensing purposes. Aptamers also display other advantages over other recognition elements such as enzymes and antibodies. They can be synthesized in vitro, requiring no animal hosts and usually with a high specificity and selectivity to just about any target from small molecules to peptides, proteins, and even whole cells [31]. The lack of an animal host means that aptamers can be synthesized to highly toxic compounds. Once a particular optimal aptamer for a certain target has been determined, it can be commercially synthesized in the pure state and often displays superior stability to other biological molecules, hence their nickname “chemical antibodies.”

Aptamers can be sourced by firstly utilizing a library of random oligonucleotides. It is possible that within this library a number of the oligonucleotides will display an affinity to the target, whereas most of them will not. They are then subjected to a process called systematic evolution of ligands by exponential (SELEX) enrichment. In this process, the library is incubated with the target and then bound molecules, that is, oligonucleotide/target complexes separated and the unbound species discarded. The bound oligonucleotides are then released from the target and then subjected to polymerase chain reaction (PCR) amplification. This then forms a new library for the process to begin again. Over a number of cycles (6–12) [31], the oligonucleotides with the strongest affinity to the target are preferred in a manner similar to natural selection. After a number of cycles, these aptamers are cloned and expressed. Figure 1.4 shows a schematic of this process.

Figure 1.4 Scheme for the systematic evolution of ligands by exponential (SELEX) enrichment process.

Source: Song et al. [31]. Reproduced with permission of Elsevier.

Aptamers bind to their targets with excellent selectivity and high affinity, dissociation constants often being nanomolar or picomolar [32]. Like antibodies, aptamers can be utilized in a variety of formats; for small molecules there is usually a simple 1 : 1 complex formed with the target encapsulated inside the aptamer. However with larger analytes the aptamer binds to the surface of the target, and different aptamers can be isolated, which bind to different areas [31]. This allows for sandwich‐type assays where two aptamers are used to enhance the biosensor response; there also exist mixed sandwich assays using an aptamer and an antibody.

One issue is that since aptamers simply form complexes with their counterparts, again there is no easily detectable product such as a redox‐active species formed. However, the easy availability and stability of aptamers also allows their functionalization with labels such as enzymes, nanoparticles, fluorescent, or redox‐active groups for use in labeled assays. Alternatively, label‐free techniques such as AC impedance, SPR, and QCM can be used to detect binding events [31].

1.6 Peptide‐Based Biosensors

Peptides are natural or synthetic polymers of amino acids and are built from the same building blocks as proteins. Since many proteins have the ability to bind targets with good selectivity and specificity, peptides of the correct amino acid sequence should be capable of doing the same [33]. Shorter peptides have a number of advantages over proteins; they will generally display better conformational and chemical stability than proteins and be much less susceptible to denaturing. Also they can be synthesized with specific sequences using well‐known solid‐phase synthesis protocols and can be easily substituted with labeling groups without affecting their activity. Especially popular is the labeling of one or both ends of the peptide with fluorescent groups [33].

These recognition receptors can be synthesized with a particular sequence or a library of peptides can be used to assess affinity to a particular target. For example, peptides can be made to specifically chelate certain metal ions even in the presence of other metal ions. Peptide‐based sensors are especially effective systems for activity of certain enzymes such as proteases. Proteases can hydrolyze peptide bonds, and certain proteases are linked to many disease states. For example, matrix metallopeptidase‐2 (MMP‐2) and MMP‐9 are thought to be important in a number of inflammatory and pathological processes as well as tumor metastasis [34–36]. Peptides can be used to assess proteinase activity. For example, quantum dots could be coated with peptides conjugated with a large number of dye molecules, fluorescence resonance energy transfer interactions occur between the dye molecules, and the dot, which quenches the dot fluorescence. When a proteinase is added, the peptide is hydrolyzed, the coating removed, and the dot fluorescence returned [37]. Activity of a variety of other materials such as kinases can also be assessed [33].

Libraries of short (<50 amino acids) peptides from random phage display can be screened against various targets as reviewed before [38]. Also in silico modeling of peptide strand interactions with targets of interest can be used to select possible receptor peptides, these can then be synthesized and assayed [38, 39]. One issue however is that immobilizing these onto a solid surface may lead to structural modifications, which remove its activity. Also peptide sequences that form the active sites of natural receptors can be synthesized and can retain the activity of the parent molecule.

1.7 MIP‐Based Biosensors

Biosensors were initially made using biological molecules such as enzymes or antibodies; however, this led to issues such as cost, difficulty in purification and isolation, and stability. The use of semisynthetic materials such as aptamers and peptides that can be synthesized or selected has addressed this issue to some extent. However, another approach is to use totally synthetic materials that mimic the behavior of enzymes or antibodies. This has led to the development of molecularly imprinted polymers (MIPs), which although not biosensors per se, are a possible solution [40–42].

For manufacturing of MIPs, the analyte of interest (often biological in nature) is mixed with a variety of polymerizable monomers and some of these will interact with the analyte. Polymerization will then be initiated and a cross‐linked polymer is formed containing entrapped analytes, which act as templates (Figure 1.5). Removal of the analyte will, if the polymer is sufficiently rigid, leave pores within the polymer, which not only match the template size and shape but also contain their internal surface groups, which will interact with the analyte [42–45]. Often this technique is combined with in silico modeling of the template interaction with a library of monomers, allowing selection of a monomer mixture that will interact strongly with the template [9, 10, 46]. MIPs display several advantages over biological materials; they have much higher stabilities and can be stored dry for months or years, synthesized in large quantities from readily available monomers, and used in nonaqueous solvents and over a range of temperatures [45].

Figure 1.5 Schematic representation of the imprinting process.

Source: Whitcombe and Vulfson [42]. Reproduced with permission of John Wiley & Sons.

A wide variety of protocols can be used. For example, inorganic polymers containing glucose were deposited onto a QCM by a sol–gel process, the glucose washed out, and the resultant system shown to act as a sensor, giving an increase in mass when exposed to aqueous glucose [47]. Polymers can also be deposited electrochemically onto electrode surfaces in the presence of a template. For example, poly(o‐phenylenediamine) could be electrochemically deposited from template solutions onto a QCM chip to give sensors for atropine (with a linear range between 8 × 10−6 and 4 × 10−3