Biosensors and Bioelectronics 61 (2014) 357–369

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Biosensors and Bioelectronics journal homepage: www.elsevier.com/locate/bios

Sensing strategies for influenza surveillance Subash C.B. Gopinath a,b,c,n, Thean-Hock Tang a,n, Yeng Chen c, Marimuthu Citartan a, Junji Tominaga b, Thangavel Lakshmipriya a a

Advanced Medical & Dental Institute (AMDI), Universiti Sains Malaysia, 13200 Kepala Batas, Penang, Malaysia Nanoelectronics Research Institute, National Institute of Advanced Industrial Science & Technology, 1-1-1 Higashi, Tsukuba 305-8562, Ibaraki, Japan c Department of Oral Biology & Biomedical Sciences and OCRCC, Faculty of Dentistry, University of Malaya, 50603 Kuala Lumpur, Malaysia b

art ic l e i nf o

a b s t r a c t

Article history: Received 2 December 2013 Received in revised form 12 April 2014 Accepted 11 May 2014 Available online 17 May 2014

Influenza viruses, which are RNA viruses belonging to the family Orthomyxoviridae, cause respiratory diseases in birds and mammals. With seasonal epidemics, influenza spreads all over the world, resulting in pandemics that cause millions of deaths. Emergence of various types and subtypes of influenza, such as H1N1 and H7N9, requires effective surveillance to prevent their spread and to develop appropriate antiinfluenza vaccines. Diagnostic probes such as glycans, aptamers, and antibodies now allow discrimination among the influenza strains, including new subtypes. Several sensors have been developed based on these probes, efforts made to augment influenza detection. Herein, we review the currently available sensing strategies to detect influenza viruses. & 2014 Elsevier B.V. All rights reserved.

Keywords: Influenza Glycan Antibody Aptamer Sensor Surveillance

Contents 1. 2. 3.

4. 5. 6. 7.

8.

9.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biological assays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Probes to sense the presence of influenza viruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Influenza detection using glycans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Influenza detection using anti-influenza antibodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Influenza detection using anti-influenza aptamers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensors for influenza detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lateral flow test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Colorimetric analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kretschmann configurations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. Surface plasmon resonance (SPR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2. Waveguide-mode sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interferometry-based platform: BioDVD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1. Capturing of the immuno-GNP-virus complex on the disc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2. Capturing the virus using immobilized anti-influenza antibody/aptamer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Strategies for enhancing the efficiency of influenza detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1. Sandwich assays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2. Fluorescence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3. Nanostructures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

n Corresponding author at: Advanced Medical & Dental Institute (AMDI), Universiti Sains Malaysia, Bertam, Kepala Batas 13200, Pulau Pinang, Malaysia. Tel.: þ 60 45622302; fax: þ 60 45622349. E-mail addresses: [email protected] (S.C.B. Gopinath), [email protected] (T.-H. Tang).

http://dx.doi.org/10.1016/j.bios.2014.05.024 0956-5663/& 2014 Elsevier B.V. All rights reserved.

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10. Future perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368

1. Introduction Flu is a severe illness caused by influenza viruses, which infect mainly the upper respiratory tract (nose, throat, bronchi, and lungs). Influenza viruses are spherical, enveloped, and range in diameter from 80 to 120 nm (Noda et al., 2006) (Fig. 1a). The three major types of influenza viruses are influenza A, B, and C. Influenza A commonly circulates among birds and mammals, often causing death (Fig. 1b), whereas influenza B occurs mainly in humans. Although influenza C is not very common, it also causes illness. Two major surface glycoproteins [hemagglutinin (HA) and neuraminidase (NA)] play an important role in influenza infection (Fig. 1a). Influenza A is classified according to its H number (HA) or N number (NA). So far, 17HA and 10NA viruses have been reported (Gopinath et al., 2013c and references therein). Two antigenic and genetic lineages (Yamagata and Victoria) of influenza B virus have been circulating since the 1980s, but they have caused lesser problems than influenza A. New influenza strains are continuously emerging. For example, the “pandemic swine flu” is caused by the H1N1 viral strain (pdm/09). “Bird flu” is mainly a subtype of H5N1 that causes illness mainly in birds. On April 1, 2013, a new bird influenza, A H7N9, was reported in China by the World Health Organization.

HA and NA antigens are the dominant targets for the host antibody response. However, frequent mutations in the amino acid sequences of these surface antigens (a process called antigenic drift) enable the viruses to evade the host immune system. HA is the main viral antigen required for membrane fusion with host cells to mediate early infection (Skehel and Wiley, 2000). HA binds to sialic acid residues (α-2,6 and α-2,3 sialic acids) on the surface of human and bird cells via three important amino acid regions (Fig. 2a and b). Traditionally, biological assays have been utilized to determine the involvement of these molecules in the infection of host cells. Commonly used biological assays for sensing influenza viruses are the hemagglutination assay, hemagglutination inhibition (HI) assay, plaque assay, and microneutralization assay.

2. Biological assays The hemagglutination assay is used to indirectly quantify the titer of influenza virus, and it is based on the specific interaction of the virus HA with the host cell surface glycan (Killian, 2008). Upon binding of the HA to the sialylated glycans of either avian or mammalian erythrocytes, a diffuse lattice is formed due to the

100 nm Hemagglutinin

Neuraminidase

Fig. 1. (a) The intact influenza virus is 80–120 nm in diameter and has two major surface proteins. Crystal structures of hemagglutinin (HA: PDB Accession: 2VIU) and neuraminidase (NA: PDB Accession: 3SAL) are shown. (b) Schematic representation of periods of observed circulation of influenza viruses. The major periods during which influenza had a severe impact on human populations are indicated.

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formation of cross-bridges between red blood cells (RBCs), and this process causes agglutination (Fig. 2c). Agglutination is mediated either by whole virus or trimeric HAs (Kuroda et al., 1986; Wu et al., 2009; Khurana et al., 2010). The reciprocal of the lowest concentration that results in complete agglutination is defined as the hemagglutinin (HA) titer/unit. One HA unit is defined as the amount of virus in 1 ml of suspension (Nishikawa et al., 2012). Gopinath and Kumar (2013) detected clear agglutination using 125 and 250 nM of commercially obtained HA derived from pdmH1N1 virus and partial agglutination using 64 nM HA. The hemagglutination assay is often performed in clinical laboratories due to its relative simplicity, speed, and ability to be visualized by the naked eye. However, because influenza viruses can switch their glycan binding preference between α-2,6 and α-2,3 sialic acids, the sensitivity of this assay is compromised (Chandrasekaran et al., 2008). In contrast, the HI assay indirectly quantifies the amount of specific antibodies against the influenza virus that are present in the sample. This is a pivotal method for probing the human immune response to vaccination against influenza viruses (Grund et al., 2011). A serum HI titer of Z40 is considered to provide meaningful protection against the virus (Teferedegne et al., 2013). HI assay shows clear display, however cannot quantify the number of infected viruses precisely and not suitable for early detection. Information about virus infectivity can be obtained by calculating the number of plaque forming units (pfu) in a virus sample (Shimizu et al., 1985; Nishikawa et al., 2012). In the plaque assay, a confluent monolayer of Madin–Darby canine kidney (MDCK) cells is infected with different dilutions of the virus. Formation of a viral plaque (an area of infection surrounded by uninfected cells) is indicative of virus infection. Live and dead cells can be differentiated by crystal violet staining (Fig. 2d), and the number of plaque forming units (pfu/mL) is calculated to determine the number of infective particles. The virus microneutralization assay is a tool that has been used to address the shortcomings associated with the HI assay, and it often is used as an adjunct or alternative to the HI assay (Teferedegne et al., 2013). This assay measures the amount of antibodies against the

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influenza virus that are present in the sample by assessing plaque reduction. Teferedegne et al. (2013) have developed a neutralization assay for the influenza virus using an endpoint assessment based on quantitative reverse-transcription PCR. Eventhough, there is a clear-cut result with virus neutralization assays, it is tedious and high-count of viruses is required compared to other sensitive sensing systems. Currently, more elegant alternatives to these biological assays are being developed based on probes such as glycans, antibodies, or aptamers.

3. Probes to sense the presence of influenza viruses 3.1. Influenza detection using glycans The surface antigen (HA) of an influenza virus particle binds terminal sialic acid residues on the host cell surface, which induces uptake of the infecting virus. These glycan chains link the HA molecules on the surface of the viruses to the host cells. The two most common glycan chains are α-2,6 and α-2,3 sialic acids (Gambaryan et al., 1997, 2004; Subbarao and Katz, 2000; Matrosovich et al., 2001; Suzuki, 2005; Kale et al., 2008; Neumann et al., 2009; Liao et al., 2010). Following uptake, the viral RNA and proteins multiply within the host cell nucleus and assemble into new viral particles on the host cell surface. Neuraminidase enzyme on the surface of the virus catalyses linkages between the terminal sialic acid and its adjacent carbohydrate moiety. Suzuki (2005) reported that the amino acids at positions 205, 226, and 228 were involved in host cell infection (Fig. 2a). Based on the differences in the sialic acid linkages, sensor systems can be formulated for typing and sub-typing of influenza viruses (Fig. 3). Suenaga et al. (2012) established a method to determine the receptor specificity of an influenza virus using glycan in conjunction with the surface plasmon resonance (SPR) technique to discriminate between human and avian influenza viruses. Recently, Gopinath et al. (2013a) differentiated human and avian

Chicken Red Blood cells

Ser 205

Leu 226

Ser 228

Hemagglutination with virus

Receptors 2,6 Sialic acid

2,3 Sialic acid

Fig. 2. (a) 3D crystal structure (PDB Accession: 2VIU) of hemagglutinin (HA). The potential glycan binding regions are indicated by spheres. (b) Two common sialic acids for influenza infection: α-2,6 (human) and α-2,3 (avian) sialic acids. (c) Hemagglutination inhibition (HI) assay. Formation of a button indicates no hemagglutination, whereas formation of a hazy layer with influenza viruses indicates hemagglutination. (d) Influenza virus neutralization assay. Plaques are shown as clear spots.

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Typing

Sub-typing H3N2

H5N1

Influenza virus

influenza A and B (typing), however for sub-typing (differentiation within subtypes of influenza A or B), only very few antibodies are reported. The ability for sub-typing can be rendered by an alternative probe known as the aptamer. 3.3. Influenza detection using anti-influenza aptamers

Sialicacid

Host cell

Fig. 3. Sialic acid mediated sensing strategy. Sialic acid forms the bridge between host-cell and hemagglutinin of influenza virus. Strategy for typing and sub-typing of influenza virus is shown.

viruses based on α-2,6 and α-2,3 sialic acids using the waveguide mode sensor method. Hidari et al. (2007) performed whole influenza virus analysis using liposome-immobilized glycan; in this study, a specific glycan that binds to the HA was immobilized on the liposome and then immobilized on a L1 sensor chip. Lee et al. (2013) proposed colorimetric detection of glycan and influenza virus interactions, and Gopinath et al. (2006b) studied the involvement of glycans in aptamer-HA interactions using an aptamer generated against HA of influenza strain B/Johannesburg/ 05/1999. Glycan as the probe can differentiate between human and avian influenza viruses but are unable to differentiate within human or avian virus subtypes. However, to enable subtyping using glycans, analysis of the sub-structures of the glycans using mass spectrometry and high performance liquid chromatography towards building a database for each of the distinct glycans can be performed. 3.2. Influenza detection using anti-influenza antibodies Antibodies are the most common probe of choice for sensing, and they are generated by immunizing an animal system with antigen. The interaction between antibody and antigen can be transduced into measurable signal changes. The first report on the whole influenza virus analyses using SPR was carried out for the interactions between influenza virus and the corresponding antibodies on a gold (Au) surface (Schofield and Dimmock, 1996). Gopinath et al. (2013a, 2013b, 2013c) used anti-HA antibody to detect and discriminate between intact viruses or HA of human and bird influenza viruses. Zhao et al. (2013) used the enzyme linked immunosorbent assay (ELISA) method to show that subtype-specific antibodies had specificity against the HA1 fragment of European porcine influenza viruses. Nomura et al. (2013) demonstrated the interactions of anti-influenza antibody against influenza virus using a new approach consisting of a prism-less SPR system equipped with charge coupled device capturing. Most reported anti-influenza antibodies are not able to discriminate between the subtypes of influenza A viruses (Gopinath et al., 2013c), although a few exception exist. For example, Egashira et al. (2008) was able to detect the HA of influenza H1N1 using an antibody-immobilized immunoliposome-Ruthenium (Ru) construct. Detection, which was carried out via electrochemiluminescence, reached attomole level (Egashira et al., 2008). Among the available antibody-based sensing methods, lateral flow test is one of the most commonly used methods for discriminating between influenza A and B. In addition to anti-HA antibodies, the lateral flow test can be conducted using anti-ribonucleoprotein antibodies as the interactive element. Due to larger size of antibodies, in most of the cases antibodies are able to differentiate between

Aptamers are artificial nucleic acids generated in vitro that can recognize a wide range of targets ranging from small molecules to the whole virus. Aptamers are generated using SELEX technology, which involves formation of a target-nucleic acid library complex followed by a process of separation and amplification of bound molecules that is repeated for several cycles (Gopinath, 2011). Using a doped RNA-library, Misono and Kumar (2005) developed a method known as SPR-based SELEX to detect the target A/Panama/ 2007/1999 (H3N2). In another study, Jeon et al. (2004) generated a DNA aptamer that blocks receptor binding region of the HA of influenza A, which can attenuate virus infectivity. The selected aptamer was shown to have an inhibitory effect against A/Texas/1/ 77 (H3N2) virus. This aptamer also hampered the binding capacity of other HAs of H3N2 influenza viruses (A/Japan/57 and A/Port Chalmers/1/73). Cheng et al. (2008) developed another DNA aptamer against the HA1 fragment of influenza A/H5N1, and Gopinath et al. (2006a, 2006b) generated two RNA aptamers against intact influenza virus A/Panama/2007/1999 and HA of B/ Johannesburg/05/1999. The efficiency of these aptamers in inhibiting HA-mediated membrane fusion was demonstrated. These aptamers have the ability to discriminate among different subtypes of influenza viruses. The anti-influenza A/Panama/2007/ 1999 aptamer has a 15-fold higher affinity to HA than commercially generated anti-influenza-A antibody. Choi et al. (2011) showed that DNA aptamers generated against HA of influenza A/chicken/Korea/MS 96/96 could bind strongly with viral particles. In addition, high affinity aptamers have been generated and found to be specific against recent pandemic influenza viruses A/H1N1pdm (Gopinath and Kumar, 2013) and H5N1 (Wang et al., 2013). Lakshmipriya et al. (2013) generated two anti-influenza aptamers against the intact virus B/Tokio/53/99 and HA of B/Jilin/20/2003 and used the aptamers to differentiate between influenza types. In another study, Negri et al. (2011) used surface-enhanced Raman spectroscopy to evaluate the affinity of nucleoprotein and aptamer. Aptamers currently are viewed as equal or better molecules than antibodies for sensing purposes, and they can be used to complement antibodies in sandwich assays (Kim et al., 2010). Due to excellent specificity of the aptamer, aptamers that are extremely specific against subtypes of influenza viruses can also be produced using boronic acid modified aptamers that focus on certain glycan substructures. However, the shortcomings of generating aptamer is the need for careful handling during the SELEX process and the requisite for stability enhancement of the aptamer prior to clinical trial.

4. Sensors for influenza detection The development of biosensors that can be used to analyze the influenza recognition elements remains a challenge in the field of medical diagnosis (Table 1). Various biosensors have been developed using different anti-influenza probes for use in diagnostic immunoassays (Gopinath et al., 2006a,b, 2010, 2013a, 2013b, 2013c; Bahgat et al., 2009; Wang et al., 2009; Watanabe et al., 2009; Watcharatanyatip et al., 2010; Suenaga et al., 2012). These sensors can be generally categorized based on their application (e.g., diagnosis, imaging, drug discovery, target validation, and molecular discrimination). Different sensing systems for use in virus surveillance are sorely needed due to the increasing emergence of new and different strains (Fig. 4a). Most currently available sensors utilize antibody or aptamer to act as

Table 1 Sensors generated for the detection of influenza viruses Influenza type

Target Virus

Probe

Sensitivity

Method

Application/advantage

Reference

SPR

H1N1

Virus

A/PR/8/34

Glycan-ligand

45.6  10  13 M

Label-free

Interactions and receptors study

SPR

H3N2

HA

A/Panama/2007/1999

Aptamer

Kd 120 pM

Label-free

SPR

H3N2

Virus

A/Panama/2007/1999

Aptamer

Kd 188 pM

Label-free

SPR

Influenza B HA

B/Johannesburg/ 05/1999

Aptamer

Kd 720 pM

Label-free

SPR

H1N1

HA

A/New Caledonia/20/99

Glycan-ligands

Kd 1.5 mM

Label-free

Detection and discrimination of influenza B Detection and discrimination of influenza B Detection and discrimination of influenza A Detection of HA

Electrochemical

H1N1

HA

A/Hiroshima/5/2001

3  10  14 g/ml

Electrochemiluminescence Sensitive detection of HA

Interferometry Interferometry ICT Waveguide

H3N2 H3N2 H1N1 H3N2

HA Virus Virus HA

A/Panama/2007/1999 A/Panama/2007/1999 A/California/12/2009 A/Panama/2007/1999

ImmunoliposomeRu Aptamer Antibody Antibody Antibody

Critchley and Dimmock (2004) Misono and Kumar (2005) Gopinath et al. (2006a) Gopinath et al. (2006b) Mandenius et al. (2008) Egashira et al. (2008)

10 nM 10 nM 1.13HA unit 1 nM

Label-free Label-free Gold Dye

Gopinath et al. (2008) Gopinath et al. (2009) Lee et al. (2010) Gopinath et al. (2010)

Interferometry Electro Immuno-assay

H3N2 H1N1

Virus Virus

A/Texas/1/77 Swine influenza virus

Antibody Antibody

Silicon nanowire sensor

—

Antibody

Interferometry SPR

H1N1 Virus H3N2 H1N1H3N2 Virus H5N1 HA

1 μg/ml 180 TCID50 (50% tissue culture infective dose) 29 viruses/μl

— A/H5N1/Vietnam

Antibody Glycan

QCM

H5N3

Glycan Antibody

Farris et al. (2010) Lee et al. (2011)

Label-free

Detection of influenza

Shen et al. (2012)

29 ng/ml— Kd 1.6  10  9

Enzymatic Label-free

Baňuls et al. (2012) Suenaga et al. (2012)

Kd 1.44  10  8

Label-free

Detection of influenza Discrmination of bird and human influenza Binding of HA and glycan

8  10 pfu/ml

Gold

Detection of influenza

Gold

Discrimination of influenza

Label-free Label-free

Sensitive detection of recent pandemic influenza Detection of influenza

Waveguide

H3N2

Virus

A/duck/Hong Kong/313/4/ 1978 A/Udorn/307/1972

Waveguide

H3N2

Virus

A/Udorn/307/1972

Antibody

—

SPR

H1N1

HA

A/California/07/ 2009

Aptamer

Kd 67 fM

Electrochemical ICT Prism-free SPR Indium-tin-oxide thin-film transistors SPR Quartz Crystal Microbalance Rat Basophilic Leukemia cell sensor Colorimetric

HA

Label-free Label-free

Detection of HA Detection of intact virus Early diagnosis of influenza Detection of human and bird influenza Detection of influenza Detection of influenza

3

Takahashi et al. (2013)

A and B H3N2 H5N1

Virus Virus Virus

— A/Udorn/307/1972 A/Goose/Qinghai/5/2005

Antibody Antibody Antibody

4.6  10 pfu/assay 0.2HAU/ml 0.8  10  10 g/ml

Gold Fluorescent Label-free

Detection of influenza viruses Detection of influenza Detection of influenza

Gopinath et al. (2013b) Gopinath et al. (2013c) Gopinath and Kumar (2013) Kiilerich-Pedersen et al. (2013) Mitamura et al. (2013) Nomura et al. (2013) Guo et al. (2013)

H5N1 H5N1

HA HA

A/Vietnam/1203/04 Avian Influenza Virus

Aptamer Aptamer

Kd 4.65 nM 0.0128HA unit

Label-free Label-free

Detection of Influenza Detection of Influenza

Wang et al. (2013) Wang and Li (2013)

H5N1

Virus

A/Bar-headed Goose/ Antibody Qinghai/61/05 B/Victoria and influenza B/ Sialic acid Yamagata

103 TCID50

Fluorescent

Detection of pathogens

Qu et al. (2013)

HA titer 512

Label-free

Detection influenza

Lee et al. (2013)

H1N1

Virus

Influenza B Virus

A/PR/8/34

Aptamer

3

10 pfu/ml 3

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Sensor

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Aptamer Pool

Antibody

Influenza Targets Separation

Amplification

H1N1

H5N1

H2N2

H7N9

H3N2 Influenza B

Fig. 4. (a) Sensor applications for influenza viruses and (b) molecules involved in influenza detection. The strategy for aptamer selection is outlined. Different types and subtypes of influenza are shown in different colors. The crystal structure of an antibody is shown (PDB Accession: 1IGT). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Capture

Capture

Lens

Lens Filter

Filter

2

Filter

Light source

Filter

Light source

Fig. 5. Schematic showing sensor systems: (a) Scattering-mode of reflection-based sensors and (b) angular shift-mode with prism-based sensor

the transducer for reporting virus interaction with probe (Fig. 4b). High affinity sensing systems can be formulated by complementing different probes, which can recognize same target from influenza virus. Optical sensors function by measuring the reflection or angular shifts of a laser beam upon irradiation of the sensing surface. The

reflected light is further aligned, captured, and analyzed for the interactions as reported in elsewhere (Fig. 5a and b). Other sensing strategies include carbon nanotubes (Tam et al., 2009; Lee et al., 2011), electrochemical sensors (Lai et al., 2012; Kiilerich-Pedersen et al., 2013), multiplex PCR (Liang et al., 2013), field flow fractionation and multi-angle light scattering (Bousse et al., 2013), quartz

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crystal microbalance (Wang and Li, 2013), multiplex loopmediated isothermal amplification assay (Mahony et al., 2013), hemadsorption (Uhlendorff et al., 2009), and real-time PCR (Amano and Cheng, 2005). As HA spans about 80% of the membrane of influenza viruses, anti-HA probes are most commonly used in sensor technology.

5. Lateral flow test The lateral flow test, also known as the lateral flow immunochromatography test (ICT), is designed to detect the presence of a particular target within a complex mixture (Hara et al., 2008; Mori et al., 2012; Mitamura et al., 2013). ICT is used in medical diagnosis from the home to the field, especially in the detection of influenza viruses. The efficiency and reliability of an ICT relies mainly on the use of the correct lateral flow. Prior to detection, a solution containing detergents is used to break the influenza viruses. The ICT consists of a series of pads (sample pad, conjugate pad, capillary pad, and absorption pad) that are capable of transporting the sample fluid spontaneously from one pad to the next. The sample pad acts as a sponge to hold the excess sample and to initiate flow. The conjugate pad contains pre-absorbed immuno-gold nanoparticles (immuno-GNPs) that bind the specific target in the sample to be tested. When this complex reaches the capillary pad containing the test lines, results become visible. For influenza detection, the test lines, which are distinct for influenza A and B, are pre-immobilized with the corresponding detection antibodies (anti-influenza A- and B-specific antibodies), whereas the control line harbors the secondary antibody (e.g., anti-rabbit or anti-mouse) (Fig. 6). Upon accumulation of the appropriate complex (target, immuno-GNP/immuno-colored particles, and anti-influenza antibody) on the test lines, a visible colored line appears (e.g., blue and red for dyed latex and GNP accumulation, respectively). The color appears on positive test lines due to the formation of a sandwich containing the target, immuno-GNP, and antibody. The most important criterion for sandwich complex formation is the presence of antibodies that have different epitopes to avoid competition. However, a sandwich can still form in cases in which anti-HA antibodies have

Fig. 6. Schematics showing how an immunochromatography test works. Signal amplications by silver nanoparticles and pattern of results are shown. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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the same epitope regions due to the formation of a trimer by HA molecules. The ICT system is cheap, easy to use, and results are visualized by the naked eye. However, the sensitivity of the system is not sufficient for the detection of influenza viruses. For this reason, ICT is usually carried after onset of high fever, as a high number of multiplied influenza viruses are present in the nasopharyngeal tract at this point in time (Watanabe et al., 2009). Gold or silver nanoparticles conjugated with antibody are usually used in ICT. Mori et al. (2012) used 50 nm gold nanoparticles and amplified the signal using silver particles 410 mM, which improved the sensitivity up to 1000 fold (Fig. 6; inset). ICT is mainly used to detect and discriminate between the major types of influenza viruses (A or B). However, this assay is not able to discriminate between influenza subtypes (i.e., within influenza A or B), as most of the antibodies in ICT are against ribonucleoprotein. Sasaki et al. (2012) generated an ICT for the detection of the pandemic influenza virus A/H1N1pdm (also known as swine flu). Generally, influenza subtypes are classified based on HA and NA, which indicates that probes that can discriminate between HA or NA are needed. Further, developing ICT which shows several channels/lines to detect and differentiate types and sub-types of influenza viruses is necessary. However, due to the design of the ICT for individual-based tests, ICT is not possible for high-throughput analysis and the need for sample pretreatment is required if the starting sample is not in the form of liquid (Posthuma-Trumpie et al., 2009).

6. Colorimetric analyses Colorimetric assays are another tool for naked eye visualization of influenza viral interactions. Colorimetry is a solution-based assay that can be used to indirectly determine the concentration of the target via absorbance of light at a certain wavelength. Tannock et al. (1989) measured the release of neutral red from influenza virus-infected MDCK cells in an automated neutralization test for influenza B virus. Using crystal violet, MTT [3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide], and neutral red, Smee et al. (2002) conducted colorimetric tests to assess the anti-influenza activities against H1N1 and H3N2 viruses. Lehtoranta et al. (2009) described a novel colorimetric cell proliferation assay for measuring neutralizing antiinfluenza antibodies against influenza viruses in human sera. In addition, several GNP-based colorimetric assays for ligand-analyte interactions have been reported (Gopinath et al., 2014; references therein). GNP-based colorimetric strategies can be developed simply by controlling the assembly and disassembly of the probes for influenza viruses on the GNPs (Fig. 7a and b). The key principle inherent in this assay is the purple-colored aggregates (attraction) that form due to target-probe complex formation. The presence of the target that forms complex with the probe allows the mono-or divalent ions to minimize repulsion between the GNPs, resulting in aggregation of the GNPs (purple colour). In the absence of the target, the probe is bound to the surface of the GNPs, thus stabilizing it against mono- or divalent ion-induced aggregation, resulting in the red dispersed (repulsion) state (Fig. 7a). Due to its simplicity and versatility, this assay can be used as an alternative to current diagnostic assays, especially for detection of small molecules (Gopinath et al., 2014). Lee et al. (2013) developed a colorimetric system that uses sialic acid as the probe for virus detection and reported that its sensitivity was 512HA titer for influenza B/Victoria and influenza B/Yamagata. In the presence of virus, formation of complex with sialic acid-conjugated GNP leads to virus mediated aggregation (Fig. 7b). GNP-based strategy can be the frontier for ASSURED diagnostic, as it is affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free and highly end-user-deliverable (Tsung-Ting et al., 2013). The problem usually encountered with

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GNP

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Fig. 7. Colorimetric-based detections. (a) Assembly and disassembly of GNP-modified aptamer. In the presence of NaCl, anti-influenza aptamers that are spontaneously absorbed on the surface of the GNPs causes the GNPs to be dispersed, producing red colour. In the presence of the target (influenza virus), the GNPs forms aggregations that produces purple colour as aptamers are separated form GNPs by complex formation with the virus. (b) Sialic acid-mediated colorimetric detection. The aggregation of GNP-conjugated sialic acids after binding with virus. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

GNP-based colorimetric assays is non-specificity, which can be alleviated with proper blocking material such as synthetic polymers (Nagasaki, 2011). GNP-based strategy is unable to deliver real-time monitoring capability of the influenza virus interactions, which is possible with Kretschmann configuration-based strategies.

7. Kretschmann configurations Biological, ICT, and colorimetric assays display visible results. In addition to these methods, real-time monitoring is another popular way of measuring influenza virus interactions (Fig. 5a and b). Kretschmann configuration-based strategies, which include SPR, waveguide mode are examples for real-time monitoring. 7.1. Surface plasmon resonance (SPR) Unless otherwise stated, SPR and the other similar sensors described operate based on similar principles that originate from the Kretschmann configuration (Citartan et al., 2013). Surface plasmons are the collective charge oscillations along the interface between two materials, and SPR refers to the excitation of these plasmons by incident light under the condition of total internal reflection (Kretschmann, 1971). Immobilization or interaction of the biomolecules with the interacting partners results in the shift of the SPR which can be measured in label-free way. Several SPR-based sensor systems are available, and they differ in the placement of the measurement chip, surface priming, composition of buffers, and immobilization procedures. SPR-based Biacore is the leading SPR technique used for influenza virus detection. Biacore technology was created in 1984 and expanded in 1995 to include the ability to evaluate biomolecular interactions; the new systems were named BIAlite, Biacore 1000, and Biacore 3000. The latest model, Biacore

T100, offers higher sensitivity and more advanced features than previous models. The main advantage of this technology is its capacity to monitor weak macromolecular interactions that cannot be detected by other sensors and its subjectability to automation. Biacore was used to evaluate the detection of different influenza viruses such as, A/Panama/2007/1999 (Misono and Kumar, 2005; Gopinath et al., 2006a) and B/Johannesburg/ 05/1999 (Gopinath et al., 2006b), pdmHIN1 (Gopinath and Kumar, 2013) and other human and avian viruses (Suenaga et al., 2012) with higher sensitivity. A number of alternative techniques for biomolecular interaction analysis that are comparable with SPR have been developed. François et al. (2011) introduced collection mode surface plasmon fiber sensors as a new biosensing platform to detect seasonal influenza virus A. SPR sensing is very commonly in use with several advantages such as, reduced size and automated with real-time measurements. However, some SPR-system like Biacore need proper maintainance and expensive. Another SPR-based system known as Autolab requires pretreatment of the sensing surface with piranha solution (70% (v/v) H2SO4, 30% (v/v) H2O2) that requires tedious washing step and can be dangerous due to the high oxidizing feature of the solution. Moreover, with these SPR-system, careful design of sensing surface which reduce the biofouling is highly necessary. 7.2. Waveguide-mode sensors Waveguide mode sensors use similar principles as those of SPR devices. The only difference is the mode used for the measurements, which is a waveguide mode in lieu of the surface mode (Gopinath et al., 2010). The term “waveguide” is used to describe a structure (dielectric medium) capable of confining and guiding electromagnetic waves. The state in which the light is propagating while maintaining a given light intensity distribution (phase velocity) is called the waveguide mode. Planar and fiber optical waveguides are the general class

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based on the dimensions and light-guiding properties. These two waveguides are further subdivided into single mode (only one light mode is guided) and multimode (with several light modes) (Schmitt and Hoffmann, 2010). J.J. Thomson has designed the first structure for guiding waves in 1893, and Oliver Lodge was experimentally tested it in 1894 (McLachlan, 1947). Waveguide mode sensors are designed based on the Kretschmann configuration; slight modifications in the dielectric environment near surfaces of the waveguide mode sensor are detected with high sensitivity by measuring changes in reflectivity. Shifts in reflectivity or phase velocity are caused by changes in the local refractive index that result from the adsorption of biomolecules on the sensing plate. Interaction of the biomolecules with their corresponding partners can also be measured (Gopinath et al., 2008, 2009a). The sensing plate, which is placed on the bottom of the prism, is illuminated, and the spectrum of reflected light is recorded using a spectrophotometer. By equipping these set-ups with different materials, light-sources, and angular prisms, the system can be configured for different purposes (Fig. 5b). Different modes of transduction that can be used with waveguide mode sensors include grating-based label-free, colorimetric resonant grating reflection, interferometric, evanescent-field fluorescence, and radiolabeling (Li et al., 2008; Schmitt and Hoffmann, 2010). Evanescent waves are near-field waves in which the intensity dissipates exponentially as the distance from the boundary (source) increases. This type of wave can be coupled with the waveguide mode to generate evanescent-field coupled waveguide mode sensors (Lukosz and Tiefenthaler, 1995). This type of sensor has been used to evaluate interactions between anti-influenza antibody and human and bird influenza viruses. These analyses involved antibody-based (Gopinath et al., 2010, 2013b, 2013c) and glycan-based discrimination of human and avian influenza viruses (Gopinath et al., 2013a) and tested the specificity of anti-influenza antibody for discrimination among H3N2 strains (Gopinath et al., 2013c). Waveguide mode sensors have some limitations, however, especially for high-throughput analyses and limitation with sample volume. Additionally, as mentioned above, sensing surface with miminized non-specificity which shows acceptable signal-tonoise ratio is important. Interferometry-based sensors overcome some of the limitations of waveguide mode and SPR-sensors.

Direct ELISA

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8. Interferometry-based platform: BioDVD Interferometry is the principle of superimposition of electromagnetic waves with similar or different phases. Superimposition of electromagnetic waves that are similar in phase yields constructive interference, whereas superimposition of waves with different phases results in destructive interference. Immobilization or interaction between biomolecules affects the interference, in which the changes in interference indicate the interaction events (Hariharan, 2007). Interferometry-based platform involves the usage of very low sample volume and that can mediate multiple interactions at the same time. BioDVD is a disc-based technology associated with interferometry. Different capturing configurations of the influenza virus on the surface of the BioDVD can be used to study the virus.

8.1. Capturing of the immuno-GNP-virus complex on the disc In general, the adsorption of proteins on the sensing surface is assisted by van der Waals, hydrophobic, and electrostatic interactions as well as hydrogen bonding. Characteristics of the sensing surface affect the density, quantity, conformation, and orientation of the adsorbed molecules (Yoshimoto et al., 2010). Shima et al. (2013) directly immobilized a complex of immuno-GNP and influenza virus on the surface of a grooved polycarbonate disc. The disc substrate overlaid with indium tin oxide film had a diameter of 12 cm, thickness of 0.6 mm, and grooves on the surface for a readout laser to trace from the rear side. Initially, the virus was mixed with the anti-HA antibody conjugated to the GNPs. Next, this virus solution was sprayed onto the surface of the disc to mimic the condition of the air-borne influenza viruses. For the signal readout, an optical disk drive tester with a laser wavelength (λ) of 405 nm and a numerical aperture of 0.65 was applied. The reflected light intensity data obtained by the optical disc drive tester was collected with an oscilloscope. The attachment of the immuno-GNP-influenza complex on the disc was confirmed by observation under a scanning electron microscope (SEM). The SEM analyses revealed that the immuno-GNPs completely covered the viral particles.

Indirect ELISA

Sandwich ELISA Fig. 8. Enzyme linked immunosorbent assay (ELISA): (a) Types of ELISAs and (b) steps involved in an ELISA experiment.

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8.2. Capturing the virus using immobilized anti-influenza antibody/ aptamer Immobilization of proteins on the sensor surface can be achieved via the interaction of the amines of the protein and the aminoreactive groups of sensor surface functionalized with N-hydroxysuccinimide (NHS), periodate, gluteraldehyde, isothio-cyanate, or active ester (Yoshimoto et al., 2010). Immobilization of biomolecules on the gold surface can be achieved via conjugation to thiol/thiol containing groups as the coupling agents (Gopinath et al., 2008). On the gold surface of a BioDVD disc, interactions of influenza with anti-influenza aptamer (Gopinath et al., 2008) and anti-influenza antibody (Gopinath et al., 2009b) were generated for the purpose of influenza detection and discrimination. Analyses were performed on a multilayer structure consisting of Au/ZnS–SiO2/AgInSbTe/ZnS–SiO2/Au. The aptamer duplexed with a thiolated DNA-oligo was attached on the gold surface of the disc before interaction with the influenza virus. To immobilize the anti-influenza antibody, activation of the antibodies was first performed using N-hydroxysuccinimide and N-ethyl-N0 -(30 dimethylaminopropyl) carbodiimide hydrochloride to prepare the reactive amine. Subsequently, these reactive amines interacted with the carboxylic acid linker of 16-mercaptohexadecanoic acid, which has a thiol linker at the other end that interacts with gold atoms, resulting in the immobilization of the antibodies on the gold surface. Next, interaction of the intact influenza virus with the antibodyimmobilized spot was conducted. To improve the sensitivity of the BioDVD, Gopinath et al. (2009b) performed computer simulations and optimized the ZnS–SiO2 layer thicknesses. Disc-based sensing systems with optimized surface chemistry is highly suitable for highthroughput analyses and for faithful output.

9. Strategies for enhancing the efficiency of influenza detection Most of the current diagnostic methods suffer from low sensitivity, high cost, and the need for species-specific reagents. Thus, new, rapid, reliable, and sensitive systems are needed for influenza diagnostics (Negri et al., 2011). Sensitivity of the sensing system can be improved in several ways, including the use of a sandwich configuration with two different probes, use of fluorescent/chemical tags/metals or other nanoparticles, and physical modifications on sensing surface. 9.1. Sandwich assays Sandwich assay is a common method to increase the sensitivity and specificity of biomolecular interactions. To design this assay, two probes with different binding sites on the influenza virus should be used to avoid competition and to ensure proper orientation. However, exceptions to this rule exist, such as in the case of the trimeric HA on the surface of the influenza virus; Baňuls et al. (2012) developed a sandwich assay using similar anti-HA antibodies. Various combinations (e.g., antibody-virus-aptamer, receptor-virus-antibody, glycanvirus-antibody, anti-HA-virus-anti-NA, mouse IgG-virus-rabbit IgG) on any sensing surface can be used for sandwich assays for influenza viral detection. ELISA used with the sandwich configuration yields better sensitivity than other ELISA strategies (i.e., direct and indirect ELISAs) (Fig. 8a and b). Watcharatanyatip et al. (2010) successfully performed multispecies detection of antibodies to influenza A viruses using a double-antigen sandwich ELISA. A sandwich assay with antibody-antigen-aptamer is highly appreciated as these two different probes can complement each other for the development of more sensitive system (Kim et al., 2010). This type of complementation gives rise to the generation of ELISA like assays such as ELASA (Enzyme linked aptamer sorbent assay).

Using commercial DVD discs, Baňuls et al. (2012) developed a sandwich assay to detect influenza virus. These discs are composed of a polycarbonate substrate, a reflective metalized layer, and a polymethyl methacrylate protective lacquer coating. Measurements were taken using a Dual Polarisation Interferometer, which consists of a helium–neon laser. In their non-competitive immune-sandwich assay, the monoclonal anti-influenza antibody was first immobilized on the glutaraldehyde modified areas. Following antibody immobilization, virus was bound to the antibody, which then interacted with another biotinylated influenza-specific antibody. Finally, the interaction was detected by the addition of streptavidin conjugated to horseradish peroxidase. Ultimately, up to 29 ng/ml of virus was detected in the substrate solution (Baňuls et al., 2012). Success of sandwich assay lies on the selection of suitable probes that is able to target different binding regions on the antigen (epitope or aptatope) with high affinity. 9.2. Fluorescence Fluorescence is one of the most commonly applied forms of signal transduction to obtain the information about influenzaprobe complex formation. In the fluorescence resonance energy transfer (FRET) method, a reporter and quencher are placed at the two extremities of a nucleic acid probe. In the absence of the target, these two molecules are in close proximity to each other, which induces fluorescence quenching. However, the target induces a conformational change of the probe, which causes the reporter and quencher to move apart and emit fluorescence (Fig. 9a). Chou and Huang (2012) have developed a quantum dot (QD)induced FRET reporter system with two oligonucleotides that specifically recognize two separate, neighboring regions of the H5 sequences of the avian influenza virus were used as the capturing and reporter probes, respectively. They were conjugated to QD655 (donor) in a molar ratio of 10:1 and Alexa Fluor 660 dye (acceptor), respectively. At target concentrations ranging from 0.5 nM to 1 μM, the QD emission decreased at 653 nm and dye emission increased at 690 nm. Using an immunofluorescence strategy, Cho et al. (2013) were able to differentiate between the newly pandemic influenza virus H1N1 and the seasonal flu virus. More efficient fluorescent-based detection can be attained by generating probes against natural fluorophores such as green fluorescent protein (GFP). Paige et al. (2011) formulated a system based on an aptamer against 3,5-dimethoxy-4-hydroxybenzylidene imidazolinone, which is a derivative of GFP. The resulting aptamer was used to generate a construct known as Spinach, which was conjugated to the 5S RNA sequence for its localization within human embryonic kidney 293 T cells. They have also generated aptamers that could bind appropriate ligands with different spectral properties with various fluorescence emissions (distinct colors). This strategy could be adopted for influenza detection with aptamers generated for each of the influenza subtypes. These aptamers could be hybridized with aptamers that bind fluorophores with different spectral properties for clear visualization (Fig. 9b). Eventhough, fluorescent-based assays are highly successful in the past, in some cases, tagging of the probe with fluorescent compounds may result in aberrant folding pattern, which cause the reduction in the efficiency of the desired probe (Citartan et al., 2013). 9.3. Nanostructures Nanostructures are materials whose structural elements have sizes that range between 1 and 100 nm (between molecular and microscopic structures) (Moriarty, 2001). Among the currently

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Fig. 9. Fluroescent-based influenza detection. (a) Strategy for using a fluorescent structure-switching aptamer and (b) proposed strategy involving an anti-fluorescent aptamer for the detection of influenza virus (Paige et al., 2011).

Fig. 10. Gold nanoparticle-based sensing of influenza viruses. Antibody-conjugated GNP on the sensing surface is shown in the left Panel and SEM observation of GNP is shown on the right at the top. ImageJ software-based analysis of GNPs on the sensing surface is shown on the right bottom.

available nanostructures, metal nanoparticles are most commonly used for sensor development. Metal nanoparticles can improve the sensing of influenza viruses via formation of a nanoparticleinfluenza virus or nanoparticle-anti-influenza probe (e.g., antibody or aptamer) complex. Metal nanoparticles offer high detection sensitivity and clear visualization (under the microscope) of their attachment on the sensing surface (Fig. 10). Noble metal nanoparticles are frequently used, as a variety of techniques are available for their synthesis (e.g., chemical methods such as chemical reduction,

photochemical reduction, coprecipitation, thermal decomposition, and hydrolysis, and physical methods such as vapor deposition, laser ablation, and grinding) (Tauran et al., 2013). Another type of nanostructures, nanorods are structures that can be a good platform as it has absorption spectrum suitable for label-free assay (Petryayeva and Krull, 2011). Negri et al. (2011) used a polyvalent aptamer on an another nanostructure (silver nanorod) as the substrate to detect the nucleoprotein of the influenza virus. Ignatovich and Novotny (2006) introduced a background-free

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real-time detection scheme that can recognize low-index nanoparticles such as single influenza viruses in water. This method is based on the measurement of the electromagnetic field amplitude of the scattered light in the absence and presence of the target. Gopinath et al. (2013a, 2013b, 2013c) used GNP-conjugated anti-influenza antibody for sensitive detection and discrimination between influenza subtypes. Creation of nanostructure by physical changes, such as nanopore/nanohole formation on nanomaterials allows for efficient collection of target molecules with proper orientation due to the formation of a large internal surface area (Yamaguchi et al., 2009; Lazzara et al., 2011). Nanopore construction is gaining intense attention in various fields such as nanoscience, engineering, and material sciences due to the nature of the well-ordered, densely packed nanoscale structures (Lau et al., 2004). Uniformity of nanoholes on sensor chips also improves the sensitivity of detection, which also permits molecular size selectivity (Rong et al., 2008; Gopinath et al., 2008; Yamaguchi et al., 2009). Deep perforations are recommended to penetrate the sensing layer. Gopinath et al. (2008) found that the sensitivity of waveguide sensors depends on the diameter of the nanoholes, as widening of the diameter increases the spectral shift by accommodating more molecules. Gopinath et al. (2009a) analyzed the interaction between the HA of A/Panama/07/1999 (H3N2) and anti-HA antibodies on the nanoholes of the sensor chip.

10. Future perspectives Surveillance is an important approach for influenza control. A rapid and accurate diagnostic method is necessary for influenza surveillance and to prevent the spread of the virus. Further, the design and structure of sensing systems should be suitable for home-to-field applications with high sensitivity. Several sensitive systems are currently available and in use in clinical practice. Although these sensing systems are able to differentiate between influenza A and B, differentiation within influenza subtypes is currently limited in most cases. Selectivity is important and it can be achieved with proper selection of probe which can differentiate types and sub-types of influenza viruses. Hence, probes that can differentiate between the subtypes of influenza viruses is needed for the detection of newly emerging influenza viruses and for augmenting influenza surveillance programs. Another issue is the appropriate anti-influenza treatments for the effective human healthcare. Especially, for the newly emerging influenza virus, researchers are looking for appropriate strategy to screen the antiinfluenza drugs. Another important criterion is the development of sensor that are suitable for point-of-care diagnostic, which enables prompt treatment to be carried out anywhere. This is especially important in rural areas with limited resource-settings.

Acknowledgements We thank Universiti Sains Malaysia (USM) for awarding an Academic Staff Training Scheme to Citartan M for this study, and we acknowledge the support provided by the Advanced Medical and Dental Institute, USM, to cover his travelling and subsistence costs while in Japan. Tang TH was supported by USM Research University Grant 1001/CIPPT/813043. Y. Chen was supported by UM.C/625/1/HIR/MOHE/MED/16/5. We thank AMDI Research committee for supporting the manuscript for English editing services. References Amano, Y., Cheng, Q., 2005. Anal. Bioanal. Chem. 381, 156–164.

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