**Abstract**

As the fourth industrial revolutions technologies intensify, cities are becoming smarter, new business models are emerging and informal enterprises are formalizing by default. Research demonstrates that the future of our world is decided by the quality of its future cities. As cities invest in information and communication technologies (ICTs) and embrace the Fourth Industrial Revolution (4IR) technologies to make life easier and solve many of the problems we face today, employment opportunities expand and citizens enjoy better lifestyle. This chapter will examine how the concept of smart cities is disrupting existing business models and creating new ones that have positively impacting Africa's informal enterprise sector. The chapter leverages abundance theory to explain the emerging phenomenon in the nexus between smart cities, new business models and informal enterprises in Sub-Saharan Africa. The study finds that indeed the concept of smart cities is indeed facilitating new business models that are formalizing the informal sector.

**Keywords:** smart cities, digital innovation, informal economies, gig economies, Africa


**3. Prevention and treatment**

Early anti-influenza drugs were synthesized by large scale screening methods without the knowledge of their chemical structures and mechanisms of action [29], whereas, the recent antivirals have been discovered based on the structure of influenza virus protein as drug targets using X-ray crystallography method. This is structure based, involving the use of organic compounds that are able to bind to viral target protein receptors [30]. These structures have high binding affinity to the viral target following chemical synthesis and effective antiviral screening using standard in vitro assays such as cell based antiviral screening [31] and biochemical evaluation [32]. Some cell based antiviral screening includes plaque assays for studying replication in virus, yield-reduction assays for quantifying specific viral antigens and dye uptake for measuring cytopathic effect. The application of bioinformatics, robotics, miniaturization strategies have led to an advanced and high-throughput drug screening of large drug libraries with unique chemical structures [33] and computational screening [34]. In vivo drug screening using various animal models such as chicken, mouse, ferret have been used to evaluate new drugs [35] this is followed by clinical trials to study its bio-safety, kinetics and tolerance in human [36].

Advances has since then seen the treatment of influenza virus infection basically through vaccines, monoclonal antibodies and antivirals drugs. Antiviral influenza drugs are mostly NA inhibitors; however, they generally have short therapeutic window and current show emerging drug resistance [37]. Till date, four (4) antiviral drugs have been approved for the treatment of influenza: the NA inhibitors oseltamivir (Tamifu), peramivir (Rapivab), zanamivir (Relenza), and the cap-dependent endonuclease inhibitor baloxavir (Xofuza) [23, 37]. Oseltamivir is the preferred treatment for patients with severe influenza. Intravenous peramivir is an option for these patients if there are contraindications to or concerns about reduced bioavailability of oral oseltamivir [24]. Baloxavir is preferred for the treatment of uncomplicated influenza in patients of age 12 years and older. A study was conducted to compare baloxavir with oseltamivir and placebo in 1436 healthy people between 12 to 65 years of age who had influenza, baloxavir and oseltamivir reduced symptom duration by approximately one day compared with placebo. Adamantanes (amantadine and rimantadine [Flumadine]) are also approved for influenza treatment but are not currently recommended because these medications are not active against influenza B, and most influenza A strains have shown resistance to adamantane for the past 10 years [24].

Vaccines remain extremely essential to the prevention of the infection. Vaccination is the most preferred method for prevention, and routine chemoprophylaxis within the community is not recommended. The first influenza vaccine was developed in 1945, and since seen several others produced. Multiple formulations of the influenza vaccine are available, including inactivated influenza vaccines (IIV); a recombinant inactivated vaccine (RIV); and a live, attenuated influenza vaccine (LAIV). LAIV shows one of the best efficacies at around 70% and tends to be more effective in children. It delivers more NA and M2 antigens, triggers mucosal responses including IgA, and has the potential for inducing CD8 T cell responses [38].

As a primary prophylactic countermeasure, annual influenza vaccination is engaged globally with the aim of limiting influenza burden. However, the effectiveness of the current available influenza vaccines is limited because they only confer protective immunity when there is antigenic similarity between the selected vaccine strains and circulating influenza isolates. The consequences of antigenic drift or shift, results in an antigenic mismatch between the current vaccines and circulating influenza isolates. Accumulation of mutations, especially at key antigenic sites in the HA globular head, due to the absence of the proofreading activity of the viral RNA polymerase and then to the selective pressure exerted by the host immune system is often responsible for the escape of influenza virus from pre-existing immunity in the case of antigenic drift [39]. There is therefore a crucial need to develop a more effective broadly-reactive (universal) influenza vaccine with the capability to confer protection against both seasonal and newly emerging pre-pandemic strains.

### **3.1 The journey to a universal flu vaccine**

In influenza virus vaccine design, the major targets of the antibody response against the virus are the surface glycoprotein antigens hemagglutinin (HA) and neuraminidase (NA). As earlier stated, Hemagglutinin (HA) and neuraminidase (NA), are the main surface glycoproteins on influenza viral particles. NA is however less abundantly expressed on the virion in comparison HA expression, with HA to NA ratio often ranging from 4:1 to 5:1 [38]. The influenza HA is responsible for binding to sialic acid, the receptor on target host cells, and there are approximately 500 molecules of HA per virion [40]. The mature form of the HA glycoprotein exists as a homotrimer containing three HA monomers that are composed of a globular head and a stem/stalk region. The receptor binding site (RBS) is present in the globular head, which is however a hypervariable region of the protein, while the stem region is majorly involved in the pH-induced fusion event triggered by endosome acidification following viral adsorption. The stem/stalk region of the HA is more conserved among and across HA subtypes belonging to the same group [38]. Antibody response elicited against this stem/stalk region forms one of the major approaches towards developing a more responsive vaccine to both current and future strains of influenza viruses (**Figure 2**).

#### *3.1.1 Stem-based universal vaccine approaches*

Influenza virus infection can elicit neutralizing antibodies against both the globular head and the stem structures of the HA viral protein. Currently, ongoing strategies for more efficacious vaccine development is aimed at eliciting antibodies that target the conserved stem region of HA since previously existing influenza vaccines only show minimal induction of stem-directed humoral immunity [3]. Several studies describe ongoing approaches to elicit stem-directed antibodies including sequential immunization with heterologous influenza strains, immunization with modified proteins by removing or glycan-masking the globular head, referred to as headless HA, through minimizing epitopes of the stem region, hyperglycosylated HA head domain, Chimeric HA, and Mosaic HA [3, 38]. Self-reactivity of this antibodies may occur due to their polyreactive profile and the proximity of the HA stem region to the cell membrane which is a crucial limitation described by scientists to this approach.

Nachbagauer et al. [40] recently presented a unique concept in the stem-based approach using the context of a LAIV with a H8 head domain and an H1 stem domain *Influenza Viruses: Targetting Conserved Viral Ha-Stem, Matrix and Nucleo-Proteins to Disarm… DOI: http://dx.doi.org/10.5772/intechopen.104770*

#### **Figure 2.**

*Showing the phylogenetic trees of (a) hemagglutinin (HA) and (b) neuraminidase (NA). The primary surface glycoproteins of influenza viruses, HA and NA, are divided into several categories and subtypes. During the last century, only viruses producing H1, H2, or H3 HAs and N1 or N2 NAs (such as H1N1, H2N2, or H3N2; circled in purple) have spread widely in the human population. The scale bars represent a 6% change in amino acid levels (source: [41]).*

(cH8/1) and a split-inactivated vaccine with an H5 head domain and an H1 stem domain (cH5/1) [40]. Using preclinical ferret investigations, the scientists assessed protection against pandemic H1N1 virus challenge using several sequential primeboost combinations and vaccination regimens. These studies show that a sequential live-attenuated followed by split-inactivated viral vaccination strategy provides superior protection against pandemic H1N1 infection. Scientists have characterized this notion as a sequential immunization and chimeric HA proteins approach to stembased universal vaccine design.

Furthermore, in a stem-based immunogens approach to the universal influenza vaccine design, based on the H1 subtype, Impagliazzo et al. created stable mini-HA stem antigens, where the best candidate demonstrated structural and binding characteristics with widely neutralizing antibodies equivalent to full-length HA, indicating correct folding. This immunogen totally protected mice in lethal heterologous and heterosubtypic challenge scenarios and lowered fever in cynomolgus monkeys following a sublethal challenge [42]. However, determining the effectiveness of antibodies targeting conserved epitopes in the HA stem region to offer protection remains a critical challenge [43].


**1. Introduction**

288 Infrared Spectroscopy – Life and Biomedical Sciences

Mamarelis, I., Pissaridi, K., Dritsa, V., Kotileas, P., Tsiligiris, V., Tzilalis, V. &

McIntosh, L.M., Jackson, M., Mantsch, H., Stranc, M.F. , Pilavdzic, D. &.Crowson, A.N.(1999)

Skin Components. *Journal of Investigative Dermatology,* Vol. 112, pp. 951–956. Nara, M., Okazaki, M., & Kagi, H. (2002). Infrared study of human serum very-low-density

Parthasarathy, S., Litvinov, D., Selvarajan, K. & Garelnabi M. (2008).Lipid peroxidation and

Ross, R. & Glomset, J. A. (1973). Atherosclerosis and the arterial smooth muscle cell. *Science*

Stary, H.C. (2000). Natural history and histological classification of atherosclerotic lesions: an update. *Arteriosclerosis, Thrombosis, and Vascular Biology*, Vol. 20, pp. 1177-1178 Steinberg, D. & Witztum, J.L. (1990). Lipoproteins and atherogenesis. *Journal of American*

Theophanides, T., Angibust, J.P. & Manfait M. (1988). *Protein and Nucleic Acid Changes*. In

Theophanides, T. (1984). *Fourier Transform Infrared Spectroscopy*. D. Reidel Publishing Co.,

Theophanides, T. (1978). *Infrared and Raman spectroscopy of biological molecules.* NATO

Woernley, D. L. (1952). Infrared Absorption Curves for Normal and Neoplastic Tissues and

Yano, K., Ohoshima, S., Gotou, Y., Kumaido, K., Moriguchi, T. & Katayama H. (2000). Direct

Measurement of Human Lung Cancerous and Noncancerous Tissues by Fourier Transform Infrared Microscopy: Can an Infrared Microscope Be Used as a Clinical

Advanced Study Institute, D Reidel Publishing Co, Dodrecht

Related Biological Substances. *Cancer Research*, Vol. 12, pp. 516

Tool? *Analytical Biochemistry*, Vol. 287, pp. 218-225

Twardowski (Ed.), Spectroscopic and Structural Studies of Biomaterials, I. Proteins,

spectroscopic study. *In Vivo*, Vol. 24, pp.883-888

*Molecular Structure*, Vol. 78, pp. 101-116

*et Biophysica Acta*, Vol. 1781, pp. 221–231

*Medical association .* Vol. 264, pp. 3047–3052

Sigma Press, Wilmslow, UK, pp. 3-14

Vol. 180, pp. 1332–1339

Dodrecht.

Anastassopoulou, J. (2010). Oxidative stress and atherogenesis. An FT-IR

Infrared Spectra of Basal Cell Carcinomas are Distinct from Non-Tumor-Bearing

and low-density lipoproteins. Implication of esterified lipid C-O stretching bands for characterizing lipoproteins. *Chemistry and Physics of Lipids,* Vol. 117, pp. 1–6. Petra, M., Anastassopoulou, J., Theologis, T. & Theophanides T. (2005). Synchrotron micro-

FT-IR spectroscopic evaluation of normal paediatric human bone. *Journal of*

decomposition — Conflicting roles in plaque vulnerability and stability. *Biochimica*

Biochemical analyses of substances rely upon the ability of techniques to identify qualitatively and quantitatively the components present and are based on physicochemical characteristics as well as chemical nature of substances being detected. While chemical analyses usually depend on reactions of a given substance and can be destructive, spectral studies are usually non-destructive and deal with describing a substance based on properties like absorption or transmission of light (e.g. UV, Visible, Infrared (IR)), light scattering ability, fluorescence /phosphorescence using various optical techniques. Thus, the technique (Fourier transform infrared) spectroscopy has gained prominence in both research and applications in different fields of science. Among the various techniques, IR spectroscopy owing to its lower potency of causing damage compared to X-rays, gamma rays and UV rays (as it is based on weak vibrational energies) has become the technique of choice during chemical analysis of substances. IR spectroscopy can not only provide information about the various components in a complex material but is also unique in its ability to be modified into different kinds of instrumentations based on requirement. The various IR spectroscopy based instruments from a simple IR based spectroscope that helps to obtain the absorbance spectra of a chemical compound to the complex imaging systems that employ computational methods in addition to the technical sophistication are based on a simple principle that every compound or a particular combination of compounds can be described by means of a FTIR (Fourier transform infra red) spectra qualitatively and quantitatively.

The guiding principle of all such analyses lies in the fact that when IR radiation of different wave numbers are simultaneously passed through a sample, specific wave numbers are absorbed based on the vibrations of molecules, creating a unique fingerprint of each sample, from a simple molecule like a protein molecule to a more complex structure like eukaryotic tissues. In spite of the fact that the cells and tissues can be discriminated based on their spectral fingerprints in the mid IR/NIR region, their signatures are the result of contribution from several biological components that at times absorb at similar or overlapping wave

<sup>\*</sup> Corresponding Author

Chemometrics of Cells and Tissues Using

**0.0**

suitability and relevance to biomedical research.

**0.5**

**1.0**

 **Absorbance (AU)**

**1.5**

**2.0**

**Esters**

IR Spectroscopy – Relevance in Biomedical Research 291

**Lipids**

**Proteins**

**1800 1600 1400 1200 1000 800**

**Wavenumber (cm-1)**

Fig. 1B. Amide I normalized, averaged spectra of (a) bacteria (*Streptococcus pneumoniae*), (b) Cervical epithelium intermediate layer and (c) colonic crypt in transverse section to show absorbance of different biological components at different regions of mid IR. Note that though the carbohydrates absorbance occurs principally in the region between 1200-900 cm-1, the pattern differs across samples, giving a preliminary information of their different origins.

data in usable and interpretable forms. Advancements in computational techniques have added to the utility of the FTIR based instruments by making spectral calculations rapid and automatic, leading to their application in diverse fields of chemistry and biology for both applied and basic research. The potential of FTIR spectroscopy thus serves not only for routine applications but also as a diagnostic tool where other optical methods become difficult to apply. In view of these developments an ever expanding field of biomedical research based on FTIR based technologies has arisen over the last few decades. With its unique abilities, the technique has been applied mostly in cancer diagnosis and monitoring, microbial identification and drug efficacy evaluations to name a few. The present chapter describes in brief the different aspects of applications of FTIR in biomedicine and their

**2. Brief historical perspective of FTIR spectroscopy in biomedical research**  IR spectroscopy has been in use for chemical analysis for several decades. The recognition of its ability to distinguish between normal and abnormal based on fingerprints of the cells and tissues has been utilized to describe changes at molecular and cellular levels ( Yang et al 1995, Malik et al 1996, Schultz et al 1996, Malins et al 1997) opened up a new area in the field of biodiagnostics. Other studies paved way for the examination of the technique as a diagnostic tool for identification of disease especially malignancy (Cohenford et al 1997, Rigas et al 1990, Wang et al 1997). This was followed by expanding the methods to study tissues from several different organs such as skin, cervix, liver among others with an emphasis on cancer identification which would be at par with conventional diagnostic techniques without the negative factors like toxicity to live tissues. Early works focused on defining the tissue variability in terms of IR spectra (Chiriboga et al 1998a,b,c, Wood et al 2004). This led to a large number of studies with different tissues and cell lines with an objective to make the technique relevant to oncology (Fukuyama et al 1999, Diem et al 2000, Gao et al 1999, Yano et al 2000, Malins et al 2003). The potential application in other

**c**

**b a**

**Carbohydrates**

**Nucleic acids**

numbers. In order to explain the contribution of different metabolites like carbohydrates, nucleic acids, proteins and lipids in a sample, spectra of pure components are collected and analyzed for specific patterns. Comparison of compound that vary in one or more functional groups aids in the determination and assignment of particular groups and defines the specificity of signature for each along the entire spectral region. Assignment of the exact contribution of each component to the entire spectra therefore makes the method quantitative as well as qualitative.

Figure 1A shows representative spectra of blood fractions obtained by FTIR-MSP (Fourier Transform Infrared Microscopy), mounted on ZnSe slides in transmission mode. It is observed that in spite of the diversity of the source and type of the samples, absorbance of IR occurs at similar wave numbers, implicating that the samples are composed of similar basic substances as mentioned above. Simultaneously it also shows how the variation occurs among these substances and what are the likely principal components of each, contributing to its unique spectra. For example, plasma has less sugar compared to the bacterial cell which has a capsule and thus has more prominent peaks in the region 900-1185. Similarly, the RBCs that lack nucleic acids show diminished peaks while WBCs show clear absorbance peaks between 1185 cm-1 and 1300 cm-1.

Fig. 1A. Amide II normalized average spectra in the region 800-1800 cm-1 of different ZnSe mounted blood components ( a-WBC, b-Plasma and c-RBC) analyzed by FTIR-MSP in transmission mode showing few peaks absorbance and the functional groups associated with it.

Thus a qualitative assessment can be made regarding the composition of a sample by looking at its spectra and also defining what type of functional groups could be contributing to absorbance at specific wave numbers.

Several tissues and organisms can show common peaks due to similarity in composition. As shown in Figure 1b, there is absorbance at wave numbers corresponding to components (like carbohydrates, proteins) that are present across a wide range of samples. Owing to large number of data due to the availability of several characteristic wave numbers for individual compounds, mathematical and computational methods are developed that can analyze spectra as per the requirement, providing users the convenience of obtaining the

numbers. In order to explain the contribution of different metabolites like carbohydrates, nucleic acids, proteins and lipids in a sample, spectra of pure components are collected and analyzed for specific patterns. Comparison of compound that vary in one or more functional groups aids in the determination and assignment of particular groups and defines the specificity of signature for each along the entire spectral region. Assignment of the exact contribution of each component to the entire spectra therefore makes the method

Figure 1A shows representative spectra of blood fractions obtained by FTIR-MSP (Fourier Transform Infrared Microscopy), mounted on ZnSe slides in transmission mode. It is observed that in spite of the diversity of the source and type of the samples, absorbance of IR occurs at similar wave numbers, implicating that the samples are composed of similar basic substances as mentioned above. Simultaneously it also shows how the variation occurs among these substances and what are the likely principal components of each, contributing to its unique spectra. For example, plasma has less sugar compared to the bacterial cell which has a capsule and thus has more prominent peaks in the region 900-1185. Similarly, the RBCs that lack nucleic acids show diminished peaks while WBCs show clear absorbance

**1600 1500 1400 1300 1200 1100 1000 900 800**

**Wavenumber (cm-1**

Fig. 1A. Amide II normalized average spectra in the region 800-1800 cm-1 of different ZnSe mounted blood components ( a-WBC, b-Plasma and c-RBC) analyzed by FTIR-MSP in transmission mode showing few peaks absorbance and the functional groups associated

Thus a qualitative assessment can be made regarding the composition of a sample by looking at its spectra and also defining what type of functional groups could be contributing

Several tissues and organisms can show common peaks due to similarity in composition. As shown in Figure 1b, there is absorbance at wave numbers corresponding to components (like carbohydrates, proteins) that are present across a wide range of samples. Owing to large number of data due to the availability of several characteristic wave numbers for individual compounds, mathematical and computational methods are developed that can analyze spectra as per the requirement, providing users the convenience of obtaining the

**1238 CH2 (bend)**

c

b

**930**

a

**972**

**)**

**1080**

1047

**1084 C-O (Sym)**

1124

1172

quantitative as well as qualitative.

peaks between 1185 cm-1 and 1300 cm-1.

**0.0**

**0.5**

**1.0**

**1.5**

**Absorbance (A.U)**

to absorbance at specific wave numbers.

with it.

**2.0**

**2.5**

**1545 amide II**

**1400 COO- (Sys)**

Fig. 1B. Amide I normalized, averaged spectra of (a) bacteria (*Streptococcus pneumoniae*), (b) Cervical epithelium intermediate layer and (c) colonic crypt in transverse section to show absorbance of different biological components at different regions of mid IR. Note that though the carbohydrates absorbance occurs principally in the region between 1200-900 cm-1, the pattern differs across samples, giving a preliminary information of their different origins.

data in usable and interpretable forms. Advancements in computational techniques have added to the utility of the FTIR based instruments by making spectral calculations rapid and automatic, leading to their application in diverse fields of chemistry and biology for both applied and basic research. The potential of FTIR spectroscopy thus serves not only for routine applications but also as a diagnostic tool where other optical methods become difficult to apply. In view of these developments an ever expanding field of biomedical research based on FTIR based technologies has arisen over the last few decades. With its unique abilities, the technique has been applied mostly in cancer diagnosis and monitoring, microbial identification and drug efficacy evaluations to name a few. The present chapter describes in brief the different aspects of applications of FTIR in biomedicine and their suitability and relevance to biomedical research.

## **2. Brief historical perspective of FTIR spectroscopy in biomedical research**

IR spectroscopy has been in use for chemical analysis for several decades. The recognition of its ability to distinguish between normal and abnormal based on fingerprints of the cells and tissues has been utilized to describe changes at molecular and cellular levels ( Yang et al 1995, Malik et al 1996, Schultz et al 1996, Malins et al 1997) opened up a new area in the field of biodiagnostics. Other studies paved way for the examination of the technique as a diagnostic tool for identification of disease especially malignancy (Cohenford et al 1997, Rigas et al 1990, Wang et al 1997). This was followed by expanding the methods to study tissues from several different organs such as skin, cervix, liver among others with an emphasis on cancer identification which would be at par with conventional diagnostic techniques without the negative factors like toxicity to live tissues. Early works focused on defining the tissue variability in terms of IR spectra (Chiriboga et al 1998a,b,c, Wood et al 2004). This led to a large number of studies with different tissues and cell lines with an objective to make the technique relevant to oncology (Fukuyama et al 1999, Diem et al 2000, Gao et al 1999, Yano et al 2000, Malins et al 2003). The potential application in other

Chemometrics of Cells and Tissues Using

Malathi, 2008, Bourassa et al 2011).

routine spectrometers.

IR Spectroscopy – Relevance in Biomedical Research 293

**Authors Tissue/Organs Analysis Region/Wave number**

Krishna et al 2007 Ovarian tissue Cluster analysis 1540-60 & 1720-1780 Ali et al 2008 Brain tumor Cluster analysis Higher region, 1735.

Yang et al 1995 Fibrosacrcoma Intensity 1085 Fujioka et al 2004 Gastric cancer Discriminant Analysis 925-1660 Podshyvavlov et al 2005 Cervical cancer PNN 900-1800

Maziak et al 2007 Oesophagus Ratios Several

Argov et al 2002 Colon ANN 900-1800 Yano et al 2000 Lung Ratios 1045,1467 Wu et al 2001 Oral tissues Intensity 1745

Andrus and Strickland 1998 Lymphoma Ratio 1020,1121 Table 1. Malignancy in different types of tissue studied by FTIR spectroscopy.

each sample would require a different approach before being mounted for analysis, the common requirement is removal of water (that interferes with the spectra) through processes like lyophilization or even heating thermo stable samples in an oven. The samples are at times dissolved in D2O to over come the effect of water especially where they are aqueous soluble. Such systems are used to study interactions between biological molecules such as proteins and nucleic acids with ligands like toxins and antibiotics (Pratibha and

A slightly different approach is required for cells and microbes where a homogeneous material is available (e.g cell lines, bacteria). Such samples require removal of extraneous materials that can confound the spectra and are usually subjected to washes by normal saline before being dried for spectral acquisition. For example, bacterial cells from cultures in log phase are harvested after treatment with chloroform and the media removed by centrifugation. Subsequently they are washed several times in normal saline (without phosphates) and mounted on discs or any IR transparent support for spectra collection (Sahu et al 2006b).

When dealing with eukaryotic cells like cell lines, the cells are harvested and washed in a buffered solution several times to get rid of the media components and finally washed in normal saline before removing the excess water by a process of air drying. Similarly, different components of blood can be separated and fractions of plasma directly dried on the support. For the cellular components of blood or fluid biopsies like fine needle extracts by different biophysical techniques can be used for obtaining a given population of cells such as RBC, WBCs, monocytes or T cells and B cells which after purification are treated similar to cell lines for spectroscopic measurements. Most of these samples can be measured with

The approach becomes more complex when dealing with spectroscopy of tissues. In case of a relatively homogeneous tissue like liver, spectra can more or less be obtained from any region of the biopsy while tissues like cervical tissues that have clearly differentiated zones would need measurements at precise locations to alleviate spectral variation due to location

Harvey et al 2007 Prostate FTIR-acoustics

Lasch and Naumann 1998 Melanoma ANN/cluster analysis

biomedical fields was expanded by studying the classification of microorganisms using the technique and using it for identifying pathogens (Kirschner et al 2001, Choosmith et al 2001, Sandt et al 2003, Essendoubi et al 2005). These studies led to the development of the concept of "biomarkers" which are parameters or statistics derived from the spectral data that help to identify or differentiate among samples. The most promising biomarkers have been repeatedly used though developments have led to utilization of more complex spectral information rather than simple ratios. For example the region between 900-1300 cm-1. There were also studies undertaken to identify universally suitable biomarkers which could be used in different cells or tissue types to identify malignancies (Sahu et al 2004b, 2005, Mordechai et al 2004). Thus, there was a parallel development of mathematical and computational methodologies to utilize the spectral data and improve the sensitivity and specificity of the diagnosis and helping clearly defining ambiguous samples or accounting for outliers. These methods include linear discriminant analysis (LDA) (Krafft et al 2006), probabilistic neural networks (PNN) (Mark et al 2004), Principal component analysis(PCA) (Muralikrishna et al 2005) artificial neural networks (ANN) (Zhang et al 2003, Lasch et al 2007) and Discriminant classification functions (DCF) (Sahu et al 2010, Bogomolny et al 2007). The methods like clustering of spectra based on either Wards algorithm using Euclidean distances or Mahalanobis distances have been used to classify tissues and used as a means of developing pseudocolor images during FPA mapping of tissues (Lasch and Naumann, 1998). Similarly while the diagnosis of tissues using FTIR was continuing, the detection of changes in single cells using more sophisticated techniques like synchrotron or SERS were undertaken (Chekun et al 2002). Studies were undertaken to elucidate the contribution of individual components like the nucleic acids which held a great promise as biomarkers (Malins et al 2005, Sahu et al 2008). The works were also supported by inducing transformation in cells using various biological and chemical reagents and studying the spectral manifestations (Ramesh et al 2001, Salman et al 2003, Bogomolny et al 2008). The other significant technical development undertaken was the fiber optic systems that could be used for surface scanning or as probes and these are based on the ATR systems. Parallel research was also carried out to help identify and exclude contaminants in samples (Wood et al 1998, Romeo et al 2003, Sahu et al 2005) or use samples from paraffin embedded systems (Ly et al 2008). The effect of physical phenomenon like Mie scattering and its contribution to the FTIR data was also studied (Kohler et al 2008, Lee et al 2007) with an aim to understand how it can interfere with and influence the basic spectral data. Thus in the last two decades progress has been made in several directions to help realize the full potential of the technique in various biomedical fields making the data both qualitatively and quantitatively relevant. Table I lists a few studies where detection of cancers was carried out using FTIR based instrumentation.

#### **3. Different types of FTIR spectroscopy and their suitability to different fields of biomedical research**

Currently the simplest and most familiar IR spectrometers existing consist of a source of IR beam, a sample holder and a detection system to monitor the absorbance. Utilizing several different matrices that are IR transparent ( KBr, BaF2, CaF2, ZnSe), it is possible to obtain the spectra from samples as diverse as pure compounds such as proteins, lipids, drugs and metabolites to homogeneous preparations such as body fluids, microorganisms, cell lines that can be mounted without destroying their chemical nature. While it is imperative that

biomedical fields was expanded by studying the classification of microorganisms using the technique and using it for identifying pathogens (Kirschner et al 2001, Choosmith et al 2001, Sandt et al 2003, Essendoubi et al 2005). These studies led to the development of the concept of "biomarkers" which are parameters or statistics derived from the spectral data that help to identify or differentiate among samples. The most promising biomarkers have been repeatedly used though developments have led to utilization of more complex spectral information rather than simple ratios. For example the region between 900-1300 cm-1. There were also studies undertaken to identify universally suitable biomarkers which could be used in different cells or tissue types to identify malignancies (Sahu et al 2004b, 2005, Mordechai et al 2004). Thus, there was a parallel development of mathematical and computational methodologies to utilize the spectral data and improve the sensitivity and specificity of the diagnosis and helping clearly defining ambiguous samples or accounting for outliers. These methods include linear discriminant analysis (LDA) (Krafft et al 2006), probabilistic neural networks (PNN) (Mark et al 2004), Principal component analysis(PCA) (Muralikrishna et al 2005) artificial neural networks (ANN) (Zhang et al 2003, Lasch et al 2007) and Discriminant classification functions (DCF) (Sahu et al 2010, Bogomolny et al 2007). The methods like clustering of spectra based on either Wards algorithm using Euclidean distances or Mahalanobis distances have been used to classify tissues and used as a means of developing pseudocolor images during FPA mapping of tissues (Lasch and Naumann, 1998). Similarly while the diagnosis of tissues using FTIR was continuing, the detection of changes in single cells using more sophisticated techniques like synchrotron or SERS were undertaken (Chekun et al 2002). Studies were undertaken to elucidate the contribution of individual components like the nucleic acids which held a great promise as biomarkers (Malins et al 2005, Sahu et al 2008). The works were also supported by inducing transformation in cells using various biological and chemical reagents and studying the spectral manifestations (Ramesh et al 2001, Salman et al 2003, Bogomolny et al 2008). The other significant technical development undertaken was the fiber optic systems that could be used for surface scanning or as probes and these are based on the ATR systems. Parallel research was also carried out to help identify and exclude contaminants in samples (Wood et al 1998, Romeo et al 2003, Sahu et al 2005) or use samples from paraffin embedded systems (Ly et al 2008). The effect of physical phenomenon like Mie scattering and its contribution to the FTIR data was also studied (Kohler et al 2008, Lee et al 2007) with an aim to understand how it can interfere with and influence the basic spectral data. Thus in the last two decades progress has been made in several directions to help realize the full potential of the technique in various biomedical fields making the data both qualitatively and quantitatively relevant. Table I lists a few studies where detection of cancers was carried out

**3. Different types of FTIR spectroscopy and their suitability to different fields**

Currently the simplest and most familiar IR spectrometers existing consist of a source of IR beam, a sample holder and a detection system to monitor the absorbance. Utilizing several different matrices that are IR transparent ( KBr, BaF2, CaF2, ZnSe), it is possible to obtain the spectra from samples as diverse as pure compounds such as proteins, lipids, drugs and metabolites to homogeneous preparations such as body fluids, microorganisms, cell lines that can be mounted without destroying their chemical nature. While it is imperative that

using FTIR based instrumentation.

**of biomedical research**

Table 1. Malignancy in different types of tissue studied by FTIR spectroscopy.

each sample would require a different approach before being mounted for analysis, the common requirement is removal of water (that interferes with the spectra) through processes like lyophilization or even heating thermo stable samples in an oven. The samples are at times dissolved in D2O to over come the effect of water especially where they are aqueous soluble. Such systems are used to study interactions between biological molecules such as proteins and nucleic acids with ligands like toxins and antibiotics (Pratibha and Malathi, 2008, Bourassa et al 2011).

A slightly different approach is required for cells and microbes where a homogeneous material is available (e.g cell lines, bacteria). Such samples require removal of extraneous materials that can confound the spectra and are usually subjected to washes by normal saline before being dried for spectral acquisition. For example, bacterial cells from cultures in log phase are harvested after treatment with chloroform and the media removed by centrifugation. Subsequently they are washed several times in normal saline (without phosphates) and mounted on discs or any IR transparent support for spectra collection (Sahu et al 2006b).

When dealing with eukaryotic cells like cell lines, the cells are harvested and washed in a buffered solution several times to get rid of the media components and finally washed in normal saline before removing the excess water by a process of air drying. Similarly, different components of blood can be separated and fractions of plasma directly dried on the support. For the cellular components of blood or fluid biopsies like fine needle extracts by different biophysical techniques can be used for obtaining a given population of cells such as RBC, WBCs, monocytes or T cells and B cells which after purification are treated similar to cell lines for spectroscopic measurements. Most of these samples can be measured with routine spectrometers.

The approach becomes more complex when dealing with spectroscopy of tissues. In case of a relatively homogeneous tissue like liver, spectra can more or less be obtained from any region of the biopsy while tissues like cervical tissues that have clearly differentiated zones would need measurements at precise locations to alleviate spectral variation due to location

Chemometrics of Cells and Tissues Using

**5. Protocols for sample preparation**

**5.1.1 Drug macromolecules interactions**

**5.1.2 Microbial cultures**

**5.1 FTIR spectroscopy of homogeneous materials**

the clumping tendency of the filaments (Salman et al 2011).

**5.1.3 Cell lines and transformed cultures**

IR Spectroscopy – Relevance in Biomedical Research 295

carbohydrates of bacteria. The ratio of the integrated absorbance at 1045 cm-1 to that at 1545 cm-1 provides an estimate of the carbohydrate absorption (Parker, 1971). When a normalization is made to the amide II band the intensities at this wavenumber can be assumed to directly correlate with the amount of carbohydrates and is similar to

As the sources of samples for FTIR spectroscopy can be different, they need special

Studies of drug interactions by FTIR spectroscopy are easily carried out in systems using D2O. Various approaches are possible. The drug and protein/nucleic acid interaction is carried out under solution conditions at predetermined stoichiometry and the mixture is then added to a film from which spectral data are obtained. The individual components are also used as references or controls. The difference spectra between the control and with the ligands is sued to quantify the amount of bound ligands or study the changes in the protein secondary structure by monitoring the shifts in peak intensities or variation in peak intensities of different functional groups (Bourasssa et al 2011). FTIR-ATR system can also be used for studying the

Bacterial cultures growing in the log phase are fixed with formaldehyde (final concentration 0.25%) and washed three times with saline containing 0.25% formaldehyde and once with saline only. The pellet is resuspended with saline to an OD450 = 0.3 (about 20 ml). One microliter of the suspension is spotted on Zinc-Selenium slides and air dried for 6 hours to remove any water in the sample under a laminar air flow chamber. Microscopic FTIR measurements are made in transmission mode using the FTIR microscope IRscope II with a mercury-cadmium-telluride (MCT) detector, coupled to the FTIR spectrometer (Bruker Equinox model 55/S, OPUS software). Absorbance is measured from an area of 100 nm diameter by setting the slit to 100 microns. Regions of thickness of about 10 microns (as seen from the ADC values) are selected and 128 scans co added for each spectrum. For each sample at least five spots are measured and the average spectra calculated. Similar procedure can be adopted for yeast cells. In case of fungi which have a tendency to grow as filaments (hyphae), the filaments are harvested and briefly tweezed or torn to get uniform untangled mass as observed under the microscope. These are then washed in water or saline before mounting on the slides. These can also be measured using ATR systems as they are highly adherent and a spread sample is likely to have non uniformity in thickness owing to

Cell lines are cultured under suitable conditions (e.g. 37°C in RPMI medium supplemented with 10% of newborn calf serum (NBCS) and the antibiotics penicillin, streptomycin) and

normalizing the amount to protein in cells and tissues using chemical analyses.

preparations adopting different kinds of procedures depending on the samples.

interactions using a dialysis system.(Krasteva et al 2006, Kumar & Barth (2011).

effects (Chiriboga et al 1998a). However these samples necessitate a microscopic evaluation before measurement. Colonic biopsies that display a more or less uniform pattern in the cross section are measured on the circles of crypts for diagnosis of malignancies. However the longitudinal sections of the crypt require a more defined location for measurement and invariably depend on the utilization of microscope. More complicated measurements of tissues where several different kind of cells are required to be measured depend on focal plane array detectors where each pixel of the measured area can be represented by a spectra. The requirement of methodologies for reconstruction of a pseudo image based on spectral characteristics become the norm and essential feature of such measurements.

### **4. FTIR instrumentation and their applications in biomedicine**

The simplest FTIR spectrometers have been most widely used to study cells and plasma samples. When a sample's spectra are obtained, they are normalized, averaged and baseline corrected. The usual practice is to undertake a baseline correction in the spectral region of interest using the rubber band form and locating the two extreme points of the region of interest. The cut spectra are then normalized again to the highest peak or the area. The different methods of baseline correction in specified regions would greatly alter the results. Hence application of similar data processing on all spectra being considered in a study becomes essential. The intensities at selected wave numbers are then used to define ratios or biomarkers that can define the criterion being sought for the diagnosis. Extracting intensity ratios is preferred than band intensity, since it yields a dimensionless quantity which is mostly independent on the exact normalization procedure used (such as Min-Max or vector normalization). Other than the intensities, many studies report shifts in peak wave numbers which can provide additional valuable information. However instruments that can measure only a few wave numbers can also be used when more sophisticated instrumentations are not available. For example measuring the band intensities of the CH2 and CH3 vibrations between 2800-3200 cm-1 can provide information about the status of the tissue (Sahu et al 2006a,2005). Similarly various other parameters have been routinely used in diagnosis of diseases and cancer. The ratio of Amide I /Amide II quantified using the integrated absorption of Amide I and Amide II (1750-1590 cm-1, 1590-1480 cm-1) was one of the first ratios found suitable for diagnosis and is an indicator for the DNA absorbance variation (Liquier and Talliander,1996, Benedetti et al., 1997; Gasparri & Muzio, 2003). RBCs (red blood cells) from humans that lack nuclei posses a ratio of unity for this parameter and maybe used as a reference when understanding nuclear changes using these wave number regions.

Similarly quantification of phosphate metabolites calculated by measuring the integrated area of phosphate symmetric (990-1145 cm-1) and antisymmetric (1190-1275 cm-1) bands have been used to understand the stages of the cell (Yang at al. 1995). The most promising phosphate absorbance that has been used in many studies is the RNA/DNA ratio dependent on the phosphate absorbance arising from the symmetric vibrations of DNA and RNA namely at 1020 and 1121 cm-1 respectively. Several other band intensities have been used individually or in combination as listed in table 1 for evaluating cells, tissues and biopsies. The band at 1045 cm-1 is attributed to the vibrational frequency of -CH2OH groups and the C-O stretching frequencies coupled with C-O bending frequencies of the C-OH groups of carbohydrates (including glucose, glycogen, etc.) as well as the capsular

effects (Chiriboga et al 1998a). However these samples necessitate a microscopic evaluation before measurement. Colonic biopsies that display a more or less uniform pattern in the cross section are measured on the circles of crypts for diagnosis of malignancies. However the longitudinal sections of the crypt require a more defined location for measurement and invariably depend on the utilization of microscope. More complicated measurements of tissues where several different kind of cells are required to be measured depend on focal plane array detectors where each pixel of the measured area can be represented by a spectra. The requirement of methodologies for reconstruction of a pseudo image based on spectral

The simplest FTIR spectrometers have been most widely used to study cells and plasma samples. When a sample's spectra are obtained, they are normalized, averaged and baseline corrected. The usual practice is to undertake a baseline correction in the spectral region of interest using the rubber band form and locating the two extreme points of the region of interest. The cut spectra are then normalized again to the highest peak or the area. The different methods of baseline correction in specified regions would greatly alter the results. Hence application of similar data processing on all spectra being considered in a study becomes essential. The intensities at selected wave numbers are then used to define ratios or biomarkers that can define the criterion being sought for the diagnosis. Extracting intensity ratios is preferred than band intensity, since it yields a dimensionless quantity which is mostly independent on the exact normalization procedure used (such as Min-Max or vector normalization). Other than the intensities, many studies report shifts in peak wave numbers which can provide additional valuable information. However instruments that can measure only a few wave numbers can also be used when more sophisticated instrumentations are not available. For example measuring the band intensities of the CH2 and CH3 vibrations between 2800-3200 cm-1 can provide information about the status of the tissue (Sahu et al 2006a,2005). Similarly various other parameters have been routinely used in diagnosis of diseases and cancer. The ratio of Amide I /Amide II quantified using the integrated absorption of Amide I and Amide II (1750-1590 cm-1, 1590-1480 cm-1) was one of the first ratios found suitable for diagnosis and is an indicator for the DNA absorbance variation (Liquier and Talliander,1996, Benedetti et al., 1997; Gasparri & Muzio, 2003). RBCs (red blood cells) from humans that lack nuclei posses a ratio of unity for this parameter and maybe used as a reference when understanding nuclear changes using these wave number

Similarly quantification of phosphate metabolites calculated by measuring the integrated area of phosphate symmetric (990-1145 cm-1) and antisymmetric (1190-1275 cm-1) bands have been used to understand the stages of the cell (Yang at al. 1995). The most promising phosphate absorbance that has been used in many studies is the RNA/DNA ratio dependent on the phosphate absorbance arising from the symmetric vibrations of DNA and RNA namely at 1020 and 1121 cm-1 respectively. Several other band intensities have been used individually or in combination as listed in table 1 for evaluating cells, tissues and biopsies. The band at 1045 cm-1 is attributed to the vibrational frequency of -CH2OH groups and the C-O stretching frequencies coupled with C-O bending frequencies of the C-OH groups of carbohydrates (including glucose, glycogen, etc.) as well as the capsular

characteristics become the norm and essential feature of such measurements.

**4. FTIR instrumentation and their applications in biomedicine**

regions.

carbohydrates of bacteria. The ratio of the integrated absorbance at 1045 cm-1 to that at 1545 cm-1 provides an estimate of the carbohydrate absorption (Parker, 1971). When a normalization is made to the amide II band the intensities at this wavenumber can be assumed to directly correlate with the amount of carbohydrates and is similar to normalizing the amount to protein in cells and tissues using chemical analyses.