Chapter 1 Emerging techniques in biological sciences Ranbir Chander Sobti1, Ahmad Ali2, Phuntsog Dolma2, Anuragini Kadwalia2, Tundup Dolma3, Jagdish Rai4 and Archana Chauhan2 1 Department of Biotechnology, Panjab University, Chandigarh, India, 2Department of Zoology, Panjab University, Chandigarh, India, 3Department of Environmental Studies, Panjab University, Chandigarh, India, 4Institute of Forensic Science & Criminology (U.I.E.A.S.T.), Panjab University, Chandigarh, India 1.1 Introduction The term “Bioscience” refers to any field of study that deals with living organisms including plants, animals, and microorganisms. Modern biology is a wide and diverse area that studies the structure, function, growth, distribution, evolution, and other characteristics of living organisms through a variety of specialized disciplines. In the modern era, the definition of this word can be expanded to include all the work done in biological fields to benefit mankind and other organisms. This includes all the progress made to date in the fields of health sciences, animal sciences, genetics, molecular biology, biochemistry, bioinformatics etc. The term “biotechnology” refers to the application of techniques and the use of living organisms or biological processes to develop agricultural, industrial, or medical products. In particular, DNA technology is used in many aspects of modern biotechnology including DNA sequencing, analysis, cutting, and pasting. Using cellular and bimolecular processes, this biology-based research produces products and technology that improve our lives and the health of our world. Science and technology support each other in the development of new technologies, while scientific knowledge is used to produce new technologies. Scientists frequently use modern technologies to investigate new things and assist in the exploration of nature. In medicine and healthcare, digital technology helps in the transformation of an unsustainable healthcare system into one that is sustainable. These are technologies that are driven by efforts to acquire or synthesize newer biological or molecular diversity, or a wider range of specificity, so that the user can pick and choose what is valuable from a broad pool of newly obtained variation. The importance of biosciences is more prominent in present times considering scientists successfully discovered a vaccine against SARS-Covid19 in a very limited time. This was achieved because of advanced technology. The need for technology in bioscience is to generate a large amount of yield from agriculture and industries, and discovering new things as the traditional way of production and also generating environmentally friendly, pest-resistance products was not effective. The breakthrough discoveries in biology were made by people trained in techniques from other disciplines. Many of the Nobel prizes in chemistry are awarded for discoveries related to biological systems. Physicists have developed techniques that have advanced the experimental science of every discipline. Mathematics is considered the mother of science, and nowadays, computational science, along with statistics, is leading the measure of developments in every sphere of science and business. Many types of equipment used in biological sciences were devised by engineers. When all these discipline areas work together for a common goal, the result is bound to be fruitful. Many technological advancements has been made so far. Some of the techniques are discussed below. 1.2 Artificial Intelligence It is very important to discuss “Artificial Intelligence” first of all because the majority of the advanced technologies used in biosciences generate output are based on this technique. The word “Artificial” refers to something that is not natural and the word “Intelligence” can be defined as the ability to receive and analyze information, store it in the Advances in Animal Experimentation and Modeling. DOI: https://doi.org/10.1016/B978-0-323-90583-1.00013-1 © 2022 Elsevier Inc. All rights reserved. 3 4 PART | A Modern technological advancements in the field of animal experimentation form of memory, and apply this knowledge for future actions. Human beings are the most intelligent of all the organisms known though higher animals, for examples, mammals show intelligent behavior. Lower animals and plants are also known to show a subtle level of intelligence. Living organisms display “Natural Intelligence.” However, the term “Artificial Intelligence” is used for the machines that are programmed to think and act independent of human intervention using the previous information stored as memory. Artificial intelligence has two major subtypesmachine learning and deep learning. Machine learning allows the machine to learn from previous data and perform a function accordingly. Deep learning works similar to machine learning but it uses many layers to analyze data just like our neurons in the nervous system form complex networks. This allows the machine to analyze large volumes of data and act accordingly. These machines can make predictions, recommendations, create visuals, differentiate different types of cells and perform many other functions which make them useful to the modern biologists. Artificial Intelligence is used these days almost in every branch of biology, health and medicine. Machine learning is used in the next generation sequencing and structural predictions of proteins. Deep learning can be used in generating images, genome analysis, prediction of genetic variants, finding sites of mutations and drug discovery. New Drugs can be designed by using computer aided drug designing as artificial intelligence can help in virtual screening of the drugs, quantitative structure activity relation analysis and in silico analysis of absorption, distribution, metabolism, excretion and toxicity properties (Zhong et al., 2018). Biomarkers can be identified using deep neural network (Mamoshina, Vieira, Putin, & Zhavoronkov, 2016). Diseases can be diagnosed and even pre diagnosed effectively without using deep neural networks and this way health care facilities can be made available even in the remote places where health care professionals are not available. 1.3 Imaging cells to molecules in 3D Imaging is one of the most important tools for biologists or scientists to evaluate and record size and form and structures, various anomalies, variation, etc. Max Knoll and Ernst Ruska at the Berlin Technische Hochschule in 1931 invented electron microscopy with higher resolution barrier. In the 19th century the theoretical and technical groundworks of the modern light microscope was created, most notably by keeping the diffraction-limit theory into consideration, but also aberration-corrected lenses and an optimized illumination mode called Köhler illumination (Köhler, 1894) were applied. Electron microscopy enabled the visualization of biological structures at a resolution of 0.1 nm. And one of the greatest challenges in imaging biological samples is their inherently low contrast, which is due to their refractive index being very close to water. Thus, generating little scatter interaction with the incident light. Various different methods for increasing contrast have been developed including phase imaging and polarization changes, staining and fluorescence. Out of these the staining and fluorescence being possibly the most far-reaching development since the invention of the light microscope (Wollman, Nudd, Hedlund, & Leake, 2015). Another improved imaging tool is Cryo-electron microscopy which forms a promising tool for determining the structure of proteins for which X-ray crystallography or nuclear magnetic resonance (NMR) is not feasible (Zhang, Minary, & Levitt, 2012). Some proteins like membrane proteins are not easy to crystalize and therefore the structure determination through X-ray crystallography is not feasible. These membrane proteins are also not suitable for NMR-spectroscopy based structure determination because it requires high purity and concentration of protein. Also, cryo electron microscopy made the very important discovery of lipid bilayer structure of cell membrane possible. Fluorescence-based confocal microscopy is used today to visualize the biological samples at molecular precision in 3D. It has confirmed the localization of molecules in subcellular organelles and other spatiotemporal changes in living systems (Table 1.1). Light Sheet Fluorescence Microscopy combined with advanced optics can be used to image cells at subcellular levels in their native state (Liu et al., 2018). Human chromosomes are prepared in a linear and conserved sequence order which undergoes additional spatial folding within the three-dimensional space of the nucleus. While, structural variations in this organization are an important source of natural genetic diversity, though the cytogenetic anomalies can also underlie a number of human diseases and disorders (Hu, Maurais, & Ly, 2020). Thus, there is a need for methods that can image chromosomes with genome-wide coverage, as well as greater genomic and optical resolution. Medical imaging is a branch of science dealing with the visualization of the internal organs of the body without using invasive techniques starting from X-ray photography of bones, ultrasound imaging of internal organs, MRI of the brain, fluorescent tracer-dye based angiography, etc. has enabled precise diagnosis of ailments. Medical imaging (X-ray, MRI, ultrasounds, etc.) is used to create visual representations of the interior of the body for clinical analysis and medical intervention of complex diseases in a short period of time. Fourier-transform infrared spectroscopy-imaging can visualize the concentration of metabolites and macromolecules in cells of a tissue, using false-color rendering (Kumar, Srinivasan, & Nikolajeff, 2018). This technique is expected to be a cost-effective cytological diagnosis tool in the future. Digital Pathology is another useful technique in which histological glass slides are converted TABLE 1.1 Brief summary of various microscope used for the identification or analysis of specimen to cell or gene. Microscope Inventor Method Application Reference Compound microscope Galileo 1609 Used an objective lens to collect light from a specimen and a second lens to magnify the image Magnify the image or specimen Wollman et al. (2015) Compound microscope Robert Hooke and Antonj van Leeuwenhoek 1665 Light passes through 2 lenses and can magnify up to 2000 3 Microbiology and microscopic structure of cork, seeds, skin, fish scales, oyster shell, the white matter upon the tongues of feverish persons, nerves, muscle fibers, fish circulatory system, insect eyes, spider physiology, mite reproduction, aquatic plants Gest (2009) Electron microscope Max Knoll and Ernst Ruska 1931 Uses beams of electrons rather than light Used to observe minute objects: viruses, DNA, parts of cells Wollman et al. (2015) Phase contrast microscopy Fritz Zernike 1934 Achieved by manipulating the transmitted, background light differently from the scattered light, which is typically phase-shifted 90 by the sample. A circular annulus is placed in front of the light source, producing a ring of illumination with much higher contrast image Microscopy technique in cell culture, thin tissue slices, lithographic patterns, fibers and live cell imaging in their natural state without previous fixation or labeling Zernike (1955) Cryo-electron microscopy Jacques Dubochet, Joachim Frank and Richard Henderson 1930s A small aliquot of sample in solution or suspension is applied to an electron microscopy grid and is blotted to a thin layer and immediately plunged into liquid ethane (around 2180 C), so that the molecules get trapped in a layer of vitrified water Determining the structure of proteins in 3D Dubochet et al. (1988) Differential interference contrast Georges Nomarski 1955 Use of a Nomarski Wollaston prism through which polarized light is sheared into two beams polarized at 90 degrees to each other Imaging unstained microbiological samples to reveal the boundaries of cells and subcellular organelles Nomarski and Weill (1955) Fluorescence microscope Otto Heimstaedt and Heinrich Lehmann 1911 13 Uses fluorescence and phosphorescence To study properties of organic or inorganic substances Rost (1992) Epifluorescence microscopes Johan S. Ploem Excitation of the fluorophore and detection of the fluorescence are done through the same light path (i.e., through the objective) Useful when imaging thick samples, over 10 µm deep Böhmer and Enderlein (2003) Confocal microscope Marvin Minsky 1955 High-resolution imaging of thick specimens (without physical sectioning) can be analyzed using fluorescent-labeled dye Assessment of different types of eye diseases, particularly imaging, qualitative analysis, and quantification of endothelial cells of the cornea. Also used as the data retrieval mechanism in some 3D optical data storage systems Hara, Morishige, Chikama, and Nishida (2003) Multiphoton microscope Winfried Denk and James Strickler 1990 Multiphoton fluorescence excitation results in the capture of high-resolution three-dimensional images of specimen tagged with highly specific fluorophores Use for imaging living, intact biological tissues on the length scale Hoover and Squier (2013) (Continued ) 6 PART | A Modern technological advancements in the field of animal experimentation TABLE 1.1 (Continued) Microscope Inventor Method Application Reference Total internal reflection fluorescence (TIRF) microscope Daniel Axelrod early 1980s Have limited specimen region in the field very adjacent to the interface between two media having different refractive indices Use in biochemistry and cell biology for visualization of cell, tracking of secretory granules etc. Axelrod (2001), Fish (2009) Brillouin microscopy Léon Brillouin Uses a low-power, focused laser beam and a high-resolution confocal spectrometer to measure the Brillouin frequency Use to create 3D structures without even labeling the biological samples Edmondson, Broglie, Adcock, and Yang (2014) CRISPR labeled fluorescence Emmanuelle Charpentier and Jennifer Doudna CRISPR labeling and imaging of protein-coding genes in living cells Use for imaging gene loci in vivo Chen, Zou, Xu, Liang, and Huang (2018), Nasri et al. (2019) into images which can further be analyzed by using deep neural networks. El Achi and Khoury (2020) recently summarized many such techniques which help in detecting hematological disorders especially cancer. The capacity of Artificial intelligence-based techniques in detecting molecular and genetic alterations in hepatic and gastro intestinal cancers from histological digital slides has lately been reported (Calderaro & Kather, 2021). Ever since the development of imagining from light microscope to advanced fluorescence microscopy has motivated by aspirations to illuminate and understand the functional anatomy of the cell. Thereby, 3D microscopy has permitted fundamental insight into the steady-state organization of a cell and its dynamics as well as the structure, turnover, mobility, and function of its components (Renz, 2013). 1.4 Microarray Microarrays are the miniature version of complex laboratory tools which is why they are also known as lab on chip. DNA Microarray or DNA chip or biochip comprises of the oligonucleotides attached on solid surfaces like glass slides. Advanced photolithography techniques made it possible to attach an oligonucleotide probe on a glass slide or other solid surfaces. Therefore, millions of oligonucleotide probes can be packed on a glass slide and all mRNA products from a tissue can be quantitatively detected at the same time using only a small sample. This is in principle southern blotting at large-scale miniaturized form. The mRNA is detected after converting it to DNA by reverse transcription and also attaching a fluorophore to it. These can be used to detect mutations, single nucleotide polymorphism (SNPs) and detection of binding site clusters by CHIP-chip sequencing. Using Artificial Intelligence, thousands of DNA Spots can be analyzed simultaneously. Many modifications of these techniques are used to diagnose various diseases. These can be used for detection of cancers and infectious diseases. Scientists at Technical University of Munich in Germany have very recently developed a rapid microarray test for identifying antibodies against SARS-Cov-2 (Hastings, 2021). RNA microarrays are used for studying various aspects of transcriptomics. Protein microarrays help in studying gene expression and proteomics (Fig. 1.1). 1.5 Genetic engineering with precision Genetic engineering is a method of altering genetic makeup in order to get the desired outcome. Modifications like (insertions, deletions, and substitutions) in a living organism’s genome are referred to as genome engineering, genome editing, and gene editing, which combine science, technology, and engineering to comprehend, develop, redesign, manufacture, and modify genetic materials in living creatures and biological systems more quickly (Sandler, 2020). Engineered gene drives are a type of genetic alteration that improve the potential of a genetic characteristic or element being inherited more frequently than the Mendelian ratio. Engineered gene drives, which stimulate the biased inheritance of specific genes, have the potential to disseminate beneficial genes throughout wild populations or inhibit hazardous species, and could be especially useful in the control of vector-borne diseases like malaria, eliminating insect-borne diseases, eradicating invasive foreign species, and even reversing pesticide and herbicide resistance in a cost-effective Emerging techniques in biological sciences Chapter | 1 7 FIGURE 1.1 Applications of DNA microarray technology. FIGURE 1.2 Genetic engineering process. and ecologically sound manner (Champer, Buchman, & Akbari, 2016). Plant genetic engineering techniques have been created to make plant breeding faster, more predictable, and adaptable to a wide range of species, for example, Bt cotton, Bt brinjal. The basic steps of genetic engineering are illustrated in (Fig. 1.2). The RNAi (RNA interference) was discovered by Fire and Mello in 1960. The phenomenon of RNAi is engaged in sequence-specific gene regulation, which is triggered by the introduction of dsRNA, which inhibits translation or suppresses 8 PART | A Modern technological advancements in the field of animal experimentation transcription. Antisense technology is less precise, efficient, and stable than RNAi technology. It has been effectively used to change the expression of genes in plants to improve quality attributes. RNAi is mediated by 21 23 nucleotide small interfering RNAs (siRNAs), which are generated by the RNAse II-like enzyme Dicer from lengthy double-stranded RNAs. The siRNAs are then added to an RNA-induced silencing complex (RISC), which identifies and cleaves mRNA that is complementary to the siRNAs (Campbell & Choy, 2005). A ribonuclease known as DICER or Dicer-like enzyme does the cleavage. Dicer enzymes are double strand (ds)-specific RNases whose cleavage products are used to provide specificity in RNAbased gene-silencing pathways (Pare & Hobman, 2007). Scientists can easily knock out a gene product from an organism using RNAi, whereas the earlier methods of gene knockout were time-consuming as well as unfeasible (Saurabh, Vidyarthi, & Prasad, 2014). According to central dogma, any gene is transcribed as an mRNA, which then translates into a protein, which impacts the organism’s phenotype. RNAi disrupts the gene-to-phenotype sequence by degrading specific mRNA transcribed from the target gene. The RNA RISC, which is activated by double-stranded RNA, causes sequence-specific mRNA degradation in the RNA-interference pathway (Sontheimer, 2005). Therefore, in principle, any gene can be silenced by adding a small RNA to the cell that is complementary to the target gene. The most modern method of genetic engineering is genome editing using programmable endonucleases. Endonucleases are employed to cause double strand breaks (DSBs) in certain target genes. The cellular DNA repair mechanism then uses nonhomologous end joining or homology-directed repair to repair the double strand break (HDR). Insertions, deletions, substitutions, and DNA recombination may occur during the process (Puchta, 2005; Puchta, Dujon, & Hohn, 1996; Symington & Gautier, 2011). Site-directed mutagenesis (SDM) and other genetic engineering approaches can be used to investigate protein structure, both chemical and three-dimensional, as well as flexibility and function. Unlike chemical and physical mutagenesis, which introduces numerous mutations across the genome at random, SDM approaches are designed to be target specific (Quétier, 2016). The Zinc finger nuclease technology (ZFN), TAL effector nucleases (TALEN) and clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPRassociated protein 9 (Cas9) are the main SDM techniques currently in use (Schiml & Puchta, 2016). In comparison to ZFN and TALEN, CRISPR-Cas9 has attracted a lot of interest recently since it is a more user-friendly and costeffective method of creating target-specific constructs. Various techniques are used for changing the DNA of an organism, starting from random mutagenesis to precise changes using CRISPR/Cas9 (Clustered regularly interspaced short palindromic repeats) gene editing, which is mostly used in genomic editing approach. The CRISPR/Cas9 comprises of a Cas9 nuclease and a single-guide RNA for genome editing (sgRNA). Cas9 can recognize and cut a specific target DNA sequence with the help of sgRNA, resulting in DSBs that cause cell repair processes and mutations at or around the DSB locations (Bao et al., 2019). Numerous guide sequences can be encoded into a single CRISPR array to allow simultaneous editing of multiple places throughout the mammalian genome, exhibiting the RNA-guided nuclease technology’s programmability and wider application (Cong et al., 2013). This method is applicable to systems ranging from cells in vitro to animals in vivo. CRISPR/Cas9 genome editing has been utilized to fix disease-causing DNA mutations ranging from a single base pair to massive deletions (Cai, Fisher, Huang, & Xie, 2016). CRISPR-Cas9 has been utilized to improve yield, quality, and nutritional value in a growing number of monocot and dicot plant species, as well as to introduce or enhance resistance to biotic and abiotic challenges, among other things. Despite worries about biosafety, genome editing is a promising technology that has the potential to contribute to food production for the benefit of the world’s rising population (El-Mounadi et al., 2020). Enzymes for DNA recombination are also used to replace a target DNA piece in the genome. These precision genetic engineering techniques are very valuable for crop improvement and the treatment of genetic diseases in humans (Cai et al., 2016). CRISPR technology can detect the DNA/RNA of a pathogen by binding to it through complementary guide RNA and giving fluorescence signals while cleaving the target polynucleotide. The feasibility of using the CRISPR-Cas system to combat viral attacks has also been evaluated (Konwarh, 2020). CRISPR-cas9 based genome editing in stem cells opens up whole new possibilities for treatment and research models of disease (Valenti, Serena, Carbonare, & Zipeto, 2019). Induced pluripotent stem cells can be created from any differentiated cell by expression of only 4 factors: Oct3/4, Sox2, c-Myc, and Klf4 (Takahashi & Yamanaka, 2006). These combinations of techniques are already being tried for genetic therapy of sickle cell anemia (Park & Bao, 2021). Systems biology (SB) is a branch of biology that focuses on these complex processes at the cellular level (Chen, Wang, & Zhang, 2009). SB is a new interdisciplinary discipline that aims to investigate and comprehend the complex behavior of biological systems at the system level rather than at the level of individual components. This is accomplished by continuously combining experimental data with known system information. To analyze the system and use the generated data, new systems biology and bioinformatics models and algorithms are required (Jayavelu, 2015). In a system biology approach, designed genetic networks and genes are constructed from component pieces available at a repository called iGEM (International Genetically Engineered Machine). This repository has DNA pieces available in Emerging techniques in biological sciences Chapter | 1 9 a format that the components can be joined in any design because a convention is followed to provide restriction sites in these DNA elements. The repository has a very rich catalog of promoters, reporters, regulatory elements, etc. These DNA components (also called BioBricks) are supplied to member labs at a nominal charge in the spirit of open science (Wang et al., 2021). In an engineering approach to biological systems, cell-free systems are used to synthesize proteins and metabolites using the necessary components of cellular machinery rather than the live cell (Rollin, Tam, & Zhang, 2013). These necessary components for synthesis or other chemical reactions are extracted from cells using separation techniques such as ultracentrifugation. Using these cell-free systems, even non-coded (unnatural) amino acids can be incorporated into the proteins to expand the chemical repertoire of proteins which may be required for more stability or novel function for the industrial application of designed proteins (Noren, Anthony-Cahill, Griffith, & Schultz, 1989). 1.6 Omics technologies The term omics refers to a scientific discipline in biology that ends with “omics.” With the recent advancement in technologies the suffix “omics” has been applied to techniques that assess some features of a vast family of biological molecules, such as the genome, proteome, transcriptome, or metabolome, respectively (Fig. 1.3; Vailati-Riboni, Palombo, & Loor, 2017). Omics studies are also referring to a group of technologies that are used to investigate the roles, connections, and activities of the many types of molecules that make up a complete cell of an organism. The comprehensive capabilities of omics technologies in the context of the cell, tissue, or organism is its distinguishing feature. They are primarily intended for the non-targeted and non-biased identification of genes, mRNAs, proteins, and metabolites in a biological sample. Each of these areas opens us previously unimaginable possibilities for understanding the physiological state and cellular activities in the cell (Roh, Nerem, & Roy, 2016; Zhang, Sun, & Ma, 2017). High-throughput methods can quickly offer a comprehensive understanding of biological activities at various levels, enabling faster resolving the challenges in health, disease detection, and cellular metabolism as well as uncover gaps in present understanding (Baidoo & Benites, 2019). Moreover, in situations where no hypothesis is known or mandated owing to a lack of data, the omics approach is appropriate for hypothesis-generating studies, as holistic techniques gather and evaluate all relevant data to establish a hypothesis, which can then be tested (Roberts, Souza, Gerszten, & Clish, 2012). The automated DNA protein sequencer and ink-jet DNA synthesizer, developed by Leroy Hood and colleagues in the early 1990s for gene expression analysis, were the first omics technologies (Hood, 2002). They also developed a protein sequencer and synthesizer for protein expression at the cellular level. Furthermore, the development of metabolomics investigations, which were initiated by Oliver, Winson, Kell, and Baganz (1998) completed the physiological cycle of biological information processing and synthesis, from gene expression through protein synthesis to metabolite alterations. 1.6.1 Genomics and transcriptomics Genomics refers to the study of the complete genome of an organism. Whereas transcriptomic refers to the study of the entire RNA (i.e., mRNA, non-coding RNA, rRNA, snRNA, and tRNA) expressed by a cell, therefore representing a FIGURE 1.3 Different branches of omics technology. 10 PART | A Modern technological advancements in the field of animal experimentation snapshot of cellular metabolism (Pagani et al., 2012). Synthetic biology generally relies on the creation of predictable and favored phenotypes, thus tight and adjustable regulation of gene expression is extremely desirable. The gathering of genome-scale data has never been simpler. However, the advancement in sequencing technologies has expanded our capacity to study and interpret the entire genomes of organisms using high throughput sequencing technology. It will also be narrowing the gap between genotype and phenotype. Genome-wide association studies have become the standard approach for identifying candidate areas associated with complex characteristics of interest in humans and other species since the advent of genomics (Gondro, Van Der Werf, & Hayes, 2013). Probe-based chips with a high number of SNP markers distributed over the genome (Capomaccio, Milanesi, Bomba, Vajana, & Ajmone-Marsan, 2015; Vailati-Riboni et al., 2017). Haemophilus influenzae is the first organism to sequence a complete genome (Fleischmann et al., 1995). Since then, numerous genomes have been sequenced including the human genome, and used for understanding the evolution of organisms, mutations in DNA, prediction of newly emerging infectious diseases, etc. (Fricke, Rasko, & Ravel, 2009). Transcriptome means all the mRNA while proteome refers to all the proteins expressed in an organism. The transcriptomic era begins when Schena, Shalon, Davis, and Brown (1995) for the first time created the “micro-array” technology. It allowed for the large-scale study of a specified set (from hundreds to thousands) of cellular mRNA using an ink-jet DNA synthesizer. Transcriptomics has been transformed by the recent advancement of high-throughput nextgeneration sequencing (NGS) technology, which allows RNA analysis using massively parallel cDNA sequencing (RNAseq; Voelkerding, Dames, & Durtschi, 2009). The restricted dynamic range of detection, which was a problem with microarray technologies, was no longer an issue with this technique. 1.6.2 Proteomics Proteomics is the study of proteins and peptides generated by cells at various phases of their development and life cycle, as well as in biological systems under specific growth conditions. Proteomics may also be used to investigate the temporal dynamics of protein expression or posttranslational modification (PTM; Catherman, Skinner, & Kelleher, 2014; VerBerkmoes, Denef, Hettich, & Banfield, 2009). A proteomic analysis, on the other hand, gives the protein inventory of a cell or tissue at a certain time point, allowing for the discovery of new biomarkers, the identification and localization of PTMs, and the investigation of protein-protein interactions (Chandramouli & Qian, 2009). In fact, all the applications of genetic engineering are possible only because of genome sequencing. Proteins are the effector molecules of almost all the biological functions in the body. Proteomics deals with the study of the structure and function of proteins including their interactions and modifications (Nesvizhskii & Aebersold, 2005; Wolters, Washburn, & Yates, 2001). Proteomic methods have been developed to detect and differentially measure protein species in complicated biological data, and cattle researchers are using them (Lippolis & Reinhardt, 2008; Sauerwein, Bendixen, Restelli, & Ceciliani, 2014). Mass spectrometry (Ms) is a method in which all chemical components in a sample are ionized and the resultant charged molecules (ions) are evaluated according to their mass-to-charge (m/z) ratios, is at the core of current proteomics (Aebersold & Mann, 2003; Marx, 2013). One- or two-dimensional polyacrylamide gel electrophoresis (1D-PAGE, 2D-PAGE) is commonly employed for a simple pre-separation of complicated protein mixtures before Ms analysis. However, other forms of liquid chromatography (LC or HPLC) are utilized to supplement or replace gel-based separation techniques to further automate the process and build a streaming pipeline analysis (Bian et al., 2020; Krisp, Yang, Van Soest, & Molloy, 2015; Wilson, Vehus, Berg, & Lundanes, 2015). The field of proteomics has numerous applications like Disease diagnosis, identification of potential vaccine candidates, food microbiology, etc. (Catherman et al., 2014). Proteomics can comprehensively study the difference in the proteome of various tissues and disease conditions (Rühl et al., 2019). The key technology in proteomics is a mass spectrometer which can determine the mass of a lot of proteins at the same time and with 1 Dalton precision. Traditionally the proteins were identified by gel electrophoresis and western blotting but various forms have not only decreased the amount of biological sample required but also decreased cost, time. Through mass spectroscopy, we can also detect even PTMs of a protein in a cell (Kleiner, 2019; Yates, 2019). 1.6.3 Metabolomics The global profiling of metabolites in a biological sample is known as the metabolome. The term metabolome is concerned with the low-weight molecules called metabolites present in a cell or tissue or a sample. A metabolomics analysis may be performed on a range of biological fluids and tissue types, and it can be done using a variety of technological platforms. To provide an integrated picture of the metabolome, metabolomics generally combines high-resolution analysis with statistical methods such as principal component analysis and partial least squares (Zhang et al., 2012). NMR is Emerging techniques in biological sciences Chapter | 1 11 FIGURE 1.4 Flow diagram of multiple data sources in a multiomics framework. one of the most widely used spectroscopic analytical methods for identifying and quantifying a wide range of organic substances in the micro-molar range, providing unbiased information about metabolite profiles. Peptides, amino acids, nucleic acids, carbohydrates, organic acids, vitamins, polyphenols, alkaloids, and inorganic species are among the compounds that may be detected with this method. In high-throughput metabolomics, Ms is increasingly being used in conjunction with other methods like chromatography or electrophoretic approaches. Ms has been the technique of choice in many metabolomic investigations because of its excellent sensitivity and the large range of covered compounds (Zhang et al., 2012). Mass spectroscopy along with various chromatographic separation techniques has also made it possible to study the complete metabolic profile, lipid profile, and carbohydrates, which are respectively called metabolomics and lipidomics and glycomics. Metabolomics is an excellent tool for the molecular phenotyping of an organism and thus can suggest how patients will respond to particular drugs or how severe disease can be for a particular patient depending upon his metabolome. The era of single omics is over now as today is the age of ‘multi-omics, the integrative approach. Using multi-omics approaches can lead to better disease diagnosis, prognosis, and treatment. The multiomic technology combined with the system biology approach and artificial intelligence can help in generating a patienttailored health care approach specific for every individual depending upon his/her genome, transcriptome, proteome, and metabolome (Fig. 1.4). 1.7 Informatics and simulations Various fields of science have been transformed by increasing computing power and information technology. In biological sciences it helps in designing complex experiments, narrowing down possibilities, and sometimes even deciphering information about processes that are not accessible through any other technique yet. For instance, simulation of protein folding has given valuable insights into the mechanism of protein folding, and combined with artificial intelligence, it can predict the structure of proteins. The experimental techniques are not adequate to probe the detailed mechanism of protein folding which is very fast (few microseconds to milliseconds). The folded structure of some proteins is also not feasible to determine experimentally, specifically for membrane proteins. Protein structure prediction has been one of the most important scientific challenges because the structure of a protein opens the path to understand its function, mechanism. One application of this is the drug target prediction and how they are likely to interact with the drugs. The best methods of protein structure prediction are announced every two years through a competition called CASP (Critical Assessment of Structure Prediction). The artificial intelligence system called alpha fold, housed at Google Inc. subsidiary DeepMind, has performed the best in CASP14 held in 2020 (Alam & Shehu, 2021). But these knowledge-based methods performing the best in CASP competitions so far are only up to 90% accurate. These methods are based on the experimentally known structure of a similar sequence of proteins. The other approach is de novo structure prediction which is based on interatomic forces 12 PART | A Modern technological advancements in the field of animal experimentation FIGURE 1.5 Integration of omics technology, systems biology and artificial Intelligence may help developed personalized healthcare systems. that folds the protein. But given the so many atoms of the protein molecule itself and the surrounding solvent, such calculations are too much for the presently available commuting power in most of the labs. The approximation of forces accounted for interatomic interactions is also not perfect. But in the future, more computing power and better actuation of forces involved are expected to determine the structures of new protein sequences by de novo methods. Although the structure prediction of new natural protein sequences remains uncertain, the state of art has advanced reasonably to design proteins like therapeutic antibodies, enzymes, etc. Michael Levitt had developed an algorithm for humanizing antibodies that can transplant the binding site from animal antibodies to the scaffold of human antibodies (Queen et al., 1989). Regarding the simulation studies in general, Albert Einstein has said, “everything that counts is not counted and everything that is counted, does not count.” Therefore, the ultimate proof of the structure or any truth is experimentation but simulations help in narrowing down possibilities. Such narrowing down of possibilities in other experiments also can be of big help. For example, in-silico drug design can narrow down the possibility to a few molecules whereas testing molecules randomly will waste a lot of resources as well as kill a lot of animals in experimentation (Gray, Sidhu, Chandrasekera, Hendriksen, & Borrebaeck, 2016). Nowadays the simulation in biological sciences is used at various levels of detail according to the scale of the system in simulation. Quantum calculations can be performed for a small drug molecule but for the larger systems, the forces have to be accounted for in a coarse-grained manner because the computing power available is limited. The systems of every level in biology are studied using simulations like, the structure of drug molecules, cellular processes, cell-cell interaction, the functioning of organs, and up to the whole organism (Fig. 1.5). Computational science is crucial for the study of biological networks at the cellular and system level. The massive data accumulating about biological systems and increasing the information storage capacity of computers is bringing in BigData analytics in biological sciences. The number of databases about various biological sciences is increasing. The Nucleic Acid Research journal maintains a comprehensive catalog of biological informatics resources at its website: https://www.oxfordjournals.org/nar/database/c/ This includes databases about research literature, protein structure, genomes of various organisms, secondary databases, up to the ecological and biodiversity databases (Rigden & Fernández, 2021). A comprehensive analysis of such massive information is possible through computational methods/algorithms only. The development of such algorithms and data structures is now a separate discipline within biological sciences called bioinformatics. Research in life sciences today is unlikely to be well-informed research without the use of bioinformatics. 1.8 Automation and miniaturization of experiments Microfluidics is basically the miniaturizing of the lab experiments at very small scale in the form of a chip hence called “lab on chip.” It involves the manipulation of fluids in small sized (in microns) channels. The miniaturization of Emerging techniques in biological sciences Chapter | 1 13 FIGURE 1.6 Application of microfluidics is various fields of biology. FIGURE 1.7 Diagram illustrating fluorescence in situ hybridization (FISH) on microfluidic chip. experimental setups through microfluidics has not only decreased the long-run cost of experiments but also enables the study of new forces and phenomena affecting systems at the micro and nanoscale (Kohl et al., 2021). Microfluidics requires less volume of chemicals (in pico to nanoliters) and the portability of setup makes it possible to design point of care devices (Dabbagh et al., 2021). Due to less volume of consumables required the cost is less therefore high throughput studies can be done at an affordable cost. These advanced setups are also used to understand the forces at nano and micro scale which is called nano-science (Fig. 1.6). Sometimes the unexpected phenomena observed at the macro scale of a system are caused by phenomena at smaller scales such as quantum phenomena. Microfluidics is particularly helpful in such cases as it can be used to study the mechanisms at nano scale level. All these features have enabled the biologists to use microfluidics in various fields such as Next Generation DNA sequencing, Polymerase Chain Reaction (PCR), Protein crystallization etc. In microfluidics, reaction volumes are in nano scale, similar to those typically found in living cells which makes the in vitro studies more accurate (Fig. 1.7). So, in recent years, microfluidics is used in drug discovery, chemical synthesis, screening of compounds and preclinical testing of drugs on living cells. Automation of repetitive experimental work using robotics has not only made experimentation easy but also more accurate, precise, and free of human errors (Tegally, San, Giandhari, & de Oliveira, 2020). Automated liquid handling systems are commercially available for proteomics, transcriptomics, and genomics research as well as for diagnostic as commercial scale. This had been feasible due to reducing the cost of electronic circuit chips (Gach, Iwai, Kim, Hillson, & Singh, 2017; Table 1.2). 14 PART | A Modern technological advancements in the field of animal experimentation TABLE 1.2 Emerging techniques in life sciences. Techniques Applications Principle Instrument infrastructure Informatics and simulation Data analysis, prediction of structure, function, and intervention design Mathematics and statistics Supercomputing facility, data Next-generation sequencing Genome sequence, gene expression Optics, electronics, nanoscience Optoelectronic fabrication Genetic engineering (CRISPR, RNAi) Crop improvement, therapeutics Molecular biology and genetics Reagents, oligonucleotide synthesis and protein purification facility Omics technologies transcriptomics, proteomics, metabolomics System biology Electrophoresis, separation of ions by their mass-to-charge ratio (m/z), oligonucleotide hybridization Mass spectrometer robotic liquid handler Spectroscopy Molecular structure determination and chemical composition of mixtures or systems Nuclear magnetic resonance (NMR), light diffraction, absorbance, optics NMR spectrometer cyclotron, synchrotron for a collimated light source Imaging Structure of molecules to cells and organs Optics, electronics Electron microscope, imaging facility BioMEMS Diagnosis, biology at micro and nanoscale details Electronics, optics fluidics, nanoscience Cleanroom facilities and micro-nano fabrication facility for microfluidic systems with optical, electrical, and mechanical systems integrated 1.9 BioMEMS (Biomedical micro electro-mechanical systems in biology) Microelectromechanical systems (Bio-MEMS) are being developed with more and more functionalities by integrating the optics, electronics, and mechanical systems in microfluidic channels (Ino et al., 2020). These BioMEMS can do precise manipulation of biosystems and also closely observe the behavior/response. There emerge some new phenomena and forces at these nanoscale dimensions such as surface plasmon response which is the interaction of electromagnetic waves with electrons. Similarly, such forces at work in biological systems can also be studied through such miniaturized experimental setups and detection schemes integrated into it. These miniaturized systems are also ultimately less costly for large-scale experiments like high throughput assays in research and diagnosis. Some of these devices like LFA (Immunochromatographic lateral flow assays) and glucometer have come up as point-of-care kits for diagnosis.(Huang et al., 2020). Microfluidics has made it possible to create a microenvironment around cells that is similar to the environment they experience in tissue or organs. Various functionalities are integrated into microfluidic channels for 3D cell culture and an organ-on-chip (Thompson, Fu, Knight, & Thorpe, 2020). The use of these organs-on-chips not only does away with ethical clearance required in case of animal experiments but they are also less time consuming and more controlled experiment, enabling a clear understanding of cause-and-effect relationship. The origin of these technologies can be attributed to flow cytometry wherein the fluid flow is controlled to line up the cells and count them using advanced optics. Quake et al. had proposed the idea of microfluidic cell sorters (Fu, Chou, Spence, Arnold, & Quake, 2002). If a reasonable purity of sorted cells is achieved then they can be very valuable for label-free sorting of sperms for gender selection in cattle, separating out cancerous cells from blood, harvesting stem cells, etc. (Fig. 1.8). Microfluidic systems are designed to automate the process of DNA profiling from minute forensic samples such as a touch of a finger (Woolf et al., 2020). These devices extract the DNA, PCR amplifies the short tandem repeat (STRs), and detect the size of amplified STRs to create the DNA profile (Hong et al., 2020). These all-in-one DNA profiling devices are used on-site such as police stations and mobile forensic labs. Latest developments in next-generation DNA sequencing like oxford nanopore technologies are a cutting-edge example of integrating molecular machines, microfluidics, and sophisticated sensors (Kumar, Cowley, & Davis, 2019). They use protein as nanopores through which the DNA is ratcheted by enzymes. This process is inspired by the processive enzymes in cells that work on DNA (Patel Emerging techniques in biological sciences Chapter | 1 15 FIGURE 1.8 Glucometer as an example of BioMEMS. et al., 2018). Then the integrated optics and electronics are used to sense the chemical identity of nucleotides passing through the nanopore to ultimately determine the sequence of the whole genome. This oxford nanopore NGS is very error-prone but future developments are expected to make these devices accurate and so cost-effective that almost all can get their genome sequenced for use in personalized medicine (Cohen, 2020). Many new techniques have emerged at the interface of various disciplines like electronics, optics, molecular biology, genetics, computational science, and nanoscience. The majority of them require expensive instruments and manufacturing facilities therefore they are widely accessible. Recently the scientific community is also emphasizing techniques that are high tech but low cost so that the benefits of science can reach common people. 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